Classification Of Genotypes Research Articles

Use of continuous genotypes for genomic prediction in sugarcane.

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

Regional Genetic Heterogeneity Among Patients with Pyruvate Kinase Deficiency

Background: Pyruvate kinase (PK) deficiency is a rare, congenital hemolytic anemia caused by homozygous or compound heterozygous mutations in the PKLR gene, with a prevalence of 3.2-8.5 individuals per million in Western populations. Over 350 pathogenic variants in the PKLR gene have been reported, with the majority being missense substitutions. Although their frequency differs across regions and populations, the geographic distribution of PKLR variants has not yet been fully investigated. Aim: To evaluate the PKLR genotype distribution by region among patients (pts) with PK deficiency in the merged real-world database of the Peak Registry (NCT03481738) and PK Deficiency Natural History Study (NHS; NCT02053480). Methods: The NHS was a global, longitudinal, observational study that enrolled pts with PK deficiency from 2014-2017 (study completed May 2020). The Peak Registry is an ongoing global registry which was initiated in 2018 to build on the NHS with expanded pt recruitment and geographic reach, broader data collection, and longer pt follow-up. Comparable data from these two studies were integrated to form a merged database providing enlarged sample size and prolonged follow-up from pts who participated in both studies. This analysis included pts with available genotype data from the NHS and Peak Registry, including those who were siblings; a large Amish cohort of pts carrying the homozygous p.R479H variant was excluded to avoid reporting bias. Genotype classifications were aligned between the two studies. Data were extracted from the merged database of NHS and Peak (data cutoff date: 13May2022) and summarized descriptively by region. Regions were defined as: Northern (N) Europe (Denmark, Ireland, Netherlands, UK); Central (C) Europe (Czech Republic, Germany, Switzerland); Southern (S) Europe (France, Italy, Spain, Portugal); Asia (S Korea, Thailand, Turkey); N America (Canada, US). Results: 362 pts were included in the analysis (N Europe, N=59; S Europe, N=62; C Europe, N=51; Asia, N=23; N America, N=167), 97 (27%) of whom were siblings. Most pts (72%) had compound heterozygous PKLR variants (Table). Homozygous mutations were most common among pts from Asia (15/23; 65%) and were least common among pts from S Europe (8/62; 13%). Almost half of pts from N Europe (49%) had a compound heterozygous missense/missense (M/M) genotype, while a similar proportion from S Europe (44%) had a compound heterozygous missense/non-missense (M/NM) genotype. A total of 724 PKLR variants were identified, including 197 unique variants, 26 of which were newly identified. Most were missense mutations (76%), followed by nonsense (10%), splice site (7%), and frameshift (5%) mutations. The majority of mutated alleles in the cohort were single nucleotide variants (90%), followed by small deletions (4%), large deletions (4%), and insertions (2%). The amino acid variant with the highest observed frequency was p.Arg510Gln, both overall (158/724; 22%), and in N Europe (31/118; 26%), C Europe (27/102; 26%), and N America (92/334; 28%), but it was not observed in Asia (0/46; 0%). The second most common variant overall, p.Arg486Trp (67/724; 9%), was the most frequent in S Europe (31/124; 25%). When stratified by genotype, p.Arg510Gln was the most common missense mutation in homozygous pts in each region (40-67%) except in Asia. In Asia, p.Thr157Ala, p.Lys541Asn, and p.Arg194Pro were the most common mutations overall (4/46; 9%), and also the most common missense mutations in homozygous pts (4/28; 14%). One hundred and seventy-four unique variants were observed in compound heterozygotes. In S Europe (27/102; 26%) and Asia (2/10; 20%), p.Arg486Trp was the most frequently observed missense mutation in compound heterozygote pts. Overall and in all regions except for Asia (where it was absent), p.Glu241Ter was the most common non-missense mutation in compound heterozygote pts (32/254; 13%); this was p.Gly540Ter (3/6; 50%) in Asia. Conclusions: This analysis, which used the largest real-world data source for pts with PK deficiency, confirms the genetic heterogeneity of the disease, showing that the mutation frequency in PKLR varies substantially based on geographic distribution and that the identification of novel genetic variants is relatively common in PK deficiency.

