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Roy N, Kabir AH, Zahan N, Mouna ST, Chakravarty S, Rahman AH, Bayzid MS. Genome wide association studies on seven yield-related traits of 183 rice varieties in Bangladesh. PLANT DIRECT 2024; 8:e593. [PMID: 38887667 PMCID: PMC11182691 DOI: 10.1002/pld3.593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 03/26/2024] [Accepted: 05/02/2024] [Indexed: 06/20/2024]
Abstract
Rice genetic diversity is regulated by multiple genes and is largely dependent on various environmental factors. Uncovering the genetic variations associated with the diversity in rice populations is the key to breed stable and high yielding rice varieties. We performed genome wide association studies (GWASs) on seven rice yielding traits (grain length, grain width, grain weight, panicle length, leaf length, leaf width, and leaf angle) based on a population of 183 rice landraces of Bangladesh. Our GWASs reveal various chromosomal regions and candidate genes that are associated with different traits in Bangladeshi rice varieties. Noteworthy was the recurrent implication of chromosome 10 in all three grain-shape-related traits (grain length, grain width, and grain weight), indicating its pivotal role in shaping rice grain morphology. Our study also underscores the involvement of transposon gene families across these three traits. For leaf related traits, chromosome 10 was found to harbor regions that are significantly associated with leaf length and leaf width. The results of these association studies support previous findings as well as provide additional insights into the genetic diversity of rice. This is the first known GWAS study on various yield-related traits in the varieties of Oryza sativa available in Bangladesh-the fourth largest rice-producing country. We believe this study will accelerate rice genetics research and breeding stable high-yielding rice in Bangladesh.
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Affiliation(s)
- Nilanjan Roy
- Department of Biomedical EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh
- Molecular, Cellular, and Developmental BiologyUniversity of KansasLawrenceKansasUSA
| | - Acramul Haque Kabir
- Department of Biomedical EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh
- Department of Biomedical EngineeringUniversity of UtahSalt Lake CityUtahUSA
| | - Nourin Zahan
- Department of Biomedical EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh
| | - Shahba Tasmiya Mouna
- Department of Biomedical EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh
| | - Sakshar Chakravarty
- Department of Computer Science and EngineeringUniversity of CaliforniaRiversideCaliforniaUSA
- Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
| | - Atif Hasan Rahman
- Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
| | - Md. Shamsuzzoha Bayzid
- Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
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2
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Depardieu C, Lenz P, Marion J, Nadeau S, Girardin MP, Marchand W, Bégin C, Treydte K, Gessler A, Bousquet J, Savard MM, Isabel N. Contrasting physiological strategies explain heterogeneous responses to severe drought conditions within local populations of a widespread conifer. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 923:171174. [PMID: 38402972 DOI: 10.1016/j.scitotenv.2024.171174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/12/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Abstract
Understanding how trees prioritize carbon gain at the cost of drought vulnerability under severe drought conditions is crucial for predicting which genetic groups and individuals will be resilient to future climate conditions. In this study, we investigated variations in growth, tree-ring anatomy as well as carbon and oxygen isotope ratios to assess the sensitivity and the xylem formation process in response to an episode of severe drought in 29 mature white spruce (Picea glauca [Moench] Voss) families grown in a common garden trial. During the drought episode, the majority of families displayed decreased growth and exhibited either sustained or increased intrinsic water-use efficiency (iWUE), which was largely influenced by reduced stomatal conductance as revealed by the dual carbon‑oxygen isotope approach. Different water-use strategies were detected within white spruce populations in response to drought conditions. Our results revealed intraspecific variation in the prevailing physiological mechanisms underlying drought response within and among populations of Picea glauca. The presence of different genetic groups reflecting diverse water-use strategies within this largely-distributed conifer is likely to lessen the negative effects of drought and decrease the overall forest ecosystems' sensitivity to it.
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Affiliation(s)
- Claire Depardieu
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC G1V 0A6, Canada; Forest Research Centre, Département des sciences du bois et de la forêt, Université Laval, Québec, QC G1V 0A6, Canada; Natural Ressources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 rue du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada.
| | - Patrick Lenz
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC G1V 0A6, Canada; Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, 1055 rue du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada
| | - Joelle Marion
- Geological Survey of Canada, Natural Resources Canada, 490 rue de la Couronne, Québec, QC G1K 9A9, Canada
| | - Simon Nadeau
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC G1V 0A6, Canada; Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, 1055 rue du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada
| | - Martin P Girardin
- Natural Ressources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 rue du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada; Centre d'étude de la forêt, Université du Québec à Montréal, C.P. 8888, Succ. Centre-ville, Montréal, QC H3C 3P8, Canada; Forest Research Institute, Université du Québec en Abitibi-Témiscamingue, 445 boul. de l'Université, Rouyn-Noranda, QC J9X 5E4, Canada
| | - William Marchand
- Natural Ressources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 rue du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada; Centre d'étude de la forêt, Université du Québec à Montréal, C.P. 8888, Succ. Centre-ville, Montréal, QC H3C 3P8, Canada; Forest Research Institute, Université du Québec en Abitibi-Témiscamingue, 445 boul. de l'Université, Rouyn-Noranda, QC J9X 5E4, Canada
| | - Christian Bégin
- Geological Survey of Canada, Natural Resources Canada, 490 rue de la Couronne, Québec, QC G1K 9A9, Canada
| | - Kerstin Treydte
- Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
| | - Arthur Gessler
- Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland; Institute of Terrestrial Ecosystems, ETH Zurich, Zurich, Switzerland
| | - Jean Bousquet
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC G1V 0A6, Canada; Forest Research Centre, Département des sciences du bois et de la forêt, Université Laval, Québec, QC G1V 0A6, Canada
| | - Martine M Savard
- Geological Survey of Canada, Natural Resources Canada, 490 rue de la Couronne, Québec, QC G1K 9A9, Canada
| | - Nathalie Isabel
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC G1V 0A6, Canada; Natural Ressources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 rue du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada
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3
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Stringer EJ, Gruber B, Sarre SD, Wardle GM, Edwards SV, Dickman CR, Greenville AC, Duncan RP. Boom-bust population dynamics drive rapid genetic change. Proc Natl Acad Sci U S A 2024; 121:e2320590121. [PMID: 38621118 PMCID: PMC11067018 DOI: 10.1073/pnas.2320590121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/06/2024] [Indexed: 04/17/2024] Open
Abstract
Increasing environmental threats and more extreme environmental perturbations place species at risk of population declines, with associated loss of genetic diversity and evolutionary potential. While theory shows that rapid population declines can cause loss of genetic diversity, populations in some environments, like Australia's arid zone, are repeatedly subject to major population fluctuations yet persist and appear able to maintain genetic diversity. Here, we use repeated population sampling over 13 y and genotype-by-sequencing of 1903 individuals to investigate the genetic consequences of repeated population fluctuations in two small mammals in the Australian arid zone. The sandy inland mouse (Pseudomys hermannsburgensis) experiences marked boom-bust population dynamics in response to the highly variable desert environment. We show that heterozygosity levels declined, and population differentiation (FST) increased, during bust periods when populations became small and isolated, but that heterozygosity was rapidly restored during episodic population booms. In contrast, the lesser hairy-footed dunnart (Sminthopsis youngsoni), a desert marsupial that maintains relatively stable population sizes, showed no linear declines in heterozygosity. These results reveal two contrasting ways in which genetic diversity is maintained in highly variable environments. In one species, diversity is conserved through the maintenance of stable population sizes across time. In the other species, diversity is conserved through rapid genetic mixing during population booms that restores heterozygosity lost during population busts.
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Affiliation(s)
- Emily J. Stringer
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology, University of Canberra, CanberraACT2617, Australia
| | - Bernd Gruber
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology, University of Canberra, CanberraACT2617, Australia
| | - Stephen D. Sarre
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology, University of Canberra, CanberraACT2617, Australia
| | - Glenda M. Wardle
- Desert Ecology Research Group, School of Life and Environmental Sciences, The University of Sydney, SydneyNSW2006, Australia
| | - Scott V. Edwards
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA02138
| | - Christopher R. Dickman
- Desert Ecology Research Group, School of Life and Environmental Sciences, The University of Sydney, SydneyNSW2006, Australia
| | - Aaron C. Greenville
- Desert Ecology Research Group, School of Life and Environmental Sciences, The University of Sydney, SydneyNSW2006, Australia
| | - Richard P. Duncan
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology, University of Canberra, CanberraACT2617, Australia
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Rahman H, Vikram P, Hu Y, Asthana S, Tanaji A, Suryanarayanan P, Quadros C, Mehta L, Shahid M, Gkanogiannis A, Thushar S, Balazadeh S, Mueller-Roeber B, Becerra Lopez-Lavalle LA, Wei T, Singh RK. Mining genomic regions associated with agronomic and biochemical traits in quinoa through GWAS. Sci Rep 2024; 14:9205. [PMID: 38649738 PMCID: PMC11035704 DOI: 10.1038/s41598-024-59565-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/12/2024] [Indexed: 04/25/2024] Open
Abstract
Quinoa (Chenopodium quinoa Willd.), an Andean crop, is a facultative halophyte food crop recognized globally for its high nutritional value and plasticity to adapt to harsh conditions. We conducted a genome-wide association study on a diverse set of quinoa germplasm accessions. These accessions were evaluated for the following agronomic and biochemical traits: days to 50% flowering (DTF), plant height (PH), panicle length (PL), stem diameter (SD), seed yield (SY), grain diameter (GD), and thousand-grain weight (TGW). These accessions underwent genotyping-by-sequencing using the DNBSeq-G400R platform. Among all evaluated traits, TGW represented maximum broad-sense heritability. Our study revealed average SNP density of ≈ 3.11 SNPs/10 kb for the whole genome, with the lowest and highest on chromosomes Cq1B and Cq9A, respectively. Principal component analysis clustered the quinoa population in three main clusters, one clearly representing lowland Chilean accessions, whereas the other two groups corresponded to germplasm from the highlands of Peru and Bolivia. In our germplasm set, we estimated linkage disequilibrium decay to be ≈ 118.5 kb. Marker-trait analyses revealed major and consistent effect associations for DTF on chromosomes 3A, 4B, 5B, 6A, 7A, 7B and 8B, with phenotypic variance explained (PVE) as high as 19.15%. Nine associations across eight chromosomes were also found for saponin content with 20% PVE by qSPN5A.1. More QTLs were identified for PL and TGW on multiple chromosomal locations. We identified putative candidate genes in the genomic regions associated with DTF and saponin content. The consistent and major-effect genomic associations can be used in fast-tracking quinoa breeding for wider adaptation across marginal environments.
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Affiliation(s)
- Hifzur Rahman
- International Center for Biosaline Agriculture, Dubai, UAE.
| | - Prashant Vikram
- International Center for Biosaline Agriculture, Dubai, UAE
- SGT University, Gurugram, Haryana, India
| | - Yulan Hu
- BGI-Research, 518083, Shenzhen, China
- BGI Research, 430074, Wuhan, China
| | | | - Abhinav Tanaji
- Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE
| | | | - Chris Quadros
- Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE
| | - Lovely Mehta
- International Center for Biosaline Agriculture, Dubai, UAE
| | | | | | | | - Salma Balazadeh
- Institute of Biology Leiden, Sylvius Laboratory, Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands
| | - Bernd Mueller-Roeber
- Department of Molecular Biology, University of Potsdam, Karl-Liebknecht-Straße 24-25, Haus 20, 14476, Potsdam, Germany
| | | | - Tong Wei
- International Center for Biosaline Agriculture, Dubai, UAE.
