Montastraea cavernosa across eight reef sites surrounding Cuba. Colonies were genotyped using nine microsatellite markers and > 9,000 single nucleotide polymorphism (SNP) markers generated using the 2bRAD approach to assess fine-scale genetic structure across these sites. Both the microsatellite and SNP analyses identified patterns of genetic differentiation among sample populations. While the microsatellite analyses did not identify significant genetic structure across the seven shallow M. cavernosa sampling sites, the SNP analyses revealed significant pairwise population differentiation, suggesting that differentiation is greater between eastern and western sites. This study provides insight into methodological differences between microsatellite and SNP markers including potential trade-offs between marker-specific biases, sample size, sequencing costs, and the ability to resolve subtle patterns of population genetic structure. Furthermore, this study suggests that locations in western Cuba may play important roles in this species’ regional metapopulation dynamics and therefore may merit incorporation into developing international management efforts in addition to the local management the sites receive.Coral reef habitats surrounding Cuba include relatively healthy, well-developed shallow and mesophotic (30–150 m) scleractinian communities at the cross-currents of the Tropical Western Atlantic (TWA). However, Cuba’s coral communities are not immune to the declines observed throughout the TWA, and there is limited information available regarding genetic connectivity, diversity, and structure among these populations. This represents an immense gap in our understanding of coral ecology and population dynamics at both local and regional scales. To address this gap, we evaluated the population genetic structure of the coral  Population genetic approaches have been widely implemented to quantify gene flow among coral populations and can be used to infer patterns of connectivity, generating critical information that can be applied to management and conservation strategies7. Assessments of coral genetic connectivity in the TWA have focused on a variety of coral species with different reproductive mechanisms and life histories10, examined wide-ranging spatial scales and depth gradients12, and employed a variety of molecular markers16. However, there have been relatively few studies focused on characterizing the connectivity patterns and dynamics in coral genetic structure across the Cuban archipelago or between Cuba and other reefs in the TWA19.In response to the precipitous declines in coral cover and health on reefs throughout the Tropical Western Atlantic (TWA) and worldwide, greater focus has been placed on quantifying larval connectivity among coral populations that may play critical roles in the persistence and recovery of regional metapopulations21. The lack of development along Cuba’s coastline and the implementation of low-input agricultural methods has led to comparatively minimal levels of local human impact on Cuba’s coral reefs24. Based on one dataset spanning from 1988–2007, Cuba has maintained relatively high coral cover, averaging 17.6% and 13.4% along the shallow reef crest and fore reef, respectively25. In contrast, when surveyed in 2011, the average coral cover across sites in the nearby Florida Keys was only 2.4%26. Study of Cuba’s immense mesophotic coral ecosystems  began with coral community characterization down to 70 m, however deeper and more detailed description of Cuba’s mesophotic communities has only recently been undertaken28. Reports from a 2017 expedition estimate as much as 80% coral cover and suggest a relatively low incidence of bleaching and disease within these coral communities29. The immense habitat area, high coral cover, and relative health of Cuba’s coral communities have contributed to their reputation as an “ecological crown jewel” of the Caribbean Sea24.An assessment of coral connectivity among Cuba’s reef ecosystems is critical as these populations may act as coral refugia within the TWA. Cuba has the largest shelf habitat in the Caribbean, supporting almost 4,000 km of reef31. In response to these declines, Cuba has implemented several strategies to protect its coastal marine ecosystems, including coral reefs. As of 2018, 25% of Cuba’s insular shelf is a designated or proposed marine protected area (MPA), encompassing an estimated 30% of Cuban coral reefs32. In addition, government agencies from Cuba and the United States established a “Sister Sanctuary” relationship among the Florida Keys and Flower Garden Banks National Marine Sanctuaries in the U.S. and the physically and ecologically connected Guanahacabibes National Park and Banco de San Antonio Marine Sanctuary in Cuba33.While Cuban coral reefs are relatively healthy, they are not immune to anthropogenic impacts. Overexploitation of coral reef fishes, land-based pollution, and multiple thermally induced bleaching events have contributed to an estimated average annual decline in coral cover of 1.75%, only slightly lower than the estimated decline for the wider TWA (2.5–2.7%).19 found low levels of genetic diversity among Orbicella faveolata sampled throughout Cuba. One notable exception was higher levels of diversity observed within the northwestern Los Colorados archipelago and higher levels of genetic differentiation between this site and other reefs. The study presented here aims to investigate patterns of genetic connectivity of a different coral species, Montastraea cavernosa , across sites surrounding Cuba. Montastraea cavernosa is a cosmopolitan34, extreme depth-generalist36, broadcast spawner37, and important reef builder38. These characteristics, in combination with a number of available molecular resources40, make M. cavernosa an excellent candidate species for population genetic studies. Previous investigations have demonstrated varying levels of horizontal and vertical genetic connectivity of M. cavernosa across local and regional spatial scales and between shallow and mesophotic depth zones41.Despite ongoing efforts to manage and conserve these threatened coral communities, there has only been one study focused on characterizing coral genetic structure across sites throughout the Cuban archipelago. Ulmo-Díaz et al43. Microsatellite markers are highly polymorphic short-sequence repeats that can be easily and inexpensively applied, especially for species where they have already been developed (e.g. M. cavernosa). However, the number of microsatellite loci that can be efficiently genotyped for multiple individuals is limited and their high mutation rate may not be reflective of the individual’s genomic dynamics on the whole44. These markers also require frequent manual allele calling, which incorporates subjectivity and potential for scoring bias45. The implementation of high-throughput sequencing technologies has enabled the development of single nucleotide polymorphism (SNP) markers through restriction site-associated DNA sequencing (RADseq) approaches47. The 2bRAD approach falls under the umbrella of RADseq techniques, and functions by employing a type IIB restriction endonuclease to cleave short, uniform length DNA sequences upstream and downstream from recognition sites dispersed throughout the genome, oftentimes generating thousands of SNP loci48. While the library preparation and sequencing can be more costly and labor-intensive than microsatellites, many studies have found SNPs to be more informative when quantifying population genetic structure, especially in cases where sample size and/or overall levels of genetic differentiation are low51.Population genetic studies have historically implemented multiple classes of molecular markers but microsatellites are one of the most widely used and versatile marker classes in population genetic research53. Detailed characterization of algal symbiont community structure traditionally relies on genetic methods including sequencing of markers like ITS2, psbAncr, and other candidate genes56. However, this additional lab work and sequencing can be time-consuming and expensive. With RADseq methods, algal symbiont sequences can be aligned to transcriptomic and genomic references available from the algal symbiont genera57. While this approach only generates a proxy for algal symbiont community make-up and is currently limited in its resolution to the genus level, it can provide preliminary insight into algal symbiont communities without additional lab or sequencing efforts. Montastraea cavernosa algal symbiont communities are primarily dominated by members of the genus Cladocopium (formerly clade C) but these corals may also maintain communities dominated by, or with background levels of, Durusdinium (clade D), Breviolum (clade B), and Symbiodinium (clade A)60.In addition to quantifying coral population genetic structure, characterization of the coral’s associated algal symbiont assemblages is also critical to our understanding of aspects of the species’ ecology. Patterns in algal symbiont community structure may complement patterns of coral host genetic structure or may provide insight into potential drivers of genetic differentiationM. cavernosa within Cuba and inform regional management strategies by quantifying connectivity among sites within established or proposed MPAs. The second objective was to compare the efficacy and resolution of both microsatellites and 2bRAD SNP genotyping to holistically describe patterns of coral population connectivity.The primary objective of this study was to characterize patterns of genetic differentiation among sample populations of Montastraea cavernosa samples were collected during May and June 2017 on a joint U.S.