A Genotype Validated Bimodal Method for the Large-Scale Identification and Phenotyping of Persons with Sickle Cell Disease Using Electronic Health Record Data

Introduction Clinical trials for using hematopoietic stem cell transplant (HSCT) with gene therapies to cure sickle cell disease (SCD) began in 2019. The Food and Drug Administration (FDA) requires that 1) all gene therapy participants receive at least 15 years of ongoing surveillance and 2) their outcomes be compared to a contemporaneous cohort that did not receive gene therapy. Previously we developed an automated contemporaneous cohort of children and adults with SCD from electronic health record (EHR) data (Cronin RM et al. 2023, Blood Adv). Our work was built upon previously explored algorithms utilizing ICD codes and laboratory data to identify persons with sickle cell anemia (SCA), a severe form of SCD (Singh et al. 2018, Blood Adv). Still, our algorithm additionally classifies other predicted genotypes. However, we could not test the SCD phenotypes against the SCD genotypes due to the absence of beta-globin gene sequence data. Here we tested the hypothesis that our Vanderbilt-EHR algorithm for identifying a contemporaneous cohort at Vanderbilt can classify their predicted genotype compared to beta-globin gene sequencing, the gold standard for SCD diagnosis, in the cohort. We subsequently tested the Vanderbilt-EHR algorithm in an established cohort of children from St. Jude Children's Research Hospital that have been deeply phenotyped. Methods We applied the Vanderbilt-EHR algorithm to a cohort of 275 samples from Vanderbilt's biorepository of DNA, BioVU, linked to de-identified medical records in a data warehouse called the Synthetic Derivative. These 275 samples were previously submitted for whole genome sequencing as part of the Genetic Variation of Heart, Lung, and Kidney Disease in Sickle Cell Disease: Pre- and Post-Curative Therapies TOPMed project (HLK-SCD phs002617). The returned genotypes were validated using the hemoglobin variant database HbVar (Giardine BM et al. 2021, Nucleic Acids Res) (Table 1). We applied the hemoglobinopathy genotype classification portion of the algorithm to the Sickle Cell Clinical Research and Intervention Program (SCCRIP) cohort from St. Jude Children's Research Hospital, which includes putative genotype diagnoses derived from a comprehensive evaluation of clinical data for each member. We performed statistical analyses of the identification and classification results of the Vanderbilt cohort and the SCCRIP cohort's classification results. Results Of the 275 genotyped samples, the algorithm correctly predicted 255 SCD cases and 10 non-SCD cases. There were three cases predicted to be false positives; however, a comprehensive analysis showed that all have laboratory values indicative of SCA (high hemoglobin S levels >60%, absent or negligible hemoglobin A levels, elevated reticulocytes, and low mean corpuscular volume) suggesting that there may be a genotyping error. The remaining 7 cases were either indeterminate in the prediction or in the genotype (Table 1). Given the small number of indeterminate results, we performed a sensitivity analysis to identify the range of performance for SCD identification. The method performed very well with over 98% sensitivity and over 96% positive predictive value (PPV) (Table 2). The lower specificity and negative predictive value (NPV) could be largely attributed to the disproportionately high number of SCD cases in the dataset compared to non-SCD cases. The SCD classification algorithm performed similarly in both the VUMC and SCCRIP cohorts (Table 2). The algorithm performs best in identifying SC and SCA types of SCD with nearly 100% accuracy for SC and over 94% accuracy for SCA. Transfusions can confound the diagnosis of SCA vs. S beta thalassemia +, but the latter has a much lower prevalence consistent with our results (Table 2). Discussion and Conclusions Larger SCD cohorts will be identified using these approaches in singular EHR databases and in de-identified data warehouses that EHR companies have developed by aggregating data across multiple EHR systems (e.g., EPIC Cosmos and Cerner Real-World Data). The ongoing need for contemporaneous comparison cohorts of persons with SCD to understand outcomes related to treatment and phenotype can be greatly assisted by accurate automated approaches. The Vanderbilt-EHR algorithm is the first to comprehensively phenotype SCD and other hemoglobinopathies involving variant hemoglobin beta alleles.

