Search

Introduction: Scoliosis is a pathological spine structure deformation, predominantly classified as "idiopathic" due to its unknown etiology. However, it has been suggested that scoliosis may be linked to polygenic backgrounds. It is crucial to identify potential Adolescent Idiopathic Scoliosis (AIS)-related genetic backgrounds before scoliosis onset. Methods: The present study was designed to intelligently parse, decompose and predict AIS-related variants in ClinVar database. Possible AIS-related variant records downloaded from ClinVar were parsed for various labels, decomposed for Dinucleotide Compositional Representation (DCR) and other traits, screened for high-risk genes with statistical analysis, and then learned intelligently with deep learning to predict high-risk AIS genotypes. Results: Results demonstrated that the present framework is composed of all technical sections of data parsing, scoliosis genotyping, genome encoding, machine learning (ML)/deep learning (DL) and scoliosis genotype predicting. 58,000 scoliosis-related records were automatically parsed and statistically analyzed for high-risk genes and genotypes, such as FBN1 , LAMA2 and SPG11. All variant genes were decomposed for DCR and other traits. Unsupervised ML indicated marked inter-group separation and intra-group clustering of the DCR of FBN1 , LAMA2 or SPG11 for the five types of variants (Pathogenic, Pathogeniclikely, Benign, Benignlikely and Uncertain). A FBN1 DCR-based Convolutional Neural Network (CNN) was trained for Pathogenic and Benign/ Benignlikely variants performed accurately on validation data and predicted 179 high-risk scoliosis variants. The trained predictor was interpretable for the similar distribution of variant types and variant locations within 2D structure units in the predicted 3D structure of FBN1. Discussion: In summary, scoliosis risk is predictable by deep learning based on genomic decomposed features of DCR. DCR-based classifier has predicted more scoliosis risk FBN1 variants in ClinVar database. DCR-based models would be promising for genotype-to-phenotype prediction for more disease types. [ABSTRACT FROM AUTHOR]

IntroductionScoliosis is a pathological spine structure deformation, predominantly classified as “idiopathic” due to its unknown etiology. However, it has been suggested that scoliosis may be linked to polygenic backgrounds. It is crucial to identify potential Adolescent Idiopathic Scoliosis (AIS)-related genetic backgrounds before scoliosis onset.MethodsThe present study was designed to intelligently parse, decompose and predict AIS-related variants in ClinVar database. Possible AIS-related variant records downloaded from ClinVar were parsed for various labels, decomposed for Dinucleotide Compositional Representation (DCR) and other traits, screened for high-risk genes with statistical analysis, and then learned intelligently with deep learning to predict high-risk AIS genotypes.ResultsResults demonstrated that the present framework is composed of all technical sections of data parsing, scoliosis genotyping, genome encoding, machine learning (ML)/deep learning (DL) and scoliosis genotype predicting. 58,000 scoliosis-related records were automatically parsed and statistically analyzed for high-risk genes and genotypes, such as FBN1, LAMA2 and SPG11. All variant genes were decomposed for DCR and other traits. Unsupervised ML indicated marked inter-group separation and intra-group clustering of the DCR of FBN1, LAMA2 or SPG11 for the five types of variants (Pathogenic, Pathogeniclikely, Benign, Benignlikely and Uncertain). A FBN1 DCR-based Convolutional Neural Network (CNN) was trained for Pathogenic and Benign/ Benignlikely variants performed accurately on validation data and predicted 179 high-risk scoliosis variants. The trained predictor was interpretable for the similar distribution of variant types and variant locations within 2D structure units in the predicted 3D structure of FBN1.DiscussionIn summary, scoliosis risk is predictable by deep learning based on genomic decomposed features of DCR. DCR-based classifier has predicted more scoliosis risk FBN1 variants in ClinVar database. DCR-based models would be promising for genotype-to-phenotype prediction for more disease types.

Context: Broadening the gene pool of staple food crops by incorporating desirable genes from wild/relative species is way to achieve higher genetic gains. The African rice species Oryza glaberrima Steud. is a source of genes for genetic improvement of Oryza sativa L. to various biotic and abiotic stresses. Aim: The study was undertaken to identify genomic regions associated with yield and yield-related traits in interspecific backcross mapping populations derived from variety IR64 (O. sativa indica) × O. glaberrima. Methods: The BC1F2 and BC1F2:3 populations were phenotyped for days to 50% flowering, plant height, number of tillers, number of productive tillers, panicle length, spikelet fertility and grain yield under irrigated conditions. The BC1F2 population was genotyped with 99 genome-wide polymorphic SSR markers. Quantitative trait loci (QTLs) associated with yield-enhancing traits were identified through single marker analysis. Key results: Significant genetic variability was observed with moderate to high heritability in mapping populations. Genotyping revealed the presence of segregation distortion and skewed genome composition. Single marker analysis identified 20 QTLs linked to the studied traits. A QTL linked to grain yield, qGY-4.1 , derived from O. glaberrima was found in both populations with high phenotypic variance. The chromosome region around marker RM510 was identified as a QTL hotspot and putative candidate genes were identified. Conclusions: Our study demonstrates that O. glaberrima can serve as a valuable genetic resource for genetic improvement of yield-enhancing traits in O. sativa cultivars. Implications: The identified QTLs regions can be employed in molecular breeding. The promising introgression lines identified can be used as pre-breeding lines in future breeding programs. African rice species Oryza glaberrima as a source of genes for improvement of Oryza sativa is well established for various biotic and abiotic stresses. In this study, yield and yield-related QTLs derived from O. glaberrima were mapped in backcross mapping populations of IR64 × O. glaberrima. The outcomes of the study will help in harnessing useful genomic regions from O. glaberrima for genetic improvement of O. sativa. [ABSTRACT FROM AUTHOR]

Summary: Speciation via hybridization and polyploidization is a major evolutionary force in plant evolution but is still poorly understood for neopolyploid groups. Challenges are attributed to high heterozygosity, low genetic divergence, and missing information on progenitors, ploidy, and reproduction. We study the large Eurasian Ranunculus auricomus species complex and use a comprehensive workflow integrating reduced‐representation sequencing (RRS) genomic data to unravel reticulate evolution, genome diversity and composition of polyploids.We rely on 97 312 restriction site‐associated DNA sequencing (RAD‐Seq) loci, 576 targeted nuclear genes (48 phased), and 71 plastid regions derived from 78 polyploid apomictic taxa and four diploid and one tetraploid putative sexual progenitor species. We applied (phylo)genomic structure, network, and single nucleotide polymorphism (SNP)‐origin analyses.Results consistently showed only 3–5 supported and geographically structured polyploid genetic groups, each containing extant sexual and one unknown progenitor species. Combined analyses demonstrated predominantly allopolyploid origins, each involving 2–3 different diploid sexual progenitor species. Young allotetraploids were characterized by subgenome dominance and nonhybrid SNPs, suggesting substantial post‐origin but little lineage‐specific evolution.The biodiversity of neopolyploid complexes can result from multiple hybrid origins involving different progenitors and substantial post‐origin evolution (e.g. homoeologous exchanges, hybrid segregation, gene flow). Reduced‐representation sequencing genomic data including multi‐approach information is efficient to delimit shallow reticulate relationships. [ABSTRACT FROM AUTHOR]