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PubMed2025

Genomic study for pregnancy loss in Brahman cattle.

Reproduction has major influence on productivity of beef cattle operations. Maintaining an animal in the herd for an extended period without producing a marketable product can result in significant economic losses, compromising the efficiency of the production system. Understanding genetic variation's role in pregnancy loss (PL) is crucial for improving reproductive success in cattle. Identifying genomic regions that influence embryo and fetal survival, as well as pinpointing candidate genes associated with PL, can enhance breeding strategies. The objective of this study was to estimate variance components and investigate genetic factors associated with PL in Brahman cattle. Phenotypic records consisted of 29,905 pregnancy (28,691) and abortion (1,214) records from nulliparous, primiparous, and multiparous cows. A total of 921 animals were genotyped using a medium-density SNP chip (∼52K markers). Variance components were estimated using a threshold model to assess the binary response to PL through a single-step genomic BLUP procedure. The heritability estimate for PL was low (0.11), but the presence of genetic variance suggests that selection for improved reproductive performance is feasible. Genome-wide association analyses identified 17 candidate regions containing 92 genes. Regions on BTA4, 7, 8, 9, 11, 12, 16, 18, 19, 21, 22, and 29 harbored genes associated with embryonic development and implantation, fertilization, G protein-coupled receptors, embryonic brain development, olfactory receptor activity, and calcium signaling. Orthologous genes were also identified in humans (Homo sapiens), rats (Rattus norvegicus), and mice (Mus musculus). The candidate regions reported in this study provide insights for identifying and selecting animals with improved reproductive performance, ultimately enhancing the productivity of Brahman cattle. Moreover, our findings contribute to a better understanding of the genetic and physiological mechanisms underlying pregnancy retention in beef cattle.

Animals↗

PubMed2026

Quo vadis, BGA? A collaborative EDNAP exercise on the challenges and progress in forensic biogeographical ancestry inference.

There is a broad consensus that forensic tests for the prediction of externally visible characteristics (EVC) and analysis of biogeographic ancestry (BGA) of an individual are technically reliable. However, interpretation of the results and population-specific genotype distribution patterns remains challenging. EVC and BGA analyses provide valuable information for population genetics studies and as investigative leads for criminal cases, as well as for historical and contemporary identification tests. However, inaccurate or incorrect predictions, for example, from subjective bias in the interpretations made, have the potential to misdirect police investigations. The legal situation regarding EVC and BGA testing varies by country: ranging from countries where it is explicitly prohibited, to those without specific regulations on biogeographic ancestry prediction, and others that have already enacted laws governing its use. The reluctance to utilize these analyses is not only due to legal restrictions and data protection concerns, but also to initial limited sets of sufficiently comprehensive forensic DNA assays. Forensic BGA marker panels typically contain up to ∼300 SNPs. This relatively small number of genetic markers, along with limited reference population data, complicates the interpretation of results from donors of unknown origin. This paper presents the results of a collaborative EDNAP study, which, for the first time, evaluated the approach to reporting EVC and BGA data between international laboratories. For the study, DNA from nine individuals with self-reported ancestry was collected and analysed using various forensic panels differing in the number and composition of ancestry-informative markers genotyped, comprising: the Precision ID mtDNA Whole Genome Panel, the VISAGE Basic Tool and the VISAGE Enhanced Tool for Appearance and Ancestry Prediction, and the Ion AmpliSeq™ PhenoTrivium Panel. To ensure full data protection, all SNP genotypes and uniparental marker haplotypes obtained were not shared with third parties. Instead, the genetic data were analysed using a range of commonly used population analysis software packages. These analysis outcomes were then distributed to twelve European forensic laboratories (both academic and law enforcement institutions), who were asked to prepare reports based on their interpretation of the phenotypes and ancestry they inferred from the analysis data. A questionnaire sent alongside the genetic information, aimed to evaluate which difficulties were encountered by the participants in processing the BGA analysis data they were given.