Screening of Eucalyptus camaldulensis clones for enhancing productivity and sustainability in problematic soils of Southern India

Among the various challenges faced by eucalyptus plantations in the Indian pulp industry, one of them is the development of resilient clones adaptable to saline soils. Large genetic diversity exists in E. camaldulensis because of high out-crossing rates and is expected to pass traits into the new developed clones. We used a salt injury score, survival percentage and proline content as a screening criterion to select new clones tolerant to high salt levels in soils. Proline has been identified as a beneficial solute in plants under stress caused by high salinity. Three months old clones of eleven new eucalyptus genotypes were transplanted in pots with free-draining coarse river sand. Four treatments comprising control, high Salt, High Salt + High pH and High pH were arranged in a randomised block design. Common salt (NaCl) was used for regulating salt concentrations while sodium bicarbonate (NaHCO3) was used for regulating the pH of the solutions. NaCl concentrations were increased every third day until the final concentration of 150 mM was reached. Similarly, pH of the solutions was also gradually increased till final pH of 10 was attained. Each treatment solution was delivered through automated drip delivery. Salt injury scores based on symptoms were positively correlated (0.812) with survival percentage and proline content. Eucalyptus genotypes were classified into five salt tolerant groups: tolerant (T), moderately tolerant (MT), moderately sensitive (MS), sensitive (S), and highly sensitive (HS). The mean values of proline content in leaves were different among the five groups, indicating that the proline content is a reliable marker for identifying salt tolerance. Proline contents in stressed seedlings were significantly different between the tolerant and highly sensitive groups, but the difference among the intermediate groups (MT, MS and S) was not significant. As the stress levels of salt and pH were increased, the sensitive hybrid clone tested showed very poor growth, which was also negatively correlated with the proline content in its leaves. Combination of high salt and high pH was also found to be correlated with reduced proline content, poor growth and survival among sensitive genotypes. In addition to the commonly used sodium and pH induced symptoms, proline content is suggested to be another useful criterion to differentiate salt-tolerant from salt-sensitive genotypes. This study helped in classification of genotypes with different levels of salt and pH tolerance, which may be used for deployment of genotypes according to changing edaphic conditions for sustainability of eucalyptus plantations.

The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models

Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-‘2011’, ‘Miraj-‘08’, and ‘Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.

A Re-Evaluation of African Swine Fever Genotypes Based on p72 Sequences Reveals the Existence of Only Six Distinct p72 Groups.

The African swine fever virus (ASFV) is currently causing a world-wide pandemic of a highly lethal disease in domestic swine and wild boar. Currently, recombinant ASF live-attenuated vaccines based on a genotype II virus strain are commercially available in Vietnam. With 25 reported ASFV genotypes in the literature, it is important to understand the molecular basis and usefulness of ASFV genotyping, as well as the true significance of genotypes in the epidemiology, transmission, evolution, control, and prevention of ASFV. Historically, genotyping of ASFV was used for the epidemiological tracking of the disease and was based on the analysis of small fragments that represent less than 1% of the viral genome. The predominant method for genotyping ASFV relies on the sequencing of a fragment within the gene encoding the structural p72 protein. Genotype assignment has been accomplished through automated phylogenetic trees or by comparing the target sequence to the most closely related genotyped p72 gene. To evaluate its appropriateness for the classification of genotypes by p72, we reanalyzed all available genomic data for ASFV. We conclude that the majority of p72-based genotypes, when initially created, were neither identified under any specific methodological criteria nor correctly compared with the already existing ASFV genotypes. Based on our analysis of the p72 protein sequences, we propose that the current twenty-five genotypes, created exclusively based on the p72 sequence, should be reduced to only six genotypes. To help differentiate between the new and old genotype classification systems, we propose that Arabic numerals (1, 2, 8, 9, 15, and 23) be used instead of the previously used Roman numerals. Furthermore, we discuss the usefulness of genotyping ASFV isolates based only on the p72 gene sequence.

