The increasing availability of multi-omics data is promising in enhancing genomic prediction in breeding and human genetics. However, integrating multi-omics data into genomic prediction models remains challenging due to complex relationships between omics layers and phenotypic outcomes. We propose Fusion Similarity Best Linear Unbiased Prediction (FSBLUP), a novel strategy that integrates genomic and intermediate omics data using a unified similarity matrix approach. FSBLUP systematically estimates how different omics layers contribute to phenotypic variation via machine-learning-optimized parameters that capture underlying genetic architecture of complex traits. FSBLUP demonstrates greater predictive accuracy than existing methods, as validated through theoretical and practical evaluations.

BACKGROUND:Multibreed genomic prediction (MBGP) is crucial for improving prediction accuracy for breeds with small populations, for which limited data are often available. Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic (co)variance in multitrait models can achieve higher joint prediction accuracy. However, the block partitioning method, a key factor influencing model performance, has not been extensively explored. RESULTS:We introduce mbBayesABLD, a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium (LD) patterns. In this model, marker effects within each block are assumed to follow normal distributions with block-specific parameters. We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation. The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models, such as GBLUP and BayesR. For the meat marbling score trait in pigs, compared with GBLUP, which does not account for heterogeneous genetic (co)variance, mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%. Furthermore, our findings indicate that a moderate level of similarity in LD patterns between breeds (with an average correlation of 0.6) is sufficient to improve the prediction accuracy of the target breed. CONCLUSIONS:This study presents a novel LD block-based approach for multibreed genomic prediction. Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.

Diaporthe species are critical plant pathogens that contribute to a disease complex responsible for substantial yield losses in soybean production worldwide. However, reports on the primary Diaporthe species causing soybean stem blight and their sensitivity to various fungicides are scarce in China. In this study, a total of 46 D. longicolla strains were isolated and identified from diseased soybean stems and rots collected from 14 regions of Heilongjiang province in 2021 and 2022. Among the eight fungicides examined, fludioxonil, mefentrifluconazole, tebuconazole, and azoxystrobin demonstrated effective inhibition for D. longicolla, with EC50 values < 0.3 µg/mL. Interestingly, the EC50 values of D. longicolla to two succinate dehydrogenase inhibitors (SDHIs), pydiflumetofen and fluopyram, were 5.47 µg/mL and over 100 µg/mL, respectively. In molecular dynamics simulations, pydiflumetofen exhibited a smaller RMSD, while fluopyram had a higher binding free energy with Sdh proteins compared to pydiflumetofen. This difference may contribute to the higher activity of pydiflumetofen in D. longicolla. Further analysis of the electrostatic potential and structural conformations of the binding pocket revealed that pydiflumetofen formed more hydrophobic interactions with SdhC and SdhD and was positioned closer to the SdhD subunit. A mixture of fludioxonil and mefentrifluconazole at a ratio of 1:5, as well as fludioxonil and pydiflumetofen at a ratio of 1:5, exhibited synergistic effects. These findings demonstrated that several fungicides could be utilized to control Diaporthe stem blight, and the difference in binding affinity to the Sdh subunit impacts sensitivity to fluopyram and pydiflumetofen.

Nonparametric models have recently been receiving increased attention due to their effectiveness in genomic prediction for complex traits. However, regular nonparametric models cannot effectively differentiate the relative importance of various SNPs, which significantly impedes the further application of these methods for genomic prediction. To enhance the fitting ability of nonparametric models and improve genomic prediction accuracy, a weighted kernel ridge regression model (WKRR) was proposed in this study. For this new method, different weights were assigned to different SNPs according to the p-values from GWAS, and then a KRR model based on these weighted SNPs was constructed for genomic prediction. Cross-validation was further adopted to choose appropriate hyper-parameters during the weighting and prediction process for generalization. We compared the predictive accuracy of WKRR with the genomic best linear unbiased prediction (GBLUP), BayesR, and unweighted KRR using both simulated and real datasets. The results showed that WKRR outperformed unweighted KRR in all simulated scenarios. Additionally, WKRR achieved an average improvement of 1.70% in accuracies across all traits in a mice dataset and 2.17% for three lactation-related traits in a cattle dataset compared to GBLUP, and yielded competitive results compared to BayesR. These findings demonstrated the great potential of weighted nonparametric models for genomic prediction.