Humans↗

PubMed2026

Towards a Standard Threshold for Genome Wide Significance in Dogs.

Genome-wide association studies (GWAS) are a foundational step in tying phenotype to genotype, relying on statistical significance thresholds to distinguish true- from false-positive signals of association. Dog genomics has long relied on per-study Bonferroni thresholds of significance, basing these on SNP chip levels of markers (~100 k to > 14 M variable sites). However, as the field progresses into whole genome imputation analyses and more powerful meta-analyses, there is a clear need to develop a standard significance threshold for common-variant GWAS. Using 1591 dogs from the broad-ancestry Dog10K dataset, we performed permutation analysis and developed GWAS thresholds for datasets using either 1% or 5% minor allele frequencies. The resultant p-values, 4.2 × 10-7 and 5.0 × 10-7 respectively, are similar to previous Bonferroni levels (p-value ~6 × 10-7), but less restrictive than the standard human p-value, 5 × 10-8, which is sometimes used in dog studies. Given the diverse haplotypes from the > 320 breeds in the Dog10K input dataset, we suggest a p-value of 4 × 10-7 as a standard significance threshold that could be applied to any dog GWAS.

Animals↗

PubMed2026

A transcriptome-wide approach for rapid pathotype discrimination of Puccinia striiformis f. sp. tritici in north-western India.

Stripe rust of wheat caused by Puccinia striiformis f. sp. tritici (Pst) remains a major constraint to wheat production in India due to the rapid evolution and frequent emergence of virulent pathotypes. Rapid and reliable discrimination of Pst pathotypes is essential for effective resistance deployment and surveillance. In the present study, transcriptome-wide simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) were exploited to develop and validate molecular markers for pathotype-specific detection of Pst pathotypes prevalent in North India (110S119, 238S119, 46S119, 110S84 and 78S84). Microsatellite mining from 6103 core orthologous clusters comprising 51,127 transcripts mined 14,634 SSR loci, from which 93 primer pairs were synthesized. However, only three SSR markers exhibited polymorphism indicating limited discrimination potential of expressed sequence-derived (EST) SSRs for pathotype differentiation. In contrast, SNP discovery through stringent variant calling and filtration yielded 186 pathotype-specific homokaryotic SNPs, of which 56 high-confidence loci were selected for Kompetitive Allele-Specific PCR (KASP) assay development. A total of 48 KASP markers were synthesized and 14 demonstrated clear pathotype- or cluster-specific polymorphism representing substantially higher resolution than SSR markers. The high SNP-to-KASP conversion efficiency (~ 95%) and reproducible fluorescence-based clustering emphasize the robustness of KASP assay. Comparative evaluation revealed that SNP-based KASP markers provide superior discriminatory capacity for closely related Pst pathotypes and represent a promising complementary molecular approach for rapid identification of predominant Indian Pst pathotypes. The validated marker panel developed in this study can complement conventional virulence phenotyping and field pathogenomics approaches for surveillance of currently known pathotypes, while continued refinement may accommodate future changes in pathogen populations.

India↗

PubMed2025

A vision of how low-coverage sequence data should contribute to genetic evaluation in the future.

