Short tandem repeats (STRs) are involved in dozens of Mendelian disorders and have been implicated in complex traits. However, genotyping arrays used in genome-wide association studies focus on single nucleotide polymorphisms (SNPs) and do not readily allow identification of STR associations. We leverage next-generation sequencing (NGS) from 479 families to create a SNP + STR reference haplotype panel. Our panel enables imputing STR genotypes into SNP array data when NGS is not available for directly genotyping STRs. Imputed genotypes achieve mean concordance of 97% with observed genotypes in an external dataset compared to 71% expected under a naive model. Performance varies widely across STRs, with near perfect concordance at bi-allelic STRs vs. 70% at highly polymorphic repeats. Imputation increases power over individual SNPs to detect STR associations with gene expression. Imputing STRs into existing SNP datasets will enable the first large-scale STR association studies across a range of complex traits. Short-tandem repeats (STR), similar to single nucleotide polymorphisms (SNP), contribute to complex traits, but their ascertainment by next-generation sequencing is costly. Here, Saini et al. provide a SNP+STR haplotype reference panel that allows imputation of STRs from SNP array data. Hundreds of thousands of individuals have been genotyped using commodity genotyping arrays. These arrays take advantage of the correlation structure between nearby variants induced by linkage disequilibrium (LD), which allows genome-wide imputation based on genotypes of only a small subset of loci4. However, GWAS based on single-nucleotide polymorphism (SNP) associations face important limitations. Even with sample sizes of up to 100,000 individuals, common SNPs still fail to explain the majority of heritability for many complex traits5.Genome-wide association studies (GWAS) have become increasingly successful at identifying genetic loci significantly associated with complex traits in humans, largely due to the enormous growth in available sample sizes6. The signal could not be localized to a single SNP and could only be explained after deep characterization of the underlying CNV. This and subsequent discoveries8 highlight the importance of considering alternative variant classes.One compelling hypothesis explaining the missing heritability dilemma is that complex variants, such as multi-allelic repeats not in strong LD with common SNPs, are important drivers of complex traits but are largely invisible to current analyses. Indeed, dissection of the strongest schizophrenia association, located in the major histocompatibility complex, revealed a poorly tagged polymorphic copy number variant (CNV) to be the causal variant9. Multiple lines of evidence support a role of STRs in complex traits12, particularly in neurological and psychiatric phenotypes. Due to their rapid mutation rates13, STRs exhibit high rates of heterozygosity14 and likely contribute at least as many de novo mutations per generation as SNPs16. Furthermore, STRs have been shown to play a significant role in regulating gene expression18, splicing21, and DNA methylation18. Intriguingly, more than 30 Mendelian disorders are caused by STR expansions via a range of mechanisms, including polyglutamine aggregation , hypermethylation (Fragile X Syndrome23), and RNA toxicity (ALS/FTD24). Furthermore, causal STRs driving existing GWAS signals have already been identified25.Short tandem repeats (STRs), consisting of repeated motifs of 1–6 bp in tandem, comprise more than 3% of the human genome28 and found that STRs and SNPs provide complementary information about the evolutionary history of a genomic region. Despite widespread SNP-STR LD, statistical phasing of STRs and SNPs is challenging for several reasons: SNP-STR LD is notably weaker than SNP-SNP LD28 due to the rapid mutation rates29 and high prevalence of recurrent mutations in STRs. As a result, the relationship between STR repeat number and SNP haplotype can be complex: the same STR allele may be present on multiple SNP haplotypes. On the other hand, a single SNP haplotype may harbor multiple distinct STR alleles. Furthermore, LD patterns at STRs vary widely as a function of properties of the repeat, such as the repeat unit length, mutation rate, and mutation step size28. Finally, STRs are prone to genotyping errors induced during PCR amplification31, further ambiguating phase information.Existing technologies have not allowed for systematic STR association studies. Next-generation sequencing (NGS) can be used to directly genotype short STRs, but NGS is still too expensive to perform on sufficiently large cohorts for GWAS of most complex traits. An alternative approach is to impute STRs into existing SNP array datasets. Previous studies have demonstrated that STRs are often in significant LD with nearby SNPs32 now enables applying this technique genome-wide. Here, we profile STRs in 479 families and use pedigree information to phase STR genotypes onto SNP haplotypes to create a genome-wide reference for imputation. We use this panel to impute STRs into an external dataset of similar ethnic background with average 97% concordance with observed STR genotypes. Imputation accuracy varies across STRs, ranging from nearly perfect concordance at bi-allelic STRs to around 70% for highly polymorphic forensic markers. We show that STR imputation achieves greater power than individual SNPs to detect underlying STR associations and demonstrate the utility of our panel by detecting STRs not previously known to be associated with gene expression. Finally, we impute genotypes at STRs previously implicated in human disorders and show that we could accurately identify specific SNP haplotypes associated with long normal alleles most at risk for expansion.Sequencing related samples allows haplotype resolution by directly tracing inheritance patterns. The recent generation of deep NGS using PCR-free protocols for hundreds of nuclear families in combination with accurate tools for genotyping STRs from NGSTo facilitate use by the community, we release a phased SNP + STR haplotype panel for samples genotyped as part of the 1000 Genomes Project (see Data availability). This resource will enable large-scale studies of STR associations in hundreds of thousands of available SNP datasets, and will likely yield significant new insights into complex traits.32 to profile autosomal STRs in each sample. HipSTR takes aligned reads and a reference set of STRs as input and outputs maximum likelihood diploid genotypes for each STR in the genome. While HipSTR infers the entire sequence of each STR allele, we focus here on differences in repeat copy number rather than sequence variation within the repeat itself. To maximize the quality of genotype calls, individuals were genotyped jointly with HipSTR’s multi-sample calling mode using phased SNP genotypes and aligned reads as input (Methods). Multi-sample calling allows HipSTR to leverage information on haplotypes discovered across all samples in the dataset to estimate per-locus error parameters and output genotype likelihoods for each possible diploid genotype. Notably, our HipSTR catalog excluded most known STRs implicated in expansion disorders such as Huntington’s Disease and hereditary ataxias, since even the normal allele range for these STRs is above or near the length of Illumina reads36. To supplement our panel, we applied a second STR genotyper, Tredparse37, to genotype a targeted set of known pathogenic STRs in our cohort  (see URLs). We focused on 1916 individuals from 479 family quads (parents and two children) that were sequenced to an average depth of 30x using Illumina’s PCR-free protocol. Based on comparison to 1000 Genomes Project samples, we estimated the cohort to consist primarily of Europeans (83%), with 2.0%, 9.0%, and 3.6% of East Asian, South Asian, and African ancestry, respectively   were highly concordant with a catalog generated from the 1000 Genomes Project00) Fig. . Allele 00) Fig. . For STRr2, is defined as the squared Pearson correlation between STR allele length and the SNP genotype. The second, termed allelic r2, treats each STR allele as a separate bi-allelic locus and is computed similar to traditional SNP-SNP LD (Methods). Similar to previous studies28, SNP-STR LD was dramatically weaker than SNP-SNP LD by both metrics  for each STR, which was on average 5.5 kb away  with the best tag SNP, suggesting that phasing would result in informative haplotypes.We examined the extent of linkage disequilibrium between STRs and nearby SNPs using two metrics. The first, termed length 40, which was recently demonstrated to perform well in phasing multi-allelic STRs41 and can incorporate pedigree information. Resulting phased haplotypes from the parent samples were merged into a single genome-wide reference panel for downstream imputation.We developed a pipeline to phase STRs onto SNP haplotypes leveraging the quad family structure Fig. . Based or2, and allelic r2 between imputed vs. observed genotypes at each STR, where observed refers to genotypes obtained by HipSTR or Tredparse. We additionally evaluated imputation performance under two null models where genotypes were either imputed randomly (random model) or always imputed as the most frequent diploid genotype (naive model) (Methods). Imputed genotypes showed an average of 96.7% concordance with observed genotypes, compared to 61.0% or 71.7% expected under the random and naive models, respectively . To further evaluate imputation performance at highly polymorphic STRs, we examined the CODIS STRs used in forensic analysis , but were on average 8.8% higher , likely as a result of our larger and more homogenous cohort. Per-locus imputation statistics for all STRs are reported in Supplementary Data We first evaluated the utility of our phased