-Cuba expedition that circumnavigated the Cuban archipelago to survey and characterize the geomorphology, biodiversity, and ecological health status of Cuba’s shallow and mesophotic coral ecosystems61. Ninety-four shallow M. cavernosa colonies were sampled from depths between 1–5 m by snorkelers using a hammer and masonry chisel. An additional two mesophotic samples were collected at 41 and 75 m, respectively, by the Mohawk remotely operated vehicle (ROV). Unfortunately, the flattened colony morphology of this species at mesophotic depths, in combination with limitations in the strength and dexterity of the ROV’s sampling apparatus, restricted the ability to effectively collect mesophotic tissue samples. A total of 96 M. cavernosa samples (~ 5 cm2 each) were collected across eight sampling sites surrounding the island  and a Qubit 2.0 fluorometer (Invitrogen). Samples were subsequently diluted to equal concentrations before microsatellite amplification and 2bRAD library preparation.All samples were preserved in TRIzol reagent and maintained at -20 °C before transport back to Florida Atlantic University (FAU) Harbor Branch Oceanographic Institute where they were stored at -80 °C prior to DNA extraction. Total genomic DNA was extracted from these samples using a modified CTAB extraction protocol as described in Eckert et al10, amplified in triplex with self-labeled fluorescent primers using the QIAGEN Type-It Microsatellite PCR kit following modified methods outlined in Eckert et al.12. Successful amplification was verified through visualization on a 2% agarose gel before amplicons were diluted in deionized water and measured using an Applied Biosystems ABI 3130xl genetic analyzer with ROX-500 size standard. Generated electropherograms were used to call alleles in GeneMapper v3.7 following protocols as described by Studivan and Voss7. Samples with loci that did not amplify successfully or were of otherwise poor quality were re-amplified to produce the most complete dataset possible. In addition, a random subset of samples was re-amplified and re-analyzed to ensure consistency across analyzer runs and allele calls. Samples were amplified and run an average of 1.3 times and only samples genotyped at six or more loci were used in further analyses.Samples were genotyped using nine previously developed microsatellite loci.48 including modifications described in the protocol’s GitHub repository (https://github.com/z0on/2bRAD_denovo). Briefly, the protocol’s five main steps include: (1) restriction digest of 200 ng of total genomic DNA with BcgI endonuclease; (2) ligation of DNA fragments with indexed adaptors; (3) pooling of indexed ligations; (4) PCR amplification of pooled ligations; (5) and size-selection and purification of ~ 180 bp fragments. Library preparation time and cost were significantly reduced through the use of 12 uniquely indexed 3′ adaptors, allowing 12 sample ligations to be pooled prior to amplification. Fully degenerate 5′ adaptors were also included to allow filtering of PCR duplicates in downstream analyses. Triplicate 2bRAD libraries were prepared for three coral samples as described in Manzello et al.57. These triplicate libraries were used downstream as a sequencing quality check and to identify natural clones. Pooled libraries were further pooled in equimolar amounts based on qPCR relative quantification. The final pooled library underwent automated size selection with Pippin Prep (Sage Science) before conducting 100-bp single-end sequencing on an Illumina NovaSeq S1 flow cell at the University of Texas at Austin’s Genome Sequencing and Analysis Facility.2bRAD SNP libraries were prepared following Wang et alhttps://github.com/z0on/2bRAD_denovo). The sequences were further quality-filtered using the fastq_quality_filter in the FASTX-Toolkit v0.0.1463 only retaining reads with at least 90% of bases with Phred quality scores ≥ 20. Trimmed and quality-filtered reads were aligned to the M. cavernosa genome 40; concatenated with available algal symbiont transcriptomes for the Symbiodiniaceae genera Symbiodinium64, Breviolum (https://sites.bu.edu/davieslab/data-code/), Cladocopium65, and Durusdinium66 using the sequence aligner Bowtie2 v2.3.567. Reads that mapped with high levels of uniqueness to each of the algal symbiont transcriptomes  were counted using custom Perl scripts as described by Manzello et al.57 and used as a proxy for the relative abundance of these four algal symbiont genera associated with each sample. Only reads that aligned to the M. cavernosa genome were included in downstream analyses of M. cavernosa population genetic structure.Reads were initially demultiplexed by the sequencing facility using PCR and TruSeq indices and were further demultiplexed by the in-line 3′ adaptor indices, deduplicated, and trimmed using custom Perl scripts , and Nei’s genetic distance were calculated in GenAlEx v6.50369. Analysis of Molecular Variance (AMOVA) was calculated in GenAlEx using FST to assess population differentiation70 . Pairwise FST value comparisons between each site were also generated in GenAlEx and associated false discovery rate (FDR)-corrected p-values were calculated71 . Population differentiation was visualized using Principal Coordinates Analysis (PCoA) based on Nei’s genetic distance.Of the 96 samples collected, 95 were successfully amplified across ≥ 6 microsatellite loci. However, only a trimmed dataset of 80 samples was used in the downstream analysis to maintain a consistent sample set between the microsatellite and 2bRAD analyses. Observed and expected heterozygosity, fixation index  was conducted on the microsatellite genotypes using the R package adegenet v2.1.175. A Mantel test (isolation-by-distance) was used to measure the correlation between Nei’s genetic distance and geographic distance among sampling populations’ geographic coordinates using the function mantel.randtest from the R package adegenet76 (999 permutations).The R package ParallelStructure v1.0 on FAU’s high-performance computing cluster to assess population structure78 . Models were run for genetic cluster values K = 1–11, or the number of sampling sites + 3 to identify any possibility of cryptic genetic clusters within sampling populations. The LOCPRIOR option was selected to include information on the sample location as a model input. The web-based program STRUCTURE HARVESTER v0.6.94 was used to generate model likelihoods for different values of K79. STRUCTURE plots were generated in R.The Bayesian model-based clustering program STRUCTURE v2.3.4 was run via the R package 57. The resulting pairwise IBS matrix was used to generate a cluster dendrogram using the function hclust in R. This dendrogram was used to identify naturally occurring clones that exhibited similar levels of genetic similarity to one another as the technical triplicate groups. The dataset was then edited to remove replicates and natural clones.ANGSD was used to compute an identity-by-state (IBS) matrix to observe sample clustering and to identify genetic clones. The IBS approach calculates the proportion of times that two randomly selected reads that contain a certain SNP locus are the same or different between two individuals. This measurement of genetic distance is particularly robust to variation in sequencing coverage among samplespoppr and adegenet. Observed and expected heterozygosities were calculated from ANGSD outputs using the Stacks v2.3 populations program80. Nei’s genetic distance and pairwise FST values were calculated using the R package StAMPP v1.5.181 (99 permutations). A PCoA of population-level Nei’s genetic distance was conducted using the cmdscale function in the R package vegan v2.5.682. A Mantel test was used to measure the correlation between Nei’s genetic distance and geographic distance among sampling populations using the function mantel.randtest from the R package adegenet (999 permutations).ANGSD was re-run with the same filter parameters on the dataset with clones removed. The newly generated IBS matrix was used to produce a new cluster dendrogram. A PCA and AMOVA (999 permutations) were conducted on the genotype likelihoods produced by ANGSD using the R packages K = 1–11 were assessed with ADMIXTURE v1.3.083, which uses the same statistical model as STRUCTURE to estimate an individual’s ancestral membership but applies an algorithm optimized for large multi-locus SNP datasets. Cross-validation error values calculated for each model identified the most likely values of K. The program NGSadmix v32 was employed to conduct ADMIXTURE analysis on genotypic likelihoods in lieu of hard-called genotypes84. NGSadmix plots were generated in R.Population structure for models of cluster values FST values using the program BAYESCAN v2.185  and plotted with R (https://github.com/z0on/2bRAD_denovo). The locations of these outlier SNP loci were compared against annotated gene regions (extended by ± 2 kb) of the M. cavernosa genome to identify any putative functional selection effect.Outlier SNP loci putatively undergoing selection were identified based on locus-population-specific differences in In the microsatellite dataset, all of the 80 samples were genotyped across six or more loci and 73 of the samples (91%) were genotyped across all nine microsatellite loci. Two pairs of samples were identified as genetic clones based on their identical multi-locus genotypes. These clones were samples collected from the Guanahacabibes and Chivirico sites. In situ collection photos were reviewed to verify that these samples were true genetic clones and not accidental re-samples of the same colony. From the 2bRAD sequencing, over 275 million reads were obtained with an average of 3.21 million reads per sample library. Following the removal of PCR duplicates and quality-filtering, there were over 127 million reads retained, averaging 1.48 million reads per sample. Hierarchical clustering based on IBS generated from the SNP dataset discerned the same two pairs of natural clones also identified in the microsatellite dataset . Heterozygosity values based on the SNP dataset were more consistent across populations , however, the Banco de San Antonio average heterozygosity values were still lower than the other populations, possibly due to low sample size.Mean observed and expected heterozygosity calculated from the microsatellite dataset were higher across all populations than the SNP dataset Fig. . HeterozThe hierarchical cluster dendrogram generated from the microsatellite dataset based on Prevosti’s genetic distance did not produce discernible clustering patterns among samples Fig. a. In condf7,155, p = 0.03; SNP: 1.88%, SS = 12,345.90, df7,155, p = 0.0001). PCoA visualization of Nei’s genetic distance matrices generated similar plots for both the microsatellite and SNP datasets, with clustering