Low-coverage sequencing refers to sequencing DNA of individuals to a low depth of coverage (e.g., 0.5X) and imputing that sequence to a genomic sequence based on reference haplotypes from individuals sequenced to a high depth of coverage (e.g., ≥10X). It has been proposed as an alternative to genotyping by Single-nucleotide polymorphisms (SNP) arrays. At least one commercial product based on it is available for agricultural species. Concerns limiting adoption in its current form are: 1) the cost of storing the huge volume of data it generates and 2) whether that additional data will result in improved accuracy of genetic evaluation. This work envisions future implementation of low-coverage sequencing to reduce storage costs and enhance genetic evaluations by leveraging the additional information in the full sequence of the pangenome to account for more genetic variation. We propose addressing the storage issue by representing genomic sequence of an individual in a pair of haplotype arrays with each element pointing to an enumerated haplotype of the sequence within one of approximately 50,000 defined genome segments. Assuming 60 million genomic variants, the infrastructure required to translate the identifier of any enumerated haplotype into its genomic sequence would require less than 10 gigabytes of binary storage. Each haplotype array element would require 2 bytes, so the marginal binary storage required to represent the genomic sequence of an individual would be about 200 kilobytes (KB), similar to the genotypes from a SNP array with 200,000 markers. This assumes no pedigree and no ambiguity of the imputation, though the latter is unrealistic. Strategies to minimize, and when necessary, to manage and efficiently represent ambiguity are proposed. The genomic sequence of an individual could be stored in about 1 KB (binary) if both parents have unambiguous sequences stored as described above. The proposed system for representing the pangenome includes algorithms for read mapping and imputation intended to leverage all known genetic variation in the target population. It is also designed to use sequencing reads generated for imputing the genomic sequence of new individuals to identify unrecognized mutations, crossovers, and structural variants, thus continuously improving the genome representation, especially if widespread use of low-coverage sequencing in livestock industries is realized. This could make improved genetic merit and management of livestock feasible without computational burden.

Animals↗

PubMed2026

Integrated multi-omics identification of m6A-SNP-related diagnostic biomarkers in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks reliable and minimally invasive biomarkers for early diagnosis. m6A-associated single-nucleotide polymorphisms (m6A-SNPs) may influence RNA methylation and gene expression, offering opportunities to identify clinically relevant diagnostic markers. METHODS: We integrated eQTLGen cis-eQTL data, RMVar m6A-SNP annotations, and ALS transcriptomic datasets to identify m6A-SNP-related genes. Random Forest and LASSO regression were combined to screen robust diagnostic markers. A nomogram was constructed and validated using independent cohorts. Immune infiltration, predicted m6A modification sites, and potential RBP-SNP interactions were assessed. Peripheral blood samples from ALS patients were used for exploratory validation of gene expression and global m6A levels. RESULTS: We identified 109 ALS-associated m6A-SNP-related genes with cis-eQTL signals and narrowed these to seven candidate diagnostic markers (TMED5, OXR1, BRI3, FEM1C, SUZ12, EIF2AK4, and TJAP1). The seven-gene model outperformed the individual markers in the training cohort and retained moderate discrimination in the independent validation cohort. ALS samples showed differences in inferred immune-cell composition, including monocytes, neutrophils, and T-cell subsets. The selected SNP loci were located near predicted m6A sites and annotated RBP-binding regions. Exploratory clinical validation showed significant upregulation of FEM1C and SUZ12 at both mRNA and protein levels, accompanied by reduced global m6A modification. CONCLUSIONS: Through multi-omics integration and exploratory clinical validation, this study identifies m6A-SNP-related candidate markers associated with ALS. The findings support further evaluation of m6A-related signatures for ALS discrimination and molecular characterization, while larger independent cohorts and additional calibration are required before clinical application.

Humans↗

PubMed2026

Breed classification of Lao People's Democratic Republic (Lao PDR) and Thai native chickens using synchrotron radiation-based Fourier transform infrared spectroscopy and genotyping by sequencing.

