Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach

Using machine learning to realize genetic site screening and genomic prediction of productive traits in pigs

Genomic prediction, which is based on solving linear mixed-model (LMM) equations, is the most popular method for predicting breeding values or phenotypic performance for economic traits in livestock. With the need to further improve the performance of genomic prediction, nonlinear methods have been considered as an alternative and promising approach. The excellent ability to predict phenotypes in animal husbandry has been demonstrated by machine learning (ML) approaches, which have been rapidly developed. To investigate the feasibility and reliability of implementing genomic prediction using nonlinear models, the performances of genomic predictions for pig productive traits using the linear genomic selection model and nonlinear machine learning models were compared. Then, to reduce the high-dimensional features of genome sequence data, different machine learning algorithms, including the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and convolutional neural network (CNN) algorithms, were used to perform genomic feature selection as well as genomic prediction on reduced feature genome data. All of the analyses were processed on two real pig datasets: the published PIC pig dataset and a dataset comprising data from a national pig nucleus herd in Chifeng, North China. Overall, the accuracies of predicted phenotypic performance for traits T1, T2, T3 and T5 in the PIC dataset and average daily gain (ADG) in the Chifeng dataset were higher using the ML methods than the LMM method, while those for trait T4 in the PIC dataset and total number of piglets born (TNB) in the Chifeng dataset were slightly lower using the ML methods than the LMM method. Among all the different ML algorithms, SVM was the most appropriate for genomic prediction. For the genomic feature selection experiment, the most stable and most accurate results across different algorithms were achieved using XGBoost in combination with the SVM algorithm. Through feature selection, the number of genomic markers can be reduced to 1 in 20, while the predictive performance on some traits can even be improved compared to using the full genome data. Finally, we developed a new tool that can be used to execute combined XGBoost and SVM algorithms to realize genomic feature selection and phenotypic prediction.

Comparative study of neuropeptide signaling systems in Hemiptera

Numerous physiological processes in insects are tightly regulated by neuropeptides and their receptors. Although they form an ancient signaling system, there is still a great deal of variety in neuropeptides and their receptors among different species within the same order. Neuropeptides and their receptors have been documented in many hemipteran insects, but the differences among them have been poorly characterized. Commercial grapevines worldwide are plagued by the bug Daktulosphaira vitifoliae (Hemiptera: Sternorrhyncha). Here, 33 neuropeptide precursors and 48 putative neuropeptide G protein-coupled receptor (GPCR) genes were identified in D. vitifoliae. Their expression profiles at the probe and feeding stages reflected potential regulatory roles in probe behavior. By comparison, we found that the Releasing Hormone-Related Peptides (GnRHs) system of Sternorrhyncha was differentiated from those of the other 2 suborders in Hemiptera. Independent secondary losses of the adipokinetic hormone/corazonin-related peptide receptor (ACP) and corazonin (CRZ) occurred during the evolution of Sternorrhyncha. Additionally, we discovered that the neuropeptide signaling systems of Sternorrhyncha were very different from those of Heteroptera and Auchenorrhyncha, which was consistent with Sternorrhyncha's phylogenetic position at the base of the order. This research provides more knowledge on neuropeptide systems and sets the groundwork for the creation of novel D. vitifoliae management strategies that specifically target these signaling pathways.

Whole genome sequencing of simmental cattle for SNP and CNV discovery

BACKGROUD

The single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) are two major genomic variants, which play crucial roles in evolutionary and phenotypic diversity.

RESULTS

In this study, we performed a comprehensive analysis to explore the genetic variations (SNPs and CNVs) of high sperm motility (HSM) and poor sperm motility (PSM) Simmental bulls using the high-coverage (25×) short-read next generation sequencing and single-molecule long reads sequencing data. A total of ~ 15 million SNPs and 2,944 CNV regions (CNVRs) were detected in Simmental bulls, and a set of positive selected genes (PSGs) and CNVRs were found to be overlapped with quantitative trait loci (QTLs) involving immunity, muscle development, reproduction, etc. In addition, we detected two new variants in LEPR, which may be related to the artificial breeding to improve important economic traits. Moreover, a set of genes and pathways functionally related to male fertility were identified. Remarkably, a CNV on SPAG16 (chr2:101,427,468 - 101,429,883) was completely deleted in all poor sperm motility (PSM) bulls and half of the bulls in high sperm motility (HSM), which may play a crucial role in the bull-fertility.

