Application of Low Coverage Genotyping by Sequencing in Selectively Bred Arctic Charr (Salvelinus alpinus)

Arctic charr (Salvelinus alpinus) is a species of high economic value for the aquaculture industry, and of high ecological value due to its Holarctic distribution in both marine and freshwater environments. Novel genome sequencing approaches enable the study of population and quantitative genetic parameters even on species with limited or no prior genomic resources. Low coverage genotyping by sequencing (GBS) was applied in a selected strain of Arctic charr in Sweden originating from a landlocked freshwater population. For the needs of the current study, animals from year classes 2013 (171 animals, parental population) and 2017 (759 animals; 13 full sib families) were used as a template for identifying genome wide single nucleotide polymorphisms (SNPs). GBS libraries were constructed using the PstI and MspI restriction enzymes. Approximately 14.5K SNPs passed quality control and were used for estimating a genomic relationship matrix. Thereafter a wide range of analyses were conducted in order to gain insights regarding genetic diversity and investigate the efficiency of the genomic information for parentage assignment and breeding value estimation. Heterozygosity estimates for both year classes suggested a slight excess of heterozygotes. Furthermore, F-ST estimates among the families of year class 2017 ranged between 0.009 - 0.066. Principal components analysis (PCA) and discriminant analysis of principal components (DAPC) were applied aiming to identify the existence of genetic clusters among the studied population. Results obtained were in accordance with pedigree records allowing the identification of individual families. Additionally, DNA parentage verification was performed, with results in accordance with the pedigree records with the exception of a putative dam where full sib genotypes suggested a potential recording error. Breeding value estimation for juvenile growth through the usage of the estimated genomic relationship matrix clearly outperformed the pedigree equivalent in terms of prediction accuracy (0.51 opposed to 0.31). Overall, low coverage GBS has proven to be a cost-effective genotyping platform that is expected to boost the selection efficiency of the Arctic charr breeding program

Data from: The effect of geographical scale of sampling on DNA barcoding

Eight years after DNA barcoding was formally proposed on a large scale, CO1 sequences are rapidly accumulating from around the world. While studies to date have mostly targeted local or regional species assemblages, the recent launch of the global iBOL project (International Barcode of Life), highlights the need to understand the effects of geographical scale on Barcoding’s goals. Sampling has been central in the debate on DNA Barcoding, but the effect of the geographical scale of sampling has not yet been thoroughly and explicitly tested with empirical data. Here we present a CO1 dataset of aquatic predaceous diving beetles of the tribe Agabini, sampled throughout Europe, and use it to investigate how the geographic scale of sampling affects 1) the estimated intraspecific variation of species, 2) the genetic distance to the most closely related heterospecific, 3) the ratio of intraspecific and interspecific variation, 4) the frequency of taxonomically recognised species found to be monophyletic, and 5) query identification performance based on six different species assignment methods. Intraspecific variation was significantly correlated with the geographical scale of sampling (R-square=0.7), and more than half of the species with 10 or more sampled individuals (N=29) showed higher intraspecific variation than 1% sequence divergence. In contrast the distance to the closest heterospecific showed a significant decrease with increasing geographical scale of sampling. The average genetic distance dropped from >7% for samples within 1km, to 6000 km apart. Over a third of the species were not monophyletic, and the proportion increased through locally, nationally, regionally and continentally restricted subsets of the data. The success of identifying queries decreased with increasing spatial scale of sampling; liberal methods declined from 100% to around 90% whereas strict methods dropped to below 50% at continental scales. The proportion of query identifications considered uncertain (more than one species <1% distance from query) escalated from zero at local, to 50% at continental scale. Finally, by resampling the most widely sampled species we show that even if samples are collected to maximise the geographical coverage, up to 70 individuals are required to sample 95% of intraspecific variation. The results show that the geographical scale of sampling has a critical impact on the global application of DNA barcoding. Scale-effects result from the relative importance of different processes determining the composition of regional species assemblages (dispersal and ecological assembly) and global clades (demography, speciation and extinction). The incorporation of geographical information, where available, will be required to obtain identification rates at global scales equivalent to those in regional barcoding studies. Our result hence provides an impetus for both smarter barcoding tools and sprouting national barcoding initiatives – smaller geographical scales deliver higher accuracy

Supplementary Figure 2: Strict consensus tree of four most parsimonious trees from complete European dataset of Agabini diving-beetles

Parsimony analysis done in TNT with driven search strategy using tree-fusing and sectorial searches

Supplementary Figure 1: Neighbour-Joining tree on complete European dataset of Agabini diving-beetles

Produced from PAUP with a Kimura-2-Parameter mode

Species level CO1 dataset in nexus format for gene-tree dating of European Agabini

Species level CO1 dataset in nexus format for gene-tree dating of European Agabin

Supplementary Figure 3: Majority-rule consensus tree from Bayesian analysis on complete European dataset of Agabini diving-beetles

Bayesian analysis done in MrBayes 3.2 with codon-partitioned GTR+I+G model and using a parsimony topology as start tree

43 most parsimonious trees from parsimony analysis in nexus format European Agabini

43 most parsimonious trees from parsimony analysis in nexus format European Agabin

Bayesian tree in nexus format European Agabini

Bayesian tree in nexus format European Agabin

Dated CO1 gene tree in nexus format on species level matrix of European Agabini

Produced from BEAST with codon partitioned GTR+I+G model