Reference Genotype Research Articles

Predictors of COVID-19 severity among a cohort of Egyptian patients

BackgroundAs the outbreak of COVID-19 progresses, prognostic markers for the identification of high-risk individuals are urgently needed. The angiotensin system is implicated in the pathogenesis of COVID-19 as ACE2 is the cellular receptor for SARS-COV-2 virus, and expression of the ACE2 gene could regulate an individual’s susceptibility to infection. In addition, the balance between ACE and ACE2 activity may play a role in the severity of COVID-19.Aim of workThe aim of the work is to explore the role of ACE1 I/D and ACE2 G8790A gene variants and serum ACE l/ACE2 ratio as risk factors for severity of COVID-19 infection.MethodsOne hundred and eighty COVID-19 patients were divided into three groups: mild (60 patients), moderate (60 patients), and severe (60 patients). The enzyme levels of ACE and ACE2 were measured by ELISA. ACE I/D (rs4646994) was assayed using PCR and ACE2 (rs2285666) gene variant was determined using real-time PCR.ResultsACE/ACE2 ratio was significantly lower in the mild group than in the moderate-to-severe group (P < 0.001). GG (reference) genotype and G allele of ACE2 were more frequent in mild group, AA (variant) genotype, and A allele were more frequent in severe group (P value < 0.001). In the multiple logistic regression, COVID-19 severity was associated with older age (> 50y) (OR 10.4, 95% CI 3.8–28.4, P < 0.001), comorbidities (OR 8.2, 95% CI 1.6–42.1, P 0.012), and higher ACE/ACE2 ratio (OR 8.3, 95% CI 3.7–18.6 P < 0.001) were independent significant predictors of severity. Haplotype analysis revealed that patients with D allele of the ACE gene combined with the A allele of the ACE2 gene had nearly double the risk of having severe COVID infection (OR = 1.9, 95% CI 1.1–3.5, P = 0.024).ConclusionOld age (> 50 years), presence of comorbidities, and a high ACE/ACE2 ratio are recognized as pivotal predictors of COVID-19 severity.

Thai pharmacogenomics database -2 (TPGxD-2) sequel to TPGxD-1, analyzing genetic variants in 26 non-VIPGx genes within the Thai population.

Next-generation sequencing (NGS) has transformed pharmacogenomics (PGx), enabling thorough profiling of pharmacogenes using computational methods and advancing personalized medicine. The Thai Pharmacogenomic Database-2 (TPGxD-2) analyzed 948 whole genome sequences, primarily from the Electricity Generating Authority of Thailand (EGAT) cohort. This study is an extension of the previous Thai Pharmacogenomic Database (TPGxD-1) and specifically focused on 26 non-very important pharmacogenes (VIPGx) genes. Variant calling was conducted using Sentieon (version 201808.08) following GATK's best workflow practices. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0. Star allele analysis was performed with Stargazer v2.0.2, which called star alleles for 22 of 26 non-VIPGx genes. The variant analysis revealed a total of 14,529 variants in 26 non-VIPGx genes, with TBXAS1 had the highest number of variants (27%). Among the 14,529 variants, 2328 were novel (without rsID), with 87 identified as clinically relevant. We also found 56 known PGx variants among the known variants (n = 12,201), with UGT2B7 (19.64%), CYP1B1 (8.9%), SLCO2B1 (8.9%), and POR (8.9%) being the most common. We reported a high frequency of intermediate metabolizers (IMs) in CYP2F1 (34.6%) and CYP4A11 (8.6%), and a high frequency of decreased functional alleles in POR (53.9%) and SLCO1B3 (34.9%) genes. This study enhances our understanding of pharmacogenomic profiling of 26 non-VIPGx genes of notable clinical importance in the Thai population. However, further validation with additional computational and reference genotyping methods is necessary, and novel alleles identified in this study should undergo further orthogonal validation.

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.

Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

Drought-induced molecular changes in crown of various barley phytohormone mutants

ABSTRACT One of the main signal transduction pathways that modulate plant growth and stress responses, including drought, is the action of phytohormones. Recent advances in omics approaches have facilitated the exploration of plant genomes. However, the molecular mechanisms underlying the response in the crown of barley, which plays an essential role in plant performance under stress conditions and regeneration after stress treatment, remain largely unclear. The objective of the present study was the elucidation of drought-induced molecular reactions in the crowns of different barley phytohormone mutants. We verified the hypothesis that defects of gibberellins, brassinosteroids, and strigolactones action affect the transcriptomic, proteomic, and hormonal response of barley crown to the transitory drought influencing plant development under stress. Moreover, we assumed that due to the strong connection between strigolactones and branching the hvdwarf14.d mutant, with dysfunctional receptor of strigolactones, manifests the most abundant alternations in crowns and phenotype under drought. Finally, we expected to identify components underlying the core response to drought which are independent of the genetic background. Large-scale analyses were conducted using gibberellins-biosynthesis, brassinosteroids-signaling, and strigolactones-signaling mutants, as well as reference genotypes. Detailed phenotypic evaluation was also conducted. The obtained results clearly demonstrated that hormonal disorders caused by mutations in the HvGA20ox2, HvBRI1, and HvD14 genes affected the multifaceted reaction of crowns to drought, although the expression of these genes was not induced by stress. The study further detected not only genes and proteins that were involved in the drought response and reacted specifically in mutants compared to the reaction of reference genotypes and vice versa, but also the candidates that may underlie the genotype-universal stress response. Furthermore, candidate genes involved in phytohormonal interactions during the drought response were identified. We also found that the interplay between hormones, especially gibberellins and auxins, as well as strigolactones and cytokinins may be associated with the regulation of branching in crowns exposed to drought. Overall, the present study provides novel insights into the molecular drought-induced responses that occur in barley crowns.

Common‐garden study of introgression at loci associated with traits adaptive to coastal environment from Quercus dentata into Quercus mongolica var. crispula

AbstractAdaptive introgression, which can provide genetic diversity resulting in the expansion to novel habitats and the creation of different ecotypes, has been proposed in oaks (genus Quercus). In northern Japan, Quercus mongolica var. crispula (Qc) is common in inland habitats, and Quercus dentata (Qd) occurs in coastal habitats. At the northern distributional limit of Qd, Quercus × angustilepidota (Qa), a hybrid taxon between Qc and Qd, occurs in coastal habitats. These taxa, Qc, Qa, and Qd, were transplanted to inland and coastal common gardens (sites). Genotypes at 27 495 loci, phenotypes of eight leaf and shoot traits, and the size of 30‐year‐old trees were measured for 224 individuals in both sites. Genotypic variation revealed a hybrid zone between Qc and Qd, including both northern‐edge Qd admixed with Qc and coastal Qa backcrossed to Qc. Phenotypes of Qa trees were intermediate between those of Qc and Qd trees. The size of Qa and Qd trees was smaller than that of Qc trees in the inland site but was larger in the coastal site, suggesting adaptation of Qa and Qd to coastal environment. Local ancestry was estimated from phased genotypes of admixed trees using reference genotypes of 47 Qc and 25 Qd trees, indicating heterogeneous ancestry along chromosomes. Admixture mapping of local ancestry detected loci significantly associated with a leaf trait, which were located in an introgressed genomic region with genes related to stress response. Further studies are necessary to elucidate the genetic basis of adaptive introgression resulting in Qd‐like phenotypes of Qa.

