Microsatellite and SNP Genotyping Services: Marker-Based Approaches for Parentage, Conservation, and Diversity Studies

Reduced-representation sequencing and whole-genome approaches have transformed population genetics, but they are not always the right tool. A wildlife forensics lab confirming the provenance of a seized ivory shipment does not need 50,000 genome-wide SNPs — it needs a reproducible panel of 15 to 20 markers that can be run on degraded DNA and compared against an INTERPOL-standardized reference database. A conservation hatchery tracking pedigrees across multiple spawning seasons needs markers whose allele calls are stable across years, instruments, and operators. An aquaculture breeding program selecting for disease resistance in 5,000 fingerlings needs per-sample genotyping costs measured in cents, not dollars.

These scenarios share a common requirement: known-locus genotyping — targeting a defined, validated set of genetic markers rather than discovering variants de novo in every sample. Two marker classes dominate this space. Microsatellites (simple sequence repeats, SSRs) have served as the workhorse of population genetics for three decades, valued for their high per-locus polymorphism, codominant inheritance, and extensive legacy datasets. Single nucleotide polymorphisms (SNPs) have grown rapidly with the advent of high-throughput genotyping platforms, offering superior standardization, higher genome-wide coverage, and better performance with degraded DNA. Neither marker type is universally superior; the choice depends on the biological question, sample characteristics, budget, and whether comparability with existing datasets matters.

This article provides a practical guide to both marker systems — from wet-lab workflows through platform selection to downstream analysis — with emphasis on the decision-making framework that connects research objectives to the appropriate genotyping technology. For a broader overview of the genotyping landscape including next-generation sequencing approaches such as GBS and ddRAD-seq, see our Genotyping and Genetic Diversity Services overview.

Figure 1: Microsatellite and SNP Marker Comparison — Multi-Allelic SSRs vs. Bi-Allelic SNPs

Microsatellite Genotyping Workflow — From DNA to Diploid Genotypes

The microsatellite genotyping workflow is conceptually straightforward but operationally demanding — success depends on careful panel design, rigorous allele scoring discipline, and awareness of the technical artifacts that distinguish experienced laboratories from novices.

Marker development and panel design

For species with existing microsatellite resources, marker selection begins with a literature survey and cross-species amplification testing. When new markers are needed, microsatellite development traditionally required constructing and screening a genomic library enriched for repeat motifs — a labor-intensive process that could take months. High-throughput sequencing has transformed this step: low-coverage whole-genome shotgun sequencing (typically 5 to 10× coverage on an Illumina platform) yields thousands of candidate SSR loci in days. Tools such as QDD and MISA identify perfect and compound repeats, design primer pairs, and filter for loci with adequate flanking sequences. A 2025 study of the Neotropical tree Anadenanthera colubrina demonstrated the power of this approach: low-coverage shotgun sequencing identified 60 candidate SSR loci, of which 25 were validated with allelic error rates below 3 percent, and sequence-based genotyping (SSRseq) revealed pervasive size homoplasy — alleles identical by length but divergent by sequence — that conventional capillary electrophoresis would have missed (Goncalves et al., Plant Ecology and Evolution, 2025). For breeding applications where trait association is the goal, transcriptome-anchored SSRs derived from RNA-seq data link markers to expressed genes, offering functional relevance beyond neutral diversity. Microsatellite marker development services support the full pipeline from genomic library construction through candidate locus validation and multiplex panel optimization.

Multiplex PCR and fluorescent labeling

Once a marker panel is selected, primers are synthesized with fluorescent labels — typically 6-FAM, VIC, NED, and PET for four-dye detection on ABI genetic analyzers. Multiplex PCR combines multiple primer pairs in a single reaction, reducing per-sample cost and hands-on time. Panel design requires balancing annealing temperatures, avoiding primer dimer formation, and ensuring that overlapping allele size ranges are assigned to different fluorescent dyes. Modern multiplex panels routinely combine 10 to 15 loci per reaction; for parentage applications requiring higher exclusion probabilities, multiple multiplex panels are pooled post-PCR for simultaneous electrophoresis. Tetrameric repeats are preferred over dinucleotide repeats when possible because they produce fewer stutter bands — the ±2 bp polymerase slippage artifacts that complicate allele calling in dinucleotide loci with high polymorphism information content (PIC) above 0.7.

