Double-digest RAD Sequencing Research Articles

Development of a universal double-digest RAD sequencing approach for a group of nonmodel, ecologically and economically important insect and fish taxa.

The generation of genome-scale data is critical for a wide range of questions in basic biology using model organisms, but also in questions of applied biology in nonmodel organisms (agriculture, natural resources, conservation and public health biology). Using a genome-scale approach on a diverse group of nonmodel organisms and with the goal of lowering costs of the method, we modified a multiplexed, high-throughput genomic scan technique utilizing two restriction enzymes. We analysed several pairs of restriction enzymes and completed double-digestion RAD sequencing libraries for nine different species and five genera of insects and fish. We found one particular enzyme pair produced consistently higher number of sequence-able fragments across all nine species. Building libraries off this enzyme pair, we found a range of usable SNPs between 4000 and 37000 SNPS per species and we found a greater number of usable SNPs using reference genomes than de novo pipelines in STACKS. We also found fewer reads in the Read 2 fragments from the paired-end Illumina Hiseq run. Overall, the results of this study provide empirical evidence of the utility of this method for producing consistent data for diverse nonmodel species and suggest specific considerations for sequencing analysis strategies.

Finding the right coverage: the impact of coverage and sequence quality on single nucleotide polymorphism genotyping error rates.

Restriction-enzyme-based sequencing methods enable the genotyping of thousands of single nucleotide polymorphism (SNP) loci in nonmodel organisms. However, in contrast to traditional genetic markers, genotyping error rates in SNPs derived from restriction-enzyme-based methods remain largely unknown. Here, we estimated genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother-offspring dyads of Hoffman's two-toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference-aligned and de novo-assembled data sets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo-assembled data sets. For example, coverage ≥5 yielded median genotyping error rates of ≥0.03 and ≥0.11 in reference-aligned and de novo-assembled data sets, respectively. Genotyping error rates declined to ≤0.01 in reference-aligned data sets with a coverage ≥30, but remained ≥0.04 in the de novo-assembled data sets. We observed approximately 10- and 13-fold declines in the number of loci sampled in the reference-aligned and de novo-assembled data sets when coverage was increased from ≥5 to ≥30 at quality score ≥30, respectively. Finally, we assessed the effects of genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the trade-off between sample size and genotyping error rates be considered prior to building sequencing libraries, reporting genotyping error rates become standard practice, and that effects of genotyping errors on inference be evaluated in restriction-enzyme-based SNP studies.

Assessing the utility of whole genome amplified DNA for next-generation molecular ecology.

DNA quantity can be a hindrance in ecological and evolutionary research programmes due to a range of factors including endangered status of target organisms, available tissue type, and the impact of field conditions on preservation methods. A potential solution to low-quantity DNA lies in whole genome amplification (WGA) techniques that can substantially increase DNA yield. To date, few studies have rigorously examined sequence bias that might result from WGA and next-generation sequencing of nonmodel taxa. To address this knowledge deficit, we use multiple displacement amplification (MDA) and double-digest RAD sequencing on the grey mouse lemur (Microcebus murinus) to quantify bias in genome coverage and SNP calls when compared to raw genomic DNA (gDNA). We focus our efforts in providing baseline estimates of potential bias by following manufacturer's recommendations for starting DNA quantities (>100ng). Our results are strongly suggestive that MDA enrichment does not introduce systematic bias to genome characterization. SNP calling between samples when genotyping both de-novo and with a reference genome are highly congruent (>98%) when specifying a minimum threshold of 20X stack depth to call genotypes. Relative genome coverage is also similar between MDA and gDNA, and allelic dropout is not observed. SNP concordance varies based on coverage threshold, with 95% concordance reached at ~12X coverage genotyping de-novo and ~7X coverage genotyping with the reference genome. These results suggest that MDA may be a suitable solution for next-generation molecular ecological studies when DNA quantity would otherwise be a limiting factor.