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Waples RS. Practical application of the linkage disequilibrium method for estimating contemporary effective population size: A review. Mol Ecol Resour 2024; 24:e13879. [PMID: 37873672 DOI: 10.1111/1755-0998.13879] [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: 06/22/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/25/2023]
Abstract
The method to estimate contemporary effective population size (Ne ) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy.
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Affiliation(s)
- Robin S Waples
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA
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Schweizer RM, Saarman N, Ramstad KM, Forester BR, Kelley JL, Hand BK, Malison RL, Ackiss AS, Watsa M, Nelson TC, Beja-Pereira A, Waples RS, Funk WC, Luikart G. Big Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field. J Hered 2021; 112:313-327. [PMID: 33860294 DOI: 10.1093/jhered/esab019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 04/13/2021] [Indexed: 02/07/2023] Open
Abstract
A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis.
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Affiliation(s)
- Rena M Schweizer
- Division of Biological Sciences, University of Montana, Missoula, MT
| | - Norah Saarman
- Department of Biology, Utah State University, Logan, UT
| | - Kristina M Ramstad
- Department of Biology and Geology, University of South Carolina Aiken, Aiken, SC
| | | | - Joanna L Kelley
- School of Biological Sciences, Washington State University, Pullman, WA
| | - Brian K Hand
- Division of Biological Sciences, University of Montana, Missoula, MT.,Flathead Lake Biological Station, University of Montana, Polson, MT
| | - Rachel L Malison
- Flathead Lake Biological Station, University of Montana, Polson, MT
| | - Amanda S Ackiss
- Wisconsin Cooperative Fishery Research Unit, University of Wisconsin Stevens Point, Stevens Point, WI
| | | | | | - Albano Beja-Pereira
- Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO-UP), InBIO, Universidade do Porto, Vairão, Portugal.,DGAOT, Faculty of Sciences, University of Porto, Porto, Portugal.,Sustainable Agrifood Production Research Centre (GreenUPorto), Faculty of Sciences, University of Porto, Porto, Portugal
| | - Robin S Waples
- Northwest Fisheries Science Center, NOAA Fisheries, Seattle, WA
| | - W Chris Funk
- Department of Biology, Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO
| | - Gordon Luikart
- Division of Biological Sciences, University of Montana, Missoula, MT.,Flathead Lake Biological Station, University of Montana, Polson, MT
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Luikart G, Antao T, Hand BK, Muhlfeld CC, Boyer MC, Cosart T, Trethewey B, Al-Chockhachy R, Waples RS. Detecting population declines via monitoring the effective number of breeders (N b ). Mol Ecol Resour 2020; 21:379-393. [PMID: 32881365 DOI: 10.1111/1755-0998.13251] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 06/10/2020] [Accepted: 06/12/2020] [Indexed: 12/16/2022]
Abstract
Estimating the effective population size and effective number of breeders per year (Nb ) can facilitate early detection of population declines. We used computer simulations to quantify bias and precision of the one-sample LDNe estimator of Nb in age-structured populations using a range of published species life history types, sample sizes, and DNA markers. Nb estimates were biased by ~5%-10% when using SNPs or microsatellites in species ranging from fishes to mosquitoes, frogs, and seaweed. The bias (high or low) was similar for different life history types within a species suggesting that life history variation in populations will not influence Nb estimation. Precision was higher for 100 SNPs (H ≈ 0.30) than for 15 microsatellites (H ≈ 0.70). Confidence intervals (CIs) were occasionally too narrow, and biased high when Nb was small (Nb < 50); however, the magnitude of bias would unlikely influence management decisions. The CIs (from LDNe) were sufficiently narrow to achieve high statistical power (≥0.80) to reject the null hypothesis that Nb = 50 when the true Nb = 30 and when sampling 50 individuals and 200 SNPs. Similarly, CIs were sufficiently narrow to reject Nb = 500 when the true Nb = 400 and when sampling 200 individuals and 5,000 loci. Finally, we present a linear regression method that provides high power to detect a decline in Nb when sampling at least five consecutive cohorts. This study provides guidelines and tools to simulate and estimate Nb for age structured populations (https://github.com/popgengui/agestrucnb/), which should help biologists develop sensitive monitoring programmes for early detection of changes in Nb and population declines.
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Affiliation(s)
- Gordon Luikart
- Flathead Lake Biological Station, Montana Conservation Genomics Laboratory, Division of Biological Sciences, University of Montana, Polson, MT, USA
| | - Tiago Antao
- The Welcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Brian K Hand
- Flathead Lake Biological Station, Montana Conservation Genomics Laboratory, Division of Biological Sciences, University of Montana, Polson, MT, USA
| | - Clint C Muhlfeld
- Northern Rocky Mountain Science Center, U.S. Geological Survey, Glacier National Park, Northern Rocky Mountain Science Center, West Glacier, MT, USA
| | | | - Ted Cosart
- Flathead Lake Biological Station, Montana Conservation Genomics Laboratory, Division of Biological Sciences, University of Montana, Polson, MT, USA
| | - Brian Trethewey
- Flathead Lake Biological Station, Montana Conservation Genomics Laboratory, Division of Biological Sciences, University of Montana, Polson, MT, USA
| | - Robert Al-Chockhachy
- U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT, USA
| | - Robin S Waples
- Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
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