Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species methods
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Introduction
The genome serves simultaneously as a basic blueprint, encoding information for proper cellular and developmental processes necessary to produce an organism, and as a historical record of the demographic processes and selective forces acting in a given lineage. Exploration of mechanistic details through biochemistry, genetics, and development has lead to a deeper understanding of how genotype leads to phenotype, while exploitation of the historical record has enabled the fields of systematics, population genetics, and molecular ecology to elucidate the pressures and processes that shape diversity in populations and divergence between species. Studies of genetic information both encoded and recorded in genomes work with the same currency-comparison of homologous sequences across individuals-but these approaches employ very different modes of inference, and as such the detai...
Introduction
To increase the breadth of RADseq applications, we have elaborated on the method described by Baird et al. by eliminating random shearing and explicitly using size selection to recover a tunable number of regions, which are distributed randomly throughout the genome. Moreover, to maximize our ability to multiplex (i.e., increase the number of samples per sequencing lane), we also have developed a two-index combinatorial tagging approach (e.g., n * m individuals using n+m indices) and an accompanying computational analysis toolkit and lightweight data management component to facilitate high-order multiplexing of many hundreds of individuals. We also developed a graph clustering-based pipeline to maximize sequence read inclusion in analysis and permit detection of orthologous haplotypes regardless of divergence (i.e., without arbitrary similarity requirements), thereby improving analysi...
Validation of ddRADseq Derived Genotypes in a Laboratory Cross
ddRADseq was used to identify SNPs between two Peromyscus species, neither of which had a genome sequence available, that were crossed as part of a QTL experiment. This yielded 1158 unique markers that were fixed within, but different between, the parental species. By calculating the fraction of recombinant genotypes and LOD of linkage between markers, we generated ( A ) 24 groups of strongly linked markers, heatmap colors represent strength of linkage in both recombination frequency (upper left) and LOD (lower right) between all pairs of markers; and ( B ) a genetic map with average inter-marker distance of 1.6 cM. ddRADseq was also used to genotype wild-caught and lab-reared individuals of P. leucopus. Our ddRADseq method permitted successful genotyping of wild-caught individuals even when the allelic variants within a population are unknown. ( C ) Estimated site frequency spectrum...
Discussion
Here we describe a combination of laboratory and computational methodology to permit highly repeatable and tunable recovery of hundreds to hundreds of thousands of randomly sampled regions from a target genome. In comparison to traditional RADseq methods, ddRADseq library preparation is less expensive and rapid (<8 hours hands-on time for dozens to hundreds of samples), completely compatible with microplate format, and can be performed using limited amounts of genomic material (<100 ng). Furthermore, due to the removal of random shearing (and therefore random recovery), correlated recovery of regions across individuals results in increased robustness to variability in read count (see " "; Supporting ). As sequencing depth required to reach saturation is a direct function of the number of regions sampled ( ), the number of individuals which can be genotyped in a single sequ...
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The genome serves simultaneously as a basic blueprint, encoding information for proper cellular and developmental processes necessary to produce an organism, and as a historical record of the demographic processes and selective forces acting in a given lineage. Exploration of mechanistic details through biochemistry, genetics, and development has lead to a deeper understanding of how genotype leads to phenotype, while exploitation of the historical record has enabled the fields of systematics, population genetics, and molecular ecology to elucidate the pressures and processes that shape diversity in populations and divergence between species. Studies of genetic information both encoded and recorded in genomes work with the same currency-comparison of homologous sequences across individuals-but these approaches employ very different modes of inference, and as such the details of a particular experiment dictate optimal marker resolution ( ). To address the need for flexibility in marker number, we describe a next-generation sequencing-based method for determining individual sequence genotypes that can be tuned to sample a large range (from hundreds to hundreds of thous...
To increase the breadth of RADseq applications, we have elaborated on the method described by Baird et al. by eliminating random shearing and explicitly using size selection to recover a tunable number of regions, which are distributed randomly throughout the genome. Moreover, to maximize our ability to multiplex (i.e., increase the number of samples per sequencing lane), we also have developed a two-index combinatorial tagging approach (e.g., n * m individuals using n+m indices) and an accompanying computational analysis toolkit and lightweight data management component to facilitate high-order multiplexing of many hundreds of individuals. We also developed a graph clustering-based pipeline to maximize sequence read inclusion in analysis and permit detection of orthologous haplotypes regardless of divergence (i.e., without arbitrary similarity requirements), thereby improving analysis sensitivity and efficiency. Our software pipeline utilizes a novel approach for filtering resulting loci independent of coverage depth and converts the resulting haplotype multiple alignments into standard SAM/BAM format for downstream analysis, such as variant detection using the Genome Analysis...
ddRADseq was used to identify SNPs between two Peromyscus species, neither of which had a genome sequence available, that were crossed as part of a QTL experiment. This yielded 1158 unique markers that were fixed within, but different between, the parental species. By calculating the fraction of recombinant genotypes and LOD of linkage between markers, we generated ( A ) 24 groups of strongly linked markers, heatmap colors represent strength of linkage in both recombination frequency (upper left) and LOD (lower right) between all pairs of markers; and ( B ) a genetic map with average inter-marker distance of 1.6 cM. ddRADseq was also used to genotype wild-caught and lab-reared individuals of P. leucopus. Our ddRADseq method permitted successful genotyping of wild-caught individuals even when the allelic variants within a population are unknown. ( C ) Estimated site frequency spectrum of a wild population of P. leucopus caught in a single Louisiana population. ( D ) Genetic structure between five populations of P. leucopus. Dots represent individuals (N = 92) and color indicates the states from which individuals were collected: LA = Louisiana; NE =...
Here we describe a combination of laboratory and computational methodology to permit highly repeatable and tunable recovery of hundreds to hundreds of thousands of randomly sampled regions from a target genome. In comparison to traditional RADseq methods, ddRADseq library preparation is less expensive and rapid (<8 hours hands-on time for dozens to hundreds of samples), completely compatible with microplate format, and can be performed using limited amounts of genomic material (<100 ng). Furthermore, due to the removal of random shearing (and therefore random recovery), correlated recovery of regions across individuals results in increased robustness to variability in read count (see " "; Supporting ). As sequencing depth required to reach saturation is a direct function of the number of regions sampled ( ), the number of individuals which can be genotyped in a single sequencing lane is inversely proportional to the number of regions recovered. For example, we chose to recover 15-25 K regions in one experiment described here, for which saturation was achieved at less than 500 K reads per individual.
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