CONCLUSIONS

In conclusion, this study provides a valuable genetic variation resource for the cattle breeding and selection programs.

Genomic and transcriptomic analyses enable the identification of important genes associated with subcutaneous fat deposition in Holstein cows

Subcutaneous fat deposition has many important roles in dairy cattle, including immunological defense and mechanical protection. The main objectives of this study are to identify key candidate genes regulating subcutaneous fat deposition in high-producing dairy cows by integrating genomic and transcriptomic datasets. A total of 1,654 genotyped Holstein cows are used to perform a genome-wide association study (GWAS) aiming to identify genes associated with subcutaneous fat deposition. Subsequently, weighted gene co-expression network analyses (WGCNA) are conducted based on RNA-sequencing data of 34 cows and de-regressed estimated breeding values of subcutaneous fat deposition. Lastly, differentially expressed (DE) mRNA, lncRNA, and differentially alternative splicing genes are obtained for 12 Holstein cows with extreme and divergent phenotypes for subcutaneous fat deposition. Forty-six protein-coding genes are identified as candidate genes regulating subcutaneous fat deposition in Holstein cattle based on the GWAS. Eleven overlapping genes are identified based on the analyses of DE genes and WGCNA. Furthermore, the candidate genes identified based on the GWAS, WGCNA, and analyses of DE genes are significantly enriched for pathways involved in metabolism, oxidative phosphorylation, thermogenesis, fatty acid degradation, and glycolysis/gluconeogenesis pathways. Integrating all findings, the NID2, STARD3, UFC1, DEDD, PPP1R1B, and USP21 genes are considered to be the most important candidate genes influencing subcutaneous fat deposition traits in Holstein cows. This study provides novel insights into the regulation mechanism underlying fat deposition in high-producing dairy cows, which will be useful when designing management and breeding strategies.

A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle

This study aimed to perform a genome-wide association analysis (GWAS) using the Random Forest (RF) approach for scanning candidate genes for age at first calving (AFC) in Nellore cattle. Additionally, potential epistatic effects were investigated using linear mixed models with pairwise interactions between all markers with high importance scores within the tree ensemble non-linear structure. Data from Nellore cattle were used, including records of animals born between 1984 and 2015 and raised in commercial herds located in different regions of Brazil. The estimated breeding values (EBV) were computed and used as the response variable in the genomic analyses. After quality control, the remaining number of animals and SNPs considered were 3,174 and 360,130, respectively. Five independent RF analyses were carried out, considering different initialization seeds. The importance score of each SNP was averaged across the independent RF analyses to rank the markers according to their predictive relevance. A total of 117 SNPs associated with AFC were identified, which spanned 10 autosomes (2, 3, 5, 10, 11, 17, 18, 21, 24, and 25). In total, 23 non-overlapping genomic regions embedded 262 candidate genes for AFC. Enrichment analysis and previous evidence in the literature revealed that many candidate genes annotated close to the lead SNPs have key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cell maturation, and pheromone recognition. Furthermore, some genomic regions previously associated with fertility and growth traits in Nellore cattle were also detected in the present study, reinforcing the effectiveness of RF for pre-screening candidate regions associated with complex traits. Complementary analyses revealed that many SNPs top-ranked in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes, remarkably in the BTAs 3, 5, 11, and 21. The reported results are expected to enhance the understanding of genetic mechanisms involved in the biological regulation of AFC in this cattle breed.

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

BACKGROUND

Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data.

RESULTS

Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided  ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties.

CONCLUSIONS

This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

Cross-population selection signatures in Canchim composite beef cattle

Analyses of livestock genomes have been used to detect selection signatures, which are genomic regions associated with traits under selection leading to a change in allele frequency. The objective of the present study was to characterize selection signatures in Canchim composite beef cattle using cross-population analyses with the founder Nelore and Charolais breeds. High-density single nucleotide polymorphism genotypes were available on 395 Canchim representing the target population, along with genotypes from 809 Nelore and 897 Charolais animals representing the reference populations. Most of the selection signatures were co-located with genes whose functions agree with the expectations of the breeding programs; these genes have previously been reported to associate with meat quality, as well as reproductive traits. Identified genes were related to immunity, adaptation, morphology, as well as behavior, could give new perspectives for understanding the genetic architecture of Canchim. Some selection signatures identified genes that were recently introduced in Canchim, such as the loci related to the polled trait.

Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model

Genome-wide association studies are a powerful tool to identify genomic regions and variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. The objectives of this study were to identify genomic regions as well as genes and pathways associated with the first-lactation milk, fat, protein, and total solid yields; fat, protein, and total solid percentage; and somatic cell score (SCS) in a Thai dairy cattle population. Effects of SNPs were estimated by a weighted single-step GWAS, which back-solved the genomic breeding values predicted using single-step genomic BLUP (ssGBLUP) fitting a single-trait random regression test-day model. Genomic regions that explained at least 0.5% of the total genetic variance were selected for further analyses of candidate genes. Despite the small number of genotyped animals, genomic predictions led to an improvement in the accuracy over the traditional BLUP. Genomic predictions using weighted ssGBLUP were slightly better than the ssGBLUP. The genomic regions associated with milk production traits contained 210 candidate genes on 19 chromosomes [Bos taurus autosome (BTA) 1 to 7, 9, 11 to 16, 20 to 21, 26 to 27 and 29], whereas 21 candidate genes on 3 chromosomes (BTA 11, 16, and 21) were associated with SCS. Many genomic regions explained a small fraction of the genetic variance, indicating polygenic inheritance of the studied traits. Several candidate genes coincided with previous reports for milk production traits in Holstein cattle, especially a large region of genes on BTA14. We identified 141 and 5 novel genes related to milk production and SCS, respectively. These novel genes were also found to be functionally related to heat tolerance (e.g., SLC45A2, IRAG1, and LOC101902172), longevity (e.g., SYT10 and LOC101903327), and fertility (e.g., PAG1). These findings may be attributed to indirect selection in our population. Identified biological networks including intracellular cell transportation and protein catabolism implicate milk production, whereas the immunological pathways such as lymphocyte activation are closely related to SCS. Further studies are required to validate our findings before exploiting them in genomic selection.

Genome-wide association studies of preweaning growth and in vivo carcass composition traits in Esme sheep

Sheep are considered as a major contributor of global food security. Moreover, sheep preweaning growth traits as well as in vivo carcass composition traits such as ultrasonic measurements of Longissimus dorsi muscle depth (UMD) and back-fat thickness (UFD) are crucially important indicators of meat yield and hot carcass composition. Despite their relative importance for productivity and profitability of a sheep production system, detected QTL for these traits are quite scarce. Therefore, we implemented GWAS for these traits using animal mixed model-based association approach provided by GenABEL in Esme sheep. Three genome-wide and 14 individual chromosome-wide associated SNPs were discovered. As a result, ESRP1, LOC105613082, ZNF641, DUSP5, TEAD1, SMOX, PTPRT, RALYL, POM121C, PHIP, LOC101106051, ZIM3, PEG3, TRPC7, FBXL4, LOC105610397, LOC105616489 and DNAAF2 were suggested as candidates. Some of the discovered genes and involved pathways were already annotated to contribute growth and development in various species including human, mice and cattle. All in all, the results of this study are expected to strongly contribute to shed a light on the underlying molecular mechanisms behind growth and carcass composition traits, with potential implications on studies aiming faster genetic improvement, targeted low-resolution SNP panel designs and genome-editing studies.

Random-effect meta-analysis of genetic parameter estimates for carcass and meat quality traits in beef cattle

Considerable variability of genetic parameter estimates is observed among different studies for the same trait, which is associated with the distinct effects included in the statistical model, population breed, and sample sizes. The random-effect meta-analysis summarizes genetic parameters considering the heterogeneity among studies. Therefore, the aim of this study was to perform a random-effect meta-analysis of heritability and genetic correlation estimates for carcass and meat quality traits in beef cattle. A total of 152 estimates of heritability and 83 genetic correlations for longissimus muscle area (LMA), back fat thickness (BFT), and marbling score (MRB) were used. High heterogeneity among published studies was observed for all traits, indicating the need of a random-effects model to perform the analysis. Estimates of heritability through the meta-analysis using the random-effects model were high (0.30 to 0.34), indicating that fast genetic progress can be obtained for these traits. However, genetic correlations had low magnitude (lower than 0.25), which suggested that all three traits should be included in the selection scheme.

