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We used whole genome resequencing of pooled individuals to develop a high‐density single‐nucleotide polymorphism (SNP) chip for Eucalyptus. Genomes of 240 trees of 12 species were sequenced at 3.5× each, and 46 997 586 raw SNP variants were subject to multivariable filtering metrics toward a multispecies, genome‐wide distributed chip content. Of the 60 904 SNPs on the chip, 59 222 were genotyped and 51 204 were polymorphic across 14 Eucalyptus species, providing a 96% genome‐wide coverage with 1 SNP/12–20 kb, and 47 069 SNPs at ≤ 10 kb from 30 444 of the 33 917 genes in the Eucalyptus genome. Given the EUChip60K multi‐species genotyping flexibility, we show that both the sample size and taxonomic composition of cluster files impact heterozygous call specificity and sensitivity by benchmarking against ‘gold standard’ genotypes derived from deeply sequenced individual tree genomes. Thousands of SNPs were shared across species, likely representing ancient variants arisen before the split of these taxa, hinting to a recent eucalypt radiation. We show that the variable SNP filtering constraints allowed coverage of the entire site frequency spectrum, mitigating SNP ascertainment bias. The EUChip60K represents an outstanding tool with which to address population genomics questions in Eucalyptus and to empower genomic selection, GWAS and the broader study of complex trait variation in eucalypts.

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.

Cultivated peanut ( L.) is an important oilseed crop that is grown extensively in Africa, Asia and America. The diseases early and late leaf spot severely constrains peanut production worldwide. Because multiple genes control resistance to leaf spot diseases, conventional breeding is a time-consuming approach for pyramiding resistance genes into a single genotype. Marker-assisted selection (MAS) would complement and accelerate conventional breeding once molecular markers tightly associated with the resistance genes are identified. In this study, we have generated a large number of SNPs through genotyping by sequencing (GBS) and constructed a high-resolution map with an average distance of 1.34 cM among 2,753 SNP markers distributed on 20 linkage groups. QTL mapping has revealed that major QTL within a confidence interval could provide an efficient way to detect putative resistance genes. Analysis of the interval sequences has indicated that a major QTL for resistance to late leaf spot anchored by two NBS-LRR resistance genes on chromosome B05. Two major QTLs located on chromosomes A03 and B04 were associated with resistance genes for early leaf spot. Sequences within the confidence interval would facilitate identifying resistance genes and applying marker-assisted selection for resistance.

Zyxin functions as a regulator of the restructuring of the actin cytoskeleton during the process of repairing tissue damage, cell movement and attachment. It has also been identified as a potential gene involved in chicken coccidiosis. In order to gain a deeper understanding of these phenomena, we employed a collection of computer-based techniques and databases to examine the amino acid sequence, structural dynamics, molecular interactions, and activities of the gene. Our analysis revealed that Zyxin contains two non-synonymous SNPs (A > C at position 22 and G > A at position 137) at exon 1. Also, there existed a non-synonymous SNPs in Exon 3 (A>C and A>T both at position 861) of the gene with Synonymous SNPs observed only in exon 3 (A>G at position 812 and 854, T > C at position 863). The genetic diversity revealed in these chicken populations indicates the presence of genetic variation, with Naked neck chickens showing a considerably higher frequency of particular SNPs. Two non-synonymous single nucleotide polymorphisms (nsSNPs) were forecasted to exert a profound influence on the structure, stability, and activities of Zyxin, thereby heightening the vulnerability to coccidiosis.

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision‐making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects' underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high‐dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two‐dimensional (spatio‐modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging‐genetic dataset and achieve an SZ prediction accuracy of 94.10% (p < .0001), outperforming state‐of‐the‐art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio‐modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies. Attentive fusion of neuroimaging and genomics data classify schizophrenia (SZ) with high precision. The attention scores provide the most contributing imaging‐genetics features for characterizing the disorder. The proposed fusion module is self‐explaining; interprets how each biological sources complement the other and leverage their combination to better understand SZ.

Premise There is a general lack of consensus on the best practices for filtering of single‐nucleotide polymorphisms (SNPs) and whether it is better to use SNPs or include flanking regions (full “locus”) in phylogenomic analyses and subsequent comparative methods. Methods Using genotyping‐by‐sequencing data from 22 Glycine species, we assessed the effects of SNP vs. locus usage and SNP retention stringency. We compared branch length, node support, and divergence time estimation across 16 datasets with varying amounts of missing data and total size. Results Our results revealed five aspects of phylogenomic data usage that may be generally applicable: (1) tree topology is largely congruent across analyses; (2) filtering strictly for SNP retention (e.g., 90–100%) reduces support and can alter some inferred relationships; (3) absolute branch lengths vary by two orders of magnitude between SNP and locus datasets; (4) data type and branch length variation have little effect on divergence time estimation; and (5) phylograms alter the estimation of ancestral states and rates of morphological evolution. Discussion Using SNP or locus datasets does not alter phylogenetic inference significantly, unless researchers want or need to use absolute branch lengths. We recommend against using excessive filtering thresholds for SNP retention to reduce the risk of producing inconsistent topologies and generating low support.

