Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that all markers contribute equally to genetic variance, which can limit its predictive accuracy, especially for traits controlled by major genes. To overcome this limitation, we integrate results from genome-wide association studies (GWAS) into an enhanced ssGBLUP framework, termed single-step genome-wide association assisted BLUP (ssGWABLUP). Our approach assigns differential weights to markers on the basis of their GWAS results, thereby increasing the contribution of effective markers while diminishing the influence of ineffective ones during the construction of the genomic relationship matrix. By incorporating pseudo quantitative trait nucleotides (pQTNs) as covariates, we aim to capture the effects of markers closely associated with major causal variants, leading to the development of the ssGWABLUP_pQTNs. Compared with weighted ssGBLUP (WssGBLUP), the ssGWABLUP model demonstrated superior accuracy and dispersion across different genetic architectures. We then compared the performance of our proposed ssGWABLUP_pQTNs model against both ssGBLUP and ssGWABLUP across various genetic scenarios. Our results demonstrate that ssGWABLUP_pQTNs outperforms other models in terms of prediction accuracy, particularly in scenarios with simpler genetic architectures. Additionally, evaluation using pig dataset confirmed the effectiveness of ssGWABLUP_pQTNs, highlighting its potential for practical breeding applications. The incorporation of pQTNs and a weighted genomic relationship matrix presents a promising and potentially scalable approach to further enhance genomic prediction, with potential implications for improving the accuracy of genomic selection in breeding programs.

Lvliang black goat (LBG) is an excellent local breed resource in China that is known for its black fur, excellent meat quality, and strong adaptability. Studying the genetic mechanism and germplasm characteristics of LBG can provide theoretical and practical basis for the protection of the genetic resources of this breed and help implement conservation and breeding. In this study, the genetic diversity of the LBG population was evaluated using whole-genome SNP data. It was found that the LBG population had a high genetic diversity and a low degree of inbreeding. According to the clustering results of male goats and the relationship between individuals, the LBG population was divided into 13 families. Then, through population structure analysis, it was found that LBG had a close genetic relationship with the Nanjiang goat and Qinggoda goat populations, and they may have the same ancestors. The LBG population has retained some ancient genetic characteristics and is a special population that integrates local genetic characteristics and foreign gene flow. Through four selection signal analyses, we detected multiple candidate genes related to economic traits (CFL2, SCD, NLRP14, etc.) and adaptability (C4BPA, FUT8, PRNP, etc.) in the LBG population. In addition, in a comparative analysis with three commercial breeds (Saanen goat, Boer goat and Angora goat) we also found multiple genes related to physical characteristics (ERG, NRG3, EDN3, etc.). Finally, we performed functional enrichment analysis on these genes and explored their genetic mechanisms. This study provides important data support for the protection and breeding of LBG and provides a new perspective for enriching the genetic diversity of goat populations.

Natural or artificial selection could shape genetic architecture, e.g., the relationship between minor allele frequency (MAF) and the effect sizes of causal variants (CVs). This study aimed to investigate the impact of the MAF–effect size relationship (as a selection signature, S) on genomic prediction and heritability estimation in livestock, using both simulated data (Holstein) and real datasets (Holstein and pigs). We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). Simulation results demonstrated that for traits under strong negative selection (S < −1), both GBLUP-S and GBLUP-SMS outperformed classic GBLUP. The prediction accuracy of GBLUP-S improved by 0.011–0.031, while GBLUP-SMS achieved a gain of 0.005–0.025. Furthermore, GBLUP-SMS exhibited lower sensitivity to variations in S-values, whereas GBLUP-S heavily relied on accurate S specification. When the true S was matched, GBLUP-SMS generated more unbiased (or comparable) heritability estimates and higher prediction accuracy relative to GBLUP-S. Critically, mismatched S in GBLUP-S led to increased bias in heritability estimates and reduced prediction accuracy. Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. Negative selection signatures were identified for cattle (S = −0.5) and pig T1, T2, and T3 (S = −1.5, −1, and −2, respectively). These findings advance the theoretical framework of GBLUP-based genomic prediction and heritability estimation.

Lüliang black goat (LBG) is a unique livestock genetic resource of great significance for the local agriculture and economic development of Shanxi, China. However, the kidding rate of LBG is relatively low, which limits efforts to improve the reproductive performance and economic benefits of this breed. Therefore, improving the kidding characteristics of LBG is crucial for increasing its economic benefits. In this study, 20 LBG does were selected for whole-genome resequencing and divided into two groups: 10 in the T group (does with three consecutive kiddings of twin kids) and 10 in the S group (does with three consecutive kiddings of single kids). Based on whole-genome resequencing data, this study comprehensively assessed the population structure and genetic diversity of LBG and explored the related genes that affect reproductive performance. Three selection signal analysis methods—Fst (Fixation Index), π (nucleotide diversity), and XP-CLR (Cross Population Composite Likelihood Ratio)—were applied to screen a total of 838 genes, and enrichment analysis was performed to identify genes closely related to the reproductive performance of LBG, including ENPP3, APC, and GLI2. A generalized linear model was used to conduct a correlation analysis between non-synonymous mutations in the three genes and the number of kids produced. Two loci that were significantly correlated with kidding number were identified (p < 0.05): GLI2 g.63400363 C>T and GLI2 g.63417538 C>T. In general, the LBG population has high genetic diversity and good prospects for genetic improvement. The findings revealed that mining high-fecundity selection characteristics provides a basis for research on goat reproductive mechanisms.

Sheep are an economically important livestock species, and reproductive performance is a key trait affecting productivity. The Dorper × Hu hybrid sheep (DHS), widely bred in China, provides a valuable model for studying the genetic basis of prolificacy. This study aimed to investigate the genomic architecture and identify candidate genes associated with high litter size in DHS using whole-genome selective sweep analysis and genome-wide association study (GWAS). A total of 31 DHS individuals with complete reproductive records were sequenced and compared with publicly available genomic data from 20 Hu sheep (HUS) and 10 Dorper sheep (DPS). Population genetic structure and diversity were assessed using phylogenetic trees, principal component analysis (PCA), and ADMIXTURE analysis. To identify key genomic regions associated with litter size, we performed selective sweep analysis between the polytocous and monotocous subpopulations of DHS using multiple methods within a 50 kb sliding window framework, including $F_{ST}$, ${\theta }_{\pi }$ ratio, XP-CLR, and XP-EHH; we also conducted GWAS. DHS exhibited a distinct genetic structure with admixed ancestry and elevated genetic diversity. Genetic diversity analysis showed that DHS retained moderate levels of heterozygosity and polymorphism, comparable to or exceeding those of its parental breeds. Comparative analysis between polytocous and monotocous DHS identified reproduction-associated genes, including MUC1, PLCB4, SIN3A, and ELAVL2, enriched in pathways such as ovarian steroidogenesis, insulin secretion, and circadian entrainment. Furthermore, genome-wide association study (GWAS) identified 140 significant loci (p < 10⁻⁵) associated with reproductive traits. From these, 10 candidate SNPs were selected for validation through single-marker association analysis in 200 DHS individuals, among which two loci—g.88680390 C>A (SLC24A2/MLLT3) and g.18197516 T>C (ABCA1)—showed significant correlations with litter size. These findings enhance our understanding of the genetic basis of prolificacy in DHS and provide valuable molecular markers for genomic selection in sheep-breeding programs.