Widespread Deviant Patterns of Heterozygosity in Whole-Genome Sequencing Due to Autopolyploidy, Repeated Elements, and Duplication.

Most population genomic tools rely on accurate single nucleotide polymorphism (SNP) calling and filtering to meet their underlying assumptions. However, genomic complexity, resulting from structural variants, paralogous sequences, and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing (WGS) data with low (1.5×) to intermediate (4.8×) coverage for four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata). The analyses revealed that deviant SNPs accounted for 22% to 62% of all SNPs in salmonid datasets and approximately 11% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs, and could be partially associated with transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, underestimated pairwise FST values, and overestimated nucleotide diversity. Considering the widespread occurrence of deviant SNPs arising from a variety of sources, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths.

Long-distance migration is a major factor driving local adaptation at continental scale in Coho salmon.

Inferring the genomic basis of local adaptation is a long-standing goal of evolutionary biology. Beyond its fundamental evolutionary implications, such knowledge can guide conservation decisions for populations of conservation and management concern. Here, we investigated the genomic basis of local adaptation in the Coho salmon (Oncorhynchus kisutch) across its entire North American range. We hypothesized that extensive spatial variation in environmental conditions and the species' homing behaviour may promote the establishment of local adaptation. We genotyped 7829 individuals representing 217 sampling locations at more than 100,000 high-quality RADseq loci to investigate how recombination might affect the detection of loci putatively under selection and took advantage of the precise description of the demographic history of the species from our previous work to draw accurate population genomic inferences about local adaptation. The results indicated that genetic differentiation scans and genetic-environment association analyses were both significantly affected by variation in recombination rate as low recombination regions displayed an increased number of outliers. By taking these confounding factors into consideration, we revealed that migration distance was the primary selective factor driving local adaptation and partial parallel divergence among distant populations. Moreover, we identified several candidate single nucleotide polymorphisms associated with long-distance migration and altitude including a gene known to be involved in adaptation to altitude in other species. The evolutionary implications of our findings are discussed along with conservation applications.

Re-evaluating Coho salmon (Oncorhynchus kisutch) conservation units in Canada using genomic data.

Conservation units (CUs) are important tools for supporting the implementation of standardized management practices for exploited species. Following the adoption of the Wild Salmon Policy in Canada, CUs were defined for Pacific salmon based on characteristics related to ecotype, life history and genetic variation using microsatellite markers as indirect measures of local adaptation. Genomic data sets have the potential to improve the definition of CUs by reducing variance around estimates of population genetic parameters, thereby increasing the power to detect more subtle patterns of population genetic structure and by providing an opportunity to incorporate adaptive information more directly with the identification of variants putatively under selection. We used one of the largest genomic data sets recently published for a nonmodel species, comprising 5662 individual Coho salmon (Oncorhynchus kisutch) from 149 sampling locations and a total of 24,542 high-quality SNPs obtained using genotyping-by-sequencing and mapped to the Coho salmon reference genome to (1) evaluate the current delineation of CUs for Coho in Canada and (2) compare patterns of population structure observed using neutral and outlier loci from genotype-environment association analyses to determine whether separate CUs that capture adaptive diversity are needed. Our results reflected CU boundaries on the whole, with the majority of sampling locations managed in the same CU clustering together within genetic groups. However, additional groups that are not currently represented by CUs were also uncovered. We observed considerable overlap in the genetic clusters identified using neutral or candidate loci, indicating a general congruence in patterns of genetic variation driven by local adaptation and gene flow in this species. Consequently, we suggest that the current CU boundaries for Coho salmon are largely well-suited for meeting the Canadian Wild Salmon Policy's objective of defining biologically distinct groups, but we highlight specific areas where CU boundaries may be refined.

A setback into a success: What can batch effects tell us about best practices in genomics?

The increasing access to high-throughput sequencing is certainly one of the major changes that molecular ecology has gone through over the last decade. With the positive trend towards more open science, most sequencing data sets are now available on public databases, which holds amazing potential, but also risks of introducing batch effects in studies combining data sets. In this issue of Molecular Ecology Resources, Lou and Therkildsen (2022) offer a timely discussion on the matter by analyzing an imperfect low-coverage Whole Genome Sequencing data set, in which they test the effects of differences in sequencing choices, DNA degradation, and read depth on routine population genomics analyses. Through a series of diagnostic tools, they uncover multiple factors producing technical artefacts that can bias estimates of genetic diversity, inference of population structure, and selection scans. For each confounding factor, they demonstrate the effectiveness of mitigation approaches and suggest other avenues to deal with the issue. In this perspective, we highlight considerations regarding (1) effects that arise from differences between batches of sequencing; (2) unavoidable heterogeneity within data sets; and (3) more general concerns around the use of next-generation sequencing in population genomics. Altogether, by exploring what may have appeared at first glimpse as a "failed" sequencing project, Lou and Therkildsen (2022) end up setting a standard of best practices to make the most of heterogeneous whole-genome sequences, opening a promising avenue towards efficient reuse of published data sets.