panel for imputation using a leave-one-out analysis in the SSC samples. For each sample, we constructed a modified reference panel with that sample’s haplotypes removed and then performed genome-wide imputation. We measured concordance, length ly Table  and alle38 with high quality SNP genotypes obtained from low coverage whole-genome sequencing (WGS) (n = 2504) or genotyping arrays . We validated imputed genotypes for subsets of 1000 Genomes samples using data obtained from three pipelines: (1) Illumina WGS + HipSTR, (2) capillary electrophoresis, and (3) 10X Genomics + HipSTR, in each case using the orthogonal data as the truth set. Each of these datasets evaluates a different aspect of our imputation pipeline. The first tests whether a pipeline identical to that used to create our reference panel can achieve similar performance on datasets collected by different groups using different protocols. Additionally, since it consists of both Europeans and non-Europeans, it allows us to evaluate imputation across a variety of population groups. The second tests whether our results are robust across STR genotyping technologies and allows us to compare imputed STRs based on statistically inferred HipSTR genotypes to those obtained experimentally using capillary electrophoresis. The third returns phased genotypes, allowing us to directly compare inferred haplotypes and phase information.We next evaluated our ability to impute STR genotypes into external datasets. For this, we focused on samples from the 1000 Genomes Projectn = 50), African (n = 50), and East Asian (n = 50) backgrounds. Per-locus concordance, length r2, and allelic r2 were highly concordant between the SSC panel and 1000 Genomes samples of European origin   WGS datasets available for 150 of the samples (see URLs) from European (0× WGS da43 (see URLs) available for a subset of samples in our panel at highly polymorphic STRs. After filtering non-European samples and STRs that could not be reliably mapped to HipSTR notation (Methods), 41 samples and 206 STRs remained for comparison. We obtained an average overall concordance of 76.9% with capillary genotypes compared with 76.4% expected based on HipSTR analysis. Per-locus concordances based on HipSTR vs. capillary genotypes were strongly correlated   Fig. .Finally, we compared imputed genotypes from the highly characterized NA12878 genome to phased data available from 10X Genomics (see URLs), a synthetic long read technology. We constructed a phased validation panel by calling HipSTR separately on reads from each phase and combining with phased SNP genotypes . Association testing simulations were performed 100 times for each STR on chromosome 21 in our dataset (Methods). As expected, the strength of association for each variant as measured by the negative log10p-value was linearly related with its length r2 with the causal variant . The advantage from STR imputation grew as a function of the number of common STR alleles . To this end, we analyzed eSTRs from samples in the Genotype-Tissue Expression46 (GTEx) dataset for which RNA-sequencing, WGS, and SNP array data were available. As a test case, we imputed STR genotypes using SNP data for chromosome 21 and tested for association with genes expressed in whole blood. For comparison, we additionally performed each association using genotypes obtained from WGS using HipSTR (Methods). A total of 2452 STR x gene tests were performed in each case. Association p-values were similarly distributed across both analyses and showed a strong departure from the uniform distribution expected under a null hypothesis of no eSTR associations , effect sizes were strongly correlated when using imputed vs. HipSTR genotypes . Furthermore, effect sizes obtained from imputed data were concordant with previously reported effect sizes in a separate cohort using a different cell type (lymphoblastoid cell lines)17   Fig. .47 to fine-map associations using the most strongly associated STR and the top 100 associated SNPs for each gene (Methods). We identified three genes with ANOVA p < 0.05 for which the STR was the top variant returned by CAVIAR. One example, a CG-rich STR in the promoter of CSTB, was previously demonstrated to act as an eSTR48 and expansions of this repeat are implicated in myoclonus epilepsy49. In each case, imputed STR genotypes were more strongly associated with gene expression compared to the best tag SNP . We additionally applied CAVIAR52 (2) longer alleles are more at risk for expansion53 and (3) allele lengths below the pathogenic range could potentially be associated with more complex phenotypes51.Finally, to determine whether alleles at known pathogenic STRs could be accurately imputed, we examined results of our imputation pipeline at 12 STRs previously implicated in expansion disorders that were included in our panel . This finding demonstrates that while we were unable to directly impute pathogenic expansion alleles, STR imputation can accurately identify which individuals are at risk for carrying expansions or pre-pathogenic mutations and the inferred haplotypes can reveal the history by which such mutations arise.Resolution of SNP-STR haplotypes can be used to infer the mutation history of a specific STR locusOur study combines available whole-genome sequencing datasets with existing bioinformatics tools to generate the first phased SNP + STR haplotype panel allowing genome-wide imputation of STRs into SNP data. Despite their exceptionally high rates of polymorphism, 92% of STRs in our panel could be imputed with at least 90% concordance, and 38% achieved greater than 99% concordance. Imputation performance varied widely across STRs, primarily due to differences in polymorphism levels across loci. Bi-allelic STRs could be imputed nearly perfectly , whereas STRs with the highest heterozygosity, including forensic markers and known pathogenic repeats, could be imputed to around 70% concordance (compared to approximately 50% expected under a naive model). We additionally show that imputation improves power to detect STR associations over standard SNP-based GWAS and could detect both known and previously unknown associations between STR lengths and expression of nearby genes.59. However, studies in large cohorts reaching hundreds of thousands of samples3, as well as deep sequencing studies to detect rare variants60, have so far not confirmed these hypotheses. An increasingly supported idea is that complex variants not well tagged by SNPs may comprise an important component of the missing heritability12. GWAS is essentially blind to contributions from highly polymorphic STRs and other repeats, despite their known importance to human disease and molecular phenotypes. Thus, STR association studies will undoubtedly uncover additional heritability that is so far unaccounted for. Notably, while autism phenotypes are available for the SSC families, this cohort is too small to perform a GWAS and was specifically ascertained for families enriched for de novo, rather than inherited, pathogenic mutations. In future work our panel can be applied to impute STRs into larger cohorts for autism and other complex traits for which tens of thousands of SNP array datasets are available.A widely recognized limitation of GWAS is the fact that common SNP associations still explain only a small fraction of heritability of most traits. Multiple explanations for this have been proposed, including minute effect sizes of individual variants and a potential role for high-impact rare variation61 and longer variable number tandem repeats (VNTRs)62 from short reads and can be used to expand our panel in the future. Overall, our STR imputation framework will enable an entire new class of variation to be interrogated by reanalyzing hundreds of thousands of existing datasets, with the potential to lead to novel genetic discoveries across a broad range of phenotypes.Our initial haplotype panel faces several important limitations. First, the majority of samples are of European origin, limiting imputation accuracy in other population groups. Second, imputation accuracy is mediocre for the most highly polymorphic STRs, some of which will ultimately have to be directly genotyped to adequately test for associations. Notably, our work relied on existing tools originally designed for SNP imputation. Further work on computational methods specifically for imputing repeats may be able to improve performance. Finally, thousands of long STRs are filtered from our panel due to the limitation imposed by short read lengths. While we have included target STRs implicated in STR expansion disorders, many long STRs are still inaccessible using current tools. New methods are now being developed for genome-wide genotyping of more complex STRsThe SSC Phase 1 dataset consists of 1916 individuals from 479 quad families. Access to SSC data was approved for this project under SFARI Base project ID 2405.1. This study was certified as exempt from institutional review board (IRB) review by the University of California San Diego IRB (Project #161286XX) since only de-identified data was accessed. Informed consents were obtained for each participating family by SSC recruitment sites in accordance with their local IRBs.63. A total of 27,185,239 variants that passed the default GATK filters and overlapped with sites reported in the 1000 Genomes Project38 phase 3 data were retained for downstream analysis.Aligned BAM and gVCF files for whole-genome sequencing data of individuals were obtained through SFARI base (see URLs) and processed on Amazon Web Services (AWS). SNP genotypes were called from gVCF files using the GATK version 3 joint calling pipeline38 and projected SSC samples onto the resulting PCs to infer sample ancestry  using SNPs from 2504 samples from Phase 3 of the 1000 Genomes Project64 version 1.4 and tabix65 version 