Lao PDR harbors substantial genetic diversity in native chicken populations, representing an important resource for sustainable production and long-term food security. This study aimed to classify five Lao native chicken breeds-Ou, Black Bone, Horn Chou, Yolk, and Chae-and to discriminate them from a Thai native breed, Leung Hang Khao (LK), using integrative genotype-based approaches. Blood samples were collected from 50 LK and Lao native chickens (32 Ou, 10 Black Bone, 9 Horn Chou, 121 Yolk, and 41 Chae). Genomic DNA was extracted and analyzed using synchrotron radiation-based Fourier-transform infrared (SR-FTIR) spectroscopy to characterize biochemical composition, while genotyping-by-sequencing (GBS) was employed to identify genome-wide single nucleotide polymorphisms (SNPs). SR-FTIR analysis revealed highly significant differences among breeds in nucleotide-associated functional groups, including thymine, adenine, guanine, cytosine, as well as DNA backbone and deoxyribose components (P < 0.001). Multivariate analyses demonstrated that principal component analysis (PCA) of SR-FTIR spectra effectively discriminated chicken breeds, while hierarchical cluster analysis (HCA) further resolved them into two major clusters with distinct sub-clusters, reflecting variation in DNA biochemical composition. In contrast, GBS analysis identified 1484 common SNPs; however, PCA based on SNP data showed limited resolution in clearly separating breeds, despite revealing similar clustering trends. Overall, the results highlight the strong discriminatory power of SR-FTIR spectroscopy for rapid and effective classification of native chicken breeds at the molecular level, outperforming SNP-based differentiation under the current marker density. This study provides novel insights into the application of synchrotron-based spectroscopic techniques in poultry genetics and contributes valuable baseline information for the conservation and utilization of Lao native chicken genetic resources.

Breed classification↗

PubMed2025

Genome-wide association study to dissect the genetic architecture of bolting-related traits in carrot (Daucus carota).

As a crucial root vegetable, the phenomenon of bolting and flowering presents a significant challenge to the commercial value of carrot. However, the genetic mechanism of carrot bolting remains to be fully elucidated. In this study, we conducted a two-year cultivation experiment with a population of 240 carrots to examine traits associated with bolting. We conducted whole-genome sequencing on these carrots and subsequently performed population structure analysis, as well as genome-wide association studies (GWAS). A total of nine single nucleotide polymorphism (SNP) loci were identified across diverse environmental conditions that exhibited significant associations with bolting speed and other traits. Furthermore, within 93 candidate genes identified, the RING-domain zinc-finger protein LOC108205243 was determined to play a significant role in the regulation of bolting traits. The SNPs and candidate genes identified in this research may serve as molecular markers for bolting traits, thereby offering essential resources for genetic engineering breeding efforts aimed at managing bolting in carrots.

Daucus carota↗

PubMed2025

Amplicon-based analyses of single-nucleotide polymorphisms reveal the genetic structure of a forest insect baculovirus.

Amplicon-based next-generation sequencing (aNGS) is a powerful tool in diagnostics and genetic studies. We developed an aNGS approach to study the population structure of the Lymantria dispar multiple nucleopolyhedrovirus (LdMNPV), a specific pathogen of the spongy moth Lymantria dispar, a devastating lepidopteran pest in European, Asian, and American deciduous forests. Naturally occurring pathogens, such as LdMNPV, are frequently reported to cause epizootics and a rapid decline of insect pest populations. DNA samples of pooled LdMNPV-infected larvae from forest regions in Northern Bavaria (Germany) were subjected to whole genome sequencing (WGS) and aNGS optimization. Then, five marker regions were identified in the genome of LdMNPV for PCR amplification, covering 21 highly specific single-nucleotide polymorphism (SNP) positions that enabled comprehensive analysis at the intra- and intersample levels. These markers were used in aNGS analyses of 70 single larvae collected in 12 forest sites, followed by SNP-based hierarchical clustering on principal components (HCPC). This approach identified three LdMNPV population clusters consisting of homogenous (pure) and heterogeneous (mixed) LdMNPV samples. To explain the genetic variability within each sample, a model based on linear optimization was developed and validated by comparing the predictions from aNGS and WGS data. The analyses showed that LdMNPV from Bavarian forests carried genetic variants highly similar to those present in the commercial product Gypchek®, developed for biocontrol. The distribution of genetic characteristics showed some trends of geographic and temporal prevalence, which are indicative of short-distance and long-distance transmission. The aNGS approach offers a fast, cost-effective, and comprehensive insight into the natural population structure of LdMNPV.

insects↗

PubMed2026

GWAS-based identification of a candidate gene and development of a predictive KASP marker for seed protein and oil contents in soybean.

