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.

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.

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.

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.

Identification of new cotton fiber-quality QTL by multiple genomic analyses and development of markers for genomic breeding

Cotton fiber is one of the main raw materials for the textile industry.In recent years,many cotton fiber quality QTL have been identified,but few were applied in breeding.In this study,a genome wide association study(GWAS)of fiber-quality traits in 265 upland cotton breeding intermediate lines(GhBreeding),combined with genome-wide selective sweep analysis(GSSA)and genomic selection(GS),revealed 25 QTL.Most of these QTL were ignored by only using GWAS.The CRISPR/Cas9 mutants of GhMYB_D13 had shorter fiber,which indicates the credibility of QTL to a certain extent.Then these QTL were verified in other cotton natural populations,5 stable QTL were found having broad potential for application in breeding.Additionally,among these 5 stable QTL,superior genotypes of 4 showed an enrichment in most improved new varieties widely cultivated currently.These findings provide insights for how to identify more QTL through combined multiple genomic analysis to apply in breeding.

Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality.Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait.Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection.We performed a genome-wide association study(GWAS)and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpe?o and non-Tuxpe?o heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms(SNPs).Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses.More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines.Incorporating genotype-by-environment(G×E)interaction increased genomic prediction accuracy.

Alleviative effects of Bacillus coagulans strains on irritable bowel syndrome-unraveling strain specificity through physiological and genomic analysis

The high intraspecies heterogeneity of Baciillus coagulans leads to significant phenotypic differences among different strains.Thus,6 B.coagulans strains were tested in the present study using an irritable bowel syndrome(IBS)animal model to determine whether the IBS-alleviating effects of B.coagulans strains are strain-specific.The results of this study showed that the ingestion of B.coagulans GBI-30,6086,and B.coagulans CCFM1041 significantly alleviated IBS symptoms in mice.In contrast,other B.coagulans strains showed no or limited alleviating effects on IBS symptoms.According to our experimental results,the two main common features of these strains were as follows:1)The resistance of vegetative cells to bile salts,and 2)ability to synthesize specific lipids and secondary metabolites.Screening strains based on these two indicators may greatly reduce costs and provide a basis for mining new functional B.coagulans strains.Our results also suggest that administration of B.coagulans could significantly regulate microbiota dysbiosis in animal models.Moreover,the close relationships between the gut microbiota,gut microbiota metabolites,and IBS were further confirmed in this study.

Genomic signatures of selection,local adaptation and production type characterisation of East Adriatic sheep breeds

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.

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.

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.

Conservation genomics provides insights into genetic resilience and adaptation of the endangered Chinese hazelnut, Corylus chinensis

Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.

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.

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.

Genomic Selection for Frogeye Leaf Spot Resistance in Soybean

Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.

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.

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.

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.

The occurrence,inheritance,and segregation of complex genomic structural variation in synthetic Brassica napus

"Synthetic"allopolyploids recreated by interspecific hybridization play an important role in providing novel genomic variation for crop improvement.Such synthetic allopolyploids often undergo rapid genomic structural variation(SV).However,how such SV arises,is inherited and fixed,and how it affects important traits,has rarely been comprehensively and quantitively studied in advanced generation synthetic lines.A better understanding of these processes will aid breeders in knowing how to best utilize synthetic allopolyploids in breeding programs.Here,we analyzed three genetic mapping populations(735 DH lines)derived from crosses between advanced synthetic and conventional Brassica napus(rapeseed)lines,using whole-genome sequencing to determine genome composition.We observed high tolerance of large structural variants,particularly toward the telomeres,and preferential selection for balanced homoeologous exchanges(duplication/deletion events between the A and C genomes resulting in retention of gene/chromosome dosage between homoeologous chromosome pairs),including stable events involving whole chromosomes("pseudoeuploidy").Given the experimental design(all three populations shared a common parent),we were able to observe that parental SV was regularly inherited,showed genetic hitchhiking effects on segregation,and was one of the major factors inducing adjacent novel and larger SV.Surprisingly,novel SV occurred at low frequencies with no significant impacts on observed fertility and yield-related traits in the advanced generation synthetic lines.However,incorporating genome-wide SV in linkage mapping explained significantly more genetic variance for traits.Our results provide a framework for detecting and understanding the occurrence and inheritance of genomic SV in breeding programs,and support the use of synthetic parents as an important source of novel trait variation.

Genetic gain and inbreeding from simulation of different genomic mating schemes for pig improvement

Background Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values(GEBV)to accelerate the speed of genetic improvement in domestic animals.But after multi-generation selection,the rate of inbreeding and the occurrence of homozygous harmful alleles might increase,which would reduce performance and genetic diversity.To mitigate the above problems,we can utilize genomic mating(GM)based upon optimal mate allocation to construct the best genotypic combinations in the next generation.In this study,we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population.These factors included:the algorithm used to derive inbreeding coefficients;the trait heritability(0.1,0.3 or 0.5);the kind of GM scheme(focused average GEBV or inbreeding);the approach for computing the genomic relationship matrix(by SNP or runs of homozygosity(ROH)).The outcomes were compared to three traditional mating schemes(random,positive assortative or negative assortative matings).In addition,the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.Results Genomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain.The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs.The GROH-based GM schemes with the maximum genetic gain resulted in 0.9%-2.6%higher rates of genetic gainΔG,and 13%-83.3%lowerΔF than positive assortative mating regardless of heritability.The rates of inbreeding were always the fastest with positive assortative mating.Results from a purebred Large White pig population,confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes.Conclusion Compared with traditional mating schemes,genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population.Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.