Rosaceae phylogenomic studies provide insights into the evolution of new genes

Rosa banksiae,known as Lady Banks'rose,is a perennial ornamental crop and a versatile herb in traditional Chinese medicine.Given the lack of genomic resources,we assembled a Hi Fi and Nanopore sequencing-derived 458.58 Mb gap-free telomere-to-telomere high-quality R.banksiae genome with a scaffold N50=63.90 Mb.The genome of R.banksiae exhibited no lineage-specific whole-genome duplication compared with other Rosaceae.The phylogenomic analysis of 13 Rosaceae and Arabidopsis through a comparative genomics study showed that numerous gene families were lineage-specific both before and after the diversification of Rosaceae.Some of these genes are candidates for new genes that have evolved from parental genes through fusion events.Fusion genes are divided into three types:Type-I and Type-II genes contain two parental genes that are generated by duplication,distributed in the same and different regions of the genome,respectively;and Type-III can only be detected in one parental gene.Here,Type-I genes are found to have more relaxed selection pressure and lower Ks values than Type-II,indicating that these newly evolved Type-I genes may play important roles in driving phenotypic evolution.Functional analysis exhibited that newly formed fusion genes can regulate the phenotype traits of plant growth and development,suggesting the functional significance of these genes.This study identifies new fusion genes that could be responsible for phenotype evolution and provides information on the evolutionary history of recently diverged species in the Rosa genus.Our data represents the major progress in understanding the new fusion genes evolution pattern of Rosaceae and provides an invaluable resource for phylogenomic studies in plants.

Genomic selection for meat quality traits based on VIS/NIR spectral information

The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.

The evolution of cancer genomic medicine in Japan and the role of the National Cancer Center Japan

The journey to implement cancer genomic medicine(CGM)in oncology practice began in the 1980s,which is considered the dawn of genetic and genomic cancer research.At the time,a variety of activating oncogenic alterations and their functional significance were unveiled in cancer cells,which led to the development of molecular targeted therapies in the 2000s and beyond.Although CGM is still a relatively new discipline and it is difficult to predict to what extent CGM will benefit the diverse pool of cancer patients,the National Cancer Center(NCC)of Japan has already contributed considerably to CGM advancement for the conquest of cancer.Looking back at these past achievements of the NCC,we predict that the future of CGM will involve the following:1)A biobank of paired cancerous and non-cancerous tissues and cells from various cancer types and stages will be developed.The quantity and quality of these samples will be compatible with omics analyses.All biobank samples will be linked to longitudinal clinical information.2)New technologies,such as whole-genome sequencing and artificial intelligence,will be introduced and new bioresources for functional and pharmacologic analyses(e.g.,a patient-derived xenograft library)will be systematically deployed.3)Fast and bidirectional translational research(bench-to-bedside and bedside-to-bench)performed by basic researchers and clinical investigators,preferably working alongside each other at the same institution,will be implemented;4)Close collaborations between academia,industry,regulatory bodies,and funding agencies will be established.5)There will be an investment in the other branch of CGM,personalized preventive medicine,based on the individual's genetic predisposition to cancer.

Construction and validation of somatic mutation-derived long noncoding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND Long non-coding RNAs(LncRNAs)have been found to be a potential prognostic factor for cancers,including hepatocellular carcinoma(HCC).Some LncRNAs have been confirmed as potential indicators to quantify genomic instability(GI).Nevertheless,GI-LncRNAs remain largely unexplored.This study established a GI-derived LncRNA signature(GILncSig)that can predict the prognosis of HCC patients.AIM To establish a GILncSig that can predict the prognosis of HCC patients.METHODS Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles.The GI-LncRNAs were then analyzed for functional enrichment.The GILncSig was established in the training set by Cox regression analysis,and its predictive ability was verified in the testing set and TCGA set.In addition,we explored the effects of the GILncSig and TP53 on prognosis.RESULTS A total of 88 GI-LncRNAs were found,and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI.The GILncSig was constructed by 5 LncRNAs(miR210HG,AC016735.1,AC116351.1,AC010643.1,LUCAT1).In the training set,the prognosis of high-risk patients was significantly worse than that of low-risk patients,and similar results were verified in the testing set and TCGA set.Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor.Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve(0.773)was higher than the two LncRNA signatures published recently.Furthermore,the GILncSig may have a better predictive performance than TP53 mutation status alone.CONCLUSION We established a GILncSig that can predict the prognosis of HCC patients,which will help to guide prognostic evaluation and treatment decisions.

