Comparison of allele-specific expression in Sistani cattle and its crossbreed with Holstein, Simmental, and Montbeliarde breeds.

The present study has analyzed the allelic-specific expression in Purebred Sistani (Bos Indicus) and their crossbreed with Holstein, Simmental, and Montbeliarde breeds (Bos Taurus). The blood samples were taken from the caudal vein of purebred Sistani cows and crossbreed Sistani's with Holstein, Simental, and Montbeliarde (4 treatments). We discovered 152,496 (Purebred Sistani), 134,285 (Sistani × Simmental), 163,362 (Sistani × Montbeliarde), and 177,042 (Sistani × Holstein) SNPs on the assembled transcriptomes. In the Purebred Sistani, 8295 (5%), Sistani × Holstein crossbreed 11,900 (7%), Sistani × Simmental crossbreed 13,187 (10%), and Sistani × Montbeliarde crossbreed 16,666 (10%) number of SNPs were identified as ASE-SNPs. In the present study, 12 SNPs types identify, of which four were transition and eight were transversion. The most common SNPs were transition types. These SNPs were present in purebred Sistani 71.84%, Sistani × Holstein crossbreed 72.65%, Sistani × Simmental crossbreed 72.60%, and Sistani × Montbeliarde crossbreed 71.94%. Ontology analysis of the expressed genes in these cows revealed the involvement of these genes in different Biological classifications. Conducting such studies in parts of the world, such as the Sistan region, where it is not possible to record accurate records of cows, is a suitable and economical method for identifying genes with different expressions.

Correction: Salimiyekta et al. Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population. Animals 2021, 11, 3492.

There was an error in the original publication [...].

Comparison of different response variables in genomic prediction using GBLUP and ssGBLUP methods in Iranian Holstein cattle.

We compared the reliability and bias of genomic evaluation of Holstein bulls for milk, fat, and protein yield with two methods of genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP). Four response variables of estimated breeding value (EBV), daughter yield deviation (DYD), de-regressed proofs based on Garrick (DRPGR) and VanRaden (DRPVR) were used as dependent variables. The effects of three weighting methods for diagonal elements of the incidence matrix associated with residuals were also explored. The reliability and the absolute deviation from 1 of the regression coefficient of the response variable on genomic prediction (Dev) using GBLUP and ssGBLUP methods were estimated in the validation population. In the ssGBLUP method, the genomic prediction reliability and Dev from un-weighted DRPGR method for milk yield were 0.44 and 0.002, respectively. In the GBLUP method, the corresponding measurements from un-weighted EBV for fat were 0.52 and 0.008, respectively. Moreover, the un-weighted DRPGR performed well in ssGBLUP with fat yield values for reliability and Dev of 0.49 and 0.001, respectively, compared to equivalent protein yield values of 0.38 and 0.056, respectively. In general, the results from ssGBLUP of the un-weighted DRPGR for milk and fat yield and weighted DRPGR for protein yield outperformed other models. The average reliability of genomic predictions for three traits from ssGBLUP was 0.39 which was 0.98% higher than the average reliability from GBLUP. Likewise, the Dev of genomic predictions was lower in ssGBLUP than GBLUP. The average Dev of predictions for three traits from ssGBLUP and GBLUP were 0.110 and 0.144, respectively. In conclusion, genomic prediction using ssGBLUP outperformed GBLUP both in terms of reliability and bias.

The Candidate Chromosomal Regions Responsible for Milk Yield of Cow: A GWAS Meta-Analysis.

Milk yield (MY) is highly heritable and an economically important trait in dairy livestock species. To increase power to detect candidate genomic regions for this trait, we carried out a meta-analysis of genome-wide association studies (GWAS). In the present study, we identified 19 studies in PubMed for the meta-analysis. After review of the studies, 16 studies passed the filters for meta-analysis, and the number of chromosomes, detected markers and their positions, number of animals, and p-values were extracted from these studies and recorded. The final data set based on 16 GWAS studies had 353,698 cows and 3950 markers and was analyzed using METAL software. Our findings revealed 1712 significant (p-value < 2.5 × 10−6) genomic loci related to MY, with markers associated with MY found on all autosomes and sex chromosomes and the majority of them found on chromosome 14. Furthermore, gene ontology (GO) annotation was used to explore biological functions of the genes associated with MY; therefore, different regions of this chromosome may be suitable as genomic regions for further research into gene expression.

