Genotyping Technologies Research Articles

High-sensitivity label-free electrochemical genosensors for carbon nanotube plasmon-assisted detection of somatic mutations in nucleic acids from formalin-fixed paraffin-embedded tissues

Currently, molecular genetic testing of somatic point mutations in the genome of cancer tumors for targeted therapy requires high-performance tools for studies of spatial gene expression and spatial genome profiling in formalin-fixed paraffin-embedded (FFPE) tissue samples. We offer new high-performance label-free electrochemical impedimetric deoxyribonucleic-acid (DNA) nanosensors. The sensors based on a platform of crystalline carbon nanotube plasmonic assemblies are fabricated by the Langmuir–Blodgett deposition technique. The carbon-nanotube assemblies are suspended on nanoporous supports. Incorporation of fast Fourier transforms in the impedance spectroscopy led to the introduction of a new information parameter, the integral value of capacitance changes. The allele-sensitive assay based on plasmon-assisted effects in the assemblies of electrically active conjugates between DNA and few-walled carbon nanotubes has been used for discrimination of allele single-nucleotide polymorphisms (SNPs) of Kirsten Rat Sarcoma viral oncogene homologue (KRAS gene). Using 19- and 20-mer single-stranded (ss) probe oligonucleotides and 35- and 47-mer toehold probes, we explored the probe length and the target location. The genotyping technology allows discrimination of single-nucleotide variations in the target 35-base oligonucleotide at the attomolar concentration level. Human genomic DNAs were isolated from FFPE colorectal cancer tumor tissue samples. The KRAS-gene exon 2, codon 12, c.35G>A mutation has been successfully discriminated in the genomic DNAs with a significance level, P, of 0.001–0.02. The assay has a sensitivity with 0.33-ng/ml limit of detection (LOD) for native genomic DNA.

An Inexpensive and Quick Method for Genotyping of HLA Variants Included in the Spanish Pharmacogenomic Portfolio of National Health System

The possibility of using the same genotyping technology (TaqMan) for all the genetic tests included in the new Spanish pharmacogenomics portfolio should enable the application of a multigenotyping platform to obtain a whole pharmacogenomics profile. However, HLA-typing is usually performed with other technologies and needs to be adapted to TaqMan assays. Our aim was to establish a set of TaqMan assays for correct typing of HLA-A*31:01, HLA-B*15:02, HLA-B*57:01, and HLA-B*58:01. Therefore, we searched for and selected SNVs described in different populations as surrogate markers for these HLA alleles, designed TaqMan assays, and tested in a set of samples with known HLA-A and HLA-B. HLA-A*31:01 was correctly typed with a combination of rs1061235 and rs17179220 (PPV 100%, 95% CI 84.6–100-%; NPV 100%, 95% CI 96.5–100.0%), HLA-B*15:02 with rs10484555 (PPV 100%, 95% CI 69.2–100.0%; NPV 100%, 95% CI 96.8–100.0%) and rs144012689 (PPV 100%, 95% CI 69.2–100.0%; NPV 100%, 95% CI 96.8–100.0%), and HLA-B*57:01 with rs2395029 (PPV 99.5%, 95% CI 72.9–99.3%; NPV 99.5%, 95% CI 98.3–100.0%). HLA-B*58:01 was typed using two allele-specific TaqMan probes mixed with a ß-Globin reference and treated as a genotyping assay (PPV 100.0%, 95% CI 81.5–100.0%; NPV 100%, 95% CI 96.8–100.0%). In conclusion, we demonstrated a clinically useful way to type HLA-A and HLA-B alleles included in the Spanish pharmacogenomics portfolio using TaqMan assays.

Genetic Parameter Estimates for Growth of Hāpuku (Groper, Polyprion oxygeneios) in Land-Based Aquaculture Using Random Regression Models