Backgrounds: Objective of this study is to conduct a genome-wide association study (GWAS) of first-parity reproductive traits in Suzi pigs to identify significant single-nucleotide polymorphisms (SNPs) or candidate genes influencing these traits. Methods: This research employed technologies including the Zhongxin 50K SNP chip, simplified genome sequencing, resequencing, and the 100K SNP liquid chip to perform genome-wide SNP detection on 898 Suzi sows. Genotype data and phenotypic data were combined to do GWAS, gene annotation, and enrichment analysis. Results: Results showed that this study obtained phenotypes of 33 first-parity reproductive traits from 574 sows. GWAS results indicated there were 10 first-parity reproductive traits significantly associated with SNPs, and these traits were AFS, AFF, NNB, NH, NW, NS, NM, ND, PB, and CCN. These 10 traits were significantly associated with 60 SNPs, with 15 (25%) located on chromosome 2-the highest proportion. The SNPs significantly associated with AFS and AFF were largely identical. Genome-wide variance component analysis revealed that among the 10 traits with significantly associated SNPs in GWAS, there were 5 traits that exhibited genome-wide heritability ≥ 0.01. Trait of NM showed the highest heritability (0.65–0.7). These significantly associated SNPs annotated 20 candidate genes, including ADAMTS19, PROP1, ZNF354B, PCARE, LUZP2, VIRMA, EPHA5, AAAS, SLCO3A1-SV2B, KIF18A-BDNF, SERGEF, DYNLRB2, HNF4G, CATSPERD, HSD11B1L, DICER1, RARG, PCDHAC2, KRT79, and HSD17B2. GO analysis of candidate genes revealed that the top three biological processes were cell adhesion, positive regulation of cell projection organization, and positive regulation of neuron projection development. KEGG results showed the top three pathways were inositol phosphate metabolism, glutamatergic synapse, and phosphatidylinositol signaling system. Conclusions: These findings provide a foundation for the reproductive breeding of Suzi pigs and offer new insights into biological breeding in pigs.

Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits of different breeds as potentially genetically related but different, and divides chromosomes into independent blocks to fit heterogeneous genetic (co)variances. Best practices of random effect (co)variance matrix priors in mbBayesAB were analyzed, and the prediction accuracies of mbBayesAB were compared with within-breed (WBGP) and other commonly used MBGP models. The results showed that assigning an inverse Wishart prior to the random effect and obtaining information on the scale of the inverse Wishart prior from the phenotype enabled mbBayesAB to achieve the highest accuracy. When combining two cattle breeds (Limousin and Angus) in reference, mbBayesAB achieved higher accuracy than the WBGP model for two weight traits. For the marbling score trait in pigs, MBGP of the Yorkshire and Landrace breeds led to a 6.27% increase in accuracy for Yorkshire validation using mbBayesAB compared to that using the WBGP model. Therefore, considering heterogeneous genetic (co)variance in MBGP is advantageous. However, determining appropriate priors for (co)variance and hyperparameters is crucial for MBGP.

MOTIVATION:Utilizing both purebred and crossbred data in animal genetics is widely recognized as an optimal strategy for enhancing the predictive accuracy of breeding values. Practically, the different genetic background among several purebred populations and their crossbred offspring populations limits the application of traditional prediction methods. Several studies endeavor to predict the crossbred performance via the partial relationship, which divides the data into distinct sub-populations based on the common genetic background, such as one single purebred population and its corresponding crossbred descendant. However, this strategy makes prediction inaccurate due to ignoring half of the parental information of crossbreed animals. Furthermore, dominance effects, although playing a significant role in crossbreeding systems, cannot be modeled under such a prediction model. RESULTS:To overcome this weakness, we developed a novel multi-breed single-step model using metafounders to assess ancestral relationships across diverse breeds under a unified framework. We proposed to use multi-breed dominance combined relationship matrices to model additive and dominance effects simultaneously. Our method provides a straightforward way to evaluate the heterosis of crossbreeds and the breeding values of purebred parents efficiently and accurately. We performed simulation and real data analyses to verify the potential of our proposed method. Our proposed model improved prediction accuracy under all scenarios considered compared to commonly used methods. AVAILABILITY AND IMPLEMENTATION:The software for implementing our method is available at https://github.com/CAU-TeamLiuJF/MAGE.