Conservation genomic investigation of an endangered conifer,Thuja sutchuenensis,reveals low genetic diversity but also low genetic load

Endangered species generally have small populations with low genetic diversity and a high genetic load.Thuja sutchuenensis is an endangered conifer endemic to southwestern China.It was once considered extinct in the wild,but in 1999 was rediscovered.However,little is known about its genetic load.We collected 67 individuals from five wild,isolated T.sutchuenensis populations,and used 636,151 SNPs to analyze the level of genetic diversity and genetic load in T.sutchuenensis to delineate the conservation units of T.sutchuenensis,based on whole transcriptome sequencing data,as well as target capture sequencing data.We found that populations of T.sutchuenensis could be divided into three groups.These groups had low levels genetic diversity and were moderately genetically differentiated.Our findings also indicate that T.sutchuenensis suffered two severe bottlenecks around the Last Glaciation Period and Last Glacial Maximum.Among Thuja species,T.sutchuenensis presented the lowest genetic load and hence might have purged deleterious mutations efficiently through purifying selection.However,distribution of fitness effects analysis indicated a high extinction risk for T.sutchuenensis.Multiple lines of evidence identified three management units for T.sutchuenensis.Although T.sutchuenensis possesses a low genetic load,low genetic diversity,suboptimal fitness,and anthropogenic pressures all present an extinction risk for this rare conifer.This might also hold true for many endangered plant species in the mountains all over the world.

Chromosome-level genome and population genomics of the intermediate horseshoe bat(Rhinolophus affinis)reveal the molecular basis of virus tolerance in Rhinolophus and echolocation call frequency variation

Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.

Genomic Profile of SARS-COV-2 Associated with COVID-19 Outbreaks in N’Djamena, Chad

Background: SARS-CoV-2 has circulated worldwide with dramatic consequences. In Chad, we have no data reported of variants. The aim of this study was to identify the SARS-CoV-2 variants that circulated during the epidemic from 2020 to 2021. Methods: This is a cross-sectional, descriptive study carried out between 2020 and 2021. Samples from patients with suspected COVID-19 were tested in five laboratories in N’Djamena. One hundred quality samples of the positives were sequenced in Kinshasa using Oxford nanopore technologies minion and the Protocol Midnight SARS-CoV2. Data were processed using Excel version 16 software. Results: Of the 100 samples sequenced, 77 (77%) produced sequences, 23 (23%) did not. The genomic profiles were wild-type Wuhan and minor mutations (19A, 19B (A), 20A (B.1, B.2), 20B (AV.1), 20D (B.1.1.1 /C.36), 20C), variant of concern Alpha (20I), variant of concern Delta (21A/J), variant of interest Eta (21D), variant of concern Omicron (21K) and unclassified variant under surveillance (B.1.640). Of these variants, the maximums were detected in patients aged 26 - 35 with 30.26% and 25.26% in 36 - 45. However, 24.67% were in travelers and 75.32% in residents, 35.06% in those vaccinated against COVID-19 and 62.33% in non-vaccinates. The estimated case-fatality rate was 2.44% (107/4374). Conclusion: This work has provided preliminary data on COVID-19 and SARS-CoV-2 variants circulating during the 2020-2021 epidemics in Chad.

Exploring the Unknown: The Application and Prospects of Artificial Intelligence in Genomics and Bioinformatics

This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.

PCR-HRM for Genomic Surveillance of SARS-CoV-2: A Variant Detection Tool in Côte d’Ivoire, West Africa

The rise of new viruses, like SARS-CoV-2 causing the COVID-19 outbreak, along with the return of antibiotic resistance in harmful bacteria, demands a swift and efficient reaction to safeguard the health and welfare of the global population. It is crucial to have effective measures for prevention, intervention, and monitoring in place to address these evolving and recurring risks, ensuring public health and international security. In countries with limited resources, utilizing recombinant mutation plasmid technology in conjunction with PCR-HRM could help differentiate the existence of novel variants. cDNA synthesis was carried out on 8 nasopharyngeal samples following viral RNA extraction. The P1 segment of the SARS-CoV-2 Spike S protein was amplified via conventional PCR. Subsequently, PCR products were ligated with the pGEM-T Easy vector to generate eight recombinant SARS-CoV-2 plasmids. Clones containing mutations were sequenced using Sanger sequencing and analyzed through PCR-HRM. The P1 segment of the S gene from SARS-CoV-2 was successfully amplified, resulting in 8 recombinant plasmids generated from the 231 bp fragment. PCR-HRM analysis of these recombinant plasmids differentiated three variations within the SARS-CoV-2 plasmid population, each displaying distinct melting temperatures. Sanger sequencing identified mutations A112C, G113T, A114G, G214T, and G216C on the P1 segment, validating the PCR-HRM findings of the variations. These mutations led to the detection of L452R or L452M and F486V protein mutations within the protein sequence of the Omicron variant of SARS-CoV-2. In summary, PCR-HRM is a vital and affordable tool for distinguishing SARS-CoV-2 variants utilizing recombinant plasmids as controls.