Genotype-by-environment interaction analysis for grain yield of barley genotypes in the warm climate of Iran

In the present study, multi-environmental trials were performed on 19 newly developed barley genotypes at five test regions across the warm climate in Iran for two consecutive years. The results of additive main effects and multiplicative interaction (AMMI), GGE biplot (GGE) model, and several BLUP-based stability parameters and integrated models (WAASB and WAASBY) were used to reveal GEI patterns and identify superior genotypes with stable grain yield across test environments. Statistical analyses revealed that grain yield was influenced by significant environments (E), genotypes (G), and GEI effects. Among AMMI-, BLUP-, and grain mean-based stability parameters, the harmonic means of genotypic values (HMGV), relative performance of genotypic value (RPGV), and harmonic mean of RPGV parameters indicated a dynamic concept of stability. Moreover, results of GGE biplot analysis identified three mega-environments with different winner genotypes in test environments. Regardless of the different years, the Ahvaz, Darab, and Moghan regions were identified as Type-II environments due to their highest discrimination power and representative ability. The grouping pattern considerably followed the ranking of genotypes in GGE biplot analysis and WAASB model. In conclusion, our results revealed that genotypes G9 and G10 had grain yield higher than the reference genotype and relative stability in specific environments with favorable environmental conditions. Genotype G5 had general adaptability and stability across all test environments. This genotype can be recommended for cultivation in a wide range of environments in the warm climate of Iran.

Natural selection on feralization genes contributed to the invasive spread of wild pigs throughout the United States.

Despite a long presence in the contiguous United States (US), the distribution of invasive wild pigs (Sus scrofa × domesticus) has expanded rapidly since the 1980s, suggesting a more recent evolutionary shift towards greater invasiveness. Contemporary populations of wild pigs represent exoferal hybrid descendants of domestic pigs and European wild boar, with such hybridization expected to enrich genetic diversity and increase the adaptive potential of populations. Our objective was to characterize how genetic enrichment through hybridization increases the invasiveness of populations by identifying signals of selection and the ancestral origins of selected loci. Our study focused on invasive wild pigs within Great Smoky Mountains National Park, which represents a hybrid population descendent from the admixture of established populations of feral pigs and an introduction of European wild boar to North America. Accordingly, we genotyped 881 wild pigs with multiple high-density single-nucleotide polymorphism (SNP) arrays. We found 233 markers under putative selection spread over 79 regions across 16 out of 18 autosomes, which contained genes involved in traits affecting feralization. Among these, genes were found to be related to skull formation and neurogenesis, with two genes, TYRP1 and TYR, also encoding for crucial melanogenesis enzymes. The most common haplotypes associated with regions under selection for the Great Smoky Mountains population were also common among other populations throughout the region, indicating a key role of putatively selective variants in the fitness of invasive populations. Interestingly, many of these haplotypes were absent among European wild boar reference genotypes, indicating feralization through genetic adaptation.

The Role of Glutathione Transferase Omega-Class Variant Alleles in Individual Susceptibility to Ovarian Cancer.

The tumor microenvironment is affected by reactive oxygen species and has been suggested to have an important role in ovarian cancer (OC) tumorigenesis. The role of glutathione transferases (GSTs) in the maintenance of redox balance is considered as an important contributing factor in cancer, including OC. Furthermore, GSTs are mostly encoded by highly polymorphic genes, which further highlights their potential role in OC, known to originate from accumulated genetic changes. Since the potential relevance of genetic variations in omega-class GSTs (GSTO1 and GSTO2), with somewhat different activities such as thioltransferase and dehydroascorbate reductase activity, has not been clarified as yet in terms of susceptibility to OC, we aimed to investigate whether the presence of different GSTO1 and GSTO2 genetic variants, individually or combined, might represent determinants of risk for OC development. Genotyping was performed in 110 OC patients and 129 matched controls using a PCR-based assay for genotyping single nucleotide polymorphisms. The results of our study show that homozygous carriers of the GSTO2 variant G allele are at an increased risk of OC development in comparison to the carriers of the referent genotype (OR1 = 2.16, 95% CI: 0.88-5.26, p = 0.08; OR2 = 2.49, 95% CI: 0.93-6.61, p = 0.06). Furthermore, individuals with GST omega haplotype H2, meaning the concomitant presence of the GSTO1*A and GSTO2*G alleles, are more susceptible to OC development, while carriers of the H4 (*A*A) haplotype exhibited lower risk of OC when crude and adjusted haplotype analysis was performed (OR1 = 0.29; 95% CI: 0.12-0.70; p = 0.007 and OR2 = 0.27; 95% CI: 0.11-0.67; p = 0.0054). Overall, our results suggest that GSTO locus variants may confer OC risk.