Investigation of high correlation with carcass traits of SNPs of the PLCB1, C/EBPα, and TDRKH genes and the combinations of SNPs using the MDR method in the Hanwoo

BACKGROUND

Recently, many researchers focus on the best way to produce high-quality meat, as the trend in food consumption today is to focus on quality. In general, consumers' preferences in beef differ depending on taste and meatiness. Therefore, researchers are interested in how the marbling score affects the flavors of meat or the various factors that make up the meatiness to captivate the consumers' tastes.

OBJECTIVE

This study identifies single nucleotide polymorphisms (SNPs) or gene combinations that affect the carcass traits of Korean cattle (Hanwoo) by using the multifactor dimensionality reduction (MDR) method.

METHODS

We collected the candidate SNPs to identify SNPs related to marbling scores from whole-exome sequencing and bovine SNP genotyping data. Using 96 Hanwoo samples, we performed PCR amplification to investigate the polymorphism status. In addition, we investigated genetic relationships between carcass traits and SNPs using 612 Hanwoo samples. Furthermore, each candidate SNP genotype and the combinations of SNP genotypes were verified to improve the accuracy of genetic relationships using MDR method.

RESULTS

Twenty-four candidate SNPs associated with carcass trait and marbling scores were identified from SNP genotyping and whole-exome sequencing. Among them, three SNP markers (c.459 T > C of the PLCB1 gene, c.271 A > C of the C/EBPα gene, and g.17257 A > G of the TDRKH gene) were showed statistically significant differences between intramuscular fat and genotypes. Especially, two candidate SNPs, including c.459 T > C located in the PLCB1 gene and c.271 A > C located in the C/EBPα gene, could be highly associated with the intramuscular fat of Hanwoo quality grade. In addition, the combination of SNP genotypes is showed higher significant differences with carcass weight, backfat thickness, and longissimus dorsi muscle area.

CONCLUSION

Three SNP genotypes and the combination of SNP genotypes in the PLCB1, C/EBPα, and TDRKH genes may be useful genetic markers for improving beef quality.

Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods

The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2 = 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2 = 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the mean-squared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method.

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Simple Summary

Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. Gene set enrichment analysis indicated that the identified candidate genes were related to biological processes and functional terms that were involved in growth and lipid metabolism. In conclusion, these results suggest that the incorporation of GWAS results and network analysis can help us to better understand the genetic bases underlying growth and carcass traits.

Abstract

In recent years, studies on the biological mechanisms underlying complex traits have been facilitated by innovations in high-throughput genotyping technology. We conducted a weighted single-step genome-wide association study (WssGWAS) to evaluate backfat thickness, carcass weight, eye muscle area, marbling score, and yearling weight in a cohort of 1540 Hanwoo beef cattle using BovineSNP50 BeadChip. The WssGWAS uncovered thirty-three genomic regions that explained more than 1% of the additive genetic variance, mostly located on chromosomes 6 and 14. Among the identified window regions, seven quantitative trait loci (QTL) had pleiotropic effects and twenty-six QTL were trait-specific. Significant pathways implicated in the measured traits through Gene Ontology (GO) term enrichment analysis included the following: lipid biosynthetic process, regulation of lipid metabolic process, transport or localization of lipid, regulation of growth, developmental growth, and multicellular organism growth. Integration of GWAS results of the studied traits with pathway and network analyses facilitated the exploration of the respective candidate genes involved in several biological functions, particularly lipid and growth metabolism. This study provides novel insight into the genetic bases underlying complex traits and could be useful in developing breeding schemes aimed at improving growth and carcass traits in Hanwoo beef cattle.

