151
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Miao J, Farhat P, Wang W, Ruhsam M, Milne R, Yang H, Tso S, Li J, Xu J, Opgenoorth L, Miehe G, Mao K. Evolutionary history of two rare endemic conifer species from the eastern Qinghai-Tibet Plateau. ANNALS OF BOTANY 2021; 128:903-918. [PMID: 34472580 PMCID: PMC8577208 DOI: 10.1093/aob/mcab114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND AIMS Understanding the population genetics and evolutionary history of endangered species is urgently needed in an era of accelerated biodiversity loss. This knowledge is most important for regions with high endemism that are ecologically vulnerable, such as the Qinghai-Tibet Plateau (QTP). METHODS The genetic variation of 84 juniper trees from six populations of Juniperus microsperma and one population of Juniperus erectopatens, two narrow-endemic junipers from the QTP that are sister to each other, was surveyed using RNA-sequencing data. Coalescent-based analyses were used to test speciation, migration and demographic scenarios. Furthermore, positively selected and climate-associated genes were identified, and the genetic load was assessed for both species. KEY RESULTS Analyses of 149 052 single nucleotide polymorphisms showed that the two species are well differentiated and monophyletic. They diverged around the late Pliocene, but interspecific gene flow continued until the Last Glacial Maximum. Demographic reconstruction by Stairway Plot detected two severe bottlenecks for J. microsperma but only one for J. erectopatens. The identified positively selected genes and climate-associated genes revealed habitat adaptation of the two species. Furthermore, although J. microsperma had a much wider geographical distribution than J. erectopatens, the former possesses lower genetic diversity and a higher genetic load than the latter. CONCLUSIONS This study sheds light on the evolution of two endemic juniper species from the QTP and their responses to Quaternary climate fluctuations. Our findings emphasize the importance of speciation and demographic history reconstructions in understanding the current distribution pattern and genetic diversity of threatened species in mountainous regions.
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Affiliation(s)
- Jibin Miao
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
- College of Science, Tibet University, Lhasa 850000, PR China
| | - Perla Farhat
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Wentao Wang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
| | - Markus Ruhsam
- Royal Botanic Garden Edinburgh, 20A Inverleith Row, Edinburgh EH3 5LR, UK
| | - Richard Milne
- Institute of Molecular Plant Sciences, The University of Edinburgh, Edinburgh EH9 3JH, UK
| | - Heng Yang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
| | - Sonam Tso
- College of Science, Tibet University, Lhasa 850000, PR China
| | - Jialiang Li
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
| | - Jingjing Xu
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
| | - Lars Opgenoorth
- Faculty of Biology and Geology, University of Marburg, 35032 Marburg, Germany
| | - Georg Miehe
- Faculty of Biology and Geology, University of Marburg, 35032 Marburg, Germany
| | - Kangshan Mao
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, Sichuan, PR China
- College of Science, Tibet University, Lhasa 850000, PR China
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152
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Jordan KW, Bradbury PJ, Miller ZR, Nyine M, He F, Fraser M, Anderson J, Mason E, Katz A, Pearce S, Carter AH, Prather S, Pumphrey M, Chen J, Cook J, Liu S, Rudd JC, Wang Z, Chu C, Ibrahim AMH, Turkus J, Olson E, Nagarajan R, Carver B, Yan L, Taagen E, Sorrells M, Ward B, Ren J, Akhunova A, Bai G, Bowden R, Fiedler J, Faris J, Dubcovsky J, Guttieri M, Brown-Guedira G, Buckler E, Jannink JL, Akhunov ED. Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation. G3-GENES GENOMES GENETICS 2021; 12:6423995. [PMID: 34751373 PMCID: PMC9210282 DOI: 10.1093/g3journal/jkab390] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 10/21/2021] [Indexed: 12/04/2022]
Abstract
To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10−14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.
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Affiliation(s)
- Katherine W Jordan
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.,USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | - Peter J Bradbury
- USDA-ARS, Plant Soil and Nutrition Research Unit, Ithaca, NY, 14853, USA
| | - Zachary R Miller
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Moses Nyine
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Fei He
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Max Fraser
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Jim Anderson
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Esten Mason
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80521, USA
| | - Andrew Katz
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80521, USA
| | - Stephen Pearce
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80521, USA
| | - Arron H Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Samuel Prather
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Michael Pumphrey
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Jianli Chen
- Department of Plant Sciences, University of Idaho, Aberdeen, ID, 83210, USA
| | - Jason Cook
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA
| | - Shuyu Liu
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Jackie C Rudd
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Zhen Wang
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Chenggen Chu
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Amir M H Ibrahim
- Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Amarillo, TX, 79106, USA
| | - Jonathan Turkus
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Eric Olson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Ragupathi Nagarajan
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Brett Carver
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Liuling Yan
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74075, USA
| | - Ellie Taagen
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Mark Sorrells
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Brian Ward
- USDA-ARS, Plant Science Research Unit, Raleigh, NC, 27695, USA
| | - Jie Ren
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.,Integrative Genomics Facility, Kansas State University, Manhattan, KS, 66506 USA
| | - Alina Akhunova
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.,Integrative Genomics Facility, Kansas State University, Manhattan, KS, 66506 USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | - Robert Bowden
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | - Jason Fiedler
- USDA-ARS, Cereal Crops Research Unit, Fargo, ND, 58102, USA
| | - Justin Faris
- USDA-ARS, Cereal Crops Research Unit, Fargo, ND, 58102, USA
| | - Jorge Dubcovsky
- Department of Plant Sciences, University of California-Davis, Davis, CA, 95616, USA
| | - Mary Guttieri
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66502, USA
| | | | - Ed Buckler
- USDA-ARS, Plant Soil and Nutrition Research Unit, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- USDA-ARS, Plant Soil and Nutrition Research Unit, Ithaca, NY, 14853, USA
| | - Eduard D Akhunov
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
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153
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The genomic landscape of Mexican Indigenous populations brings insights into the peopling of the Americas. Nat Commun 2021; 12:5942. [PMID: 34642312 PMCID: PMC8511047 DOI: 10.1038/s41467-021-26188-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/22/2021] [Indexed: 12/30/2022] Open
Abstract
The genetic makeup of Indigenous populations inhabiting Mexico has been strongly influenced by geography and demographic history. Here, we perform a genome-wide analysis of 716 newly genotyped individuals from 60 of the 68 recognized ethnic groups in Mexico. We show that the genetic structure of these populations is strongly influenced by geography, and our demographic reconstructions suggest a decline in the population size of all tested populations in the last 15-30 generations. We find evidence that Aridoamerican and Mesoamerican populations diverged roughly 4-9.9 ka, around the time when sedentary farming started in Mesoamerica. Comparisons with ancient genomes indicate that the Upward Sun River 1 (USR1) individual is an outgroup to Mexican/South American Indigenous populations, whereas Anzick-1 was more closely related to Mesoamerican/South American populations than to those from Aridoamerica, showing an even more complex history of divergence than recognized so far.
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154
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Impacts of environmental conditions, and allelic variation of cytosolic glutamine synthetase on maize hybrid kernel production. Commun Biol 2021; 4:1095. [PMID: 34535763 PMCID: PMC8448750 DOI: 10.1038/s42003-021-02598-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 08/24/2021] [Indexed: 11/19/2022] Open
Abstract
Cytosolic glutamine synthetase (GS1) is the enzyme mainly responsible of ammonium assimilation and reassimilation in maize leaves. The agronomic potential of GS1 in maize kernel production was investigated by examining the impact of an overexpression of the enzyme in the leaf cells. Transgenic hybrids exhibiting a three-fold increase in leaf GS activity were produced and characterized using plants grown in the field. Several independent hybrids overexpressing Gln1-3, a gene encoding cytosolic (GS1), in the leaf and bundle sheath mesophyll cells were grown over five years in different locations. On average, a 3.8% increase in kernel yield was obtained in the transgenic hybrids compared to controls. However, we observed that such an increase was simultaneously dependent upon both the environmental conditions and the transgenic event for a given field trial. Although variable from one environment to another, significant associations were also found between two GS1 genes (Gln1-3 and Gln1-4) polymorphic regions and kernel yield in different locations. We propose that the GS1 enzyme is a potential lead for producing high yielding maize hybrids using either genetic engineering or marker-assisted selection. However, for these hybrids, yield increases will be largely dependent upon the environmental conditions used to grow the plants. Amiour et al. use a multi-year field trial evaluation and association mapping to determine if increased enzyme activity and native allelic variations at the GS1 loci in maize contribute to differences in grain yield. Overexpression of GS1 and polymorphisms in the corresponding loci were associated with kernel yield, indicating that GS1 expression can directly control kernel production and that GS1 has a potential lead in the production of high yielding maize hybrids depending on environmental conditions.
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155
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Wang M, Yuan D, Zou X, Wang Z, Yeh HY, Liu J, Wei LH, Wang CC, Zhu B, Liu C, He G. Fine-Scale Genetic Structure and Natural Selection Signatures of Southwestern Hans Inferred From Patterns of Genome-Wide Allele, Haplotype, and Haplogroup Lineages. Front Genet 2021; 12:727821. [PMID: 34504517 PMCID: PMC8421688 DOI: 10.3389/fgene.2021.727821] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 07/29/2021] [Indexed: 12/15/2022] Open
Abstract
The evolutionary and admixture history of Han Chinese have been widely discussed via traditional autosomal and uniparental genetic markers [e.g., short tandem repeats, low-density single nucleotide polymorphisms). However, their fine-scale genetic landscapes (admixture scenarios and natural selection signatures) based on the high-density allele/haplotype sharing patterns have not been deeply characterized. Here, we collected and generated genome-wide data of 50 Han Chinese individuals from four populations in Guizhou Province, one of the most ethnolinguistically diverse regions, and merged it with over 3,000 publicly available modern and ancient Eurasians to describe the genetic origin and population admixture history of Guizhou Hans and their neighbors. PCA and ADMIXTURE results showed that the studied four populations were homogeneous and grouped closely to central East Asians. Genetic homogeneity within Guizhou populations was further confirmed via the observed strong genetic affinity with inland Hmong-Mien people through the observed genetic clade in Fst and outgroup f3/f4-statistics. qpGraph-based phylogenies and f4-based demographic models illuminated that Guizhou Hans were well fitted via the admixture of ancient Yellow River Millet farmers related to Lajia people and southern Yangtze River farmers related to Hanben people. Further ChromoPainter-based chromosome painting profiles and GLOBETROTTER-based admixture signatures confirmed the two best source matches for southwestern Hans, respectively, from northern Shaanxi Hans and southern indigenes with variable mixture proportions in the historical period. Further three-way admixture models revealed larger genetic contributions from coastal southern East Asians into Guizhou Hans compared with the proposed inland ancient source from mainland Southeast Asia. We also identified candidate loci (e.g., MTUS2, NOTCH4, EDAR, ADH1B, and ABCG2) with strong natural selection signatures in Guizhou Hans via iHS, nSL, and ihh, which were associated with the susceptibility of the multiple complex diseases, morphology formation, alcohol and lipid metabolism. Generally, we provided a case and ideal strategy to reconstruct the detailed demographic evolutionary history of Guizhou Hans, which provided new insights into the fine-scale genomic formation of one ethnolinguistically specific targeted population from the comprehensive perspectives of the shared unlinked alleles, linked haplotypes, and paternal and maternal lineages.
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Affiliation(s)
- Mengge Wang
- Guangzhou Forensic Science Institute, Guangzhou, China.,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Didi Yuan
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Xing Zou
- College of Basic Medicine, Chongqing University, Chongqing, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Hui-Yuan Yeh
- School of Humanities, Nanyang Technological University, Singapore, Singapore
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Lan-Hai Wei
- State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Bofeng Zhu
- Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China.,Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.,Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi'an Jiaotong University, Xi'an, China
| | - Chao Liu
- Guangzhou Forensic Science Institute, Guangzhou, China.,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Guanglin He
- School of Humanities, Nanyang Technological University, Singapore, Singapore.,State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
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156
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Kumar L, Farias K, Prakash S, Mishra A, Mustak MS, Rai N, Thangaraj K. Dissecting the genetic history of the Roman Catholic populations of West Coast India. Hum Genet 2021; 140:1487-1498. [PMID: 34424406 DOI: 10.1007/s00439-021-02346-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/12/2021] [Indexed: 10/20/2022]
Abstract
Migration and admixture history of populations have always been curious and an interesting theme. The West Coast of India harbours a rich diversity, bestowing various ethno-linguistic groups, with many of them having well-documented history of migrations. The Roman Catholic is one such distinct group, whose origin was much debated. While some historians and anthropologists relating them to ancient group of Gaud Saraswat Brahmins, others relating them for being members of the Jews Lost Tribes in the first Century migration to India. Historical records suggests that this community was later forcibly converted to Christianity by the Portuguese in Goa during the Sixteenth Century. Till date, no genetic study was done on this group to infer their origin and genetic affinity. Hence, we analysed 110 Roman Catholics from three different locations of West Coast of India including Goa, Kumta and Mangalore using both uniparental and autosomal markers to understand their genetic history. We found that the Roman Catholics have close affinity with the Indo-European linguistic groups, particularly Brahmins. Additionally, we detected genetic signal of Jews in the linkage disequilibrium-based admixture analysis, which was absent in other Indo-European populations, who are inhabited in the same geographical regions. Haplotype-based analysis suggests that the Roman Catholics consist of South Asian-specific ancestry and showed high drift. Ancestry-specific historical population size estimation points to a possible bottleneck around the time of Goan inquisition (fifteenth century). Analysis of the Roman Catholics data along with ancient DNA data of Neolithic and bronze age revealed that the Roman Catholics fits well in a basic model of ancient ancestral composition, typical of most of the Indo-European caste groups of India. Mitochondrial DNA (mtDNA) analysis suggests that most of the Roman Catholics have aboriginal Indian maternal genetic ancestry; while the Y chromosomal DNA analysis indicates high frequency of R1a lineage, which is predominant in groups with higher ancestral North Indian (ANI) component. Therefore, we conclude that the Roman Catholics of Goa, Kumta and Mangalore regions are the remnants of very early lineages of Brahmin community of India, having Indo-Europeans genetic affinity along with cryptic Jewish admixture, which needs to be explored further.
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Affiliation(s)
- Lomous Kumar
- CSIR-Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad, Telangana, 500007, India
| | - Kranti Farias
- Canadian Institute for Jewish Research, Montreal, Canada
| | - Satya Prakash
- CSIR-Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad, Telangana, 500007, India
| | - Anshuman Mishra
- Institute of Advanced Materials, IAAM, Gammalkilsvägen 18, 590 53, Ulrika, Sweden
| | - Mohammed S Mustak
- Department of Applied Zoology, Mangalore University, Mangalore, 574199, India
| | - Niraj Rai
- Birbal Sahni Institute of Palaeosciences, Uttar Pradesh, 53 University Road, Lucknow, 226007, India.
| | - Kumarasamy Thangaraj
- CSIR-Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad, Telangana, 500007, India.
- DBT-Centre for DNA Fingerprinting and Diagnostics, Uppal, Hyderabad, 500007, India.