The impact and complete genome characterisation of viruses involved in outbreaks of gastroenteritis in a farrow-to-finish holding

Viral enteric pathogens continuously burden intensive pig farming, causing gastrointestinal diseases of epidemic and endemic nature. The present study investigated two diarrhoea outbreaks on a large farrow-to-finish holding and subsequent circulation of outbreak-related enteric viruses. These viruses were characterised by whole genome sequencing, and statistical evaluation of the impact on specific production metrics was performed. The results provided evidence that the Porcine epidemic diarrhoea virus–swine enteric coronavirus (PEDV–SeCoV) S gene recombinant strain was responsible for the first outbreak, whilst Rotavirus A (RVA) in a mixed infection with Rotavirus B (RVB) and porcine kobuvirus (PKV) probably caused the second diarrhoea outbreak. Whole genome characterisation revealed a porcine origin of all viruses involved and significant heterogeneity of RVB strain, proposing four novel genotypes and changes in RVB VP1 genotype classification. The statistical evaluation confirmed only a minor disturbance in the number of weaned pigs per sow, with statistical forecasting showing positive trends. A follow-up study corroborated the endemicity of RVA and PKV, in contrast to PEDV–SeCoV. Punctual, comprehensive and timely investigation of diarrhoea outbreaks is a prerequisite for applying adequate pig health and biosecurity management. Calculating such outbreaks' impact on production metrics can potentially shape future decisions on management improvements.

Assessment of variability parameters and diversity of panicle architectural traits associated with yield in rice (Oryza sativa L.)

The rice panicle, a pivotal reproductive structure, signifies the transition from vegetative to reproductive growth in plants. Comprising components such as the rachis, primary and secondary branches, seed quantities and branch lengths, panicle architecture profoundly influences grain production. This study delves into the diversity of panicle architecture traits and scrutinizes variability parameters across 69 distinct rice genotypes. Our findings underscore substantial variations in panicle architecture traits among genotypes. Particularly noteworthy are traits with the highest coefficient of variation (CV%), encompassing the count of secondary branches, single plant yield, productive tillers per plant, seeds per secondary branch and panicle weight. Correlation analysis reveals robust positive connections between panicle weight, the number of filled grains per panicle, 1000-grain weight and single plant yield. The number of secondary branches exhibits the most substantial phenotypic coefficient of variation (PCV%) at 47.14%, accompanied by a genotypic coefficient of variation (GCV%) of 43.57%. Traits such as days to 50% flowering, plant height and number of filled grains per panicle manifest high heritability (97.04%, 91.24% and 76.22% respectively) and notable genetic advancement (23.11%, 39.62% and 47.49%). The principal component analysis identifies the primary component (PC1) as the principal contributor to variance. Biplot analysis accentuates positive correlations between attributes like the number of filled grains per panicle, panicle length, plant height, primary branch count, panicle weight, seeds per primary branch and the number of secondary branches with single plant yield. By employing Mahalanobis D2 statistics, the classification of genotypes into 6 distinct clusters reveals clusters III and IV as distinguished by their significant inter-cluster and intra-cluster distances. This comprehensive analysis unveils the potential for harnessing panicle architecture traits to enhance grain production and advances our comprehension of intricate relationships within diverse rice genotypes.

Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

Deep learning techniques play an important role in plant phenotype research, due to their powerful data processing and modeling capabilities. Multi-task learning has been researched for plant phenotype analysis, which can combine different plant traits and allow for a consideration of correlations between multiple phenotypic features for more comprehensive analysis. In this paper, an intelligent and optimized multi-task learning method for the phenotypic analysis of Arabidopsis thaliana is proposed and studied. Based on the VGG16 network, hard parameter sharing and task-dependent uncertainty are used to weight the loss function of each task, allowing parameters associated with genotype classification, leaf number counting, and leaf area prediction tasks to be learned jointly. The experiments were conducted on the Arabidopsis thaliana dataset, and the proposed model achieved weighted classification accuracy, precision, and Fw scores of 96.88%, 97.50%, and 96.74%, respectively. Furthermore, the coefficient of determination R2 values in the leaf number and leaf area regression tasks reached 0.7944 and 0.9787, respectively.