- BGI-Research, 518083, Shenzhen, China.
- BGI Research, 430074, Wuhan, China.
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Sezen UU, Shue JE, Worthy SJ, Davies SJ, McMahon SM, Swenson NG. Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech. Proc Biol Sci 2024; 291:20232338. [PMID: 38593851 PMCID: PMC11003779 DOI: 10.1098/rspb.2023.2338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/05/2024] [Indexed: 04/11/2024] Open
Abstract
Transcriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33 042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the single nucleotide polymorphism-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-autumn transitions for two consecutive years involved 4197 differentially expressed protein coding genes. Using Populus orthologues we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed gene ontology terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech.
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Affiliation(s)
- U. Uzay Sezen
- Smithsonian Environmental Research Center, 647 Contees Wharf Rd, Edgewater, MD 21037, USA
| | - Jessica E. Shue
- Smithsonian Environmental Research Center, 647 Contees Wharf Rd, Edgewater, MD 21037, USA
| | - Samantha J. Worthy
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Stuart J. Davies
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Gamboa, Panama
- Department of Botany, National Museum of Natural History, Smithsonian Institution, Washington DC 20560, USA
| | - Sean M. McMahon
- Smithsonian Environmental Research Center, 647 Contees Wharf Rd, Edgewater, MD 21037, USA
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Gamboa, Panama
| | - Nathan G. Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
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6
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Wolf ESA, Vela S, Wilker J, Davis A, Robert M, Infante V, Venado RE, Voiniciuc C, Ané JM, Vermerris W. Identification of genetic and environmental factors influencing aerial root traits that support biological nitrogen fixation in sorghum. G3 (BETHESDA, MD.) 2024; 14:jkad285. [PMID: 38096484 PMCID: PMC10917507 DOI: 10.1093/g3journal/jkad285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/19/2023] [Indexed: 03/08/2024]
Abstract
Plant breeding and genetics play a major role in the adaptation of plants to meet human needs. The current requirement to make agriculture more sustainable can be partly met by a greater reliance on biological nitrogen fixation by symbiotic diazotrophic microorganisms that provide crop plants with ammonium. Select accessions of the cereal crop sorghum (Sorghum bicolor (L.) Moench) form mucilage-producing aerial roots that harbor nitrogen-fixing bacteria. Breeding programs aimed at developing sorghum varieties that support diazotrophs will benefit from a detailed understanding of the genetic and environmental factors contributing to aerial root formation. A genome-wide association study of the sorghum minicore, a collection of 242 landraces, and 30 accessions from the sorghum association panel was conducted in Florida and Wisconsin and under 2 fertilizer treatments to identify loci associated with the number of nodes with aerial roots and aerial root diameter. Sequence variation in genes encoding transcription factors that control phytohormone signaling and root system architecture showed significant associations with these traits. In addition, the location had a significant effect on the phenotypes. Concurrently, we developed F2 populations from crosses between bioenergy sorghums and a landrace that produced extensive aerial roots to evaluate the mode of inheritance of the loci identified by the genome-wide association study. Furthermore, the mucilage collected from aerial roots contained polysaccharides rich in galactose, arabinose, and fucose, whose composition displayed minimal variation among 10 genotypes and 2 fertilizer treatments. These combined results support the development of sorghums with the ability to acquire nitrogen via biological nitrogen fixation.
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Affiliation(s)
- Emily S A Wolf
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, Gainesville, FL 32609, USA
| | - Saddie Vela
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, Gainesville, FL 32609, USA
| | - Jennifer Wilker
- Department of Bacteriology, University of Wisconsin, Madison, WI 53706, USA
| | - Alyssa Davis
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32610, USA
| | - Madalen Robert
- Independent Junior Research Group–Designer Glycans, Leibniz Institute of Plant Biochemistry, 06120 Halle (Saale), Germany
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32609, USA
| | - Valentina Infante
- Department of Bacteriology, University of Wisconsin, Madison, WI 53706, USA
| | - Rafael E Venado
- Department of Bacteriology, University of Wisconsin, Madison, WI 53706, USA
| | - Cătălin Voiniciuc
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32609, USA
| | - Jean-Michel Ané
- Department of Bacteriology, University of Wisconsin, Madison, WI 53706, USA
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Wilfred Vermerris
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32610, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL 32610, USA
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Bančič J, Odeny DA, Ojulong HF, Josiah SM, Buntjer J, Gaynor RC, Hoad SP, Gorjanc G, Dawson IK. Genomic and phenotypic characterization of finger millet indicates a complex diversification history. THE PLANT GENOME 2024; 17:e20392. [PMID: 37986545 DOI: 10.1002/tpg2.20392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/10/2023] [Accepted: 08/16/2023] [Indexed: 11/22/2023]
Abstract
Advances in sequencing technologies mean that insights into crop diversification can now be explored in crops beyond major staples. We use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the whole and sub-genome level. A set of 8778 SNPs and 13 agronomic traits was used to characterize a diverse panel of 423 landraces from Africa and Asia. Through principal component analysis (PCA) and discriminant analysis of principal components, four distinct groups of accessions were identified that coincided with the primary geographic regions of finger millet cultivation. Notably, East Africa, presumed to be the crop's origin, exhibited the lowest genetic diversity. The PCA of phenotypic data also revealed geographic differentiation, albeit with differing relationships among geographic areas than indicated with genomic data. Further exploration of the sub-genomes A and B using neighbor-joining trees revealed distinct features that provide supporting evidence for the complex evolutionary history of finger millet. Although genome-wide association study found only a limited number of significant marker-trait associations, a clustering approach based on the distribution of marker effects obtained from a ridge regression genomic model was employed to investigate trait complexity. This analysis uncovered two distinct clusters. Overall, the findings suggest that finger millet has undergone complex and context-specific diversification, indicative of a lengthy domestication history. These analyses provide insights for the future development of finger millet.
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Affiliation(s)
- Jon Bančič
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian, UK
- Scotland's Rural College (SRUC), Kings Buildings, Edinburgh, UK
| | - Damaris A Odeny
- International Crops Research Institute for the Semi-Arid Tropics, ICRAF House, Gigiri Nairobi, Kenya
| | - Henry F Ojulong
- International Crops Research Institute for the Semi-Arid Tropics, ICRAF House, Gigiri Nairobi, Kenya
| | - Samuel M Josiah
- Department of Horticulture, University of Georgia, Athens, Georgia, USA
| | - Jaap Buntjer
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian, UK
| | - R Chris Gaynor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian, UK
| | - Stephen P Hoad
- Scotland's Rural College (SRUC), Kings Buildings, Edinburgh, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian, UK
| | - Ian K Dawson
- Scotland's Rural College (SRUC), Kings Buildings, Edinburgh, UK
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8
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Winn ZJ, Hudson-Arns E, Hammers M, DeWitt N, Lyerly J, Bai G, St Amand P, Nachappa P, Haley S, Mason RE. HaploCatcher: An R package for prediction of haplotypes. THE PLANT GENOME 2024; 17:e20412. [PMID: 37968867 DOI: 10.1002/tpg2.20412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/17/2023]
Abstract
Wheat (Triticum aestivum L.) is crucial to global food security but is often threatened by diseases, pests, and environmental stresses. Wheat-stem sawfly (Cephus cinctus Norton) poses a major threat to food security in the United States, and solid-stem varieties, which carry the stem-solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker-assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled "HaploCatcher" was developed to predict specific haplotypes of interest in genome-wide genotyped lines. A training population of 1056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome-wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker-derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k-nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross-validation. Estimated group means of lines classified by haplotypes from PCR-derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole-genome sequenced early generation material.
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Affiliation(s)
- Zachary James Winn
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Emily Hudson-Arns
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Mikayla Hammers
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Noah DeWitt
- School of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Jeanette Lyerly
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Guihua Bai
- USDA Agricultural Research Service, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Paul St Amand
- USDA Agricultural Research Service, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Punya Nachappa
- Department of Agricultural Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Scott Haley
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Richard Esten Mason
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
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9
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Kamble U, He X, Navathe S, Kumar M, Patial M, Kabir MR, Singh G, Singh GP, Joshi AK, Singh PK. Genome-wide association mapping for field spot blotch resistance in South Asian spring wheat genotypes. THE PLANT GENOME 2024; 17:e20425. [PMID: 38221748 DOI: 10.1002/tpg2.20425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/03/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024]
Abstract
Spot blotch caused by Bipolaris sorokiniana ((Sacc.) Shoemaker) (teleomorph: Cochliobolus sativus [Ito and Kuribayashi] Drechsler ex Dastur) is an economically important disease of warm and humid regions. The present study focused on identifying resistant genotypes and single-nucleotide polymorphism (SNP) markers associated with spot blotch resistance in a panel of 174 bread spring wheat lines using field screening and genome-wide association mapping strategies. Field experiments were conducted in Agua Fria, Mexico, during the 2019-2020 and 2020-2021 cropping seasons. A wide range of phenotypic variation was observed among genotypes tested during both years. Twenty SNP markers showed significant association with spot blotch resistance on 15 chromosomes, namely, 1A, 1B, 2A, 2B, 2D, 3A, 3B, 4B, 4D, 5A, 5B, 6A, 6B, 7A, and 7B. Of these, two consistently significant SNPs on 5A, TA003225-0566 and TA003225-1427, may represent a new resistance quantitative trait loci. Further, in the proximity of Tsn1 on 5B, AX-94435238 was the most stable and consistent in both years. The identified genomic regions could be deployed to develop spot blotch-resistant genotypes, particularly in the spot blotch-vulnerable wheat growing areas.
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Affiliation(s)
- Umesh Kamble
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, Haryana, India
| | - Xinyao He
- International Maize and Wheat Improvement Centre (CIMMYT) Apedo, Mexico City, Mexico
| | | | - Manjeet Kumar
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Madhu Patial
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - Gyanendra Singh
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, Haryana, India
| | | | | | - Pawan Kumar Singh
- International Maize and Wheat Improvement Centre (CIMMYT) Apedo, Mexico City, Mexico
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10
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Kinoshita S, Sakurai K, Hamazaki K, Tsusaka T, Sakurai M, Kurosawa T, Aoki Y, Shirasawa K, Isobe S, Iwata H. Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction. Genes (Basel) 2023; 14:2137. [PMID: 38136959 PMCID: PMC10742415 DOI: 10.3390/genes14122137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/24/2023] [Accepted: 11/25/2023] [Indexed: 12/24/2023] Open
Abstract
Red perilla is an important medicinal plant used in Kampo medicine. The development of elite varieties of this species is urgently required. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on compound phenotypes (i.e., conventional selection) is expensive and time consuming. Here, we propose genomic selection (GS) and marker-assisted selection (MAS), which use marker information for selection, as suitable selection methods for medicinal plants, and we evaluate the effectiveness of these methods in perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and evaluating the accuracy of genomic prediction (GP). We found that GP had a sufficiently high accuracy for all traits, confirming that GS is an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that using these populations in breeding may simultaneously enhance multiple target traits. This study contributes to research on the genetic mechanisms of the major medicinal compounds of red perilla, as well as the breeding efficiency of this medicinal plant.