Hāpuku (Polyprion oxygeneios) is a promising candidate for aquaculture production in New Zealand. Methods for spawning, juvenile production, and growout to harvest entirely on land, where water quality, pathogens, environmental impacts, and genetic “pollution” can be tightly controlled, have been developed, and genetic improvement to optimise land-based production is the obvious next step. However, estimates of genetic parameters are required to design a rigorous, disciplined, and effective selective breeding program. By using existing data consisting of irregularly spaced repeated measurements of fork length and live body weight collected on wild-collected founders and two generations of captively reared progeny, we evaluated the species’ genetic potential for improvement in growth. We first tested a range of univariate random regression models to identify the best-fitting models for these data. Subsequently, using a bivariate model, we estimated variance components for growth trajectories of fork length and whole body weight. With one to six records available per fish, the best-fitting univariate models included only a fixed effect for contemporary groups and fixed and random genetic third-order Legendre polynomials. More complex models that included full-sib family and/or permanent environmental effects produced unacceptable constrained and/or non-positive-definite solutions. Both traits are moderately heritable at all stages of the growout phase (~0.4–0.5), and the genetic correlation patterns between daily breeding values estimated via the covariance function are different for length and weight. Genetic correlations for length between all pairs of age-specific breeding values are positive and strong (>0.7) and change gradually and smoothly with increasing temporal separation. For weight, these correlations deteriorate more rapidly with increasing time lags between measurements and become negative for some age pairings. We conclude that random regression analyses are a valuable tool for extracting genetic information from irregularly spaced repeated measurements of fish size, speculate that emerging technologies for high-throughput genotyping and phenotyping will add to the value of this approach in the near future, and reason that a breeding strategy that rigorously takes into account the potentially unfavourable genetic correlations between breeding values for weight at some ages will further adapt hāpuku to land-based systems and enhance the profitability commercial-scale production.

A low-cost dpMIG-seq method for elucidating complex inheritance in polysomic crops: a case study in tetraploid blueberry.

Next-generation sequencing (NGS) library construction often requires high-quality DNA extraction, precise adjustment of DNA concentration, and restriction enzyme digestion to reduce genome complexity, which results in increased time and cost in sample preparation and processing. To address these challenges, a PCR-based method for rapid NGS library preparation, named dpMIG-seq, has been developed and proven effective for high-throughput genotyping. However, the application of dpMIG-seq has been limited to diploid and polyploid species with disomic inheritance. In this study, we obtained genome-wide single nucleotide polymorphism (SNP) markers for tetraploid blueberry to evaluate genotyping and downstream analysis outcomes. Comparison of genotyping qualities inferred across samples with different DNA concentrations and multiple bioinformatics approaches revealed high accuracy and reproducibility of dpMIG-seq-based genotyping, with Pearson's correlation coefficients between replicates in the range of 0.91 to 0.98. Furthermore, we demonstrated that dpMIG-seq enables accurate genotyping of samples with low DNA concentrations. Subsequently, we applied dpMIG-seq to a tetraploid F1 population to examine the inheritance probability of parental alleles. Pairing configuration analysis supported the random meiotic pairing of homologous chromosomes on a genome-wide level. On the other hand, preferential pairing was observed on chr-11, suggesting that there may be an exception to the random pairing. Genotypic data suggested quadrivalent formation within the population, although the frequency of quadrivalent formation varied by chromosome and cultivar. Collectively, the results confirmed applicability of dpMIG-seq for allele dosage genotyping and are expected to catalyze the adoption of this cost-effective and rapid genotyping technology in polyploid studies.

Milletomics: a metabolomics centered integrated omics approach toward genetic progression.

Producing alternative staple foods like millet will be essential to feeding ten billion people by 2050. The increased demand for millet is driving researchers to improve its genetic variation. Millets include protein, dietary fiber, phenolic substances, and flavonoid components. Its climate resilience makes millet an appealing crop for agronomic sustainability. Integrative omics technologies could potentially identify and develop millets with desirable phenotypes that may have high agronomic value. Millets' salinity and drought tolerance have been enhanced using transcriptomics. In foxtail, finger, and pearl millet, proteomics has discovered salt-tolerant protein, phytohormone-focused protein, and drought tolerance. Metabolomics studies have revealed that certain metabolic pathways including those involving lignin, flavonoids, phenylpropanoid, and lysophospholipids are critical for many processes, including seed germination, photosynthesis, energy metabolism, and the synthesis of bioactive chemicals necessary for drought tolerance. Metabolomics integration with other omics revealed metabolome engineering and trait-specific metabolite creation. Integrated metabolomics and ionomics are still in the development stage, but they could potentially assist in comprehending the pathway of ionomers to control nutrient levels and biofortify millet. Epigenomic analysis has shown alterations in DNA methylation patterns and chromatin structure in foxtail and pearl millets in response to abiotic stress. Whole-genome sequencing utilizing next-generation sequencing is the most proficient method for finding stress-induced phytoconstituent genes. New genome sequencing enables novel biotechnological interventions including genome-wide association, mutation-based research, and other omics approaches. Millets can breed more effectively by employing next-generation sequencing and genotyping by sequencing, which may mitigate climate change. Millet marker-assisted breeding has advanced with high-throughput markers and combined genotyping technologies.