BACKGROUND: Soybean [Glycine max (L.) Merrill] is one of the most widely cultivated crops worldwide. Its seeds contain about 40% protein and 20% oil, serving as essential nutrient sources for humans. Given the nutritional importance of seed protein and oil, identifying genes that regulate their levels is crucial for improving soybean seed quality. OBJECTIVE: This study aimed to identify genetic factors associated with seed protein and oil content using a genome-wide association study (GWAS). METHODS: Seed protein and oil contents were quantified in 192 soybean mutant accessions in a mutant diversity pool (MDP), and GWAS was conducted using 17,631 SNPs filtered from genotyping-by-sequencing. Expression of a candidate gene was examined across seed developmental stages (R5 to R7), and a significant SNP was converted into a Kompetitive Allele-Specific PCR (KASP) marker for validation. RESULTS: GWAS detected significant SNPs associated with seed protein and oil content. Chr20_7635098 was identified as a nonsynonymous SNP located in the exon of Glyma.20g042400. This gene showed differential expression across seed developmental stages between mutant accessions with contrasting protein and oil contents. The KASP marker for Chr20_7635098 was validated using the MDP and six domestic soybean cultivars showing predictive accuracies of ≥ 80.50% for protein content and ≥ 61.18% for oil content. CONCLUSION: Overall, this study identified a candidate gene linked to both seed protein and oil content, providing valuable insights for molecular breeding strategies aimed at efficiently improving these nutritional traits.

Glycine max↗

PubMed2026

Genetic regulation of AIF1 shapes immune and liver injury profiles in chronic alcohol use.

BACKGROUNDIn chronic alcohol consumers, immune cells may drive the progression from mild liver injury to more severe alcohol-associated liver disease (ALD), including alcohol-associated hepatitis (AAH) and cancer. Liver macrophages, both resident and infiltrating, express allograft inflammatory factor 1 (AIF1), which is upregulated during inflammation and enhances immune activation.METHODSUsing serum and urine samples from 868 individuals classified as having alcohol use disorder or not, based on DSM-IV/V criteria, along with serum and liver biopsy tissue from a second cohort of 27 patients diagnosed with AAH, we evaluated the impact of the AIF1 promoter single-nucleotide polymorphism (SNP) (rs3132451; C/C, C/G, G/G) on liver function markers and immune cell profiles.RESULTSAIF1 transcript levels were genotype dependent: C/C homozygotes expressed 5.2% of the levels observed in G/G individuals, while C/G heterozygotes expressed 46%. Unlike most SNPs associated with harmful effects, the G/G genotype is highly prevalent, present in about 70% of patients. Among chronic alcohol users, G/G individuals exhibited elevated markers of liver injury and a more than 3-fold increase in hepatic immune cells, including infiltrating AIF1+ macrophages and neutrophils. Despite similar durations of alcohol misuse, G/G individuals had higher Model for End-Stage Liver Disease scores compared with C/G individuals, indicating a significantly greater 90-day mortality risk. Notably, some immune abnormalities, such as elevated neutrophils, persisted in G/G males even after alcohol abstinence.CONCLUSIONThese findings suggest that functional genetic variation in AIF1 may contribute to the severity and persistence of ALD.TRIAL REGISTRATIONClinicalTrials.gov NCT02231840.FUNDINGResearch support was provided from the National Institute on Alcohol Abuse and Alcoholism of the NIH under grants 1ZIAAA000440-02 and R24AA025017.

Humans↗

PubMed2026

SNP genotyping in Pseudotsuga menziesii and Pinus radiata using targeted genotyping-by-sequencing (GBS): improved Bayesian SNP calling using a beta-binomial distribution and other optimized input parameters.