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Affiliation(s)
- Sei Kinoshita
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan; (S.K.); (K.S.)
| | - Kengo Sakurai
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan; (S.K.); (K.S.)
| | - Kosuke Hamazaki
- RIKEN Center for Advanced Intelligence Project, Kashiwa, Chiba 227-0871, Japan;
| | - Takahiro Tsusaka
- TSUMURA & CO., Ami, Ibaraki 300-1155, Japan; (T.T.); (M.S.); (T.K.); (Y.A.)
| | - Miki Sakurai
- TSUMURA & CO., Ami, Ibaraki 300-1155, Japan; (T.T.); (M.S.); (T.K.); (Y.A.)
| | - Terue Kurosawa
- TSUMURA & CO., Ami, Ibaraki 300-1155, Japan; (T.T.); (M.S.); (T.K.); (Y.A.)
| | - Youichi Aoki
- TSUMURA & CO., Ami, Ibaraki 300-1155, Japan; (T.T.); (M.S.); (T.K.); (Y.A.)
| | - Kenta Shirasawa
- Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan; (K.S.); (S.I.)
| | - Sachiko Isobe
- Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan; (K.S.); (S.I.)
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan; (S.K.); (K.S.)
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11
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Krause MD, Piepho HP, Dias KOG, Singh AK, Beavis WD. Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:252. [PMID: 37987845 PMCID: PMC10663270 DOI: 10.1007/s00122-023-04470-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/25/2023] [Indexed: 11/22/2023]
Abstract
KEY MESSAGE Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of [Formula: see text] kg/ha[Formula: see text]/yr[Formula: see text] ([Formula: see text] bu/ac[Formula: see text]/yr[Formula: see text]). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha[Formula: see text]/yr[Formula: see text] (0.27-0.59 bu/ac[Formula: see text]/yr[Formula: see text]) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.
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Affiliation(s)
| | | | - Kaio O G Dias
- Department of General Biology, Federal University of Viçosa, Viçosa, Brazil
| | - Asheesh K Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA
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12
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Ma Y, Yang W, Zhang H, Wang P, Liu Q, Li F, Du W. Genetic analysis of phenotypic plasticity identifies BBX6 as the candidate gene for maize adaptation to temperate regions. FRONTIERS IN PLANT SCIENCE 2023; 14:1280331. [PMID: 37964997 PMCID: PMC10642939 DOI: 10.3389/fpls.2023.1280331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023]
Abstract
Introduction Climate changes pose a significant threat to crop adaptation and production. Dissecting the genetic basis of phenotypic plasticity and uncovering the responsiveness of regulatory genes to environmental factors can significantly contribute to the improvement of climate- resilience in crops. Methods We established a BC1F3:4 population using the elite inbred lines Zheng58 and PH4CV and evaluated plant height (PH) across four environments characterized by substantial variations in environmental factors. Then, we quantified the correlation between the environmental mean of PH (the mean performance in each environment) and the environmental parameters within a specific growth window. Furthermore, we performed GWAS analysis of phenotypic plasticity, and identified QTLs and candidate gene that respond to key environment index. After that, we constructed the coexpression network involving the candidate gene, and performed selective sweep analysis of the candidate gene. Results We found that the environmental parameters demonstrated substantial variation across the environments, and genotype by environment interaction contributed to the variations of PH. Then, we identified PTT(35-48) (PTT is the abbreviation for photothermal units), the mean PTT from 35 to 48 days after planting, as the pivotal environmental index that closely correlated with environmental mean of PH. Leveraging the slopes of the response of PH to both the environmental mean and PTT(35-48), we successfully pinpointed QTLs for phenotypic plasticity on chromosomes 1 and 2. Notably, the PH4CV genotypes at these two QTLs exhibited positive contributions to phenotypic plasticity. Furthermore, our analysis demonstrated a direct correlation between the additive effects of each QTL and PTT(35-48). By analyzing transcriptome data of the parental lines in two environments, we found that the 1009 genes responding to PTT(35-48) were enriched in the biological processes related to environmental sensitivity. BBX6 was the prime candidate gene among the 13 genes in the two QTL regions. The coexpression network of BBX6 contained other genes related to flowering time and photoperiod sensitivity. Our investigation, including selective sweep analysis and genetic differentiation analysis, suggested that BBX6 underwent selection during maize domestication. Discussion Th is research substantially advances our understanding of critical environmental factors influencing maize adaptation while simultaneously provides an invaluable gene resource for the development of climate-resilient maize hybrid varieties.
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Affiliation(s)
- Yuting Ma
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenyan Yang
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Pingxi Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Liu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fenghai Li
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Wanli Du
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
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13
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Della Coletta R, Fernandes SB, Monnahan PJ, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Importance of genetic architecture in marker selection decisions for genomic prediction. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:220. [PMID: 37819415 DOI: 10.1007/s00122-023-04469-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023]
Abstract
KEY MESSAGE We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Samuel B Fernandes
- Department of Crop, Soil and Environmental Sciences at University of Arkansas, Fayetteville, AR, 72701, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Mark A Mikel
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Martin O Bohn
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA.
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14
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Guhlin J, Le Lec MF, Wold J, Koot E, Winter D, Biggs PJ, Galla SJ, Urban L, Foster Y, Cox MP, Digby A, Uddstrom LR, Eason D, Vercoe D, Davis T, Howard JT, Jarvis ED, Robertson FE, Robertson BC, Gemmell NJ, Steeves TE, Santure AW, Dearden PK. Species-wide genomics of kākāpō provides tools to accelerate recovery. Nat Ecol Evol 2023; 7:1693-1705. [PMID: 37640765 DOI: 10.1038/s41559-023-02165-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 07/11/2023] [Indexed: 08/31/2023]
Abstract
The kākāpō is a critically endangered, intensively managed, long-lived nocturnal parrot endemic to Aotearoa New Zealand. We generated and analysed whole-genome sequence data for nearly all individuals living in early 2018 (169 individuals) to generate a high-quality species-wide genetic variant callset. We leverage extensive long-term metadata to quantify genome-wide diversity of the species over time and present new approaches using probabilistic programming, combined with a phenotype dataset spanning five decades, to disentangle phenotypic variance into environmental and genetic effects while quantifying uncertainty in small populations. We find associations for growth, disease susceptibility, clutch size and egg fertility within genic regions previously shown to influence these traits in other species. Finally, we generate breeding values to predict phenotype and illustrate that active management over the past 45 years has maintained both genome-wide diversity and diversity in breeding values and, hence, evolutionary potential. We provide new pathways for informing future conservation management decisions for kākāpō, including prioritizing individuals for translocation and monitoring individuals with poor growth or high disease risk. Overall, by explicitly addressing the challenge of the small sample size, we provide a template for the inclusion of genomic data that will be transformational for species recovery efforts around the globe.
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Affiliation(s)
- Joseph Guhlin
- Genomics Aotearoa, Biochemistry Department, School of Biomedical Sciences, University of Otago, Dunedin, Aotearoa New Zealand
| | - Marissa F Le Lec
- Genomics Aotearoa, Biochemistry Department, School of Biomedical Sciences, University of Otago, Dunedin, Aotearoa New Zealand
| | - Jana Wold
- School of Biological Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
| | - Emily Koot
- The New Zealand Institute for Plant and Food Research Ltd, Palmerston North, Aotearoa New Zealand
| | - David Winter
- School of Natural Sciences, Massey University, Palmerston North, Aotearoa New Zealand
| | - Patrick J Biggs
- School of Natural Sciences, Massey University, Palmerston North, Aotearoa New Zealand
- School of Veterinary Science, Massey University, Palmerston North, Aotearoa New Zealand
| | - Stephanie J Galla
- School of Biological Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
- Department of Biological Sciences, Boise State University, Boise, ID, USA
| | - Lara Urban
- Department of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, Aotearoa New Zealand
- Helmholtz Pioneer Campus, Helmholtz Zentrum Muenchen, Neuherberg, Germany
- Helmholtz AI, Helmholtz Zentrum Muenchen, Neuherberg, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Yasmin Foster
- Department of Zoology, University of Otago, Dunedin, Aotearoa New Zealand
| | - Murray P Cox
- School of Natural Sciences, Massey University, Palmerston North, Aotearoa New Zealand
- Department of Statistics, University of Auckland, Auckland, Aotearoa New Zealand
| | - Andrew Digby
- Kākāpō Recovery Programme, Department of Conservation, Invercargill, Aotearoa New Zealand
| | - Lydia R Uddstrom
- Kākāpō Recovery Programme, Department of Conservation, Invercargill, Aotearoa New Zealand
| | - Daryl Eason
- Kākāpō Recovery Programme, Department of Conservation, Invercargill, Aotearoa New Zealand
| | - Deidre Vercoe
- Kākāpō Recovery Programme, Department of Conservation, Invercargill, Aotearoa New Zealand
| | - Tāne Davis
- Rakiura Tītī Islands Administering Body, Invercargill, Aotearoa New Zealand
| | - Jason T Howard
- Neurogenetics of Language Lab, The Rockefeller University, New York, NY, USA
- Mirxes, Cambridge, MA, USA
| | - Erich D Jarvis
- The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Fiona E Robertson
- Department of Zoology, University of Otago, Dunedin, Aotearoa New Zealand
| | - Bruce C Robertson
- Department of Zoology, University of Otago, Dunedin, Aotearoa New Zealand
| | - Neil J Gemmell
- Department of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, Aotearoa New Zealand
| | - Tammy E Steeves
- School of Biological Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
| | - Anna W Santure
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa New Zealand
| | - Peter K Dearden
- Genomics Aotearoa, Biochemistry Department, School of Biomedical Sciences, University of Otago, Dunedin, Aotearoa New Zealand.
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15
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Pons C, Casals J, Brower M, Sacco A, Riccini A, Hendrickx P, Figás MDR, Fisher J, Grandillo S, Mazzucato A, Soler S, Zamir D, Causse M, Díez MJ, Finkers R, Prohens J, Monforte AJ, Granell A. Diversity and genetic architecture of agro-morphological traits in a core collection of European traditional tomato. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5896-5916. [PMID: 37527560 PMCID: PMC10540738 DOI: 10.1093/jxb/erad306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/28/2023] [Indexed: 08/03/2023]
Abstract
European traditional tomato varieties have been selected by farmers given their consistent performance and adaptation to local growing conditions. Here we developed a multipurpose core collection, comprising 226 accessions representative of the genotypic, phenotypic, and geographical diversity present in European traditional tomatoes, to investigate the basis of their phenotypic variation, gene×environment interactions, and stability for 33 agro-morphological traits. Comparison of the traditional varieties with a modern reference panel revealed that some traditional varieties displayed excellent agronomic performance and high trait stability, as good as or better than that of their modern counterparts. We conducted genome-wide association and genome-wide environment interaction studies and detected 141 quantitative trait loci (QTLs). Out of those, 47 QTLs were associated with the phenotype mean (meanQTLs), 41 with stability (stbQTLs), and 53 QTL-by-environment interactions (QTIs). Most QTLs displayed additive gene actions, with the exception of stbQTLs, which were mostly recessive and overdominant QTLs. Both common and specific loci controlled the phenotype mean and stability variation in traditional tomato; however, a larger proportion of specific QTLs was observed, indicating that the stability gene regulatory model is the predominant one. Developmental genes tended to map close to meanQTLs, while genes involved in stress response, hormone metabolism, and signalling were found within regions affecting stability. A total of 137 marker-trait associations for phenotypic means and stability were novel, and therefore our study enhances the understanding of the genetic basis of valuable agronomic traits and opens up a new avenue for an exploitation of the allelic diversity available within European traditional tomato germplasm.