Development and application of the GenoBaits WheatSNP16K array to accelerate wheat genetic research and breeding

Single-nucleotide polymorphisms (SNPs) are widely used as molecular markers for constructing genetic linkage maps in wheat. Compared with available SNP-based genotyping platforms, a genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology has become the favored genotyping technology because it is less demanding and more cost effective, flexible, and user-friendly. In this study, a new GenoBaits WheatSNP16K (GBW16K) GBTS array was designed using datasets generated by the wheat 660K SNP array and resequencing platforms in our previous studies. The GBW16K array contains 14 868 target SNP regions that are evenly distributed across the wheat genome, and 37 669 SNPs in these regions can be identified in a diversity panel consisting of 239 wheat accessions from around the world. Principal component and neighbor-joining analyses using the called SNPs are consistent with the pedigree information and geographic distributions or ecological environments of the accessions. For the GBW16K marker panel, the average genetic diversity among the 239 accessions is 0.270, which is sufficient for linkage map construction and preliminary mapping of targeted genes or quantitative trait loci (QTLs). A genetic linkage map, constructed using the GBW16K array-based genotyping of a recombinant inbred line population derived from a cross of the CIMMYT wheat line Yaco“S” and the Chinese landrace Mingxian169, enables the identification of Yr27, Yr30, and QYr.nwafu-2BL.4 for adult-plant resistance to stripe rust from Yaco“S” and of Yr18 from Mingxian169. QYr.nwafu-2BL.4 is different from any previously reported gene/QTL. Three haplotypes and six candidate genes have been identified for QYr.nwafu-2BL.4 on the basis of haplotype analysis, micro-collinearity, gene annotation, RNA sequencing, and SNP data. This array provides a new tool for wheat genetic analysis and breeding studies and for achieving durable control of wheat stripe rust.

Assessment of DNA quality for whole genome library preparation

In recent years, more sophisticated DNA technologies for genotyping have enabled considerable progress in various fields such as clinical genetics, archaeogenetics and forensic genetics. DNA samples previously rejected as too challenging to analyze due to low amounts of degraded DNA can now provide useful information. To increase the chances of success with the new methodologies, it is crucial to know the fragment size of the template DNA molecules, and whether the DNA in a sample is mostly single or double stranded. With this knowledge, an appropriate library preparation method can be chosen, and the DNA shearing parameters of the protocol can be adjusted to the DNA fragment size in the sample. In this study, we first developed and evaluated a user-friendly fluorometry-based protocol for estimation of DNA strandedness. We also evaluated different capillary electrophoresis methods for estimation of DNA fragmentation levels. Next, we applied the developed methodologies to a broad variety of DNA samples processed with different DNA extraction protocols. Our findings show that both the applied DNA extraction method and the sample type affect the DNA strandedness and fragmentation. The established protocols and the gained knowledge will be applicable for future sequencing-based high-density SNP genotyping in various fields.

MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test.

Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.

Genomic patterns of selection in morphometric traits across diverse Indian cattle breeds.

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima's D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

Beyond the standard GWAS - a guide for plant biologists.

Classic genome-wide association studies (GWAS) look for associations between individual SNPs and phenotypes of interest. With the rapid progress of high-throughput genotyping and phenotyping technologies, GWAS have become increasingly powerful for detecting genetic determinants and their molecular mechanisms underpinning natural phenotypic variation. However, GWAS frequently yield results with neither expected nor promising loci, nor any significant associations. This is often because associations between SNPs and a single phenotype are confounded, for example with the environment, other traits, or complex genetic structures. Such confounding can mask true genotype-phenotype associations, or inflate spurious associations. To address these problems, numerous methods have been developed that go beyond the standard model. Such advanced GWAS models are flexible and can offer improved statistical power for understanding the genetics underlying complex traits. Despite this advantage, these models have not been widely adopted and implemented compared to the standard GWAS approach, partly because this literature is diverse and often technical. In this review, our aim is to provide an overview of the application and the benefits of various advanced GWAS models for handling complex traits and genetic structures, targeting plant biologists who wish to carry out GWAS more effectively.

Genetic Insights into Age-Related Macular Degeneration.

