Reduced-representation Sequencing Data Research Articles

Genetic Diversity Assessment in Plants from Reduced Representation Sequencing Data.

Genetic diversity refers to the variety of genetic traits within a population or a species. It is an essential aspect of both plant ecology and plant breeding because it contributes to the adaptability, survival, and resilience of populations in changing environments. This chapter outlines a pipeline for estimating genetic diversity statistics from reduced representation or whole genome sequencing data. The pipeline involves obtaining DNA sequence reads, mapping the corresponding reads to a reference genome, calling variants from the alignments, and generating an unbiased estimation of nucleotide diversity and divergence between populations. The pipeline is suitable for single-end Illumina reads and can be adjusted for paired-end reads. The resulting pipeline provides a comprehensive approach for aligning and analyzing sequencing data to estimate genetic diversity.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

Genomic signals of local adaptation across climatically heterogenous habitats in an invasive tropical fruit fly (Bactrocera tryoni)

Local adaptation plays a key role in the successful establishment of pest populations in new environments by enabling them to tolerate novel biotic and abiotic conditions experienced outside their native range. However, the genomic underpinnings of such adaptive responses remain unclear, especially for agriculturally important pests. We investigated population genomic signatures in the tropical/subtropical Queensland fruit fly, Bactrocera tryoni, which has an expanded range encompassing temperate and arid zones in Australia, and tropical zones in the Pacific Islands. Using reduced representation sequencing data from 28 populations, we detected allele frequency shifts associated with the native/invasive status of populations and identified environmental factors that have likely driven population differentiation. We also determined that precipitation, temperature, and geographic variables explain allelic shifts across the distribution range of B. tryoni. We found spatial heterogeneity in signatures of local adaptation across various climatic conditions in invaded areas. Specifically, disjunct invasive populations in the tropical Pacific Islands and arid zones of Australia were characterised by multiple significantly differentiated single nucleotide polymorphisms (SNPs), some of which were associated with genes with well-understood function in environmental stress (e.g., heat and desiccation) response. However, invasive populations in southeast Australian temperate zones showed higher gene flow with the native range and lacked a strong local adaptive signal. These results suggest that population connectivity with the native range has differentially affected local adaptive patterns in different invasive populations. Overall, our findings provide insights into the evolutionary underpinnings of invasion success of an important horticultural pest in climatically distinct environments.

Molecular phylogeny and inflorescence evolution of Prunus (Rosaceae) based on RAD-seq and genome skimming analyses

Prunus is an economically important genus widely distributed in the temperate Northern Hemisphere. Previous studies on the genus using a variety of loci yielded conflicting phylogenetic hypotheses. Here, we generated nuclear reduced representation sequencing data and plastid genomes for 36 Prunus individuals and two outgroups. Both nuclear and plastome data recovered a well-resolved phylogeny. The species were divided into three main clades corresponding to their inflorescence types, - the racemose group, the solitary-flower group and the corymbose group - with the latter two sister to one another. Prunus was inferred to have diversified initially in the Late Cretaceous around 67.32 million years ago. The diversification of the three major clades began between the Paleocene and Miocene, suggesting that paleoclimatic events were an important driving force for Prunus diversification. Ancestral state reconstructions revealed that the most recent common ancestor of Prunus had racemose inflorescences, and the solitary-flower and corymb inflorescence types were derived by reduction of flower number and suppression of the rachis, respectively. We also tested the hybrid origin hypothesis of the racemose group proposed in previous studies. Prunus has undergone extensive hybridization events, although it is difficult to identify conclusively specific instances of hybridization when using SNP data, especially deep in the phylogeny. Our study provides well-resolved nuclear and plastid phylogenies of Prunus, reveals substantial cytonuclear discord at shallow scales, and sheds new light on inflorescence evolution in this economically important lineage.