1.2, respectively. HipSTR32 version v0.5 was called individually per STR with default parameters. Phased SNPs were provided as input to allow HipSTR to perform physical phasing when possible. Resulting VCF files from each batch were merged to create a genome-wide callset in VCF format.STRs were jointly genotyped on the AWS EC2 platform in batches of 500 STRs. We streamed the corresponding region of each BAM file and of the phased SNP VCF files to a local EBS volume attached to each EC2 instance using samtools66 hg19.genomicSuperDups table) were removed from the callset using intersectBed67 v2.25.0; (ii) Pentanucleotides and hexanucleotides containing homopolymer runs of at least 5 or 6 nucleotides, respectively, in the hg19 reference genome were removed as they were found to contain an excess of indels in the homopolymer regions; (iii) STRs with call rate <80%; (iv) STRs with expected heterozygosity <0.095, corresponding to a minor allele frequency of 5% for bi-allelic markers, were removed to restrict to polymorphic STRs; (v) STRs with significantly more or fewer heterozygous genotypes compared to expectation under Hardy-Weinberg equilibrium (p < 0.01) as suggested previously68. After filtering, 453,671 STRs remained in our panel.HipSTR calls were filtered using the filter_vcf.py script in the HipSTR package with suggested parameters . We used the following criteria to remove problematic STRs from the callset: (i) STRs overlapping segmental duplications . Expected heterozygosity was computed using the PyVCF package (see URLs) for the 1000 Genomes calls and using a custom script for the SSC data to collapse alleles of identical length into a single allele. STRs passing all filters described above included in the comparison. Analysis was restricted to STRs with at least 500 calls in the 1000 Genomes dataset.STR genotypes for 1000 Genomes samples generated by Willems et al.36 and are based on 307 controls of Welsh origin. Frequencies for DM1 were obtained from Fig. 1 of Ambrose et al.35 and are based on 254 controls of Chinese origin. Frequencies for HDL were obtained from Fig. 1 of Figley et al.34 and are based on 352 controls of North American Caucasian origin. Frequencies for SCA7 were obtained from Fig. 1 of Gouw et al.33 and are based on 180 controls of European origin. Frequencies for HTT are based on data in the phv00173896.v1.p1 variable of dbGaP study phs000371.v1.p1 (Genetic modifiers of Huntington’s Disease) based on the shorter allele of 2802 patients with Huntington’s Disease.Control distributions for Fig. 69 version 2.r837 with 1000 Genomes Phase 3 genotypes as a reference panel and ignoring pedigree information. SHAPEIT’s duoHMM70 version 0.1.7 method was used to refine phased haplotypes using pedigree structure and correcting for Mendelian errors.SNP genotypes were phased using SHAPEIT40 version 4.0 was used to phase each STR separately using phased SNP genotypes, pedigree information, and unphased STR genotypes as input. In order to leverage the HipSTR genotype likelihoods (GL field), Beagle requires all samples to have GL information. To accommodate this, phasing was performed in two steps. First, samples with missing data were removed and the remaining samples were phased using the -gl Beagle flag. Next, missing samples were added back to the VCF and all samples were jointly phased in a second Beagle round using default parameters. In this step Beagle additionally imputed any calls with missing genotypes. Genotype values (GT field) were used for the STRs genotyped using Tredparse as it does not report genotype likelihoods, and phasing and imputation of STRs was done in a single step. Phased STRs and SNPs for only the unrelated parent samples from each locus were then merged into a single genome-wide reference panel in VCF format.BeagleX = {x1, x2,...xn} be the true STR genotypes for samples 1..n and Y =  {y1, y2,...yn} be the imputed STR genotypes. Each genotype xi is defined as xi =  where xi1 and xi2 give the (unordered) lengths of the two STR alleles for a diploid sample and similarly for Y. We then define the following metrics:Let ci was defined as: 1 if both genotypes match (xi1 = yi1and xi2 = yi2 or xi2 = yi1and xi1 = yi2); 0 if neither imputed allele matched a true allele; else 0.5 if one but not both imputed alleles matched the true alleles. Genotype concordance for an STR is the average over all the samples Genotype concordance di = xi1 + xi2 and Xd = {d1, d2,...,dn}. Length r2 is computed asDefine the STR genotype dosage as the sum of the lengths of the two alleles at a given site: a, define Xa = {a1, a2,...,an} where r2 is computed as cov2/(Var(Xa)Var(Ya)).For a given allele length r2.The best tag SNP for an STR is defined as the SNP within 50 kb with the highest length For all concordance metrics, outlier genotypes containing alleles seen less than three times in the entire cohort were removed from the analysis.i, j) ∈ {1,...,n}2 and  ∈ {1,...,n}2, n is the number of alleles, fx gives the frequency of allele x, and C gives the concordance between genotypes  and  as defined above. For example, for a bi-allelic marker with allele frequencies f1 and f2 expected genotype concordance under the random model is given by r2 and allelic r2 were computed by comparing genotypes imputed randomly based on population allele frequencies to true genotypes at each STR. Concordance under the naive model was computed by comparing each sample’s genotype to the most frequent diploid genotype. Length r2 and allelic r2 are not defined under the naive model since all imputed genotypes are identical.For each STR, we additionally computed the expected value of each metric under a random model where genotypes are imputed randomly based on the frequency of underlying alleles and a naive model where genotypes are imputed to be the most common diploid genotype. Expected genotype concordance under the random model was calculated as STRs were imputed into SNP data downloaded from the 1000 Genomes Project site from three sources . Prior to comparing genotypes, offsets were calculated to match HipSTR lengths to the length of Marshfield STRs as previously described14. STRs with imperfect repeat structures were removed. Capillary genotypes were rounded down to the nearest number of repeat units.Capillary electrophoresis genotypes for 209 samples at 721 Marshfield STRs were downloaded from the Payseur Lab website (see URLs). PCR product sizes were converted to length differences in bp from the reference genome using product size annotations10X Genomics data for NA12878 was obtained from the NA12878 Gemline Genome v2 available on the 10X Genomics website (see URLs). We extracted reads belonging to phase 1 or 2 from the phased, barcoded BAM based on the HP tag into separate BAM files. HipSTR v0.6.1 was called separately on each BAM with non-default parameters --def-stutter-model --min-reads 5 --use-unpaired and with --haploid-chrs containing a list of all autosomal chromosomes to force a haploid genotyping model. Haploid STR calls were obtained for both phases at 118,353 STRs. We identified the nearest heterozygous SNP to each STR that was genotyped in both the 10X data and in our phased panel. STRs for which the nearest SNP had discordant genotypes in the two datasets were discarded leaving 116,764 STRs for analysis.P = βG + E, where P is a vector of standard normalized phenotypes, β gives the effect size, E gives the error term drawn from a normal distribution N, and G is a vector of the sum of genotype lengths for each individual scaled to have mean 0 and variance 1. For each simulated phenotype dataset, we tested the causal STR, the imputed STR genotypes, and the best tag SNP (strongest length r2) within 50 kb of the STR for association. Association tests were performed using the Python statsmodels library OLS method (see URLs).We analyzed parental genotypes for 5838 STRs across chromosome 21 that passed filtering and quality control as described above. For each STR, we simulated quantitative phenotype datasets under the model: pi) = βXi where pi is the probability that sample i is a case and Xi is the scaled genotype for individual i as described above. Association tests were performed using the Python statsmodels Logit method.We performed additional simulations under a case–control model shown in Supplementary Fig. P = βG2 + E where G is a vector of the squared sum of allele lengths scaled by the mean allele length, and P, β, E are as described above. Two sets of association tests were performed: the first tested for association between STR length and phenotype  through dbGaP under phs000424.v7.p2. This included high-coverage (30 × ) Illumina whole-genome sequencing (WGS) data from 650 unrelated samples, Omni 2.5 SNP genotypes for 450 samples, and gene-level RPKM values for whole blood in 336 samples. STRs were genotyped from WGS data using HipSTR v0.5 and subject to the same quality filtering as SSC samples. STRs were additionally imputed to Omni2.5 data with Beagle as described above. Downstream analyses were restricted to the 336 samples with available whole blood expression data. These samples consisted of 284 European, 45 African American, 3 Asian, and 3 Amerindian samples and 2 samples with no population label available.Y = βX + C + ε, where X denotes STR genotype lengths, Y denotes expression values, β denotes the effect size, C denotes various covariates, and ε is the error term. Following our previous study17, we used STR dosage, defined as the sum of repeat lengths of the two alleles for each sample, to define STR genotypes. All repeat lengths are reported as length differences from the hg19 reference, with 0 representing the reference allele. STR dosages were scaled to have mean 0 and variance 1. Genes with median expression of 0 were excluded and expression values for remaining genes were quantile normalized to a