Genomic data support management of anadromous Arctic Char fisheries in Nunavik by highlighting neutral and putatively adaptive genetic variation.

Distinguishing neutral and adaptive genetic variation is one of the main challenges in investigating processes shaping population structure in the wild, and landscape genomics can help identify signatures of adaptation to contrasting environments. Arctic Char (Salvelinus alpinus) is an anadromous salmonid and the most harvested fish species by Inuit people, including in Nunavik (Québec, Canada), one of the most recently deglaciated regions in the world. Unlike many other anadromous salmonids, Arctic Char occupy coastal habitats near their natal rivers during their short marine phase restricted to the summer ice-free period. Our main objective was to document putatively neutral and adaptive genomic variation in anadromous Arctic Char populations from Nunavik and bordering regions to inform local fisheries management. We used genotyping by sequencing (GBS) to genotype 18,112 filtered single nucleotide polymorphisms (SNP) in 650 individuals from 23 sampling locations along >2000 km of coastline. Our results reveal a hierarchical genetic structure, whereby neighboring hydrographic systems harbor distinct populations grouped by major oceanographic basins: Hudson Bay, Hudson Strait, Ungava Bay, and Labrador Sea. We found genetic diversity and differentiation to be consistent both with the expected postglacial recolonization history and with patterns of isolation-by-distance reflecting contemporary gene flow. Results from three gene-environment association methods supported the hypothesis of local adaptation to both freshwater and marine environments (strongest associations with sea surface and air temperatures during summer and salinity). Our results support a fisheries management strategy at a regional scale, and other implications for hatchery projects and adaptation to climate change are discussed.

Geometrical-based quasi-aspheric surface description and design method for miniature, low-distortion, wide-angle camera lens.

In addition to utilizing traditional aspheric surfaces, complicated geometric curves for meeting stringent design requirements can also be adopted in optical systems. In this paper, we investigate two geometric shape modeling schemes, namely, pedal and cosine curves, which allow for representation of an S-shaped profile for the optical design of a camera lens. To obtain a powerful tool for representing a quasi-aspheric surface (QAS) to resemble the designed form surface, we linearly combine the pedal/cosine function with a base conic section. The detailed parameterization process of representation is discussed in this paper. Subsequently, an existing starting point that has similar specifications to that of the design requirements is selected. During the optimization process, a least-squares fitting algorithm is implemented to obtain the optimal coefficient values of the proposed QAS representation, and then the parameters (radii, air thickness, lens thickness, coefficients, materials, etc.) of the optical system are set to optimize the optical performance, gradually aiming to minimize the predefined merit function. We demonstrate the applicability of the proposed geometric modeling schemes via two design examples. In comparison to a conventional aspheric camera lens of the same specifications, the optical performance with respect to field of view and distortion has been improved due to higher degrees of design freedom. We believe that the proposed technology of geometric modeling schemes promises to improve optical performance due to these higher degrees of freedom and appears to be applicable to many different camera lenses.

Simplified projection technique to correct geometric and chromatic lens aberrations using plenoptic imaging.

Plenoptic imaging has been used in the past decade mainly for 3D reconstruction or digital refocusing. It was also shown that this technology has potential for correcting monochromatic aberrations in a standard optical system. In this paper, we present an algorithm for reconstructing images using a projection technique while correcting defects present in it that can apply to chromatic aberrations and wide-angle optical systems. We show that the impact of noise on the reconstruction procedure is minimal. Trade-offs between the sampling of the optical system needed for characterization and image quality are presented. Examples are shown for aberrations in a classic optical system and for chromatic aberrations. The technique is also applied to a wide-angle full field of view of 140° (FFOV 140°) optical system. This technique could be used in order to further simplify or minimize optical systems.

Virtual camera calibration using optical design software.

Camera calibration is a critical step in many vision applications. It is a delicate and complex process that is highly sensitive to environmental conditions. This paper presents a novel virtual calibration technique that can be used to study the impact of various factors on the calibration parameters. To highlight the possibilities of the method, the calibration parameters' behavior has been studied regarding the effects of tolerancing and temperature for a specific lens. This technique could also be used in many other promising areas to make calibration in the laboratory or in the field easier.