METHODS AND RESULTS: The amplification of genomic DNA with 32 ISSR markers detected an average of 97.64% polymorphism while 35.15% and 51.08% polymorphism per population and geographical zone, respectively. Analysis of molecular variance revealed significant variation within population 75% and between population 25% whereas within region 84% and between region 16%. The Bidillali exposed greater number of locally common band i.e., NLCB (≤ 25%) = 25 and NLCB (≤ 50%) = 115 were shown by Cancaraki while the lowest was recorded as NLCB (≤ 25%) = 6 and NLCB (≤ 50%) = 72 for Roko and Maibergo, accordingly. The highest PhiPT value was noted between Roko and Katawa (0.405*) whereas Nei's genetic distance was maximum between Roko and Karu (0.124). Based on Nei's genetic distance, a radial phylogenetic tree was constructed that assembled the entire accessions into 3 major clusters for further confirmation unrooted NJ vs NNet split tree analysis based on uncorrected P distance exposed the similar result. Principal coordinate analysis showed variation as PC1 (15.04%) > PC2 (5.81%).
CONCLUSIONS: The current study leads to prompting the genetic improvement and future breeding program by maximum utilization and better conservation of existing accessions. The accessions under Cancaraki and Jatau are population documented for future breeding program due to their higher genetic divergence and homozygosity.
RESULTS: The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods.
CONCLUSION: Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.
RESULTS: Our results demonstrate genetic control for FCR in tilapia, with a heritability estimate of 0.32 ± 0.11. Response to selection estimates showed FCR could be efficiently improved by selective breeding. Due to low genetic correlations, selection for growth traits would not improve FCR. However, weight loss at fasting has a high genetic correlation with FCR (0.80 ± 0.25) and a moderate heritability (0.23), and could be an easy to measure and efficient criterion to improve FCR by selective breeding in tilapia.
CONCLUSION: At this age, FCR is genetically determined in Nile tilapia. A selective breeding program could be possible and could help enabling the development of a more sustainable aquaculture production.
METHODS: DNA of 400 cynomolgus macaques from 10 Chinese breeding farms was genotyped to characterize their regional origin and rhesus ancestry proportion. A nested PCR assay was used to detect Plasmodium cynomolgi infection in sampled individuals.
RESULTS: All populations exhibited high levels of genetic heterogeneity and low levels of inbreeding and genetic subdivision. Almost all individuals exhibited an Indochinese origin and a rhesus ancestry proportion of 5%-48%. The incidence of P. cynomolgi infection in cynomolgus macaques is strongly associated with proportion of rhesus ancestry.
CONCLUSIONS: The varying amount of rhesus ancestry in cynomolgus macaques underscores the importance of monitoring their genetic similarity in malaria research.