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Affiliation(s)
- Clara Pons
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana (COMAV), Universitat Politècnica de València, València, Spain
- Instituto de Biología Molecular y Celular de Plantas (IBMCP). Consejo Superior de Investigaciones Científicas (CSIC), Universitat Politècnica de València, València, Spain
| | - Joan Casals
- Department of Agri-Food Engineering and Biotechnology/Miquel Agustí Foundation, Universitat Politècnica de Catalunya, Campus Baix Llobregat, Esteve Terrades 8, 08860 Castelldefels, Spain
| | - Matthijs Brower
- Wageningen University & Research, Plant Breeding, POB 386, NL-6700 AJ Wageningen, The Netherlands
| | - Adriana Sacco
- Institute of Biosciences and BioResources (IBBR), National Research Council of Italy (CNR), Via Università 133, 80055 Portici, Italy
| | - Alessandro Riccini
- Department of Agriculture and Forest Sciences (DAFNE), Università degli Studi della Tuscia, Viterbo, Italy
| | - Patrick Hendrickx
- Wageningen University & Research, Plant Breeding, POB 386, NL-6700 AJ Wageningen, The Netherlands
| | - Maria del Rosario Figás
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana (COMAV), Universitat Politècnica de València, València, Spain
| | - Josef Fisher
- Hebrew University of Jerusalem, Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Rehovot, Israel
| | - Silvana Grandillo
- Institute of Biosciences and BioResources (IBBR), National Research Council of Italy (CNR), Via Università 133, 80055 Portici, Italy
| | - Andrea Mazzucato
- Department of Agriculture and Forest Sciences (DAFNE), Università degli Studi della Tuscia, Viterbo, Italy
| | - Salvador Soler
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana (COMAV), Universitat Politècnica de València, València, Spain
| | - Dani Zamir
- Hebrew University of Jerusalem, Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Rehovot, Israel
| | - Mathilde Causse
- INRAE, UR1052, Génétique et Amélioration des Fruits et Légumes 67 Allée des Chênes, Domaine Saint Maurice, CS60094, Montfavet, 84143, France
| | - Maria José Díez
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana (COMAV), Universitat Politècnica de València, València, Spain
| | - Richard Finkers
- Wageningen University & Research, Plant Breeding, POB 386, NL-6700 AJ Wageningen, The Netherlands
| | - Jaime Prohens
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana (COMAV), Universitat Politècnica de València, València, Spain
| | - Antonio Jose Monforte
- Instituto de Biología Molecular y Celular de Plantas (IBMCP). Consejo Superior de Investigaciones Científicas (CSIC), Universitat Politècnica de València, València, Spain
| | - Antonio Granell
- Instituto de Biología Molecular y Celular de Plantas (IBMCP). Consejo Superior de Investigaciones Científicas (CSIC), Universitat Politècnica de València, València, Spain
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16
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Feldmann MJ, Covarrubias-Pazaran G, Piepho HP. Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures. G3 (BETHESDA, MD.) 2023; 13:jkad148. [PMID: 37405459 PMCID: PMC10468314 DOI: 10.1093/g3journal/jkad148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023]
Abstract
Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.
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Affiliation(s)
- Mitchell J Feldmann
- Department of Plant Sciences, University of California Davis, One Shields Ave, Davis, CA 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, El Batán, 56130 Texcoco, Edo. de México, México
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart 70599, Germany
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17
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Mora-Poblete F, Maldonado C, Henrique L, Uhdre R, Scapim CA, Mangolim CA. Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach. FRONTIERS IN PLANT SCIENCE 2023; 14:1153040. [PMID: 37593046 PMCID: PMC10428628 DOI: 10.3389/fpls.2023.1153040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 07/12/2023] [Indexed: 08/19/2023]
Abstract
Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.
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Affiliation(s)
| | - Carlos Maldonado
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Luma Henrique
- Department of Agronomy, State University of Maringá, Paraná, Brazil
| | - Renan Uhdre
- Department of Agronomy, State University of Maringá, Paraná, Brazil
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18
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Ledesma A, Ribeiro FAS, Uberti A, Edwards J, Hearne S, Frei U, Lübberstedt T. Molecular characterization of doubled haploid lines derived from different cycles of the Iowa Stiff Stalk Synthetic (BSSS) maize population. FRONTIERS IN PLANT SCIENCE 2023; 14:1226072. [PMID: 37600186 PMCID: PMC10433169 DOI: 10.3389/fpls.2023.1226072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright's F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program.
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Affiliation(s)
- Alejandro Ledesma
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | | | - Alison Uberti
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Jode Edwards
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
| | - Sarah Hearne
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico
| | - Ursula Frei
- Department of Agronomy, Iowa State University, Ames, IA, United States
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19
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Chen SP, Tung CW, Wang PH, Liao CT. A statistical package for evaluation of hybrid performance in plant breeding via genomic selection. Sci Rep 2023; 13:12204. [PMID: 37500737 PMCID: PMC10374541 DOI: 10.1038/s41598-023-39434-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023] Open
Abstract
Hybrid breeding employs heterosis, which could potentially improve the yield and quality of a crop. Genomic selection (GS) is a promising approach for the selection of quantitative traits in plant breeding. The main objectives of this study are to (i) propose a GS-based approach to identify potential parental lines and superior hybrid combinations from a breeding population, which is composed of hybrids produced by a half diallel mating design; (ii) develop a software package for users to carry out the proposed approach. An R package, designated EHPGS, was generated to facilitate the employment of the genomic best linear unbiased model considering additive plus dominance marker effects for the hybrid performance evaluation. The R package contains a Bayesian statistical algorithm for calculating genomic estimated breeding value (GEBVs), GEBV-based specific combining ability, general combining ability, mid-parent heterosis, and better-parent heterosis. Three datasets that have been published in literature, including pumpkin (Cucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the use of EHPGS.
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Affiliation(s)
- Szu-Ping Chen
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Chih-Wei Tung
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Pei-Hsien Wang
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Chen-Tuo Liao
- Department of Agronomy, National Taiwan University, Taipei, Taiwan.
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20
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Heilmann PG, Frisch M, Abbadi A, Kox T, Herzog E. Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. FRONTIERS IN PLANT SCIENCE 2023; 14:1178902. [PMID: 37546247 PMCID: PMC10401275 DOI: 10.3389/fpls.2023.1178902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.
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Affiliation(s)
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | | | | | - Eva Herzog
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
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21
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García-García I, Méndez-Cea B, González de Andrés E, Gazol A, Sánchez-Salguero R, Manso-Martínez D, Horreo JL, Camarero JJ, Linares JC, Gallego FJ. Climate and Soil Microsite Conditions Determine Local Adaptation in Declining Silver Fir Forests. PLANTS (BASEL, SWITZERLAND) 2023; 12:2607. [PMID: 37514222 PMCID: PMC10384727 DOI: 10.3390/plants12142607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/15/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
Ongoing climatic change is threatening the survival of drought-sensitive tree species, such as silver fir (Abies alba). Drought-induced dieback had been previously explored in this conifer, although the role played by tree-level genetic diversity and its relationship with growth patterns and soil microsite conditions remained elusive. We used double digest restriction-site-associated DNA sequencing (ddRADseq) to describe different genetic characteristics of five silver fir forests in the Spanish Pyrenees, including declining and non-declining trees. Single nucleotide polymorphisms (SNPs) were used to investigate the relationships between genetics, dieback, intraspecific trait variation (functional dendrophenotypic traits and leaf traits), local bioclimatic conditions, and rhizosphere soil properties. While there were no noticeable genetic differences between declining and non-declining trees, genome-environment associations with selection signatures were abundant, suggesting a strong influence of climate, soil physicochemical properties, and soil microbial diversity on local adaptation. These results provide novel insights into how genetics and diverse environmental factors are interrelated and highlight the need to incorporate genetic data into silver fir forest dieback studies to gain a better understanding of local adaptation.
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Affiliation(s)
- Isabel García-García
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Belén Méndez-Cea
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | | | - Antonio Gazol
- Instituto Pirenaico de Ecología (IPE-CSIC), 50059 Zaragoza, Spain
| | - Raúl Sánchez-Salguero
- Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, 41013 Sevilla, Spain
| | - David Manso-Martínez
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Jose Luis Horreo
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - J Julio Camarero
- Instituto Pirenaico de Ecología (IPE-CSIC), 50059 Zaragoza, Spain
| | - Juan Carlos Linares
- Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, 41013 Sevilla, Spain
| | - Francisco Javier Gallego
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
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22
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Labroo MR, Endelman JB, Gemenet DC, Werner CR, Gaynor RC, Covarrubias-Pazaran GE. Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:147. [PMID: 37291402 DOI: 10.1007/s00122-023-04377-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 05/05/2023] [Indexed: 06/10/2023]
Abstract
KEY MESSAGE Reciprocal recurrent selection sometimes increases genetic gain per unit cost in clonal diploids with heterosis due to dominance, but it typically does not benefit autopolyploids. Breeding can change the dominance as well as additive genetic value of populations, thus utilizing heterosis. A common hybrid breeding strategy is reciprocal recurrent selection (RRS), in which parents of hybrids are typically recycled within pools based on general combining ability. However, the relative performances of RRS and other breeding strategies have not been thoroughly compared. RRS can have relatively increased costs and longer cycle lengths, but these are sometimes outweighed by its ability to harness heterosis due to dominance. Here, we used stochastic simulation to compare genetic gain per unit cost of RRS, terminal crossing, recurrent selection on breeding value, and recurrent selection on cross performance considering different amounts of population heterosis due to dominance, relative cycle lengths, time horizons, estimation methods, selection intensities, and ploidy levels. In diploids with phenotypic selection at high intensity, whether RRS was the optimal breeding strategy depended on the initial population heterosis. However, in diploids with rapid-cycling genomic selection at high intensity, RRS was the optimal breeding strategy after 50 years over almost all amounts of initial population heterosis under the study assumptions. Diploid RRS required more population heterosis to outperform other strategies as its relative cycle length increased and as selection intensity and time horizon decreased. The optimal strategy depended on selection intensity, a proxy for inbreeding rate. Use of diploid fully inbred parents vs. outbred parents with RRS typically did not affect genetic gain. In autopolyploids, RRS typically did not outperform one-pool strategies regardless of the initial population heterosis.
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Affiliation(s)
- Marlee R Labroo
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jeffrey B Endelman
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Dorcus C Gemenet
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Christian R Werner
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Giovanny E Covarrubias-Pazaran
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico.
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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23
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Keele GR. Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population. G3 (BETHESDA, MD.) 2023; 13:jkad027. [PMID: 36735601 PMCID: PMC10085760 DOI: 10.1093/g3journal/jkad027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 12/30/2022] [Accepted: 01/23/2023] [Indexed: 02/04/2023]
Abstract
Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.