Fragment analysis and allele scoring

Amplified fragments are separated by capillary electrophoresis on instruments such as the ABI 3730xl or 3500xl, with an internal size standard (e.g., GeneScan 500 LIZ) in each lane for precise sizing. Allele calling software — GeneMapper, GeneMarker, or the open-source alternatives Fragman and STRyper (Peccoud, PLOS ONE, 2025) — converts migration times to base-pair sizes and assigns integer allele designations. This binning step is where inter-laboratory variability originates: different laboratories may apply different bin margins, and the same allele can be called as 142 bp in one facility and 143 bp in another if calibration standards drift. Consistent use of an allelic ladder — a synthetic mixture of all known alleles for each locus — run alongside samples on every plate is the single most effective quality-control practice for cross-run and cross-laboratory reproducibility. For projects spanning multiple years or requiring data integration across laboratories, microsatellite genotyping services with standardized allele-calling protocols can eliminate this source of noise.

Quality control and error detection

Three common artifacts require systematic screening. Null alleles — PCR failure at one or both alleles due to primer-site mutations — cause apparent homozygote excess and can severely bias parentage exclusion. The software Micro-Checker identifies loci with null allele signals, and Cervus estimates null allele frequencies during parentage simulations. Stutter peaks, typically 2 to 4 bp shorter than the true allele, are inherent to repeat amplification and must be distinguished from genuine alleles, particularly in mixed-population samples where a stutter peak from one individual's allele can be mistaken for another individual's true allele. Allelic dropout, where one allele amplifies preferentially in a heterozygote, is exacerbated by low-quality DNA and can convert a true heterozygote into an apparent homozygote. Each locus should be assessed for Hardy-Weinberg equilibrium within populations, with Bonferroni-corrected significance thresholds. Loci showing consistent deviation across multiple populations are candidates for exclusion. Genotyping error rates, estimated from blind replicates comprising 5 to 10 percent of samples, should remain below 3 percent for parentage studies and below 5 percent for population-level diversity estimation.

Figure 2: Microsatellite Genotyping Workflow — Five-Stage Laboratory and Analysis Pipeline

SNP Genotyping Platforms — Choosing the Right Technology for Your Scale

SNP genotyping spans an enormous range of throughput and multiplex capacity, from single-plex assays run on standard qPCR instruments to genome-wide arrays interrogating millions of loci. Platform selection is dictated by two variables: the number of SNPs and the number of samples. A 5-SNP panel for 2,000 samples demands a different technology than a 500-SNP panel for 50 samples, and choosing wrong can multiply costs without improving data quality.

Low-plex: TaqMan and KASP for targeted diagnostics

When the question involves 1 to 50 SNPs across hundreds or thousands of samples, qPCR-based chemistries are the most cost-effective choice. TaqMan assays use dual-labeled hydrolysis probes with allele-specific fluorescent reporters; the 5' nuclease activity of Taq polymerase generates signal only when the probe is perfectly complementary to the target sequence, yielding call rates routinely above 98 percent. TaqMan is the gold standard for high-accuracy genotyping applications where individual genotype calls require unambiguous, auditable results, but per-SNP costs are high due to custom probe synthesis. KASP (Kompetitive Allele-Specific PCR) achieves similar accuracy at lower cost by using universal FRET reporter oligos — only two labeled oligos are needed regardless of the SNP being interrogated, making KASP particularly economical for breeding programs running moderate numbers of SNPs. Both TaqMan and KASP are well-suited to non-invasive and degraded samples because the short amplicon lengths (typically 60 to 120 bp) amplify successfully from fragmented DNA that would fail in larger-panel approaches. For projects requiring TaqMan SNP genotyping validation of candidate variants, custom assay design and high-throughput processing are available.

