Genetic diversity and positive signatures of selection in indigenous cattle breeds of Iran.

Animal domestication, climate changes over time, and artificial selection have played significant roles in shaping the genome structure of various animal species, including cattle. These processes have led to the emergence of several indigenous cattle breeds with distinct genetic characteristics. This study focused on unraveling the genetic diversity and identifying candidate genomic regions in eight indigenous cattle breeds of Iran. The data consisted of ∼777 962 single nucleotide polymorphisms (SNPs) of 89 animals from Iranian indigenous cattle scattered throughout the country. We employed various methods, including integrated haplotype score, FST, and cross-population composite likelihood ratio, to conduct a genome scan for detecting selection signals within and between cattle populations. Average observed heterozygosity across the populations was 0.36, with a range of 0.32-0.40. In addition, negative and low rates of inbreeding (FIS) in the populations were observed. The genome-wide analysis revealed several genomic regions that harbored candidate genes associated with production traits (e.g., MFSD1, TYW5, ADRB2, BLK, and CRTC3), adaptation to local environmental constraints (CACNA2D1, CXCL3, and GRO1), and coat color (DYM). Finally, the study of the reported quantitative trait loci (QTL) regions in the cattle genome demonstrated that the identified regions were associated with QTL related to important traits such as milk composition, body weight, daily gain, feed conversion, and residual feed intake. Overall, this study contributes to a better understanding of the genetic diversity and potential candidate genes underlying important traits in Iranian indigenous cattle breeds, which can inform future breeding and conservation efforts.

A predictive assessment of genetic correlations between traits in chickens using markers.

BACKGROUND: Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. METHODS: A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 - λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the "optimum" λ was determined using cross-validation. RESULTS: Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW-HHP and BM-HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together. CONCLUSIONS: Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

Influence of milking frequency on genetic parameters associated with the milk production in the first and second lactations of Iranian Holstein dairy cows using random regression test day models.

BACKGROUND: The milk yield can be affected by the frequency of milking per day, in dairy cows. Previous studies have shown that the milk yield is increased by 6-25 % per lactation when the milking frequency is increased from 2 to 3 times per day while the somatic cell count is decreased. To investigate the effect of milking frequency (3X vs. 4X) on milk yield and it's genetic parameters in the first and second lactations of the Iranian Holstein dairy cows, a total of 142,604 test day (TD) records of milk yield were measured on 20,762 cows. RESULTS: Heritability estimates of milk yield were 0.25 and 0.19 for 3X milking frequency and 0.34 and 0.26 for 4X milking frequency throughout the first and second lactations, respectively. Repeatability estimates of milk yield were 0.70 and 0.71 for 3X milking frequency and 0.76 and 0.77 for 4X milking frequency, respectively. In comparison with 3X milking frequency, the milk yield of the first and second lactations was increased by 11.6 and 12.2 %, respectively when 4X was used (p < 0.01). CONCLUSIONS: Results of this research demonstrated that increasing milking frequency led to an increase in heritability and repeatability of milk yield. The current investigation provided clear evidences for the benefits of using 4X milking frequency instead of 3X in Iranian Holstein dairy cows.