Are single nucleotide polymorphisms in the IL-2 gene biomarkers for Hashimoto's thyroiditis?

Hashimoto's thyroiditis (HT) is an autoimmune disorder that can lead to hypothyroidism. The pathophysiology of HT involves the production of antithyroid antibodies that attack the thyroid tissue, causing inflammation and progressive fibrosis. Recent studies demonstrated a strong correlation between Interleukin-2 (IL-2) levels and the development of autoimmune diseases, suggesting that this cytokine may play a crucial role in the pathogenesis of HT. In this study, we determined the presence of the point mutation +114T/G in the IL-2 gene in patients with HT compared with a control group, and also the serum level of anti-thyroid peroxidase (TPOAbs) and anti-thyroglobulin (TgAbs) antibodies in HT patients with vs. without the mutation. The sequences of the IL-2 gene obtained from subjects were determined by the Sanger sequencing method. Our study did not reveal that the +114T/G polymorphism of the IL-2 gene is a susceptibility or protective factor for HT. No significant correlations were observed between the reference genotype, hetero- and homozygous +114T/G polymorphism and TPOAbs, respectively TgAbs serum levels in HT patients. Further studies of more cases are needed to identify more polymorphisms in the IL-2 gene and study their correlations with HT.

Synergistic approach of PCR-based fragment length analysis and amplicon deep sequencing reveals rich diversity of S-alleles in sweet cherries from the Caucasian region of origin.

The self-incompatibility system in sweet cherry (Prunus avium L.) prevents fertilization with own or genetically related pollen, and is genetically determined by the multi-allelic S-locus. Therefore, determining S-alleles is crucial for plant breeding and fruit production, as it enables the selection of compatible combinations of S-genotypes for successful pollination. In this study, S-alleles were identified in a total of 260 genotypes from the Caucasian region, the species' center of origin. S-allele genotyping was conducted using PCR fragment length analysis with the standard marker PaConsI-F/R2 and reference genotypes, complemented by sequence analysis through amplicon deep sequencing. The genotypes collected from Azerbaijan and Turkey exhibit a high allelic richness at the S-locus, particularly compared to modern sweet cherry cultivars worldwide. Nine previously undescribed S-alleles were identified and designated as S45, S46, S47, S48, S49, S50, S51, S52 and S53. Given the expected high diversity for other traits, this plant material represents a valuable resource for further breeding research and introgression of new traits in future breeding programs. Furthermore, our results underscore that fragment length alone may not be sufficient for unambiguous assignment of S-alleles due to minimal length differences between different alleles. To address this issue, an S-allele reference ladder was developed using the rich diversity for precise assignment of the S-alleles. This tool can be applied in future experiments as a robust and cost-effective method for accurate S-genotyping across different runs and laboratories. Additionally, several selected S-genotypes were planted in a trial field and will be maintained as an S-allele reference collection.

Effect of DIO2 Gene Polymorphism on Thyroid Hormone Levels and Its Correlation with the Severity of Schizophrenia in a Pakistani Population.

Low levels of triiodothyronine (T3) in the brain lead to increased dopamine receptor sensitivity, potentially resulting in schizophrenia. Iodothyronine deiodinase 2 (DIO2) is the only enzyme which converts tetraiodothyronine (T4) to T3 in the brain. DIO2 polymorphism of rs225014 results in the expression of non-functioning DIO2. Therefore, this study aimed to investigate the association of rs255014 with schizophrenia and its impact on thyroid hormone levels. This study included 150 schizophrenia cases and 150 controls. DNA was extracted from blood and subjected to PCR and amplicon sequencing. Serum thyroid profiles were determined using chemiluminescent magnetic microparticle immunoassay. Statistical analyses involved independent sample t-tests, Chi-square, and Pearson's correlation tests. The results revealed a higher frequency of the reference genotype (TT) in controls compared to cases (p < 0.05). However, rs225014 did not influence serum thyroid levels or the severity of schizophrenia (p > 0.05). Interestingly, control subjects exhibited significantly higher T3 levels (p < 0.001) than cases. Regardless of the genotype (TT or CC), the control group had higher mean T3 levels than the corresponding case group (p < 0.05). In conclusion, rs225014 is associated with schizophrenia and has no effect on serum thyroid hormone levels.