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157
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van Eeden G, Uren C, van der Spuy G, Tromp G, Möller M. Local ancestry inference in heterogeneous populations-Are recent recombination events more relevant? Brief Bioinform 2021; 22:6337894. [PMID: 34343255 DOI: 10.1093/bib/bbab300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/29/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
To date, numerous software tools have been developed to infer recombination maps. Many of these software tools infer the recombination rate from linkage disequilibrium, and therefore they infer recombination many generations into the past. Other recently developed methods rely on the inference of recent recombination events to determine the recombination rate, such as identity by descent- and local ancestry inference (LAI)-based tools. Methods that mainly use recent recombination events to infer the recombination rate might be more relevant for certain analyses like LAI. We therefore describe a protocol for creating high-resolution, population-specific recombination maps using methods that mainly use recent recombination events and a method that uses recent and distant recombination events for recombination rate inference. Subsequently, we compared the effect of using maps inferred by these two paradigms on LAI accuracy.
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Affiliation(s)
| | | | - Gian van der Spuy
- Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa
| | - Gerard Tromp
- South African Tuberculosis Bioinformatics Initiative (SATBBI), South Africa
| | - Marlo Möller
- Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa
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158
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Matsunami M, Koganebuchi K, Imamura M, Ishida H, Kimura R, Maeda S. Fine-Scale Genetic Structure and Demographic History in the Miyako Islands of the Ryukyu Archipelago. Mol Biol Evol 2021; 38:2045-2056. [PMID: 33432348 PMCID: PMC8097307 DOI: 10.1093/molbev/msab005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The Ryukyu Archipelago is located in the southwest of the Japanese islands and is composed of dozens of islands, grouped into the Miyako Islands, Yaeyama Islands, and Okinawa Islands. Based on the results of principal component analysis on genome-wide single-nucleotide polymorphisms, genetic differentiation was observed among the island groups of the Ryukyu Archipelago. However, a detailed population structure analysis of the Ryukyu Archipelago has not yet been completed. We obtained genomic DNA samples from 1,240 individuals living in the Miyako Islands, and we genotyped 665,326 single-nucleotide polymorphisms to infer population history within the Miyako Islands, including Miyakojima, Irabu, and Ikema islands. The haplotype-based analysis showed that populations in the Miyako Islands were divided into three subpopulations located on Miyakojima northeast, Miyakojima southwest, and Irabu/Ikema. The results of haplotype sharing and the D statistics analyses showed that the Irabu/Ikema subpopulation received gene flows different from those of the Miyakojima subpopulations, which may be related with the historically attested immigration during the Gusuku period (900 − 500 BP). A coalescent-based demographic inference suggests that the Irabu/Ikema population firstly split away from the ancestral Ryukyu population about 41 generations ago, followed by a split of the Miyako southwest population from the ancestral Ryukyu population (about 16 generations ago), and the differentiation of the ancestral Ryukyu population into two populations (Miyako northeast and Okinawajima populations) about seven generations ago. Such genetic information is useful for explaining the population history of modern Miyako people and must be taken into account when performing disease association studies.
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Affiliation(s)
- Masatoshi Matsunami
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
| | - Kae Koganebuchi
- Advanced Medical Research Center, Faculty of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
| | - Minako Imamura
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan.,Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara-Cho, Japan
| | - Hajime Ishida
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
| | - Ryosuke Kimura
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
| | - Shiro Maeda
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan.,Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara-Cho, Japan
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159
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Tarekegn GM, Khayatzadeh N, Liu B, Osama S, Haile A, Rischkowsky B, Zhang W, Tesfaye K, Dessie T, Mwai OA, Djikeng A, Mwacharo JM. Ethiopian indigenous goats offer insights into past and recent demographic dynamics and local adaptation in sub-Saharan African goats. Evol Appl 2021; 14:1716-1731. [PMID: 34295359 PMCID: PMC8287980 DOI: 10.1111/eva.13118] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 07/15/2020] [Accepted: 08/13/2020] [Indexed: 12/13/2022] Open
Abstract
Knowledge on how adaptive evolution and human socio-cultural and economic interests shaped livestock genomes particularly in sub-Saharan Africa remains limited. Ethiopia is in a geographic region that has been critical in the history of African agriculture with ancient and diverse human ethnicity and bio-climatic conditions. Using 52K genome-wide data analysed in 646 individuals from 13 Ethiopian indigenous goat populations, we observed high levels of genetic variation. Although runs of homozygosity (ROH) were ubiquitous genome-wide, there were clear differences in patterns of ROH length and abundance and in effective population sizes illustrating differences in genome homozygosity, evolutionary history, and management. Phylogenetic analysis incorporating patterns of genetic differentiation and gene flow with ancestry modelling highlighted past and recent intermixing and possible two deep ancient genetic ancestries that could have been brought by humans with the first introduction of goats in Africa. We observed four strong selection signatures that were specific to Arsi-Bale and Nubian goats. These signatures overlapped genomic regions with genes associated with morphological, adaptation, reproduction and production traits due possibly to selection under environmental constraints and/or human preferences. The regions also overlapped uncharacterized genes, calling for a comprehensive annotation of the goat genome. Our results provide insights into mechanisms leading to genome variation and differentiation in sub-Saharan Africa indigenous goats.
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Affiliation(s)
- Getinet M. Tarekegn
- Department of Animal Production and TechnologySchool of Animal Sciences and Veterinary MedicineBahir Dar UniversityBahir DarEthiopia
- Department of Animal Breeding and GeneticsSwedish University of Agricultural Sciences (SLU)UppsalaSweden
| | - Negar Khayatzadeh
- Department of Sustainable Agricultural SystemsDivision of Livestock SciencesUniversity of Natural Resources and Life SciencesViennaAustria
| | - Bin Liu
- Inner Mongolia Agricultural UniversityHohhotChina
| | - Sarah Osama
- The University of QueenslandSaint LuciaQLDAustralia
| | - Aynalem Haile
- Small Ruminant GenomicsInternational Centre for Agricultural Research in the Dry Areas (ICARDA)Addis AbabaEthiopia
| | - Barbara Rischkowsky
- Small Ruminant GenomicsInternational Centre for Agricultural Research in the Dry Areas (ICARDA)Addis AbabaEthiopia
| | | | - Kassahun Tesfaye
- Department of Microbial, Cellular and Molecular BiologyAddis Ababa UniversityAddis AbabaEthiopia
| | - Tadelle Dessie
- International Livestock Research Institute (ILRI)Addis AbabaEthiopia
| | - Okeyo A. Mwai
- International Livestock Research Institute (ILRI)NairobiKenya
| | - Appolinaire Djikeng
- Animal and Veterinary Sciences Group, SRUC and Centre for Tropical Livestock Genetics and Health (CTLGH)The Roslin InstituteEaster BushMidlothianUK
| | - Joram M. Mwacharo
- Small Ruminant GenomicsInternational Centre for Agricultural Research in the Dry Areas (ICARDA)Addis AbabaEthiopia
- Animal and Veterinary Sciences Group, SRUC and Centre for Tropical Livestock Genetics and Health (CTLGH)The Roslin InstituteEaster BushMidlothianUK
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160
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Population-scale peach genome analyses unravel selection patterns and biochemical basis underlying fruit flavor. Nat Commun 2021; 12:3604. [PMID: 34127667 PMCID: PMC8203738 DOI: 10.1038/s41467-021-23879-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/17/2021] [Indexed: 02/05/2023] Open
Abstract
A narrow genetic basis in modern cultivars and strong linkage disequilibrium in peach (Prunus persica) has restricted resolution power for association studies in this model fruit species, thereby limiting our understanding of economically important quality traits including fruit flavor. Here, we present a high-quality genome assembly for a Chinese landrace, Longhua Shui Mi (LHSM), a representative of the Chinese Cling peaches that have been central in global peach genetic improvement. We also map the resequencing data for 564 peach accessions to this LHSM assembly at an average depth of 26.34× per accession. Population genomic analyses reveal a fascinating history of convergent selection for sweetness yet divergent selection for acidity in eastern vs. western modern cultivars. Molecular-genetics and biochemical analyses establish that PpALMT1 (aluminum-activated malate transporter 1) contributes to their difference of malate content and that increases fructose content accounts for the increased sweetness of modern peach fruits, as regulated by PpERDL16 (early response to dehydration 6-like 16). Our study illustrates the strong utility of the genomics resources for both basic and applied efforts to understand and exploit the genetic basis of fruit quality in peach.
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161
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Rapid detection of identity-by-descent tracts for mega-scale datasets. Nat Commun 2021; 12:3546. [PMID: 34112768 PMCID: PMC8192555 DOI: 10.1038/s41467-021-22910-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/01/2021] [Indexed: 01/08/2023] Open
Abstract
The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections.
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162
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Cortellari M, Barbato M, Talenti A, Bionda A, Carta A, Ciampolini R, Ciani E, Crisà A, Frattini S, Lasagna E, Marletta D, Mastrangelo S, Negro A, Randi E, Sarti FM, Sartore S, Soglia D, Liotta L, Stella A, Ajmone-Marsan P, Pilla F, Colli L, Crepaldi P. The climatic and genetic heritage of Italian goat breeds with genomic SNP data. Sci Rep 2021; 11:10986. [PMID: 34040003 PMCID: PMC8154919 DOI: 10.1038/s41598-021-89900-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/29/2021] [Indexed: 02/04/2023] Open
Abstract
Local adaptation of animals to the environment can abruptly become a burden when faced with rapid climatic changes such as those foreseen for the Italian peninsula over the next 70 years. Our study investigates the genetic structure of the Italian goat populations and links it with the environment and how genetics might evolve over the next 50 years. We used one of the largest national datasets including > 1000 goats from 33 populations across the Italian peninsula collected by the Italian Goat Consortium and genotyped with over 50 k markers. Our results showed that Italian goats can be discriminated in three groups reflective of the Italian geography and its geo-political situation preceding the country unification around two centuries ago. We leveraged the remarkable genetic and geographical diversity of the Italian goat populations and performed landscape genomics analysis to disentangle the relationship between genotype and environment, finding 64 SNPs intercepting genomic regions linked to growth, circadian rhythm, fertility, and inflammatory response. Lastly, we calculated the hypothetical future genotypic frequencies of the most relevant SNPs identified through landscape genomics to evaluate their long-term effect on the genetic structure of the Italian goat populations. Our results provide an insight into the past and the future of the Italian local goat populations, helping the institutions in defining new conservation strategy plans that could preserve their diversity and their link to local realities challenged by climate change.
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Affiliation(s)
- Matteo Cortellari
- Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milan, Italy
| | - Mario Barbato
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti and BioDNA Centro di ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122, Piacenza, Italy
| | - Andrea Talenti
- Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milan, Italy.
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | - Arianna Bionda
- Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milan, Italy
| | - Antonello Carta
- Unità di Ricerca di Genetica e Biotecnologie, Agris Sardegna, 07100, Sassari, Italy
| | - Roberta Ciampolini
- Dipartimento di Scienze Veterinarie, Università di Pisa, Viale delle Piagge 2, 56124, Pisa, Italy
| | - Elena Ciani
- Dipartimento di Bioscienze Biotecnologie e Biofarmaceutica, Università degli Studi di Bari, Via Orabona 4, 70126, Bari, Italy
| | - Alessandra Crisà
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015, Monterotondo, Rome, Italy
| | - Stefano Frattini
- Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milan, Italy
| | - Emiliano Lasagna
- Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06121, Perugia, Italy
| | - Donata Marletta
- Department of Agriculture, Food and Environment, University of Catania, Via Valdisavoia 5, 95123, Catania, Italy
| | - Salvatore Mastrangelo
- Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, 90128, Palermo, Italy
| | - Alessio Negro
- Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milan, Italy
| | - Ettore Randi
- Department of Chemistry and Bioscience, Faculty of Engineering and Science, University of Aalborg, Aalborg, Denmark
| | - Francesca M Sarti
- Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06121, Perugia, Italy
| | - Stefano Sartore
- Dipartimento di Scienze Veterinarie, Università degli Studi di Torino, largo Braccini 2, 10095, Grugliasco, Italy
| | - Dominga Soglia
- Dipartimento di Scienze Veterinarie, Università degli Studi di Torino, largo Braccini 2, 10095, Grugliasco, Italy
| | - Luigi Liotta
- Dipartimento di Scienze Veterinarie, University of Messina, Messina, Italy
| | - Alessandra Stella
- Institute of Biology and Biotechnology in Agriculture, National Research Council (CNR), Milan, Italy
| | - Paolo Ajmone-Marsan
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti and BioDNA Centro di ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122, Piacenza, Italy
| | - Fabio Pilla
- Dipartimento Agricoltura, Ambiente e Alimenti Universitá degli Studi del Molise, 86100, Campobasso, Italy
| | - Licia Colli
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti and BioDNA Centro di ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122, Piacenza, Italy
| | - Paola Crepaldi
- Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milan, Italy
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163
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Ma X, Yang W, Gao Y, Pan Y, Lu Y, Chen H, Lu D, Xu S. Genetic origins and sex-biased admixture of the Huis. Mol Biol Evol 2021; 38:3804-3819. [PMID: 34021754 PMCID: PMC8382924 DOI: 10.1093/molbev/msab158] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The Hui people are unique among Chinese ethnic minorities in that they speak the same language as Han Chinese (HAN) but practice Islam. However, as the second-largest minority group in China numbering well over 10 million, the Huis are under-represented in both global and regional genomic studies. Here, we present the first whole-genome sequencing effort of 234 Hui individuals (NXH) aged over 60 who have been living in Ningxia, where the Huis are mostly concentrated. NXH are genetically more similar to East Asian than to any other global populations. In particular, the genetic differentiation between NXH and HAN (FST = 0.0015) is only slightly larger than that between northern and southern HAN (FST = 0.0010), largely attributed to the western ancestry in NXH (∼10%). Highly differentiated functional variants between NXH and HAN were identified in genes associated with skin pigmentation (e.g., SLC24A5), facial morphology (e.g., EDAR), and lipid metabolism (e.g., ABCG8). The Huis are also distinct from other Muslim groups such as the Uyghurs (FST = 0.0187), especially, NXH derived much less western ancestry (∼10%) compared with the Uyghurs (∼50%). Modeling admixture history indicated that NXH experienced an episode of two-wave admixture. An ancient admixture occurred ∼1,025 years ago, reflecting the intensive west-east contacts during the late Tang Dynasty, and the Five Dynasties and Ten Kingdoms period. A recent admixture occurred ∼500 years ago, corresponding to the Ming Dynasty. Notably, we identified considerable sex-biased admixture, i.e., excess of western males and eastern females contributing to the NXH gene pool. The origins and the genomic diversity of the Hui people imply the complex history of contacts between western and eastern Eurasians.
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Affiliation(s)
- Xixian Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wenjun Yang
- Key Laboratory of Fertility Preservation and Maintenance, the General Hospital, Ningxia Medical University, Yinchuan, Ningxia 750004 China
| | - Yang Gao
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, 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
| | - Yan 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.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, 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
| | - 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
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China.,Human Phenome Institute, Fudan University, Shanghai 201203, China
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164
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The genetic structure of Norway. Eur J Hum Genet 2021; 29:1710-1718. [PMID: 34002043 PMCID: PMC8560852 DOI: 10.1038/s41431-021-00899-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 03/01/2021] [Accepted: 04/14/2021] [Indexed: 11/08/2022] Open
Abstract
The aim of the present study was to describe the genetic structure of the Norwegian population using genotypes from 6369 unrelated individuals with detailed information about places of residence. Using standard single marker- and haplotype-based approaches, we report evidence of two regions with distinctive patterns of genetic variation, one in the far northeast, and another in the south of Norway, as indicated by fixation indices, haplotype sharing, homozygosity, and effective population size. We detect and quantify a component of Uralic Sami ancestry that is enriched in the North. On a finer scale, we find that rates of migration have been affected by topography like mountain ridges. In the broader Scandinavian context, we detect elevated relatedness between the mid- and northern border areas towards Sweden. The main finding of this study is that despite Norway's long maritime history and as a former Danish territory, the region closest to mainland Europe in the south appears to have been an isolated region in Norway, highlighting the open sea as a barrier to gene flow into Norway.