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble-stacking (E-S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E-S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E-S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E-S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

Signatures of selection in Charolais beef cattle identified by genome-wide analysis

Charolais cattle are one of the most important breeds for meat production worldwide; in México, its selection is mainly made by live weight traits. One strategy for mapping important genomic regions that might influence productive traits is the identification of signatures of selection. This type of genomic features contains loci with extended linkage disequilibrium (LD) and homozygosity patterns that are commonly associated with sites of quantitative trait locus (QTL). Therefore, the objective of this study was to identify the signatures of selection in Charolais cattle genotyped with the GeneSeek Genomic Profiler Bovine HD panel consisting of 77 K single nucleotide polymorphisms (SNPs). A total 61,311 SNPs and 819 samples were used for the analysis. Identification of signatures of selection was carried out using the integrated haplotype score (iHS) methodology implemented in the rehh R package. The top ten SNPs with the highest piHS values were located on BTA 4, 5, 6 and 14. By identifying markers in LD with top ten SNPs, the candidate regions defined were mapped to 52.8-59.3 Mb on BTA 4; 67.5-69.3 on BTA 5; 39.5-41.0 Mb on BTA 6; and 26.4-29.6 Mb on BTA 14. The comparison of these candidate regions with the bovine QTLdb effectively confirmed the association (p < 0.05) with QTL related to growth traits and other important productive traits. The genomic regions identified in this study indicated selection for growth traits on the Charolais population via the conservation of haplotypes on various chromosomes. These genomic regions and their associated genes could serve as the basis for haplotype association studies and for the identification of causal genes related to growth traits.

Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle

Background

Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness.

Results

The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes.

Conclusions

GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0713-4) contains supplementary material, which is available to authorized users.

A copy number variant scan in the autochthonous Valdostana Red Pied cattle breed and comparison with specialized dairy populations

Copy number variants (CNVs) are an important source of genomic structural variation, recognized to influence phenotypic variation in many species. Many studies have focused on identifying CNVs within and between human and livestock populations alike, but only few have explored population-genetic properties in cattle based on CNVs derived from a high-density SNP array. We report a high-resolution CNV scan using Illumina’s 777k BovineHD Beadchip for Valdostana Red Pied (VRP), an autochthonous Italian dual-purpose cattle population reared in the Alps that did not undergo strong selection for production traits. After stringent quality control and filtering, CNVs were called across 108 bulls using the PennCNV software. A total of 6,784 CNVs were identified, summarized to 1,723 CNV regions (CNVRs) on 29 autosomes covering a total of ~59 Mb of the UMD3.1 assembly. Among the mapped CNVRs, there were 812 losses, 832 gains and 79 complexes. We subsequently performed a comparison of CNVs detected in the VRP and those available from published studies in the Italian Brown Swiss (IBS) and Mexican Holstein (HOL). A total of 171 CNVRs were common to all three breeds. Between VRP and IBS, 474 regions overlapped, while only 313 overlapped between VRP and HOL, indicating a more similar genetic background among populations with common origins, i.e. the Alps. The principal component, clustering and admixture analyses showed a clear separation of the three breeds into three distinct clusters. In order to describe the distribution of CNVs within and among breeds we used the pair V_ST statistic, considering only the CNVRs shared to more than 5 individuals (within breed). We identified unique and highly differentiated CNVs (n = 33), some of which could be due to specific breed selection and adaptation. Genes and QTL within these regions were characterized.

Genome-wide scan reveals divergent selection among taurine and zebu cattle populations from different regions

In this study, to identify genomic signatures of divergent selection, we genotyped 10 cattle breeds/populations (n = 275), representing eight Ethiopian cattle populations (n = 229) and two zebu populations (n = 46) adapted to tropical and sub-tropical environments, using the high-density single-nucleotide polymorphisms (SNPs) derived mainly from Bos indicus breeds, and using five reference taurine breeds (n = 212). Population genetic differentiation (F_ST ) values across sliding windows were estimated between zebu and reference combined taurine breeds. The most differentiated regions (F_ST  ≥ 0.53), representing the top 1% smoothed F_ST values, were considered to represent regions under diversifying selection. In total, 285 and 317 genes were identified in the comparisons of Ethiopian cattle with taurine and Asian zebu with taurine respectively. Some of these genes are involved in stress responses/thermo-tolerance and DNA damage repair (HSPA4, HSF1, CMPK1 and EIF2AK4), pigmentation (ERBB3 and MYO1A), reproduction/fertility (UBE2D3, ID3 and PSPC1), immune response (PIK3CD and AKIRIN2) and body stature and size (MBP2, LYN and NPM1). Additionally, the candidate genes were associated with functional terms (e.g. cellular response to stress, DNA repair, inflammatory response) important for physiological adaptation to environmental stresses. The results of our study may shed light on the influence of artificial and natural selection in shaping the genomic diversity of modern cattle breeds and also may serve as a basis for further genetic investigation of traits of tropical adaptation in cattle.