Association of IL13 polymorphisms with susceptibility to myocardial infarction: A case-control study in Chinese population.

Inflammatory cytokines play a major role in the pathogenesis of myocardial infarction (MI). Although information on the importance of interleukin 13 (IL13) in human MI is limited, it has been well documented in the mouse model. Genetic variation in the IL13 gene has been associated with the structure and expression of the IL13. In the present study, we hypothesized that IL13 common genetic variants would be associated with a predisposition to the development of MI. The present study enrolled 305 MI patients and 310 matched healthy controls. Common genetic polymorphisms in the IL13 gene (rs20541, rs1881457, and rs1800925) were genotyped using the TaqMan SNP genotyping method. Plasma levels of IL13 were measured using an enzyme-linked immunosorbent assay (ELISA). In MI patients, minor alleles of the IL13 rs1881457 and rs1800925 polymorphisms were less common than in healthy controls [rs1881457: AC (P = 0.004, OR = 0.61), C (P = 0.001, OR = 0.66); rs1800925: CT (P = 0.006, OR = 0.59)]. Further haplotype analysis of three studied SNPs revealed a significant association with predisposition to MI. Interestingly, IL13 rs1881457 and rs1800925 were linked to plasma levels of IL13: the reference genotype had higher levels, heterozygotes were intermediate, and the alternate genotype had the lowest levels. In the Chinese population, IL13 (rs1881457 and rs180092) variants are associated with different plasma IL13 levels and offer protection against MI development. However, additional research is required to validate our findings in different populations, including descent samples.

Comparison of massively parallel sequencing to capillary electrophoresis for short tandem repeat genotyping of trace DNA.

In forensic genetics, massively parallel sequencing (MPS) offers several advantages over the current golden standard, capillary electrophoresis (CE): additional sequence information, shorter amplicon lengths, and the simultaneous analysis of many markers. These benefits result in a reduced number of reactions necessary while improving the amount of data obtained, thereby conserving valuable sample extracts. This proves particularly advantageous for the analysis of trace DNA. This study assessed the suitability of MPS for short tandem repeat (STR) typing of low template samples compared with results obtained through CE. The MPS genotypes showed higher concordance to reference genotypes, with donor alleles being more frequently assigned to be the major contributor, meeting the requirements for database entry. However, the MPS workflow is more time-consuming and associated with higher costs.

Improving gene expression prediction models through the use of meta‐analysis summary statistics