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165
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Carress H, Lawson DJ, Elhaik E. Population genetic considerations for using biobanks as international resources in the pandemic era and beyond. BMC Genomics 2021; 22:351. [PMID: 34001009 PMCID: PMC8127217 DOI: 10.1186/s12864-021-07618-x] [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: 10/16/2020] [Accepted: 04/14/2021] [Indexed: 12/11/2022] Open
Abstract
The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.
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Affiliation(s)
- Hannah Carress
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK
| | - Daniel John Lawson
- School of Mathematics and Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Eran Elhaik
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK. .,Department of Biology, Lund University, Lund, Sweden.
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166
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Freyman WA, McManus KF, Shringarpure SS, Jewett EM, Bryc K, Auton A. Fast and Robust Identity-by-Descent Inference with the Templated Positional Burrows-Wheeler Transform. Mol Biol Evol 2021; 38:2131-2151. [PMID: 33355662 PMCID: PMC8097300 DOI: 10.1093/molbev/msaa328] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Estimating the genomic location and length of identical-by-descent (IBD) segments among individuals is a crucial step in many genetic analyses. However, the exponential growth in the size of biobank and direct-to-consumer genetic data sets makes accurate IBD inference a significant computational challenge. Here we present the templated positional Burrows-Wheeler transform (TPBWT) to make fast IBD estimates robust to genotype and phasing errors. Using haplotype data simulated over pedigrees with realistic genotyping and phasing errors, we show that the TPBWT outperforms other state-of-the-art IBD inference algorithms in terms of speed and accuracy. For each phase-aware method, we explore the false positive and false negative rates of inferring IBD by segment length and characterize the types of error commonly found. Our results highlight the fragility of most phased IBD inference methods; the accuracy of IBD estimates can be highly sensitive to the quality of haplotype phasing. Additionally, we compare the performance of the TPBWT against a widely used phase-free IBD inference approach that is robust to phasing errors. We introduce both in-sample and out-of-sample TPBWT-based IBD inference algorithms and demonstrate their computational efficiency on massive-scale data sets with millions of samples. Furthermore, we describe the binary file format for TPBWT-compressed haplotypes that results in fast and efficient out-of-sample IBD computes against very large cohort panels. Finally, we demonstrate the utility of the TPBWT in a brief empirical analysis, exploring geographic patterns of haplotype sharing within Mexico. Hierarchical clustering of IBD shared across regions within Mexico reveals geographically structured haplotype sharing and a strong signal of isolation by distance. Our software implementation of the TPBWT is freely available for noncommercial use in the code repository (https://github.com/23andMe/phasedibd, last accessed January 11, 2021).
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167
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Gebreyesus G, Aagaard Poulsen N, Krogh Larsen M, Bach Larsen L, Skipper Sørensen E, Würtz Heegaard C, Buitenhuis B. Vitamin B 12 and transcobalamin in bovine milk: Genetic variation and genome-wide association with loci along the genome. JDS COMMUNICATIONS 2021; 2:127-131. [PMID: 36339496 PMCID: PMC9623645 DOI: 10.3168/jdsc.2020-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/19/2021] [Indexed: 06/16/2023]
Abstract
In human nutrition, bovine milk is an essential source of bioavailable vitamin B12 and B12-binding proteins, including transcobalamin. In this study, we estimated genetic parameters for milk content of vitamin B12 and transcobalamin using milk samples from 341 and 663 Danish Holstein cows, respectively. Additionally, we conducted whole-genome association analysis to identify SNP and genes associated with vitamin B12 and transcobalamin. Our results indicated moderate to high heritability for vitamin B12 (0.37 ± 0.18) and transcobalamin (0.61 ± 0.13) content in the Danish Holstein. With a significance threshold of -log10 P-value > 5.87, significant associations were detected between SNP in Bos taurus autosome (BTA)17 and the log-transformed transcobalamin content of milk; no significant association was detected for vitamin B12. The significant region in BTA17 was imputed to full sequence for further fine mapping, and the SNP with the most significant associations to transcobalamin were assigned to the transcobalamin 2 (TCN2) gene.
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Affiliation(s)
- Grum Gebreyesus
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, PO Box 50, DK-8830 Tjele, Denmark
| | - Nina Aagaard Poulsen
- Department of Food Science, Aarhus University, Agro Food Park 48, DK-8200 Aarhus N, Denmark
| | - Mette Krogh Larsen
- Department of Food Science, Aarhus University, Agro Food Park 48, DK-8200 Aarhus N, Denmark
- Arla Foods Amba, Mæalkevejen 4, DK-6920 Videbæk, Denmark
| | - Lotte Bach Larsen
- Department of Food Science, Aarhus University, Agro Food Park 48, DK-8200 Aarhus N, Denmark
| | - Esben Skipper Sørensen
- Molecular Nutrition, Department of Molecular Biology and Genetics, Aarhus University, Gustav Wieds Vej 10, DK-8000 Aarhus C, Denmark
| | - Christian Würtz Heegaard
- Molecular Nutrition, Department of Molecular Biology and Genetics, Aarhus University, Gustav Wieds Vej 10, DK-8000 Aarhus C, Denmark
| | - Bart Buitenhuis
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, PO Box 50, DK-8830 Tjele, Denmark
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168
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Benson KA, Murray SL, Senum SR, Elhassan E, Conlon ET, Kennedy C, Conlon S, Gilbert E, Connaughton D, O'Hara P, Khamis S, Cormican S, Brody LC, Molloy AM, Lynch SA, Casserly L, Griffin MD, Carton R, Yachnin K, Harris PC, Cavalleri GL, Conlon P. The genetic landscape of polycystic kidney disease in Ireland. Eur J Hum Genet 2021; 29:827-838. [PMID: 33454723 PMCID: PMC8110806 DOI: 10.1038/s41431-020-00806-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 02/08/2023] Open
Abstract
Polycystic kidney diseases (PKDs) comprise the most common Mendelian forms of renal disease. It is characterised by the development of fluid-filled renal cysts, causing progressive loss of kidney function, culminating in the need for renal replacement therapy or kidney transplant. Ireland represents a valuable region for the genetic study of PKD, as family sizes are traditionally large and the population relatively homogenous. Studying a cohort of 169 patients, we describe the genetic landscape of PKD in Ireland for the first time, compare the clinical features of patients with and without a molecular diagnosis and correlate disease severity with autosomal dominant pathogenic variant type. Using a combination of molecular genetic tools, including targeted next-generation sequencing, we report diagnostic rates of 71-83% in Irish PKD patients, depending on which variant classification guidelines are used (ACMG or Mayo clinic respectively). We have catalogued a spectrum of Irish autosomal dominant PKD pathogenic variants including 36 novel variants. We illustrate how apparently unrelated individuals carrying the same autosomal dominant pathogenic variant are highly likely to have inherited that variant from a common ancestor. We highlight issues surrounding the implementation of the ACMG guidelines for variant pathogenicity interpretation in PKD, which have important implications for clinical genetics.
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Affiliation(s)
- Katherine A Benson
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Susan L Murray
- Department of Nephrology, Beaumont Hospital, Dublin, Ireland
| | - Sarah R Senum
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | | | - Eoin T Conlon
- Department of Nephrology, Beaumont Hospital, Dublin, Ireland
| | - Claire Kennedy
- Department of Nephrology, Beaumont Hospital, Dublin, Ireland
| | - Shane Conlon
- Department of Nephrology, Beaumont Hospital, Dublin, Ireland
| | - Edmund Gilbert
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Paul O'Hara
- Department of Renal Medicine, University Hospital Limerick, Limerick, Ireland
| | - Sarah Khamis
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Sarah Cormican
- Regenerative Medicine Institute (REMEDI) at CÚRAM Centre for Research in Medical Devices, School of Medicine, National University of Ireland Galway, Galway, Ireland
- Nephrology Department, Galway University Hospitals, Saolta University Healthcare Group, Galway, Ireland
| | - Lawrence C Brody
- Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Anne M Molloy
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Sally Ann Lynch
- Department of Clinical Genetics, Children's University Hospital, Temple Street, Dublin, Ireland
| | - Liam Casserly
- Department of Renal Medicine, University Hospital Limerick, Limerick, Ireland
| | - Matthew D Griffin
- Regenerative Medicine Institute (REMEDI) at CÚRAM Centre for Research in Medical Devices, School of Medicine, National University of Ireland Galway, Galway, Ireland
- Nephrology Department, Galway University Hospitals, Saolta University Healthcare Group, Galway, Ireland
| | - Robert Carton
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Gianpiero L Cavalleri
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons in Ireland, Dublin, Ireland.
| | - Peter Conlon
- School of Pharmacy and Biomolecular Science, Royal College of Surgeons in Ireland, Dublin, Ireland.
- Department of Nephrology, Beaumont Hospital, Dublin, Ireland.
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169
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Yan W, Wang B, Chan E, Mitchell-Olds T. Genetic architecture and adaptation of flowering time among environments. THE NEW PHYTOLOGIST 2021; 230:1214-1227. [PMID: 33484593 PMCID: PMC8193995 DOI: 10.1111/nph.17229] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/07/2021] [Indexed: 05/17/2023]
Abstract
The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.
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Affiliation(s)
- Wenjie Yan
- College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
- Department of Biology, Duke University, Box 90338, Durham, NC 27708, USA
| | - Baosheng Wang
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou, 510650 China
| | - Emily Chan
- Department of Biology, Duke University, Box 90338, Durham, NC 27708, USA
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170
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Kutanan W, Liu D, Kampuansai J, Srikummool M, Srithawong S, Shoocongdej R, Sangkhano S, Ruangchai S, Pittayaporn P, Arias L, Stoneking M. Reconstructing the Human Genetic History of Mainland Southeast Asia: Insights from Genome-Wide Data from Thailand and Laos. Mol Biol Evol 2021; 38:3459-3477. [PMID: 33905512 PMCID: PMC8321548 DOI: 10.1093/molbev/msab124] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Thailand and Laos, located in the center of Mainland Southeast Asia (MSEA), harbor diverse ethnolinguistic groups encompassing all five language families of MSEA: Tai-Kadai (TK), Austroasiatic (AA), Sino-Tibetan (ST), Hmong-Mien (HM), and Austronesian (AN). Previous genetic studies of Thai/Lao populations have focused almost exclusively on uniparental markers and there is a paucity of genome-wide studies. We therefore generated genome-wide SNP data for 33 ethnolinguistic groups, belonging to the five MSEA language families from Thailand and Laos, and analyzed these together with data from modern Asian populations and SEA ancient samples. Overall, we find genetic structure according to language family, albeit with heterogeneity in the AA-, HM-, and ST-speaking groups, and in the hill tribes, that reflects both population interactions and genetic drift. For the TK speaking groups, we find localized genetic structure that is driven by different levels of interaction with other groups in the same geographic region. Several Thai groups exhibit admixture from South Asia, which we date to ∼600-1000 years ago, corresponding to a time of intensive international trade networks that had a major cultural impact on Thailand. An AN group from Southern Thailand shows both South Asian admixture as well as overall affinities with AA-speaking groups in the region, suggesting an impact of cultural diffusion. Overall, we provide the first detailed insights into the genetic profiles of Thai/Lao ethnolinguistic groups, which should be helpful for reconstructing human genetic history in MSEA and selecting populations for participation in ongoing whole genome sequence and biomedical studies.
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Affiliation(s)
- Wibhu Kutanan
- Department of Biology, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Dang Liu
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Jatupol Kampuansai
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand.,Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Metawee Srikummool
- Department of Biochemistry, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand
| | - Suparat Srithawong
- Department of Biology, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Rasmi Shoocongdej
- Department of Archaeology, Faculty of Archaeology, Silpakorn University, Bangkok, Thailand
| | - Sukrit Sangkhano
- School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand
| | - Sukhum Ruangchai
- Department of Physics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Pittayawat Pittayaporn
- Department of Linguistics and Southeast Asian Linguistics Research Unit, Faculty of Arts, Chulalongkorn University, Bangkok, Thailand
| | - Leonardo Arias
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.,Centre for Linguistics, Faculty of Humanities, Leiden University, Leiden, The Netherlands
| | - Mark Stoneking
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
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171
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Liu D, Duong NT, Ton ND, Van Phong N, Pakendorf B, Van Hai N, Stoneking M. Extensive Ethnolinguistic Diversity in Vietnam Reflects Multiple Sources of Genetic Diversity. Mol Biol Evol 2021; 37:2503-2519. [PMID: 32344428 PMCID: PMC7475039 DOI: 10.1093/molbev/msaa099] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Vietnam features extensive ethnolinguistic diversity and occupies a key position in Mainland Southeast Asia. Yet, the genetic diversity of Vietnam remains relatively unexplored, especially with genome-wide data, because previous studies have focused mainly on the majority Kinh group. Here, we analyze newly generated genome-wide single-nucleotide polymorphism data for the Kinh and 21 additional ethnic groups in Vietnam, encompassing all five major language families in Mainland Southeast Asia. In addition to analyzing the allele and haplotype sharing within the Vietnamese groups, we incorporate published data from both nearby modern populations and ancient samples for comparison. In contrast to previous studies that suggested a largely indigenous origin for Vietnamese genetic diversity, we find that Vietnamese ethnolinguistic groups harbor multiple sources of genetic diversity that likely reflect different sources for the ancestry associated with each language family. However, linguistic diversity does not completely match genetic diversity: There have been extensive interactions between the Hmong-Mien and Tai-Kadai groups; different Austro-Asiatic groups show different affinities with other ethnolinguistic groups; and we identified a likely case of cultural diffusion in which some Austro-Asiatic groups shifted to Austronesian languages during the past 2,500 years. Overall, our results highlight the importance of genome-wide data from dense sampling of ethnolinguistic groups in providing new insights into the genetic diversity and history of an ethnolinguistically diverse region, such as Vietnam.