of the Guanahacabibes, Cayo Jutías, Cayo Sabinal, Cabo Lucrecia, Cayo Anclitas, and Chivirico populations while the Banco de San Antonio and Isla de la Juventud populations were relatively distanced from the main cluster , with all shallow to shallow pairwise population FST comparisons found to be non-significant ), which suggested that K = 1 was the most likely number of genetic clusters . All of these loci had positive alpha values suggesting that they are undergoing diversifying selection85. Four of these loci were identified within ± 2 kb of an M. cavernosa annotated gene region with assigned gene ontologies 40 functioning primarily in inorganic ion transport, protein modification and turnover, and signal transduction  were identified as candidate SNPs undergoing selection by BAYESCAN based on their significant locus-specific Cladocopium transcriptome. Only a small portion of reads aligned to Symbiodinium, Breviolum, or Durusdinium transcriptomes, suggesting that Cladocopium is the dominant genus of algal symbionts hosted by M. cavernosa in Cuba  and did not show large differences in proxy symbiont associations from the majority of the shallow populations across the other seven sites.The majority of reads that aligned to the algal symbiont transcriptomes aligned to the uba Fig. . The mesMontastraea cavernosa from eight locations surrounding the Cuban archipelago exhibited significant levels of genetic differentiation among sites. PCoA, PCA, isolation by distance analysis, and pairwise FST values generated from the SNP dataset generally support a pattern of higher differentiation between western and eastern sites , more ephemeral, and localized hydrodynamic regimes compared to the western sites which may result in decreased levels of immigration and higher levels of local larval retention91. To evaluate the “local” and “regional” cluster hypothesis, future studies should assess the levels of genetic connectivity of each of these clusters to other population outgroups across the TWA.Much of the east–west differentiation pattern observed in this study is driven by 15 samples identified in the SNP dataset that clustered together and away from the other samples in the IBS cluster dendrogram and PCA Figs. b, 5d. ThM. cavernosa is presently unknown in situ, laboratory studies of larval survivorship and settlement rates have suggested that larvae become competent to settle after ~ 4 days and have an average larval life expectancy of ~ 15 days93. The Gulf Stream current system reaches its peak annual transport levels in late July, coinciding with M. cavernosa spawning events that occur following the full moons between July and September88. Based on the velocities of these current systems and estimated maximum pelagic larval duration of M. cavernosa, populations from western and northwestern Cuba potentially exhibit some level of larval connectivity with other reef populations in the region, including the Mesoamerican reef, Flower Garden Banks, and Florida Keys. This hypothesis is consistent with conclusions made by a number of biophysical modeling studies. Studies modeling connectivity of snapper species throughout Cuba found higher levels of larval retention in the north-central and southeastern regions and relatively higher levels of connectivity and export in northwestern and southwestern sites94. Additionally, another study assessed regional connectivity networks of multiple reef-associated species including the scleractinian corals O. annularis and Porites astreoides95. O. annularis has similar life history characteristics to M. cavernosa, it is a broadcast spawner, releasing gametes in the late summer with an estimated maximum pelagic larval duration of 30 days95. In contrast, P. astreoides is a brooding species that releases larvae throughout the spring and summer but with a much shorter estimated maximum pelagic larval duration, 7 days96. Despite these varied life histories, the biophysical model identified populations in western Cuba and north-central Cuba as highly connected source/sink regions for both of these species, while southern Cuba tended to experience higher self-regional recruitment and lower connectivity to upstream sites95.While the duration of the obligatory pre-competency period and pelagic larval duration of M. cavernosa found high levels of genetic connectivity between mesophotic populations in Belize and shallow populations in the Dry Tortugas despite a separation of more than 1,000 km7. Sites in western Cuba may serve as a “stepping-stone” for connectivity between these two populations. Therefore, incorporation of M. cavernosa samples from Cuba into regional connectivity studies combining high-resolution population genetics and biophysical modeling approaches may offer greater insight into regional connectivity patterns and context for the observed patterns of genetic differentiation within Cuba.A regional population genetic study of FST values, PCoAs, and PCAs generated from both the microsatellite and SNP dataset demonstrated comparatively high levels of differentiation between Banco de San Antonio and the other sample populations. Given the low sample size from a single mesophotic site, this study cannot make generalized observations on vertical connectivity between shallow and mesophotic coral populations in Cuba. However, these results suggest that further collection of mesophotic coral samples, especially with paired shallow site sample collection to minimize horizontal distance, is critical to fully characterize the connectivity dynamics of this species across Cuba’s extensive shallow and mesophotic habitat. Patterns of connectivity between shallow and mesophotic M. cavernosa have been variable in other locations across the TWA. Montastraea cavernosa from the Flower Garden Banks exhibit relatively high levels of vertical connectivity, in contrast to populations on the Belize Barrier Reef which exhibit a strong genetic break between shallow and upper mesophotic depth zones12. Montastraea cavernosa populations in Florida show varying levels of genetic differentiation across a large geographic range with lower mesophotic populations at Pulley Ridge exhibiting high levels of differentiation from downstream, shallow populations in the northern Florida Keys, and upper-mesophotic populations in the Dry Tortugas functioning as a genetic intermediate between the two41.Notably, the highest levels of observed genetic differentiation were between Banco de San Antonio, the only mesophotic population, and all other shallow populations. Pairwise Acropora cervicornis populations in the Florida Reef Tract were considered outlier loci97. Similarly, outlier SNP loci accounted for 3.3% of the total SNPs identified across populations of Acropora palmata throughout the Caribbean98. While species-level, sequencing, or methodological differences may be driving this distinction in the observed number of SNP loci, these relatively few outlier loci suggest that selective forces on the studied populations are minimal compared to neutral drivers of genetic differentiation among M. cavernosa populations in Cuba99.Outlier SNPs identified as candidate loci undergoing selection by BAYESCAN represented a minority of the SNP loci identified by ANGSD . The prevalence of outlier SNP loci in this study is much lower than what has been observed in other coral population genetic studies based on RADseq SNP approaches. For example, 3.1% of the identified SNP loci across 101. Although, given that these two loci were close to one another and identified in the same gene region, they may be misattributed structural variants. A third outlier SNP is located near a gene region involved in responses to oxidative stress in O. faveolata larvae exposed to UV radiation102. The last outlier SNP was identified in a region annotated as a gene family involved in signal transduction mechanisms that may play roles in coral fertilization and/or in maintaining coral-algal symbiosis104. As this methodology only identifies candidate outlier loci potentially undergoing selection, further transcriptomic and physiological studies would be needed to fully understand any putative functional effects.Of the identified outlier loci, four are within or near annotated gene regions that may have functional implications for corals Table . Two outCladocopium genus from the Cuban M. cavernosa samples in this study is consistent with numerous reports that M. cavernosa maintains symbioses with this algal genus across site, depth, and time107. However, Cladocopium is a species-rich genus encompassing genetically, physiologically, and ecologically diverse members109. Presently, amplicon sequencing of markers such as ITS2 is needed to assess algal symbiont assemblages to the sub-genus level54. Hypothetically, if more algal symbiont genomes are sequenced and published, RADseq methods could be used to effectively conduct simultaneous SNP mining for both coral host and algal symbionts. Nevertheless, the approach used in this study and others demonstrates the ability to use coral holobiont sequence data generated with SNP approaches to broadly characterize in hospite algal symbionts57.The dominance of reads aligning to the FST calculations, and STRUCTURE/ADMIXTURE analyses, the dataset based on > 9,000 SNPs more clearly and consistently identified ecologically relevant patterns of genetic differentiation in Cuban M. cavernosa populations than the dataset based on nine microsatellite markers. In addition, genetic parameters such as heterozygosity were less biased by a low sample size per population in the SNP dataset than the microsatellite dataset. While both approaches were able to identify signatures of significant differentiation between mesophotic and shallow samples, the microsatellite dataset was unable to identify significant genetic differentiation among the seven shallow populations. Notably, assessing the microsatellite dataset alone would suggest that shallow M. cavernosa across Cuba are highly admixed and well-connected , individual sample size per population is low, or when patterns of population differentiation are especially subtle.Across multiple population genetic analyses, including hierarchical clustering of samples, PCoA, PCA, AMOVA, pairwise ted Fig. a. These 112. However, the