Large‑scale transcriptomic and genomic analyses reveal a novel functional gene SERPINB6 for chicken carcass traits

Background Carcass traits are crucial indicators of meat production efficiency.However,the molecular regulatory mechanisms associated with these traits remain unclear.Results In this study,we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms.Based on association analyses with the elastic net(EN)model,we identified 12 candidate genes(AMY1A,AP3B2,CEBPG,EEF2,EIF4EBP1,FGFR1,FOXD3,GOLM1,LOC107052698,PABPC1,SERPINB6 and TBC1D16)for 4 carcass-related traits,namely live weight,dressed weight,eviscerated weight,and breast muscle weight.SERPINB6 was identified as the only overlapping gene by 3 analyses,EN model analysis,weighted gene co-expression network analysis and differential expression analysis.Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts.Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression.Furthermore,a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3′UTR of SERPINB6.Conclusions Collectively,our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation.Additionally,the downstream variant rs317934171 regulates SERPINB6 expression.These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Metabologenomics and network pharmacology to understand the molecular mechanism of cancer research

In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cases 2023 November 6;11(31):7593-7609.Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats.Numerous methods for chemical warfare,communication,nutrition acquisition,and stress prevention are made possible by these specialized metabolites.Metabolomics is a popular technique for collecting direct mea-surements of metabolic activity from many biological systems.However,con-fusing metabolite identification is a typical issue,and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity.Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologe-nomics.This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes.Combining metabolomics and genomics data is beneficial.Furthermore,computer networking proposes that using meta-bolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.CONCLUSION The genome and metabolites of biological organisms are not fully characterized with current technologies.However,increasing high-throughput metabolomics and genomics data provide promising generation of paired data sets to understand the molecular mechanism of biochemical processes as well as determining targets for pharmaceutical drug design.Contemporary network infrastructures to integrate omics analysis can provide molecular mechanism of biochemical pathways.Furthermore,clinical data may be integrated to gene expression–metabolite expression by system genetics approach.Calculating pair-wise correlations and weighted correlation network analysis provide the basis of this integration[11-13].The occurrence of strong correlations between classified metabolites and co-expression transcripts implies either various roles of metabolites or linkages between metabolic pathways and the immune system.

Identifying Comprehensive Genomic Alterations and Potential Neoantigens for Cervical Cancer Immunotherapy in a Cohort of Chinese Squamous Cell Carcinoma of the Cervix

Objective Genomic alterations and potential neoantigens for cervical cancer immunotherapy were identified in a cohort of Chinese patients with cervical squamous cell carcinoma(CSCC).Methods Whole-exome sequencing was used to identify genomic alterations and potential neoantigens for CSCC immunotherapy.RNA Sequencing was performed to analyze neoantigen expression.Results Systematic bioinformatics analysis showed that C>T/G>A transitions/transversions were dominant in CSCCs.Missense mutations were the most frequent types of somatic mutation in the coding sequence regions.Mutational signature analysis detected signature 2,signature 6,and signature 7 in CSCC samples.PIK3CA,FBXW7,and BICRA were identified as potential driver genes,with BICRA as a newly reported gene.Genomic variation profiling identified 4,960 potential neoantigens,of which 114 were listed in two neoantigen-related databases.Conclusion The present findings contribute to our understanding of the genomic characteristics of CSCC and provide a foundation for the development of new biotechnology methods for individualized immunotherapy in CSCC.

Rice-wheat comparative genomics:Gains and gaps

Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits.Several wheat genes and gene families have been characterized based on their rice orthologs.Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops.Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping,deduce their putative functions and biochemical pathways,and develop molecular markers for marker-assisted breeding.A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering,gene editing,or wide crossing.

Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.

Comparative genomic analysis revealed that dietary habits affected the adaptation of Bifidobacterium bifidum to the intestinal tract in different geographic populations

Recent research on the genome of Bifidobacterium bifidum has mainly focused on the isolation sources(intestinal tract niche)recently,but reports on the isolation region are limited.This study analyzed the differences in the genome of B.bifidum isolated from different geographical populations by comparative genomic analysis.Results at the genome level indicated that the GC content of American isolates was significantly higher than that of Chinese and Russian isolates.The phylogenetic tree,based on 919 core genes showed that B.bifidum might be related to the geographical characteristics of isolation region.Furthermore,functional annotation analysis demonstrated that copy numbers of carbohydrate-active enzymes(CAZys)involved in the degradation of polysaccharide from plant and host sources in B.bifidum were high,and 18 CAZys showed significant differences across different geographical populations,indicating that B.bifidum had adapted to the human intestinal environment,especially in the groups with diets rich in fiber.Dietary habits were one of the main reasons for the differences of B.bifidum across different geographical populations.Additionally,B.bifidum exhibited high diversity,evident in glycoside hydrolases,the CRISPR-Cas system,and prophages.This study provides a genetic basis for further research and development of B.bifidum.