As an important genotyping platform, SNP chips are essential for implementing genomic selection. In this article, we introduced the development of a liquid SNP chip panel for dairy goats. This panel contains 54,188 SNPs based on genotyping by targeted sequencing (GBTS) technology. The source of SNPs in the panel were from the whole-genome resequencing of 110 dairy goats from three European and two Chinese indigenous dairy goat breeds. The performance of this liquid SNP chip panel was evaluated by genotyping 200 additional goats. Fifteen of them were randomly selected for whole-genome resequencing. The average capture ratio of the panel design loci was 98.41%, and the genotype concordance with resequencing reached 98.02%. We further used this chip panel to conduct genome-wide association studies (GWAS) to detect genetic loci that affect coat color in dairy goats. A single significant association signal for hair color was found on chromosome 8 at 31.52–35.02 Mb. The TYRP1 gene, which is associated with coat color in goats, was identified to be located at this genomic region (chromosome 8: 31,500,048-31,519,064). The emergence of high-precision and low-cost liquid microarrays will improve the analysis of genomics and breeding efficiency of dairy goats.

Excreta traits comprise a very important characteristic in breeding that have been neglected for a long time. With the growth of intensive pig farming, plenty of environment problems have been raised, and people have begun to pay attention to pig excreta behaviors from genetics and breeding perspectives. However, the genetic architecture of excreta traits remains unclear. To investigate the genetic architecture of excreta traits in pigs, eight excreta traits and feed conversion ratio (FCR) were analyzed in this study. We performed genome-wide association studies (GWASs) on 213 Yorkshire pigs and estimated genetic parameters for a total number of 290 pigs, comprising 213 Yorkshire, 52 Landrace and 25 Duroc. After analysis, eight and 22 genome-wide significant SNPs were detected for FCR and the eight excreta traits in single-trait GWASs separately, and 18 were detected in a multi-trait meta-analysis for excreta traits, six of which were detected in both the single-trait and the multi-trait GWAS. Eighty, 182 and 133 genes were detected within 1 Mb of the genome-wide significant SNPs for FCR, excreta traits and multi-trait meta-analysis, respectively. Five candidate genes (BCKDC, DBT, ANKRD7, SHPRH and HCRT) with biochemical and physiological effects relevant to feed efficiency and excreta traits might be interesting markers for future breeding. Meanwhile, functional enrichment analysis indicates that most of the significant pathways are associated with the glutathione catabolic process, DNA topological change and replication fork protection complex. This study reveals the architecture of excreta traits in commercial pigs and offers an opportunity for decreasing the pollution from excreta using genomic selection in pigs.

Feed efficiency (FE) traits are key factors that can influence the economic benefits of pig production. However, little is known about the genetic architecture of FE and FE-related traits. This study aimed to identify SNPs and candidate genes associated with FE and FE-related traits, namely, average daily feed intake (ADFI), average daily gain (ADG), the feed conversion ratio (FCR), and residual feed intake (RFI). The phenotypes of 5823 boars with genotyped data (50 K BeadChip) from 1365 boars from a nucleus farm were used to perform a genome-wide association study (GWAS) of two breeds, Duroc and Yorkshire. Moreover, we performed a genetic parameter estimation for four FE and FE-related traits. The heritabilities of the FE and FE-related traits ranged from 0.13 to 0.36, and there were significant genetic correlations (−0.69 to 0.52) of the FE and FE-related traits with two growth traits (age at 100 kg and backfat thickness at 100 kg). A total of 61 significant SNPs located on eight different chromosomes associated with the four FE and FE-related traits were identified. We further identified four regions associated with FE and FE-related traits that have not been previously reported, and they may be potential novel QTLs for FE. Considering their biological functions, we finally identified 35 candidate genes relevant for FE and FE-related traits, such as the widely reported MC4R and INSR genes. A gene enrichment analysis showed that FE and FE-related traits were highly enriched in the biosynthesis, digestion, and metabolism of biomolecules. This study deepens our understanding of the genetic mechanisms of FE in pigs and provides valuable information for using marker-assisted selection in pigs to improve FE.