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Affiliation(s)
- Gregory R Keele
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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24
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Pélissier R, Ducasse A, Ballini E, Frouin J, Violle C, Morel JB. A major genetic locus in neighbours controls changes of gene expression and susceptibility in intraspecific rice mixtures. THE NEW PHYTOLOGIST 2023; 238:835-844. [PMID: 36710512 DOI: 10.1111/nph.18778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Reports indicate that intraspecific neighbours alter the physiology of focal plants, and with a few exceptions, their molecular responses to neighbours are unknown. Recently, changes in susceptibility to pathogen resulting from such interactions were demonstrated, a phenomenon called neighbour-modulated susceptibility (NMS). However, the genetics of NMS and the associated molecular responses are largely unexplored. Here, we analysed in rice the modification of biomass and susceptibility to the blast fungus pathogen in the Kitaake focal genotype in the presence of 280 different neighbours. Using genome-wide association studies, we identified the loci in the neighbour that determine the response in Kitaake. Using a targeted transcriptomic approach, we characterized the molecular responses in focal plants co-cultivated with various neighbours inducing a reduction in susceptibility. Our study demonstrates that NMS is controlled by one major locus in the rice genome of its neighbour. Furthermore, we show that this locus can be associated with characteristic patterns of gene expression in focal plant. Finally, we propose an hypothesis where Pi could play a role in explaining this case of NMS. Our study sheds light on how plants affect the physiology in their neighbourhood and opens perspectives for understanding plant-plant interactions.
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Affiliation(s)
- Rémi Pélissier
- PHIM, CEFE, Institut Agro, INRAE, CIRAD, Univ Montpellier, 34000, Montpellier, France
| | - Aurélie Ducasse
- PHIM, INRAE, CIRAD, Institut Agro, Univ Montpellier, 34000, Montpellier, France
| | - Elsa Ballini
- PHIM, INRAE, CIRAD, Institut Agro, Univ Montpellier, 34000, Montpellier, France
| | - Julien Frouin
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, 34000, Montpellier, France
| | - Cyrille Violle
- CEFE, CNRS, EPHE, IRD, Univ Montpellier, 34000, Montpellier, France
| | - Jean-Benoit Morel
- PHIM, INRAE, CIRAD, Institut Agro, Univ Montpellier, 34000, Montpellier, France
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25
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Michel S, Löschenberger F, Ametz C, Bürstmayr H. Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:79. [PMID: 36952008 PMCID: PMC10036288 DOI: 10.1007/s00122-023-04312-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 01/27/2023] [Indexed: 06/17/2023]
Abstract
A novel genomic selection strategy offers the unique opportunity to develop qualitative race-specific resistant varieties that possess high levels of the more durable quantitative race-nonspecific resistance in their genetic background. Race-specific qualitative resistance genes (R-genes) are conferring complete resistance in many pathosystems, but are frequently overcome by new virulent pathogen races. Once the deployed R-genes are overcome, a wide variation of quantitative disease resistance (QDR) can be observed in a set of previously race-specific, i.e., completely resistant genotypes-a phenomenon known as "vertifolia effect." This race-nonspecific QDR is considered to be more durable in the long term, but provides merely a partial protection against pathogens. This simulation study aimed to detangle race-specific R-gene-mediated resistance of pending selection candidates and the QDR in their genetic background by employing different genomic selection strategies. True breeding values that reflected performance data for rust resistance in wheat were simulated, and used in a recurrent genomic selection based on several prediction models and training population designs. Using training populations that were devoid of race-specific R-genes was thereby pivotal for an efficient improvement of QDR in the long term. Marker-assisted preselection for the presence of R-genes followed by a genomic prediction for accumulating the many small to medium effect loci underlying QDR in the genetic background of race-specific resistant genotypes appeared furthermore to be a promising approach to select simultaneously for both types of resistance. The practical application of such a knowledge-driven genomic breeding strategy offers the opportunity to develop varieties with multiple layers of resistance, which have the potential to prevent intolerable crop losses under epidemic situations by displaying a high level of QDR even when race-specific R-genes have been overcome by evolving pathogen populations.
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Affiliation(s)
- Sebastian Michel
- Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
| | | | - Christian Ametz
- Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | - Hermann Bürstmayr
- Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
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26
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Endelman JB. Fully efficient, two-stage analysis of multi-environment trials with directional dominance and multi-trait genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:65. [PMID: 36949348 PMCID: PMC10033618 DOI: 10.1007/s00122-023-04298-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Abstract
R/StageWise enables fully efficient, two-stage analysis of multi-environment, multi-trait datasets for genomic selection, including support for dominance heterosis and polyploidy. Plant breeders interested in genomic selection often face challenges to fully utilizing multi-trait, multi-environment datasets. R package StageWise was developed to go beyond the capabilities of most specialized software for genomic prediction, without requiring the programming skills needed for more general-purpose software for mixed models. As the name suggests, one of the core features is a fully efficient, two-stage analysis for multiple environments, in which the full variance-covariance matrix of the Stage 1 genotype means is used in Stage 2. Another feature is directional dominance, including for polyploids, to account for inbreeding depression in outbred crops. StageWise enables selection with multi-trait indices, including restricted indices with one or more traits constrained to have zero response. For a potato dataset with 943 genotypes evaluated over 6 years, including the Stage 1 errors in Stage 2 reduced the Akaike Information Criterion (AIC) by 29, 67, and 104 for maturity, yield, and fry color, respectively. The proportion of variation explained by heterosis was largest for yield but still only 0.03, likely because of limited variation for the genomic inbreeding coefficient. Due to the large additive genetic correlation (0.57) between yield and maturity, naïve selection on an index combining yield and fry color led to an undesirable response for later maturity. The restricted index coefficients to maximize genetic merit without delaying maturity were identified. The software and three vignettes are available at https://github.com/jendelman/StageWise .
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Affiliation(s)
- Jeffrey B Endelman
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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27
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Pfeilsticker TR, Jones RC, Steane DA, Vaillancourt RE, Potts BM. Molecular insights into the dynamics of species invasion by hybridisation in Tasmanian eucalypts. Mol Ecol 2023; 32:2913-2929. [PMID: 36807951 DOI: 10.1111/mec.16892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/26/2022] [Accepted: 01/26/2023] [Indexed: 02/22/2023]
Abstract
In plants where seed dispersal is limited compared with pollen dispersal, hybridisation may enhance gene exchange and species dispersal. We provide genetic evidence of hybridisation contributing to the expansion of the rare Eucalyptus risdonii into the range of the widespread Eucalyptus amygdalina. These closely related tree species are morphologically distinct, and observations suggest that natural hybrids occur along their distribution boundaries and as isolated trees or in small patches within the range of E. amygdalina. Hybrid phenotypes occur outside the range of normal dispersal for E. risdonii seed, yet in some hybrid patches small individuals resembling E. risdonii occur and are hypothesised to be a result of backcrossing. Using 3362 genome-wide SNPs assessed from 97 individuals of E. risdonii and E. amygdalina and 171 hybrid trees, we show that (i) isolated hybrids match the genotypes expected of F1 /F2 hybrids, (ii) there is a continuum in the genetic composition among the isolated hybrid patches from patches dominated by F1 /F2 -like genotypes to those dominated by E. risdonii-backcross genotypes, and (iii) the E. risdonii-like phenotypes in the isolated hybrid patches are most-closely related to proximal larger hybrids. These results suggest that the E. risdonii phenotype has been resurrected in isolated hybrid patches established from pollen dispersal, providing the first steps in its invasion of suitable habitat by long-distance pollen dispersal and complete introgressive displacement of E. amygdalina. Such expansion accords with the population demographics, common garden performance data, and climate modelling which favours E. risdonii and highlights a role of interspecific hybridisation in climate change adaptation and species expansion.
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Affiliation(s)
- Thais R Pfeilsticker
- School of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, Australia
| | - Rebecca C Jones
- School of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, Australia
| | - Dorothy A Steane
- School of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, Australia
| | - René E Vaillancourt
- School of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, Australia
| | - Brad M Potts
- School of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, Australia
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28
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Méndez-Cea B, García-García I, Sánchez-Salguero R, Lechuga V, Gallego FJ, Linares JC. Tree-Level Growth Patterns and Genetic Associations Depict Drought Legacies in the Relict Forests of Abies marocana. PLANTS (BASEL, SWITZERLAND) 2023; 12:873. [PMID: 36840220 PMCID: PMC9959318 DOI: 10.3390/plants12040873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
The frequency and intensity of drought events are increasing worldwide, challenging the adaptive capacity of several tree species. Here, we evaluate tree growth patterns and climate sensitivity to precipitation, temperature, and drought in the relict Moroccan fir Abies marocana. We selected two study sites, formerly stated as harboring contrasting A. marocana taxa (A. marocana and A. tazaotana, respectively). For each tree, dendrochronological methods were applied to quantify growth patterns and climate-growth sensitivity. Further, ddRAD-seq was performed on the same trees and close saplings to obtain single nucleotide polymorphisms (SNPs) and related genotype-phenotype associations. Genetic differentiation between the two studied remnant populations of A. marocana was weak. Growth patterns and climate-growth relationships were almost similar at the two sites studied, supporting a negative effect of warming. Growth trends and tree size showed associations with SNPs, although there were no relationships with phenotypes related to climatic sensitivity. We found significant differences in the SNPs subjected to selection in the saplings compared to the old trees, suggesting that relict tree populations might be subjected to genetic differentiation and local adaptation to climate dryness. Our results illustrate the potential of tree rings and genome-wide analysis to improve our understanding of the adaptive capacity of drought-sensitive forests to cope with ongoing climate change.
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Affiliation(s)
- Belén Méndez-Cea
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Isabel García-García
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Raúl Sánchez-Salguero
- Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, 41013 Sevilla, Spain
| | - Víctor Lechuga
- Centro de Estudios Avanzados en Ciencias de la Tierra, Energía y Medio Ambiente (CEACTEMA), Universidad de Jaén, 23071 Jaén, Spain
| | - Francisco Javier Gallego
- Departamento de Genética, Fisiología y Microbiología, Unidad de Genética, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Juan C. Linares
- Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, 41013 Sevilla, Spain
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Yamamoto E, Yabe S, Inari-Ikeda M, Yoshida H, Morinaka Y, Matsuoka M, Kitano H. Independent control of organ number and distribution pattern in rice panicle. FRONTIERS IN PLANT SCIENCE 2023; 14:1119770. [PMID: 36824199 PMCID: PMC9941638 DOI: 10.3389/fpls.2023.1119770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
As the determinants of yield products, rice panicle traits are important targets for breeding. Despite their importance in grain filling and subsequent yield productivity, knowledge on the organ distribution pattern in rice panicles is limited owing to the lack of objective evaluation methods. In this study, we developed a method for quantifying rice panicle organ distribution patterns. To validate our method for practical application in biology, we integrated this method into a quantitative trait locus (QTL) analysis and identified QTLs for panicle organ distribution patterns in rice. Interestingly, Grain number 1 (Gn1), a major QTL of organ number, was not identified as a QTL for distribution pattern, indicating that the number and distribution of panicle organs are independently controlled. This study provides insight into rice panicle organ distribution patterns that will help improve breeding targeting rice panicle architecture.