Estimation of Soybean Yield by Combining Maturity Group Information and Unmanned Aerial Vehicle Multi-Sensor Data Using Machine Learning

Accurate and rapid estimation of the crop yield is essential to precision agriculture. Critical to crop improvement, yield is a primary index for selecting excellent genotypes in crop breeding. Recently developed unmanned aerial vehicle (UAV) platforms and advanced algorithms can provide powerful tools for plant breeders. Genotype category information such as the maturity group information (M) can significantly influence soybean yield estimation using remote sensing data. The objective of this study was to improve soybean yield prediction by combining M with UAV-based multi-sensor data using machine learning methods. We investigated three types of maturity groups (Early, Median and Late) of soybean, and collected the UAV-based hyperspectral and red–green–blue (RGB) images at three key growth stages. Vegetation indices (VI) and texture features (Te) were extracted and combined with M to predict yield using partial least square regression (PLSR), Gaussian process regression (GPR), random forest regression (RFR) and kernel ridge regression (KRR). The results showed that (1) the method of combining M with remote sensing data could significantly improve the estimation performances of soybean yield. (2) The combinations of three variables (VI, Te and M) gave the best estimation accuracy. Meanwhile, the flowering stage was the optimal single time point for yield estimation (R2 = 0.689, RMSE = 408.099 kg/hm2), while using multiple growth stages produced the best estimation performance (R2 = 0.700, RMSE = 400.946 kg/hm2). (3) By comparing the models constructed by different algorithms for different growth stages, it showed that the models built by GPR showed the best performances. Overall, the results of this study provide insights into soybean yield estimation based on UAV remote sensing data and maturity information.

Classification of genotypes based on the VP1 gene of feline calicivirus and study of cross-protection between different genotypes.

Feline calicivirus (FCV) causes upper respiratory tract diseases and even death in cats, thereby acting as a great threat to feline animals. Currently, FCV prevention is mainly achieved through vaccination, but the effectiveness of vaccination is limited. In this study, 105 FCV strain VP1 sequences with clear backgrounds were downloaded from the NCBI and subjected to a maximum likelihood method for systematic evolutionary analysis. Based on the genetic analysis results, FCV-positive sera were prepared using SPF mice and Chinese field cats as target animals, followed by a cross-neutralization assay conducted on the different genotype strains and in vivo challenge tests were carried out to further verify with the strain with best cross-protection effect. The results revealed that FCV was mainly divided into two genotypes: GI and GII. The GI genotype strains are prevalent worldwide, but all GII genotype strains were isolated from Asia, indicating a clear geographical feature. This may form resistance to FCV prevention in Asia. The in vitro neutralization assay conducted using murine serum demonstrated that the cross-protection effect varied among strains. A strain with broad-spectrum neutralization properties, DL39, was screened. This strain could produce neutralizing titers (10 × 23.08-10 × 20.25) against all strains used in this study. The antibody titers against the GI strains were 10 × 23.08-10 × 20.5 and those against the GII strains were 10 × 20.75-10 × 20.25. Preliminary evidence suggested that the antibody titer of the DL39 strain against GI was higher than that against GII. Subsequent cross-neutralization assays with cat serum prepared with the DL39 strain and each strain simultaneously yielded results similar to those described above. In vivo challenge tests revealed that the DL39 strain-immunized cats outperformed the positive controls in all measures. The results of several trials demonstrated that strain DL39 can potentially be used as a vaccine strain. The study attempted to combine the genetic diversity and phylogenetic analysis of FCV with the discovery of potential vaccines, which is crucial for developing highly effective FCV vaccines.

Association Between Genotype and Phenotype Severity in ABCA4-Associated Retinopathy