Genomewide association study for production and meat quality traits in Canchim beef cattle

The commercial value of the bovine carcass is determined by a set of traits, such as weight, yield, back fat thickness, and marbling; therefore, the genetic improvement of growth, meat, and carcass quality traits is an important tool to add value to the supply chain. Genomewide association studies (GWAS) enable the identification of loci that control phenotypic expression of quantitative traits (QTL). Therefore, the objective of this work was to perform a GWAS to identify genomic regions and genes associated with growth, carcass traits, and meat quality in Canchim beef cattle. These traits were yearling weight (YW), rib eye area (REA), back fat thickness (BFT), and marbling (MARB). To increase sample size and marker density, genotype imputation was performed, and only markers imputed with greater than 95% accuracy were used. Genomewide association study was performed using a Bayesian approach, by the Bayes B statistical method, incorporating genotypes and phenotypes from 614 animals from both the Canchim breed and the MA genetic group (offspring of Charolais bulls and one-half Canchim + one-half Zebu cows). This investigation identified 1 and 4 genomic regions explaining 0.23 and 7.35% of the genetic variance for REA and YW, respectively. These regions harbor a total of 19 genes, 7 of which were classified for biological functions by functional analysis. Significant associations were not observed for BFT and MARB. The identification of QTL that had been previously described in the literature reinforces associations found in this study.

Candidate genes for male and female reproductive traits in Canchim beef cattle

Background

Beef cattle breeding programs in Brazil have placed greater emphasis on the genomic study of reproductive traits of males and females due to their economic importance. In this study, genome-wide associations were assessed for scrotal circumference at 210 d of age, scrotal circumference at 420 d of age, age at first calving, and age at second calving, in Canchim beef cattle. Data quality control was conducted resulting in 672,778 SNPs and 392 animals.

Results

Associated SNPs were observed for scrotal circumference at 420 d of age (435 SNPs), followed by scrotal circumference at 210 d of age (12 SNPs), age at first calving (six SNPs), and age at second calving (four SNPs). We investigated whether significant SNPs were within genic or surrounding regions. Biological processes of genes were associated with immune system, multicellular organismal process, response to stimulus, apoptotic process, cellular component organization or biogenesis, biological adhesion, and reproduction.

Conclusions

Few associations were observed for scrotal circumference at 210 d of age, age at first calving, and age at second calving, reinforcing their polygenic inheritance and the complexity of understanding the genetic architecture of reproductive traits. Finding many associations for scrotal circumference at 420 d of age in various regions of the Canchim genome also reveals the difficulty of targeting specific candidate genes that could act on fertility; nonetheless, the high linkage disequilibrium between loci herein estimated could aid to overcome this issue. Therefore, all relevant information about genomic regions influencing reproductive traits may contribute to target candidate genes for further investigation of causal mutations and aid in future genomic studies in Canchim cattle to improve the breeding program.

Electronic supplementary material

The online version of this article (doi:10.1186/s40104-017-0199-8) contains supplementary material, which is available to authorized users.

Study on the introgression of beef breeds in Canchim cattle using single nucleotide polymorphism markers

The aim of this study was to evaluate the level of introgression of breeds in the Canchim (CA: 62.5% Charolais—37.5% Zebu) and MA genetic group (MA: 65.6% Charolais—34.4% Zebu) cattle using genomic information on Charolais (CH), Nelore (NE), and Indubrasil (IB) breeds. The number of animals used was 395 (CA and MA), 763 (NE), 338 (CH), and 37 (IB). The Bovine50SNP BeadChip from Illumina panel was used to estimate the levels of introgression of breeds considering the Maximum likelihood, Bayesian, and Single Regression method. After genotype quality control, 32,308 SNPs were considered in the analysis. Furthermore, three thresholds to prune out SNPs in linkage disequilibrium higher than 0.10, 0.05, and 0.01 were considered, resulting in 15,286, 7,652, and 1,582 SNPs, respectively. For k = 2, the proportion of taurine and indicine varied from the expected proportion based on pedigree for all methods studied. For k = 3, the Regression method was able to differentiate the animals in three main clusters assigned to each purebred breed, showing more reasonable according to its biological viewpoint. Analyzing the data considering k = 2 seems to be more appropriate for Canchim-MA animals due to its biological interpretation. The usage of 32,308 SNPs in the analyses resulted in similar findings between the estimated and expected breed proportions. Using the Regression approach, a contribution of Indubrasil was observed in Canchim-MA when k = 3 was considered. Genetic parameter estimation could account for this breed composition information as a source of variation in order to improve the accuracy of genetic models. Our findings may help assemble appropriate reference populations for genomic prediction for Canchim-MA in order to improve prediction accuracy. Using the information on the level of introgression in each individual could also be useful in breeding or crossing design to improve individual heterosis in crossbred cattle.

