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Population Genomics: Applications in Healthcare, Conservation, Agriculture, and More

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  • Population Genomics: Applications in Healthcare, Conservation, Agriculture, and More

    population genomics has applications in wildlife conservation and agriculture
    Foundation, co-evolution with NGS, and impact

    Population genomics is the use of DNA data from entire populations to understand the genetic makeup of a group of individuals, human or otherwise. We previously covered the basics of population genomics and how next-generation sequencing (NGS) expanded the field here.1 Since then, population genomics has co-evolved with NGS to achieve measurable impacts in several areas.

    This article will cover:

    ● How PacBio HiFi long-read sequencing was used to uncover a structural variant of the gene KMT2E, and how that may change rare disease diagnostics and management.

    ● The improvement of conservation of an endangered squirrel species, thanks to SNP detection through targeted amplicon sequencing.

    ● The use of population-scale whole-genome sequencing (WGS) with short- and long-read combinations to understand genetic differences between traditional and improved rice varieties.

    ● Other recent studies and potential future breakthroughs in population genomics as NGS continues to evolve.


    Population genomics in healthcare: New diagnosis thanks to pangenome graphs

    The reference human genome allows researchers to compare human genetic data with it, but it does not offer perfect coverage, having missing regions because they are hard to sequence, and lacking population data.

    A common problem with reference genomes has been detecting structural variants (which often lead to disease), due to these being present in repeat regions or other areas where short-read genome sequencing offers poor resolution.

    Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions.2 A draft of a human pangenomic graph was published in 2023 to help with these issues.3

    In 2024, the use of PacBio HiFi reads allowed for the generation of better data for a pangenome graph, in which a structural variant of KMT2E (a 14.5 kbp deletion) was found, characterized, and predicted to lead to a non-functional protein. The protein encoded regulates histone methylation, and is required for normal brain development.4

    The individual with this structural variant presented symptoms expected from abnormal brain development, and the deletion was identified in the maternal genome too, who presented cognitive symptoms as well.

    This breakthrough allows for better screening of genetic disease transmission and opens up future therapies to correct the deletion through gene therapy.

    The study identified over 200,000 structural variants, with nearly 1,000 affecting coding sequences. This highlights the clinical diagnostic applications of pangenomes and the quantity of data that can be generated with better NGS approaches.

    mage 1: Infographic illustrating pangenome graphs for human healthcare applications. Four-panel visualization shows: (1) limitations of reference human genome with unsequenced regions, (2) long-read and short-read NGS sequencing generating population genomics data, (3) data assembled in circular pangenome graph structures revealing structural variants affecting genes, and (4) clinical application in diagnostics with medical symbols. Demonstrates rare disease diagnosis workflow using population-scale pangenomics and PacBio HiFi sequencing technology.


    Figure 1. New NGS techniques have helped increase human genome data, including unsequenced regions from the reference human genome. Assembly of data in pangenome graphs allowed analysis and revealed potential diagnostic and healthcare applications.


    Environmental impact: NGS from minimally invasive DNA collection helps conservation efforts

    Population monitoring of animal species can be hard, as collecting high-quality DNA samples requires invasive procedures (blood drawn, tissue samples). Minimally invasive sampling (like buccal swabs) results in less quantity and lower quality DNA, and common NGS protocols don’t generate reliable data from such samples.

    This challenge is being overcome by advances in NGS and library prep methods. In a 2024 study, a team showed how Genotyping-in-Thousands by sequencing (GT-seq), a technique that was first developed in 2015, allowed them to understand and monitor the population of Idaho squirrels better than previous sequencing methods.5,6

    Idaho squirrels are an endangered species. GT-seq was used on buccal swabs of the squirrels, which worked well thanks to multi-primer PCR enrichment steps before Illumina sequencing. In a follow-up study, the same team used GT-seq to inform recovery options and actions for the threatened squirrel species.7

    GT-seq achieved 73% genotyping success with minimally invasive samples, improving genetic monitoring without blood or tissue sampling. It also successfully identified different subpopulations, which may benefit from separate recovery and conservation strategies.

    Image 2: Infographic illustrating how GT-seq was used in conservation for endangered Idaho ground squirrels. Four-step workflow visualization: (1) endangered squirrel species requiring population monitoring, (2) minimally invasive DNA sampling using buccal swabs and GT-seq amplicon sequencing, (3) population structure heat map showing subpopulation distributions and genetic differentiation, and (4) conservation tree symbolizing biodiversity protection efforts. Highlights non-invasive genetic monitoring for wildlife management and endangered species recovery.


    Figure 2. Minimally invasive DNA sampling is especially beneficial in conservation studies. A new technique called GT-seq allows sequencing from these samples despite lower-quality DNA and has helped gather data on endangered Idaho squirrels.

    Agricultural data: Understanding transposable elements in rice to improve varieties

    Pangenomes have had their impact on agriculture too. Specifically, studies in rice have made use of them to identify transposable elements (TEs) that drive agricultural value from these cereal plants.

    TEs in plants were first connected to performance by the famous Barbara McClintock study from 1951 on maize color variability.8 Recently, a team of researchers used short- and long-read NGS to create a pangenome map of rice genomes, which indicated TEs had a role in plant performance.9

    Afterwards, they studied both existing traditional Asian rice varieties and improved varieties (varieties bred and selected for better traits like more grain yield or stress resistance). They found that 24.7% of genes (3,480 of 14,076) with different expression among the two pools were associated with TE variations, and of those, the expression changes of 544 genes were mapped only to TE variation.10 This knowledge can be used for future engineering or breeding and screening of further improved rice varieties.