cost-savings associated with using microsatellites are often limited to species where these markers have already undergone the relatively expensive sequencing needed to develop them. Other aspects of microsatellite marker application, including primer labeling, can also increase the cost of using this method. Furthermore, in order to accurately assess population genetic parameters using microsatellite markers, some studies have recommended as many as 25–30 individuals per sample population, which may offset any potential per-sample cost savings114. In contrast, simulation studies have shown that even with extremely low sample sizes (n = 2) genetic diversity and population genetic parameters can be accurately assessed using SNP markers as long as > 1,000 SNP loci are included in the analysis116. In studies where sample collection may be limited by logistical or biological constraints , the extra time and financial investment to generate and analyze thousands of SNP loci may more efficiently and effectively identify ecologically relevant patterns of population genetic structure.There are still trade-offs involved with using a RADseq based SNP approach versus a microsatellite approach, and there are times where a microsatellite approach may be all that is necessary to address the research questions of interest. The library preparation and high-throughput sequencing required by RADseq approaches tend to be more expensive and time-consuming than using microsatellite markers, and oftentimes require isolation of higher-quality DNAM. cavernosa across eight sites surrounding the Cuban archipelago. The complementary but differing results between microsatellite and SNP analyses for this sample set highlight some important trade-offs between these two approaches that researchers and MPA managers should consider when designing future coral population genetic studies. Sample collection accessibility and effort, sequencing costs, marker type and number, and relative desire to resolve subtle patterns in genetic differentiation are all considerations for method selection. For example, in this first investigation of M. cavernosa population genetics for Cuba, subtle but significant differentiation among sample populations was more clearly identified by the SNP approach. These results have important management implications on both local (within Cuba) and regional (throughout the TWA) scales. Coral reef management plans should consider local-scale connectivity dynamics when developing MPA regulations, possibly enforcing stricter policies in more highly connected coral populations to improve the efficacy of the MPA network as a whole. High levels of genetic connectivity for M. cavernosa have already been identified among highly distant reef populations 7 and perhaps Cuba may play an important role as a regional stepping stone among reefs in both the Gulf of Mexico and the western Caribbean. Future studies must obtain an understanding of Cuban coral populations’ role in regional metapopulation dynamics in order to effectively design networks of MPAs on an international scale for the mutual coral conservation benefit of multiple countries within this highly-connected system.This study used nine previously developed microsatellite loci and > 9,000 SNP loci generated using a 2bRAD approach to quantify the genetic structure of Supplementary file1


Zoonotic visceral leishmaniasis (VL) is endemic in the Mediterranean basin. However, large-scale comparative analyses of the commercial kits for the serological diagnosis of this neglected disease are lacking. This study compared the performances of four enzyme-linked immunosorbent assays (ELISA) and two immunochromatographic tests (ICT) as screening tests for the serodiagnosis of human VL in the Mediterranean region.Leishmania infantum IgG, Bordier Leishmania infantum, Ridascreen Leishmania IgG, and Vircell Leishmania). The population with proven VL (n = 181) included 65 immunocompromised patients. Significantly higher percentages of false-negative results were obtained with all assays in immunocompromised patients, compared with the immunocompetent population. In the whole population, sensitivity and specificity ranged from 80.7% to 93.9% and from 95.7% to 100%, respectively. The maximum accuracy was observed with the Bordier and Vircell ELISA kits (96.2%), and the lowest accuracy with Ridascreen reagent (88.7%). New thresholds of positivity are proposed for the Bordier, Vircell and NovaLisa ELISA kits to achieve 95% sensitivity with the highest possible specificity. Western blot (WB), used as a confirmation method, showed 100% sensitivity and identified 10.1% of asymptomatic carriers among the control population from the South of France.Serum samples from 319 patients living in France, Tunisia or Morocco were tested using two ICT (IT LEISH and TruQuick LEISH IgG/IgM Meridian) and four ELISA reagents  and four enzyme-linked immunosorbent assays , available in France and used as screening tests. The Bordier and Vircell ELISA reagents displayed the highest analytic performance compared with all the other kits. However, no test displayed 95% sensitivity at the thresholds recommended by the manufacturers. To reach this sensitivity value with the highest possible specificity, new thresholds are proposed for the Bordier, Vircell and NovaLisa ELISA reagents. Positive and negative results obtained with the six screening tests were confirmed by Western blot (100% sensitivity). Overall, this study presents a comparative analysis of commercially available kits for the serodiagnosis of human VL and provides additional information on the prevalence of asymptomatic carriers in the Mediterranean basin.Human visceral leishmaniasis (VL) is a neglected disease endemic in the Mediterranean region, caused by  Leishmania infantum is a neglected, yet severe, zoonotic disease that occurs mainly in Latin America and in the Mediterranean region . Because of the low quantity of some samples, among the 340 sera included in this study, 339 sera were screened with LDBio, 338 with IT-LEISH, 335 with TruQuick, 339 with Bordier and Ridascreen, 335 with NovaLisa, and 322 with Vircell. Among these sera, a complete dataset was available for 319 samples. The VL population (n = 181) included 98 immunocompetent and 65 immunocompromised patients , and 18 patients with unknown immune status who were included in the immunocompetent group (n = 116). To compare the performance of the different ICT and ELISA kits, we assessed their sensitivity, specificity and accuracy in the 319 samples tested with all kits . We inteSensitivity and specificity of 95% and 98%, respectively, are recommended for VL diagnosis . HoweverWestern blotting is used in the routine practice to confirm equivocal or positive results obtained by initial screening tests. Western blot analysis of the serum samples from patients with proven VL (n = 181) confirmed the results in 100% of cases . On the L. infantum in humans [Leishmania antibodies . The Among the six assays used for antibody screening, none reached the recommended sensitivity of 95% for VL diagnosis (14). Therefore, we lowered the positivity thresholds of the ELISA reagents to obtain a sensitivity of 95% with the highest possible specificity. As no grey zone is defined by the manufacturer of Bordier, we recommend interpreting the index values between 0.7 and 1 as equivocal or positive results. By using these cut-off values, the Bordier kit displayed the highest specificity (>99%). We propose to broaden the cut-off values recommended by the Vircell manufacturer from 9–11 to 8–11, yielding a specificity comparable to that of Bordier. We also suggest reducing the NovaLisa positivity threshold from 9 to 6, although this would entail a significant decrease of specificity and the use of additional confirmation tests, compared with the Bordier and Vircell assays. Moreover, the cut-off values (1.5–11) of the Ridascreen kit required to achieve a sensitivity of 95% significantly reduced specificity (81.9%), therefore making this test less suitable for routine screening.Few studies evaluated ICT performance for the diagnosis of zoonotic VL . Howeveret al. compared one ICT (IT LEISH) and three ELISA  kits in Brazil [et al., except for Ridascreen (specificity <85% in Brazil). Conversely, while Ridascreen displayed the highest sensitivity value in Brazil, we found that this test had the lowest sensitivity among all kits we evaluated. On the other hand, the sensitivity and specificity of the IT LEISH ICT were similar in both studies, although our values were slightly higher in the whole population. These differences might be explained by antigen variability in parasites from different geographic regions and by cross-reactivity with other endemic pathogens, as highlighted by the World Health Organization (WHO) evaluation of ICT [et al. [Recently, Freire n Brazil . The oven of ICT . Finally [et al. ,7 that r [et al. . In a re [et al. . These rRegarding WB, this technique is currently used to confirm the positive results obtained by serological screening. Its specificity should be discussed by taking into account the existence of asymptomatic carriers. The Leishmanin skin test , blood cIn conclusion, the Vircell and Bordier ELISA kits displayed significantly higher accuracy rates in the whole population than the other tests. Nevertheless, we had to decrease the cut-off values to improve the performance of the four ELISA tests. For field studies, the TruQuick ICT is the most suitable with satisfactory performances. In immunocompromised patients, the index values of all ELISA reagents were significantly lower than in the immunocompetent population and false-negative results were more frequent, as shown previously . This kiS1 ChecklistThe STARD checklist describes the design of the current study in order to improve reporting accuracy and completeness.(DOCX)Click here for additional data file.S1 FigP: positive; N: negative; I: indeterminate; Orange case: target condition present; Green case: target condition absent.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableNegative = 0; Positive = 1; Grey cell = unknown; Pink cell = immunosuppression; Orange cell = grey zone; Yellow cell = discrepancy.(XLSX)Click here for additional data file.


