Abstract
Developing genomic insights is challenging in nonmodel species for which resources are often scarce and prohibitively costly. Here, we explore the potential of a recently established approach using Pool‐seq data to generate a de novo genome assembly for mining exons, upon which Pool‐seq data are used to estimate population divergence and diversity. We do this for two pairs of sympatric populations of brown trout (Salmo trutta): one naturally sympatric set of populations and another pair of populations introduced to a common environment. We validate our approach by comparing the results to those from markers previously used to describe the populations (allozymes and individual‐based single nucleotide polymorphisms [SNPs]) and from mapping the Pool‐seq data to a reference genome of the closely related Atlantic salmon (Salmo salar). We find that genomic differentiation (F ST) between the two introduced populations exceeds that of the naturally sympatric populations (F ST = 0.13 and 0.03 between the introduced and the naturally sympatric populations, respectively), in concordance with estimates from the previously used SNPs. The same level of population divergence is found for the two genome assemblies, but estimates of average nucleotide diversity differ (π¯ ≈ 0.002 and π¯ ≈ 0.001 when mapping to S. trutta and S. salar, respectively), although the relationships between population values are largely consistent. This discrepancy might be attributed to biases when mapping to a haploid condensed assembly made of highly fragmented read data compared to using a high‐quality reference assembly from a divergent species. We conclude that the Pool‐seq‐only approach can be suitable for detecting and quantifying genome‐wide population differentiation, and for comparing genomic diversity in populations of nonmodel species where reference genomes are lacking.
Highlights
Understanding the importance of genetic variation for species' per‐ sistence continues to be a major research goal in population genetic and evolutionary studies (Allendorf & Ryman, 2002; Bernatchez, 2016; Soulé & Wilcox, 1980)
We explore the Pool‐seq‐only approach of Neethiraj et al (2017) using the brown trout (Salmo trutta) which belongs to the family Salmonidae that is characterized by large genomes (c. 3 giga base pairs (Gbp)) with the added complexity of a whole‐genome duplication event that occurred roughly 90 million years ago (MYA) followed by subse‐ quent, and ongoing, rediploidization (Berthelot et al, 2014; Lien et al, 2016; Nugent, Easton, Norman, Ferguson, & Danzmann, 2017)
We mapped our pools to a high‐quality reference genome from Atlantic salmon (S. salar), a closely related species, to compare to our Pool‐seq‐only results
Summary
Understanding the importance of genetic variation for species' per‐ sistence continues to be a major research goal in population genetic and evolutionary studies (Allendorf & Ryman, 2002; Bernatchez, 2016; Soulé & Wilcox, 1980). Our aim is to explore the potential of an exon mining through Pool‐seq approach to characterize the genomic variation and dif‐ ferentiation among brown trout populations We ask whether this approach is suitable for answering population genomics questions by studying two pairs of sympatric populations for which we can make well informed hypotheses based on previous work (Andersson, Jansson, et al, 2017; Andersson, Johansson, Sundbom, Ryman, & Laikre, 2017; Palm & Ryman, 1999; Palmé et al, 2013). We use Pool‐seq data from one of these populations to generate a de novo brown trout as‐ sembly, and map the Pool‐seq data to this reference to estimate pool‐specific diversity and pairwise differentiation These results are compared to the differentiation found from previous analyses of the same populations using allozymes and SNPs (Andersson, Jansson, et al, 2017; Andersson, Johansson, et al, 2017; Palm & Ryman, 1999; Palmé et al, 2013). We further contrast the outcome from mapping the Pool‐seq data to our draft assembly to mapping against the ref‐ erence genome of a related species by repeating the analyses for Pool‐seq data mapped to an available Atlantic salmon genome (Lien et al, 2016)
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