Simulation Analysis on Genomic Selection of Grouper (Epinephelus coioides) Breeding for Categorical Traits

Background: The basic principle of genome selection (GS) is to establish a model of genome estimated breeding value (GEBV) by using single-nucleotide polymorphisms (SNPs) covering the entire genome. Despite the decreasing cost of high-throughput genotyping, the GS strategy remains expensive due to the need for phenotyping and genotyping for a large number of samples. Simulation analysis of genome selection is a popular, lower-cost method to determine an optimal breeding program of GS. Objective: To evaluate the utility of simulation data to study the influence of different factors on algorithms. This could be helpful for developing genome selection breeding strategies, especially for stress and resistance traits of fish. Methods: Real data of orange-spotted grouper (Epinephelus coioides) were obtained from a previous genome-wide association study. Ammonia tolerance, different population sizes, SNP density, QTL number, kinship (base mutation rate), and heritability were considered. All of the phenotypes and genotypes were generated by AlphaSimR simulation software. Four genome selection algorithms (gBLUP, rrBLUP, BayesA, and BayesC) were tested to derive GEBV, and their accuracies (area under the curve, AUC) were compared. Results: In different scenarios, the AUC ranges from 0.4237 to 0.6895 for BayesA, 0.4282 to 0.6878 for BayesC, 0.4278 to 0.6798 for gBLUP, and 0.4346 to 0.6834 for rrBLUP. The mean AUC of these four algorithms was not significantly different (0.547–0.548). The accuracies of the four genome selection algorithms were similar but had different predictive performances in specific scenarios. The gBLUP was most stable, and the rrBLUP was slightly better at predicting low heritability traits. When the number of individuals was small, the BayesA and BayesC algorithms were more robust. Conclusion: A practical GS scheme should be optimized in accordance with marker density, heritability, and reference population size. Adequate preliminary research is necessary. The results provide a framework for the design of genomic selection schemes in E. coioides breeding.

Detection of Whole Genome Selection Signatures of Pakistani Teddy Goat

Whole genome pooled sequence data of 12 Pakistani Teddy goats is analyzed for positive selection signatures as their breed defining characteristics. Selection imprints left in the Teddy genome are unveiled by genomic differentiation after the successful paired-end alignment of 635,357,043 reads with (ARS1) reference genome assembly. Pooled-heterozygosity ( ) and Tajima’s D (TD) are applied for validation and getting better hits of selection signals, while pairwise FST statistics is conducted on Teddy vs. Bezoar (wild goat ancestor) for genomic differentiation. Annotation of regions under positive selection reveals 59 genes underlying production and adaptive traits. score ≥ 5 detected six windows having highest scores on Chr. 29, 9, 25, 15 and 14 that harbor HRASLS5, LACE1 and AXIN1 genes which are candidate for embryonic development, lactation and body height. Secondly, TD value of ≤ -2.2 showed 4 windows with very strong hits on Chr.5 & 9 harbor STIM1 and ADM genes related to body mass and weight. Lastly, FST analysis generated three strong signals with threshold ≤ 0.42 on Chr.12 & 5 harbor ITGB1 gene associated with milk production & lactation traits. Other significant selection signatures encompass genes associated with wool production, prolificacy, immunity and coat colors. In brief, this study identified the genes under selection in this Pakistani goat breed that will be helpful to refining future breeding policies and converging required productive traits within and across other goat breeds and to explore full genetic potential of this valued livestock species.

Does Choice Matter? Reference-Based Alignment for Molecular Epidemiology of Tuberculosis

When using genome sequencing for molecular epidemiology, short sequence reads are aligned to an arbitrary reference strain to detect single nucleotide polymorphisms. We investigated whether reference genome selection influences epidemiological inferences ofMycobacterium tuberculosistransmission by aligning sequence reads from 162 closely related lineage 4 (Euro-American) isolates to 7 different genomes. Phylogenetic trees were consistent with use of all but the most divergent genomes, suggesting that reference choice can be based on considerations other thanM. tuberculosislineage.