Revealing the complete mtDNA genome sequence of Cemani chicken (Gallus gallus) by using Nanopore sequencing analysis.

Objective: This study aimed to identify, discover and explore the characteristics of the mtDNA genomes of Cemani chicken (Gallus gallus). Methods: This study used gDNA of Cemani chicken isolated from liver tissue. mtDNA sequencing was performed using WGS mtDNA analysis with nanopore technology by Oxford Nanopore Technologies GridION. Bioinformatics and data analysis were then performed. Results: This study showed that the length of the mtDNA genome is 16,789 bp, consisting of two ribosomal RNA (12S rRNA, 16S rRNA), 22 transfer RNA genes (trnR, trnG, trnK, trnD, trnS, trnY, trnC, trnN, trnA, trnW, trnM, trnQ, trnl, trnL, trnV, trnF, trnP, trnT, trnE, trnL, trnS, trnH), 13 protein-coding genes (PCGs) (ND4l, ND3, COX3, ATP6, ATP8, COX2, COX1, ND2, ND1, CYTB, ND6, ND5, ND4), and a noncoding control region (Dloop). Furthermore, analysis showed there were polymorphic sites and amino acid alterations when mtDNA Cemani chicken was aligned with references from GenBank. Conclusion: Site (988T>*) in Dloop genes and (328A>G) in ND3 genes which alter glycine to stop codon, were specific markers found only in Cemani chicken.

Genetic characteristics of complete mtDNA genome sequence of Indonesian local rabbit (Oryctolagus cuniculus).

BACKGROUND: Indonesian local rabbit (Oryctolagus cuniculus) is a local breed in Indonesia. We reveal the mitochondrial genome sequence of the Indonesian local Rabbit for the first time. A better understanding of the mechanisms underlying these beneficial aspects of local breeds over imported ones requires detailed genetic investigations, of which mtDNA genome sequencing is of particular importance. Such an investigation will solve the major issues of misidentification with Javanese hares (Lepus nigricollis) and maternal lineage. In addition, this information will guide better statistics on the Indonesian local rabbit breed population and strategies for its conservation and breeding plans. This study aimed to identify and explore the characteristics of the mtDNA genomes of Indonesian local rabbits. RESULT: This study observed that the length of the mtDNA genome is 17,469 bp, consisting of two ribosomal RNA (12S rRNA, 16S rRNA), 22 transfer RNA genes (trnR, trnG, trnK, trnD, trnS, trnY, trnC, trnN, trnA, trnW, trnM, trnQ, trnl, trnL, trnV, trnF, trnP, trnT, trnE, trnL, trnS, trnH), 13 protein-coding genes (PCGs) (ND4l, ND3, COX3, ATP6, ATP8, COX2, COX1, ND2, ND1, CYTB, ND6, ND5, ND4), a replication origin, and a noncoding control region (D-loop). CONCLUSIONS: mtDNA genome of Indonesian local rabbit was the longest and had the most extended D-loop sequence among the other references of Oryctolagus cuniculus. Other specific differences were also found in the percentage of nucleotides and variation in most of the PCGs when they were aligned with Oryctolagus cuniculus references from GenBank. Indonesian local Rabbits strongly suspected brought from Europe during the colonial period in Indonesia.

Bayesian estimates of genetic parameters of non-return rate and success in first insemination in Japanese Black cattle.

OBJECTIVE: The objective of present study was to estimate heritability of non-return rate (NRR) and success of first insemination (SFI) by using the Bayesian approach with Gibbs sampling. METHODS: Heifer Traits were denoted as NRR-h and SFI-h, and cow traits as NRR-c and SFI-c. The variance covariance components were estimated using threshold model under Bayesian procedures THRGIBBS1F90. RESULTS: The SFI was more relevant to evaluating success of insemination because a high percentage of animals that demonstrated no return did not successfully conceive in NRR. Estimated heritability of NRR and SFI in heifers were 0.032 and 0.039 and the corresponding estimates for cows were 0.020 and 0.027. The model showed low values of Geweke (p-value ranging between 0.012 and 0.018) and a low Monte Carlo chain error, indicating that the amount of a posteriori for the heritability estimate was valid for binary traits. Genetic correlation between the same traits among heifers and cows by using the two-trait threshold model were low, 0.485 and 0.591 for NRR and SFI, respectively. High genetic correlations were observed between NRR-h and SFI-h (0.922) and between NRR-c and SFI-c (0.954). CONCLUSION: SFI showed slightly higher heritability than NRR but the two traits are genetically correlated. Based on this result, both two could be used for early indicator for evaluate the capacity of cows to conceive.