Excessive and asymmetrical removal of heterozygous sites by maxSH biases downstream population genetic inference: Implications for hybridization between two primroses

AbstractTechniques of reduced‐representation sequencing (RRS) have revolutionized ecological and evolutionary genomics studies. Precise establishment of orthologs is a critical challenge for RRS, especially when a reference genome is absent. The proportion of shared heterozygous sites across samples is an alternative criterion for filtering paralogs. In the prevailing pipeline for variant calling of RRS data – PYRAD/IPYRAD, maxSH is an often overlooked parameter with implications to detecting and filtering paralogs according to shared heterozygosity. Using empirical genotyping by sequencing data of two primroses (Primula alpicola Stapf and Primula florindae Ward) and their putative hybrids, and extra data sets of Californian golden cup oaks, we explore the impact of maxSH on filtering paralogs and further downstream analyses. Our study sheds light on the simultaneous validity and risk of filtering paralogs using maxSH, and its significant effects on downstream analyses of outlier detection, population assignment, and demographic modeling, emphasizing the importance of attention to detail during bioinformatic processes. The mutual confirmation between results of population assignment and demographic modeling in this study suggested maxSH = 0.10 has a potentially excessive and asymmetrical effect on the removal of truly shared heterozygous sites as paralogs. These results indicate that hybridization origin hypotheses of putative hybrids represented by results with maxSH = 0.25 and 0.50 are more credible. In conclusion, we revealed the critical hazard of paralogs filtration according to sharing heterozygosity at first, so that we propose to use specific protocols, rather than maxSH, to filter potential paralogs for closely related lineages.

Population genetics of a recent range expansion and subsequent loss of migration in monarch butterflies

Range expansions—whether permanent or transient—strongly influence the distribution of genetic variation in space. Monarch butterflies are best known for long‐distance seasonal migration within North America but are also established as nonmigratory populations around the world, including on Pacific Islands. Previous research has highlighted stepwise expansion across the Pacific, though questions remain about expansion timing and the population genetic consequences of migration loss. Here, we present reduced‐representation sequencing data for 275 monarchs from North America (n = 85), 12 Pacific Islands (n = 136) and three locations in Australia (n = 54), with the goal of understanding (i) how the monarch's Pacific expansion has shaped patterns of population genetic variation and (ii) how loss of migration has influenced spatial patterns of differentiation. We find support for previously described stepwise dispersal across the Pacific and document an additional expansion from Hawaii into the Mariana Islands. Nonmigratory monarchs within the Mariana Islands show strong patterns of differentiation, despite their proximity; by contrast, migratory North American samples form a single genetically panmictic population across the continent. Estimates of Pacific establishment timing are highly uncertain (~100–1,000,000 years ago) but overlap with historical records that indicate a recent expansion. Our data support (i) a recent expansion across the Pacific whose timing overlaps with available historical records of establishment and (ii) a strong role for seasonal migration in determining patterns of spatial genetic variation. Our results are noteworthy because they demonstrate how the evolution of partial migration can drive population differentiation over contemporary timescales.

Genetics and Plasticity Are Responsible for Ecogeographical Patterns in a Recent Invasion.

Patterns of covariation between phenotype and environment are presumed to be reflective of local adaptation, and therefore translate to a meaningful influence on an individual’s overall fitness within that specific environment. However, these environmentally driven patterns may be the result of numerous and interacting processes, such as genetic variation, epigenetic variation, or plastic non-heritable variation. Understanding the relative importance of different environmental variables on underlying genetic patterns and resulting phenotypes is fundamental to understanding adaptation. Invasive systems are excellent models for such investigations, given their propensity for rapid evolution. This study uses reduced representation sequencing data paired with phenotypic data to examine whether important phenotypic traits in invasive starlings (Sturnus vulgaris) within Australia appear to be highly heritable (presumably genetic) or appear to vary with environmental gradients despite underlying genetics (presumably non-heritable plasticity). We also sought to determine which environmental variables, if any, play the strongest role shaping genetic and phenotypic patterns. We determined that environmental variables—particularly elevation—play an important role in shaping allelic trends in Australian starlings and may also reinforce neutral genetic patterns resulting from historic introduction regime. We examined a range of phenotypic traits that appear to be heritable (body mass and spleen mass) or negligibly heritable (e.g. beak surface area and wing length) across the starlings’ Australian range. Using SNP variants associated with each of these phenotypes, we identify key environmental variables that correlate with genetic patterns, specifically that temperature and precipitation putatively play important roles shaping phenotype in this species. Finally, we determine that overall phenotypic variation is correlated with underlying genetic variation, and that these interact positively with the level of vegetation variation within a region, suggesting that ground cover plays an important role in shaping selection and plasticity of phenotypic traits within the starlings of Australia.

Labile sex chromosomes in the Australian freshwater fish family Percichthyidae.