standard normal distribution. We included sex, population structure, and technical variation in expression as covariates. For population structure, we used the top 15 principal components resulting from perform principal components analysis on the matrix of SNP genotypes from each sample. To control for technical variation in expression, we applied PEER factor correction73 using 83 PEER factors.We performed separate eSTR analyses using HipSTR and imputed genotypes. In each case, we performed a separate association test between gene expression and each STR within 100 kb of the gene using a model p-value. We then compared two linear models: Y ~ eSNP (SNP-only model) vs. Y ~ eSNP + eSTR (SNP + STR model) using the anova_lm function in the python statsmodels.api.stats module. We used CAVIAR v1.0 to further fine-map eSTR signals against the top 100 eSNPs within 100 kb of each gene. Pairwise-LD between the eSTR and eSNPs was estimated using the Pearson correlation between SNP dosages  and STR dosages .We used model comparison to determine whether the best eSTR for each gene explained variation in gene expression beyond a model consisting of the best eSNP. For each gene with an eSTR we determined the lead eSNP with the strongest ATN1 implicated in DRPLA was taken from Table 1 of Veneziano et al.55 and consists of rs4963516, rs1007924, rs7310941, rs7303722, rs2239167, rs34199021, rs2071075, rs2071076, and rs2159887 with hg19 alleles G, A, G, T, A, A, T, C, and C, respectively. Distance from the founder haplotype was calculated as the number of mismatches.The founder haplotype for the expansion allele in https://base.sfari.org/. For HipSTR, see https://github.com/tfwillems/HipSTR. For Beagle, see https://faculty.washington.edu/browning/beagle/b4_0.html. For 1000 Genomes phased Affy6.0 and Omni2.5 SNP data, see ftps.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/shapeit2_scaffolds/hd_chip_scaffolds/. For 1000 Genomes Phase 3, see http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. For 1000 Genomes STR data, see http://strcat.teamerlich.org/download. For Marshfield Capillary electrophoresis data, see https://payseur.genetics.wisc.edu/strpData.htm. For Marshfield marker annotations, see https://web.stanford.edu/group/rosenberglab/data/pembertonEtAl2009/Pemberton_AdditionalFile1_11242009.txt. For NA12878 10X Genomics data, see https://support.10xgenomics.com/genome-exome/datasets/2.2.1/NA12878_WGS_v2. For High-coverage Illumina sequencing for 1000 Genomes samples, see https://www.ebi.ac.uk/ena/data/view/PRJEB20654. For PyVCF, see https://github.com/jamescasbon/PyVCF. For Python statsmodels, see http://www.statsmodels.org/stable/index.html.For Simons Simplex Collection, see https://github.com/gymreklab/snpstr-imputation.Analysis scripts and Jupyter notebooks for reproducing the figures in this study are provided in the Github repository Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2


To evaluate microvasculature dropout in the optic disc (Mvd-D) using optical coherence tomography angiography (OCTA) and investigate factors associated with Mvd-D in primary open-angle glaucoma (POAG) eyes.One hundred twenty-three eyes of 123 POAG patients were included from the Diagnostic Innovations in Glaucoma Study. The 3.0×3.0-mm optic nerve head OCTA scans were acquired using a spectral-domain OCT instrument. Images with whole-signal-mode were evaluated. Eyes were classified into 3 categories . Mvd-D and pseudo-Mvd-D had complete loss of OCTA signals on the temporal side of the optic disc on the en face projection image. They were distinguished base on the visualization of the anterior lamina cribrosa in the horizontal B-scans of that area. No Mvd-D was defined when no complete signal loss of OCTA signals was observed. Covariates including focal lamina cribrosa defects assessed by swept-source OCT and microvasculature dropout in the parapapillary region (Mvd-P) were analyzed.P = 0.001), worse visual field mean deviation , higher prevalence of focal lamina cribrosa defect , and higher prevalence of Mvd-P .Forty-two, 37, and 44 eyes were identified as having Mvd-D, pseudo-Mvd-D, and no Mvd-D, respectively. The eyes with Mvd-D showed significantly lower intraocular pressure, worse visual field mean deviation, larger cup-to-disc ratio, thinner circumpapillary retinal nerve fiber layer (cpRNFL), and lower circumpapillary vessel density within the RNFL than the eyes with pseudo-Mvd-D or the eyes without Mvd-D. Multivariable logistic regression analysis showed significant associations of Mvd-D with larger cup-to-disc ratio (OR, 1.08; OCTA-derived Mvd-D was strongly associated with the presences of Mvd-P and focal lamina cribrosa defects, and these 3 findings were topographically associated with each other. Ocular blood flow is thought to have an important role in the pathology of glaucoma.–3 In parImaging of the perfused microvasculature of the optic disc with OCTA provides qualitative and quantitative information that previously has not been possible to acquire. Moreover, in addition to being non-invasive, the actual measurements are performed relatively quickly.–12 PeripThe purpose of the current study was to evaluate and characterize the Mvd-D in POAG patients using OCTA, and also to clarify the relationship between Mvd-D and clinical features including focal LC damage and Mvd-P.POAG patients from the Diagnostic Innovations in Glaucoma Study (DIGS)  who underwent OCTA , spectral-domain OCT (SD-OCT) ONH imaging , and swept-source OCT (SS-OCT)  were included in this observational cross-sectional study. The included patients completed OCTA examinations from January, 2015 to September, 2016. Details of the DIGS protocol have been described previously. This stuAll participants underwent an ophthalmologic examination, including assessment of best-corrected visual acuity, refractive error, slit-lamp biomicroscopy, intraocular pressure (IOP) measurement with Goldmann applanation tonometry, gonioscopy, central corneal thickness (CCT) measured with ultrasound pachymetry , axial length measured by the IOLMaster , dilated fundus examination, simultaneous stereophotography of the optic disc, standard automated perimetry , SD-OCT, OCTA, and SS-OCT. The IOP obtained on the day of the OCTA examination was used in this analysis.POAG was defined as the presence of glaucomatous optic nerve damage  and associated repeatable visual field (VF) damage. Glaucomatous VF damage was defined as a Glaucoma Hemifield Test outside normal limits or a pattern standard deviation (PSD) outside 95% normal limits confirmed on 2 consecutive, reliable  tests.Eyes with a history of intraocular surgery (except for glaucoma surgery or uncomplicated cataract surgery), secondary causes of glaucoma, nonglaucomatous optic neuropathies, vascular or nonvascular retinopathies, and other ocular or systemic diseases known to impair the VF were excluded from the investigation. When both eyes were eligible, one eye from each patient was randomly selected for analysis.Two blood pressure (BP) measurements obtained in a resting, seated position were taken at least 5 minutes apart using an Omron Automatic  instrument. Mean ocular perfusion pressure (MOPP) was calculated as MOPP = 2/3 (1/3 systolic BP + 2/3 diastolic BP)—IOP.All participants underwent VF testing using the 24–2 pattern SITA on the Humphrey Field Analyzer (Carl Zeiss Meditec) within 6 months of imaging. Only reliable tests  were included. The quality of VF tests was also reviewed by the Visual Field Assessment Center staff to identify and exclude VFs with evidence of inattention or inappropriate fixation, artifacts such as eyelid and lens rim artifacts, fatigue effects, and abnormal results caused by diseases other than glaucoma.The Avanti SD-OCT system has a light source with a 70-kHz axial line rate and a center wavelength of 840 nm. The axial and transverse resolutions of this system were approximately 5 μm and 15μm. The acquired optic disc cubes consisted of 304 B-scans containing 304 A-scans each. The ONH map consists of a 6.0×6.0-mm cube scan which is used to automatically calculate disc area and cup-to-disc (C/D) ratio. In addition, average circumpapillary RNFL (cpRNFL) thickness is calculated in a 10-pixel-wide band along a circle of 3.45 mm in diameter centered on the ONH. Only good-quality images with a signal strength index (SSI) of 37 or more and without segmentation failures or artifacts were included.