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Fantastic Beasts and How To Sequence Them: Ecological Genomics for Obscure Model Organisms

2017; Elsevier BV; Volume: 34; Issue: 2 Linguagem: Inglês

10.1016/j.tig.2017.11.002

1362-4555

Mikhail V. Matz,

Genetic and phenotypic traits in livestock

Genotyping applications are undergoing a shift from high-coverage, reduced representation sequencing to low-coverage, whole-genome sequencing. Approaches based on full allele frequency spectrum (AFS) to study population structure, migration rates, and historical population sizes are gaining popularity. There has been a rise in functional genomics studies of acclimatization and adaptation, powered by cost-efficient methods for genome-wide gene expression and DNA methylation analysis. ‘Third-generation’ sequencing technologies (PacBio and Oxford Nanopore Technologies) have been proven to produce high-quality genome and transcriptome references, and to directly detect epigenetically modified DNA bases. The application of genomic approaches to ‘obscure model organisms’ (OMOs), meaning species with no prior genomic resources, enables increasingly sophisticated studies of the genomic basis of evolution, acclimatization, and adaptation in real ecological contexts. I consider here ecological questions that can be addressed using OMOs, and indicate optimal sequencing and data-handling solutions for each case. With this I hope to promote the diversity of OMO-based projects that would capitalize on the peculiarities of the natural history of OMOs and could feasibly be completed within the scope of a single PhD thesis. The application of genomic approaches to ‘obscure model organisms’ (OMOs), meaning species with no prior genomic resources, enables increasingly sophisticated studies of the genomic basis of evolution, acclimatization, and adaptation in real ecological contexts. I consider here ecological questions that can be addressed using OMOs, and indicate optimal sequencing and data-handling solutions for each case. With this I hope to promote the diversity of OMO-based projects that would capitalize on the peculiarities of the natural history of OMOs and could feasibly be completed within the scope of a single PhD thesis. the same as site frequency spectrum (SFS), a histogram of the number of segregating variants binned by their frequency. Can be n-dimensional for n populations ([41Robinson J.D. et al.Sampling strategies for frequency spectrum-based population genomic inference.BMC Evol. Biol. 2014; 14: 254Crossref PubMed Scopus (51) Google Scholar, 52Gutenkunst R.N. et al.Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data.PLoS Genet. 2009; 5e1000695Crossref PubMed Scopus (1030) Google Scholar] for illustrations). genotyping of several polymorphic markers per linkage disequilibrium (LD) block, which is the typical distance between markers in the genome across which their genotypes remain correlated as a result of infrequent recombination. portion of the genome represented in the mature (spliced) RNA. performing analyses based on probabilities of alternative genotypes at each SNP without trying to decide which genotype is true [42Alex Buerkle C. Gompert Z. Population genomics based on low coverage sequencing: how low should we go?.Mol. Ecol. 2013; 22: 3028-3035Crossref PubMed Scopus (136) Google Scholar, 92Nielsen R. et al.SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data.PLoS One. 2012; 7e37558Crossref PubMed Scopus (233) Google Scholar]. This method is designed for lower-coverage data (as low as 1.5–2×) and is implemented in the software package ANGSD (analysis of next-generation sequencing data [93Korneliussen T. et al.ANGSD: analysis of next generation sequencing data.BMC Bioinform. 2014; 15: 356Crossref PubMed Scopus (1131) Google Scholar]). profiling of genotypes in one or more populations looking for genomic regions exhibiting unusual patterns. Typically used to look for signatures of natural selection or introgression. identifying the most likely genotype at each SNP site and performing downstream analyses assuming that these genotypes are true. Applicable for data with 10× or better coverage [40Han E. et al.Characterizing bias in population genetic inferences from low-coverage sequencing data.Mol. Biol. Evol. 2014; 31: 723-735Crossref PubMed Scopus (55) Google Scholar]. a family of diverse genotyping methods [45Andrews K.R. et al.Harnessing the power of RADseq for ecological and evolutionary genomics.Nat. Rev. Genet. 2016; 17: 81-92Crossref PubMed Scopus (813) Google Scholar, 46Puritz J.B. et al.Demystifying the RAD fad.Mol. Ecol. 2014; 23: 5937-5942Crossref PubMed Scopus (146) Google Scholar] that sequence short fragments of the genome adjacent to recognition site(s) for specific restriction endonuclease(s). methods for sequencing long individual nucleic acid molecules; these include single molecule real-time (SMRT) sequencing by PacBio and nanopore sequencing by Oxford Nanopore Technologies (ONT).