Sex-specific ecology has management implications, but rapid sex-chromosome turnover in fishes hinders sex-marker development for monomorphic species. We used annotated genomes and reduced-representation sequencing data for two Australian percichthyids, Macquarie perch Macquaria australasica and golden perch M.ambigua, and whole genome resequencing for 50Macquarie perch of each sex, to identify sex-linked loci and develop an affordable sexing assay. In silico pool-seq tests of 1,492,004Macquarie perch SNPs revealed that a 275-kb scaffold was enriched for gametologous loci. Within this scaffold, 22loci were sex-linked in a predominantly XY system, with females being homozygous for the X-linked allele at all 22, and males having the Y-linked allele at >7. Seven XY-gametologous loci (all males, but no females, are heterozygous or homozygous for the male-specific allele) were within a 146-bp region. A PCR-RFLP sexing assay targeting one Y-linked SNP, tested in 66known-sex Macquarie perch and two of each sex of three confamilial species, plus amplicon sequencing of 400bp encompassing the 146-bp region, revealed that the few sex-linked positions differ between species and between Macquarie perch populations. This indicates sex-chromosome lability in Percichthyidae, supported by nonhomologous scaffolds containing sex-linked loci for Macquarie- and golden perches. The present resources facilitate genomic research in Percichthyidae, including formulation of hypotheses about candidate genes of interest such as transcription factor SOX1b that occurs in the 275-kb scaffold ~38kb downstream of the 146-bp region containing seven XY-gametologous loci. Sex-linked markers will be useful for determining genetic sex in some populations and studying sex chromosome turnover.

Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub.

The spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush (Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations (222 individuals; 38,615 loci) spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic‐environment association (GEA) analyses using both redundancy analysis (RDA) and a machine‐learning approach (Gradient Forest) indicated environmental variables (precipitation seasonality, slope, aspect, elevation, and annual precipitation) influenced spatial genomic structure and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA‐based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables (e.g., precipitation seasonality) that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.

Sex assignment in a non-model organism in the absence of field records using Diversity Arrays Technology (DArT) data

Conservation genomics research often relies on accurate sex information to make inferences about species demography, dispersal, and population structure. However, field determined sex data are not always available and can be subject to human error, while laboratory sex assignment methods such as PCR assays can often be costly and challenging for non-model species. Conservation genomics programs increasingly use reduced-representation genome sequencing to assess neutral and functional genetic diversity, population structure, gene flow and pedigrees in threatened species. Here we demonstrate that sex can be determined from reduced-representation sequencing data produced by the increasingly popular Diversity Arrays Technology sequencing workflow (DArT-seq) using a program originally designed for application to shotgun data. This program—sexassign—compares the “dosage” of sequencing reads mapping to autosomes versus the X chromosome. In the present study, sexassign was used to identify the sex of 60 field-collected Greater Stick-Nest Rat (Leporillus conditor) samples, despite the absence of an annotated reference genome for the species. This “read-dosage” approach is not only more accurate and affordable than traditional sex assignment methods, but can be applied to any diploid organism with a heterogametic sex determination system—including non-model and understudied species of conservation importance—by using FASTQs generated by DArT.

A demonstration of conservation genomics for threatened species management.

As species extinction rates increase, genomics provides a powerful tool to support intensive management of threatened species. We use the Tasmanian devil (Sarcophilus harrisii) to demonstrate how conservation genomics can be implemented in threatened species management. We conducted whole genome sequencing (WGS) of 25 individuals from the captive breeding programme and reduced-representation sequencing (RRS) of 98 founders of the same programme. A subset of the WGS samples was also sequenced by RRS, allowing us to directly compare genome-wide heterozygosity with estimates from RRS data. We found good congruence in interindividual variation and gene-ontology classifications between the two data sets, indicating that our RRS data reflect the genome well. We also attempted genome-wide association studies with both data sets (regarding breeding success), but the genomic data suffered from small sample size, while the RRS data suffered from lack of precision, highlighting a key trade-off in the design of conservation genomic research. Nevertheless, we identified a number of candidate genes that may be associated with variation in breeding success. Individual heterozygosity, as measured by WGS or RRS, was not associated with breeding success in captivity but was negatively associated with litter sizes of breeding females in the RRS data set. Our findings enable conservation managers to have confidence in RRS data while understanding its limitations, and provide avenues for further investigation into which processes underlie variation in breeding success in captive Tasmanian devils. We caution, however, that deep functional insights using RRS may be impaired by a lack of precision, especially when marker density is low.