Medium-plex: MassARRAY for the validation sweet spot

Agena Bioscience's MassARRAY system occupies a unique niche: 10 to 50 SNPs multiplexed in a single reaction across hundreds to thousands of samples. The iPLEX chemistry uses single-base primer extension with mass-modified terminators; the extension products are resolved by MALDI-TOF mass spectrometry, which discriminates alleles by mass rather than fluorescence. This eliminates optical artifacts and spectral overlap, achieving genotyping accuracy above 99 percent for most SNP contexts. MassARRAY is the platform of choice for GWAS replication — taking the top 20 to 50 associated SNPs from a discovery GBS or ddRAD-seq study and validating them in an independent cohort of 1,000 to 5,000 individuals. The capital equipment cost is significant, but for medium-plex, large-sample workflows, the per-data-point cost is lower than TaqMan and the multiplex flexibility exceeds what arrays offer at this scale. For high-throughput MassARRAY SNP genotyping, multiplex panels are custom-designed and optimized for each project's variant set.

High-plex: targeted amplicon sequencing and arrays

When the panel exceeds 50 to 100 SNPs, two approaches dominate. Targeted amplicon sequencing — also known as GT-seq or amplicon-seq — uses multiplex PCR to amplify target regions followed by NGS on Illumina platforms. A 2025 study demonstrated a 144-SNP GT-seq panel for snow leopards that achieved allele-calling accuracy above 96.7 percent from field-collected fecal samples, with the full panel costing a fraction of what individual TaqMan assays for the same loci would require (Solari et al., Molecular Ecology Resources, 2025). SNP microarrays — pre-designed chips containing hundreds of thousands to millions of probes — are the standard for human GWAS and agricultural genomics but require species-specific array development, making them cost-prohibitive for most non-model organisms unless a community array already exists. The recent development of pipelines such as mPCRselect, which optimizes SNP selection for population assignment power while minimizing the number of loci, has made targeted amplicon panels increasingly practical for non-model conservation species (Armstrong et al., Molecular Ecology Resources, 2025). For projects transitioning from discovery to targeted validation, SNP microarray services and whole-genome SNP genotyping provide options across the throughput spectrum.

Figure 3: SNP Genotyping Platform Selection Matrix — Throughput vs. Plex-Level Decision Framework

Applications — Parentage, Conservation, and Breeding from Genotype Data

The analytical methods applied to microsatellite and SNP genotype data are well-established, but the quality of biological inference depends more on marker selection and sampling design than on which software package is used.

Parentage assignment

In aquaculture, captive breeding, and wildlife management, parentage analysis reconstructs pedigrees when controlled crosses are impractical — for example, in communally spawned fish cohorts, multi-sire wild populations, or reintroduction programs where founders were not individually tracked. Cervus 3.0 remains the most widely used likelihood-based tool: it calculates LOD scores (log-odds of parentage) for each candidate parent-offspring pair, determines critical thresholds through simulation, and reports assignments at relaxed (80 percent) and strict (95 percent) confidence levels. A 2025 refinement — the Pairwise method — improves on traditional Cervus by computing trio-specific rather than global critical thresholds, incorporating locus-specific genotyping error rates, and using forward-backward simulations that better control false-positive assignments when relatives are present among candidates (Amiri Roudbar et al., Ecology and Evolution, 2025). COLONY takes a complementary full-probability approach, simultaneously inferring sibship structures and full pedigrees, which is advantageous when parents are unsampled and families must be reconstructed from offspring genotypes alone. A practical benchmark: a panel of 12 to 15 highly polymorphic tetrameric microsatellites (mean He ≥ 0.6) typically achieves combined exclusion probabilities above 99.99 percent when both parents are unknown, sufficient for the vast majority of aquaculture and wildlife applications. For species with low genetic diversity — bottlenecked populations where only a handful of polymorphic microsatellites exist — 50 to 80 carefully selected SNPs can provide equivalent or superior parentage resolution. Parentage testing services support applications from aquaculture pedigree reconstruction to wildlife forensics.