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Affiliation(s)
- Eiji Yamamoto
- Graduate School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
| | - Shiori Yabe
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Mayuko Inari-Ikeda
- Bioscience and Biotechnology Center, Nagoya University, Nagoya, Aichi, Japan
| | - Hideki Yoshida
- Institute of Fermentation Sciences, Fukushima University, Fukushima, Fukushima, Japan
| | - Yoichi Morinaka
- Department of Sustainable Agri-Culture, Fukui Prefectural University, Awara, Fukui, Japan
| | - Makoto Matsuoka
- Institute of Fermentation Sciences, Fukushima University, Fukushima, Fukushima, Japan
| | - Hidemi Kitano
- Bioscience and Biotechnology Center, Nagoya University, Nagoya, Aichi, Japan
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Garcia-Abadillo J, Morales L, Buerstmayr H, Michel S, Lillemo M, Holzapfel J, Hartl L, Akdemir D, Carvalho HF, Isidro-Sánchez J. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding. FRONTIERS IN PLANT SCIENCE 2023; 13:1057914. [PMID: 36714712 PMCID: PMC9876611 DOI: 10.3389/fpls.2022.1057914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/24/2022] [Indexed: 06/18/2023]
Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.
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Affiliation(s)
- J. Garcia-Abadillo
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - L. Morales
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - H. Buerstmayr
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - S. Michel
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - M. Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - L. Hartl
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - D. Akdemir
- CIBMTR (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis, MN, United States
| | - H. F. Carvalho
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - J. Isidro-Sánchez
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Fang DD, Thyssen GN, Wang M, Jenkins JN, McCarty JC, Jones DC. Genomic confirmation of Gossypium barbadense introgression into G. hirsutum and a subsequent MAGIC population. Mol Genet Genomics 2023; 298:143-152. [PMID: 36346467 DOI: 10.1007/s00438-022-01974-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
Introgression of superior fiber traits from Pima cotton (Gossypium barbadense, GB) into high yield Upland cotton (G. hirsutum) has been a breeding objective for many years in a few breeding programs in the world. However, progress has been very slow due to introgression barriers resulting from whole genome hybridization between the two species. To minimize such barriers, chromosome substitution lines (CS-B) from Pima cotton 3-79 in an Upland cotton cultivar TM-1 were developed. A multiparent advanced generation inter-cross (MAGIC) population consisting of 180 recombinant inbred lines (RILs) was subsequently made using the 18 CS-B lines and three Upland cotton cultivars as parents. In this research, we sequenced the whole genomes of the 21 parents and 180 RILs to examine the G. barbadense introgression. Of the 18 CS-B lines, 11 contained the target GB chromosome or chromosome segment, two contained more than two GB chromosomes, and five did not have the expected introgression. Residual introgression in non-target chromosomes was prevalent in all CS-B lines. A clear structure existed in the MAGIC population and the 180 RILs were distributed into three groups, i.e., high, moderate, and low GB introgression. Large blocks of GB chromosome introgression were still present in some RILs after five cycles of random-mating, an indication of recombination suppression or other unknown reasons present in the population. Identity by descent analysis revealed that the MAGIC RILs contained less introgression than expected. This research presents an insight on understanding the complex problems of introgression between cotton species.
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Affiliation(s)
- David D Fang
- Cotton Fiber Bioscience Research Unit, USDA-ARS, Southern Regional Research Center, New Orleans, LA, 70124, USA.
| | - Gregory N Thyssen
- Cotton Fiber Bioscience Research Unit, USDA-ARS, Southern Regional Research Center, New Orleans, LA, 70124, USA
| | - Maojun Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Johnie N Jenkins
- Genetics and Sustainable Agriculture Research Unit, USDA-ARS, Mississippi State, MS, 39762, USA
| | - Jack C McCarty
- Genetics and Sustainable Agriculture Research Unit, USDA-ARS, Mississippi State, MS, 39762, USA
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Morales L, Ametz C, Dallinger HG, Löschenberger F, Neumayer A, Zimmerl S, Buerstmayr H. Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:23. [PMID: 36692839 PMCID: PMC9873752 DOI: 10.1007/s00122-023-04249-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
We used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods. Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008-2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive ability and genetic variance overall, but differences between models were small. Including QTL as covariates improved predictive ability in some years where highly significant QTL had been detected via genome-wide association analysis. Predictive ability was moderate within years (cross-validated) but poor in cross-year frameworks.
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Affiliation(s)
- Laura Morales
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, University of Natural Resources and Life Sciences Vienna, Tulln, Austria.
| | | | - Hermann Gregor Dallinger
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
- Saatzucht Donau GmbH and CoKG, Probstdorf, Austria
| | | | | | - Simone Zimmerl
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Hermann Buerstmayr
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
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Budhlakoti N, Mishra DC, Majumdar SG, Kumar A, Srivastava S, Rai SN, Rai A. Integrated model for genomic prediction under additive and non-additive genetic architecture. FRONTIERS IN PLANT SCIENCE 2022; 13:1027558. [PMID: 36531414 PMCID: PMC9749549 DOI: 10.3389/fpls.2022.1027558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM's performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.
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Affiliation(s)
- Neeraj Budhlakoti
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dwijesh Chandra Mishra
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sayanti Guha Majumdar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Sudhir Srivastava
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - S. N. Rai
- Bioinformatics and Biostatistics Department, University of Louisville, Louisville, KY, United States
| | - Anil Rai
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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He X, Zhang F, He F, Shen Y, Yu LX, Zhang T, Kang J. Accuracy of genomic selection for alfalfa biomass yield in two full-sib populations. FRONTIERS IN PLANT SCIENCE 2022; 13:1037272. [PMID: 36388566 PMCID: PMC9650308 DOI: 10.3389/fpls.2022.1037272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Alfalfa (Medicago sativa) is one of the most important leguminous forages, widely planted in temperate and subtropical regions. As a homozygous tetraploid, its complex genetic background limits genetic improvement of biomass yield attributes through conventional breeding methods. Genomic selection (GS) could improve breeding efficiency by using high-density molecular markers that cover the whole genome to assess genomic breeding values. In this study, two full-sib F1 populations, consisting of 149 and 392 individual plants (P149 and P392), were constructed using parents with differences in yield traits, and the yield traits of the F1 populations were measured for several years in multiple environments. Comparisons of individual yields were greatly affected by environments, and the best linear unbiased prediction (BLUP) could accurately represent the original yield data. The two hybrid F1 populations were genotyped using GBS and RAD-seq techniques, respectively, and 47,367 and 161,170 SNP markers were identified. To develop yield prediction models for a single location and across locations, genotypic and phenotypic data from alfalfa yields in multiple environments were combined with various prediction models. The prediction accuracies of the F1 population, including 149 individuals, were 0.11 to 0.70, and those of the F1 population, consisting of 392 individuals, were 0.14 to 0.67. The BayesC and RF models had the highest average prediction accuracy of 0.60 for two F1 populations. The accuracy of the prediction models for P392 was higher than that of P149. By analyzing multiple prediction models, moderate prediction accuracies are obtained, although accuracies will likely decline across multiple locations. Our study provided evidence that GS can accelerate the improvement of alfalfa yield traits.
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Affiliation(s)
- Xiaofan He
- School of Grassland Science, Beijing Forestry University, Beijing, China
| | - Fan Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fei He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuhua Shen
- College of Chemistry and Life Sciences, Chifeng University, Chifeng, China
| | - Long-Xi Yu
- Plant Germplasm Introduction and Testing Research, United States Department of Agriculture-Agricultural Research Service, Prosser, WA, United States
| | - Tiejun Zhang
- School of Grassland Science, Beijing Forestry University, Beijing, China
| | - Junmei Kang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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Heritable and Nonheritable Rumen Bacteria Are Associated with Different Characters of Lactation Performance of Dairy Cows. mSystems 2022; 7:e0042222. [PMID: 36102532 PMCID: PMC9600476 DOI: 10.1128/msystems.00422-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Recent studies have reported that some rumen microbes are heritable. However, it is necessary to clarify the functions and specific contributions of the heritable rumen microbes to cattle phenotypes (microbiability) in comparison with those that are nonheritable. This study aimed to identify the distribution and predicted functions of heritable and nonheritable bacterial taxa at species level in the rumen of dairy cows and their respective contributions to energy-corrected milk yield, protein content and yield, and fat content and yield in milk. Thirty-two heritable and 674 nonheritable bacterial taxa were identified at species level, and the functional analysis revealed that predicted microbial functions for both groups were mainly enriched for energy, amino acid, and ribonucleotide metabolism. The mean microbiability (to reflect a single taxon's contribution) of heritable bacteria was found to range from 0.16% to 0.33% for the different milk traits, whereas the range for nonheritable bacteria was 0.03% to 0.06%. These findings suggest a strong contribution by host genetics in shaping the rumen microbiota, which contribute significantly to milk production traits. Therefore, there is an opportunity to further improve milk production traits through attention to host genetics and the interaction with the rumen microbiota. IMPORTANCE Rumen bacteria produce volatile fatty acids which exert a far-reaching influence on hepatic metabolism, mammary gland metabolism, and animal production. In the current study, 32 heritable and 674 nonheritable bacterial taxa at species level were identified, and shown to have different microbiability (overall community contribution) and mean microbiability (the average of a single taxon's contribution) for lactation performance. The predicted functions of heritable and nonheritable bacterial taxa also differed, suggesting that targeted nutritional and genetic breeding approaches could be used to manipulate them to improve dairy cow performance.
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Genome-wide association studies and genomic selection assays made in a large sample of cacao (Theobroma cacao L.) germplasm reveal significant marker-trait associations and good predictive value for improving yield potential. PLoS One 2022; 17:e0260907. [PMID: 36201531 PMCID: PMC9536643 DOI: 10.1371/journal.pone.0260907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 09/13/2022] [Indexed: 11/19/2022] Open
Abstract
A genome-wide association study (GWAS) was undertaken to unravel marker-trait associations (MTAs) between SNP markers and phenotypic traits. It involved a subset of 421 cacao accessions from the large and diverse collection conserved ex situ at the International Cocoa Genebank Trinidad. A Mixed Linear Model (MLM) in TASSEL was used for the GWAS and followed by confirmatory analyses using GAPIT FarmCPU. An average linkage disequilibrium (r2) of 0.10 at 5.2 Mb was found across several chromosomes. Seventeen significant (P ≤ 8.17 × 10-5 (-log10 (p) = 4.088)) MTAs of interest, including six that pertained to yield-related traits, were identified using TASSEL MLM. The latter accounted for 5 to 17% of the phenotypic variation expressed. The highly significant association (P ≤ 8.17 × 10-5) between seed length to width ratio and TcSNP 733 on chromosome 5 was verified with FarmCPU (P ≤ 1.12 × 10-8). Fourteen MTAs were common to both the TASSEL and FarmCPU models at P ≤ 0.003. The most significant yield-related MTAs involved seed number and seed length on chromosome 7 (P ≤ 1.15 × 10-14 and P ≤ 6.75 × 10-05, respectively) and seed number on chromosome 1 (P ≤ 2.38 × 10-05), based on the TASSEL MLM. It was noteworthy that seed length, seed length to width ratio and seed number were associated with markers at different loci, indicating their polygenic nature. Approximately 40 candidate genes that encode embryo and seed development, protein synthesis, carbohydrate transport and lipid biosynthesis and transport were identified in the flanking regions of the significantly associated SNPs and in linkage disequilibrium with them. A significant association of fruit surface anthocyanin intensity co-localised with MYB-related protein 308 on chromosome 4. Testing of a genomic selection approach revealed good predictive value (genomic estimated breeding values (GEBV)) for economic traits such as seed number (GEBV = 0.611), seed length (0.6199), seed width (0.5435), seed length to width ratio (0.5503), seed/cotyledon mass (0.6014) and ovule number (0.6325). The findings of this study could facilitate genomic selection and marker-assisted breeding of cacao thereby expediting improvement in the yield potential of cacao planting material.