AbstractBackgroundBy focusing on a gene‐level rather than a single‐variant basis, transcriptome‐wide association studies (TWAS) assess the role of genetically regulated gene expression in disease risk and improve statistical power by reducing the multiple testing burden by over a 100‐fold. A key limitation of current TWAS approaches is the requirement for dense individual‐level datasets with both genotypes and gene expression measures to produce effective expression prediction models. We sought to improve the statistical power of AD TWAS by leveraging easily accessible summary statistics taken from large‐scale expression quantitative trait loci (eQTL) meta‐analyses.MethodWe used eQTLGen Whole Blood cis‐eQTL data (N = 31,684) as our source of meta‐analysis summary statistics. To perform gene‐based elastic‐net regressions using the summary statistics, we used the lassosum R package (Mak et al. 2017) with 1000 Genomes data as a genomic reference and GTEx v8 genotype, normalized Whole Blood gene expression and covariate data (N = 670) as a testing panel for model validation and dynamic selection of the optimal mixing/penalty parameters. The SNP‐weights generated by our models were subsequently applied to AD meta‐analysis summary statistics from Kunkle et al. 2019 (AD Cases = 21,982; Controls = 44,944) and Bellenguez et al. 2022 (AD/ ‘proxy’ AD Cases = 111,326; Controls = 677,663) via S‐PrediXcan to assess whether predicted gene expression is associated with AD risk.ResultWe found on average a 3.3% (SD: 0.05%) significant improvement in the predictive performance for 93.3% of genes models (N = 2,787) when using the same SNPs as the PredictDB model. Using models with the restricted SNP set, we identified four genome‐wide significant gene expression models (CTDNEP, p = 3.2e‐9; STK4, p = 1.6e‐13; RPS14, p = 8.8e‐38; POLR1H, p = 5.6e‐45) using Kunkle et al. 2019 summary statistics relative to only one using the PredictDB model (FBXO46, p = 6.6e‐15). When applying the Bellenguez et al. 2022 summary statistics, which contains a broader AD phenotype thru the inclusion of ‘proxy’ cases, we identified 12 genome‐wide significant gene expression models (Range: CHRNA2, p = 2.6e‐8; SLC24A4, p = 4.5e‐16) associated with AD.ConclusionOur findings show AD association studies based on TWAS approaches may be improved by meta‐analysis reference panels.

Genetic relatedness of rice landraces in North East India with wild relatives using chloroplast markers

AbstractThe evolutionary relationship between domesticated Oryza species and their wild relatives in North East India is not well understood. To improve the understanding of the evolutionary relationship, this study investigates the genetic diversity of 68 indigenous rice landraces from North East India, ten O. rufipogon genotypes, and nine O. nivara genotypes using chloroplast variable repeat markers which, due to non-recombination and uniparental inheritance, enable better phylogenetic inference. Reference genotypes IR64 (indica) and Nipponbare (japonica) were included to characterize various phylogenetic clusters. Using distance-based hierarchical clustering, model-based structuring and principal component analysis, selected landraces from the three North Eastern Indian states of Assam, Manipur and Arunachal Pradesh were grouped into two phylogenetically different clusters that represented the IR64 and Nipponbare groupings. Interestingly, despite the fact that a cluster analysis combining North East landraces and wild relatives likewise produced two separate clusters (cluster I: Nipponbare, cluster II: IR64), the majority of the wild relatives were only clustered in the IR64 cluster. This suggests that the two distinct evolutionary histories of the rice landraces in North East India are distinguished by their genetic affinity for wild relatives and their variation in the indica and japonica pools. These results highlight chloroplast divergence influencing the genetic diversity of North East landraces with wild relatives. Further, these findings will enable in-depth studies on the functional significance of chloroplast diversity on trait adaptation in rice landraces.

Abstract C005: Inferring global ancestry using scRNA sequencing reads of the fallopian tube and ovarian cancer