Multi-omics Approach Reveals Influenza-A Virus Target Genes Associated Genomic,Clinical and Immunological Characteristics in Cancers

Objective To examine the precise function of influenza A virus target genes(IATGs)in malignancy.Methods Using multi-omics data from the TCGA and TCPA datasets,33 tumor types were evaluated for IATGs.IATG expression in cancer cells was analyzed using transcriptome analysis.Copy number variation(CNV)was assessed using GISTICS 2.0.Spearman’s analysis was used to correlate mRNA expression with methylation levels.GSEA was used for the enrichment analysis.Pearson’s correlation analysis was used to examine the association between IATG mRNA expression and IC50.The ImmuCellAI algorithm was used to calculate the infiltration scores of 24 immune cell types.Results In 13 solid tumors,IATG mRNA levels were atypically expressed.Except for UCS,UVM,KICH,PCPG,THCA,CHOL,LAMI,and MESO,most cancers contained somatic IATG mutations.The main types of CNVs in IATGs are heterozygous amplifications and deletions.In most tumors,IATG mRNA expression is adversely associated with methylation.RT-PCR demonstrated that EGFR,ANXA5,CACNA1C,CD209,UVRAG were upregulated and CLEC4M was downregulated in KIRC cell lines,consistent with the TCGA and GTEx data.Conclusion Genomic changes and clinical characteristics of IATGs were identified,which may offer fresh perspectives linking the influenza A virus to cancer.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Mitogenomic variation in the Black-throated Tit(Aegithalos concinnus):Conserved structure,concerted evolution of duplicate control regions and multiple distinct evolutionary lineages

The mitochondrial genome is a prominent research topic due to its indispensable role in organisms and its application in many research disciplines.However,few studies have investigated intraspecies mitogenomic variation.In this study,69 mitogenomes of the Black-throated Tit(Aegithalos concinnus)were assembled and annotated from a large number of short reads generated using high-throughput sequencing technology.Comparative analyses revealed that mitogenomic characteristics such as length,gene and nucleotide composition,codon usage,and duplicated control regions were relatively conserved despite substantial intraspecies morphological changes.Yet,all the individuals from the subspecies A.c.iredalei had one more nucleotide in the 12S rRNA than the other studied subspecies.Phylogenetic analyses showed five distinct lineages based on the complete mitogenomes and the 13 combined protein-coding genes,whereas only four lineages were observed when using the duplicate control regions.Most interestingly,each lineage had both copies of the control regions of the comprising individuals,indicating that the paralogous control regions were more similar than the orthologous sequences from the distinct lineages.This suggested the control regions had undergone concerted evolution.The Black-throated Tit has complex evolutionary history and needs further investigating the taxonomic status of these lineages,as well as the underlying evolutionary processes.Our findings call for more research on intraspecies mitogenomic variation.

Genomic divergence and mutation load in the Begonia masoniana complex from limestone karsts

Understanding genome-wide diversity,inbreeding,and the burden of accumulated deleterious mutations in small and isolated populations is essential for predicting and enhancing population persistence and resilience.However,these effects are rarely studied in limestone karst plants.Here,we re-sequenced the nuclear genomes of 62 individuals of the Begonia masoniana complex(B.liuyanii,B.longgangensis,B.masoniana and B.variegata)and investigated genomic divergence and genetic load for these four species.Our analyses revealed four distinct clusters corresponding to each species within the complex.Notably,there was only limited admixture between B.liuyanii and B.longgangensis occurring in overlapping geographic regions.All species experienced historical bottlenecks during the Pleistocene,which were likely caused by glacial climate fluctuations.We detected an asymmetric historical gene flow between group pairs within this timeframe,highlighting a distinctive pattern of interspecific divergence attributable to karst geographic isolation.We found that isolated populations of B.masoniana have limited gene flow,the smallest recent population size,the highest inbreeding coefficients,and the greatest accumulation of recessive deleterious mutations.These findings underscore the urgency to prioritize conservation efforts for these isolated population.This study is among the first to disentangle the genetic differentiation and specific demographic history of karst Begonia plants at the whole-genome level,shedding light on the potential risks associated with the accumulation of deleterious mutations over generations of inbreeding.Moreover,our findings may facilitate conservation planning by providing critical baseline genetic data and a better understanding of the historical events that have shaped current population structure of rare and endangered karst plants.