One of the major causes of vision impairment among elderly people in developed nations is age-related macular degeneration (AMD). The distinctive features of AMD are the accumulation of extracellular deposits called drusen and the gradual deterioration of photoreceptors and nearby tissues in the macula. AMD is a complex and multifaceted disease influenced by several factors such as aging, environmental risk factors, and a person's genetic susceptibility to the condition. The interaction among these factors leads to the initiation and advancement of AMD, where genetic predisposition plays a crucial role. With the advent of high-throughput genotyping technologies, many novel genetic loci associated with AMD have been identified, enhancing our knowledge of its genetic architecture. The common genetic variants linked to AMD are found on chromosome 1q32 (in the complement factor H gene) and 10q26 (age-related maculopathy susceptibility 2 and high-temperature requirement A serine peptidase 1 genes) loci, along with several other risk variants. This review summarizes the common genetic variants of complement pathways, lipid metabolism, and extracellular matrix proteins associated with AMD risk, highlighting the intricate pathways contributing to AMD pathogenesis. Knowledge of the genetic underpinnings of AMD will allow for the future development of personalized diagnostics and targeted therapeutic interventions, paving the way for more effective management of AMD and improved outcomes for affected individuals.

Plasmodium vivax populations in the western Greater Mekong Subregion evaluated using a genetic barcode.

An improved understanding of the Plasmodium vivax populations in the Great Mekong Subregion (GMS) is needed to monitor the progress of malaria elimination. This study aimed to use a P. vivax single nucleotide polymorphism (SNP) barcode to evaluate the population dynamics and explore the gene flow among P. vivax parasite populations in the western GMS (China, Myanmar and Thailand). A total of 315 P. vivax patient samples collected in 2011 and 2018 from four regions of the western GMS were genotyped for 42 SNPs using the high-throughput MassARRAY SNP genotyping technology. Population genetic analysis was conducted to estimate the genetic diversity, effective population size, and population structure among the P. vivax populations. Overall, 291 samples were successfully genotyped at 39 SNPs. A significant difference was observed in the proportion of polyclonal infections among the five P. vivax populations (P = 0.0012, Pearson Chi-square test, χ2 = 18.1), with western Myanmar having the highest proportion (96.2%, 50/52) in 2018. Likewise, the average complexity of infection was also highest in western Myanmar (1.31) and lowest in northeast Myanmar (1.01) in 2018. The older samples from western China in 2011 had the highest pairwise nucleotide diversity (π, 0.388 ± 0.046), expected heterozygosity (He, 0.363 ± 0.02), and the largest effective population size. In comparison, in the neighboring northeast Myanmar, the more recent samples in 2018 showed the lowest values (π, 0.224 ± 0.036; He, 0.220 ± 0.026). Furthermore, the 2018 northeast Myanmar parasites showed high and moderate genetic differentiation from other populations with FST values of 0.162-0.252, whereas genetic differentiation among other populations was relatively low (FST ≤ 0.059). Principal component analysis, phylogeny, and STRUCTURE analysis showed that the P. vivax population in northeast Myanmar in 2018 substantially diverged from other populations. Although the 42 SNP barcode is a valuable tool for tracking parasite origins of worldwide parasite populations, a more extended barcode with additional SNPs is needed to distinguish the more related parasite populations in the western GMS.

Exploring crop genomics: role of molecular markers in identifying genetic diversity and characterization

and facilitating germplasm characterization for crop improvement. Understanding and harnessing crop genetic diversity are paramount with the escalating demand for sustainable agricultural practices and the need to address global food security challenges. Molecular markers have emerged as powerful tools enabling precise identification, characterization, and utilization of genetic resources for crop enhancement. The paper provides an in-depth analysis of various molecular marker techniques, encompassing DNA-based markers such as single nucleotide polymorphisms (SNPs), simple sequence repeats (SSRs), and amplified fragment length polymorphisms (AFLPs), as well as RNA-based markers like expression sequence tags (ESTs) and microRNAs. By elucidating genetic relationships, population structure, and phylogenetic analysis, these markers facilitate the conservation and utilization of crop germplasm. Moreover, the review highlights recent advancements in high-throughput genotyping technologies and bioinformatics tools, which have revolutionized crop genomic research. These advancements enable comprehensive genome-wide analyses, accelerating breeding efforts for the development of improved crop varieties with enhanced traits such as yield, quality, and stress tolerance. Integration of molecular marker-assisted selection (MAS) into breeding programs further enhances the efficiency and precision of crop improvement strategies. Despite the significant contributions of molecular markers to crop genomics, challenges persist. The review addresses these challenges and discusses potential solutions, emphasizing the importance of collaborative efforts, data sharing, and interdisciplinary research endeavors. By overcoming these hurdles, the agricultural community can fully harness the potential of genomics for sustainable crop improvement, thus addressing global food security challenges in the face of changing environmental conditions and population growth. In conclusion, understanding the role of molecular markers in crop genomics is crucial for optimizing breeding strategies, conserving genetic resources, and developing resilient crop varieties to meet the demands of a growing population and ensure food security in a changing world.