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Weisweiler M, Arlt C, Wu PY, Van Inghelandt D, Hartwig T, Stich B. Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3511-3529. [PMID: 36029318 PMCID: PMC9519679 DOI: 10.1007/s00122-022-04197-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.
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Affiliation(s)
- Marius Weisweiler
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Christopher Arlt
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Po-Ya Wu
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Delphine Van Inghelandt
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Thomas Hartwig
- Institute for Molecular Physiology, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Benjamin Stich
- Institute for Quantitative Genetics and Genomics of Plants, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Universitätsstraße 1, 40225, Düsseldorf, Germany.
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Robert P, Goudemand E, Auzanneau J, Oury FX, Rolland B, Heumez E, Bouchet S, Caillebotte A, Mary-Huard T, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3337-3356. [PMID: 35939074 DOI: 10.1007/s00122-022-04170-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.
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Affiliation(s)
- Pauline Robert
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - François-Xavier Oury
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, Estrées-Mons, France
| | - Sophie Bouchet
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Antoine Caillebotte
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Jacques Le Gouis
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Renaud Rincent
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France.
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Jackson N, Littleford-Colquhoun BL, Strickland K, Class B, Frere CH. Selection in the city: Rapid and fine-scale evolution of urban eastern water dragons. Evolution 2022; 76:2302-2314. [PMID: 35971751 DOI: 10.1111/evo.14596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 06/27/2022] [Accepted: 07/18/2022] [Indexed: 01/22/2023]
Abstract
Oceanic archipelagos have long been treated as a Petri dish for studies of evolutionary and ecological processes. Like archipelagos, cities exhibit similar patterns and processes, such as the rapid phenotypic divergence of a species between urban and nonurban environments. However, on a local scale, cities can be highly heterogenous, where geographically close populations can experience dramatically different environmental conditions. Nevertheless, we are yet to understand the evolutionary and ecological implications for populations spread across a heterogenous cityscape. To address this, we compared neutral genetic divergence to quantitative trait divergence within three native riparian and four city park populations of an iconic urban adapter, the eastern water dragon. We demonstrated that selection is likely acting to drive divergence of snout-vent length and jaw width across native riparian populations that are geographically isolated and across city park populations that are geographically close yet isolated by urbanization. City park populations as close as 0.9 km exhibited signs of selection-driven divergence to the same extent as native riparian populations isolated by up to 114.5 km. These findings suggest that local adaptation may be occurring over exceptionally small geographic and temporal scales within a single metropolis, demonstrating that city parks can act as archipelagos for the study of rapid evolution.
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Affiliation(s)
- Nicola Jackson
- Global Change Ecology Research Group, University of the Sunshine Coast, Sippy Downs, QLD, 4556, Australia
| | - Bethan L Littleford-Colquhoun
- Global Change Ecology Research Group, University of the Sunshine Coast, Sippy Downs, QLD, 4556, Australia.,Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, Rhode Island, 02912, US.,Institute at Brown for Environment and Society, Brown University, Providence, Rhode Island, 02912, US
| | - Kasha Strickland
- Global Change Ecology Research Group, University of the Sunshine Coast, Sippy Downs, QLD, 4556, Australia.,Department of Aquaculture and Fish Biology, Hólar University, Sauðarkrókur, 550, Iceland
| | - Barbara Class
- Global Change Ecology Research Group, University of the Sunshine Coast, Sippy Downs, QLD, 4556, Australia
| | - Celine H Frere
- Global Change Ecology Research Group, University of the Sunshine Coast, Sippy Downs, QLD, 4556, Australia.,School of Biological Sciences, University of Queensland, St. Lucia, QLD, 4072, Australia
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Genome-wide association study identifies a gene responsible for temperature-dependent rice germination. Nat Commun 2022; 13:5665. [PMID: 36175401 PMCID: PMC9523024 DOI: 10.1038/s41467-022-33318-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/13/2022] [Indexed: 11/08/2022] Open
Abstract
Environment is an important determinant of agricultural productivity; therefore, crops have been bred with traits adapted to their environment. It is assumed that the physiology of seed germination is optimised for various climatic conditions. Here, to understand the genetic basis underlying seed germination, we conduct a genome-wide association study considering genotype-by-environment interactions on the germination rate of Japanese rice cultivars under different temperature conditions. We find that a 4 bp InDel in one of the 14-3-3 family genes, GF14h, preferentially changes the germination rate of rice under optimum temperature conditions. The GF14h protein constitutes a transcriptional regulatory module with a bZIP-type transcription factor, OREB1, and a florigen-like protein, MOTHER OF FT AND TFL 2, to control the germination rate by regulating abscisic acid (ABA)-responsive genes. The GF14h loss-of-function allele enhances ABA signalling and reduces the germination rate. This allele is found in rice varieties grown in the northern area and in modern cultivars of Japan and China, suggesting that it contributes to the geographical adaptation of rice. This study demonstrates the complicated molecular system involved in the regulation of seed germination in response to temperature, which has allowed rice to be grown in various geographical locations.
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Bhat JA, Adeboye KA, Ganie SA, Barmukh R, Hu D, Varshney RK, Yu D. Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.). Front Genet 2022; 13:953833. [PMID: 36419833 PMCID: PMC9677453 DOI: 10.3389/fgene.2022.953833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/22/2022] [Indexed: 11/09/2022] Open
Abstract
Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.
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Affiliation(s)
- Javaid Akhter Bhat
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- International Genome Center, Jiangsu University, Zhenjiang, China
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
| | | | - Showkat Ahmad Ganie
- Plant Molecular Science and Centre of Systems and Synthetic Biology, Department of Biological Sciences, Royal Holloway University of London, Surrey, United Kingdom
| | - Rutwik Barmukh
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Dezhou Hu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Murdoch’s Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Perth, WA, Australia
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
| | - Deyue Yu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
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Crain J, Larson S, Dorn K, DeHaan L, Poland J. Genetic architecture and QTL selection response for Kernza perennial grain domestication traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2769-2784. [PMID: 35763029 PMCID: PMC9243872 DOI: 10.1007/s00122-022-04148-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Analysis of multi-year breeding program data revealed that the genetic architecture of an intermediate wheatgrass population was highly polygenic for both domestication and agronomic traits, supporting the use of genomic selection for new crop domestication. Perennial grains have the potential to provide food for humans and decrease the negative impacts of annual agriculture. Intermediate wheatgrass (IWG, Thinopyrum intermedium, Kernza®) is a promising perennial grain candidate that The Land Institute has been breeding since 2003. We evaluated four consecutive breeding cycles of IWG from 2016 to 2020 with each cycle containing approximately 1100 unique genets. Using genotyping-by-sequencing markers, quantitative trait loci (QTL) were mapped for 34 different traits using genome-wide association analysis. Combining data across cycles and years, we found 93 marker-trait associations for 16 different traits, with each association explaining 0.8-5.2% of the observed phenotypic variance. Across the four cycles, only three QTL showed an FST differentiation > 0.15 with two corresponding to a decrease in floret shattering. Additionally, one marker associated with brittle rachis was 216 bp from an ortholog of the btr2 gene. Power analysis and quantitative genetic theory were used to estimate the effective number of QTL, which ranged from a minimum of 33 up to 558 QTL for individual traits. This study suggests that key agronomic and domestication traits are under polygenic control and that molecular methods like genomic selection are needed to accelerate domestication and improvement of this new crop.
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Affiliation(s)
- Jared Crain
- Department of Plant Pathology, Kansas State University, 4024 Throckmorton Plant Sciences Center, Manhattan, KS, 66506, USA
| | - Steve Larson
- USDA-ARS, Forage and Range Research, Utah State University, Logan, UT, 84322, USA
| | - Kevin Dorn
- USDA-ARS, Soil Management and Sugarbeet Research, Fort Collins, CO, 80526, USA
| | - Lee DeHaan
- The Land Institute, 2440 E. Water Well Rd, Salina, KS, 67401, USA
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, 4024 Throckmorton Plant Sciences Center, Manhattan, KS, 66506, USA.
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
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Lin M, Qiao P, Matschi S, Vasquez M, Ramstein GP, Bourgault R, Mohammadi M, Scanlon MJ, Molina I, Smith LG, Gore MA. Integrating GWAS and TWAS to elucidate the genetic architecture of maize leaf cuticular conductance. PLANT PHYSIOLOGY 2022; 189:2144-2158. [PMID: 35512195 PMCID: PMC9342973 DOI: 10.1093/plphys/kiac198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/28/2022] [Indexed: 05/11/2023]
Abstract
The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed. Dissecting the genetic architecture of natural variation for maize (Zea mays L.) leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we performed an integrated genome- and transcriptome-wide association studies (GWAS and TWAS) to identify candidate genes putatively regulating variation in leaf gc. Of the 22 plausible candidate genes identified, 4 were predicted to be involved in cuticle precursor biosynthesis and export, 2 in cell wall modification, 9 in intracellular membrane trafficking, and 7 in the regulation of cuticle development. A gene encoding an INCREASED SALT TOLERANCE1-LIKE1 (ISTL1) protein putatively involved in intracellular protein and membrane trafficking was identified in GWAS and TWAS as the strongest candidate causal gene. A set of maize nested near-isogenic lines that harbor the ISTL1 genomic region from eight donor parents were evaluated for gc, confirming the association between gc and ISTL1 in a haplotype-based association analysis. The findings of this study provide insights into the role of regulatory variation in the development of the maize leaf cuticle and will ultimately assist breeders to develop drought-tolerant maize for target environments.
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Affiliation(s)
- Meng Lin
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Pengfei Qiao
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | | | - Miguel Vasquez
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California 92093, USA
| | | | - Richard Bourgault
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Marc Mohammadi
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Michael J Scanlon
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Isabel Molina
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Laurie G Smith
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California 92093, USA
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Gamal El‐Dien O, Shalev TJ, Yuen MMS, Stirling R, Daniels LD, Breinholt JW, Neves LG, Kirst M, Van der Merwe L, Yanchuk AD, Ritland C, Russell JH, Bohlmann J. Genomic selection reveals hidden relatedness and increased breeding efficiency in western redcedar polycross breeding. Evol Appl 2022; 15:1291-1312. [PMID: 36051463 PMCID: PMC9423091 DOI: 10.1111/eva.13463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/19/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Western redcedar (WRC) is an ecologically and economically important forest tree species characterized by low genetic diversity with high self-compatibility and high heartwood durability. Using sequence capture genotyping of target genic and non-genic regions, we genotyped 44 parent trees and 1520 offspring trees representing 26 polycross (PX) families collected from three progeny test sites using 45,378 SNPs. Trees were phenotyped for eight traits related to growth, heartwood and foliar chemistry associated with wood durability and deer browse resistance. We used the genomic realized relationship matrix for paternity assignment, maternal pedigree correction, and to estimate genetic parameters. We compared genomics-based (GBLUP) and two pedigree-based (ABLUP: polycross and reconstructed full-sib [FS] pedigrees) models. Models were extended to estimate dominance genetic effects. Pedigree reconstruction revealed significant unequal male contribution and separated the 26 PX families into 438 FS families. Traditional maternal PX pedigree analysis resulted in up to 51% overestimation in genetic gain and 44% in diversity. Genomic analysis resulted in up to 22% improvement in offspring breeding value (BV) theoretical accuracy, 35% increase in expected genetic gain for forward selection, and doubled selection intensity for backward selection. Overall, all traits showed low to moderate heritability (0.09-0.28), moderate genotype by environment interaction (type-B genetic correlation: 0.51-0.80), low to high expected genetic gain (6.01%-55%), and no significant negative genetic correlation reflecting no large trade-offs for multi-trait selection. Only three traits showed a significant dominance effect. GBLUP resulted in smaller but more accurate heritability estimates for five traits, but larger estimates for the wood traits. Comparison between all, genic-coding, genic-non-coding and intergenic SNPs showed little difference in genetic estimates. In summary, we show that GBLUP overcomes the PX limitations, successfully captures expected historical and hidden relatedness as well as linkage disequilibrium (LD), and results in increased breeding efficiency in WRC.
