Dealing with paralogy in RADseq data: in silico detection and single nucleotide polymorphism validation in Robinia pseudoacacia L. Issue 20 (22nd September 2016) - Verdu, Cindy F.; Guichoux, Erwan; Quevauvillers, Samuel; De Thier, Olivier; Laizet, Yec'han; Delcamp, Adline; Gévaudant, Frédéric; Monty, Arnaud; Porté, Annabel J.; Lejeune, Philippe; Lassois, Ludivine; Mariette, Stéphanie
Abstract: The RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications. Abstract : Paralogy can impact the estimate of genetic diversity and single nucleotide polymorphism (SNP) development from RADseq data. We explored putative paralogy in a dataset obtained on Robinia pseudoacacia L. Thanks to a filtering approach, we validated a set of SNPs useful for genotyping in the species.