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Affiliation(s)
- Dang Liu
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Nguyen Thuy Duong
- Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Nguyen Dang Ton
- Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Nguyen Van Phong
- Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Brigitte Pakendorf
- Laboratoire Dynamique du Langage, UMR5596, CNRS & Université de Lyon, Lyon, France
| | - Nong Van Hai
- Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Mark Stoneking
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
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172
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Wu Z, Bortoluzzi C, Derks MFL, Liu L, Bosse M, Hiemstra SJ, Groenen MAM, Crooijmans RPMA. Heterogeneity of a dwarf phenotype in Dutch traditional chicken breeds revealed by genomic analyses. Evol Appl 2021; 14:1095-1108. [PMID: 33897823 PMCID: PMC8061282 DOI: 10.1111/eva.13183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/29/2020] [Accepted: 12/06/2020] [Indexed: 12/14/2022] Open
Abstract
The growth of animals is a complex trait, in chicken resulting in a diverse variety of forms, caused by a heterogeneous genetic basis. Bantam chicken, known as an exquisite form of dwarfism, has been used for crossbreeding to create corresponding dwarf counterparts for native fowls in the Dutch populations. Here, we demonstrate the heterogeneity of the bantam trait in Dutch chickens and reveal the underlying genetic causes, using whole-genome sequence data from matching pairs of bantam and normal-sized breeds. During the bantam-oriented crossbreeding, various bantam origins were used to introduce the bantam phenotype, and three major bantam sources were identified and clustered. The genome-wide association studies revealed multiple genetic variants and genes associated with bantam phenotype, including HMGA2 and PRDM16, genes involved in body growth and stature. The comparison of associated variants among studies illustrated differences related to divergent bantam origins, suggesting a clear heterogeneity among bantam breeds. We show that in neo-bantam breeds, the bantam-related regions underwent a strong haplotype introgression from the bantam source, outcompeting haplotypes from the normal-sized counterpart. The bantam heterogeneity is further confirmed by the presence of multiple haplotypes comprising associated alleles, which suggests the selection of the bantam phenotype is likely subject to a convergent direction across populations. Our study demonstrates that the diverse history of human-mediated crossbreeding has contributed to the complexity and heterogeneity of the bantam phenotype.
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Affiliation(s)
- Zhou Wu
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Chiara Bortoluzzi
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Martijn F. L. Derks
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Langqing Liu
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Mirte Bosse
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Sipke Joost Hiemstra
- Centre for Genetic Resources, the Netherlands (CGN) of Wageningen University & ResearchWageningenThe Netherlands
| | - Martien A. M. Groenen
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
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173
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Hill CB, Angessa TT, Zhang XQ, Chen K, Zhou G, Tan C, Wang P, Westcott S, Li C. A global barley panel revealing genomic signatures of breeding in modern Australian cultivars. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 106:419-434. [PMID: 33506596 DOI: 10.1111/tpj.15173] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 01/08/2021] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
The future of plant cultivar improvement lies in the evaluation of genetic resources from currently available germplasm. Today's gene pool of crop genetic diversity has been shaped during domestication and more recently by breeding. Recent efforts in plant breeding have been aimed at developing new and improved varieties from poorly adapted crops to suit local environments. However, the impact of these breeding efforts is poorly understood. Here, we assess the contributions of both historical and recent breeding efforts to local adaptation and crop improvement in a global barley panel by analysing the distribution of genetic variants with respect to geographic region or historical breeding category. By tracing the impact that breeding had on the genetic diversity of Hordeum vulgare (barley) released in Australia, where the history of barley production is relatively young, we identify 69 candidate regions within 922 genes that were under selection pressure. We also show that modern Australian barley varieties exhibit 12% higher genetic diversity than historical cultivars. Finally, field-trialling and phenotyping for agriculturally relevant traits across a diverse range of Australian environments suggests that genomic regions under strong breeding selection and their candidate genes are closely associated with key agronomic traits. In conclusion, our combined data set and germplasm collection provide a rich source of genetic diversity that can be applied to understanding and improving environmental adaptation and enhanced yields.
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Affiliation(s)
- Camilla Beate Hill
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Tefera Tolera Angessa
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Xiao-Qi Zhang
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Kefei Chen
- Agriculture and Food, Department of Primary Industries and Regional Development, 3 Baron-Hay Ct, South Perth, WA, 6151, Australia
- Statistics for the Australian Grains Industry (SAGI) West, Faculty of Science and Engineering, Curtin University, Kent Street, Bentley, WA, 6102, Australia
| | - Gaofeng Zhou
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
- Agriculture and Food, Department of Primary Industries and Regional Development, 3 Baron-Hay Ct, South Perth, WA, 6151, Australia
| | - Cong Tan
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Penghao Wang
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
| | - Sharon Westcott
- Agriculture and Food, Department of Primary Industries and Regional Development, 3 Baron-Hay Ct, South Perth, WA, 6151, Australia
| | - Chengdao Li
- Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia
- Agriculture and Food, Department of Primary Industries and Regional Development, 3 Baron-Hay Ct, South Perth, WA, 6151, Australia
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174
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Capodiferro MR, Aram B, Raveane A, Rambaldi Migliore N, Colombo G, Ongaro L, Rivera J, Mendizábal T, Hernández-Mora I, Tribaldos M, Perego UA, Li H, Scheib CL, Modi A, Gòmez-Carballa A, Grugni V, Lombardo G, Hellenthal G, Pascale JM, Bertolini F, Grieco GS, Cereda C, Lari M, Caramelli D, Pagani L, Metspalu M, Friedrich R, Knipper C, Olivieri A, Salas A, Cooke R, Montinaro F, Motta J, Torroni A, Martín JG, Semino O, Malhi RS, Achilli A. Archaeogenomic distinctiveness of the Isthmo-Colombian area. Cell 2021; 184:1706-1723.e24. [PMID: 33761327 PMCID: PMC8024902 DOI: 10.1016/j.cell.2021.02.040] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 10/20/2020] [Accepted: 02/18/2021] [Indexed: 01/09/2023]
Abstract
The recently enriched genomic history of Indigenous groups in the Americas is still meager concerning continental Central America. Here, we report ten pre-Hispanic (plus two early colonial) genomes and 84 genome-wide profiles from seven groups presently living in Panama. Our analyses reveal that pre-Hispanic demographic events contributed to the extensive genetic structure currently seen in the area, which is also characterized by a distinctive Isthmo-Colombian Indigenous component. This component drives these populations on a specific variability axis and derives from the local admixture of different ancestries of northern North American origin(s). Two of these ancestries were differentially associated to Pleistocene Indigenous groups that also moved into South America, leaving heterogenous genetic footprints. An additional Pleistocene ancestry was brought by a still unsampled population of the Isthmus (UPopI) that remained restricted to the Isthmian area, expanded locally during the early Holocene, and left genomic traces up to the present day.
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Affiliation(s)
| | - Bethany Aram
- Department of Geography, History and Philosophy, the Pablo de Olavide University of Seville, Seville 41013, Spain
| | - Alessandro Raveane
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy; Laboratory of Hematology-Oncology, European Institute of Oncology IRCCS, Milan 20141, Italy
| | - Nicola Rambaldi Migliore
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Giulia Colombo
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Linda Ongaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Javier Rivera
- Department of History and Social Sciences, Universidad del Norte, Barranquilla 080001, Colombia
| | - Tomás Mendizábal
- Patronato Panamá Viejo, Panama City 0823-05096, Panama; Coiba Scientific Station (COIBA AIP), City of Knowledge, Clayton 0843-03081, Panama
| | - Iosvany Hernández-Mora
- Department of History and Social Sciences, Universidad del Norte, Barranquilla 080001, Colombia
| | - Maribel Tribaldos
- Gorgas Memorial Institute for Health Studies, Panama City 0816-02593, Panama
| | - Ugo Alessandro Perego
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Hongjie Li
- Department of Anthropology, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
| | - Christiana Lyn Scheib
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Alessandra Modi
- Department of Biology, University of Florence, Florence 50122, Italy
| | - Alberto Gòmez-Carballa
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; GenPoB Research Group, Instituto de Investigación Sanitarias (IDIS), Hospital Clínico Universitario de Santiago de Compostela (SERGAS), 15706 Galicia, Spain
| | - Viola Grugni
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Gianluca Lombardo
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Garrett Hellenthal
- UCL Genetics Institute (UGI), Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Juan Miguel Pascale
- Gorgas Memorial Institute for Health Studies, Panama City 0816-02593, Panama
| | - Francesco Bertolini
- Laboratory of Hematology-Oncology, European Institute of Oncology IRCCS, Milan 20141, Italy
| | | | - Cristina Cereda
- Genomic and Post-Genomic Center, National Neurological Institute C. Mondino, Pavia 27100, Italy
| | - Martina Lari
- Department of Biology, University of Florence, Florence 50122, Italy
| | - David Caramelli
- Department of Biology, University of Florence, Florence 50122, Italy
| | - Luca Pagani
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Biology, University of Padua, Padua 35121, Italy
| | - Mait Metspalu
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Ronny Friedrich
- Curt Engelhorn Center Archaeometry (CEZA), Mannheim 68159, Germany
| | - Corina Knipper
- Curt Engelhorn Center Archaeometry (CEZA), Mannheim 68159, Germany
| | - Anna Olivieri
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Antonio Salas
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, 15782 Galicia, Spain; GenPoB Research Group, Instituto de Investigación Sanitarias (IDIS), Hospital Clínico Universitario de Santiago de Compostela (SERGAS), 15706 Galicia, Spain
| | - Richard Cooke
- Smithsonian Tropical Research Institute, Panama City 0843-03092, Panama; Sistema Nacional de Investigadores, Secretaría Nacional de Ciencia y Tecnología, Ciudad del Saber, Clayton 0816-02852, Panama
| | - Francesco Montinaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Biology-Genetics, University of Bari, Bari 70125, Italy
| | - Jorge Motta
- Gorgas Memorial Institute for Health Studies, Panama City 0816-02593, Panama
| | - Antonio Torroni
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Juan Guillermo Martín
- Department of History and Social Sciences, Universidad del Norte, Barranquilla 080001, Colombia; Coiba Scientific Station (COIBA AIP), City of Knowledge, Clayton 0843-03081, Panama
| | - Ornella Semino
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy
| | - Ripan Singh Malhi
- Department of Anthropology, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
| | - Alessandro Achilli
- Department of Biology and Biotechnology "L. Spallanzani," University of Pavia, Pavia 27100, Italy.
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175
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Wang X, Ju Z, Jiang Q, Zhong J, Liu C, Wang J, Hoff JL, Schnabel RD, Zhao H, Gao Y, Liu W, Wang L, Gao Y, Yang C, Hou M, Huang N, Regitano LCA, Porto-Neto LR, Decker JE, Taylor JF, Huang J. Introgression, admixture, and selection facilitate genetic adaptation to high-altitude environments in cattle. Genomics 2021; 113:1491-1503. [PMID: 33771637 DOI: 10.1016/j.ygeno.2021.03.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/17/2021] [Accepted: 03/21/2021] [Indexed: 12/30/2022]
Abstract
Domestication and subsequent selection of cattle to form breeds and biological types that can adapt to different environments partitioned ancestral genetic diversity into distinct modern lineages. Genome-wide selection particularly for adaptation to extreme environments left detectable signatures genome-wide. We used high-density genotype data for 42 cattle breeds and identified the influence of Bos grunniens and Bos javanicus on the formation of Chinese indicine breeds that led to their divergence from India-origin zebu. We also found evidence for introgression, admixture, and migration in most of the Chinese breeds. Selection signature analyses between high-altitude (≥1800 m) and low-altitude adapted breeds (<1500 m) revealed candidate genes (ACSS2, ALDOC, EPAS1, EGLN1, NUCB2) and pathways that are putatively involved in hypoxia adaptation. Immunohistochemical, real-time PCR and CRISPR/cas9 ACSS2-knockout analyses suggest that the up-regulation of ACSS2 expression in the liver promotes the metabolic adaptation of cells to hypoxia via the hypoxia-inducible factor pathway. High altitude adaptation involved the introgression of alleles from high-altitude adapted yaks into Chinese Bos taurus taurus prior to their formation into recognized breeds and followed by selection. In addition to selection, adaptation to high altitude environments has been facilitated by admixture and introgression with locally adapted cattle populations.
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Affiliation(s)
- Xiuge Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Zhihua Ju
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Qiang Jiang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Jifeng Zhong
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China; Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu 210014, China
| | - Chengkun Liu
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Jinpeng Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Jesse L Hoff
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA; Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Han Zhao
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China; College of Life Sciences, Shandong Normal University, Jinan, Shandong 250014, China
| | - Yaping Gao
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Wenhao Liu
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Lingling Wang
- Shandong OX Livestock Breeding Co., Ltd., Jinan, Shandong 250131, China
| | - Yundong Gao
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China; Shandong OX Livestock Breeding Co., Ltd., Jinan, Shandong 250131, China
| | - Chunhong Yang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Minghai Hou
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China
| | - Ning Huang
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan 650201, China
| | | | | | - Jared E Decker
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Jeremy F Taylor
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA.
| | - Jinming Huang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250131, China; College of Life Sciences, Shandong Normal University, Jinan, Shandong 250014, China.
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176
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Abstract
A key link to understand human history in Island Southeast Asia is the Philippine archipelago and its poorly investigated genetic diversity. We analyzed the most comprehensive set of population-genomic data for the Philippines: 1,028 individuals covering 115 indigenous communities. We demonstrate that the Philippines were populated by at least five waves of human migration. The Cordillerans migrated into the Philippines prior to the arrival of rice agriculture, where some remain as the least admixed East Asians carrying an ancestry shared by all Austronesian-speaking populations, thereby challenging an exclusive out-of-Taiwan model of joint farming–language–people dispersal. Altogether, our findings portray the Philippines as a crucial gateway, with a multilayered history, that ultimately changed the genetic landscape of the Asia-Pacific region. Island Southeast Asia has recently produced several surprises regarding human history, but the region’s complex demography remains poorly understood. Here, we report ∼2.3 million genotypes from 1,028 individuals representing 115 indigenous Philippine populations and genome-sequence data from two ∼8,000-y-old individuals from Liangdao in the Taiwan Strait. We show that the Philippine islands were populated by at least five waves of human migration: initially by Northern and Southern Negritos (distantly related to Australian and Papuan groups), followed by Manobo, Sama, Papuan, and Cordilleran-related populations. The ancestors of Cordillerans diverged from indigenous peoples of Taiwan at least ∼8,000 y ago, prior to the arrival of paddy field rice agriculture in the Philippines ∼2,500 y ago, where some of their descendants remain to be the least admixed East Asian groups carrying an ancestry shared by all Austronesian-speaking populations. These observations contradict an exclusive “out-of-Taiwan” model of farming–language–people dispersal within the last four millennia for the Philippines and Island Southeast Asia. Sama-related ethnic groups of southwestern Philippines additionally experienced some minimal South Asian gene flow starting ∼1,000 y ago. Lastly, only a few lowlanders, accounting for <1% of all individuals, presented a low level of West Eurasian admixture, indicating a limited genetic legacy of Spanish colonization in the Philippines. Altogether, our findings reveal a multilayered history of the Philippines, which served as a crucial gateway for the movement of people that ultimately changed the genetic landscape of the Asia-Pacific region.
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177
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Pérez-Enciso M, Steibel JP. Phenomes: the current frontier in animal breeding. Genet Sel Evol 2021; 53:22. [PMID: 33673800 PMCID: PMC7934239 DOI: 10.1186/s12711-021-00618-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/22/2021] [Indexed: 12/13/2022] Open
Abstract
Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and ‘missingness’ of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.
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Affiliation(s)
- Miguel Pérez-Enciso
- ICREA, Passeig de Lluís Companys 23, 08010, Barcelona, Spain. .,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Bellaterra, 08193, Barcelona, Spain.
| | - Juan P Steibel
- Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA.,Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA
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178
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Jain A, Sharma D, Bajaj A, Gupta V, Scaria V. Founder variants and population genomes-Toward precision medicine. ADVANCES IN GENETICS 2021; 107:121-152. [PMID: 33641745 DOI: 10.1016/bs.adgen.2020.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Human migration and community specific cultural practices have contributed to founder events and enrichment of the variants associated with genetic diseases. While many founder events in isolated populations have remained uncharacterized, the application of genomics in clinical settings as well as for population scale studies in the recent years have provided an unprecedented push towards identification of founder variants associated with human health and disease. The discovery and characterization of founder variants could have far reaching implications not only in understanding the history or genealogy of the disease, but also in implementing evidence based policies and genetic testing frameworks. This further enables precise diagnosis and prevention in an attempt towards precision medicine. This review provides an overview of founder variants along with methods and resources cataloging them. We have also discussed the public health implications and examples of prevalent disease associated founder variants in specific populations.