–15The OCTA images were acquired using the Avanti SD-OCT system and analyzed with the Angiovue software. Each of the acquired optic disc cubes (3.0×3.0-mm and 4.5×4.5-mm) consisted of 304 clusters of 2 repeat B-scans containing 304 A-scans each. A split-spectrum amplitude-decorrelation angiography algorithm was employed to improve the signal-to noise ratio by splitting the spectrum to generate multiple repeat OCT frames from the 2 original repeat OCT frames.Image quality of all scans was reviewed according to a standard protocol established by the Imaging Data Evaluation and Analysis reading center at the Hamilton Glaucoma Center. Trained Vessel density within the peripapillary RNFL in scans with a 4.5×4.5-mm field of view centered on the ONH was used to measure circumpapillary vessel density (cpVD) as previously reported.,14 AveraThe Mvd-D and Mvd-P were evaluated on the 3.0×3.0-mm ONH en face projection image using the OCTA whole-signal mode image, which was constructed from all of the OCTA signals below the inner limiting membrane (ILM) as shown in To classify the eyes into 3 groups based on Mvd-D, first the complete loss of OCTA signals, which was 200 μm or more wide and 100 μm or more in length, was evaluated on the temporal side of the optic disc on the en face projection image for all included eyes Figs . To deciMvd-P was defined as a complete loss of the choriocapillaris or the microvasculature within the scleral flange on both 3.0×3.0-mm ONH en face and horizontal vessel density maps as previously reported.,23 BriefIn summary of the definition of no Mvd-D, pseudo-Mvd-D, and Mvd-D1. No Mvd-D: No complete signal loss of OCTA signals is observed on the temporal side of the optic disc on the en face projection image.2. Pseudo-Mvd-D: Complete loss of OCTA signals is observed on the temporal side of the optic disc on the en face projection image, but the anterior LC portion is not well visualized in that area.3. Mvd-D: Complete loss of OCTA signals is observed on the temporal side of the optic disc on the en face projection image, and the anterior LC portion is well visualized in that area.Detection of Mvd-D and Mvd-P was performed by 2 independent observers  masked to the patients’ baseline characteristics . DiscrepImages centered on the optic disc were obtained over a 6×6-mm area with DRI OCT-1 to determine the presence of focal LC defects. Details on the device have been described elsewhere.,21 BriefIn determining the presence of focal LC defects, 2 independent observers , masked to the clinical information of the study participants, reviewed the horizontal B-scan and en face SS-OCT images. The evaluation of LC defects was performed as previously described.,17–20,23P value of less than 0.10 in the univariable analysis. The kappa (κ) coefficient was calculated to evaluate interobserver agreement in determining the Mvd-D and focal LC defects.[P values less than 0.05 were considered statistically significant.Clinical characteristics and examination measurements were compared among groups using analysis of variance (ANOVA) and post-hoc Tukey honestly significant difference tests. Categorical variables were compared using the chi-square test. Univariable logistic regression analysis was performed to identify parameters associated with the Mvd-D. Multivariable logistic regression analysis was performed using covariates with a  defects. All statP < 0.001; Mvd-P: kappa = 0.88; P < 0.001; and focal LC defects: kappa = 0.84; P < 0.001).[One hundred twenty-three eyes of 123 POAG patients are included in this report. Of the 149 eyes of 149 subjects that initially met patient eligibility criteria, 11 eyes were excluded because of poor-quality OCTA images, 3 eyes because of poor-quality SD-OCT scans, and 12 eyes because of poor-quality SS-OCT scans. One eye was excluded because of failure of the two observers to reach consensus for the determination of the focal LC defect. Inter-observer agreement for determination of Mvd-D, Mvd-P, and focal LC defects was excellent .P = 0.008; other covariates, P < 0.001). The vessel densities inside the optic disc and the SSI were significantly lower in the Mvd-D group and the pseudo-Mvd-D group than the no Mvd-D group . All other covariates were not significantly different between the pseudo-Mvd-D and no Mvd-D group. This implies that the pseudo-Mvd-D and no Mvd-D eyes had similar characteristics in our study.Forty-four (36%) eyes of 123 eyes of POAG patients were classified as the no Mvd-D group Figs , and 42 P = 0.007; P = 0.005), larger C/D ratio (both P < 0.001), worse VF MD (both P < 0.001), lower cpVD (both P < 0.001), thinner cpRNFL , presence of focal LC defect (both P < 0.001), and presence of Mvd-P (both P < 0.001). Lower SSI was associated significantly with the presence of Mvd-D only when the eyes in the pseudo-Mvd-D group were excluded from the analysis (P = 0.026).The univariable and multivariable logistic regression analyses of the association between the presence of Mvd-D and clinical and ocular characteristics are presented in P < 0.001, respectively); therefore, these 4 variables were included separately in the multivariable model to avoid multicollinearity (P = 0.001) and worse VF MD  also remained as significant factors associated with the presence of Mvd-D.C/D ratio, VF MD, cpVD, and cpRNFL thickness were significantly correlated . Further investigations are needed to clarify this issue.In the current study, IOP was significantly lower in the Mvd-D group than the other groups . One posThe current study has several limitations. First, the loss of microvasculature was investigated using whole-signal-mode images. Consequently, only eyes with unambiguous Mvd, to reduce the potential influence of the artifacts with vascular/rim shadowing or projection onto the underlying tissues, were identified. This metIn conclusion, OCTA-derived Mvd-D was strongly and topographically associated with the presences of Mvd-P and focal LC defects. Mvd-D and Mvd-P may be developmentally related to focal LC damage in glaucoma.S1 Table(DOCX)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.


Brachionus plicatilis shows diverging local adaptation in its life-history traits in relation to estimated environmental predictability in its habitats. Here, we used an integrative approach —combining environmental, phenotypic and genomic data for the same populations– to understand the genomic basis of this diverging adaptation. Firstly, a novel draft genome for B. plicatilis was assembled. Then, genome-wide polymorphisms were studied using genotyping by sequencing on 270 clones from nine populations in eastern Spain. As a result, 4,543 high-quality SNPs were identified and genotyped. More than 90 SNPs were found to be putatively under selection with signatures of diversifying and balancing selection. Over 140 SNPs were correlated with environmental or phenotypic variables revealing signatures of local adaptation, including environmental predictability. Putative functions were associated to most of these SNPs, since they were located within annotated genes. Our results reveal associations between genomic variation and the degree of environmental predictability, providing genomic evidence of adaptation to local conditions in natural rotifer populations.Environmental fluctuations are ubiquitous and thus essential for the study of adaptation. Despite this, genome evolution in response to environmental fluctuations —and more specifically to the degree of environmental predictability– is still unknown. Saline lakes in the Mediterranean region are remarkably diverse in their ecological conditions, which can lead to divergent local adaptation patterns in the inhabiting aquatic organisms. The facultatively sexual rotifer  Different evolutionary responses are expected to result from adaptation to environmental fluctuations and their degree of predictability4. One of these, phenotypic plasticity, occurs when the individuals modify their phenotype accordingly to environmental conditions, without genetic change5. Another well-studied response is adaptive tracking, in which environmental variation results in correlated variation in mean population traits, since natural selection may favour different phenotypes over evolutionary time by acting on the heritable variation in the population8. When organisms have dormant stages such as seed banks or diapausing egg banks10 adaptive tracking may result in the maintenance of high levels of genetic and ecologically relevant diversity in populations11, which could provide strong signatures of balancing selection in genomes. A third response, bet hedging, occurs when a single genotype produces different phenotypes  or a “low-risk” phenotype , so that it lowers the variance in its reproductive success but maximizes the reproductive success in the long-term12. It has been suggested that the evolution of bet hedging and phenotypic plasticity could involve regulatory genes in the genome14. However, to date, few molecular mechanisms underlying some of these evolutionary strategies have been identified, and these were mainly in relation to phenotypic plasticity (e.g. in Daphnia sp.16). With the advent of cost-effective genomic technologies, it is now possible to screen hundreds or thousands of genes and assess which ones are diverging in their allele frequencies, in order to assess how they affect evolutionary fitness in the wild17. The ultimate objective is to map multilocus genotypes to phenotypes to environments19. Therefore, from a fundamental and applied perspective, it is important to know how genomes respond to environmental fluctuations —and more specifically to unpredictable environments22–, a topic still scarcely investigated.Environmental fluctuations —either predictable or unpredictable– occur to a varying extent in any locality, so that adaptation to the degree and type of environmental fluctuation is a critical need for an organism to persist in a locality. Interestingly, climate change, over-exploitation, and habitat alteration often cause not just an environmental novelty in the average conditions, but an increase of fluctuations, with an important unpredictable component23. In particular, Mediterranean saline lakes are strongly seasonal and temporally unpredictable environments at several time scales24. In addition, these habitats are also affected by global processes, such as climate change or the North Atlantic Oscillation25. Given the lack of detailed long-term, in situ records of most of these habitats, Franch-Gras et al.26 analysed long-term trends in water-surface area using a 27 years long series of remote sensing data on water-surface area to characterize the relative importance of environmental fluctuations in a set of saline lakes in eastern Spain. The indices available to estimate the degree of predictability from time-series data, basically, decompose the time series based on periodic and stochastic variation27 and associate these with predictable and unpredictable fluctuations, respectively. Franch-Gras et al.26 estimated the water-surface area of each lake in each satellite image and calculated the predictable and unpredictable components of variation of that data by using different models that reflected the effect of fluctuations on different types of