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Affiliation(s)
- Omnia Gamal El‐Dien
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Pharmacognosy Department, Faculty of PharmacyAlexandria UniversityAlexandriaEgypt
| | - Tal J. Shalev
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Macaire M. S. Yuen
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | | | - Lori D. Daniels
- Department of Forest and Conservation SciencesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Jesse W. Breinholt
- Rapid GenomicsGainesvilleFloridaUSA
- Intermountain HealthcareIntermountain Precision GenomicsSt. GeorgeUtahUSA
| | | | - Matias Kirst
- School of Forest, Fisheries and Geomatic SciencesUniversity of FloridaGainesvilleFloridaUSA
| | - Lise Van der Merwe
- British Columbia Ministry of ForestsLands and Natural Resource Operations and Rural DevelopmentVictoriaBritish ColumbiaCanada
| | - Alvin D. Yanchuk
- British Columbia Ministry of ForestsLands and Natural Resource Operations and Rural DevelopmentVictoriaBritish ColumbiaCanada
| | - Carol Ritland
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Forest and Conservation SciencesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - John H. Russell
- British Columbia Ministry of ForestsLands and Natural Resource Operations and Rural DevelopmentVictoriaBritish ColumbiaCanada
| | - Joerg Bohlmann
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Forest and Conservation SciencesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of BotanyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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Buntaran H, Bernal-Vasquez AM, Gordillo A, Sahr M, Wimmer V, Piepho HP. Assessing the response to genomic selection by simulation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2891-2905. [PMID: 35831462 PMCID: PMC9325815 DOI: 10.1007/s00122-022-04157-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
We propose a simulation approach to compute response to genomic selection on a multi-environment framework to provide breeders the number of entries that need to be selected from the population to have a defined probability of selecting the truly best entry from the population and the probability of obtaining the truly best entries when some top-ranked entries are selected. The goal of any plant breeding program is to maximize genetic gain for traits of interest. In classical quantitative genetics, the genetic gain can be obtained from what is known as "Breeder's equation". In the past, only phenotypic data were used to compute the genetic gain. The advent of genomic prediction (GP) has opened the door to the utilization of dense markers for estimating genomic breeding values or GBV. The salient feature of GP is the possibility to carry out genomic selection with the assistance of the kinship matrix, hence improving the prediction accuracy and accelerating the breeding cycle. However, estimates of GBV as such do not provide the full information on the number of entries to be selected as in the classical response to selection. In this paper, we use simulation, based on a fitted mixed model for GP in a multi-environmental framework, to answer two typical questions of a plant breeder: (1) How many entries need to be selected to have a defined probability of selecting the truly best entry from the population; (2) what is the probability of obtaining the truly best entries when some top-ranked entries are selected.
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Affiliation(s)
- Harimurti Buntaran
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany
| | | | - Andres Gordillo
- KWS-LOCHOW GmbH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Morten Sahr
- KWS-LOCHOW GmbH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Valentin Wimmer
- KWS SAAT SE Co. KGaA, Grimsehlstaße. 31, 37574, Einbeck, Germany
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany.
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Boatwright JL, Sapkota S, Jin H, Schnable JC, Brenton Z, Boyles R, Kresovich S. Sorghum Association Panel whole-genome sequencing establishes cornerstone resource for dissecting genomic diversity. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:888-904. [PMID: 35653240 PMCID: PMC9544330 DOI: 10.1111/tpj.15853] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 05/26/2023]
Abstract
Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25-72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5-kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six Fst peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.
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Affiliation(s)
- J. Lucas Boatwright
- Department of Plant and Environmental SciencesClemson UniversityClemsonSouth Carolina29634USA
- Advanced Plant TechnologyClemson UniversityClemsonSouth Carolina29634USA
| | - Sirjan Sapkota
- Advanced Plant TechnologyClemson UniversityClemsonSouth Carolina29634USA
| | - Hongyu Jin
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska68588USA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska68588USA
| | | | - Richard Boyles
- Department of Plant and Environmental SciencesClemson UniversityClemsonSouth Carolina29634USA
- Pee Dee Research and Education CenterClemson UniversityFlorenceSouth Carolina29506USA
| | - Stephen Kresovich
- Department of Plant and Environmental SciencesClemson UniversityClemsonSouth Carolina29634USA
- Advanced Plant TechnologyClemson UniversityClemsonSouth Carolina29634USA
- Feed the Future Innovation Lab for Crop ImprovementCornell UniversityIthacaNew York14850USA
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Bhati PK, Juliana P, Singh RP, Joshi AK, Vishwakarma MK, Poland J, Govindan V, Shrestha S, Crespo-Herrera L, Mondal S, Huerta-Espino J, Kumar U. Dissecting the Genetic Architecture of Phenology Affecting Adaptation of Spring Bread Wheat Genotypes to the Major Wheat-Producing Zones in India. FRONTIERS IN PLANT SCIENCE 2022; 13:920682. [PMID: 35873987 PMCID: PMC9298574 DOI: 10.3389/fpls.2022.920682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
Spring bread wheat adaptation to diverse environments is supported by various traits such as phenology and plant architecture. A large-scale genome-wide association study (GWAS) was designed to investigate and dissect the genetic architecture of phenology affecting adaptation. It used 48 datasets from 4,680 spring wheat lines. For 8 years (2014-2021), these lines were evaluated for days to heading (DH) and maturity (DM) at three sites: Jabalpur, Ludhiana, and Samastipur (Pusa), which represent the three major Indian wheat-producing zones: the Central Zone (CZ), North-Western Plain Zone (NWPZ), and North-Eastern Plain Zone (NEPZ), respectively. Ludhiana had the highest mean DH of 103.8 days and DM of 148.6 days, whereas Jabalpur had the lowest mean DH of 77.7 days and DM of 121.6 days. We identified 119 markers significantly associated with DH and DM on chromosomes 5B (76), 2B (18), 7D (10), 4D (8), 5A (1), 6B (4), 7B (1), and 3D (1). Our results clearly indicated the importance of the photoperiod-associated gene (Ppd-B1) for adaptation to the NWPZ and the Vrn-B1 gene for adaptation to the NEPZ and CZ. A maximum variation of 21.1 and 14% was explained by markers 2B_56134146 and 5B_574145576 linked to the Ppd-B1 and Vrn-B1 genes, respectively, indicating their significant role in regulating DH and DM. The results provide important insights into the genomic regions associated with the two phenological traits that influence adaptation to the major wheat-producing zones in India.
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Affiliation(s)
- Pradeep Kumar Bhati
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Ravi Prakash Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Arun Kumar Joshi
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Manish Kumar Vishwakarma
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Jesse Poland
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Sandesh Shrestha
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | | | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Julio Huerta-Espino
- Campo Experimental Valle de México-INIFAP, Carretera los Reyes-Texcoco, Texcoco, Mexico
| | - Uttam Kumar
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
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Juliana P, Govindan V, Crespo-Herrera L, Mondal S, Huerta-Espino J, Shrestha S, Poland J, Singh RP. Genome-Wide Association Mapping Identifies Key Genomic Regions for Grain Zinc and Iron Biofortification in Bread Wheat. FRONTIERS IN PLANT SCIENCE 2022; 13:903819. [PMID: 35845653 PMCID: PMC9280339 DOI: 10.3389/fpls.2022.903819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 05/19/2022] [Indexed: 05/02/2023]
Abstract
Accelerating breeding efforts for developing biofortified bread wheat varieties necessitates understanding the genetic control of grain zinc concentration (GZnC) and grain iron concentration (GFeC). Hence, the major objective of this study was to perform genome-wide association mapping to identify consistently significant genotyping-by-sequencing markers associated with GZnC and GFeC using a large panel of 5,585 breeding lines from the International Maize and Wheat Improvement Center. These lines were grown between 2018 and 2021 in an optimally irrigated environment at Obregon, Mexico, while some of them were also grown in a water-limiting drought-stressed environment and a space-limiting small plot environment and evaluated for GZnC and GFeC. The lines showed a large and continuous variation for GZnC ranging from 27 to 74.5 ppm and GFeC ranging from 27 to 53.4 ppm. We performed 742,113 marker-traits association tests in 73 datasets and identified 141 markers consistently associated with GZnC and GFeC in three or more datasets, which were located on all wheat chromosomes except 3A and 7D. Among them, 29 markers were associated with both GZnC and GFeC, indicating a shared genetic basis for these micronutrients and the possibility of simultaneously improving both. In addition, several significant GZnC and GFeC associated markers were common across the irrigated, water-limiting drought-stressed, and space-limiting small plots environments, thereby indicating the feasibility of indirect selection for these micronutrients in either of these environments. Moreover, the many significant markers identified had minor effects on GZnC and GFeC, suggesting a quantitative genetic control of these traits. Our findings provide important insights into the complex genetic basis of GZnC and GFeC in bread wheat while implying limited prospects for marker-assisted selection and the need for using genomic selection.
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Affiliation(s)
| | - Velu Govindan
- International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | | | - Julio Huerta-Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, Chapingo, Mexico
| | - Sandesh Shrestha
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | - Jesse Poland
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Ravi P. Singh
- International Maize and Wheat Improvement Center, Texcoco, Mexico
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Mijangos JL, Gruber B, Berry O, Pacioni C, Georges A.
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v2: an accessible genetic analysis platform for conservation, ecology, and agriculture. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Jose Luis Mijangos
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology University of Canberra Bruce ACT Australia
| | - Bernd Gruber
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology University of Canberra Bruce ACT Australia
| | - Oliver Berry
- Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Crawley WA Australia
| | - Carlo Pacioni
- Department of Environment, Land, Water, and Planning Arthur Rylah Institute for Environmental Research Heidelberg VIC Australia
- Environmental and Conservation Sciences School of Veterinary and Life Sciences, Murdoch University Murdoch WA Australia
| | - Arthur Georges
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology University of Canberra Bruce ACT Australia
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50
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Feldmann MJ, Piepho HP, Knapp SJ. Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses. G3 GENES|GENOMES|GENETICS 2022; 12:6571389. [PMID: 35442424 PMCID: PMC9157152 DOI: 10.1093/g3journal/jkac080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/17/2022] [Indexed: 11/23/2022]
Abstract
Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries’ genetic values and predict unobserved entries’ genetic values. One of the main parameters of such models is genomic variance (σg2), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (hg2); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of σg2 and hg2. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of σg2 and hg2 in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.
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Affiliation(s)
- Mitchell J Feldmann
- Department of Plant Sciences, University of California , Davis, CA 95616, USA
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim , 70593 Stuttgart, Germany
| | - Steven J Knapp
- Department of Plant Sciences, University of California , Davis, CA 95616, USA
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