Triticum aestivum L.) is a major concern particularly since Nepal is ranked the 4th most vulnerable nation globally to climate change. The genetic diversity and population structure of Nepali spring wheat have not been reported. This study aims to improve the exploitation of more diverse and under-utilized genetic resources to contribute to current and future breeding efforts for global food security.Appropriate information about genetic diversity and population structure of germplasm improves the efficiency of plant breeding. The low productivity of Nepali bread wheat (We used genotyping-by-sequencing (GBS) to characterize a panel of 318 spring wheat accessions from Nepal including 166 landraces, 115 CIMMYT advanced lines, and 34 Nepali released varieties. We identified 95 K high-quality SNPs. The greatest genetic diversity was observed among the landraces, followed by CIMMYT lines, and released varieties. Though we expected only 3 groupings corresponding to these 3 seed origins, the population structure revealed two large, distinct subpopulations along with two smaller and scattered subpopulations in between, with significant admixture. This result was confirmed by principal component analysis (PCA) and UPGMA distance-based clustering. The pattern of LD decay differed between subpopulations, ranging from 60 to 150 Kb. We discuss the possibility that germplasm explorations during the 1970s–1990s may have mistakenly collected exotic germplasm instead of local landraces and/or collected materials that had already cross-hybridized since exotic germplasm was introduced starting in the 1950s.We suggest that only a subset of wheat “landraces” in Nepal are authentic which this study has identified. Targeting these authentic landraces may accelerate local breeding programs to improve the food security of this climate-vulnerable nation. Overall, this study provides a novel understanding of the genetic diversity of wheat in Nepal and this may contribute to global wheat breeding initiatives.Supplementary information accompanies this paper at 10.1186/s12870-020-02722-8. The study explores the population structure and genetic diversity of spring wheat germplasm in Nepal, not reported previously. The findings were unexpected and provide a novel understanding of the germplasm.Triticum aestivum L.). Bread wheat is the third most important staple cereal crop in the world with a global production of 757 million metric tonnes in 2017 .Genomic DNA of each wheat accession was extracted from leaf tissues collected in the field, using DNeasy Plant Mini Kits  according to the manufacturer’s protocol. SNP genotyping was performed using a GBS approach, and Ion Torrent sequence reads (50–160 bp) were processed using the Fast-GBS pipeline . In brieThe distribution of SNPs in the genome was visualized and analyzed by using TASSEL v.5.2.48 . GeneticThe model-based Bayesian clustering software fastSTRUCTURE v 1.0  was usedIn addition, TASSEL v.5.2.48  was usedhclust function .Additional file 2 Table S2. Summary of distribution of SNPs in wheat genomes across 21 chromosomes in the Nepali Wheat Diversity Panel.Additional file 3 Table S3. Assignment of individual accession in the Nepali Wheat Diversity Panel to different subpopulation based on Q-matrix obtained from fastSTRUCTURE.Additional file 4 Table S4. The frequency of genotypes in the Nepali Wheat Diversity Panel as differentiated into different subpopulations based on Q-matrix obtained from fastSTRUCTURE.


Ranunculus notabilis s.l. (R. auricomus complex) and its effects on outcrossing, inbreeding, female fitness, and heterozygosity. We performed selfing and intra‐ and interlineage crossings by bagging 481 flowers  followed by germination experiments. We compared seed set and germination rates, and related them to genetic distance and genome‐wide heterozygosity (thousands of RADseq loci). Selfings (2.5%) unveiled a significantly lower seed set compared with intra‐ (69.0%) and interlineage crossings (69.5%). Seed set of intra‐ (65%) compared to interpopulation crossings (78%) was significantly lower. In contrast, all treatments showed comparable germination rates (32%–43%). Generalized linear regressions between seed set and genetic distance revealed positive relationships in general and between lineages, and a negative one within lineages. Seed set was the main decisive factor for female fitness. Germination rates were not related to genetic distance at any level, but were positively associated with heterozygosity in interlineage crossings. Experiments confirmed full crossability and predominant outcrossing among sexual R. notabilis s.l. lineages. However, up to 5% (outliers 15%–31%) of seeds were formed by selfing, probably due to semi‐self‐compatibility in a multi‐locus gametophytic SI system. Less seed set in intrapopulation crossings, and higher seed set and germination rates from crossings of genetically more distant and heterozygous lineages (interlineage) indicate negative inbreeding and positive outbreeding effects. In GP scenarios, sexual species with small and/or isolated populations can suffer from decreased female fitness due to their breeding system. This factor, among others, probably limits range expansion of sexuals.The larger distribution area of asexuals compared with their sexual relatives in geographical parthenogenesis (GP) scenarios has been widely attributed to the advantages of uniparental reproduction and polyploidy. However, potential disadvantages of sexuals due to their breeding system have received little attention so far. Here, we study the breeding system of five narrowly distributed sexual lineages of  Ranunculus notabilis s.l (R. auricomus complex) and related them to genetic distance and genome‐wide heterozygosity. Results suggest that inbreeding depression particularly affects intrapopulation  crossings, and that positive outbreeding effects particularly influence interlineage crossings. In GP scenarios, sexual species with small and/or isolated populations can suffer from decreased female fitness due to their breeding system.Potential disadvantages of sexual plant species due to their breeding system have received little attention in geographical parthenogenesis (GP) scenarios so far. We compared seed set and germination rates of five sexual lineages of  SeveralSo far little attention has been paid to the potential disadvantages of sexual species in GP scenarios. Sexuality in plants and animals is by far the most abundant and widespread mode of reproduction, and benefits from short‐term advantages of recombination Burt, . In mostHieracium  among individuals/lineages on reproductive fitness. The R. auricomus complex shows a typical pattern of geographical parthenogenesis, with five allopatric sexual species in Southern to Central Europe  Are these lineages fully cross‐compatible, as expected within a species? (c) Do interlineage crossings differ in their seed set from intralineage ones ? (d) Do germination rates differ in inter‐ and intralineage crossings? (e) How do genetic distance and genome‐wide heterozygosity differ among lineages? (f) How do these measures relate to reproductive fitness?By using experimental crossings and analyses of key genetic measures, we aim at clarifying the role of breeding systems of sexual plant species in GP scenarios. We address the following questions: (a) Are lineages within 22.1R. notabilis s.l. in the present study, covering the whole range of this Illyrian species. Four of them were formerly described by Dunkel et al. , lineage 4 “R. peracris,” and lineage 5 “R. subcarniolicus”). Up to 16 individuals per wild population and one to three populations per lineage were sampled in Slovenia and Austria from 2011 to 2018  following two different treatments: among individuals of a lineage including intra‐ and interpopulation crossings  and among individuals of different lineages . In 2020, we added the seed set of three additional individuals of lineage 3 to increase the sample size.We harvested ripened achenes from May to June 2019. Per bag (one flower per bag), achenes were categorized into viable versus aborted achenes and counted separately. Viable achenes were recognized by color (brown to dark brown), endosperm development (hard and full achene bodies), size (larger), and removability from the receptaculum . We sowed seeds on the 19th of September 2019 and stored pots under similar garden conditions (garden beds were protected against direct rain and sunlight). To prevent frost damage on young seedlings, we transferred pots into climate growth chambers and raised plants under conditions of 10 hr photoperiod and 12°C air temperature . Cool and dark storage should simulate autumn/winter conditions to overcome seed dormancy  as described in Karbstein et al.  to check for significant group differences. If results were significant, we calculated pairwise Wilcoxon rank‐sum tests with Holm correction to assess pairwise group differences.To examine differences between seed set or germination rates and different treatments , and interlineage) of different R. notabilis s.l. samples out of the total 45 sexual R. auricomus ones used by Karbstein et al.  and the Neighbor‐Net algorithm using the program SPLITSTREE vers. 4.14.6 . Raw values, mean values per crossing ID, and mean values per lineage  were used in further data analyses.R. auricomus species , and among sexual diploid R. notabilis s.l. lineages.We performed the Kruskal–Wallis test to check for significantly different genetic distances among lineage combinations , lineage 1—lineage 2 (interlineage)). The same tests were also conducted to assess significant differences in heterozygosity among other sexual diploid To evaluate relationships between seed set or germination rates and mean genetic distances  among individuals of 3df = 2, p < .001; Figure p < .001) seed set compared with intra‐ and interlineage crossings . Seed set was significantly lower in intra‐ compared with interpopulation crossings , and, in general, investigated differences might be due to the low sample size of lineage 1. Intralineage R. notabilis s.l. crossings revealed no significant differences in seed set among lineages . Interlineage crossings significantly differed also in relation to germination rates . Lineage 3 (46.70%) differed significantly from lineage 1 (p < .05), but not from lineages 2, 4, and 5 (p > .05).Selfings of df = 14, p < .001; Table R. notabilis s.l. compared to R. cassubicifolius s.l.  but not compared to all other diploid sexual species of the R. auricomus complex  between seed set or germination rates and mean genetic distance compared to intralineage crossings . Intra‐ and interpopulation crossings showed similar relationships  between seed set or germination rates and mean heterozygosity compared to intralineage crossings  as reported from other congeneric species . Finally, pseudo‐self‐compatibility could influence the actual efficiency of SI systems: extreme temperatures and early or late pollinations could reduce the expression of SI proteins, and hence allow for occasional self‐pollen tube growth , disturbances of meiosis and sporogenesis, and shifts to apomixis  crossings. We also found higher female fitness (seed set) with increasing genetic distance in all and interlineage crossings, and higher germination rates with increasing heterozygosity in interlineage crossings. These results support an interpretation that inbreeding depression particularly affects intrapopulation  crossings, and that positive outbreeding effects particularly influence interlineage crossings. Breeding systems and genetic properties of sexual species are important, although not the exclusive factor for the restricted distribution ranges of sexual species and have to be considered for understanding GP patterns.We confirm predominant self‐incompatibility of sexual None declared.Kevin Karbstein: Conceptualization (supporting); Formal analysis (lead); Investigation ; Methodology (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Elisabeth Rahmsdorf: Conceptualization (supporting); Formal analysis (supporting); Investigation (lead); Methodology (supporting); Visualization (supporting); Writing‐original draft ; Writing‐review & editing (supporting). Salvatore Tomasello: Conceptualization (supporting); Investigation (supporting); Supervision (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Ladislav Hodač: Investigation (supporting); Resources (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Elvira Hörandl: Conceptualization (lead); Formal analysis (supporting); Funding acquisition (lead); Methodology ; Project administration (lead); Resources (supporting); Supervision (lead); Visualization (supporting); Writing‐original draft (lead); Writing‐review & editing (lead).Fig S1‐S10Click here for additional data file.


