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.

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.

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.

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.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection(MAS)and genomic selection(GS)breeding have greatly improved the efficiency of rice breeding.Due to the influences of epistasis and gene pleiotropy,ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome.In this study,113 indica rice varieties(V)and their 565 testcross hybrids(TC)were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits.The original traits and general combining ability of the parents,as well as the original traits and midparent heterosis of TC,were subjected to genome-wide association analysis.In total,381 primary significantly associated loci(SAL)and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected.Among these loci,322 candidate genes located within or nearby the SALs were screened,204 of which were cloned genes.A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs.All the SALs were used to construct genetic networks,in which 91 pleiotropic loci were investigated.Additionally,we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs,primary SALs,secondary SALs or epistatic effect SALs as covariates.Although the prediction accuracies of the four models were generally not significantly different in the TC dataset,the incorporation of primary SALs,secondary SALs,and epistatic effect SALs significantly improved the prediction accuracies of 5(26%),3(16%),and 11(58%)traits in the V dataset,respectively.These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines.They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Genetic dissection of and genomic selection for seed weight, pod length,and pod width in soybean

A biparental soybean population of 364 recombinant inbred lines(RILs)derived from Zhongdou 41×ZYD02.878 was used to identify quantitative trait loci(QTL)associated with hundred-seed weight(100-SW),pod length(PL),and pod width(PW).100-SW,PL,and PW showed moderate correlations among one another,and 100-SW was correlated most strongly with PW(0.64–0.74).Respectively 74,70,75 and19 QTL accounting for 38.7%–78.8%of total phenotypic variance were identified by inclusive composite interval mapping,restricted two-stage multi-locus genome-wide association analysis,3 variancecomponent multi-locus random-SNP-effect mixed linear model analysis,and conditional genome-wide association analysis.Of these QTL,189 were novel,and 24 were detected by multiple methods.Six loci were associated with 100-SW,PL,and PW and may be pleiotropic loci.A total of 284 candidate genes were identified in colocalizing QTL regions,including the verified gene Seed thickness 1(ST1).Eleven genes with functions involved in pectin biosynthesis,phytohormone,ubiquitin-protein,and photosynthesis pathways were prioritized by examining single nucleotide polymorphism(SNP)variation,calculating genetic differentiation index,and inquiring gene expression.The prediction accuracies of genomic selection(GS)for 100-SW,PL,and PW based on single trait-associated markers reached 0.82,0.76,and 0.86 respectively,but selection index(SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy.These results shed light on the genetic basis of 100-SW,PL,and PW and provide GS models for these traits with potential application in breeding programs.

Genomic Mutations Within the Host Microbiome: Adaptive Evolution or Purifying Selection

Next-generation sequencing technology has transformed our ability to assess the taxonomic composition functions of host-associated microbiota and microbiomes. More human microbiome research projects—particularly those that explore genomic mutations within the microbiome—will be launched in the next decade. This review focuses on the coevolution of microbes within a microbiome, which shapes strain-level diversity both within and between host species. We also explore the correlation between microbial genomic mutations and common metabolic diseases, and the adaptive evolution of pathogens and probiotics during invasion and colonization. Finally, we discuss advances in methods and algorithms for annotating and analyzing microbial genomic mutations.

Zea mays(L.) P1 locus for cob glume color identified as a post-domestication selection target with an effect on temperate maize genomes

Artificial selection during domestication and post-domestication improvement results in loss of genetic diversity near target loci. However, the genetic locus associated with cob glume color and the nature of the genomic pattern surrounding it was elusive and the selection effect in that region was not clear. An association mapping panel consisting of 283 diverse modern temperate maize elite lines was genotyped by a chip containing over 55,000 evenly distributed SNPs. Ten-fold resequencing at the target region on 40 of the panel lines and 47 tropical lines was also undertaken. A genome-wide association study(GWAS) for cob glume color confirmed the P1 locus, which is located on the short arm of chromosome 1, with a-log10 P value for surrounding SNPs higher than the Bonferroni threshold(α/n, α < 0.001) when a mixed linear model(MLM) was implemented. A total of 26 markers were identified in a 0.78 Mb region surrounding the P1 locus, including 0.73 Mb and 0.05 Mb upstream and downstream of the P1 gene, respectively. A clear linkage disequilibrium(LD) block was found and LD decayed very rapidly with increasing physical distance surrounding the P1 locus. The estimates of π and Tajima's D were significantly(P < 0.001) lower at both ends compared to the locus. Upon comparison of temperate and tropical lines at much finer resolution by resequencing(180-fold finer than chip SNPs), a more structured LD block pattern was found among the 40 resequenced temperate lines. All evidence indicates that the P1 locus in temperate maize has not undergone neutral evolution but has been subjected to artificial selection during post-domestication selection or improvement. The information and analytical results generated in this study provide insights as to how breeding efforts have affected genome evolution in crop plants.

