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Yang Z, Zhao A, Teng M, Li M, Wang H, Wang X, Liu Z, Zeng Q, Hu L, Hu J, Bao Z, Huang X. Signatures of selection in Mulinia lateralis underpinning its rapid adaptation to laboratory conditions. Evol Appl 2024; 17:e13657. [PMID: 38357357 PMCID: PMC10866071 DOI: 10.1111/eva.13657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 01/17/2024] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
The dwarf surf clam, Mulinia lateralis, is considered as a model species for bivalves because of its rapid growth and short generation time. Recently, successful breeding of this species for multiple generations in our laboratory revealed its acquisition of adaptive advantages during artificial breeding. In this study, 310 individuals from five different generations were genotyped with 22,196 single nucleotide polymorphisms (SNPs) with the aim of uncovering the genetic basis of their adaptation to laboratory conditions. Results revealed that M. lateralis consistently maintained high genetic diversity across generations, characterized by high observed heterozygosity (H o: 0.2733-0.2934) and low levels of inbreeding (F is: -0.0244-0.0261). Population analysis indicated low levels of genetic differentiation among generations of M. lateralis during artificial breeding (F st <0.05). In total, 316 genomic regions exhibited divergent selection, with 168 regions under positive selection. Furthermore, 227 candidate genes were identified in the positive selection regions, which have functions including growth, stress resistance, and reproduction. Notably, certain selection signatures with significantly higher F st value were detected in genes associated with male reproduction, such as GAL3ST1, IFT88, and TSSK2, which were significantly upregulated during artificial breeding. This suggests a potential role of sperm-associated genes in the rapid evolutionary response of M. lateralis to selection in laboratory conditions. Overall, our findings highlight the phenotypic and genetic changes, as well as selection signatures, in M. lateralis during artificial breeding. This contributes to understanding their adaptation to laboratory conditions and underscores the potential for using this species to explore the adaptive evolution of bivalves.
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Affiliation(s)
- Zujing Yang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Ang Zhao
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Mingxuan Teng
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Moli Li
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Hao Wang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Xuefeng Wang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Zhi Liu
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
| | - Qifan Zeng
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
- Laboratory of Tropical Marine Germplasm Resources and Breeding EngineeringSanya Oceanographic Institution, Ocean University of ChinaSanyaChina
| | - Liping Hu
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
- Yantai Marine Economic Research InstituteYantaiChina
| | - Jingjie Hu
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
- Laboratory of Tropical Marine Germplasm Resources and Breeding EngineeringSanya Oceanographic Institution, Ocean University of ChinaSanyaChina
| | - Zhenmin Bao
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
- Laboratory of Tropical Marine Germplasm Resources and Breeding EngineeringSanya Oceanographic Institution, Ocean University of ChinaSanyaChina
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina
| | - Xiaoting Huang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life SciencesOcean University of ChinaQingdaoChina
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina
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Wang J, Lv X, Feng L, Dong A, Liang D, Wu R. A Tracing Model for the Evolutionary Equilibrium of Octoploids. Front Genet 2022; 12:794907. [PMID: 35154248 PMCID: PMC8831725 DOI: 10.3389/fgene.2021.794907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/30/2021] [Indexed: 11/19/2022] Open
Abstract
Testing Hardy-Weinberg equilibrium (HWE) is a fundamental approach for inferring population diversity and evolution, but its application to octoploids containing eight chromosome sets has not well been justified. We derive a mathematical model to trace how genotype frequencies transmit from parental to offspring generations in the natural populations of autooctoploids. We find that octoploids, including autooctolpoids undergoing double reduction, attach asymptotic HWE (aHWE) after 15 generations of random mating, in a contrast to diploids where one generation can assure exact equilibrium and, also, different from tetraploids that use 5 generations to reach aHWE. We develop a statistical procedure for testing aHWE in octoploids and apply it to analyze a real data set from octoploid switchgrass distributed in two ecologically different regions, demonstrating the usefulness of the test procedure. Our model provides a tool for studying the population genetic diversity of octoploids, inferring their evolutionary history, and identifying the ecological relationship of octoploid-genome structure with environmental adaptation.
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Affiliation(s)
- Jing Wang
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Xuemin Lv
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Li Feng
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Ang Dong
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Dan Liang
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- *Correspondence: Dan Liang, ; Rongling Wu,
| | - Rongling Wu
- Departments of Public Health Sciences and Statistics, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA, United States
- *Correspondence: Dan Liang, ; Rongling Wu,
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