The development of unlabeled probes-high resolution melting (UP-HRM) marker on SAD, IAA27 and ACC genes of oil palm

Saputra TI, Roberdi, Nugroho YA, Artutiningsih W, Purba OS, Maryanto SD, Yono D, Utomo C, Liwang T. 2021. The development of unlabeled probes-high resolution melting (UP-HRM) marker on SAD, IAA27 and ACC genes of oil palm. Biodiversitas 22: 3356-3362. The unlabeled probes-high resolution melting (UP-HRM) marker is a useful tool for detecting of single nucleotide polymorphisms (SNPs). The objectives of this study were to develop UP-HRM markers to differentiate specific SNPs patterns on oil palm. The marker was developed and tested with Elaeis guineensis (Eg), Elaeis oleifera (Eo), Eo x Eg (hybrid), and was validated with 53 individuals of BC1F1 populations ((Eo x Eg) x Eg). Four UP-HRM markers were developed based on 2 SNPs in the stearoyl-acyl-carrier-protein 9-desaturase (EgSAD), 1 SNP in the auxin-responsive protein IAA27-like (EgIAA27), and 1 SNP in the 1-amino cyclopropane-1-carboxylate oxidase (EgACC) genes. The SNP discovery result showed that Eg was represented a reference homozygote genotype, while Eo was represented as an alternative homozygote genotype and the Eo x Eg hybrid was represented as a heterozygote genotype in all genes. The typical UP-HRM melt curve graph was successfully generated. This result was consistent with each genotype model for all four markers. The UP-HRM markers can distinguish each genotype according to the single-pass sequencing results. Furthermore, dendrogram analysis on validation divided 53 BC1F1 samples into three cluster groups.

Cross-Species Application of Illumina iScan Microarrays for Cost-Effective, High-Throughput SNP Discovery

Microarrays can be a cost-effective alternative to high-throughput sequencing for discovering novel single-nucleotide polymorphisms (SNPs). Illumina’s iScan platform dominates the market, but their commercial microarray products are designed for model organisms. Further, the platform outputs data in a proprietary format. This cannot be easily converted to human-readable genotypes or be merged with pre-existing data. To address this, we present and validate a novel pipeline to facilitate data analysis from cross-species application of Illumina microarrays. This facilitates the generation of a compatible VCF from iScan data and the merging of this with a second VCF comprising genotypes derived from other samples and sources. Our pipeline includes a custom script, iScanVCFMerge (presented as a Python package), which we validate using iScan data from three great ape genera. We conclude that cross-species application of microarrays can be a rapid, cost-effective approach for SNP discovery in non-model organisms. Our pipeline surmounts the common challenges of integrating iScan genotypes with pre-existing data.

Effects of single nucleotide polymorphism ascertainment on population structure inferences

Single nucleotide polymorphism (SNP) data are widely used in research on natural populations. Although they are useful, SNP genotyping data are known to contain bias, normally referred to as ascertainment bias, because they are conditioned by already confirmed variants. This bias is introduced during the genotyping process, including the selection of populations for novel SNP discovery and the number of individuals involved in the discovery panel and selection of SNP markers. It is widely recognized that ascertainment bias can cause inaccurate inferences in population genetics and several methods to address these bias issues have been proposed. However, especially in natural populations, it is not always possible to apply an ideal ascertainment scheme because natural populations tend to have complex structures and histories. In addition, it was not fully assessed if ascertainment bias has the same effect on different types of population structure. Here we examine the effects of bias produced during the selection of population for SNP discovery and consequent SNP marker selection processes under three demographic models: the island, stepping-stone, and population split models. Results show that site frequency spectra and summary statistics contain biases that depend on the joint effect of population structure and ascertainment schemes. Additionally, population structure inferences are also affected by ascertainment bias. Based on these results, it is recommended to evaluate the validity of the ascertainment strategy prior to the actual typing process because the direction and extent of ascertainment bias vary depending on several factors.