Growth rate plays a critical role in the pig industry and is related to quantitative traits controlled by many genes. Here, we aimed to identify causative mutations and candidate genes responsible for pig growth traits. In this study, 2360 Duroc pigs were used to detect significant additive, dominance, and epistatic effects associated with growth traits. As a result, a total number of 32 significant SNPs for additive or dominance effects were found to be associated with various factors, including adjusted age at a specified weight (AGE), average daily gain (ADG), backfat thickness (BF), and loin muscle depth (LMD). In addition, the detected additive significant SNPs explained 2.49%, 3.02%, 3.18%, and 1.96% of the deregressed estimated breeding value (DEBV) variance for AGE, ADG, BF, and LMD, respectively, while significant dominance SNPs could explain 2.24%, 13.26%, and 4.08% of AGE, BF, and LMD, respectively. Meanwhile, a total of 805 significant epistatic effects SNPs were associated with one of ADG, AGE, and LMD, from which 11 sub-networks were constructed. In total, 46 potential genes involved in muscle development, fat deposition, and regulation of cell growth were considered as candidates for growth traits, including CD55 and NRIP1 for AGE and ADG, TRIP11 and MIS2 for BF, and VRTN and ZEB2 for LMD, respectively. Generally, in this study, we detected both new and reported variants and potential candidate genes for growth traits of Duroc pigs, which might to be taken into account in future molecular breeding programs to improve the growth performance of pigs.

全基因组重测序已经成为生物信息学获取数据的最主要途径之一,但不同二代测序平台的数据质量及稳定性可能存在差异,从而对研究结果产生影响.本研究基于MGISEQ-2000、HiSeq 2000和NovaSeq 60003个测序平台分别对猪DNA样本进行全基因组重测序,分析比较3个平台的原始数据质量、比对质量和SNP变异检测情况,同时以猪50K芯片分型结果为标准评价测序数据SNP分型的准确性,以检验上述3个平台之间是否存在系统性差异.结果表明,3个平台的原始数据量及GC含量相近,测序深度均达到送测要求,平均测序深度在20X以上,HiSeq 2000和NovaSeq 6000平台Q30以上reads所占比例高于MGISEQ-2000(P<0.01),但两者的重复reads所占比例也高于MGISEQ-2000(P<0.01).MGISEQ-2000在双端比对率、平均比对深度和覆盖度超过20X的位点所占比例方面均高于其他2个平台(P<0.05),而NovaSeq 6000在平均比对深度和覆盖度超过20X的位点占比上又高于HiSeq 2000(P<0.05).MGISEQ-2000的SNP位点判型准确性与HiSeq 2000相似,二者平均准确性均达到97.21％且高于NovaSeq 6000.来自NovaSeq 6000平台的2个样本的SNP位点判型准确性结果远低于其他2个平台的结果,且其在测序稳定性上不如MGISEQ-2000和HiSeq 2000.综上,MGISEQ-2000、HiSeq 2000和NovaSeq 60003个平台的测序性能存在一定差异,但三者的测序数据均达到了生物信息学分析的要求,其中MGISEQ-2000平台在测序质量和稳定性上表现出色,可以为生物信息学分析提供更可靠的重测序数据.

试验旨在研究智能化饲喂对晋汾白猪哺乳母猪采食量、体况和生产性能的影响.试验选择20头晋汾白猪母猪,分为2组.试验组采用智能饲喂器饲喂,对照组采用人工饲喂.结果表明:哺乳12?16 d时,智能饲喂母猪的背膘厚度变化显著小于人工饲喂(P<0.05),智能饲喂的仔猪断奶体重变异系数显著小于人工饲喂(P<0.05);两种不同的饲喂方式下母猪的平均日采食量、断奶发情间隔、仔猪平均日增重和断奶重均无显著差异(P>0.05).试验结果表明,在整个哺乳期(1～28d)中,与人工饲喂相比,智能饲喂能显著提高断奶仔猪的均匀度.