organisms. Thus, predictability is defined as the organism’s ability to anticipate and adjust to future environmental conditions26. Franch-Gras et al.28 concluded that the predictability metrics based on Colwell’s approach30 and on the presence/absence of water were the most appropriate for small aquatic invertebrates28 (see methods for more details). Remarkably, the saline lakes studied showed a wide range of predictability, when predictability was assessed taking into account the point of view of small aquatic invertebrates26.The Mediterranean region typically exhibits very different environmental regimes in habitats even within relatively small geographical scales33. These rotifers combine clonal proliferation with occasional bouts of sexual reproduction which results in the production of diapausing eggs. These eggs are long-lived, and are able to persist through adverse environmental conditions, including drought. In this temperate region, rotifer populations are temporary —not being active all year round– and colonize the water column during the so-called growing season. Thus, sex and the concomitant diapausing-egg production are critical for population persistence. In the case of organisms established as clones hatching after a sexual reproduction phase , such as aphids, rotifers and cladocerans, genetic slippage due to a phase of clonal selection followed by sexual reproduction can result in phenotypic traits changing in a direction even opposite to selection34. In addition, rotifer diapausing egg banks maintain high levels of standing genetic variance for life history traits37, which indicates that they should have a high potential to adapt to environmental fluctuations using adaptive tracking8. Moreover, theory predicts low-risk strategies to occur in rotifer populations inhabiting unpredictable habitats that would consist on (1) early production of diapausing eggs38 and/or (2) intermediate hatching fraction of diapausing eggs39. Indeed, rotifer populations show signatures of local adaptation to salinity and temperature in their life history traits37. Recently, Franch-Gras et al.28 found high within-population genetic variation in two life-history traits associated to diapause in nine populations of the rotifer Brachionus plicatilis inhabiting saline lakes with varying degrees of environmental predictability26. These traits were: (1) the propensity for sexual reproduction40 as a proxy of the timing of diapausing egg production41; and (2) the diapausing egg hatching fraction . The propensity for sex differed among populations and was inversely correlated with lake predictability, suggesting a conservative bet-hedging strategy that would protect against unexpectedly short growing seasons.Cyclically parthenogenetic rotifers are common small invertebrates in ponds and lakes. These passively-dispersed aquatic organisms have been used successfully to study the evolutionary responses to environmental fluctuations described above44. One of these technologies, genotyping by sequencing (GBS)45 is a cost-effective technique to screen genome-wide patterns of diversity by using restriction enzymes to reduce the genome complexity and has been used successfully to identify genomic regions under selection in several non-model animals48. It allows the genotyping of a high number of individuals and the discovery of typically large numbers of single nucleotide polymorphisms (SNPs).The study of the genetic basis of adaptation in non-model organisms has recently been fuelled by the rapid development of genomic technologies, especially next generation sequencingB. plicatilis clones studied in Franch-Gras et al.28 in populations characterised for their environmental predictability by Franch-Gras26. Since no B. plicatilis genome was available, whole genome sequencing data were obtained in order to assemble the first draft genome for this species with a structural and functional annotation. This genome was used as a reference to facilitate (1) GBS analysis of population structure and (2) discovery of putative loci under selection. We investigated the correlation of loci under selection to life-history traits and environmental variables and identified genes associated to these loci. In this study we firstly, gather evidence of genomic association to local adaptation patterns by considering several factors; and then we focus on the degree of environmental predictability as a factor having a wide range in the studied lakes26 and to which the inhabiting populations have been shown to present patterns of local adaptation28.Here, we take advantage of such a well-characterized study system of populations to study the genomic basis of local adaptation to environmental features. We studied the genome-wide patterns of genetic variability and differentiation in response to environmental predictability using GBS in the same set of B. plicatilis HYR-1 genome size was estimated to be 115.77 Mb with an heterozygosity of 0.65%. Out of the four assemblers used, the Platanus assembly showed the best features, with the highest N50, no evidence of contamination and high proportion of complete and partial gene sequences in CEGMA completeness report .A total of 52 Gb of raw sequence data were obtained for the 270 clones from the nine populations. Out of the 630,460,573 raw reads, 379,536,357 quality-filtered barcoded reads were obtained. The SNP calling pipeline yielded 12,856 SNPs, resulting in 4,543 SNPs after filtering. Hardy-Weinberg proportions for each SNP and population showed that most SNPs do not differ from Hardy-Weinberg expectations . The overall FST (averaged over loci) was 0.18. Pairwise FST averaged across loci .The overall expected heterozygosity (averaged over loci) was 0.17 and the within population expected heterozygosity ranged from 0.15 to 0.23 (Table FST than the background of the genome (i.e. they are putatively under diversifying selection) and 81 have significantly lower FST than the background of the genome  were identified as candidates to be under selection. 12 of these SNPs had significantly higher Using the Bayenv approach, significant correlation with environmental and phenotypic variables was found for a number of SNPs ranging from 34 to 39 Fig. . None ofThe GWAS analysis using GenABEL yielded a single SNP significantly associated to the propensity for sex, this SNP was not detected by Bayenv nor by BayeScan. No SNP was significantly associated to hatching fraction.Most of the SNPs detected with BayeScan or Bayenv were located within genes  in which the available genetic resources are still scarce 49. We identified candidate regions for diversifying selection, some of them are correlated with environmental predictability and some of these are correlated with life-history traits and other ecological factors. Moreover, we found a large set of genes showing signatures of balancing selection in rotifer populations.We used a powerful, integrated approach —combining environmental, phenotypic and genomic data for the same set of populations– to identify genomic regions putatively under selection to local conditions, focusing on environmental predictability in rotifer populations. Moreover, we report a new draft genome assembly for B. plicatilis. The estimated (haploid) genome size is 115.77 Mb, similar to the assembly size obtained (108.5 Mb) and the previous estimates for different B. plicatilis strains using flow-cytometry (between 111.7–128.8 Mb)50. The k-mer analysis revealed that the heterozygosity of the B. plicatilis genome is relatively high (0.65%). This is consistent with the finding that Platanus assembler yielded better results than other assemblers, since it is known to have a good performance when assembling highly heterozygous genomes51. The genome was highly compact, with a large number of predicted gene models . This compactness produced a high number of genes physically associated to the SNPs putatively under selection. Therefore the analysis was restricted to those genes where these SNPs were in their coding regions. Although this approach might potentially produce a loss of information, since introns and inter-genic space changes can also affect gene expression, the fast linkage decay with physical distance found after a linkage disequilibrium analysis  and transcriptomes of congeneric species  the recently published Brachionus calyciflorus genome49. It would be desirable both to improve the quality of the genome assembly, for instance, by the inclusion of both more sequencing data (such as mate-pairs or PacBio platform sequencing) and gene mapping information and transcriptome data.We have described and annotated the first draft genome assembly of 2; Campillo et al.)37 and small 28 spatial scales, in the Mediterranean region. Brachionus plicatilis populations in Spain harbour a high degree of ecologically relevant genetic variation frequently involving sexual reproduction and diapause strategies56. This variation has been found to be correlated with environmental features such as salinity55 and environmental predictability28. However, the genetic basis of local adaptation in this populations remained unknown. There are very few studies considering the genetic basis of local adaptation in aquatic invertebrates. Orsini et al.57 related several genes of Daphnia magna to a set of natural anthropogenic stressors using an experimental evolution approach, and identified repeatable patterns of local adaptation. Roulin et al.16 identified one gene in the same species associated to a photoreceptor inducing a plastic response of diapause that is involved in local adaptation patterns.We identified 12 candidate genes under diversifying selection in the nine rotifer populations studied using Bayescan. These results indicate that rotifer genomes show signatures of adaptation to local conditions (not necessarily to predictability). Previous