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Affiliation(s)
- Abhinav Jain
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Disha Sharma
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Anjali Bajaj
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Vishu Gupta
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Vinod Scaria
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
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179
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Naseri A, Tang K, Geng X, Shi J, Zhang J, Shakya P, Liu X, Zhang S, Zhi D. Personalized genealogical history of UK individuals inferred from biobank-scale IBD segments. BMC Biol 2021; 19:32. [PMID: 33593342 PMCID: PMC7888130 DOI: 10.1186/s12915-021-00964-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/19/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The genealogical histories of individuals within populations are of interest to studies aiming both to uncover detailed pedigree information and overall quantitative population demographic histories. However, the analysis of quantitative details of individual genealogical histories has faced challenges from incomplete available pedigree records and an absence of objective and quantitative details in pedigree information. Although complete pedigree information for most individuals is difficult to track beyond a few generations, it is possible to describe a person's genealogical history using their genetic relatives revealed by identity by descent (IBD) segments-long genomic segments shared by two individuals within a population, which are identical due to inheritance from common ancestors. When modern biobanks collect genotype information for a significant fraction of a population, dense genetic connections of a person can be traced using such IBD segments, offering opportunities to characterize individuals in the context of the underlying populations. Here, we conducted an individual-centric analysis of IBD segments among the UK Biobank participants that represent 0.7% of the UK population. RESULTS We made a high-quality call set of IBD segments over 5 cM among all 500,000 UK Biobank participants. On average, one UK individual shares IBD segments with 14,000 UK Biobank participants, which we refer to as "relatives." Using these segments, approximately 80% of a person's genome can be imputed. We subsequently propose genealogical descriptors based on the genetic connections of relative cohorts of individuals sharing at least one IBD segment and show that such descriptors offer important information about one's genetic makeup, personal genealogical history, and social behavior. Through analysis of relative counts sharing segments at different lengths, we identified a group, potentially British Jews, who has a distinct pattern of familial expansion history. Finally, using the enrichment of relatives in one's neighborhood, we identified regional variations of personal preference favoring living closer to one's extended families. CONCLUSIONS Our analysis revealed genetic makeup, personal genealogical history, and social behaviors at the population scale, opening possibilities for further studies of individual's genetic connections in biobank data.
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Affiliation(s)
- Ardalan Naseri
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Kecong Tang
- Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA
| | - Xin Geng
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Junjie Shi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jing Zhang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pramesh Shakya
- Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA
| | - Xiaoming Liu
- USF Genomics, College of Public Health, University of South Florida, Tampa, FL, 33612, USA
| | - Shaojie Zhang
- Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA.
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Center for Precision Health, School of Biomedical Informatics, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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180
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Seidensticker D, Hubau W, Verschuren D, Fortes-Lima C, de Maret P, Schlebusch CM, Bostoen K. Population collapse in Congo rainforest from 400 CE urges reassessment of the Bantu Expansion. SCIENCE ADVANCES 2021; 7:7/7/eabd8352. [PMID: 33579711 PMCID: PMC7880602 DOI: 10.1126/sciadv.abd8352] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 12/28/2020] [Indexed: 06/12/2023]
Abstract
The present-day distribution of Bantu languages is commonly thought to reflect the early stages of the Bantu Expansion, the greatest migration event in African prehistory. Using 1149 radiocarbon dates linked to 115 pottery styles recovered from 726 sites throughout the Congo rainforest and adjacent areas, we show that this is not the case. Two periods of more intense human activity, each consisting of an expansion phase with widespread pottery styles and a regionalization phase with many more local pottery styles, are separated by a widespread population collapse between 400 and 600 CE followed by major resettlement centuries later. Coinciding with wetter climatic conditions, the collapse was possibly promoted by a prolonged epidemic. Comparison of our data with genetic and linguistic evidence further supports a spread-over-spread model for the dispersal of Bantu speakers and their languages.
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Affiliation(s)
- Dirk Seidensticker
- Department of Languages and Cultures, BantUGent-UGent Centre for Bantu Studies, Ghent University, Ghent, Belgium.
| | - Wannes Hubau
- Department of Languages and Cultures, BantUGent-UGent Centre for Bantu Studies, Ghent University, Ghent, Belgium.
- Royal Museum for Central Africa, Service of Wood Biology, Tervuren, Belgium
| | - Dirk Verschuren
- Department of Biology, Limnology Unit, Ghent University, Ghent, Belgium
| | - Cesar Fortes-Lima
- Department of Organismal Biology, Human Evolution, Uppsala University, Uppsala, Sweden
| | - Pierre de Maret
- Faculté de Philosophie et Sciences sociales, Université libre de Bruxelles, Brussels, Belgium
| | - Carina M Schlebusch
- Department of Organismal Biology, Human Evolution, Uppsala University, Uppsala, Sweden
- Palaeo-Research Institute, University of Johannesburg, Auckland Park, South Africa
- SciLifeLab, Uppsala, Sweden
| | - Koen Bostoen
- Department of Languages and Cultures, BantUGent-UGent Centre for Bantu Studies, Ghent University, Ghent, Belgium
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181
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Ho-Le TP, Tran HTT, Center JR, Eisman JA, Nguyen HT, Nguyen TV. Assessing the clinical utility of genetic profiling in fracture risk prediction: a decision curve analysis. Osteoporos Int 2021; 32:271-280. [PMID: 32789607 DOI: 10.1007/s00198-020-05403-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/23/2020] [Indexed: 10/23/2022]
Abstract
UNLABELLED Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD. INTRODUCTION Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction. METHODS The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds. RESULTS In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models. CONCLUSIONS The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.
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Affiliation(s)
- T P Ho-Le
- Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia
- Faculty of Engineering and Information Technology, Hatinh University, Hatinh, Vietnam
| | - H T T Tran
- Faculty of Engineering and Information Technology, Hatinh University, Hatinh, Vietnam
| | - J R Center
- Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia
- St Vincent Clinical School, UNSW Sydney, Sydney, Australia
| | - J A Eisman
- Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia
- St Vincent Clinical School, UNSW Sydney, Sydney, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia
| | - H T Nguyen
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia
| | - T V Nguyen
- Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia.
- St Vincent Clinical School, UNSW Sydney, Sydney, Australia.
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia.
- School of Biomedical Engineering, University of Technology, Sydney, Australia.
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182
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Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, Pitsillides AN, LeFaive J, Lee SB, Tian X, Browning BL, Das S, Emde AK, Clarke WE, Loesch DP, Shetty AC, Blackwell TW, Smith AV, Wong Q, Liu X, Conomos MP, Bobo DM, Aguet F, Albert C, Alonso A, Ardlie KG, Arking DE, Aslibekyan S, Auer PL, Barnard J, Barr RG, Barwick L, Becker LC, Beer RL, Benjamin EJ, Bielak LF, Blangero J, Boehnke M, Bowden DW, Brody JA, Burchard EG, Cade BE, Casella JF, Chalazan B, Chasman DI, Chen YDI, Cho MH, Choi SH, Chung MK, Clish CB, Correa A, Curran JE, Custer B, Darbar D, Daya M, de Andrade M, DeMeo DL, Dutcher SK, Ellinor PT, Emery LS, Eng C, Fatkin D, Fingerlin T, Forer L, Fornage M, Franceschini N, Fuchsberger C, Fullerton SM, Germer S, Gladwin MT, Gottlieb DJ, Guo X, Hall ME, He J, Heard-Costa NL, Heckbert SR, Irvin MR, Johnsen JM, Johnson AD, Kaplan R, Kardia SLR, Kelly T, Kelly S, Kenny EE, Kiel DP, Klemmer R, Konkle BA, Kooperberg C, Köttgen A, Lange LA, Lasky-Su J, Levy D, Lin X, Lin KH, Liu C, Loos RJF, Garman L, Gerszten R, Lubitz SA, Lunetta KL, Mak ACY, Manichaikul A, Manning AK, Mathias RA, McManus DD, McGarvey ST, Meigs JB, Meyers DA, Mikulla JL, Minear MA, Mitchell BD, Mohanty S, Montasser ME, Montgomery C, Morrison AC, Murabito JM, Natale A, Natarajan P, Nelson SC, North KE, O'Connell JR, Palmer ND, Pankratz N, Peloso GM, Peyser PA, Pleiness J, Post WS, Psaty BM, Rao DC, Redline S, Reiner AP, Roden D, Rotter JI, Ruczinski I, Sarnowski C, Schoenherr S, Schwartz DA, Seo JS, Seshadri S, Sheehan VA, Sheu WH, Shoemaker MB, Smith NL, Smith JA, Sotoodehnia N, Stilp AM, Tang W, Taylor KD, Telen M, Thornton TA, Tracy RP, Van Den Berg DJ, Vasan RS, Viaud-Martinez KA, Vrieze S, Weeks DE, Weir BS, Weiss ST, Weng LC, Willer CJ, Zhang Y, Zhao X, Arnett DK, Ashley-Koch AE, Barnes KC, Boerwinkle E, Gabriel S, Gibbs R, Rice KM, Rich SS, Silverman EK, Qasba P, Gan W, Papanicolaou GJ, Nickerson DA, Browning SR, Zody MC, Zöllner S, Wilson JG, Cupples LA, Laurie CC, Jaquish CE, Hernandez RD, O'Connor TD, Abecasis GR. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 2021; 590:290-299. [PMID: 33568819 PMCID: PMC7875770 DOI: 10.1038/s41586-021-03205-y] [Citation(s) in RCA: 899] [Impact Index Per Article: 299.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 01/07/2021] [Indexed: 02/08/2023]
Abstract
The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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Affiliation(s)
- Daniel Taliun
- 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
| | - Daniel N Harris
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Michael D Kessler
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jedidiah Carlson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zachary A Szpiech
- Department of Biology, Pennsylvania State University, University Park, PA, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA
| | - Raul Torres
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah A Gagliano Taliun
- 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
| | | | | | - Hyun Min Kang
- 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
| | | | - Jonathon LeFaive
- 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
| | - Seung-Been Lee
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Xiaowen Tian
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Brian L Browning
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Sayantan Das
- 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
| | | | | | - Douglas P Loesch
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Amol C Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas W Blackwell
- 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
| | - Albert V Smith
- 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
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiaoming Liu
- USF Genomics, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Dean M Bobo
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
| | | | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | | | | | - Rebecca L Beer
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Michael Boehnke
- 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
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James F Casella
- Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
- Division of Pediatric Hematology, Johns Hopkins University, Baltimore, MD, USA
| | - Brandon Chalazan
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Mina K Chung
- Department of Cardiovascular Medicine, Heart & Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Clary B Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Dawood Darbar
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Michelle Daya
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Susan K Dutcher
- McDonnell Genome Institute, Washington University, St Louis, MO, USA
- Department of Genetics, Washington University, St Louis, MO, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Diane Fatkin
- Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Kensington, New South Wales, Australia
- Cardiology Department, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Tasha Fingerlin
- National Jewish Health, Center for Genes, Environment and Health, Denver, CO, USA
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Christian Fuchsberger
- 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
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Mark T Gladwin
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel J Gottlieb
- VA Boston Healthcare System, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael E Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
- Tulane University Translational Science Institute, Tulane University, New Orleans, LA, USA
| | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jill M Johnsen
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Andrew D Johnson
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Robert Kaplan
- Albert Einstein College of Medicine, New York, NY, USA
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
| | - Shannon Kelly
- Department of Epidemiology, Vitalant Research Institute, San Francisco, CA, USA
- Department of Pediatrics, UCSF Benioff Children's Hospital, Oakland, CA, USA
- Division of Pediatric Hematology, UCSF Benioff Children's Hospital, Oakland, CA, USA
| | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Douglas P Kiel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert Klemmer
- 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
| | - Barbara A Konkle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Leslie A Lange
- Department of Medicine, University of Colorado at Denver, Aurora, CO, USA
| | - Jessica Lasky-Su
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Levy
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Xihong Lin
- Biostatistics and Statistics, Harvard University, Boston, MA, USA
| | - Keng-Han Lin
- 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
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lori Garman
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | | | | | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Alisa K Manning
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David D McManus
- Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Stephen T McGarvey
- International Health Institute, Brown University, Providence, RI, USA
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, The Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | - Julie L Mikulla
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mollie A Minear
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Sanghamitra Mohanty
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
- Department of Internal Medicine, Dell Medical School, Austin, TX, USA
| | - May E Montasser
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne M Murabito
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Andrea Natale
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sarah C Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jacob Pleiness
- 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
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - D C Rao
- Division of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Dan Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sebastian Schoenherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Jeong-Sun Seo
- Precision Medicine Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Macrogen Inc, Seoul, Republic of Korea
- Gong Wu Genomic Medicine Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA
| | - Vivien A Sheehan
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Wayne H Sheu
- Taichung Veterans General Hospital Taiwan, Taichung City, Taiwan
| | | | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | | | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - David J Van Den Berg
- Center for Genetic Epidemiology, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ramachandran S Vasan
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce S Weir
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Scott T Weiss
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine-Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Yingze Zhang
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xutong Zhao
- 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
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Kathleen C Barnes
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston, Houston, TX, USA
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Stacey Gabriel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Pankaj Qasba
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Weiniu Gan
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - George J Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Northwest Genomics Center, Seattle, WA, USA
- Brotman Baty Institute, Seattle, WA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Sebastian Zöllner
- 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
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Framingham Heart Study, Framingham, MA, USA.
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Cashell E Jaquish
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Timothy D O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Gonçalo R Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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183
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Wang Z, Jiang Y, Bi H, Lu Z, Ma Y, Yang X, Chen N, Tian B, Liu B, Mao X, Ma T, DiFazio SP, Hu Q, Abbott RJ, Liu J. Hybrid speciation via inheritance of alternate alleles of parental isolating genes. MOLECULAR PLANT 2021; 14:208-222. [PMID: 33220509 DOI: 10.1016/j.molp.2020.11.008] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/11/2020] [Accepted: 11/13/2020] [Indexed: 05/21/2023]
Abstract
It is increasingly realized that homoploid hybrid speciation (HHS), which involves no change in chromosome number, is an important mechanism of speciation. HHS will likely increase in frequency as ecological and geographical barriers between species are continuing to be disrupted by human activities. HHS requires the establishment of reproductive isolation between a hybrid and its parents, but the underlying genes and genetic mechanisms remain largely unknown. In this study, we reveal by integrated approaches that reproductive isolation originates in one homoploid hybrid plant species through the inheritance of alternate alleles at genes that determine parental premating isolation. The parent species of this hybrid species are reproductively isolated by differences in flowering time and survivorship on soils containing high concentrations of iron. We found that the hybrid species inherits alleles of parental isolating major genes related to flowering time from one parent and alleles of major genes related to iron tolerance from the other parent. In this way, it became reproductively isolated from one parent by the difference in flowering time and from the other by habitat adaptation (iron tolerance). These findings and further modeling results suggest that HHS may occur relatively easily via the inheritance of alternate parental premating isolating genes and barriers.