Clinical association of FKBP5 gene in systemic lupus erythematosus patients

This study aimed to determine the impact of genetic variation in FKBP prolyl isomerase 5 (FKBP5) on mRNA and protein expression levels, on glucocorticoids (GC) responsiveness and its clinical association in systemic lupus erythematosus (SLE). The genotyping of the FKBP5-rs1360780 was performed using TaqMan SNP genotyping technology. The mRNA expression level in peripheral blood mononuclear cells was evaluated using realtime-PCR. The immunohistochemistry staining was used to analyze FKBP5 protein expression level in renal biopsies. A risk association was revealed between rs1360780 > C allele and SLE pathogenesis. We found an up-regulation in the FKBP5 mRNA expression level in SLE patients with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score ≤ 6 compared to patients with SLEDAI score > 6. Our results revealed an altered renal cell expression of FKBP5 protein in patients with lupus nephritis compared to control samples. However, no association was observed between the FKBP5 and the response to GC treatment. Our study is the first to demonstrate a link between SLE and FKBP5 gene, in terms of mRNA and protein expression levels, and the variant of the SNP rs1360780 in SLE, suggesting a susceptibility role of rs1360780 > C.

Quantifying variations associated with dental caries reveals disparity in effect allele frequencies across diverse populations

BackgroundDental caries (DC) is a multifaceted oral condition influenced by genetic and environmental factors. Recent advancements in genotyping and sequencing technologies, such as Genome-Wide Association Studies (GWAS) have helped researchers to identify numerous genetic variants associated with DC, but their prevalence and significance across diverse global populations remain poorly understood as most of the studies were conducted in European populations, and very few were conducted in Asians specifically in Indians.AimThis study aimed to evaluate the genetic affinity of effect alleles associated with DC to understand the genetic relationship between global populations with respect to the Indian context.MethodologyThis present study used an empirical approach in which variants associated with DC susceptibility were selected. These variants were identified and annotated using the GWAS summary. The genetic affinity was evaluated using Fst.ResultsThe effect of allele frequencies among different populations was examined, revealing variations in allele distribution. African populations exhibited higher frequencies of specific risk alleles, whereas East Asian and European populations displayed distinct profiles. South Asian populations showed a unique genetic cluster.ConclusionOur study emphasises the complex genetic landscape of DC and highlights the need for population-specific research as well as validation of GWAS-identified markers in Indians before defining them as established candidate genes.

IGHV Mutational Status in a Cohort of Bulgarian CLL Patients: High Unmutated CLL Prevalence in North-East Bulgaria.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in adults. One of the best established CLL prognostic markers is the somatic hypermutational status of the IGHV gene which is a part of the immunoglobulin heavy chain variable region. Technology for IGHV genotyping has been optimized and has been applied in routine diagnostics for the first time in Bulgaria. A total of 105 patients with CLL from different Bulgarian regions were tested. IGHV mutational status was determined by Sanger sequencing on total genomic DNA (gDNA) or RNA extracted from mononuclear cells. All sequencing profiles were analyzed with the IMGT/V-QUEST tool. Within the course of the analysis a high percentage of IGHV unmutated status was established in the Varna district on the Black Sea (Northeast Bulgaria). In addition, the IGHV genotyping performed on gDNA revealed a rare case with multiple rearrangements. The present data from IGHV genotyping will help in choosing the proper treatment for the benefit of Bulgarian CLL patients.