work has shown local adaptation in life history traits at intermediate , however we used a powerful correlation analysis (Bayenv) to identify candidate SNPs strongly associated with environmental predictability, hydroperiod length, salinity and two life history traits related to diapause. This correlation-based analysis uses information of important environmental and phenotypic variables for each lake/population, which are not considered in the FST method (BayeScan), resulting in the ability to detect both adaptive responses to each variable or genes that might determine diapausing-related traits. Three out of the 12 SNPs found to be putatively under diversifying selection by BayeScan were also highlighted as strongly correlated by Bayenv, what highlights the importance of the studied variables in driving local rotifer adaptation. Therefore these three SNPs are very strong candidates to be under diversifying selection in response to hatching fraction, propensity for sex and salinity. In addition, the GWAS analysis using GenABEL yielded one SNP significantly associated to the propensity for sex.We are aware that et al.28. In addition, the weak relationship between the degree of predictability and the hatching fraction observed by Franch-Gras et al.28 is consistent with our results at the genomic level. Only four SNPs detected by Bayenv (located in two genes) were correlated with both variables simultaneously. This implies that only 11.7% of the SNPs correlated with the degree of predictability were also correlated with the hatching fraction. The 37 SNPs found to be correlated with propensity for sex in Bayenv plus the one obtained by GWAS and the 38 SNPs found to be correlated with the hatching fraction are strong candidates to be underlying the genomic basis of each of these traits.From those SNPs that Bayenv related to the variables, 10 (located in ninegenes) were correlated simultaneously to both the propensity for sex and the degree of environmental predictability Fig. . As a re28 regarding the low influence of hydroperiod length on the propensity for sex once the effect of environmental predictability is discounted.Though our main interest was on unravelling the genomic responses to unpredictable environments, we also explored the effects of lake area and geographic distance on population genetic parameters, and adaptive responses to other environmental parameters such as hydroperiod length and salinity, as they are likely additional factors to explain adaptive genetic divergence among populations or can act as confounding factors. Our results agree with the findings by Franch-GrasDaphnia populations caused by this selective scenario has been reported11. Interestingly, fluctuations of water-surface area —an important feature and a proxy for other features such as variations in the physicochemical conditions are poorly correlated among the lakes studied26. Most of the SNPs correlated with any of the variables studied here are located within genes, which showed a range of gene ontologies, so that reliable inferences are difficult. However, a specific insight on gene functionality was achieved for the genes associated to the propensity for sex and environmental predictability, because the set showed an enrichment of genes that have been putatively associated to binding functions. Interestingly, both phenotypic plasticity and bet hedging have been proposed to be regulated by epigenetic mechanisms14. In addition, genes associated to hatching were found to be enriched in signal transducer activity and genes associated to salinity to regulation of cell cycle or of biological processes. After examining individual gene functions, some interesting putative functions of genes associated to specific variables are discussed below. Indeed, a gene associated to hatching fraction is described as “coronin-7 isoform X1” and is involved in the actin filament polymerization, which is upregulated in diapausing mosquitos60. A gene associated to hydroperiod, described as “estrogen receptor beta”, which increases reproduction when being activated by a ligand in the rotifer Brachionus manjavacas61. Moreover, a gene associated to predictability is described as “POU class transcription factor 1-like”, a transcription factor which is more expressed in non-diapause than in diapause pupae in the moth Helicoverpa armigera62. A gene associated to propensity for sex in this study described as “speedy A” has a “male meiotic nuclear division” GO term, as is involved in reproductive meiosis in mice63 and pigs64. Finally, a gene associated to salinity is described as “G2 mitotic-specific cyclin partial” and has a “regulation of cell cycle” GO term, the arrest of the cell cycle in diapause has been associated to the G2 stage in Bombyx mori65. Although tantalizing, these are tentative gene function descriptions and caution should be taken when considering these results. Undoubtedly, additional information on rotifer genome and transcriptomics would refine the functionality of genes that are candidates to be targets of selection by the degree of environmental predictability.A large number of SNPs that are correlated with the studied environmental variables  do not correlate with life-history traits (i.e. propensity for sex or hatching fraction), suggesting that the former acted as selective pressures on traits not considered here. Conversely, a high number of SNPs correlated with the life-history traits studied were not correlated with the environmental variables. Obviously, not all the genetic variability associated to these fitness components is responding to selection by these particular environmental variables. Other selective pressures not considered here —such as lake trophic status or temperature– could be acting. Nonetheless, through adaptive tracking, genes causing divergence in life-history traits might be responding to short-term fluctuations that are uncoupled across localities. Genetic differentiation in 66. In addition, we confirmed that this result is not due to artifactually over-merging duplicated genes  or loci with conflicting fitness effects in different life-cycle stages35 could be important in the studied B. plicatilis populations. Indeed, these results are consistent with the high polymorphism of B. plicatilis genomes despite prolonged clonal phases of proliferation of their populations35, with the large intrapopulation genetic diversity for ecologically relevant life history traits37. The GWAS analysis allowed this inter-clonal variability to be analysed, and yielded one SNP associated to propensity for sex.We identified a high proportion of putative SNPs under selection with signatures of balancing or purifying selection, 54 out of the 66 genomic regions (i.e. scaffolds) under selection. We found that most of these SNPs (74.1%) had both minor allele frequencies higher than the genome average and positive Tajima D values, suggesting that they are under balancing rather than purifying selection or being false positivesB. plicatilis are needed in order to find out further SNPs involved in adaptation to local conditions, especially when the whole genome sequencing techniques become affordable enough to apply them to a large set of individuals in population genetics studies.Given that we used a genome representation technique , which does not screen all available SNPs in the genome, our analysis resulted in an average distance between SNPs is 25.73 kb (115.77 Mb/4500), which is over the distance at which the linkage disequilibrium drops  was slightly lower than the estimate obtained by Montero-Pau et al.77 with microsatellite markers (FST = 0.25) for the same geographical area. Consistently to what Montero-Pau et al.77 observed using mitochondrial data, we found a significant relation between genetic diversity at the genomic scale (measured as estimated heterozygosity) and lake size, suggesting that lake size is a reliable proxy for effective population size in B. plicatilis populations.Genome-wide patterns of genetic variation did not reveal a geographically based population structure, which was not unexpected given the proximity of the studied populations. In previous studies, isolation-by-distance has been reported in this species at a larger regional scale26 and their phenotypic diversity26. Our genomic analysis identifies and screens a large number of high quality SNPs  in a small —but dense in gene regions– genome, assembled and annotated here. Some of these SNPs were putatively under selection, with strong signatures of both diversifying and balancing selection. Moreover, a large number of SNPs were also correlated with environmental predictability and other environmental and phenotypic variables. Remarkably, most of these SNPs were located within functionally annotated genes on the genome. Our results provide evidence of genomic adaptive responses to local conditions in natural rotifer populations, focusing on the degree of environmental predictability.We have used a powerful approach in a set of natural rotifer populations integrating genomic data with data on environmental features of their habitatsBrachionus plicatilis clones from nine Spanish salt lakes differing in predictability conditions  with the complete time series of each lake. With this contingency matrix, as described in Colwell29 and Stearns30, predictability was calculated as the summation of two values: constancy and contingency. The time-series for water-surface area and the resulting predictability values associated to each pond are shown in Fig. 78 and the Mediterranean region salt lakes have a wide range of salinity79, therefore salinity was also measured during the sampling of the lake sediments (May-September 2013). Note that this salinity measure is based on a single visit, thus although it is a value that may be indicative of the salinity of each of the