High-Throughput Sequencing Strategy for Microsatellite Genotyping Using Neotropical Fish as a Model

Abstract

Genetic diversity and population studies are essential for conservation and wildlife management programs. However, monitoring requires the analysis of multiple loci from many samples. These processes can be laborious and expensive. The choice of microsatellites and PCR calibration for genotyping are particularly daunting. Here we optimized a low-cost genotyping method using multiple microsatellite loci for simultaneous genotyping of up to 384 samples using next-generation sequencing (NGS). We designed primers with adapters to the combinatorial barcoding amplicon library and sequenced samples by MiSeq. Next, we adapted a bioinformatics pipeline for genotyping microsatellites based on read-length and sequence content. Using primer pairs for eight microsatellite loci from the fish Prochilodus costatus, we amplified, sequenced, and analyzed the DNA of 96, 288, or 384 individuals for allele detection. The most cost-effective methodology was a pseudo-multiplex reaction using a low-throughput kit of 1 M reads (Nano) for 384 DNA samples. We observed an average of 325 reads per individual per locus when genotyping eight loci. Assuming a minimum requirement of 10 reads per loci, two to four times more loci could be tested in each run, depending on the quality of the PCR reaction of each locus. In conclusion, we present a novel method for microsatellite genotyping using Illumina combinatorial barcoding that dispenses exhaustive PCR calibrations, since non-specific amplicons can be eliminated by bioinformatics analyses. This methodology rapidly provides genotyping data and is therefore a promising development for large-scale conservation-genetics studies.

Keywords: conservation genetics; fish; genotyping; microsatellite; next-generation sequencing.

FIGURE 1

Schematic representation of the genotyping strategy. (A) Amplification protocol for generation of the amplicons for sequencing. (B) Data analysis pipeline. The reads from the sequencing are filtered based on the quality of the bases and the length of the read, using the PRINSEQ tool. Filtered reads are aligned against the reference sequence using the software Bowtie2 with high sensitivity parameter. Then, the reads are realigned against the reference, using tools from the GATK package, to increase the confidence in genotyping. Subsequently, alleles are detected and quantified using the RepeatSeq tool. Results are filtered to decrease the false negative rate using the GenotypeMicrosat.pl script. Finally, the result of the genotyping is converted into the format required by population genetics analysis software using the same script.

FIGURE 2

Average (mean) read yield in run 1 (n = 5 individuals). Multiplex reactions were performed in A with eight loci (proC10, proC18, proC22, proC36, proC37, proC44, proC48, proC49), in B with four loci (proC18, proC36, proC37, proC44), and in C with two loci (proC48 and proC49). Monoplex reactions were performed in D (proC10), E (proC18), F (proC36), G (proC48), and H (proC49). Error bars are standard deviation. MiSeq Reagent Kit v2 1 M reads was used.

FIGURE 3

Average number of reads generated per individual in three distinct runs using MiSeq Reagent Kit v2 1 M reads: run 4 (96 individuals), run 5 (288 individuals), and run 6 (384 individuals). • Represents the mean number of reads for each number of samples tested. Linear regression is represented by the dashed line (^……). For more details, see Table 2.