A review of machine learning models applied to genomic prediction in animal breeding

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there’s no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs

Genetic diversity and breed-informative SNPs identification in domestic pig populations using coding SNPs

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products.Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations.Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei’s GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures.Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome

Anàlisi de gens relacionats amb el metabolisme lipídic en cabrum

El contingut en greix de la llet de cabra té importants conseqüències en el procés de fabricació de formatges. Els resultats obtinguts en aquest estudi han permès conèixer la seqüència nucleotídica de gens relacionats amb el metabolisme lipídic en cabres, una informació essencial per analitzar la seva funció i expressió

RNA-Sequence analysis of primary alveolar macrophages after in vitro infection with porcine reproductive and respiratory syndrome virus strains of differing virulence

Porcine reproductive and respiratory syndrome virus (PRRSV) mainly infects porcine alveolar macrophages (PAMs), resulting in porcine reproductive and respiratory syndrome (PRRS) in pigs. Most of the transcriptomic studies on PAMs infected with PRRSV conducted thus far have made use of microarray technology. Here, we investigated the transcriptome of PAMs in vitro at 12 h post-infection with two European PRRSV strains characterized by low (Lelystad, LV) and high (Lena) virulence through RNA-Seq. The expression levels of genes, isoforms, alternative transcription start sites (TSS) and differential promoter usage revealed a complex pattern of transcriptional and post-transcriptional gene regulation upon infection with the two strains. Gene ontology analysis confirmed that infection of PAMs with both the Lena and LV strains affected signaling pathways directly linked to the innate immune response, including interferon regulatory factors (IRF), RIG1-like receptors, TLRs and PKR pathways. The results confirmed that interferon signaling is crucial for transcriptional regulation during PAM infection. IFN-beta 1 and IFN-alpha omega, but not IFN-alpha, were up-regulated following infection with either the LV or Lena strain. The down-regulation of canonical pathways, such as the interplay between the innate and adaptive immune responses, cell death and TLR3/TLR7 signaling, was observed for both strains, but Lena triggered a stronger down-regulation than LV. This analysis contributes to a better understanding of the interactions between PRRSV and PAMs and outlines the differences in the responses of PAMs to strains with different levels of virulence, which may lead to the development of new PRRSV control strategies

Pig immune response to general stimulus and to porcine reproductive and respiratory syndrome virus infection: a meta-analysis approach

BACKGROUND: The availability of gene expression data that corresponds to pig immune response challenges provides compelling material for the understanding of the host immune system. Meta-analysis offers the opportunity to confirm and expand our knowledge by combining and studying at one time a vast set of independent studies creating large datasets with increased statistical power. In this study, we performed two meta-analyses of porcine transcriptomic data: i) scrutinized the global immune response to different challenges, and ii) determined the specific response to Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) infection. To gain an in-depth knowledge of the pig response to PRRSV infection, we used an original approach comparing and eliminating the common genes from both meta-analyses in order to identify genes and pathways specifically involved in the PRRSV immune response. The software Pointillist was used to cope with the highly disparate data, circumventing the biases generated by the specific responses linked to single studies. Next, we used the Ingenuity Pathways Analysis (IPA) software to survey the canonical pathways, biological functions and transcription factors found to be significantly involved in the pig immune response. We used 779 chips corresponding to 29 datasets for the pig global immune response and 279 chips obtained from 6 datasets for the pig response to PRRSV infection, respectively. RESULTS: The pig global immune response analysis showed interconnected canonical pathways involved in the regulation of translation and mitochondrial energy metabolism. Biological functions revealed in this meta-analysis were centred around translation regulation, which included protein synthesis, RNA-post transcriptional gene expression and cellular growth and proliferation. Furthermore, the oxidative phosphorylation and mitochondria dysfunctions, associated with stress signalling, were highly regulated. Transcription factors such as MYCN, MYC and NFE2L2 were found in this analysis to be potentially involved in the regulation of the immune response. The host specific response to PRRSV infection engendered the activation of well-defined canonical pathways in response to pathogen challenge such as TREM1, toll-like receptor and hyper-cytokinemia/ hyper-chemokinemia signalling. Furthermore, this analysis brought forth the central role of the crosstalk between innate and adaptive immune response and the regulation of anti-inflammatory response. The most significant transcription factor potentially involved in this analysis was HMGB1, which is required for the innate recognition of viral nucleic acids. Other transcription factors like interferon regulatory factors IRF1, IRF3, IRF5 and IRF8 were also involved in the pig specific response to PRRSV infection. CONCLUSIONS: This work reveals key genes, canonical pathways and biological functions involved in the pig global immune response to diverse challenges, including PRRSV infection. The powerful statistical approach led us to consolidate previous findings as well as to gain new insights into the pig immune response either to common stimuli or specifically to PRRSV infection