A genome scan of recent positive selection signatures in three sheep populations

Domesticated sheep have been exposed to artificial selection for the production of fiber, meat, and milk as well as to natural selection. Such selections are likely to have imposed distinctive selection signatures on the sheep genome. Therefore, detecting selection signatures across the genome may help elucidate mechanisms of selection and pinpoint candidate genes of interest for further investigation. Here, detection of selection signatures was conducted in three sheep breeds, Sunite （n=66）, German Mutton （n=159）, and Dorper （n=93）, using the Illumina OvineSNP50 Genotyping BeadChip array. Each animal provided genotype information for 43 273 autosomal single nucleotide polymorphisms （SNPs）. We adopted two complementary haplotype-based statistics of relative extended haplotype homozygosity （REHH） and the cross-popu- lation extended haplotype homozygosity （XP-EHH） tests. In total, 707,755, and 438 genomic regions subjected to positive selection were identified in Sunite, German Mutton, and Dorper sheep, respectively, and 42 of these regions were detected using both REHH and XP-EHH analyses. These genomic regions harbored many important genes, which were enriched in gene ontology terms involved in muscle development, growth, and fat metabolism. Fourteen of these genomic regions overlapped with those identified in our previous genome-wide association studies, further indicating that these genes under positive selection may underlie growth developmental traits. These findings contribute to the identification of candidate genes of interest and aid in understanding the evolutionary and biological mechanisms for controlling complex traits in Chinese and western sheep.

Development of Genome-Wide Scan for Selection Signature in Farm Animals

Identifying targets of positive selection in farm animals has, until recently, been frustratingly slow, relying on the analysis of individual candidate genes. Genomics, however, has provided the necessary resources to systematically interrogate the entire genome for signatures of selection. This review described important recent results derived from the application of genome-wide scan to the study of genetic changes in farm animals. These included findings of regions of the genome that showed breed differentiation, evidence of selective sweeps within individual genomes and signatures of demographic events. Particular attention is focused on the study of the implications for domestication. To date, sixteen genome-wide scans for recent or ongoing positive selection have been performed in farm animals. A key challenge is to begin synthesizing these newly constructed maps of selection into a coherent narrative of animal breed evolutionary history and derive a deeper mechanistic understanding of how animal populations improve or evolve. The major insights from the surveyed studies are highlighted and directions for future study are suggested.

Genome Mapping to Enhance Efficient Marker-Assisted Selection and Breeding of the Oil Palm (<i>Elaeis guineensis</i>Jacq.)

The oil palm (Elaeis guineensis Jacq.) is one of the major cultivated crops among the economically important palm species. It is cultivated mainly for its edible oil. For a perennial crop like oil palm, the use of Marker Assisted Selection (MAS) techniques helps to reduce the breeding cycle and improve the economic products. Genetic and physical maps are important for sequencing experiments since they show the exact positions of genes and other distinctive features in the chromosomal DNA. This review focuses on the role of genome mapping in oil palm breeding. It assesses the role of genome mapping in oil palm breeding and discusses the major factors affecting such mapping. Generating a high-density map governed by several factors, for instance, marker type, marker density, number of mapped population, and software used are the major issues treated. The general conclusion is that genome mapping is pivotal in the construction of a genetic linkage map. It helps to detect QTL and identify genes that control quantitative traits in oil palm. In perspective, the use of high-density molecular markers with a large number of markers, a large number mapping population, and up-to-date software is necessary for oil palm genome mapping.

Genome Maps, Genetic Diversity and Marker-Assisted Selection for Soybean Improvement

Soybean [Glycine max (L.) Merr.] is a major legume used for human and livestock consumption. It has protein quality and oil contents that closely meet the dietary requirements for both humans and animals (Lusas, 2004).