231 Anomaly detection for breed purity analysis

Abstract Recent advancements in genotyping technologies have revolutionized our ability to estimate breed composition in pigs. The classic compositional regression used for this purpose requires a multibreed panel to detect crossbreeding and its application is limited to breed in the panel. In this study, we presented a protocol based on classic anomaly detection algorithms that use single breed panels to detect crossbreeding. By utilizing these methods, we aim to identify and assess breed outliers within large pig genotype panels. The dataset consisted of 44,616 SNP genotypes recorded in purebred Large White, Landrace, Duroc, Pietrain (n = 2,000 from each breed) and 1,010 crossbred pigs (50% Pietrain, 25% Large White, 25% Landrace). Large White pigs (n = 1,500) were randomly selected as reference set. The remaining 500 Large White pigs, other purebred pigs, crossbred pigs, and simulated crossbred animals of known proportion of Large White segments were test sets. We applied two anomaly detection methods: 1) Principal Component Analysis (PCA) and 2) deep learning autoencoders (AE). For PCA the top 600 eigen vectors capturing 85% cumulative proportion of variance in the reference set were selected. Test set genotypes were projected onto the principal components and then they were reconstructed to the full genotype set. For AE, we used a previously published sparse convolutional denoising autoencoder (SCDA) that consisted of two main components: an encoder and a decoder. The encoder compressed the genotypes to 128 components and the decoder reconstructed the set to the original 44,616 SNP. We utilized the model trained in the reference set to reconstruct test set genotypes of the same breed, of alternative breed and of crossbred. Accuracy of reconstruction of genotypes was computed using correlation and mean square error. The proportion of rejected genotypes was used to assess the ability of anomaly detection algorithms to detect crossbreeding. For each criterion, the threshold to reject genotypes was selected as the value at which 5% of Large White animals were rejected (specificity = 0.95). Results are summarized in (Table 1). PCA provided better properties to detect genomic outliers compared with AE. PCA anomaly detection was able to detect purebred individuals with probability 1.0. It also detected over 90% of crossbred individuals that were 75% Large Whites. Crossbred with a lower proportion of Large White genomic segments were detected with P < 0.5. Anomaly detection algorithms can be used to detect genomic outliers but with relatively low sensitivity. More work is needed to optimize these algorithms for breeding applications.

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets.

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

Association of histamine-N-methyl transferase gene polymorphisms with carnosine content in red-brown Korean native chickens.

Carnosine and anserine affect the meat flavor. The contents of carnosine and anserine in meat are affected by genetic and environmental factors. This study aimed to discover the single-nucleotide polymorphisms (SNPs) in the histamine-N-methyl transferase (HNMT) and histamine-N-methyl transferase-like (HNMT-like) genes and to associate them with the content of carnosine and anserine in Korean native chickenred brown line (KNC-R). This study used a total of 384 birds (males, n = 192; females, n = 192) aged 10 weeks old, for genotyping HNMT and HNMT-like genes. One synonymous SNP (rs29009298C/T) of the HNMT gene was genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods whereas four missense SNPs (rs734406537G/A; rs736514667A/G; rs15881680G/A and rs316765035T/C) of the HNMT gene, and one missense SNP rs737657949A/C of the HNMT-like gene were genotyped by PCR allele competitive extension (PACE) genotyping technology. Two-way analysis of variance of the R program was used to associate HNMT genotypes with the contents of carnosine and anserine in KNC-R chickens. There were significant associations (p<0.05) between the genotypes of the synonymous SNP:rs29009298C/T, missense SNP rs736514667A/G of the HNMT gene and the content of carnosine in KNC-Rs. This study also reported the sex effect on the carnosine content, where females had more content of carnosine compared to that of male KNC-R. Two SNPs (synonymous: rs735769522C/T) and missense: rs736514667A/G) in the HNMT gene might be used as genetic markers in the selection and breeding of chickens with better taste and high-flavored meat.

KASP: a high-throughput genotyping system and its applications in major crop plants for biotic and abiotic stress tolerance.

Advances in plant molecular breeding have resulted in the development of new varieties with superior traits, thus improving the crop germplasm. Breeders can screen a large number of accessions without rigorous and time-consuming phenotyping by marker-assisted selection (MAS). Molecular markers are one of the most imperative tools in plant breeding programmes for MAS to develop new cultivars possessing multiple superior traits. Single nucleotide polymorphisms (SNPs) are ideal for MAS due to their low cost, low genotyping error rates, and reproducibility. Kompetitive Allele Specific PCR (KASP) is a globally recognized technology for SNP genotyping. KASP is an allele-specific oligo extension-based PCR assay that uses fluorescence resonance energy transfer (FRET) to detect genetic variations such as SNPs and insertions/deletions (InDels) at a specific locus. Additionally, KASP allows greater flexibility in assay design, which leads to a higher success rate and the capability to genotype a large population. Its versatility and ease of use make it a valuable tool in various fields, including genetics, agriculture, and medical research. KASP has been extensively used in various plant-breeding applications, such as the identification of germplasm resources, quality control (QC) analysis, allele mining, linkage mapping, quantitative trait locus (QTL) mapping, genetic map construction, trait-specific marker development, and MAS. This review provides an overview of the KASP assay and emphasizes its validation in crop improvement related to various biotic and abiotic stress tolerance and quality traits.