Conservation genetics

Microsatellites continue to play an outsized role in conservation despite the SNP revolution, partly because decades of published microsatellite data enable temporal comparisons that SNP-based studies cannot yet match. A 2025 study of captive non-human primate colonies demonstrated the practical value of this continuity, developing seven microsatellite multiplex panels that enable parentage determination across macaque, chimpanzee, gibbon, and baboon populations — achieving rigorous pedigree resolution from hair and fecal samples (de Groot et al., Ecology and Evolution, 2025). Standard analyses include: genetic diversity estimation (observed and expected heterozygosity, allelic richness rarefied to equal sample size, inbreeding coefficient Fis), population differentiation (pairwise Fst, AMOVA with hierarchical population groupings), and bottleneck detection. Bottleneck detection is a particular strength of microsatellites: the BOTTLENECK software tests for heterozygosity excess relative to allelic diversity under mutation-drift equilibrium, a signal that persists for 2 to 4 Ne generations after a population contraction. The M-ratio test (number of alleles divided by allele size range) is sensitive over longer timescales, complementing the heterozygosity-excess approach. SNP-based conservation studies increasingly use targeted panels of 100 to 300 diagnostic SNPs — such as the 196-locus GT-seq panel developed for gray wolf, eastern wolf, and coyote species identification, which simultaneously provides individual ID (probability of identity = 6.71 × 10⁻⁴¹), sex inference (97.2 percent accuracy), and kinship estimation from non-invasive samples (Hervey et al., Ecology and Evolution, 2025). For species identification and genetic monitoring, DNA barcoding services offer complementary species-level resolution.

Marker-assisted breeding

In plant and animal breeding, the genotyping platform decision is driven primarily by throughput and cost per data point. For bi-parental mapping populations and marker-assisted backcrossing involving fewer than 50 loci, KASP assays on standard qPCR instruments provide the most economical path. For genomic selection in large training populations — several thousand individuals, hundreds to low thousands of SNPs — targeted amplicon sequencing or SNP arrays become cost-effective, with per-sample costs below $15 to $20 when panel size and sample throughput are optimized. The emerging integration of microsatellite and SNP data in breeding is notable: SSRs are retained as quality-control markers for verifying accession identity and tracking known functional alleles, while SNPs provide the genome-wide coverage for genomic estimated breeding values. For breeding programs scaling from discovery to production, skim sequencing provides an intermediate-density genotyping option that bridges the gap between marker panels and whole-genome approaches.

In Practice: Three Common Genotyping Scenarios

Three genotyping scenarios illustrate the marker selection logic. First, parentage and pedigree reconstruction — for example, a potato breeding program genotyping F1 hybrid individuals with 12-SSR multiplex panels to confirm parentage and detect self-fertilized contaminants. Second, population genetic surveys of non-model species — peach palm (Bactris gasipaes) populations across the Amazon basin characterized at 15 microsatellite loci to quantify genetic diversity, gene flow, and domestication patterns. Third, marker-assisted selection — a disease-resistance panel of 25 KASP markers screened across a large segregating progeny population to select individuals carrying resistance alleles at multiple QTL. Each scenario illustrates the critical principle that marker choice follows from the biological question, not the technology's novelty.

Figure 4: Parentage and Conservation Applications — From Genotypes to Biological Inference

Microsatellite vs. SNP — Choosing the Right Marker for Your Study

The decision between microsatellites and SNPs is not about which technology is superior — it is about which marker class aligns with the study's analytical goals, sample constraints, and long-term data utility.