Genomic selection methods for crop improvement:Current status and prospects

With marker and phenotype information from observed populations, genomic selection （GS） can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in hese methods for crop improvement are reviewed in this paper.

Genomic selection: A breakthrough technology in rice breeding

Rice(Oryza sativa)provides a staple food source for more than half the world population.However,the current pace of rice breeding in yield growth is insufficient to meet the food demand of the everincreasing global population.Genomic selection(GS)holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured.Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding.However,several affecting factors and limitations require careful consideration when performing GS.In this review,we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding.We also highlight effective strategies to increase the predictive ability of various models,including GS models incorporating functional markers,genotype by environment interactions,multiple traits,selection index,and multiple omic data.Finally,we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)

In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory,the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models(RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection(GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value(GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than midparent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs.

Development of a 50K SNP Array for Japanese Flounder and Its Application in Genomic Selection for Disease Resistance

Single nucleotide polymorphism(SNP)armays are a powerful genotyping tool used in genetic research and genomic breeding programs.Japanese flounder(Paralichthys olivaceus)is an economically-important aquaculture flatfish in many countries.However,the lack of high-efficient genotyping tools has impeded the genomic breeding programs for Japanese flounder.We developed a 50K Japanese flounder SNP array,"Yuxin No.1,"and report its utility in genomic selection(GS)for disease resistance to bacterial pathogens.We screened more than 42,.2 million SNPs from the whole-genome resequencing data of 1099 individuals and selected 48697 SNPs that were evenly distributed across the genome to anchor the array with Affymetrix Axiom genotyping technology.Evaluation of the array performance with 168 fishs howed that 74.7%of the loci were successfully genotyped with high call rates(>98%)and that the poly-morphic SNPs had good cluster separations.More than 85%of the SNPs were concordant with SNPs obtained from the whole-genome resequencing data.To validate"Yuxin No.1"for GS,the arrayed geno-typing data of 27 individuals from a candidate population and 931 individuals from a reference popula-tion were used to calculate the genomic estimated breeding values(GEBVs)for disease resistance toEdwardsiella tarda.There was a 21.2%relative increase in the accuracy of GEBV using the weighted geno-mic best linear unpiased prediction(wGBLUJP),compared to traditional pedigree-based best linear unbi-ased prediction(ABLUP),suggesting good performance of the'Yuxin No.1"SNP array for GS.In summary,we developed the"Yuxin No.1"50K SNP array,which provides a useful platform for high-quality geno-typing that may be beneficial to the genomic selective breeding of Japanese flounder.

Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Epigenetics is the study of phenotypic variations that do not alter DNA sequences.Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers.One of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer.Therefore,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences.Currently,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying loci.At each DNA methylation site,a beta value(β)is used to reflect the methylation intensity.Therefore,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user bias.This study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites.The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research Institute.The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data sets.The NBDC-GA outperformed the IGA in terms of convergence performance.Furthermore,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,respectively.The proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities that were previously linked to cancer.

Genome-wide detection of selective signatures in a Jinhua pig population

The aim of this study was to detect evidence for signatures of recent selection in the Jinhua pig genome.These results can be useful to better understand the regions under selection in Jinhua pigs and might shed some lights on groups of genes that control production traits.In the present study,we performed extended haplotype homozygosity(EHH)tests to identify significant core regions in 202 Jinhua pigs.A total of 26161 core regions spanning 636.42 Mb were identified,which occupied approximately 28%of the genome across all autosomes,and 1158 significant(P<0.01)core haplotypes were selected.Genes in these regions were related to several economically important traits,including meat quality,reproduction,immune responses and exterior traits.A panel of genes including ssc-mir-365-2,KDM8,RABEP2,GSG1L,RHEB,RPH3AL and a signal pathway of PI3K-Akt were detected with the most extreme P-values.The findings in our study could draw a comparatively genome-wide map of selection signature in the pig genome,and also help to detect functional candidate genes under positive selection for further genetic and breeding research in Jinhua and other pigs.

A Personalized Digital Code from Unique Genome Fingerprinting Pattern for Use in Identification and Application on Blockchain

With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.