Dryas iulia appears to have undergone a mode of evolution different from that of other members of its subfamily (Heliconiinae). While other species constitute highly subdivided and inbred populations, those of D. iulia are thought to be large and uniform. Analyzing six samples from Southern Brazil (state of Rio Grande do Sul) in relation to three enzyme systems (EST, LAP, and PGM) and their mtDNA RFLP patterns, we found that they are very similar at the molecular level. The F statistics for enzyme polymorphism data revealed that inbreeding makes a great contribution to the population homozygosity, since FIS equals 0.1322 and FST equals 0.0023. Since the chi-square test showed that FST is not significant, we conclude that all localities belong to the same population. The mtDNA differentiation was about 12 times greater than for nuclear genes; FST was equivalent to 0.0265. We suggest that this difference is due to a higher dispersal of males, in relation to females.

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CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia (CAH) cases, a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provide critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Here, we analyzed the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of CYP21A2. SNVs of CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. All SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy of between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews’ correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). From our analysis, we identified four predictors of CYP21A2 variant pathogenicity with good performance, CADD, ConSurf, DANN, and PolyPhen2. These results can be used for future analysis to infer the impact of uncharacterized SNVs in CYP21A2.