Dibutyryl-cAMP affecting fat deposition of finishing pigs by decreasing the inflammatory system related to insulin sensitive or lipolysis

Background

The mechanism of db-cAMP regulating fat deposition and improving lean percentage is unclear and needs to be further studied.

Methods

Eighteen 100-day-old Duroc × Landrance × Large White barrows (49.75 ± 0.75 kg) were used for experiment 1, and 15 eighteen 135-day-old barrows (78.34 ± 1.22 kg) were used for experiment 2 to investigate the effects of dietary dibutyryl-cAMP (db-cAMP) on fat deposition in finishing pigs. Pigs were fed with a corn-soybean meal-based diet supplemented with 0 or 15 mg/kg db-cAMP, and both experiments lasted 35 days, respectively.

Results

The results showed that db-cAMP decreased the backfat thickness, backfat percentage, and diameter of backfat cells without changing the growth performance or carcass characteristics in both experiments, and this effect was more marked in experiment 1 than in experiment 2; db-cAMP enhanced the activity of the growth hormone–insulin-like growth factor-1 (GH-IGF-1) axis and pro-opiomelanocortin (POMC) system in both experiments, which suppressed the accumulation of backfat deposition; microarray analysis showed that db-cAMP suppressed the inflammatory system within the adipose tissue related to insulin sensitivity, which also reduced fat synthesis.

Conclusions

In summary, the effect of db-cAMP on suppressing fat synthesis and accumulation is better in the earlier phase than in the later phase of finishing pigs, and db-cAMP plays this function by increasing the activity of the GH-IGF-1 axis and POMC system, while decreasing the inflammatory system within the adipose tissue related to insulin sensitive or lipolysis.

Integration of Random Forest with population-based outlier analyses provides insight on the genomic basis and evolution of run timing in Chinook salmon (Oncorhynchus tshawytscha)

Anadromous Chinook salmon populations vary in the period of river entry at the initiation of adult freshwater migration, facilitating optimal arrival at natal spawning. Run timing is a polygenic trait that shows evidence of rapid parallel evolution in some lineages, signifying a key role for this phenotype in the ecological divergence between populations. Studying the genetic basis of local adaptation in quantitative traits is often impractical in wild populations. Therefore, we used a novel approach, Random Forest, to detect markers linked to run timing across 14 populations from contrasting environments in the Columbia River and Puget Sound, USA. The approach permits detection of loci of small effect on the phenotype. Divergence between populations at these loci was then examined using both principle component analysis and FST outlier analyses, to determine whether shared genetic changes resulted in similar phenotypes across different lineages. Sequencing of 9107 RAD markers in 414 individuals identified 33 predictor loci explaining 79.2% of trait variance. Discriminant analysis of principal components of the predictors revealed both shared and unique evolutionary pathways in the trait across different lineages, characterized by minor allele frequency changes. However, genome mapping of predictor loci also identified positional overlap with two genomic outlier regions, consistent with selection on loci of large effect. Therefore, the results suggest selective sweeps on few loci and minor changes in loci that were detected by this study. Use of a polygenic framework has provided initial insight into how divergence in a trait has occurred in the wild.

Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories

BACKGROUND

Domestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history.

METHODS

In this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations.

RESULTS

Genome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected.

CONCLUSIONS

This study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.

