Utility of allozyme supernatant and formalin-fixed samples for
phylogenomic and population genomic analyses
Formalin-fixed samples clustered with their corresponding specimen
replicates in the phylogeny, despite having above-average levels of
missing data (Fig. 4; S6). This suggests that these samples are amenable
to phylogenetic analyses. By contrast, formalin-fixed samples did not
cluster with corresponding specimen replicates in PC space, likely due
to higher levels of missing data. Smith et al. (2020) found that missing
data at informative sites biased phylogenomic estimation when using
historical samples. Similar unevenness in missing data may partially
explain some of the separation between the formalin-fixed and
supernatant/pellet replicates in PC space in our study. In particular,
when we pruned our data to only capture-based replicates and retained
loci shared by 90% of samples (713 SNPs, n=25), the amount of missing
data seems to load heavily on PC2 (12%), with the greatest separation
between the formalin-fixed samples with the largest amounts of missing
data and all other replicates. The two formalin-fixed samples with< 20% missing data clustered with their corresponding
replicates, further indicating that missing data is likely driving this
pattern. Rates of allelic mismatch (13–28%) could also explain some of
the variation in PC space between capture-based replicates, though Ewart
et al. (2019) found that simulated rates of allelic mismatch up to 10%
did not impact PCA analyses in their study. Likewise, Stronen et al.
(2018) report a similar pattern between historical and contemporary
sample genotypes using Illumina BeadChips, where samples cluster along
PC1 by sample-type (historical vs. contemporary), and along PC2 by
geography, suggesting that this pattern may be common in studies using
historical samples and not restricted to only target-capture approaches
(but see van der Valk et al. 2019).
Finally, we inferred significantly different estimates of nucleotide
diversity in formalin-fixed replicates compared with the supernatant and
pellet replicates, although removing sites with high levels of missing
data accounted for some, but not all, of this discrepancy. A similar
pattern was reported by Ewart et al. (2019), who found a bias towards
homozygosity in historical samples leading to lower estimates of genetic
diversity, and a bias in missing data towards heterozygous sites, which
may partially explain why removing sites with more missingness gave
higher estimates of genetic diversity for formalin-fixed samples. We
inferred a non-significant difference in heterozygosity among the
capture-based replicates, suggesting that either allelic mismatch or
biases in the types of sites that are missing may best explain variation
in nucleotide diversity estimates. When sample sizes are large enough,
estimating genetic diversity using only loci and individuals with low
missingness will likely reduce these biases, although the appropriate
threshold will likely vary by study. Based on the systematic biases we
report here, we recommend future studies make thoughtful choices about
data assembly and SNP filtering when using formalin-fixed samples for
demographic parameter estimation.