Information content and marker number

A single microsatellite locus with 10 to 20 alleles provides more information for parentage and individual identification than a single biallelic SNP. Empirically, 2.5 to 5.5 SNPs are needed to match the exclusion probability of one highly polymorphic microsatellite, depending on allele frequency distributions. This means that a 15-locus microsatellite panel providing combined exclusion probability above 99.99 percent requires roughly 40 to 80 SNPs for equivalent parentage power. However, the marginal cost of adding SNPs is far lower than adding microsatellites — a targeted amplicon panel of 100 SNPs costs little more to sequence than a 50-SNP panel, whereas doubling microsatellite loci doubles both multiplex PCR development effort and fragment analysis runtime.

Cross-laboratory standardization

This is where SNPs hold a decisive advantage. A SNP genotype is a nucleotide identity — A, C, G, or T — which is unambiguous regardless of platform, laboratory, or analyst. Microsatellite alleles are electrophoretic mobility measurements subject to instrument-specific sizing, dye-specific mobility shifts, and analyst-specific binning decisions. A genotype called as 142/148 at one facility may be reported as 143/149 at another, and harmonizing legacy microsatellite datasets across laboratories is a well-documented source of frustration in conservation genetics. This difference has practical consequences: if your study will contribute to a long-term monitoring program with data generated across multiple laboratories and decades, SNPs are the more sustainable choice. If your study is a self-contained project analyzed in a single laboratory and benefits from existing microsatellite reference data, the standardization advantage of SNPs is irrelevant.

Marker development cost and effort

Developing microsatellite markers for a species with no genomic resources historically required constructing and screening an enriched genomic library — a multi-month, multi-thousand-dollar effort. Low-coverage whole-genome sequencing has reduced this to days and hundreds of dollars, making de novo SSR development accessible for virtually any eukaryotic species. SNP discovery for non-model organisms has similarly benefited from reduced-representation sequencing: a single ddRAD-seq or GBS run on 50 to 100 individuals can identify 10,000 to 50,000 SNPs, from which the most informative subset can be selected for targeted assay development. Discussions of ddRAD-seq for SNP discovery and population genomic analysis are detailed in our guide on ddRAD-Seq and RAD-Seq for population genetics and phylogenomics. For projects where SNP discovery from genome-wide data is the first step, genotyping by sequencing (GBS) offers efficient large-scale variant discovery.

Sample quality and DNA requirements

Microsatellite amplicons typically span 80 to 300 bp, while SNP genotyping assays can target fragments as short as 60 bp. For degraded samples — fecal DNA, hair shafts, museum skins, formalin-fixed tissue — the shorter target length of SNP assays provides a meaningful advantage in amplification success rate. This is one reason why non-invasive wildlife monitoring has increasingly adopted SNP panels. However, microsatellites retain two advantages with difficult samples: their multi-allelic nature means that partial profiles (amplification at only 10 of 15 loci) often remain informative, and stutter artifacts that complicate allele calling are more predictable and manageable than the allele dropout patterns in SNP panels.

Cost trajectories

For small studies — fewer than 100 samples, fewer than 20 loci — microsatellite genotyping remains cost-competitive, particularly when existing primer panels are available and no marker development is needed. For large studies — hundreds to thousands of samples or more than 50 markers — SNP platforms become increasingly economical, with the crossover point depending on species, available genomic resources, and the specific platforms accessible to the research team. A practical recommendation: if your study requires more than 30 markers and more than 200 samples, a targeted SNP panel is likely more cost-effective than microsatellites; if your study requires fewer than 15 markers and fewer than 100 samples, the lower startup costs of microsatellite genotyping typically favor SSRs. For studies that fall between these thresholds, a formal cost comparison including marker development, wet-lab consumables, instrument time, and bioinformatics should inform the decision.