Abstract Introduction Single cell RNA sequencing (scRNA-seq) is a powerful technique to profile the transcriptome of each single cell in the sample to characterize its gene expression profile. Final 10x scRNA-seq libraries are comprised of standard Illumina paired end constructs. Read 2 encodes the sequence of the actual cDNA fragment used in library preparation to amplify the RNA sequences, so read 2 is a result of transcription from the DNA encoded in a particular gene. We hypothesize that single nucleotide polymorphisms (SNPs) and global ancestry can be inferred from read 2. Methods Using the read 2, FASTQ files were mapped to the reference genome using the STAR algorithm (2-pass), subsequent steps were performed according to GATK best practices. For reference populations, we selected 2142 unrelated individuals from the 1000 genomes project with admixture &amp;gt;80% in a single ancestral population using k=5 ancestral populations. SNPs quality control for the FT samples and reference included call rates of &amp;lt; 95% or minor allele frequency (MAF) &amp;lt; 1% which were filtered out. Genotypes of fallopian tube (FT) cases were merged with pruned reference genotypes (markers) and intersecting SNPs were used for further analysis. Data was visualized using PCA and ADMIXTURE (k=5). Clinical and demographic variables available in our dataset included age at time of surgery, race, ethnicity, country of birth and specific germline mutation. Whole genome sequencing on the matched germline DNA of the same samples was performed as a validation step. Results A total of 34 cases underwent scRNA-seq. FT with germline mutations included seven BRCA1, seven BRCA2, five PALB2, two RAD51, two MLH1 and one ATM. Additionally, we sequenced six FT with no germline mutations and four ovarian cancer tumors. Self-identified race included 2 Asian, 9 Black, 16 White Hispanic and 6 White non-hispanic. A total of 730 382 SNPs intersected between all samples and pruned references. PCA visualization showed correlation between self-identified race and inferred global ancestry using five ancestral populations. Furthermore, there was high correlation between country of birth, position of cases in the PCA and ADMIXTURE with K=5. Conclusion Inferring global ancestry from scRNA seq reads will help estimate the genetic ancestry from data in which this variable is missing or was not captured. Genetic ancestry is a powerful tool that should be considered when generalizing gene expression results to populations. Citation Format: Alex P. Sanchez-Covarrubias, Ashlee Sealy, Dylan Thompson, David Samuel, Ayodele Omotoso, Destiny Burnett, Matthew Schlumbrecht, Sophia George. Inferring global ancestry using scRNA sequencing reads of the fallopian tube and ovarian cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C005.

Identification and Analysis of Candidate Genes Associated with Maize Fusarium Cob Resistance Using Next-Generation Sequencing Technology

The pressure to reduce mineral fertilization and the amount of pesticides used has become a factor limiting production growth, as has the elimination of many crop protection chemicals from the market. A key condition for this to be an effective form of protection is the use of varieties with higher levels of resistance. The most effective and fastest way to assist in the selection and control of pathogens is the conducting of genome-wide association studies. These are useful tools for identifying candidate genes, especially when combined with QTL mapping to map and validate loci for quantitative traits. The aim of this study was to identify new markers coupled to genes that determine maize plant resistance to fusarium head blight through the use of next-generation sequencing, association and physical mapping, and to optimize diagnostic procedures to identify selected molecular markers coupled to plant resistance to this fungal disease. As a result of field experiments and molecular analyses, molecular markers coupled to potential genes for resistance to maize ear fusariosis were selected. The newly selected markers were tested against reference genotypes. As a result of the analyses, it was found that two markers (11801 and 20607) out of the ten that were tested differentiated between susceptible and resistant genotypes. Marker number 11801 proved to be the most effective, with a specious product of 237 bp appearing for genotypes 1, 3, 5, 9 and 10. These genotypes were characterized by a field resistance of 4–6 on the 9° scale (1 being susceptible, 9 being resistant) and for all genotypes except 16 and 20, which were characterized by a field resistance of 9. In the next step, this marker will be tested on a wider population of extreme genotypes in order to use it for the preliminary selection of fusarium-resistant genotypes, and the phosphoenolpyruvate carboxylase kinase 1 gene coupled to it will be subjected to expression analysis.

Untapped Genetic Resources for Breeding Acidic Soil-Adapted Chickpea (Cicer arietinum L.) Cultivars

