Genetic Relatedness Analysis in Natural Populations
Summary
Genetic relatedness analysis quantifies the degree of shared ancestry among individuals within and between populations by measuring how alleles are inherited from common forebears. Historically rooted in pedigree assessment, modern approaches employ molecular markers such as microsatellites and single nucleotide polymorphisms to infer kinship, inbreeding and identity-by-descent from genome-wide data. These metrics illuminate social structure, mating systems and dispersal, inform conservation strategies by identifying units for management, and underpin studies of quantitative trait heritability and disease susceptibility. High-resolution estimates of relatedness support close-kin mark–recapture techniques to gauge population size and demographic trends, and enhance statistical models of population structure by refining genetic relationship matrices. Advances in sequencing and computational methods have driven a shift towards probabilistic models that accommodate inbreeding, admixture and marker heterogeneity, enabling accurate estimation of pairwise kinship in wild and fragmented populations. Such insights are crucial to safeguard genetic diversity, guide restoration programmes and predict adaptive potential under environmental change.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Genetic Relatedness Analysis in Natural Populations publication trend
The graph below shows the total number of articles in genetic relatedness analysis in natural populations across all publications each year (not limited to Nature Index journals).
Technical terms
Kinship coefficient: Probability that alleles sampled from two individuals are identical-by-descent.
Inbreeding coefficient: Probability that two alleles within an individual are identical-by-descent.
Identity-by-descent (IBD): Alleles shared by individuals due to inheritance from a common ancestor.
Effective population size (Ne): The size of an idealised population that experiences genetic drift at the same rate as the actual population.
Close-kin mark–recapture (CKMR): A genetic method using observed related pairs to estimate population size and demography.
Single nucleotide polymorphism (SNP): A genomic locus at which individuals differ by a single base-pair.
Jacquard genetic identity coefficients: Probabilities of each possible allele-sharing state between two individuals at a locus.
References
- Estimation of inbreeding and kinship coefficients via latent identity-by-descent states. Bioinformatics (2024).
- Estimating effective population size using close‐kin mark–recapture. Methods in Ecology and Evolution (2024).
- Estimation of Jacquard’s genetic identity coefficients with bi-allelic variants by constrained least-squares. Heredity (2024).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.