How genomic insights into the evolutionary history of clouded leopards inform their conservation

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Abstract

Clouded leopards ( Neofelis spp.), a morphologically and ecologically distinct lineage of big cats, are severely threatened by habitat loss and fragmentation, targeted hunting, and other human activities. The long-held poor understanding of their genetics and evolution has undermined the effectiveness of conservation actions. Here, we report a comprehensive investigation of the whole genomes, population genetics, and adaptive evolution of Neofelis. Our results indicate the genus Neofelis arose during the Pleistocene, coinciding with glacial-induced climate changes to the distributions of savannas and rainforests, and signatures of natural selection associated with genes functioning in tooth, pigmentation, and tail development, associated with clouded leopards’ unique adaptations. Our study highlights high-altitude adaptation as the main factor driving nontaxonomic population differentiation in Neofelis nebulosa. Population declines and inbreeding have led to reduced genetic diversity and the accumulation of deleterious variation that likely affect reproduction of clouded leopards, highlighting the urgent need for effective conservation efforts.

Abstract

Genomic research illuminates the evolutionary history and conservation urgency of clouded leopards.

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Most cited references 89

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RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

Alexandros Stamatakis (2014)

Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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PLINK: a tool set for whole-genome association and population-based linkage analyses.

Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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Author and article information

Contributors

Jiaqing Yuan:

ORCID: https://orcid.org/0000-0002-0539-3806

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draft

Guiqiang Wang:

ORCID: https://orcid.org/0009-0001-2989-5966

Role: Formal analysisRole: InvestigationRole: Visualization

Le Zhao:

ORCID: https://orcid.org/0000-0003-0505-9936

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Andrew C. Kitchener:

ORCID: https://orcid.org/0000-0003-2594-0827

Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing - original draftRole: Writing - review & editing

Ting Sun:

ORCID: https://orcid.org/0000-0003-3992-0819

Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Visualization

Wu Chen:

ORCID: https://orcid.org/0000-0002-4033-254X

Role: ConceptualizationRole: ResourcesRole: ValidationRole: Visualization

Chen Huang: Role: Investigation

Chen Wang:

ORCID: https://orcid.org/0000-0001-6711-7420

Role: Resources

Xiao Xu: Role: Formal analysisRole: InvestigationRole: Validation

Jinhong Wang:

ORCID: https://orcid.org/0000-0002-0669-2939

Role: InvestigationRole: ValidationRole: Writing - review & editing

Huimeng Lu:

ORCID: https://orcid.org/0000-0002-5334-1402

Role: Formal analysisRole: Software

Lulu Xu:

ORCID: https://orcid.org/0000-0001-7315-4738

Role: Formal analysisRole: InvestigationRole: ValidationRole: VisualizationRole: Writing - original draft

Qigao Jiangzuo:

ORCID: https://orcid.org/0000-0003-4773-5349

Role: ConceptualizationRole: MethodologyRole: Validation

William J. Murphy:

ORCID: https://orcid.org/0000-0003-3699-0723

Role: ResourcesRole: Writing - original draftRole: Writing - review & editing

Dongdong Wu:

ORCID: https://orcid.org/0000-0001-7101-7297

Role: ConceptualizationRole: Project administrationRole: SupervisionRole: Writing - original draftRole: Writing - review & editing

Gang Li:

ORCID: https://orcid.org/0000-0001-7400-5486

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: 06 October 2023

Publication date (Electronic, pub): 06 October 2023

Volume: 9

Issue: 40

Electronic Location Identifier: eadh9143

Affiliations

[ ¹ ]College of Life Sciences, Shaanxi Normal University, Xi’an, China.

[ ² ]QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C., School of Bioscience and Engineering, Shaanxi University of Technology, Hanzhong, China.

[ ³ ]Department of Natural Sciences, National Museums Scotland, Chambers Street, Edinburgh EH1 1JF, UK.

[ ⁴ ]School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH9 3PX, UK.

[ ⁵ ]Guangzhou Zoo, Guangzhou Wildlife Research Center, Guangzhou, China.

[ ⁶ ]School of Life Sciences, Northwestern Polytechnical University, Xi’an, China.

[ ⁷ ]Key Laboratory of Vertebrate Evolution and Human Origins of Chinese Academy of Sciences, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China.

[ ⁸ ]Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.

[ ⁹ ]State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.

Author notes

[* ]Corresponding author. Email: gli@ 123456snnu.edu.cn (G.L.); wudongdong@ 123456mail.kiz.ac.cn (D.W.)

[†]

These authors contributed equally to this work.

Author information

Jiaqing Yuan https://orcid.org/0000-0002-0539-3806

Guiqiang Wang https://orcid.org/0009-0001-2989-5966

Le Zhao https://orcid.org/0000-0003-0505-9936

Andrew C. Kitchener https://orcid.org/0000-0003-2594-0827

Ting Sun https://orcid.org/0000-0003-3992-0819

Wu Chen https://orcid.org/0000-0002-4033-254X

Chen Wang https://orcid.org/0000-0001-6711-7420

Jinhong Wang https://orcid.org/0000-0002-0669-2939

Huimeng Lu https://orcid.org/0000-0002-5334-1402

Lulu Xu https://orcid.org/0000-0001-7315-4738

Qigao Jiangzuo https://orcid.org/0000-0003-4773-5349

William J. Murphy https://orcid.org/0000-0003-3699-0723

Dongdong Wu https://orcid.org/0000-0001-7101-7297

Gang Li https://orcid.org/0000-0001-7400-5486

Article

Publisher ID: adh9143

DOI: 10.1126/sciadv.adh9143

PMC ID: 10558132

PubMed ID: 37801506

SO-VID: 1521952d-5730-490b-8430-949a36b98ea8

Copyright © Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

History

Date received : 22 March 2023

Date accepted : 06 September 2023

Funding

Funded by: The National Science Foundation of China;

Award ID: 31970391

Funded by: the Natural Science Basic Research Program of Shaanxi;

Award ID: 2020JM-280

Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;

Award ID: GK201902008

Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;

Award ID: 2020TS054

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How genomic insights into the evolutionary history of clouded leopards inform their conservation

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Climate Change: Open Access

Most cited references 89

RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

PLINK: a tool set for whole-genome association and population-based linkage analyses.

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