Much research has shown that people tend to view genes in rather deterministic ways—often termed genetic essentialism. We explored how people would view the causes of ethnic stereotypes in contexts where human genetic variability was either emphasized or downplayed. In two studies with over 1600 participants we found that people viewed ethnic stereotypes to be more of a function of underlying genetics after they read an article describing how ancestry can be estimated by geographic distributions of gene frequencies than after reading an article describing how relatively homogeneous the human genome was or after reading a control essay. Moreover, people were more likely to attribute ethnic stereotypes to genes when they scored higher on a measure of genetic essentialism or when they had less knowledge about genes. Our understanding of stereotypes is a function of our understanding of genetics. Csanád Szegedi, then member of the European Parliament, converted to living his life as an Orthodox Jew at the age of 30. What makes his conversion remarkable is that shortly before his conversion he was the vice president of the anti-Semitic Jobbik Party in Hungary, and had published a book full of anti-Semitic sentiment. So why would such a person convert to Judaism? It appears to have been based on something that he learned about his own ancestry. Szegedi reports having been stunned to learn that his maternal grandmother was Jewish, a fact she had kept secret . That isSzegedi is not alone in viewing his genetic ancestry as the key to his identity. Consumer genomic companies inform people about the possible geographic origins of their ancestors, and many people have reacted to their test results by changing how they identify themselves, such as choosing different ethnic identities when completing a census, joining new communities, cheering for different national sports teams, and learning new languages see . These rPeople do tend to be interested in the ethnic identity of others e.g., , 8, espeWhy did Szegedi convert to Judaism when he learned about his Jewish ancestry? Or, more generally, why do people often view genes as holding the key to their identity , 11? A cAn influential account of psychological essentialism proposes that people struggle to form concrete mental representations of what essences actually are, and they come to rely on essence placeholders upon which they project their intuitions about essences . While pWhile there are indeed genetic causes that can operate in highly deterministic ways that resemble these essentialist tendencies, such as fully penetrant monogenic conditions , the so-called “fourth law of behavioral genetics” states that “a typical human behavioral trait is associated with very many genetic variants, each of which accounts for a very small percentage of the behavioral variability” , p. 305.How might people’s genetic essentialist biases affect how they understand ethnicity? While the field of cultural psychology has documented the many ways that people’s cultural experiences shape their ways of thinking e.g., , 27, it One study presented German students with either an essay that described how geographic ancestry could be revealed by genomic analyses or a control essay on an unrelated topic . Particiany differences between these populations as being more likely the result of genes. Because genes are often perceived as carving up the social world into homogenous and discrete categories Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Review of "Exploring people’s thoughts about the causes of ethnic stereotypes"The authors present two studies in which they manipulated  participants’ beliefs about human genetic homogeneity versus heterogeneity and then measured participants’ endorsement of various ethnic stereotypes and attributions of these stereotypes of genetic and experiential causes. In both studies, participants in the Genetic Difference condition (compared to those in the Genetic Similarity condition) displayed a stronger tendency to attribute ethnic stereotypes to genetic causes.I appreciate the authors’ well-executed studies with high statistical power. I also appreciate the authors’ clear and thorough presentation of the relevant literature. I do, however, have a number of suggestions that I feel would improve the paper.1. The authors provide a nice review of the existing literature on beliefs about biology/genetics and ethnic stereotyping. Indeed, a good deal of studies by researchers like Keller, Chao, Plaks, Eberhard, Halperin all point in the same direction: When people place more weight on biology/genetics, they are more likely to invoke ethnic stereotypes and more likely to endorse the accuracy of such stereotypes. For this reason, the authors could stand to articulate more explicitly how their studies are different from - and add something significant to - the existing literature.2. I looked closely at the articles the authors used in the manipulation and noticed that there is no material about cultural variation. This is important because the authors seem keenly interested in pitting genetics against experience/culture in participants’ attributions. The fact that the manipulation is only on the genetic side of the equation means that this is not an ideal way to test which type of causation  looms larger in people’s minds. A more complete design would have included four articles that would have conformed to a 2x2 design: Genetic Similarity / Cultural Similarity, Genetic Similarity / Cultural Difference, Genetic Difference / Cultural Similarity, Genetic Difference / Cultural Difference. This would have allowed for a fairer assessment of participants’relative weighing of genetic vs cultural causes. In other words, if participants’ ratings were to show a greater difference  between the Difference vs. Similarity conditions on the Genetic dimension than on the Cultural Dimension, this would provide stronger evidence for the authors’ claims. But with a full 2 X 2 design, the data *could* turn out differently. It could reveal some interesting Gene X Culture interactions in laypeople’s minds on at least some of the stereotype items. For example, people might believe that Indians like spicy food for a combination of genetic *and* cultural reasons.Indeed, many (most?) people likely hold some version of a simple hybrid model that involves combining both genetic and experiential/cultural components in an additive fashion. What differs between individuals is merely the percentages they assign to genes and culture.Another thing I notice about the articles is that there are several, if not *confounds*, at least ways in which the two are not directly comparable. For example, although the Genetic Difference article discusses how human genetic variation could occur, it never actually mentions in an absolute sense how much of the entire genome varies from person to person or region to region. In contrast, the Genetic Similarity article *does* feature prominently material about how much overall genetic variation there is . Thus, the manipulation is not really ‘little overall variation’ vs. ‘lots of overall variation’ - it’s more like ‘little variation’ vs. ‘silent’. Other differences between the articles: The Similarity article introduces two concepts  that are not mentioned at all in the Difference article. In other words, there is more being manipulated here than simply genetic sameness/difference. As such, we don’t know exactly what is causing the effect.3. Regarding the data analyses, it would have been extremely helpful and informative if the authors had gone beyond reporting a few t-tests / correlation matrices. For example, they could have tested at least a few of the numerous potentially fascinating mediational models that are there in the data waiting to be tested. For example: genetic essentialism tendencies -> attribution to genetic causes -> stereotypes are accurate. Or: SDO->genetic essentialism tendencies -> attribution to genetic causes -> stereotypes are accurate. There are any number of additional mediational tests and moderated mediation tests that would provide additional insight into why the authors obtain their pattern they obtain.In a related vein, I would have liked to see more discussion of the cognitive versus motivational origins of genetic essentialism. By ‘cognitive’ I mean the innocuous (but incorrect) beliefs that contribute to genetic essentialism. But ‘motivational’ I’m referring to the ideological/value systems that rely on genetic essentialism being true. One example of that is SDO. Another is probably Just World beliefs. In other words, well-meaning people with genuinely egalitarian aspirations may believe that people on different continents are genetically different simply because that’s the message they’ve received from their culture  and they’ve had no reason to dispute that. Thus, these people would be more likely to believe in some version of ‘separate but equal’. Others, however, may have a more ideologically-driven investment in believing that people from different parts of the world are genetically different. They may be more likely to believe in ‘separate and UNequal’.This question of where the genetic beliefs come from has important implications for how easily people might be persuaded by articles like the ones the authors present. I suspect that people with a more ideologically-driven belief in either Genetic Difference or Genetic Similarity would be less persuadable by the opposing article than those who acquired their beliefs simply by passively absorbing messages from their culture.Finally, I had a bit of an issue with how the authors used the word ‘stereotype’. I’ve just done a quick search and it turns out that Japan *is* at or near the top of the world in life expectancy and the Netherlands *is* at or near the top in average height. I raise this because the definition of the word ‘stereotype’ according to Webster’s dictionary is: “a widely held but fixed and oversimplified image or idea of a particular type of person or thing.” In other words, the ‘stereotype’ component only enters once you add in the genetic component - that’s what gives it the “fixed” and “oversimplified” aspect. But before you do that, , it’s not so much a ‘stereotype’ as it is a correlation. Put differently, genetics takes a correlation (being Dutch - height) and turns it into causation (being Dutch *causes* height) - and THAT’S when it becomes a ‘stereotype’ in the true sense of the word.On the whole, however, these are all fairly addressable concerns. I can see a future version of this paper being published in PLOSONE.Reviewer #2: Summary and General Impression:Across