A Multireference-Based Whole Genome Assembly for the Obligate Ant-Following Antbird, Rhegmatorhina melanosticta (Thamnophilidae)

Current generation high-throughput sequencing technology has facilitated the generation of more genomic-scale data than ever before, thus greatly improving our understanding of avian biology across a range of disciplines. Recent developments in linked-read sequencing (Chromium 10×) and reference-based whole-genome assembly offer an exciting prospect of more accessible chromosome-level genome sequencing in the near future. We sequenced and assembled a genome of the Hairy-crested Antbird (Rhegmatorhina melanosticta), which represents the first publicly available genome for any antbird (Thamnophilidae). Our objectives were to (1) assemble scaffolds to chromosome level based on multiple reference genomes, and report on differences relative to other genomes, (2) assess genome completeness and compare content to other related genomes, and (3) assess the suitability of linked-read sequencing technology for future studies in comparative phylogenomics and population genomics studies. Our R. melanosticta assembly was both highly contiguous (de novo scaffold N50 = 3.3 Mb, reference based N50 = 53.3 Mb) and relatively complete (contained close to 90% of evolutionarily conserved single-copy avian genes and known tetrapod ultraconserved elements). The high contiguity and completeness of this assembly enabled the genome to be successfully mapped to the chromosome level, which uncovered a consistent structural difference between R. melanosticta and other avian genomes. Our results are consistent with the observation that avian genomes are structurally conserved. Additionally, our results demonstrate the utility of linked-read sequencing for non-model genomics. Finally, we demonstrate the value of our R. melanosticta genome for future researchers by mapping reduced representation sequencing data, and by accurately reconstructing the phylogenetic relationships among a sample of thamnophilid species.

From reference genomes to population genomics: comparing three reference-aligned reduced-representation sequencing pipelines in two wildlife species

BackgroundRecent advances in genomics have greatly increased research opportunities for non-model species. For wildlife, a growing availability of reference genomes means that population genetics is no longer restricted to a small set of anonymous loci. When used in conjunction with a reference genome, reduced-representation sequencing (RRS) provides a cost-effective method for obtaining reliable diversity information for population genetics. Many software tools have been developed to process RRS data, though few studies of non-model species incorporate genome alignment in calling loci. A commonly-used RRS analysis pipeline, Stacks, has this capacity and so it is timely to compare its utility with existing software originally designed for alignment and analysis of whole genome sequencing data. Here we examine population genetic inferences from two species for which reference-aligned reduced-representation data have been collected. Our two study species are a threatened Australian marsupial (Tasmanian devil Sarcophilus harrisii; declining population) and an Arctic-circle migrant bird (pink-footed goose Anser brachyrhynchus; expanding population). Analyses of these data are compared using Stacks versus two widely-used genomics packages, SAMtools and GATK. We also introduce a custom R script to improve the reliability of single nucleotide polymorphism (SNP) calls in all pipelines and conduct population genetic inferences for non-model species with reference genomes.ResultsAlthough we identified orders of magnitude fewer SNPs in our devil dataset than for goose, we found remarkable symmetry between the two species in our assessment of software performance. For both datasets, all three methods were able to delineate population structure, even with varying numbers of loci. For both species, population structure inferences were influenced by the percent of missing data.ConclusionsFor studies of non-model species with a reference genome, we recommend combining Stacks output with further filtering (as included in our R pipeline) for population genetic studies, paying particular attention to potential impact of missing data thresholds. We recognise SAMtools as a viable alternative for researchers more familiar with this software. We caution against the use of GATK in studies with limited computational resources or time.

Multiple forms of selection shape reproductive isolation in a primate hybrid zone.

Speciation occurs when populations diverge and become reproductively isolated from each other. Natural selection is commonly accepted to play a large role in this process, and it has been widely assumed that reproductive isolation often results as a by-product of divergence driven by adaptation in allopatry. When such populations come into secondary contact, reinforcement can act to strengthen reproductive isolation, but the frequency and importance of this process are still unknown. Here, we explored genomic signatures of selection in allopatry and sympatry for loci associated with reproductive isolation using a natural primate hybrid zone. By analysing reduced-representation sequencing data, we quantified admixture and population structure across a howler monkey hybrid zone and examined the relationship between locus-specific differentiation and introgression. We detected extensive admixture that was mostly limited to the narrow contact zone. Loci with reduced introgression into the heterospecific genomic background (the pattern expected for loci associated with reproductive isolation due to selection against hybrids) were significantly more differentiated between allopatric parental populations than loci with neutral and increased introgression, supporting the hypothesis that reproductive isolation is a by-product of divergence in allopatry. Further, loci with reduced introgression showed greater differentiation in sympatry than in allopatry, suggesting a role for reinforcement. Thus, our results reflect multiple forms of selection that have shaped reproductive isolation in this system. We conclude that reproductive isolation may have initially been driven by divergence in allopatry, but later reinforced by divergent selection in sympatry.