ABCA4-associated retinopathy is a common inherited retinal disease, and its phenotype spans from late-onset macular dystrophy to extensive cone-rod degeneration. Over 2000 disease-causing variants in the ABCA4 gene have been identified. To investigate genotype-phenotype correlations in ABCA4-associated retinopathy. This cohort study took place at a single referral center for inherited retinal diseases in Italy. Data were prospectively acquired from January 2015 to June 2022. Patients diagnosed with an inherited retinal disease related to biallelic ABCA4 variants were included for analysis. Genotype, classified into 4 groups according to the presence of the (1) p.Gly1961Glu allele, (2) a hypomorphic allele, (3) at least 1 moderate variant (moderate genotypes), or (4) 2 biallelic severe variants (severe genotypes). Total decreased autofluorescence (TDAF) and definitely decreased autofluorescence (DDAF) areas, inner and outer retinal volumes, and the respective progression rate. A total of 71 patients (median [IQR] age, 34 [22.4-47.2] years; 40 [56%] female) were included in the study, and 54 (76%) were followed up for a median (IQR) of 3.5 (1.6-4.7) years. Compared with moderate genotypes, those with the p.Gly1961Glu allele had smaller TDAF lesions by 61% (95% CI, -78% to -33%; P < .001) and DDAF lesions by 77% (95% CI, -93% to -18%; P = .02), along with slower growth rates for both TDAF (0.05 mm/y; 95% CI, 0.01-0.07; P < .001) and DDAF (0.06 mm/y; 95% CI, 0-0.12; P = .004). Hypomorphic alleles were associated with a thicker inner (+0.19 mm3; 95% CI, +0.02 to +0.36; P = .03) and outer retinal volume (+0.16 mm3; 95% CI, +0.03 to +0.28; P = .01) compared with moderate genotypes as well as a slower TDAF growth rate (0.05 mm/y; 95% CI, 0.01-0.08; P = .007). Severe genotypes had a 7-fold larger TDAF area (95% CI, 3.4-14.7; P < .001) and 11-fold larger DDAF area (95% CI, 2.9-42.1; P < .001) compared with moderate genotypes, along with faster growth rates estimated at 0.16 mm/y for TDAF (95% CI, 0.12-0.20; P < .001) and 0.17 mm/y for DDAF (95% CI, 0.12-0.23; P < .001). In this study of ABCA4-associated retinopathy, a 4-tier classification of genotypes was found to capture substantial variation in disease phenotype severity. These findings could prove beneficial for the prognostication of patients and warrant consideration of genotype in the design of future clinical trials.

Application of Near-Infrared Reflectance Spectroscopy for Predicting Damage Severity in a Diverse Panel of Tectona grandis Caused by Ceratocystis fimbriata.

Tectona grandis Linn., also known as teak, is a highly valued species with adaptability to a wide range of climatic conditions and high tolerance to soil variations, making it an attractive option for both commercial and conservation purposes. In this sense, the classification of cultivated teak genotypes is crucial for both breeding programs and conservation efforts. This study examined the relationship between traits related to damage in the stem of teak plants caused by Ceratocystis fimbriata (a soil-borne pathogen that negatively impacts the productivity of teak plantations) and the spectral reflectance of 110 diverse clones, using near-infrared spectroscopy (NIRS) data and partial least squares regression (PLSR) analysis. Cross-validation models had R2 = 0.894 (ratio of standard error of prediction to standard deviation: RPD = 3.1), R2 = 0.883 (RPD = 2.7), and R2 = 0.893 (RPD = 2.8) for predicting stem lesion area, lesion length, and severity of infection, respectively. Teak genotypes (clones) can benefit from the creation of a calibration model utilizing NIRS-generated data paired with PLSR, which can effectively screen the magnitude of damage caused by the fungus. Overall, while the study provides valuable information for teak breeding and conservation efforts, a long-term perspective would be essential to evaluate the sustainability of teak genotypes over various growth stages and under continuous pathogen pressure.

Morphological Description of Chickpea (Cicer arietanum L) Genotypes Using DUS Characterization

The cultivated chickpea (Cicer arietinum) holds great importance as a pulse crop in India. The identification and classification of diverse genotypes are crucial for implementing effective strategies to improve this crop. This study was conducted to get a comprehensive morphological characterization of desi chickpea genotypes using the DUS (Distinctness, Uniformity, and Stability) descriptors suggested by the Protection of Plant Varieties and Farmer's Rights Authority, Government of India, in 2018. Environmental conditions, such as temperature, light, humidity, and nutrient availability, can influence plant variability. Different environments impose selective pressures on plants resulting in variability within plant populations. The objective of the investigation was to identify and classify diverse chickpea genotypes based on 17 different qualitative traits observed in a field experiment. Among the 17 DUS traits only one trait exhibited a consistent phenotype (monomorphic), six traits displayed two distinct phenotypes (dimorphic), nine traits exhibited three distinct phenotypes (trimorphic), and only one trait showed more than three phenotypic variations (polymorphic) among all the chickpea genotypes studied. This indicates the presence of significant genetic variability within the chickpea germplasm, offering the potential for assigning different morphological profiles for varietal identification and characterization. In particular, for features like seed and foliar colour, pod size, leaflet size, and seed shape, it was found that a high level of diversity within the chickpea germplasm using Shannon's diversity indices. The characterization of these genotypes enabled the development of distinct profiles for each line, facilitating their identification and evaluation as elite chickpea lines.