two studies, the researchers examined how ideas of genetic essentialism impact the perceived causes of ethnic stereotypes. In Study 1, participants read one of two articles which described the ability to estimate ancestry through genetics or the homogeneity of the human genome, or read a control article discussing an unrelated topic. They were then asked to describe the extent to which ethnic stereotypes were caused by genetic and environmental factors on a numerical scale. Among participants who believed ethnic stereotypes were accurate, those in the control condition and the ancestry via genetics condition viewed stereotypes as being more related to genetics and less related to environmental factors than those in the homogenous genome condition. Across conditions, however, participants rated environmental factors as the majority influence on ethnic stereotypes. Genetic essentialist beliefs were also associated with greater attribution of genetics to stereotypes.In Study 2, the authors replicated Study 1 with some additions. They expanded their list of stereotypes, included a measure of knowledge about genetics, and measured attitudes towards the ethnic groups described in the list of stereotypes. The finding that those in the genetic ancestry condition were more likely to attribute stereotypes to genetic causes was replicated; however, those in the control condition were most similar to the homogenous genome condition, unlike Study 1.Overall, the manuscript is well-written and well-organized. It provides an interesting extension to past research on genetic essentialism and racial attitudes by examining the influence of genetic information on perceptions of the origins of stereotypical behavior. Both studies use large sample sizes and the procedures for excluding participants are well-explained. The statistics used are appropriate and corrections for multiple comparisons and effect sizes are clearly present. The authors present interesting ideas on the real-world implications of this research given the popularity of ancestry-related genomic testing and the ability for increased knowledge about genes as a means of reducing essentialist attitudes.Suggestions for Improvement1. The general discussion discusses three major findings: that reading about genetic differences used to determine ancestry increases essentialist views of stereotypes, that people have essentialist views of genetics in general, and that overall genetic knowledge is low. In my read, the support for the first conclusion is strong. However, the scores for the first genetic essentialism scale are just above the midpoint in both studies. I would appreciate more explanation of how this finding is interpreted as generally more essentialist views.2. Similarly, the authors state that overall genetic knowledge is low. This was measured only in Study 2, with a score of just over 6 / 9 correct. While this does not indicate a high level of knowledge about genes, I don’t know that I would characterize this as a low level of knowledge.3. Overall, the discussion is quite short. I’d like to see some discussion of how the results of this research build upon and/or replicate previous work in this area.4. Both studies include an analysis of the effects of social dominance orientation. However, it is not explained why this variable is included and social dominance orientation is not discussed in the introduction.5. Information about participants’ racial/ethnic backgrounds is not provided. If this is available, it should be included in the participants section.6. I would appreciate more information about the development of the stereotypes for both studies. How were these stereotypes selected? Was there any consideration of differential interpretations for stereotypes related to physical appearance compared to stereotypes about habits, personality, etc. It seems it would be easier to mentally link some of these stereotypes to genes compared to others.7. Minor – There are numerous issues with APA format, particularly in the References section**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, AttachmentPLOS ONE Article Review 11-2020.docxSubmitted filename: Click here for additional data file. 2 Dec 2020Dear Dr. Wang,Thank you for allowing us to send a revised version of our manuscript for consideration. We greatly appreciate the opportunity and we are also grateful to the reviewers for providing constructive comments that have helped us make this a better manuscript.We outline, in point form, our responses to your and the reviewers’ comments along with any changes that we have made. We respond to them in the order that they appeared in your decision letter to us. EditorPlease provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained . If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.We added the following to the Methods Sections in both studies (pp. 10 & 16):“Participants provided informed written consent. All participants were over 18.”Thank you for stating the following in your Competing Interests section: "No."http://journals.plos.org/plosone/s/submit-nowPlease complete your Competing Interests on the online submission form to state any Competing Interests. If you have no competing interests, please state "The authors have declared that no competing interests exist.", as detailed online in our guide for authors at This information should be included in your cover letter; we will change the online submission form on your behalf.Note that we have declared that no competing interests exist. Reviewer 11. The authors provide a nice review of the existing literature on beliefs about biology/genetics and ethnic stereotyping. Indeed, a good deal of studies by researchers like Keller, Chao, Plaks, Eberhard, Halperin all point in the same direction: When people place more weight on biology/genetics, they are more likely to invoke ethnic stereotypes and more likely to endorse the accuracy of such stereotypes. For this reason, the authors could stand to articulate more explicitly how their studies are different from - and add something significant to - the existing literature.We thank the reviewer for this suggestion, and we have now added a section to the introduction where we discuss how our study is different from and adds to the existing literature (p. 8):“While the extant literature has found that genetic information can affect the ways people think about different ethnicities, we explore whether thinking about genetic differences between different populations leads people to think of any differences between these populations as being more likely the result of genes. Because genes are often perceived as carving up the social world into homogenous and discrete categories , different populations may be seen as fundamentally different which might increase the psychological distance people feel towards them.”2. I looked closely at the articles the authors used in the manipulation and noticed that there is no material about cultural variation. This is important because the authors seem keenly interested in pitting genetics against experience/culture in participants’ attributions. The fact that the manipulation is only on the genetic side of the equation means that this is not an ideal way to test which type of causation  looms larger in people’s minds. A more complete design would have included four articles that would have conformed to a 2x2 design: Genetic Similarity / Cultural Similarity, Genetic Similarity / Cultural Difference, Genetic Difference / Cultural Similarity, Genetic Difference / Cultural Difference. This would have allowed for a fairer assessment of participants’relative weighing of genetic vs cultural causes. In other words, if participants’ ratings were to show a greater difference  between the Difference vs. Similarity conditions on the Genetic dimension than on the Cultural Dimension, this would provide stronger evidence for the authors’ claims. But with a full 2 X 2 design, the data *could* turn out differently. It could reveal some interesting Gene X Culture interactions in laypeople’s minds on at least some of the stereotype items. For example, people might believe that Indians like spicy food for a combination of genetic *and* cultural reasons.Indeed, many (most?) people likely hold some version of a simple hybrid model that involves combining both genetic and experiential/cultural components in an additive fashion. What differs between individuals is merely the percentages they assign to genes and culture.Another thing I notice about the articles is that there are several, if not *confounds*, at least ways in which the two are not directly comparable. For example, although the Genetic Difference article discusses how human genetic variation could occur, it never actually mentions in an absolute sense how much of the entire genome varies from person to person or region to region. In contrast, the Genetic Similarity article *does* feature prominently material about how much overall genetic variation there is . Thus, the manipulation is not really ‘little overall variation’ vs. ‘lots of overall variation’ - it’s more like ‘little variation’ vs. ‘silent’. Other differences between the articles: The Similarity article introduces two concepts  that are not mentioned at all in the Difference article. In other words, there is more being manipulated here than simply genetic sameness/difference. As such, we don’t know exactly what is causing the effect.We thank the reviewer for this insightful point. We have added two paragraphs to the discussion where we discuss the limitations of the manipulations (p. 25): “Furthermore, the two experimental manipulations focused solely on the role of genes in the similarity/difference among different populations. While this design allowed us to distinguish the effects of perceiving different populations as genetically similar or different on people’s attributions of stereotypes to genetic causes, it cannot speak to the role of perceived cultural similarities/differences between populations. It is possible that emphasizing cultural differences or similarities would also influence people’s judgments of the causes of ethnic stereotypes. In addition, while we tried to make the essays convincing and based on accurate scientific explanations, the two manipulations are not precise polar opposites of each other. For example, the genetic similarity condition describes explicitly that there is relatively little genetic variation among human populations while the genetic difference condition describes how ancestry can be traced based on genetic maps of the world that show how people from different parts of the