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that lowfrequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. These polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.

Climate change and strait isolation during the glacial period had a profound effect on the differentiation of gymnosperms on both sides of the Taiwan Strait. The taxonomic status and population structure of Cunninghamia konishii (Taiwan) and C. lanceolata (mainland China) remain contentious due to conflicting morphological and molecular evidence. Thus, we sampled 92 accessions from seven natural populations, six from mainland China and one from Taiwan, and conducted high‐throughput genotyping‐by‐sequencing (GBS) analysis. The northern marginal population exhibited the lowest genetic diversity (θπ = 4.828 × 10−3), while the Taiwan population had the highest (θπ = 5.821 × 10−3), reflecting its role as a glacial refugium, while mainland populations retained lower diversity due to post‐glacial bottlenecks. There was little difference in Tajima's D values of selection pressure between mainland China and Taiwan. However, significant gene flow (Nm = 2.839) was observed, combined with low FST values (0.072–0.122), which indicate low genetic differentiation among C. lanceolata and C. konishii. Migration analysis indicated a high probability of unidirectional gene flow from mainland China to Taiwan, with the Dongshan Land Bridge facilitating pre‐glacial gene flow. We conclude that C. konishii represents an ecotype of C. lanceolata , shaped by environmental plasticity and incomplete isolation. This study enhances our understanding of the gene flow and evolutionary processes shaping the species and offers new insights into their taxonomic classification. This study investigates the genetic diversity and taxonomic status of Cunninghamia lanceolata and Cunninghamia konishii, exploring their differentiation due to climate change and glacial isolation. Genotyping‐by‐sequencing (GBS) analysis of 92 accessions revealed minimal genetic differentiation and significant gene flow from mainland China to Taiwan, suggesting that C. konishii is an ecotype of C. lanceolata , rather than a distinct species.

Bread wheat (Triticum aestivum L.) is one of the world’s most important crops. Maintaining wheat yield gains across all of its major production areas is a key target toward underpinning global food security. Brazil is a major wheat producer in South America, generating grain yields of around 6.8 million tons per year. Here, we establish and genotype a wheat association mapping resource relevant to contemporary Brazilian wheat breeding programs. The panel of 558 wheat accessions was genotyped using an Illumina iSelect 90,000 single nucleotide polymorphism array. Following quality control, the final data matrix consisted of 470 accessions and 22,475 polymorphic genetic markers (minor allele frequency ≥5%, missing data <5%). Principal component analysis identified distinct differences between materials bred predominantly for the northern Cerrado region, compared to those bred for southern Brazilian agricultural areas. We augmented the genotypic data with 26 functional Kompetitive Allele-Specific PCR (KASP) markers to identify the allelic combinations at genes with previously known effects on agronomically important traits in the panel. This highlighted breeding targets for immediate consideration – notably, increased Fusarium head blight resistance via the Fhb1 locus. To demonstrate the panel’s likely future utility, genome-wide association scans for several phenotypic traits were undertaken. Significant (Bonferroni corrected P < 0.05) marker-trait associations were detected for Fusarium kernel damage (a proxy for type 2 Fusarium resistance), identifying previously known quantitative trait loci in the panel. This association mapping panel represents an important resource for Brazilian wheat breeding, allowing future genetic studies to analyze multiple agronomic traits within a single genetically diverse population.

The application of next generation sequencing (NGS) for the analysis of mitochondrial (mt) DNA, short tandem repeats (STRs), and single nucleotide polymorphism (SNPs) has demonstrated great promise for challenging forensic specimens, such as degraded, limited, and mixed samples. Target enrichment using probe capture rather than PCR amplification offers advantages for analysis of degraded DNA since two intact PCR primer sites in the template DNA molecule are not required. Furthermore, NGS software programs can help remove PCR duplicates to determine initial template copy numbers of a shotgun library. Moreover, the same shotgun library prepared from a limited DNA source can be enriched for mtDNA as well as nuclear markers by hybrid capture with the relevant probe panels. Here, we demonstrate the use of this strategy in the analysis of limited and mock degraded samples using our custom probe capture panels for massively parallel sequencing of the whole mtgenome and 426 SNP markers. We also applied the mtgenome capture panel in a mixed sample and analyzed using both phylogenetic and variant frequency based bioinformatics tools to resolve the minor and major contributors. Finally, the results obtained on individual telogen hairs demonstrate the potential of probe capture NGS analysis for both mtDNA and nuclear SNPs for challenging forensic specimens.