BACKGROUND: Single-nucleotide polymorphism markers (SNPs) have important applications in gene conservation, breeding, and fundamental genetics research. Our long-term goal is to develop routine approaches for SNP genotyping in forest trees. Ideally, these approaches would be inexpensive, able to accommodate a wide range of samples and SNPs, available through commercial providers, and produce high-quality SNP data. RESULTS: Using targeted genotyping-by-sequencing (GBS), we developed SNP assays for two highly heterozygous tree species, Douglas-fir (Pseudotsuga menziesii) and radiata pine (Pinus radiata). Using Douglas-fir haploid and diploid data, we optimized Bayesian SNP calling by testing four input parameters: (1) allele and genotype prior probabilities, (2) Rho, the beta-binomial dispersion parameter, (3) estimated read error (BayesReadError), and (4) the logPO cutoff used to filter low confidence SNP calls. logPO is the Bayesian posterior odds ratio for a called SNP. Compared to assuming a binomial distribution of read counts (Rho = 0), the beta-binomial distribution (Rho = 0.33) substantially reduced call error and heterozygote undercalling. Compared to the other Bayesian parameters, genotype priors had little effect on genotyping success. For Douglas-fir, we tested 5,360 SNP assays, and then studied the performance of the best 4,000. For radiata pine, we tested 6,000 SNP assays, and then studied the performance of the best 4,570. In Douglas-fir and radiata pine, our Bayesian approach resulted in median call rates of 95% to 98% for the top-ranked SNPs, with an estimated call error of 1.60% for known homozygous genotypes and 2.27% for known heterozygotes. In radiata pine, median and mean call rates were above 91% for GBS and SNP genotyping using an Axiom fixed genotyping array. Additionally, the median correspondence between the GBS and Axiom genotypes was about 98% overall (mean 96%). CONCLUSIONS: By optimizing Bayesian SNP calling, selecting the best 4-5 K SNPs, and excluding samples with low DNA amounts, we substantially reduced call error and heterozygote undercalling, resulting in SNP genotypes that were nearly identical to genotypes obtained using the Axiom array. Furthermore, genotyping performance should increase even further if our SNP rankings were used to develop less complex probe pools that target fewer SNPs.

Pinus↗

PubMed2026

Scalable medium-density genotyping platforms for cultivar identification, pedigree authentication, marker-assisted and genomic selection, and other applications in strawberry.

A broad spectrum of high-density genotyping approaches, including single-nucleotide polymorphism (SNP) arrays, genotyping-by-sequencing, and whole-genome reduced-representation sequencing, have been shown to perform well in strawberry (Fragaria × ananassa), despite the inherent complexity of the octoploid genome. While these approaches are effective, their routine deployment in breeding programs can be constrained by cost, computational requirements, and workflow complexity. In parallel, many breeding programs continue to rely on locus-specific assays for marker-assisted selection, resulting in fragmented and inefficient genotyping strategies. Here, we describe medium-density amplicon-based genotyping platforms for strawberry designed to provide cost-effective, turnkey solutions that integrate markers used for marker-assisted selection with genome-wide markers suitable for genomic prediction in a single laboratory assay. These platforms were developed by targeting 1,650 or 4,811 target SNPs via amplicon sequencing, and are interoperable with existing high-density genotyping resources, including a widely used 50K SNP array, thereby facilitating data integration across platforms. We benchmarked their performance relative to the 50K SNP array across breeding-relevant applications, including identity and purity testing, pedigree authentication, marker-assisted selection, and genomic selection, and further evaluated the feasibility of genotype imputation to enhance genome-wide information content. Across analyses, the 1,650- and 4,811-amplicon platforms produced results comparable to higher-density platforms while substantially reducing genotyping cost and analytical overhead. This work demonstrates that targeted amplicon-based genotyping can support efficient, scalable, and integrated genome-informed breeding, enabling the routine application of both marker-assisted and genomic selection within strawberry breeding workflows. Open-source R workflows are provided to support streamlined analyses in breeding contexts.