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Affiliation(s)
- Zefu Wang
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Yuanzhong Jiang
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Hao Bi
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Zhiqiang Lu
- State Key Laboratory of Grassland Agro-Ecosystem, Innovation Institute of Ecology and Life Sciences, Lanzhou University, Lanzhou 730000, China; CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Yazhen Ma
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Xiaoyue Yang
- State Key Laboratory of Grassland Agro-Ecosystem, Innovation Institute of Ecology and Life Sciences, Lanzhou University, Lanzhou 730000, China
| | - Ningning Chen
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Bin Tian
- State Key Laboratory of Grassland Agro-Ecosystem, Innovation Institute of Ecology and Life Sciences, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Biodiversity Conservation in Southwest China, State Forestry and Grassland Administration, Southwest Forestry University, Kunming 650224, China
| | - Bingbing Liu
- Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China
| | - Xingxing Mao
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Tao Ma
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China
| | - Stephen P DiFazio
- Department of Biology, West Virginia University, Morgantown, WV 25606, USA
| | - Quanjun Hu
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China.
| | - Richard J Abbott
- School of Biology, University of St Andrews, St Andrews KY16 9TH, UK.
| | - Jianquan Liu
- Key Laboratory for Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences & State Key Lab of Hydraulics & Mountain River Engineering, Sichuan University, Chengdu 610065, China; State Key Laboratory of Grassland Agro-Ecosystem, Innovation Institute of Ecology and Life Sciences, Lanzhou University, Lanzhou 730000, China.
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184
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Kling D, Phillips C, Kennett D, Tillmar A. Investigative genetic genealogy: Current methods, knowledge and practice. Forensic Sci Int Genet 2021; 52:102474. [PMID: 33592389 DOI: 10.1016/j.fsigen.2021.102474] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/12/2021] [Accepted: 01/27/2021] [Indexed: 12/15/2022]
Abstract
Investigative genetic genealogy (IGG) has emerged as a new, rapidly growing field of forensic science. We describe the process whereby dense SNP data, commonly comprising more than half a million markers, are employed to infer distant relationships. By distant we refer to degrees of relatedness exceeding that of first cousins. We review how methods of relationship matching and SNP analysis on an enlarged scale are used in a forensic setting to identify a suspect in a criminal investigation or a missing person. There is currently a strong need in forensic genetics not only to understand the underlying models to infer relatedness but also to fully explore the DNA technologies and data used in IGG. This review brings together many of the topics and examines their effectiveness and operational limits, while suggesting future directions for their forensic validation. We further investigated the methods used by the major direct-to-consumer (DTC) genetic ancestry testing companies as well as submitting a questionnaire where providers of forensic genetic genealogy summarized their operation/services. Although most of the DTC market, and genetic genealogy in general, has undisclosed, proprietary algorithms we review the current knowledge where information has been discussed and published more openly.
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Affiliation(s)
- Daniel Kling
- Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden; Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway.
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Santiago de Compostela, Spain.
| | - Debbie Kennett
- Research Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Andreas Tillmar
- Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden; Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
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185
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Nyine M, Adhikari E, Clinesmith M, Aiken R, Betzen B, Wang W, Davidson D, Yu Z, Guo Y, He F, Akhunova A, Jordan KW, Fritz AK, Akhunov E. The Haplotype-Based Analysis of Aegilops tauschii Introgression Into Hard Red Winter Wheat and Its Impact on Productivity Traits. FRONTIERS IN PLANT SCIENCE 2021; 12:716955. [PMID: 34484280 PMCID: PMC8416154 DOI: 10.3389/fpls.2021.716955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 07/20/2021] [Indexed: 05/13/2023]
Abstract
The introgression from wild relatives have a great potential to broaden the availability of beneficial allelic diversity for crop improvement in breeding programs. Here, we assessed the impact of the introgression from 21 diverse accessions of Aegilops tauschii, the diploid ancestor of the wheat D genome, into 6 hard red winter wheat cultivars on yield and yield component traits. We used 5.2 million imputed D genome SNPs identified by the whole-genome sequencing of parental lines and the sequence-based genotyping of introgression population, including 351 BC1F3:5 lines. Phenotyping data collected from the irrigated and non-irrigated field trials revealed that up to 23% of the introgression lines (ILs) produce more grain than the parents and check cultivars. Based on 16 yield stability statistics, the yield of 12 ILs (3.4%) was stable across treatments, years, and locations; 5 of these lines were also high yielding lines, producing 9.8% more grain than the average yield of check cultivars. The most significant SNP- and haplotype-trait associations were identified on chromosome arms 2DS and 6DL for the spikelet number per spike (SNS), on chromosome arms 2DS, 3DS, 5DS, and 7DS for grain length (GL) and on chromosome arms 1DL, 2DS, 6DL, and 7DS for grain width (GW). The introgression of haplotypes from A. tauschii parents was associated with an increase in SNS, which was positively correlated with a heading date (HD), whereas the haplotypes from hexaploid wheat parents were associated with an increase in GW. We show that the haplotypes on 2DS associated with an increase in the spikelet number and HD are linked with multiple introgressed alleles of Ppd-D1 identified by the whole-genome sequencing of A. tauschii parents. Meanwhile, some introgressed haplotypes exhibited significant pleiotropic effects with the direction of effects on the yield component traits being largely consistent with the previously reported trade-offs, there were haplotype combinations associated with the positive trends in yield. The characterized repertoire of the introgressed haplotypes derived from A. tauschii accessions with the combined positive effects on yield and yield component traits in elite germplasm provides a valuable source of alleles for improving the productivity of winter wheat by optimizing the contribution of component traits to yield.
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Affiliation(s)
- Moses Nyine
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Elina Adhikari
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Marshall Clinesmith
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Robert Aiken
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Bliss Betzen
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Wei Wang
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Dwight Davidson
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Zitong Yu
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Yuanwen Guo
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Fei He
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Alina Akhunova
- Integrated Genomics Facility, Kansas State University, Manhattan, KS, United States
| | - Katherine W. Jordan
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
- United States Department of Agriculture, Agricultural Research Service Hard Winter Wheat Genetics Research Unit, Manhattan, KS, United States
| | - Allan K. Fritz
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Eduard Akhunov
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
- *Correspondence: Eduard Akhunov
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186
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Zhang W, Luo C, Scossa F, Zhang Q, Usadel B, Fernie AR, Mei H, Wen W. A phased genome based on single sperm sequencing reveals crossover pattern and complex relatedness in tea plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 105:197-208. [PMID: 33118252 DOI: 10.1111/tpj.15051] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 05/27/2023]
Abstract
For diploid organisms that are highly heterozygous, a phased haploid genome can greatly aid in functional genomic, population genetic and breeding studies. Based on the genome sequencing of 135 single sperm cells of the elite tea cultivar 'Fudingdabai', we herein phased the genome of Camellia sinensis, one of the most popular beverage crops worldwide. High-resolution genetic and recombination maps of Fudingdabai were constructed, which revealed that crossover (CO) positions were frequently located in the 5' and 3' ends of annotated genes, while CO distributions across the genome were random. The low CO frequency in tea can be explained by strong CO interference, and CO simulation revealed the proportion of interference insensitive CO ranged from 5.2% to 11.7%. We furthermore developed a method to infer the relatedness between tea accessions and detected complex kinship and genetic signatures of 106 tea accessions. Among them, 59 accessions were closely related with Fudingdabai and 31 of them were first-degree relatives. We additionally identified genes displaying allele specific expression patterns between the two haplotypes of Fudingdabai and genes displaying significantly differential expression levels between Fudingdabai and other haplotypes. These results lay the foundation for further investigation of genetic and epigenetic factors underpinning the regulation of gene expression and provide insights into the evolution of tea plants as well as a valuable genetic resource for future breeding efforts.
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Affiliation(s)
- Weiyi Zhang
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Cheng Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Federico Scossa
- Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, 14476, Germany
- Council for Agricultural Research and Economics, Research Center for Genomics and Bioinformatics, Via Ardeatina 546, Rome, 00178, Italy
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Björn Usadel
- Institute for Biological Data Science, Heinrich Heine University, Düsseldorf, Germany
- Institute of Bio- and Geosciences, IBG-4: Bioinformatics, CEPLAS, Forschungszentrum Jülich, Leo-Brandt-Straße, Jülich, 52425, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, 14476, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Hanwei Mei
- Shanghai Agrobiological Gene Center, Shanghai, 201106, China
| | - Weiwei Wen
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
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187
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Hao C, Jiao C, Hou J, Li T, Liu H, Wang Y, Zheng J, Liu H, Bi Z, Xu F, Zhao J, Ma L, Wang Y, Majeed U, Liu X, Appels R, Maccaferri M, Tuberosa R, Lu H, Zhang X. Resequencing of 145 Landmark Cultivars Reveals Asymmetric Sub-genome Selection and Strong Founder Genotype Effects on Wheat Breeding in China. MOLECULAR PLANT 2020; 13:1733-1751. [PMID: 32896642 DOI: 10.1016/j.molp.2020.09.001] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/19/2020] [Accepted: 09/02/2020] [Indexed: 05/18/2023]
Abstract
Controlled pedigrees and the multi-decade timescale of national crop plant breeding programs offer a unique experimental context for examining how selection affects plant genomes. More than 3000 wheat cultivars have been registered, released, and documented since 1949 in China. In this study, a set of 145 elite cultivars selected from historical points of wheat breeding in China were re-sequenced. A total of 43.75 Tb of sequence data were generated with an average read depth of 17.94× for each cultivar, and more than 60.92 million SNPs and 2.54 million InDels were captured, based on the Chinese Spring RefSeq genome v1.0. Seventy years of breeder-driven selection led to dramatic changes in grain yield and related phenotypes, with distinct genomic regions and phenotypes targeted by different breeders across the decades. There are very clear instances illustrating how introduced Italian and other foreign germplasm was integrated into Chinese wheat programs and reshaped the genomic landscape of local modern cultivars. Importantly, the resequencing data also highlighted significant asymmetric breeding selection among the three sub-genomes: this was evident in both the collinear blocks for homeologous chromosomes and among sets of three homeologous genes. Accumulation of more newly assembled genes in newer cultivars implied the potential value of these genes in breeding. Conserved and extended sharing of linkage disequilibrium (LD) blocks was highlighted among pedigree-related cultivars, in which fewer haplotype differences were detected. Fixation or replacement of haplotypes from founder genotypes after generations of breeding was related to their breeding value. Based on the haplotype frequency changes in LD blocks of pedigree-related cultivars, we propose a strategy for evaluating the breeding value of any given line on the basis of the accumulation (pyramiding) of beneficial haplotypes. Collectively, our study demonstrates the influence of "founder genotypes" on the output of breeding efforts over many decades and also suggests that founder genotype perspectives are in fact more dynamic when applied in the context of modern genomics-informed breeding.
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Affiliation(s)
- Chenyang Hao
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chengzhi Jiao
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Novogene Bioinformatics Institute, Beijing 100083, China
| | - Jian Hou
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Tian Li
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongxia Liu
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yuquan Wang
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jun Zheng
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hong Liu
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhihong Bi
- Novogene Bioinformatics Institute, Beijing 100083, China
| | - Fengfeng Xu
- Novogene Bioinformatics Institute, Beijing 100083, China
| | - Jing Zhao
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lin Ma
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yamei Wang
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Uzma Majeed
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xu Liu
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Rudi Appels
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport, and Resources, 5 Ring Road, La Trobe University, Bundoora, VIC 3083, Australia
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Hongfeng Lu
- Novogene Bioinformatics Institute, Beijing 100083, China.
| | - Xueyong Zhang
- Key Laboratory of Crop Gene Resources and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs/The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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188
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The genetic structure and adaptation of Andean highlanders and Amazonians are influenced by the interplay between geography and culture. Proc Natl Acad Sci U S A 2020; 117:32557-32565. [PMID: 33277433 DOI: 10.1073/pnas.2013773117] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Western South America was one of the worldwide cradles of civilization. The well-known Inca Empire was the tip of the iceberg of an evolutionary process that started 11,000 to 14,000 years ago. Genetic data from 18 Peruvian populations reveal the following: 1) The between-population homogenization of the central southern Andes and its differentiation with respect to Amazonian populations of similar latitudes do not extend northward. Instead, longitudinal gene flow between the northern coast of Peru, Andes, and Amazonia accompanied cultural and socioeconomic interactions revealed by archeology. This pattern recapitulates the environmental and cultural differentiation between the fertile north, where altitudes are lower, and the arid south, where the Andes are higher, acting as a genetic barrier between the sharply different environments of the Andes and Amazonia. 2) The genetic homogenization between the populations of the arid Andes is not only due to migrations during the Inca Empire or the subsequent colonial period. It started at least during the earlier expansion of the Wari Empire (600 to 1,000 years before present). 3) This demographic history allowed for cases of positive natural selection in the high and arid Andes vs. the low Amazon tropical forest: in the Andes, a putative enhancer in HAND2-AS1 (heart and neural crest derivatives expressed 2 antisense RNA1, a noncoding gene related to cardiovascular function) and rs269868-C/Ser1067 in DUOX2 (dual oxidase 2, related to thyroid function and innate immunity) genes and, in the Amazon, the gene encoding for the CD45 protein, essential for antigen recognition by T and B lymphocytes in viral-host interaction.
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189
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Nait Saada J, Kalantzis G, Shyr D, Cooper F, Robinson M, Gusev A, Palamara PF. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nat Commun 2020; 11:6130. [PMID: 33257650 PMCID: PMC7704644 DOI: 10.1038/s41467-020-19588-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/02/2020] [Indexed: 12/14/2022] Open
Abstract
Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample's birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits.
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Affiliation(s)
| | | | - Derek Shyr
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Fergus Cooper
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Alexander Gusev
- Brigham & Women's Hospital, Division of Genetics, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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190
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Zhao J, Wurigemule, Sun J, Xia Z, He G, Yang X, Guo J, Cheng HZ, Li Y, Lin S, Yang TL, Hu X, Du H, Cheng P, Hu R, Chen G, Yuan H, Zhang XF, Wei LH, Zhang HQ, Wang CC. Genetic substructure and admixture of Mongolians and Kazakhs inferred from genome-wide array genotyping. Ann Hum Biol 2020; 47:620-628. [PMID: 33059477 DOI: 10.1080/03014460.2020.1837952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND Mongolian populations are widely distributed geographically, showing abundant ethnic diversity with geographic and tribal differences. AIM To infer the genetic substructure, admixture and ancient genetic sources of Mongolians together with Kazakhs. SUBJECTS AND METHODS We genotyped more than 690,000 genome-wide SNPs from 33 Mongolian and Chinese Kazakh individuals and compared these with both ancient and present-day Eurasian populations using Principal Component Analysis (PCA), ADMIXTURE, Refine-IBD, f statistics, qpWave and qpAdm. RESULTS We found genetic substructures within Mongolians corresponding to Ölöd, Chahar, and Inner Mongolian clusters, which was consistent with tribe classifications. Mongolian and Kazakh groups derived about 6-40% of West Eurasian related ancestry, most likely from Bronze Age Steppe populations. The East Asian related ancestry in Mongolian and Kazakh groups was well represented by the Neolithic DevilsCave related nomadic lineage, comprising 42-64% of studied groups. We also detected 10-51% of Han Chinese related ancestry in Mongolian and Kazakh groups, especially in Inner Mongolians. The average admixture times for Inner Mongolian, Mongolian_Chahar, Mongolian_Ölöd and Chinese Kazakh were about 1381, 626, 635 and 632 years ago, respectively, with Han and French as the sources. CONCLUSION The DevilsCave related ancestry was once widespread westwards covering a wide geographical range from Far East Russia to the Mongolia Plateau. The formation of present-day Mongolic and Turkic-speaking populations has also received genetic influence from agricultural expansion.