Effect of insulin-like growth factor 1 gene on growth traits of Kejobong goat and its growth analysis.

AIM: This study aimed to identify the effect of the insulin-like growth factor 1 (IGF1) gene on growth, to uncover the genetic marker at the IGF1 gene, and to predict growth performance by analyzing growth models of Kejobong goats based on their genotype. MATERIALS AND METHODS: DNA and records of body weight (BW) and body measurements (BM) of Kejobong goats were collected, the IGF1 gene was amplified from the DNA template by polymerase chain reaction (PCR); the PCR products were then sequenced to determine single nucleotide polymorphisms (SNP). Linear mixed model (LMM) was used to analyze the association between SNP and growth traits. Four non-linear growth models were analyzed using non-LMM to describe the growth model and to compare the growth within genotypes. RESULTS: An SNP at intron 4 (g5752G→C) genotyped into GG and CC was significantly associated with BW and BM. Goats of genotype GG had a significantly higher BW and BM (p<0.05) than those of genotype CC. Growth analysis showed that the von Bertalanffy model was the most fit for describing BW, the Brody model for chest width and hip height, the Gompertz and Logistic models for heart girth, and the von Bertalanffy and Gompertz models for hip width. CONCLUSION: An SNP at intron 4 of the IGF1 gene was associated with the growth trait and was usable as a genetic marker candidate for improvement of growth traits of Kejobong goats while von Bertalanffy model provides proper and accurate estimates of parameters to describe the growth performance of Kejobong goats.

Genetics of heifer reproductive traits in Japanese Black cattle.

OBJECTIVE: The objective of this study was to identify environmental factors strongly associated with and to estimate genetic parameters of reproductive traits in Japanese Black heifers. METHODS: Data included reproduction records of Japanese Black heifers born between 2004 and 2014. First service non-return rate (NRR) to 56 days from first to successful insemination (FS), number of services per conception (IN), age at first calving (AFC) and gestation length (GL) were analyzed with the use of the general linear model (GLM). Genetic parameters were estimated with the use of the univariate animal model of the residual maximum likelihood (REML). RESULT: Averages of reproductive traits over eleven years were assessed, and the effects of farm, year, month, AI technician and interaction of farm × year on the traits were determined. Estimated heritability of FS was very low and that of AFC was higher than that of the other traits. A close genetic relation was observed among NRR, IN and FS; however, their heritabilities were very low. AFC shows favorable genetic correlation with IN and FS. CONCLUSION: Low heritabilities of the most reproductive traits in Japanese Black heifers are strongly influenced by farm management practices, and that large residual variances make genetic evaluation difficult. Among the reproductive traits, AFC is potentially more useful for genetic improvement of heifer reproductive traits because it has high heritability and favorable genetic correlations with IN and FS.

Different penalty methods for assessing interval from first to successful insemination in Japanese Black heifers

Objective: The objective of this study was to determine the best approach for handling missing records of first to successful insemination (FS) in Japanese Black heifers. Methods: Of a total of 2367 records used, 206 (8.7%) of open heifers were missing. Four penalty methods based on the number of inseminations were set as follows: C1 (FS average according to the number of inseminations), C2 (constant number of days; 359), C3 (maximum number of FS days to each insemination) and C4 (average of FS at the last insemination and FS of C2). C5 was generated by adding a constant number (21d) to the highest number of FS days in each contemporary group. The bootstrap method was used to compare among the 5 methods in terms of bias, mean squared error (MSE) and coefficient of correlation between estimated breeding value (EBV) of non-censored data and censored data. Three percentages (5%, 10% and 15%) were investigated using the random censoring scheme. The univariate animal model was used to conduct genetic analysis. Results: Heritability of FS in non-censored data was 0.012±0.016, slightly lower than the average estimate from the five penalty methods. C1, C2, and C3 showed lower standard errors of estimated heritability but demonstrated inconsistent results for different percentages of missing records. C4 showed moderate standard errors but more stable ones for all percentages of the missing records, whereas C5 showed the highest standard errors compared with noncensored data. The MSE in C4 heritability was 0.633×10­4, 0.879×10­4, 0.876×10­4 and 0.866 ×10­4 for 5%, 8.7%, 10%, and 15%, respectively, of the missing records. Thus, C4 showed the lowest and the most stable MSE of heritability; the coefficient of correlation for EBV was 0.88; 0.93 and 0.90 for heifer, sire and dam, respectively Conclusion: C4 demonstrated the highest positive correlation with the non-censored data set and was consistent within different percentages of the missing records. We concluded that C4 was the best penalty method for missing records due to the stable value of estimated parameters and the highest coefficient of correlation.