Fragaria↗

PubMed2026

Development of Genome-Derived InDel Markers and Genetic Diversity Analysis of Caragana acanthophylla in Xinjiang, China.

Caragana acanthophylla Kom. is an ecologically important drought-tolerant shrub in Xinjiang, China, but species-specific molecular markers for germplasm characterization remain limited. We sampled 93 individuals from 11 localities representing the currently known distribution of C. acanthophylla in Xinjiang. Three individuals per locality (33 in total) were whole-genome resequenced, yielding 2,873,410 high-quality SNPs and 5,679,915 InDels. Genome-wide SNP-based PCA and genetic relationship analysis provided an independent high-resolution assessment of the 33 resequenced individuals. From 34 candidate primer pairs, eight polymorphic InDel markers with stable amplification and clear genotyping profiles were retained and applied to all 93 individuals. The SNP dataset revealed clear regional differentiation and finer locality-associated relationships. Analysis of the same 33 individuals with the eight InDel loci recovered part of this broad pattern, particularly the differentiation of the western YL materials, but showed lower fine-scale resolution. Across all 93 individuals, the InDel panel revealed moderate to low marker-level genetic diversity and detectable regional differentiation. AMOVA attributed 67.00% of the variation to differences among the 11 original sampling localities, while the five exploratory analytical groups showed a similar among-group component (68.37%). The Mantel correlation detected across all 93 individuals (r = 0.801, p < 0.001) disappeared after YL was excluded (r = -0.032, p = 0.724), indicating that the overall spatial signal was largely driven by the geographic separation of YL. These results support the eight-marker panel as a practical, low-cost tool for preliminary germplasm characterization and broader sample screening, while genome-wide SNP data provide substantially greater resolution for population-level inference.

Caragana acanthophylla↗

PubMed2026

Genetic inference in social insects: The continued utility of microsatellites in the sociogenomic era.

Social insects differ from many other biological systems because colonies function as integrated reproductive, ecological, and evolutionary units, often conceptualized as superorganisms. This organization makes genetic inference inherently hierarchical, often requiring genotyping across multiple biological levels: the colony, the population, the individual, and, in some cases, the cellular level. Although whole-genome sequencing and single-nucleotide polymorphism (SNP)-based approaches are now widely used in population genomics, microsatellites or short tandem repeats (STRs) remain a useful approach for cost-effective, low-input, and highly replicated genotyping, particularly in the hierarchical sampling designs common in social insect studies. Here, we review the utility and limitations of microsatellites in social insect research using a three-tiered framework spanning colony-, population-, and individual- or cellular-level analyses. Across these scales, microsatellites are especially valuable for colony delimitation, kinship inference, diagnostic screening of known reproductive systems, and low-input genotyping. By comparing the suitability of microsatellites with that of SNP-based and broader genomic approaches across these applications, this review links marker choice to biological scale, sampling design, and inferential goal in studies of social insects.

Journal Article↗

PubMed2006

Pooled DNA genotyping on Affymetrix SNP genotyping arrays.

BACKGROUND: Genotyping technology has advanced such that genome-wide association studies of complex diseases based upon dense marker maps are now technically feasible. However, the cost of such projects remains high. Pooled DNA genotyping offers the possibility of applying the same technologies at a fraction of the cost, and there is some evidence that certain ultra-high throughput platforms also perform with an acceptable accuracy. However, thus far, this conclusion is based upon published data concerning only a small number of SNPs. RESULTS: In the current study we prepared DNA pools from the parents and from the offspring of 30 parent-child trios that have been extensively genotyped by the HapMap project. We analysed the two pools with Affymetrix 10 K Xba 142 2.0 Arrays. The availability of the HapMap data allowed us to validate the performance of 6843 SNPs for which we had both complete individual and pooled genotyping data. Pooled analyses averaged over 5-6 microarrays resulted in highly reproducible results. Moreover, the accuracy of estimating differences in allele frequency between pools using this ultra-high throughput system was comparable with previous reports of pooling based upon lower throughput platforms, with an average error for the predicted allelic frequencies differences between the two pools of 1.37% and with 95% of SNPs showing an error of < 3.2%. CONCLUSION: Genotyping thousands of SNPs with DNA pooling using Affymetrix microarrays produces highly accurate results and can be used for genome-wide association studies.

