A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system

Abstract

Genotyping novel complex multigene systems is particularly challenging in non-model organisms.Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most next-generation sequencing genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and copy number variation (CNV) within a PCR remains poorly described. Here we investigate the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, Gallus gallus, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined "optimal" primers, we simulated a non-model species situation by designing "naïve" primers, with sequence data from closely related Galliform species. We applied a novel open-source genotyping pipeline (ACACIA) to the data, and compared its performance with another, previously published, pipeline.ACACIA yielded very high allele calling accuracy (>98%). Non-chimeric artefacts increased linearly with increasing CNV but chimeric artefacts leveled when amplifying more than 4-6 alleles.As expected, we found heterogeneous amplification efficiency of allelic variants when coamplifying multiple loci. Using our validated ACACIA pipeline and the example data of this study, we discuss in detail the pitfalls researchers should avoid in order to reliably genotype complex multigene systems. ACACIA and the datasets used in this study are publicly available at GitLab and FigShare(https://gitlab.com/psc_santos/ACACIA and https://figshare.com/projects/ACACIA/66485).

Evidence of MHC class I and II influencing viral and helminth infection via the microbiome in a non-human primate

“…Sequence data was processed using the ACACIA pipeline [98] (code available under https://gitlab.com/psc_santos/ACACIA) using a proportion threshold (low-por) of 0.01 and 0.10 for allele calling on the MHCI and MHCII-DRB dataset, respectively. MHCI is a highly variable region characterized by a series of duplications [96,97], while the MHCII DRB gene is non-duplicated in mouse lemurs [95,99,100].…”

Can extreme MHC class I diversity be a feature of a wide geographic range? The example of Seba’s short-tailed bat (Carollia perspicillata)

“…For allele calling and genotyping and to eliminate artefacts, we followed our previously published bioinformatics pipeline (Santos et al 2016;Sommer et al 2013;Gillingham et al 2019). Briefly, we merged the paired reads and cleaned the data from the PhiX sequences with the merging tool of FLASH (Fast Length Adjustment of SHort reads) software (Magoč and Salzberg 2011).…”

“…The positions (components) in the alignment with higher-thanbackground entropy suggesting polymorphic sites were used to cluster divergent variants and to distinguish true alleles from a background variation attributed to sequencing errors, i.e., artefacts. After artefact removal, we subsequently assigned alleles to individuals by using a customized Python script (Santos et al 2016;Gillingham et al 2019). Replicates were handled blindly.…”