Figure 5: Microsatellite vs. SNP Decision Framework — Application, Cost, and Reproducibility Dimensions

Study Design Considerations — Sampling, Power, and Quality Control

Regardless of marker choice, several study design principles apply universally. Sample size per population should meet minimum thresholds: 20 to 30 individuals for reliable allele frequency estimation in population genetic studies, 8 to 12 for parentage assignment when candidate parents are well-sampled, and 50 to 100 for accurate linkage disequilibrium estimation when designing SNP panels for association studies. Pilot genotyping of 8 to 16 samples on the candidate marker panel validates amplification success rate, polymorphism, and multiplex compatibility before committing to the full cohort. A well-designed pilot answers three questions before scale-up funds are spent: (a) do the 8 to 16 test samples span the expected diversity range of the full study, (b) do all multiplex panels amplify cleanly with both positive and negative controls yielding expected results, and (c) does the realized exclusion probability or Fst resolution meet the study's inferential thresholds?

Species-specific considerations shape marker selection and power. Inbred or recently bottlenecked populations present fewer polymorphic microsatellites — the European bison (Bison bonasus), reduced to approximately two founder genomes, yielded only 2 informative loci out of 17 tested, making SNP panels the only viable route to parentage resolution. Outbred species with high standing diversity, by contrast, routinely achieve combined exclusion probabilities above 99.99 percent with 12 to 15 SSR markers. The availability of a reference genome — even a draft assembly — shifts the cost calculus substantially toward SNPs, because known flanking sequences enable primer design for targeted amplicon panels without the trial-and-error optimization that de novo SSR multiplexing requires.

Budget-driven triage: if the available budget supports only one round of genotyping, prioritize markers that directly serve the primary biological question. For parentage and individual identification, tetrameric microsatellites remain the most cost-effective per unit of exclusion power. For population structure and genome-wide diversity, 50 to 100 SNPs provide sufficient resolution for Fst-based and clustering analyses. For studies that require both — parentage plus population structure — a dual-marker pilot genotyping 8 to 16 samples with both systems identifies which marker class delivers adequate power for the secondary question, potentially eliminating the need for a second full-cohort round.

For studies transitioning from microsatellites to SNPs — or integrating both marker types — genotyping a subset of samples with both marker systems enables cross-calibration. The resulting dataset can serve as a Rosetta Stone for translating legacy microsatellite-based conclusions into the SNP framework, preserving the value of historical data while enabling the analytical advantages of modern SNP approaches. This calibration step is particularly important for long-term monitoring programs where comparability across decades is a core objective.

When trait association is the ultimate goal — connecting genotype to phenotype for marker-assisted selection or functional validation — SNP discovery by reduced-representation sequencing followed by targeted genotyping of the most informative variants represents a proven cost-efficient path. Our guide on GWAS experimental design with GBS discusses the statistical and experimental design considerations for connecting genotype to phenotype at scale.

Figure 6: Study Design Decision Flow — From Biological Question to Genotyping Strategy

FAQ

What is the difference between microsatellites and SNPs?

Microsatellites (SSRs) are tandem repeats of 1–6 bp motifs with multiple alleles per locus, genotyped by fragment length on capillary electrophoresis. SNPs are single-base changes with two alleles per locus, genotyped by a variety of platforms including qPCR, mass spectrometry, and sequencing. SSRs provide more information per locus; SNPs provide more loci per dollar at scale and superior cross-laboratory standardization.

How many microsatellite markers do I need for parentage analysis?

A panel of 12 to 15 highly polymorphic tetrameric microsatellites (mean He ≥ 0.6) typically achieves combined exclusion probabilities above 99.99 percent when both parents are unknown. For species with low genetic diversity, 50 to 80 SNPs may provide equivalent or better resolution.

What is the most cost-effective SNP genotyping method for fewer than 50 markers?

For fewer than 50 SNPs across hundreds to thousands of samples, KASP assays offer the best cost-to-performance ratio, using standard qPCR instruments and universal fluorescent reporters. If only 1 to 5 SNPs are needed at very high accuracy, TaqMan is preferred. For 10 to 50 SNPs across thousands of samples, MassARRAY provides the lowest per-data-point cost.