RAD-QTL Mapping Reveals Both Genome-Level Parallelism and Different Genetic Architecture Underlying the Evolution of Body Shape in Lake Whitefish (Coregonus clupeaformis) Species Pairs

Parallel changes in body shape may evolve in response to similar environmental conditions, but whether such parallel phenotypic changes share a common genetic basis is still debated. The goal of this study was to assess whether parallel phenotypic changes could be explained by genetic parallelism, multiple genetic routes, or both. We first provide evidence for parallelism in fish shape by using geometric morphometrics among 300 fish representing five species pairs of Lake Whitefish. Using a genetic map comprising 3438 restriction site−associated DNA sequencing single-nucleotide polymorphisms, we then identified quantitative trait loci underlying body shape traits in a backcross family reared in the laboratory. A total of 138 body shape quantitative trait loci were identified in this cross, thus revealing a highly polygenic architecture of body shape in Lake Whitefish. Third, we tested for evidence of genetic parallelism among independent wild populations using both a single-locus method (outlier analysis) and a polygenic approach (analysis of covariation among markers). The single-locus approach provided limited evidence for genetic parallelism. However, the polygenic analysis revealed genetic parallelism for three of the five lakes, which differed from the two other lakes. These results provide evidence for both genetic parallelism and multiple genetic routes underlying parallel phenotypic evolution in fish shape among populations occupying similar ecological niches.

Towards the identification of the loci of adaptive evolution

1. Establishing the genetic and molecular basis underlying adaptive traits is one of the major goals of evolutionary geneticists in order to understand the connection between genotype and phenotype and elucidate the mechanisms of evolutionary change. Despite considerable effort to address this question, there remain relatively few systems in which the genes shaping adaptations have been identified. 2. Here, we review the experimental tools that have been applied to document the molecular basis underlying evolution in several natural systems, in order to highlight their benefits, limitations and suitability. In most cases, a combination of DNA, RNA and functional methodologies with field experiments will be needed to uncover the genes and mechanisms shaping adaptation in nature.

Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning approach

Background

Genome wide association studies are now widely used in the livestock sector to estimate the association among single nucleotide polymorphisms (SNPs) distributed across the whole genome and one or more trait. As computational power increases, the use of machine learning techniques to analyze large genome wide datasets becomes possible.

Methods

The objective of this study was to identify SNPs associated with the three traits simulated in the 16th MAS-QTL workshop dataset using the Random Forest (RF) approach. The approach was applied to single and multiple trait estimated breeding values, and on yield deviations and to compare them with the results of the GRAMMAR-CG method.

Results

The two QTL mapping methods used, GRAMMAR-CG and RF, were successful in identifying the main QTLs for trait 1 on chromosomes 1 and 4, for trait 2 on chromosomes 1, 4 and 5 and for trait 3 on chromosomes 1, 2 and 3.

Conclusions

The results of the RF approach were confirmed by the GRAMMAR-CG method and validated by the effective QTL position, even if their approach to unravel cryptic genetic structure is different. Furthermore, both methods showed complementary findings. However, when the variance explained by the QTL is low, they both failed to detect significant associations.

Genome-wide association for growth traits in Canchim beef cattle

Studies are being conducted on the applicability of genomic data to improve the accuracy of the selection process in livestock, and genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to identify genomic regions and genes that play roles in birth weight (BW), weaning weight adjusted for 210 days of age (WW), and long-yearling weight adjusted for 420 days of age (LYW) in Canchim cattle. GWAS were performed by means of the Generalized Quasi-Likelihood Score (GQLS) method using genotypes from the BovineHD BeadChip and estimated breeding values for BW, WW, and LYW. Data consisted of 285 animals from the Canchim breed and 114 from the MA genetic group (derived from crossings between Charolais sires and ½ Canchim + ½ Zebu dams). After applying a false discovery rate correction at a 10% significance level, a total of 4, 12, and 10 SNPs were significantly associated with BW, WW, and LYW, respectively. These SNPs were surveyed to their corresponding genes or to surrounding genes within a distance of 250 kb. The genes DPP6 (dipeptidyl-peptidase 6) and CLEC3B (C-type lectin domain family 3 member B) were highlighted, considering its functions on the development of the brain and skeletal system, respectively. The GQLS method identified regions on chromosome associated with birth weight, weaning weight, and long-yearling weight in Canchim and MA animals. New candidate regions for body weight traits were detected and some of them have interesting biological functions, of which most have not been previously reported. The observation of QTL reports for body weight traits, covering areas surrounding the genes (SNPs) herein identified provides more evidence for these associations. Future studies targeting these areas could provide further knowledge to uncover the genetic architecture underlying growth traits in Canchim cattle.