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Abstract Increased knowledge of heritable traits in Atlantic Salmon (Salmo salar) is important to overcome bottlenecks in salmonid aquaculture. Atlantic salmonid populations, both landlocked and anadromous, represent an interesting model to gain insight into anadromy related traits, most notably, the probability to smoltify. While a previous study has identified several genomic regions diverging between anadromous and landlocked populations across the species range, the present study explores these data further with the aim to uncover if some of these genomic regions are linked to beneficial genetic traits associated with smoltification. In this study 17 of these loci were monitored in 669 anadromous salmon originating from 36 full-sibling families that had been reared under common garden conditions. The Smolt Index was calculated, using multiple visual markers, and provided a means of assessing smoltification stage. One SNP, located in Ssa04, showed a significant association with probability to smoltify, where individuals homozygous for the landlocked variant (LL) displayed a decrease in probability of smoltifying after one winter when compared with the homozygous for the anadromous variant (AA). This effect was independent of individual fish size. A separate common garden study comprising 200 individuals from either anadromous or landlocked strains showed that expression levels of ncor1, a thyroid mediator hormone located on the same chromosomal region (Ssa04), were significantly reduced in landlocked individuals post smoltification but remained constant in their anadromous counterparts. This study therefore suggests that while size is still the most important trigger for the induction of smoltification, there may also be an additional genetic component or trigger that has been ‘lost’ during the years deprived of SW transfer. In conclusion, the LL genotype identified here could potentially be used by the industry to delay smoltification and may also represent one of the first clues to the genetic regulation of smoltification in Atlantic salmon.

In promiscuous species, fitness estimates obtained from genetic parentage may often reflect both pre- and post-copulatory components of sexual selection. Directly observing copulations can help isolate the role of pre-copulatory selection, but such behavioral data are difficult to obtain in the wild and may also overlook post-copulatory factors that alter the relationship between mating success and reproductive success. To overcome these limitations, we combined genetic parentage analysis with behavioral estimates of size-specific mating in a wild population of brown anole lizards (Anolis sagrei). Males of this species are twice as large as females and multiple mating among females is common, suggesting the scope for both pre- and post-copulatory processes to shape sexual selection on male body size. Our genetic estimates of reproductive success revealed strong positive directional selection for male size, which was also strongly associated with the number of mates inferred from parentage. In contrast, a male's size was not associated with the fecundity of his mates or his competitive fertilization success. By simultaneously tracking copulations in the wild via the transfer of colored powder to females by males from different size quartiles, we independently confirmed that large males were more likely to mate than small males. We conclude that body size is primarily under pre-copulatory sexual selection in brown anoles, and that post-copulatory processes do not substantially alter the strength of this selection. Our study also illustrates the utility of combining both behavioral and genetic methods to estimate mating success to disentangle pre- and post-copulatory processes in promiscuous species. [ABSTRACT FROM AUTHOR]

Conservation and management professionals often work across jurisdictional boundaries to identify broad ecological patterns. These collaborations help to protect populations whose distributions span political borders. One common limitation to multijurisdictional collaboration is consistency in data recording and reporting. This limitation can impact genetic research, which relies on data about specific markers in an organism's genome. Incomplete overlap of markers between separate studies can prevent direct comparisons of results. Standardized marker panels can reduce the impact of this issue and provide a common starting place for new research. Genotyping‐in‐thousands (GTSeq) is one approach used to create standardized marker panels for nonmodel organisms. Here, we describe the development, optimization, and early assessments of a new GTSeq panel for use with walleye (Sander vitreus) from the Great Lakes region of North America. High genome‐coverage sequencing conducted using RAD capture provided genotypes for thousands of single nucleotide polymorphisms (SNPs). From these markers, SNP and microhaplotype markers were chosen, which were informative for genetic stock identification (GSI) and kinship analysis. The final GTSeq panel contained 500 markers, including 197 microhaplotypes and 303 SNPs. Leave‐one‐out GSI simulations indicated that GSI accuracy should be greater than 80% in most jurisdictions. The false‐positive rates of parent‐offspring and full‐sibling kinship identification were found to be low. Finally, genotypes could be consistently scored among separate sequencing runs >94% of the time. Results indicate that the GTSeq panel that we developed should perform well for multijurisdictional walleye research throughout the Great Lakes region. [ABSTRACT FROM AUTHOR]

Abstract The use of high‐throughput, low‐density sequencing approaches has dramatically increased in recent years in studies of eco‐evolutionary processes in wild populations and domestication in commercial aquaculture. Most of these studies focus on identifying panels of SNP loci for a single downstream application, whereas there have been few studies examining the trade‐offs for selecting panels of markers for use in multiple applications. Here, we detail the use of a bioinformatic workflow for the development of a dual‐purpose SNP panel for parentage and population assignment, which included identifying putative SNP loci, filtering for the most informative loci for the two tasks, designing effective multiplex PCR primers, optimizing the SNP panel for performance, and performing quality control steps for downstream applications. We applied this workflow to two adjacent Alaskan Sockeye Salmon populations and identified a GTseq panel of 142 SNP loci for parentage and 35 SNP loci for population assignment. Only 50–75 panel loci were necessary for >95% accurate parentage, whereas population assignment success, with all 172 panel loci, ranged from 93.9% to 96.2%. Finally, we discuss the trade‐offs and complexities of the decision‐making process that drives SNP panel development, optimization, and testing.

The use of high‐throughput, low‐density sequencing approaches has dramatically increased in recent years in studies of eco‐evolutionary processes in wild populations and domestication in commercial aquaculture. Most of these studies focus on identifying panels of SNP loci for a single downstream application, whereas there have been few studies examining the trade‐offs for selecting panels of markers for use in multiple applications. Here, we detail the use of a bioinformatic workflow for the development of a dual‐purpose SNP panel for parentage and population assignment, which included identifying putative SNP loci, filtering for the most informative loci for the two tasks, designing effective multiplex PCR primers, optimizing the SNP panel for performance, and performing quality control steps for downstream applications. We applied this workflow to two adjacent Alaskan Sockeye Salmon populations and identified a GTseq panel of 142 SNP loci for parentage and 35 SNP loci for population assignment. Only 50–75 panel loci were necessary for >95% accurate parentage, whereas population assignment success, with all 172 panel loci, ranged from 93.9% to 96.2%. Finally, we discuss the trade‐offs and complexities of the decision‐making process that drives SNP panel development, optimization, and testing. [ABSTRACT FROM AUTHOR]