world are genetically distinct. Hence, direct comparisons of the potency of the two experimental essays are complicated by the different kinds of information that each essay contains.”3. Regarding the data analyses, it would have been extremely helpful and informative if the authors had gone beyond reporting a few t-tests / correlation matrices. For example, they could have tested at least a few of the numerous potentially fascinating mediational models that are there in the data waiting to be tested. For example: genetic essentialism tendencies -> attribution to genetic causes -> stereotypes are accurate. Or: SDO->genetic essentialism tendencies -> attribution to genetic causes -> stereotypes are accurate. There are any number of additional mediational tests and moderated mediation tests that would provide additional insight into why the authors obtain their pattern they obtain.In a related vein, I would have liked to see more discussion of the cognitive versus motivational origins of genetic essentialism. By ‘cognitive’ I mean the innocuous (but incorrect) beliefs that contribute to genetic essentialism. But ‘motivational’ I’m referring to the ideological/value systems that rely on genetic essentialism being true. One example of that is SDO. Another is probably Just World beliefs. In other words, well-meaning people with genuinely egalitarian aspirations may believe that people on different continents are genetically different simply because that’s the message they’ve received from their culture  and they’ve had no reason to dispute that. Thus, these people would be more likely to believe in some version of ‘separate but equal’. Others, however, may have a more ideologically-driven investment in believing that people from different parts of the world are genetically different. They may be more likely to believe in ‘separate and UNequal’.This question of where the genetic beliefs come from has important implications for how easily people might be persuaded by articles like the ones the authors present. I suspect that people with a more ideologically-driven belief in either Genetic Difference or Genetic Similarity would be less persuadable by the opposing article than those who acquired their beliefs simply by passively absorbing messages from their culture.We thank the reviewer for these thoughtful points. We have not done any of these mediational analyses as our paper is not intending to explore what predicts the perceived accuracy of stereotypes. While interesting, this seems to be a much different question from what we were investigating and would require a rather different experimental design to investigate it appropriately. We have now added the following brief paragraph to the discussion where we address the interesting point about the possible cognitive and motivational origins of genetic essentialism (pp. 26-27): “While we have demonstrated here that genetic essentialist beliefs are correlated with the ways that people conceive of population differences, we have not provided any evidence regarding where these beliefs come from. There are likely some cognitive and motivational precursors of genetic essentialist beliefs, and this remains an important question for future research.”Finally, I had a bit of an issue with how the authors used the word ‘stereotype’. I’ve just done a quick search and it turns out that Japan *is* at or near the top of the world in life expectancy and the Netherlands *is* at or near the top in average height. I raise this because the definition of the word ‘stereotype’ according to Webster’s dictionary is: “a widely held but fixed and oversimplified image or idea of a particular type of person or thing.” In other words, the ‘stereotype’ component only enters once you add in the genetic component - that’s what gives it the “fixed” and “oversimplified” aspect. But before you do that, , it’s not so much a ‘stereotype’ as it is a correlation. Put differently, genetics takes a correlation (being Dutch - height) and turns it into causation (being Dutch *causes* height) - and THAT’S when it becomes a ‘stereotype’ in the true sense of the word.We feel we have used the word “stereotype” in ways that are consistent with the literature and with the Webster’s dictionary definition that the reviewer cites. There is a rather large literature that has explored the accuracy of different kinds of stereotypes , which finds that many stereotypes do possess varying degrees of accuracy. In general, the field does not use different terms for those stereotypes which have an accurate basis and those which do not.On the whole, however, these are all fairly addressable concerns. I can see a future version of this paper being published in PLOSONE.Reviewer 21. The general discussion discusses three major findings: that reading about genetic differences used to determine ancestry increases essentialist views of stereotypes, that people have essentialist views of genetics in general, and that overall genetic knowledge is low. In my read, the support for the first conclusion is strong. However, the scores for the first genetic essentialism scale are just above the midpoint in both studies. I would appreciate more explanation of how this finding is interpreted as generally more essentialist views.We agree with the reviewer that we cannot conclude from these studies that genetic essentialism in general is high and, indeed, we did not intend to draw that conclusion. Our intention was to highlight that our findings show that people view ethnic stereotypes to be more likely to be caused by genes three conditions: a) after reading an article about how people’s ancestry can be assessed by their genome, b) when people have more essentialist views, and c) when people have less knowledge about genes. We have now rephrased the discussion on these findings to make this conclusion clearer (p. 23):“Across two studies we found that people were more likely to view genes as underlying ethnic differences under the following conditions: a) when people had recently read an article describing how people’s ancestry can be assessed by examining their genomes ; b) when people tend to have more deterministic and essentialist views of genes in general; and c) when people have relatively less general knowledge about genes.”2. Similarly, the authors state that overall genetic knowledge is low. This was measured only in Study 2, with a score of just over 6 / 9 correct. While this does not indicate a high level of knowledge about genes, I don’t know that I would characterize this as a low level of knowledge.We agree that our findings do not speak to the idea that genetic knowledge in general is low and we thank the reviewer for noting that our conclusion seemed to suggest this. Our intention here was not to say that genetic knowledge in general is low, but rather that when genetic knowledge is low, then people are more likely to view ethnic stereotypes as being caused by genes. We have now rephrased the discussion to make this clearer in point 1 above.3. Overall, the discussion is quite short. I’d like to see some discussion of how the results of this research build upon and/or replicate previous work in this area.We thank the reviewer for this suggestion, and we have now added a section to the discussion where we discuss how the results build on previous work (p. 24):“Previous work has found that encounters with genetic information can affect the ways that people think about ethnicity. For example, reading about genetic ancestry led Germans to have an ingroup preference for Western Europeans over Eastern Europeans , reading about variability in the human genome led participants to evaluate ingroup and outgroup faces in a more dichotomous way , and reading about genetic similarities between Jews and Arabs led American Jewish participants to support peacemaking efforts in the Middle East less. While our study didn’t find any difference in attitudes towards the different ethnic groups, it points to a possible reason for the findings from previous research: When people think about the genetic differences between different populations, they come to think of any differences between those populations as being caused more by genes. Because genes tend to be seen as ultimate causes, immutable, natural and as carving the social world up into homogeneous and discrete categories , different populations come to be seen as fundamentally different.” 4. Both studies include an analysis of the effects of social dominance orientation. However, it is not explained why this variable is included and social dominance orientation is not discussed in the introduction. We thank the reviewer for calling our attention to this omission. We now describe this rationale on page 9.“Last, because social dominance orientation is associated with more prejudicial attitudes towards outgroups , we also explored how this variable would relate to people’s understandings about the causes of ethnic stereotypes.“5. Information about participants’ racial/ethnic backgrounds is not provided. If this is available, it should be included in the participants section.We thank the reviewer for calling our attention to this omission. We collected information about participants’ ethnic background for and added that to the:Study 1 (pp. 9-10):“We collected data from 425 Americans through MTurk in case some participants do not pass comprehension checks .”Study 2 (p. 16):“We collected data from 1238 Americans through MTurk in case some participants did not pass comprehension checks .”6. I would appreciate more information about the development of the stereotypes for both studies. How were these stereotypes selected? Was there any consideration of differential interpretations for stereotypes related to physical appearance compared to stereotypes about habits, personality, etc. It seems it would be easier to mentally link some of these stereotypes to genes compared to others.We thank the reviewer for this oversight and have now added our rationale on p. 10. “We generated this list of stereotypes from internet searches, discussions with various individuals, and by reading a list of racial stereotypes .”7. Minor – There are numerous issues with APA format, particularly in the References sectionWe followed the submission guidelines which state that the references should be in Vancouver style, not in APA style. We thank you for your very helpful review. On behalf of the authors,Steven Heine 2 Jan 2021Exploring people’s thoughts about the causes of ethnic stereotypesPONE-D-20-30825R1Dear Dr. Heine,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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