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Affiliation(s)
- Jing Zhao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Wurigemule
- School of Ethnology and Sociology, Inner Mongolia University, Huhhot, China
| | - Jin Sun
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Ziyang Xia
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Guanglin He
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jianxin Guo
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Hui-Zhen Cheng
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Yingxiang Li
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Song Lin
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xi Hu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Hua Du
- Xi'an AMS Center, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Peng Cheng
- Xi'an AMS Center, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Rong Hu
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | | | - Haibing Yuan
- National Demonstration Center for Experimental Archaeology Education and Department of Archaeology, Sichuan University, Chengdu, China
| | | | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Hu-Qin Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
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191
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Brandão A, Paulo P, Maia S, Pinheiro M, Peixoto A, Cardoso M, Silva MP, Santos C, Eeles RA, Kote-Jarai Z, Muir K, Schleutker J, Wang Y, Pashayan N, Batra J, Grönberg H, Neal DE, Nordestgaard BG, Tangen CM, Southey MC, Wolk A, Albanes D, Haiman CA, Travis RC, Stanford JL, Mucci LA, West CML, Nielsen SF, Kibel AS, Cussenot O, Berndt SI, Koutros S, Sørensen KD, Cybulski C, Grindedal EM, Park JY, Ingles SA, Maier C, Hamilton RJ, Rosenstein BS, Vega A, Kogevinas M, Wiklund F, Penney KL, Brenner H, John EM, Kaneva R, Logothetis CJ, Neuhausen SL, Ruyck KD, Razack A, Newcomb LF, Lessel D, Usmani N, Claessens F, Gago-Dominguez M, Townsend PA, Roobol MJ, Teixeira MR. The CHEK2 Variant C.349A>G Is Associated with Prostate Cancer Risk and Carriers Share a Common Ancestor. Cancers (Basel) 2020; 12:E3254. [PMID: 33158149 PMCID: PMC7694218 DOI: 10.3390/cancers12113254] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 02/07/2023] Open
Abstract
The identification of recurrent founder variants in cancer predisposing genes may have important implications for implementing cost-effective targeted genetic screening strategies. In this study, we evaluated the prevalence and relative risk of the CHEK2 recurrent variant c.349A>G in a series of 462 Portuguese patients with early-onset and/or familial/hereditary prostate cancer (PrCa), as well as in the large multicentre PRACTICAL case-control study comprising 55,162 prostate cancer cases and 36,147 controls. Additionally, we investigated the potential shared ancestry of the carriers by performing identity-by-descent, haplotype and age estimation analyses using high-density SNP data from 70 variant carriers belonging to 11 different populations included in the PRACTICAL consortium. The CHEK2 missense variant c.349A>G was found significantly associated with an increased risk for PrCa (OR 1.9; 95% CI: 1.1-3.2). A shared haplotype flanking the variant in all carriers was identified, strongly suggesting a common founder of European origin. Additionally, using two independent statistical algorithms, implemented by DMLE+2.3 and ESTIAGE, we were able to estimate the age of the variant between 2300 and 3125 years. By extending the haplotype analysis to 14 additional carrier families, a shared core haplotype was revealed among all carriers matching the conserved region previously identified in the high-density SNP analysis. These findings are consistent with CHEK2 c.349A>G being a founder variant associated with increased PrCa risk, suggesting its potential usefulness for cost-effective targeted genetic screening in PrCa families.
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Affiliation(s)
- Andreia Brandão
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
| | - Paula Paulo
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
| | - Sofia Maia
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
| | - Manuela Pinheiro
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
| | - Ana Peixoto
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.P.); (C.S.)
| | - Marta Cardoso
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
| | - Maria P. Silva
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
| | - Catarina Santos
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.P.); (C.S.)
| | - Rosalind A. Eeles
- The Institute of Cancer Research, London SM2 5NG, UK; (R.A.E.); (Z.K.-J.)
- Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Zsofia Kote-Jarai
- The Institute of Cancer Research, London SM2 5NG, UK; (R.A.E.); (Z.K.-J.)
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester M13 9PL, UK;
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - UKGPCS Collaborators
- The Institute of Cancer Research, London SW7 3RP, UK; (UKGPCS Collaborators); (The IMPACT Study Steering Committee and Collaborators)
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, FI-20014 Turun Yliopisto, 20050 Turku, Finland;
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, P.O. Box 52, 20521 Turku, Finland
| | - Ying Wang
- Department of Population Science, American Cancer Society, 250 Williams Street, Atlanta, GA 30303, USA;
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London WC1E 7HB, UK;
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD 4059, Australia; (J.B.); (APCB BioResource)
- Translational Research Institute, Brisbane, QLD 4102, Australia
| | - APCB BioResource
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD 4059, Australia; (J.B.); (APCB BioResource)
- Translational Research Institute, Brisbane, QLD 4102, Australia
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77 Stockholm, Sweden; (H.G.); (F.W.)
| | - David E. Neal
- Nuffield Department of Surgical Sciences, University of Oxford, Room 6603, Level 6, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, UK;
- Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge CB2 0RE, UK
| | - Børge G. Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark; (B.G.N.); (S.F.N.)
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200 Copenhagen, Denmark
| | - Catherine M. Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M3-C102, Seattle, WA 98109-1024, USA;
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia;
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia
- Department of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3004, Australia
| | - Alicja Wolk
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden;
- Department of Surgical Sciences, Uppsala University, 75185 Uppsala, Sweden
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, ML 20892, USA; (D.A.); (S.I.B.); (S.K.)
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90015, USA;
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK;
| | - Janet L. Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, DC 98109-1024, USA; (J.L.S.); (L.F.N.); (Canary PASS Investigators)
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, DC 98195, USA
| | - Lorelei A. Mucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA;
| | - Catharine M. L. West
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester M13 9PL, UK;
| | - Sune F. Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark; (B.G.N.); (S.F.N.)
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200 Copenhagen, Denmark
| | - Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, 75 Francis Street, Boston, MA 02115, USA;
| | - Olivier Cussenot
- Sorbonne Universite, GRC n 5, AP-HP, Tenon Hospital, 4 rue de la Chine, F-75020 Paris, France;
- CeRePP, Tenon Hospital, F-75020 Paris, France
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, ML 20892, USA; (D.A.); (S.I.B.); (S.K.)
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, ML 20892, USA; (D.A.); (S.I.B.); (S.K.)
| | - Karina Dalsgaard Sørensen
- Department of Molecular Medicine, Aarhus University Hospital, Palle Juul-Jensen Boulevard 99, 8200 Aarhus N, Denmark;
- Department of Clinical Medicine, Aarhus University, DK-8200 Aarhus N, Denmark
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115 Szczecin, Poland;
| | - Eli Marie Grindedal
- Department of Medical Genetics, Oslo University Hospital, 0424 Oslo, Norway;
| | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA;
| | - Sue A. Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90015, USA;
| | - Christiane Maier
- Humangenetik Tuebingen, Paul-Ehrlich-Str 23, D-72076 Tuebingen, Germany;
| | - Robert J. Hamilton
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada;
- Department of Surgery (Urology), University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Barry S. Rosenstein
- Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Box 1236, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA;
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica, 15706 Santiago de Compostela, Spain;
- Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago De Compostela, Spain
- CIBER of Rare Diseases (CIBERER), 28029 Madrid, Spain
| | | | - Manolis Kogevinas
- ISGlobal, 08036 Barcelona, Spain;
- IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain
- Campus del Mar, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77 Stockholm, Sweden; (H.G.); (F.W.)
| | - Kathryn L. Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, MA 02184, USA;
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany;
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Esther M. John
- Departments of Epidemiology & Population Health and of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94304, USA;
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431 Sofia, Bulgaria;
| | - Christopher J. Logothetis
- Department of Genitourinary Medical Oncology, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA;
| | - Susan L. Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, 1500 East Duarte Road, Duarte, CA 91010, USA;
| | - Kim De Ruyck
- Faculty of Medicine and Health Sciences, Basic Medical Sciences, Ghent University, Proeftuinstraat 86, 9000 Gent, Belgium;
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia;
| | - Lisa F. Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, DC 98109-1024, USA; (J.L.S.); (L.F.N.); (Canary PASS Investigators)
- Department of Urology, University of Washington, 1959 NE Pacific Street, Box 356510, Seattle, WA 98195, USA
| | - Canary PASS Investigators
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, DC 98109-1024, USA; (J.L.S.); (L.F.N.); (Canary PASS Investigators)
- Department of Urology, University of Washington, 1959 NE Pacific Street, Box 356510, Seattle, WA 98195, USA
| | - Davor Lessel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany;
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, Canada;
- Division of Radiation Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB T6G 1Z2, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Campus Gasthuisberg, University of Leuven, Herestraat 49, P.O. Box 901, 3000 Leuven, Belgium;
| | - Manuela Gago-Dominguez
- Group of Genomic Medicine, Galician Public Foundation of Genomic Medicine, Health Research Institute of Santiago de Compostela (IDIS), Galician Healthcare Service (SERGAS) University of Santiago de Compostela, 15782 Santiago de Compostela, Spain;
- Moores Cancer Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093-0012, USA
| | - Paul A. Townsend
- Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, National Institute for Health Research (NIHR) Manchester Biomedical Research Centre, Health Innovation Manchester, University of Manchester, Manchester M13 9PL, UK;
| | - Monique J. Roobol
- Department of Urology, Erasmus University Medical Center, 3015 CE Rotterdam, The Netherlands;
| | | | | | - Manuel R. Teixeira
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.B.); (P.P.); (S.M.); (M.P.); (M.C.); (M.P.S.)
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal; (A.P.); (C.S.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Biomedical Sciences Institute Abel Salazar (ICBAS), University of Porto, 4050-313 Porto, Portugal
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192
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Samuels DC, Below JE, Ness S, Yu H, Leng S, Guo Y. Alternative Applications of Genotyping Array Data Using Multivariant Methods. Trends Genet 2020; 36:857-867. [PMID: 32773169 PMCID: PMC7572808 DOI: 10.1016/j.tig.2020.07.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 10/23/2022]
Abstract
One of the forerunners that pioneered the revolution of high-throughput genomic technologies is the genotyping microarray technology, which can genotype millions of single-nucleotide variants simultaneously. Owing to apparent benefits, such as high speed, low cost, and high throughput, the genotyping array has gained lasting applications in genome-wide association studies (GWAS) and thus accumulated an enormous amount of data. Empowered by continuous manufactural upgrades and analytical innovation, unconventional applications of genotyping array data have emerged to address more diverse genetic problems, holding promise of boosting genetic research into human diseases through the re-mining of the rich accumulated data. Here, we review several unconventional genotyping array analysis techniques that have been built on the idea of large-scale multivariant analysis and provide empirical application examples. These unconventional outcomes of genotyping arrays include polygenic score, runs of homozygosity (ROH)/heterozygosity ratio, distant pedigree computation, and mitochondrial DNA (mtDNA) copy number inference.
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Affiliation(s)
- David C Samuels
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Jennifer E Below
- Devision of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Scott Ness
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA
| | - Hui Yu
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA
| | - Shuguang Leng
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA
| | - Yan Guo
- Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87109, USA.
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193
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Bianco E, Laval G, Font-Porterias N, García-Fernández C, Dobon B, Sabido-Vera R, Sukarova Stefanovska E, Kučinskas V, Makukh H, Pamjav H, Quintana-Murci L, Netea MG, Bertranpetit J, Calafell F, Comas D. Recent Common Origin, Reduced Population Size, and Marked Admixture Have Shaped European Roma Genomes. Mol Biol Evol 2020; 37:3175-3187. [PMID: 32589725 DOI: 10.1093/molbev/msaa156] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The Roma Diaspora-traditionally known as Gypsies-remains among the least explored population migratory events in historical times. It involved the migration of Roma ancestors out-of-India through the plateaus of Western Asia ultimately reaching Europe. The demographic effects of the Diaspora-bottlenecks, endogamy, and gene flow-might have left marked molecular traces in the Roma genomes. Here, we analyze the whole-genome sequence of 46 Roma individuals pertaining to four migrant groups in six European countries. Our analyses revealed a strong, early founder effect followed by a drastic reduction of ∼44% in effective population size. The Roma common ancestors split from the Punjabi population, from Northwest India, some generations before the Diaspora started, <2,000 years ago. The initial bottleneck and subsequent endogamy are revealed by the occurrence of extensive runs of homozygosity and identity-by-descent segments in all Roma populations. Furthermore, we provide evidence of gene flow from Armenian and Anatolian groups in present-day Roma, although the primary contribution to Roma gene pool comes from non-Roma Europeans, which accounts for >50% of their genomes. The linguistic and historical differentiation of Roma in migrant groups is confirmed by the differential proportion, but not a differential source, of European admixture in the Roma groups, which shows a westward cline. In the present study, we found that despite the strong admixture Roma had in their diaspora, the signature of the initial bottleneck and the subsequent endogamy is still present in Roma genomes.
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Affiliation(s)
- Erica Bianco
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - Guillaume Laval
- Human Evolutionary Genetics Unit, Department of Genomes and Genetics, UMR 2000, CNRS, Institut Pasteur, Paris, France
| | - Neus Font-Porterias
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - Carla García-Fernández
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - Begoña Dobon
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - Rubén Sabido-Vera
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - Emilija Sukarova Stefanovska
- Research Center for Genetic Engineering and Biotechnology "Georgi D. Efremov", Macedonian Academy of Science and Arts, Skopje, Macedonia
| | - Vaidutis Kučinskas
- Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Halyna Makukh
- Institute of Hereditary Pathology of the Ukrainian Academy of Medical Sciences, Lviv, Ukraine
| | - Horolma Pamjav
- Department of Reference Sample Analysis, Institute of Forensic Genetics, Hungarian Institute for Forensic Sciences, Budapest, Hungary
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Department of Genomes and Genetics, UMR 2000, CNRS, Institut Pasteur, Paris, France.,Chair Human Genomics and Evolution, Collège de France, Paris, France
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.,Department for Genomics & Immunoregulation, Life and Medical Sciences 12 Institute (LIMES), University of Bonn, Bonn, Germany
| | - Jaume Bertranpetit
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - Francesc Calafell
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
| | - David Comas
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain
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194
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Choudhury A, Aron S, Botigué LR, Sengupta D, Botha G, Bensellak T, Wells G, Kumuthini J, Shriner D, Fakim YJ, Ghoorah AW, Dareng E, Odia T, Falola O, Adebiyi E, Hazelhurst S, Mazandu G, Nyangiri OA, Mbiyavanga M, Benkahla A, Kassim SK, Mulder N, Adebamowo SN, Chimusa ER, Muzny D, Metcalf G, Gibbs RA, Rotimi C, Ramsay M, Adeyemo AA, Lombard Z, Hanchard NA. High-depth African genomes inform human migration and health. Nature 2020; 586:741-748. [PMID: 33116287 PMCID: PMC7759466 DOI: 10.1038/s41586-020-2859-7] [Citation(s) in RCA: 168] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/07/2020] [Indexed: 01/05/2023]
Abstract
The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals-comprising 50 ethnolinguistic groups, including previously unsampled populations-to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon-but in other genes, variants denoted as 'likely pathogenic' in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health.