Alleles↗

PubMed2026

Prenatal SNP-array chromosomal microarray analysis in 3,549 pregnancies: indication-specific yields and clinical implications.

BACKGROUND: SNP-based chromosomal microarray analysis (CMA) is widely used in invasive prenatal diagnosis, yet real-world performance across contemporary referral pathways, especially in the NIPT era, remains incompletely characterized. METHODS: We retrospectively analyzed 3,549 prenatal invasive samples tested by SNP array, and evaluated diagnostic yield overall and by referral indication and ultrasound phenotype. RESULTS: In total, we identified 398 pathogenic or likely pathogenic (P/LP) variants across 386 fetuses, resulting in an overall diagnostic yield of 10.9% (386/3,549). These findings comprised 223 aneuploidies and 175 pathogenic CNVs. In contrast, variants of uncertain significance (VOUS) were detected in 12.0% (426/3,549) of cases. Diagnostic yields were heavily stratified by indication: yields peaked in NIPT high-risk referrals (38.9%) and were intermediate in ultrasound-based cases (~ 11%), but dropped significantly in the advanced maternal age (AMA; 4.2%) and serum screening (~ 5-6%) groups. Conversely, VOUS rates remained remarkably stable across all referral categories. Sub-analysis of ultrasound abnormalities revealed that multisystem anomalies conferred the highest risk (27.3%), driven predominantly by aneuploidies; among soft markers, increased nuchal translucency (NT) emerged as the strongest predictor of chromosomal pathology. CONCLUSIONS: In our cohort, SNP-array identified clinically actionable findings in 10.9% of cases. NIPT enriched diagnostic yields, particularly for aneuploidies, and NT thickness was strongly associated with pathogenic findings. These results support an indication-based approach to genomic testing, with NIPT as a triage tool for aneuploidy and CMA for high-risk populations, while improving VOUS counseling.

Humans↗

PubMed2025

Role of functional genes for seed vigor related traits through genome-wide association mapping in finger millet (Eleusine coracana L. Gaertn.).

Finger millet (Eleusine coracana (L.) Gaertn.) is a calcium-rich, nutritious and resilient crop that thrives even in harsh environmental conditions. In such ecologies, seed longevity and seedling vigor are crucial for sustainable crop production amid climate change. The current study explores the genetics of accelerated aging on seed longevity traits across 221 diverse accessions of finger millet through genome-wide association approach (GWAS). A significant variation was identified in germination percentage, germination rate indices, mean germination time, seedling vigor indices and dry weight upon aging treatment. GWAS model from 11,832 high-quality SNPs identified through Genotyping-by-Sequencing (GBS) approach produced 491 marker-trait associations (MTAs) for 27 traits, of which 54 were FDR-corrected. A pleiotropic SNP, FM_SNP_9478 identified on chromosome 7B was associated with the traits viz., germination after aging, germination index after aging and their relative measures. Functional annotation revealed DET1 and expansin-A2 influenced seed coat integrity, critical for germination and aging resilience. Probable protein phosphatase 2C3 and piezo-type ion channels contributed to mechanical sensing and stress adaptation in seeds. Beta-amylase and acetyl-CoA carboxylase 2 were identified for seed metabolism and stress response. These insights lay the framework for targeted breeding efforts to improve seed quality and resilience under diverse production conditions.

Eleusine↗