Applying an association weight matrix in weighted genomic prediction of boar taint compounds

An association weight matrix identified biological pathways associated with bull fertility traits in a multi-breed population

Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix-partial correlation information theory (AWM-PCIT) approach. Beyond a simple list of candidate genes, AWM-PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility-associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower-density genotypes. Sequence-level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes-KDM5C, LRRK2, and PME-was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.

Genomic Prediction and Genome-Wide Association Study for Boar Taint Compounds

With a perspective future ban on surgical castration in Europe, selecting pigs with reduced ability to accumulate boar taint (BT) compounds (androstenone, indole, skatole) in their tissues seems a promising strategy. BT compound concentrations were quantified in the adipose tissue of 1075 boars genotyped at 29,844 SNPs. Traditional and SNP-based breeding values were estimated using pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP), respectively. Heritabilities for BT compounds were moderate (0.30-0.52). The accuracies of GBLUP and PBLUP were significantly different for androstenone (0.58 and 0.36, respectively), but comparable for indole and skatole (~0.43 and ~0.47, respectively). Several SNP windows, each explaining a small percentage of the variance of BT compound concentrations, were identified in a genome-wide association study (GWAS). A total of 18 candidate genes previously associated with BT (MX1), reproduction traits (TCF21, NME5, PTGFR, KCNQ1, UMODL1), and fat metabolism (CTSD, SYT8, TNNI2, CD81, EGR1, GIPC2, MIGA1, NEGR1, CCSER1, MTMR2, LPL, ERFE) were identified in the post-GWAS analysis. The large number of genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be partially ascribed to the pig line investigated, which is selected for ham quality and not for lean growth.

Improvement of Genomic Predictions in Small Breeds by Construction of Genomic Relationship Matrix Through Variable Selection

Genomic selection has been increasingly implemented in the animal breeding industry, and it is becoming a routine method in many livestock breeding contexts. However, its use is still limited in several small-population local breeds, which are, nonetheless, an important source of genetic variability of great economic value. A major roadblock for their genomic selection is accuracy when population size is limited: to improve breeding value accuracy, variable selection models that assume heterogenous variance have been proposed over the last few years. However, while these models might outperform traditional and genomic predictions in terms of accuracy, they also carry a proportional increase of breeding value bias and dispersion. These mutual increases are especially striking when genomic selection is performed with a low number of phenotypes and high shrinkage value—which is precisely the situation that happens with small local breeds. In our study, we tested several alternative methods to improve the accuracy of genomic selection in a small population. First, we investigated the impact of using only a subset of informative markers regarding prediction accuracy, bias, and dispersion. We used different algorithms to select them, such as recursive feature eliminations, penalized regression, and XGBoost. We compared our results with the predictions of pedigree-based BLUP, single-step genomic BLUP, and weighted single-step genomic BLUP in different simulated populations obtained by combining various parameters in terms of number of QTLs and effective population size. We also investigated these approaches on a real data set belonging to the small local Rendena breed. Our results show that the accuracy of GBLUP in small-sized populations increased when performed with SNPs selected via variable selection methods both in simulated and real data sets. In addition, the use of variable selection models—especially those using XGBoost—in our real data set did not impact bias and the dispersion of estimated breeding values. We have discussed possible explanations for our results and how our study can help estimate breeding values for future genomic selection in small breeds.