Phylogenotyping of infectious bursal disease virus in Vietnam according to the newly unified genotypic classification scheme.

Since 1987, infectious bursal disease virus (IBDV) has circulated and evolved in Vietnam, but little is known about the genotypes present. IBDV samples were collected in 1987, 2001-2006, 2008, 2011, 2015-2019, and 2021 in 18 provinces. We conducted phylogenotyping analysis based on an alignment of 143 VP2-HVR (hypervariable region) sequences from 64 Vietnamese isolates (26 previous and 38 additional sequences and two vaccines, and alignment of 82 VP1 B-marker sequences, including one vaccine and four Vietnamese field strains. The analysis identified three A-genotypes, A1, A3, and A7, and two B-genotypes, B1 and B3, among the Vietnamese IBDV isolates. The lowest average evolutionary distance (8.6%) was seen between the A1 and A3 genotypes, and the highest (21.7%) was between A5 and A7, while there was a distance of 14% between B1 and B3 and 17% between B3 and B2. Unique signature residues were observed for genotypes A2, A3, A5, A6, and A8, which could be used for genotypic discrimination. A timeline statistical summary revealed that the A3-genotype predominated (79.8% presence) in Vietnam from 1987 to 2021 and that it remained the dominant IBDV genotype over the last five years (2016-2021). The current study contributes to a better understanding of the circulating genotypes and evolution of IBDV in Vietnam and worldwide.

Case–control association study of congenital heart disease from a tertiary paediatric cardiac centre from North India

BackgroundCongenital Heart diseases (CHDs) account for 1/3rd of all congenital birth defects. Etiopathogenesis of CHDs remain elusive despite extensive investigations globally. Phenotypic heterogeneity witnessed in this developmental disorder reiterate gene-environment interactions with periconceptional factors as risk conferring; and genetic analysis of both sporadic and familial forms of CHD suggest its multigenic basis. Significant association of de novo and inherited variants have been observed. Approximately 1/5th of CHDs are documented in the ethnically distinct Indian population but genetic insights have been very limited. This pilot case–control based association study was undertaken to investigate the status of Caucasian SNPs in a north Indian cohort.MethodA total of 306 CHD cases sub-classified into n = 198 acyanotic and n = 108 cyanotic types were recruited from a dedicated tertiary paediatric cardiac centre in Palwal, Haryana. 23 SNPs primarily prioritized from Genome-wide association studies (GWAS) on Caucasians were genotyped using Agena MassARRAY Technology and test of association was performed with adequately numbered controls.ResultsFifty percent of the studied SNPs were substantially associated in either allelic, genotypic or sub-phenotype categories validating their strong correlation with disease manifestation. Of note, strongest allelic association was observed for rs73118372 in CRELD1 (p < 0.0001) on Chr3, rs28711516 in MYH6 (p = 0.00083) and rs735712 in MYH7 (p = 0.0009) both on Chr 14 and were also significantly associated with acyanotic, and cyanotic categories separately. rs28711516 (p = 0.003) and rs735712 (p = 0.002) also showed genotypic association. Strongest association was observed with rs735712(p = 0.003) in VSD and maximum association was observed for ASD sub-phenotypes.ConclusionsCaucasian findings were partly replicated in the north Indian population. The findings suggest the contribution of genetic, environmental and sociodemographic factors, warranting continued investigations in this study population.

