A genomic‐based vision on the genetic diversity and key performance traits in selectively bred Arctic charr ( Salvelinus alpinus )

Genetic diversity patterns in farmed rainbow trout (Oncorhynchus mykiss) populations using genome-wide SNP and haplotype data

Rainbow trout is one of the most popular aquaculture species worldwide, with a long history of domestication. However, limited information exists about the genetic diversity of farmed rainbow trout populations globally, with most available reports relying on low-throughput genotyping technologies. Notably, no information exists about the genetic diversity status of farmed rainbow trout in Sweden. Double-digest restriction-site-associated DNA sequencing was performed on more than 500 broodfish from two leading producers in Sweden and from the country's national breeding program. Following the detection of single nucleotide polymorphisms (SNPs), genetic diversity was studied by using either individual SNPs (n = 8680; one SNP retained per 300 bp sequence reads) or through SNP haplotypes (n = 20 558; all SNPs retained in 300 bp sequence reads). Similar amounts of genetic diversity were found amongst the three populations when individual SNPs were used. Furthermore, principal component analysis and discriminant analysis of principal components suggested two genetic clusters with the two industry populations grouped together. Genetic differentiation based on the FST fixation index was ~0.01 between the industry populations and ~0.05 when those were compared with the breeding program. Preliminary estimates of effective population size (Ne ) and inbreeding (based on runs of homozygosity; FROH ) were similar amongst the three populations (Ne  ≈ 50-80; median FROH  ≈ 0.11). Finally, the haplotype-based analysis suggested that animals from the breeding program had higher shared coancestry levels than those from the other two populations. Overall, our study provides novel insights into the genetic diversity and structure of Sweden's three main farmed rainbow trout populations, which could guide their future management.

Seasonal and age-related changes in sperm quality of farmed arctic charr (Salvelinus alpinus)

BACKGROUND

Substantial variation in male fertility is regularly observed in farmed Arctic charr. However, detailed investigations of its fluctuation during a reproductive season and across years are lacking. Furthermore, information about the effect of underlying genetic factors influencing sperm quality is scarce. The current study focused on seasonal and age-related factors that may affect sperm quality characteristics in males reared in natural and delayed photoperiods. Animals were sampled three times for two consecutive years, and sperm quality parameters were recorded using a computer-assisted sperm analysis (CASA) system. Thereafter, high-throughput sequencing technologies were applied, aiming to identify genomic regions related to the variation of sperm quality throughout the reproductive season.

RESULTS

An across-season variation in the recorded sperm quality parameters was evident. Overall, 29% and 42% of males from the natural and delayed spawning groups had a highly variable total progressive motility. Males at four years of age showed significantly higher sperm motility and velocities during the early October and November recordings compared to the following year when the same animals were five years of age. On the other hand, the opposite was observed regarding sperm concentration during the last sampling. A genome-wide FST scan detected SNP differentiation among males with high and low variability in total progressive motility (PM) on eight chromosomes (FST > 0.17), Genome wide windows with the highest FST contained SNPs in proximity (within 250 kb up- and downstream distance) to 16 genes with sperm quality biological functions in mammalian species.

CONCLUSION

Our findings provide a detailed view of seasonal, age-related, and genetic effects on sperm quality and can be used to guide decisions on broodstock selection and hatchery management.

Whole‐genome re‐sequencing provides key genomic insights in farmed Arctic charr ( Salvelinus alpinus ) populations of anadromous and landlocked origin from Scandinavia

Arctic charr (Salvelinus alpinus) is a niche-market high-value species for Nordic aquaculture. Similar to other salmonids, both anadromous and landlocked populations are encountered. Whole-genome re-sequencing (22X coverage) was performed on two farmed populations of anadromous (Sigerfjord; n = 24) and landlocked (Arctic Superior; n = 24) origin from Norway and Sweden respectively. More than 5 million SNPs were used to study their genetic diversity and to scan for selection signatures. The two populations were clearly distinguished through principal component analysis, with the mean fixation index being ~0.12. Furthermore, the levels of genomic inbreeding estimated from runs of homozygosity were 6.23% and 8.66% for the Norwegian and the Swedish population respectively. Biological processes that could be linked to selection pressure associated primarily with the anadromous background and/or secondarily with domestication were suggested. Overall, our study provided insights regarding the genetic composition of two main strains of farmed Arctic charr from Scandinavia. At the same time, ample genomic resources were produced in the magnitude of millions of SNPs that could assist the transition of Nordic Arctic charr farming in the genomics era.

Comparing mixed models and Random Forest association tests using naturalGWAS and a Striped Bass SNP dataset

In this study, we used the phenotype simulation package naturalGWAS to test the performance of Zhao's Random Forest method in comparison to an uncorrected Random Forest test, latent factor mixed models (LFMM), genome-wide efficient mixed models (GEMMA), and confounder adjusted linear regression (CATE). We created 400 sets of phenotypes, corresponding to five effect sizes and 2, 5, 15, or 30 causal loci, simulated from two empirical datasets containing SNPs from Striped Bass representing three and 13 populations. All association methods were evaluated for their ability to detect genotype-phenotype associations based on power, false discovery rates, and number of false positives. Genomic inflation was highest for uncorrected Random Forest and LFMM tests and lowest for Gemma and Zhao's Random Forest. All association tests had similar power to detect causal loci, and Zhao's Random Forest had the lowest false discovery rate in all scenarios. To measure the performance of association tests in small datasets with few loci surrounding a causal gene we also ran analyses again after removing causal loci from each dataset. All association tests were only able to find true positives, defined as loci located within 30k bp of a causal locus, in 3%-18% of simulations. In contrast, at least one false positive was found in 17%-44% of simulations. Zhao's Random Forest again identified the fewest false positives of all association tests studied. The ability to test the power of association tests for individual empirical datasets can be an extremely useful first step when designing a GWAS study.