    The impact of the study is likely to ripple across other plant species of agricultural value, allowing better understanding and modulation of gene expression to create climate-optimized, high-yield resistant variants.

    Image 3: Agricultural population genomics infographic showing transposable elements driving rice plant quality improvements. Four-stage diagram: (1) comparison of traditional versus improved rice varieties using pangenome graphs, (2) transposable element (TE) activity affecting DNA and gene expression, (3) precision screening and genome engineering of rice plants for desired traits, and (4) agricultural field representing enhanced crop breeding and food security outcomes. Overall, it illustrates how TE variations in rice genomes may enable climate-optimized high-yield variety development.


    Figure 3. Comparison of traditional rice varieties versus improved varieties from breeding in the last century showed transposable elements play a crucial role in gene expression of these plants, and were responsible for desirable traits. This knowledge might allow screening of future varieties or even direct genetic engineering of rice plants, and the findings could be extrapolated to other crops.

    An Expanding Frontier

    These are just a few of the breakthroughs that we are seeing thanks to the improvement of NGS capabilities, better population genomics analysis tools, and testing methods. Nevertheless, these case studies show how the field has matured from foundational projects like the 1000 Genomes project into practical applications with measurable outcomes for healthcare, conservation, and food security worldwide.

    And more examples are yet to yield results, but are no less important.

    Some examples are:

    ● Myanmar studies on malaria parasites and the population changes during elimination programs, to understand how effective the actions are.11

    ● Research on German shepherd dogs, showing how genetic bottlenecks due to World War II have reduced the genetic health of the breed.12

    ● Biobank efforts to gather human data and know how drugs may negatively affect individuals with certain genetic mutations, to allow for future precision medicine initiatives.13


    The integration of better long-read sequencing, other NGS advances, biobank data, and better ways to analyze and integrate the data will eventually allow for more and better applications of population genomics.

    References

    1. Benjamin Atha, An Introduction to Population Genomics, SEQanswers, 2024. https://www.seqanswers.com/articles/...ation-genomics

    2. Guarracino A, Heumos S, Nahnsen S, et al. ODGI: understanding pangenome graphs. Bioinformatics. 2022;38(13):3319-3326. doi:https://academic.oup.com/bioinformat...3/3319/6585331

    3. Liao WW, Asri M, Ebler J, et al. A draft human pangenome reference. Nature. 2023;617:312-324. doi:https://doi.org/10.1038/s41586-023-05896-x

    4. Groza C, Schwendinger-Schreck C, Cheung WA, et al. Pangenome graphs improve the analysis of structural variants in rare genetic diseases. Nat Commun. 2024;15:657. doi:https://doi.org/10.1038/s41467-024-44980-2

    5. Garrett MJ, Nerkowski SA, Kieran S, et al. Development and validation of a GT-seq panel for genetic monitoring in a threatened species using minimally invasive sampling. Ecol Evol. 2024;14(5):e11321. doi:https://doi.org/10.1002/ece3.11321

    6. Campbell NR, Harmon SA, Narum SR. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on low coverage sequencing. Mol Ecol Resour. 2015;15(4):905-917. doi:https://doi.org/10.1111/1755-0998.12357

    7. Garrett MJ, Conway CJ, Waits LP, et al. Genetic Variation and Metapopulation Structure Inform Recovery Goals in a Threatened Species. Genes (Basel). 2025;16(6):694. doi:https://doi.org/10.3390/genes16060694

    8. McClintock B. Chromosome organization and genic expression. Cold Spring Harb Symp Quant Biol. 1951;16:13-47. doi:https://doi.org/10.1101/SQB.1951.016.01.003

    9. Li X, Dai X, He H, et al. A pan-TE map highlights transposable elements underlying domestication and agronomic traits in Asian rice. Natl Sci Rev. 2024;11(6):nwae188. doi:https://academic.oup.com/nsr/article...wae188/7687832

    10. Li X, Dai X, He H, et al. Uncovering the breeding contribution of transposable elements from landraces to improved varieties through pan-genome-wide analysis in rice. Front Plant Sci. 2025;16:1573546. doi:https://doi.org/10.3389/fpls.2025.1573546

    11. Li X, Arya GA, Thu AM, et al. Malaria parasite population genomics during an elimination program in Eastern Myanmar. bioRxiv. 2025. doi:https://doi.org/10.1101/2025.05.21.655408

    12. Scarsbrook L, Spatola GJ, Dreger DL, et al. A 120-y time series of genomes reveals the consequences of closed breeding in German Shepherd Dogs. Proc Natl Acad Sci U S A. 2025;122(48):e2421755122. doi:https://doi.org/10.1073/pnas.2421755122

    13. Wiley LK, Shortt JA, Roberts ER, et al. Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine. Am J Hum Genet. 2024;111(1):11-23. doi:https://doi.org/10.1016/j.ajhg.2023.12.001

    About the author: Darío Sánchez Martín is a scientific writer and molecular biologist, specializing in biotechnology, molecular biology, and nanotechnology. He holds a Ph.D. in Biotechnology from Uppsala University, where he developed nanoparticle-based visual and magnetic assays to detect antimicrobial resistance genes. He co-founded and works at Helixa Communications as a scientific writer, helping life-science companies turn complex science into clear and persuasive content for diverse audiences.
    Last edited by SEQadmin2; 03-09-2026, 07:49 AM.
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