#2528 APOL1 GENOTYPING AND PROTEINURIC KIDNEY DISEASE IN EUROPE

Abstract Background and Aims Apolipoprotein L1 (APOL1) toxic gain-of-function variants (G1 or G2) are genetic factors driving a broad spectrum of progressive, proteinuric nephropathies referred to as APOL1-mediated kidney disease (AMKD). APOL1 genotyping is not routinely performed in kidney disease care, and prevalence of APOL1 variants among persons with chronic kidney disease (CKD) in Europe is not well known. These variants are common in persons of recent African ancestry. We report interim data of a global study estimating the prevalence of APOL1 genotypes in participants of recent African ancestry and proteinuric CKD, with a focus on data from Europe. Method Enrollment in this ongoing study will include up to 2,500 participants across different geographies who are of recent African ancestry and have focal segmental glomerulosclerosis (FSGS) or other proteinuric nondiabetic kidney disease (NDKD). The study includes a single visit during which blood samples are collected from participants to determine their APOL1 genotype using a validated polymerase chain reaction (PCR)-based assay. The percent of participants with two APOL1 variants and percent of participants in each genotype category (e.g., G1/G1, G1/G2, G2/G2) are assessed; genetic counseling services are available to participants, if desired. Results This interim analysis included 1,256 participants of whom 174 (13.9%) were from Europe. As shown in the table, among the 88 participants with FSGS and 86 participants with proteinuric NDKD, 54 (61.4%) and 32 (37.2%) have two APOL1 variants, respectively. Conclusion Our study will generate one of the largest global APOL1 genotyping data sets in participants with proteinuric kidney disease. These data begin to address a critical knowledge gap and highlight the importance of APOL1 genotyping in kidney disease care to identify AMKD, potentially optimize disease management, and enable referral for interventional clinical trials evaluating targeted therapies for AMKD.

Analysis of evolutionary imprints among the gut bacteria in phytobiotic supplemented Gallus gallus domesticus

The gut microbiota is an essential part of metabolism, assists in the breakdown of complex carbohydrates, proteins and lipids that enter into the digestive tract. Numerous microbial metabolites thus produced can have local and systemic effects which may influence health positively or negatively. The microbial population's dominance in the gut depends on the number of compounds present in the organ. Hence we have focused on analyzing the role of Moringa oleifera as phytobiotic supplementation in enhancing the beneficial gut microbiota in chicken models. Probiotics improve gut health in chickens through several mechanisms, including tight junction enhancement, nutrient uptake, niche colonization and co-aggregation with enteropathogens. The bacteria from the gut samples obtained from M.oleifera treated chickens were analyzed for various standard morphological and biochemical tests, genotypic classification using 16S rRNA gene sequencing and evaluation of evolutionary marks among the gut bacteria to determine whether they fit the criteria for probiotic traits. A variety of the consequences of beneficial gut microorganisms for hosts could attribute to bacterial communities' processes and the host's capacity for influencing the same processes. According to our study, phylogenetic analysis and taxonomy of the host significantly shape the intestinal flora across a range of host taxonomic levels. However, host ecology (i.e., diet) can further alter these gut flora, particularly in the case of many closely related host species.

Classification of bread wheat genotypes by machine learning algorithms

Classification of bread wheat genotypes by machine learning algorithms

Classification of soybean genotypes during the seedling stage in controlled drought and salt stress environments using the decision tree algorithm

AbstractSoybean is one of the most important oilseed crops grown worldwide. However, abiotic stresses such as drought and salinity can seriously affect soybean production, especially in tropical climate conditions. To evaluate the adaptability and stability of soybean genotypes under abiotic stress conditions, some studies have proposed a multitrait tool to select stress‐tolerant soybean genotypes through a multitrait stability index (MTSI). This index can be used under stressful environmental conditions to quantify the genotypic stability of soybean cultivars. Our study is based on an unprecedented approach, where we propose to use a machine learning algorithm called ‘Random Forest’ to obtain a classification model based on a decision tree algorithm. The decision tree data structure can be used even by nonexperts facilitating the decision‐making process for genotype selection. The proposed model evaluated the importance of six shoot and root morphological variables and predicted from which controlled growth environment the soybean plants originated. Using this model more than 73% of the genotypic patterns were learned correctly. Besides that, this model can also predict and rank the most critical variables in the development of soybean genotypes, having obtained results very similar to recent field research. The research is important for plant breeders who seek an early selection of soybean seedlings for drought and saline stresses.