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Affiliation(s)
- Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laura R Botigué
- Center for Research in Agricultural Genomics (CRAG), Plant and Animal Genomics Program, CSIC-IRTA-UAB-UB, Barcelona, Spain
| | - Dhriti Sengupta
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gerrit Botha
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Taoufik Bensellak
- System and Data Engineering Team, Abdelmalek Essaadi University, ENSA, Tangier, Morocco
| | - Gordon Wells
- Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa.,South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.,Africa Health Research Institute, Durban, South Africa
| | - Judit Kumuthini
- Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa.,South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yasmina J Fakim
- Department of Agriculture and Food Science, Faculty of Agriculture, University of Mauritius, Reduit, Mauritius.,Department of Digital Technologies,Faculty of Information, Communication & Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Anisah W Ghoorah
- Department of Digital Technologies,Faculty of Information, Communication & Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Eileen Dareng
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Institute of Human Virology Nigeria, Abuja, Nigeria
| | - Trust Odia
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Oluwadamilare Falola
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria.,Department of Computer and Information Sciences, Covenant University, Ota, Nigeria
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Gaston Mazandu
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Oscar A Nyangiri
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Mamana Mbiyavanga
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institute Pasteur of Tunis, Tunis, Tunisia
| | - Samar K Kassim
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Abbaseya, Cairo, Egypt
| | - Nicola Mulder
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Sally N Adebamowo
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, University of Maryland Baltimore, Baltimore, MD, USA.,University of Maryland Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute for Infectious, Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | | | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Zané Lombard
- Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Neil A Hanchard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
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195
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Tuazon AMDA, Lott P, Bohórquez M, Benavides J, Ramirez C, Criollo A, Estrada-Florez A, Mateus G, Velez A, Carmona J, Olaya J, Garcia E, Polanco-Echeverry G, Stultz J, Alvarez C, Tapia T, Ashton-Prolla P, Vega A, Lazaro C, Tornero E, Martinez-Bouzas C, Infante M, De La Hoya M, Diez O, Browning BL, Rannala B, Teixeira MR, Carvallo P, Echeverry M, Carvajal-Carmona LG. Haplotype analysis of the internationally distributed BRCA1 c.3331_3334delCAAG founder mutation reveals a common ancestral origin in Iberia. Breast Cancer Res 2020; 22:108. [PMID: 33087180 PMCID: PMC7579869 DOI: 10.1186/s13058-020-01341-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 09/16/2020] [Indexed: 12/02/2022] Open
Abstract
Background The BRCA1 c.3331_3334delCAAG founder mutation has been reported in hereditary breast and ovarian cancer families from multiple Hispanic groups. We aimed to evaluate BRCA1 c.3331_3334delCAAG haplotype diversity in cases of European, African, and Latin American ancestry. Methods BC mutation carrier cases from Colombia (n = 32), Spain (n = 13), Portugal (n = 2), Chile (n = 10), Africa (n = 1), and Brazil (n = 2) were genotyped with the genome-wide single nucleotide polymorphism (SNP) arrays to evaluate haplotype diversity around BRCA1 c.3331_3334delCAAG. Additional Portuguese (n = 13) and Brazilian (n = 18) BC mutation carriers were genotyped for 15 informative SNPs surrounding BRCA1. Data were phased using SHAPEIT2, and identical by descent regions were determined using BEAGLE and GERMLINE. DMLE+ was used to date the mutation in Colombia and Iberia. Results The haplotype reconstruction revealed a shared 264.4-kb region among carriers from all six countries. The estimated mutation age was ~ 100 generations in Iberia and that it was introduced to South America early during the European colonization period. Conclusions Our results suggest that this mutation originated in Iberia and later introduced to Colombia and South America at the time of Spanish colonization during the early 1500s. We also found that the Colombian mutation carriers had higher European ancestry, at the BRCA1 gene harboring chromosome 17, than controls, which further supported the European origin of the mutation. Understanding founder mutations in diverse populations has implications in implementing cost-effective, ancestry-informed screening.
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Affiliation(s)
| | - Paul Lott
- Genome Center, University of California Davis, Davis, CA, USA
| | | | | | | | | | | | | | - Alejandro Velez
- Hospital Pablo Tobon Uribe, Medellín, Colombia.,Dinamica IPS, Medellín, Colombia
| | | | - Justo Olaya
- Hospital Universitario Hernando Moncaleano Perdomo, Neiva, Colombia
| | - Elisha Garcia
- Genome Center, University of California Davis, Davis, CA, USA
| | | | - Jacob Stultz
- Genome Center, University of California Davis, Davis, CA, USA
| | | | - Teresa Tapia
- Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Patricia Ashton-Prolla
- Department of Genetics, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.,Post-graduate Course in Genetics and Molecular Biology, UFRGS, Porto Alegre, Brazil.,Medical Genetics Service, Hospital de Clinicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | | | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela, Spain
| | - Conxi Lazaro
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell Program-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Eva Tornero
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell Program-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | | | - Mar Infante
- Cancer Genetics Group, Institute of Genetics and Molecular Biology (UVa-CSIC), Valladolid, Spain
| | - Miguel De La Hoya
- Laboratorio de Oncología Molecular, Hospital Clínico San Carlos. IdISSC (Instituto de Investigación Sanitaria San Carlos), Madrid, Spain
| | - Orland Diez
- Grupo de Cáncer Hereditario, Instituto Oncológico Vall d'Hebron (VHIO), Madrid, Spain
| | - Brian L Browning
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | | | - Bruce Rannala
- Department of Evolution and Ecology, University of California Davis, Davis, CA, USA
| | - Manuel R Teixeira
- Portuguese Oncology Institute of Porto (IPO Porto) and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Pilar Carvallo
- Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Luis G Carvajal-Carmona
- Genome Center, University of California Davis, Davis, CA, USA. .,Division de Investigaciones, Fundacion de Genética y Genómica, Ibague, Colombia. .,University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA. .,Department of Biochemistry and Molecular Medicine, University of California Davis, Sacramento, CA, USA.
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196
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Genomic Patterns of Introgression in Interspecific Populations Created by Crossing Wheat with Its Wild Relative. G3-GENES GENOMES GENETICS 2020; 10:3651-3661. [PMID: 32737066 PMCID: PMC7534432 DOI: 10.1534/g3.120.401479] [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: 01/01/2023]
Abstract
Introgression from wild relatives is a valuable source of novel allelic diversity for breeding. We investigated the genomic patterns of introgression from Aegilops tauschii, the diploid ancestor of the wheat D genome, into winter wheat (Triticum aestivum) cultivars. The population of 351 BC1F3:5 lines was selected based on phenology from crosses between six hexaploid wheat lines and 21 wheat-Ae. tauschii octoploids. SNP markers developed for this population and a diverse panel of 116 Ae. tauschii accessions by complexity-reduced genome sequencing were used to detect introgression based on the identity-by-descent analysis. Overall, introgression frequency positively correlated with recombination rate, with a high incidence of introgression at the ends of chromosomes and low in the pericentromeric regions, and was negatively related to sequence divergence between the parental genomes. Reduced introgression in the pericentromeric low-recombining regions spans nearly 2/3 of each chromosome arm, suggestive of the polygenic nature of introgression barriers that could be associated with multilocus negative epistasis between the alleles of wild and cultivated wheat. On the contrary, negative selection against the wild allele of Tg, controlling free-threshing trait and located in the high-recombining chromosomal region, led to reduced introgression only within ∼10 Mbp region around Tg. These results are consistent with the effect of selection on linked variation described by the Hill-Robertson effect, and offer insights into the introgression population development for crop improvement to maximize retention of introgressed diversity across entire genome.
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197
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Han Z, Hu Y, Tian Q, Cao Y, Si A, Si Z, Zang Y, Xu C, Shen W, Dai F, Liu X, Fang L, Chen H, Zhang T. Genomic signatures and candidate genes of lint yield and fibre quality improvement in Upland cotton in Xinjiang. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2002-2014. [PMID: 32030869 PMCID: PMC7540456 DOI: 10.1111/pbi.13356] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 01/21/2020] [Indexed: 06/10/2023]
Abstract
Xinjiang has been the largest and highest yield cotton production region not only in China, but also in the world. Improvements in Upland cotton cultivars in Xinjiang have occurred via pedigree selection and/or crossing of elite alleles from the former Soviet Union and other cotton producing regions of China. But it is unclear how genomic constitutions from foundation parents have been selected and inherited. Here, we deep-sequenced seven historic foundation parents, comprising four cultivars introduced from the former Soviet Union (108Ф, C1470, 611Б and KK1543) and three from United States and Africa (DPL15, STV2B and UGDM), and re-sequenced sixty-nine Xinjiang modern cultivars. Phylogenetic analysis of more than 2 million high-quality single nucleotide polymorphisms allowed their classification two groups, suggesting that Xinjiang Upland cotton cultivars were not only spawned from 108Ф, C1470, 611Б and KK1543, but also had a close kinship with DPL15, STV2B and UGDM. Notably, identity-by-descent (IBD) tracking demonstrated that the former Soviet Union cultivars have made a huge contribution to modern cultivar improvement in Xinjiang. A total of 156 selective sweeps were identified. Among them, apoptosis-antagonizing transcription factor gene (GhAATF1) and mitochondrial transcription termination factor family protein gene (GhmTERF1) were highly involved in the determination of lint percentage. Additionally, the auxin response factor gene (GhARF3) located in inherited IBD segments from 108Ф and 611Б was highly correlated with fibre quality. These results provide an insight into the genomics of artificial selection for improving cotton production and facilitate next-generation precision breeding of cotton and other crops.
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Affiliation(s)
- Zegang Han
- State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingChina
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
| | - Yan Hu
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
| | - Qin Tian
- Key Laboratory of China Northwestern Inland RegionMinistry of AgricultureCotton Research InstituteXinjiang Academy of Agricultural and Reclamation ScienceShiheziChina
| | - Yiwen Cao
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
| | - Aijun Si
- Key Laboratory of China Northwestern Inland RegionMinistry of AgricultureCotton Research InstituteXinjiang Academy of Agricultural and Reclamation ScienceShiheziChina
| | - Zhanfeng Si
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
| | - Yihao Zang
- State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingChina
| | - Chenyu Xu
- State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingChina
| | - Weijuan Shen
- State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingChina
| | - Fan Dai
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
| | - Xia Liu
- Esquel GroupWanchai, Hong KongChina
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
| | - Hong Chen
- Key Laboratory of China Northwestern Inland RegionMinistry of AgricultureCotton Research InstituteXinjiang Academy of Agricultural and Reclamation ScienceShiheziChina
| | - Tianzhen Zhang
- State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingChina
- Zhejiang Provincial Key Laboratory of Crop Genetic ResourcesInstitute of Crop SciencePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina
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198
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Surbakti S, Parker HG, McIntyre JK, Maury HK, Cairns KM, Selvig M, Pangau-Adam M, Safonpo A, Numberi L, Runtuboi DYP, Davis BW, Ostrander EA. New Guinea highland wild dogs are the original New Guinea singing dogs. Proc Natl Acad Sci U S A 2020; 117:24369-24376. [PMID: 32868416 PMCID: PMC7533868 DOI: 10.1073/pnas.2007242117] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
New Guinea singing dogs (NGSD) are identifiable by their namesake vocalizations, which are unlike any other canid population. Their novel behaviors and potential singular origin during dog domestication make them an attractive, but elusive, subject for evolutionary and conservation study. Although once plentiful on the island of New Guinea (NG), they were presumed to currently exist only in captivity. This conclusion was based on the lack of sightings in the lowlands of the island and the concurrent expansion of European- and Asian-derived dogs. We have analyzed the first nuclear genomes from a canid population discovered during a recent expedition to the highlands of NG. The extreme altitude (>4,000 m) of the highland wild dogs' (HWD) observed range and confirmed vocalizations indicate their potential to be a wild NGSD population. Comparison of single-nucleotide polymorphism genotypes shows strong similarity between HWD and the homogeneous captive NGSD, with the HWD showing significantly higher genetic diversity. Admixture analyses and estimation of shared haplotypes with phylogenetically diverse populations also indicates the HWD is a novel population within the distinct evolutionary lineage of Oceanic canids. Taken together, these data indicate the HWD possesses a distinct potential to aid in the conservation of NGSD both in the wild and under human care.
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Affiliation(s)
- Suriani Surbakti
- Department of Biology, Universitas Cenderawasih, Jayapura, Papua 99224, Indonesia
| | - Heidi G Parker
- National Human Genome Research Institute, National Institutes of Health, Bethesda MD 20892
| | - James K McIntyre
- New Guinea Highland Wild Dog Foundation, Fernandina Beach, FL 32034
| | - Hendra K Maury
- Department of Biology, Universitas Cenderawasih, Jayapura, Papua 99224, Indonesia
| | - Kylie M Cairns
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Meagan Selvig
- Department of Conservation Biology, University of Göttingen, 37073 Göttingen Germany
| | - Margaretha Pangau-Adam
- Department of Biology, Universitas Cenderawasih, Jayapura, Papua 99224, Indonesia
- Department of Conservation Biology, University of Göttingen, 37073 Göttingen Germany
| | - Apolo Safonpo
- Department of Biology, Universitas Cenderawasih, Jayapura, Papua 99224, Indonesia
| | - Leonardo Numberi
- Department of Biology, Universitas Cenderawasih, Jayapura, Papua 99224, Indonesia
| | - Dirk Y P Runtuboi
- Department of Biology, Universitas Cenderawasih, Jayapura, Papua 99224, Indonesia
| | - Brian W Davis
- Department of Veterinary Integrative Biosciences, Texas A&M University College of Veterinary Medicine, College Station, TX 77843
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda MD 20892;
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199
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Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F 2 chicken population. Heredity (Edinb) 2020; 126:293-307. [PMID: 32989280 PMCID: PMC8026619 DOI: 10.1038/s41437-020-00365-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 08/18/2020] [Accepted: 08/30/2020] [Indexed: 02/07/2023] Open
Abstract
Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F2 resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1-6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.
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200
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Dutch population structure across space, time and GWAS design. Nat Commun 2020; 11:4556. [PMID: 32917883 PMCID: PMC7486932 DOI: 10.1038/s41467-020-18418-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 08/21/2020] [Indexed: 11/09/2022] Open
Abstract
Previous genetic studies have identified local population structure within the Netherlands; however their resolution is limited by use of unlinked markers and absence of external reference data. Here we apply advanced haplotype sharing methods (ChromoPainter/fineSTRUCTURE) to study fine-grained population genetic structure and demographic change across the Netherlands using genome-wide single nucleotide polymorphism data (1,626 individuals) with associated geography (1,422 individuals). We identify 40 haplotypic clusters exhibiting strong north/south variation and fine-scale differentiation within provinces. Clustering is tied to country-wide ancestry gradients from neighbouring lands and to locally restricted gene flow across major Dutch rivers. North-south structure is temporally stable, with west-east differentiation more transient, potentially influenced by migrations during the middle ages. Despite superexponential population growth, regional demographic estimates reveal population crashes contemporaneous with the Black Death. Within Dutch and international data, GWAS incorporating fine-grained haplotypic covariates are less confounded than standard methods.
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