Globally, more than half of potentially arable land is acidic, and aluminum (Al) is the primary factor limiting plant growth and crop productivity on acidic soils worldwide. The development and utilization of Al-tolerant crops is a sustainable approach to enhancing crop production on acidic soils. For this purpose, screening available genetic resources under Al-stressed conditions is a crucial initial step. Hence, the present study aimed to evaluate the Al tolerance of 264 Ethiopian chickpea landraces under hydroponic conditions without Al (control) and with 120 µM Al (Al treatment). Significant (p &lt; 0.001) variations were detected among the genotypes for all studied traits under control (0 µM Al) and 120 µM Al concentration. The relative growth values for the 120 µM Al/0 µM Al ratio was also significant, indicating the presence of a considerable amount of genetic variation in Ethiopian chickpea landraces in terms of Al tolerance. Based on relative root growth (RRG) as an Al-tolerance parameter, the genotypes were grouped into five distinct (p &lt; 0.001) classes. The highest RRG value (1.59) was obtained for genotype ETC_209008, followed by ETC_41184 and ETC_212589, while ETC_208995 had the lowest RRG value of 0.27. Of the total landraces screened, 35% had higher RRG values than the tolerant genotype ETC_WL_1_2016 used as a reference, indicating the presence of adequate genotypes capable of outperforming the reference genotype on acidic soils. The genotypes identified in the present study may serve as sources of novel alleles in genes regulating Al tolerance in chickpea that can be utilized in breeding programs to improve the crop’s adaptation to acidic soils, thus contributing to smallholder farmers’ increased nutritional and food security.

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

AbstractSynthesis of crop trial data can generate insights that are not available from the analysis of individual studies, but such synthesis is often constrained by the heterogeneity of data among studies. Rank‐based data synthesis provides the flexibility to combine data of heterogeneous types and from different sources. We demonstrate the application of rank‐based data synthesis of heterogeneous trial data to assess the effect of climatic factors on the reaction of several Musa genotypes to black leaf streak disease (BLSD; caused by Pseudocercospora fijiensis [sexual morph: Mycosphaerella fijiensis]). We aggregated data from the main public repositories of Musa trial data. We applied model‐based recursive partitioning with the Plackett–Luce model, using climatic data as covariates. The model identified the maximum length of the dry spell as the main variable influencing differences in genotypic response to BLSD, dividing the aggregated trial dataset into humid and dry environments. We found differences in the reaction of genotypes to BLSD between these environments. In humid environments, NARITA 8 was found to be the most resistant genotype, while in dry environments FHIA‐01 was the best performing improved genotype. We also assessed reliability, which is the probability of outperforming the reference genotype (Calcutta 4). In humid environments, NARITA 2, NARITA 8, and FHIA‐01 had the highest reliability, while in dry environments only the landrace Saba surpassed 50% reliability. The information generated by our data synthesis approach supports selecting Musa genotypes for further evaluations at new locations.

A MinION-based Long-Read Sequencing Application With One-Step PCR for the Genetic Diagnosis of 21-Hydroxylase Deficiency.

Recently developed long-read sequencing (LRS) technology has been considered an option for CYP21A2 analysis. However, the clinical use of LRS for CYP21A2 analysis is limited. This study's objective is to develop an efficient and low-cost LRS system for CYP21A2 screening. A DNA fragment library was prepared in a single polymerase chain reaction (PCR) that covers the entire CYP21A2 gene and all known junctions caused by TNXB gene structural rearrangements, yielding a single 8-kb product of CYP21A2 or CYP21A1P/CYP21A2 chimera. After barcoding, the PCR products were sequenced on a MinION-based platform with Flongle Flow Cell R9.4.1 and R10.4.1. The reference genotypes of 55 patients with 21-hydroxylase deficiency (21OHD) were established using the conventional method with multiplex ligation-dependent probe amplification (MLPA) and nested PCR. LRS using Flongle Flow Cell R9.4.1 yielded consistent results. Additionally, the recently updated LRS "duplex" analysis with Flongle flow cell R10.4.1 was tested to reveal an advantage of accurately sequencing a variant located on the homopolymer region. By introducing a barcode system, the cost was reduced to be comparable to that of conventional analysis. A novel single-nucleotide variation was discovered at the acceptor site of intron 7, c.940-1G > C. We also identified a subtype of the classical chimeric junction CH2, "CH2a," in the region from the latter part of intron 5 to exon 6. We successfully established a novel low-cost and highly accurate LRS system for 21OHD genetic analysis. Our study provides insight into the feasibility of LRS for diagnosing 21OHD and other genetic diseases caused by structural rearrangements.