Can microsatellite and SNP data be combined in the same analysis?

They cannot be analyzed in the same genotype matrix, but complementary analysis is common — for example, using microsatellites for parentage and individual identification while using SNPs for population structure and genome-wide diversity. Cross-calibration by genotyping a shared subset of samples with both marker types preserves comparability.

Do I need a reference genome for SNP genotyping?

Not necessarily. SNPs discovered by reduced-representation sequencing (GBS, ddRAD-seq, or RNA-seq) can be converted to targeted genotyping assays without a reference genome, although a reference makes assay design more efficient and enables annotation of variant context.

How much DNA do I need for microsatellite genotyping?

A minimum of 20 to 50 nanograms per multiplex PCR reaction, though 100 nanograms provides margin for repeat analysis. For non-invasive samples (hair, feces), DNA quantity is often limiting; SNP panels with shorter amplicon targets (60–120 bp) typically amplify more successfully from degraded samples than microsatellites (80–300 bp).

Why are microsatellites still used given the advantages of SNPs?

Three reasons: (1) decades of published microsatellite data enable temporal comparisons that SNP-based studies cannot yet match, (2) for small studies with fewer than 15 markers and 100 samples, microsatellites are cost-competitive and require no specialized equipment beyond a capillary sequencer, and (3) microsatellites remain the standard for wildlife forensics where reference databases are built on SSR markers.

What software is used for microsatellite parentage analysis?

Cervus 3.0 (likelihood-based, LOD scores with simulation-derived confidence) and COLONY (full-probability pedigree reconstruction) are the most widely used. The 2025 Pairwise method refines Cervus by using trio-specific critical thresholds and locus-specific error rates.

References:

1. de Groot NG, Bontrop RE, Doxiadis GG, Otting N, Heijmans CM. Genetic conservation and population management of non-human primates: parentage determination using seven microsatellite-based multiplexes. Ecology and Evolution. 2025;15(4):e71216. https://doi.org/10.1002/ece3.71216
2. Amiri Roudbar M, Mousavi SF, et al. Pairwise paternity assignment with forward-backward simulations: refining CERVUS using trio-based likelihood and locus-specific error rates. Ecology and Evolution. 2025;15(10):e72230. https://doi.org/10.1002/ece3.72230
3. Solari KA, Anjum S, Lonsinger RC, Waits LP, McCarthy KP, Alexander PD, Anwar M, Hussain S, Mahmood T, Khan H, Zahoor B. Next-generation snow leopard population assessment tool: multiplex-PCR SNP panel for individual identification from faeces. Molecular Ecology Resources. 2025;25(4):e14074. https://doi.org/10.1111/1755-0998.14074
4. Armstrong EE, Perryman C, Campana MG, Maldonado JE, Fleischer RC. A pipeline and recommendations for population and individual diagnostic SNP selection in non-model species. Molecular Ecology Resources. 2025;25(3):e14048. https://doi.org/10.1111/1755-0998.14048
5. Hervey KS, Wheeldon T, Mills KJ, Patterson BR, Crawley S, White BN. A 196-locus GT-seq panel for noninvasive monitoring of gray wolves, eastern wolves, and coyotes. Ecology and Evolution. 2025;15(4):e71240. https://doi.org/10.1002/ece3.71240
6. Goncalves AL, García MV, Chancerel E, Lepais O, Heuertz M. High-throughput sequence-based microsatellite genotyping for the non-model Neotropical tree species Anadenanthera colubrina (Leguminosae). Plant Ecology and Evolution. 2025;158(1):43-52. https://doi.org/10.5091/plecevo.138834
7. Peccoud J. STRyper: a macOS application for microsatellite genotyping and chromatogram management. PLOS ONE. 2025;20(2):e0318806. https://doi.org/10.1371/journal.pone.0318806

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