Short communication: Effect of αS1-casein (CSN1S1) and κ-casein (CSN3) genotypes on milk composition in Murciano-Granadina goats

The effects of the caprine alpha(S1)-casein (CSN1S1) polymorphisms on milk quality have been widely demonstrated. However, much less is known about the consequences of the kappa-casein (CSN3) genotype on milk composition in goats. Moreover, the occurrence of interactions between CSN3 and CSN1S1 genotypes has not been investigated. In this study, an association analysis between CSN1S1 and CSN3 genotypes and milk quality traits was performed in 89 Murciano-Granadina goats. Total milk yield as well as total protein, fat, solids-not-fat, lactose, alpha(S1)-casein (CSN1S1), and alpha(S2)-casein (CSN1S2) contents were recorded every other month during a whole lactation (316 observations). Data analysis using a linear mixed model for repeated observations revealed no interaction between the CSN1S1 and CSN3 genotypes. With regard to the effect of the CSN3 locus, AB and BB genotypes were significantly associated with higher levels of total casein and protein content compared with the AA CSN3 genotype. In strong contrast with French breeds, the CSN1S1 genotype did not affect protein, casein, and fat concentrations in Murciano-Granadina goats. These results highlight the importance of taking into consideration the CSN3 genotype when performing selection for milk composition in dairy goats

Effects of αs1-casein (CSN1S1) and κ-casein (CSN3) genotypes on milk coagulation properties in Murciano-Granadina goats

The effects of the caprine αs1-casein (CSN1S1) polymorphisms on milk quality and cheese yield have been widely studied in French and Italian goat breeds. Much less is known about the consequences of κ-casein (CSN3) genotype on the technological and coagulation properties of goat milk. In the current study, we have performed an association analysis between polymorphisms at the goat CSN1S1 and CSN3 genes and milk coagulation (rennet coagulation time, curdling rate and curd firmness) and technological (time to cutting of curd and cheese yield) properties. In this analysis, we have included 193 records from 74 Murciano-Granadina goats (with genotypes constituted by different combinations of alleles B, E and F of the gene CSN1S1 and alleles A and B of the gene CSN3) distributed in three herds, which were collected bimonthly during a whole lactation. Data analysis, using a linear mixed model for repeated observations, revealed significant associations between CSN1S1 genotypes and the rate of the curdling process. In this way, milk from EE goats had a significantly higher curdling rate than milk from BB individuals (PAB, P<0·05) but not on cheese yield. No interaction between the CSN1S1 and CSN3 genotypes was observed.This work was funded by a grant from the Spanish Ministry of Education and Science (AGL2002-04304-C03-02- GAN).Peer reviewe

Polymorphism of the goat agouti signaling protein gene and its relationship with coat color in Italian and Spanish breeds

Agouti signaling protein (ASIP) is one of the key players in the modulation of hair pigmentation in mammals. Binding to the melanocortin 1 receptor, ASIP induces the synthesis of phaeomelanin, associated with reddish brown, red, tan, and yellow coats. We have sequenced 2.8 kb of the goat ASIP gene in 48 individuals and identified two missense (Cys126Gly and Val128Gly) and two intronic polymorphisms. In silico analysis revealed that the Cys126Gly substitution may cause a structural change by disrupting a highly conserved disulfide bond. We studied its segregation in 12 Spanish and Italian goat breeds (N = 360) with different pigmentation patterns and found striking differences in the frequency of the putative loss-of-function Gly126 allele (Italian 0.43, Spanish Peninsular 0.08), but we did not observe a clear association with coat color. This suggests that the frequency of this putative loss-of-function allele has evolved under the influence of demographic rather than selection factors in goats from these two geographical areas.This research was partially funded by a grant (RZ2007-00005-C02-01) from the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (Spain).Peer reviewe