A Genome-Wide Scan for Signatures of Selection in Kurdish Horse Breed.

The genetic diversity and genomic regions being under putative natural selection in Kurdish horse population were studied. The samples from 72 horses were genotyped by using GGP Equine 70K SNP arrays. The Ne Slope (NeS) analyses revealed that a sharp decline in Ne has probably occurred around four generations ago, and high frequency of ROH with 2-4 Mbp in length suggested that the inbreeding has probably occurred around 20 generations ago. The effective population size (Ne) was 104 horses up to three generations ago and the average inbreeding (FROH) was 0.047(± 0.045). Using de-correlated composite of multiple selection signals (DCMS) and runs of homozygosity (ROH) analyses the genomic regions being under putative selection were detected. By using DCMS, a total of 148 significant SNP (FDR < 0.05) were identified, 40% of which were located on ECA9, where the greatest peak was observed. This genomic region harbors several known QTL which are associated with withers height (body size). Also, significant genomic regions (FDR < 0.05), harboring QTL associated with insect bite hypersensitivity (IBH), hair density and coat texture, alternate gaits, guttural pouch tympany and temperament were identified. By using outputs of ROH analyses, two hotspot regions (i.e., 30% of individuals was considered as threshold), were identified on ECA7 (50.11-54.36 Mbp) and ECA11 (26.10-29.07 Mbp) harboring QTL associated with withers height, alternate gait and IBH. In summary, the genomic regions being under putative natural selection which harbors known QTL associated with body size and IBH, among others, were introduced. Nevertheless, additional functional and comparative studies are necessary to corroborate their effect on the observed genetic and phenotypic diversity of the Kurdish horses.

Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population.

The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5-90 days) and records including late lactation (181-305 days) were 0.77-0.87 for cows and 0.81-0.94 for sires. These results show that we can select sires according to their daughters' early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Effect of cyclical cold stress during embryonic development on aspects of physiological responses and HSP70 gene expression of chicks.

The objective of the present study was to evaluate the effects of cyclical lower incubation temperature at different embryonic ages on the hatchability, body and organs weights, thyroid hormones, and liver HSP70 gene expression of newly hatched chicks. In a completely randomized design, fertile eggs of a broiler breeder (34 weeks of age) were assigned to three treatment groups with six replicates and 145 eggs per each. The treatment groups were as: control group (C) that eggs were incubated at 37.6°C during the whole incubation period; incubation temperature was decreased to 36°C for 3h per day at embryonic age from 12 to 14 (T1); and incubation temperature was decreased to 36°C for 3h per day at embryonic age from 15 to 17 (T2). No significant difference was found among treatments for hatchability (P>0.05). There were no differences (P>0.05) among treatments for body weight and liver weight, while heart weight of chicks in T1 and T2 groups were significantly higher than the control group (P<0.05). There were no differences (P>0.05) among treatments for the levels of thyroid hormones, however, the levels of both hormones tended to increase in chicks exposed to cold stress (T1 and T2). Chicks in T2 group had higher liver HSP70 gene expression compared with those in T1 and the control group (P<0.05). Cold stress in both incubation periods had no significant effect on the plasma levels of aspartate aminotransferase and alanine aminotransferase. Treatments had no effect on the plasma levels of glucose, cholesterol and triglyceride. The results of this study suggest that cyclical lower incubation temperatures (36°C) at the embryonic age from day 15-17 could induce the liver HSP70 gene expression, without negative effects on the hatchability and body weight of hatched chicks.

Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation.

Comparison of three boosting methods in parent-offspring trios for genotype imputation using simulation study.

BACKGROUND: Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. METHODS: In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. RESULTS: Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. CONCLUSIONS: The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.