Reconstructing phylogeny from reduced-representation genome sequencing data without assembly or alignment.

Reduced-representation genome sequencing such as RADseq aids the analysis of genomes by reducing the quantity of data, thereby lowering both sequencing costs and computational burdens. RADseq was initially designed for studying genetic variation across genomes at the population level, but has also proved to be suitable for interspecific phylogeny reconstruction. RADseq data pose challenges for standard phylogenomic methods, however, due to incomplete coverage of the genome and large amounts of missing data. Alignment-free methods are both efficient and accurate for phylogenetic reconstructions with whole genomes and are especially practical for nonmodel organisms; nonetheless, alignment-free methods have not been applied with reduced genome sequencing data. Here, we test a full-genome assembly- and alignment-free method, AAF, in application to RADseq data and propose two procedures for reads selection to remove reads from restriction sites that were not found in taxa being compared. We validate these methods using both simulations and real data sets. Reads selection improved the accuracy of phylogenetic construction in every simulated scenario and the two real data sets, making AAF as good or better than a comparable alignment-based method, even though AAF had much lower computational burdens. We also investigated the sources of missing data in RADseq and their effects on phylogeny reconstruction using AAF. The AAF pipeline modified for RADseq or other reduced-representation sequencing data, phyloRAD, is available on github (https://github.com/fanhuan/phyloRAD).

GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data.

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.

Accurate continuous geographic assignment from low- to high-density SNP data.

Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies. We introduce a novel statistical method for geopositioning individuals of unknown origin from genotypes. Our method is based on a geostatistical model trained with a dataset of georeferenced genotypes. Statistical inference under this model can be implemented within the theoretical framework of Integrated Nested Laplace Approximation, which represents one of the major recent breakthroughs in statistics, as it does not require Monte Carlo simulations. We compare the performance of our method and an alternative method for geospatial inference, SPA in a simulation framework. We highlight the accuracy and limits of continuous spatial assignment methods at various scales by analyzing genotype datasets from a diversity of species, including Florida Scrub-jay birds Aphelocoma coerulescens, Arabidopsis thaliana and humans, representing 41-197,146 SNPs. Our method appears to be best suited for the analysis of medium-sized datasets (a few tens of thousands of loci), such as reduced-representation sequencing data that become increasingly available in ecology. http://www2.imm.dtu.dk/∼gigu/Spasiba/ gilles.b.guillot@gmail.com Supplementary data are available at Bioinformatics online.

Degenerate adaptor sequences for detecting PCR duplicates in reduced representation sequencing data improve genotype calling accuracy.

RAD-tag is a powerful tool for high-throughput genotyping. It relies on PCR amplification of the starting material, following enzymatic digestion and sequencing adaptor ligation. Amplification introduces duplicate reads into the data, which arise from the same template molecule and are statistically nonindependent, potentially introducing errors into genotype calling. In shotgun sequencing, data duplicates are removed by filtering reads starting at the same position in the alignment. However, restriction enzymes target specific locations within the genome, causing reads to start in the same place, and making it difficult to estimate the extent of PCR duplication. Here, we introduce a slight change to the Illumina sequencing adaptor chemistry, appending a unique four-base tag to the first index read, which allows duplicate discrimination in aligned data. This approach was validated on the Illumina MiSeq platform, using double-digest libraries of ants (Wasmannia auropunctata) and yeast (Saccharomyces cerevisiae) with known genotypes, producing modest though statistically significant gains in the odds of calling a genotype accurately. More importantly, removing duplicates also corrected for strong sample-to-sample variability of genotype calling accuracy seen in the ant samples. For libraries prepared from low-input degraded museum bird samples (Mixornis gularis), which had low complexity, having been generated from relatively few starting molecules, adaptor tags show that virtually all of the genotypes were called with inflated confidence as a result of PCR duplicates. Quantification of library complexity by adaptor tagging does not significantly increase the difficulty of the overall workflow or its cost, but corrects for differences in quality between samples and permits analysis of low-input material.