ponds it does not integrate its temporal variability.ons Fig. , Table 528 was allowed to grow as described in Supplementary Method A monoclonal culture from one individual stem female hatched from a diapausing egg isolated from Hoya Rasa lake (HYR1 clone)80 to remove (1) Illumina adapters, (2) low quality leading or trailing regions  or unidentified nucleotides (i.e. Ns), (3) reads with average Phred score < 100, and (4) regions (5-base sliding window) with average Phred score < 20. Thereafter, reads were corrected using a Bloom filter-based error correction tool (BLESS v0.16)81. The paired reads were then merged using Usearch v. 882. Pre-assembly assessment of k-mer distributions were obtained by KMC83 and profiling by GenomeScope84.The sequenced reads were trimmed using Trimmomatic v.0.32De novo genome assemblies were constructed with several assemblers using default parameters  CEGMA v. 2.5 (Core Eukaryotic Genes Mapping Approach)87, a tool that assesses genome completeness based on the presence of a set of core eukaryotic genes, and (3) Assemblathon scripts88 to obtain length-based statistics, such as N50. Platanus assembly was chosen as the reference assembly (see Results) and downstream analyses were performed on this assembly.89 was initially used as an ab initio gene prediction software. After that, GeneMark predictions were provided to run MAKER2 v. 2.31.890 an annotation pipeline that identifies repetitive elements, aligns expressed sequence tags (ESTs), and uses protein homology evidence to generate further gene predictions. MAKER2 was run twice. The first MAKER2 round was performed with permissive settings  the core eukaryotic gene models obtained from CEGMA, (2) ESTs from B. plicatilis NCBI database, (accession: 10-07-2016), (3) available transcriptomes from congeneric species 54, and (5) the Swiss-Prot database. The annotation output from MAKER2 first run was converted into a training set to run Augustus. Finally, the Augustus and MAKER2 (1st pass) outputs were provided to perform a second MAKER2 pass with more stringent settings 94 against a subset (metazoa only) of the NCBI’s non-redundant (nr) protein database. Finally, Blast2GO v.4.1.995 was run with default settings to integrate results from BLAST and InterProScan and to retrieve Gene Ontology (GO) terms and annotation.The functional annotation of the final set of predicted genes was performed by identifying protein domains with InterProScan v.5B. plicatilis was allowed to grow as described in Supplementary Method et al.45, using the restriction enzyme ApeKI (GC[A-T]GC) for digestion. A library with unique barcodes for each clone, plus blank samples, was created.Each of the 270 clones of 96, in which all reads were trimmed to the same length (64 bp) and identical reads were collapsed into tags. These tags were then aligned against the reference genome using the Burrows-Wheeler alignment tool (BWA)97 and SNPs were called from aligned tags. Pipeline default parameters were used except for two cases, in which more conservative values than default were used: (1) at the Minimum length of aligned base pair to store the Sequence Alignment/Map (SAM) entry ; and (2) at the Minimum locus coverage .SNPs were called from the raw DNA sequences using the GBS pipeline as implemented in TASSEL-GBS v2 (TASSEL 5)98. In order to obtain confidently assigned genotypes, those genotypes at each sample supported by less than six reads were excluded (considered as not genotyped). A set of filters were applied to retain those SNPs highly informative, of high quality and not representing over-merged repetitive regions. Firstly, SNPs required at least 50% of the clones genotyped in each population. Second, minor allele frequency (MAF) had to be higher than 1%. Third, only two alleles were present. Fourth, the average read depth among clones had to be lower than 150 reads48. Fifth, less than 60% of clones had to be heterozygotes in each SNP.The set of SNPs were quality filtered using custom developed scripts  for each pairwise comparison, and within-population genetic diversity was measured as the average among loci expected heterozygosity (He), observed heterozygosity (Ho) and inbreeding coefficient (FIS). Mantel tests were used to test for isolation-by-distance patterns in IBDWS v.3.23102 using the geographic distance between lakes calculated using Google Earth (2008) and pairwise FST as genetic distance. The relationship between heterozygosity (He) and log-transformed lake area , a model-free multivariate ordination method, was performed as implemented in the “adegenet” packageea Table  was test105 was used to estimate the posterior probability that a given SNP is affected by selection based on the allele frequencies in each population. It identifies those SNPs with significantly higher —i.e. diversifying selection– or lower —i.e. balancing/purifying selection– differentiation among populations than expected under a model of neutral evolution18. Briefly, prior odds of 10 were used to identify the top candidate SNPs under selection with a false discovery rate of 0.05. Secondly, we used Bayenv2106 to identify loci correlated with particular environmental and phenotypic variables. Bayenv uses a Bayesian approach that takes into account population structure -from covariance matrices- when testing for correlations between environmental and population allelic frequencies106. To do so, the full set of SNPs were used for the construction of 10 covariance matrices using different priors 107. The association with genetic variation was tested for three environmental variables known for the lakes under study 26, and two phenotypic variables previously estimated for the genotyped populations . Following Hancock et al.108 we used the BF of all of the SNPs to calculate a rank statistic . The rank statistic was averaged between runs to obtain the mean rank of each SNP. SNPs were considered as potentially under selection when their mean rank was above 0.99. Thirdly, a genome-wide association study (GWAS) was performed for both life-history traits in order to explore genotype-phenotype association by using GenABEL R package109 with EIGENSTRAT methodology110. In this approach the inter-clonal variability is analysed by considering individual phenotypic values and genotypes, which was hidden behind the averages used in the Bayenv approach. This methodology uses PCA to correct for population stratification. For each SNP and phenotypic variable association analyzed, it provides the Bonferroni adjusted p-value and the R2. SNPs with p-value lower than 0.05 are considered to be putatively associated to the phenotype.To identify SNPs likely to be differentiated as a result of selection, three different approaches were used. Firstly, BayeScan 2.1111 was used to find genes in a flanking region of 0, 2.5 or 5 Kb upstream and downstream from the focal SNPs. As expected from the compact genome of B. plicatilis . GBS raw sequences were deposited in the ENA (accession number SRP151997). The Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession REGN00000000. The version described in this paper is version REGN01000000.Supplementary material file informationDataset 1Dataset 2Dataset 3


The exploration of ‘gene–environment interactions’ (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our ‘adaptive combination of Bayes factors method’ (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable. Evidence of ‘gene–environment interactions’ (G × E) has been found in some phenotypes and complex diseases . Genes aL disease-associated SNPs that are nearly in linkage equilibrium is aggregated by defining a GRS of the ith subject as lth SNP of that subject and lth SNP (n is the sample size).Many G × E studies avoid the harsh multiple-testing penalty by constructing a genetic risk score (GRS) according to previous GWAS findings and then testing the significance of the interaction term (GRS × E) in a regression model , 17–19. ocument} . Informaith subject by summing his/her total number of risk alleles. For example, the gene × physical activity interaction in obesity was identified using an unweighted GRS  and  for smaller effect sizes and larger effect sizes, respectively.We let 50% of D (20 or 50) and the size of Our setting of e was the random error term following the standard normal distribution. We let 50% of d = 1, …, D, where D = 20 or 50) were uniformly drawn from the intervals  and  for smaller effect sizes and larger effect sizes, respectively. Likewise, the abovementioned situations (1–3) were accordingly considered in the simulations of continuous traits.Continuous traits were generated by the following model:D and the size of Y in e was simulated from the standard normal distribution. In total, 7652 SNPs that passed the screening stage were analyzed according to Our assumption of D (20 or 50) and the size of Similarly, we obtained the standardized We then evaluated the power performance of the polygenic approaches in the presence of SNP main effects. The binary traits were simulated according to the following model:s  and s  were uniformly drawn from the interval  for smaller effect sizes, and were uniformly drawn from  for larger effect sizes.e was the random error term following the standard normal distribution. d = 1, …, D) and d = 0.5D + 1, …, 1.5D, where D = 20 or 50) were uniformly drawn from  for smaller effect sizes and were uniformly drawn from  for larger effect sizes.The continuous traits were simulated according to the following model:D present only main effects, SNPs (0.5D + 1) ∼ D exhibit both main effects and SNP × E interactions, and SNPs (D + 1) ∼ 1.5D exhibit only SNP × E interactions on traits. According to our observation from real data analyses , the P-value of M* (or I*) is 10 times the minimum P-value of the 10 GRS-M (or GRS-I) tests. An M* (or I*) test is claimed to be significant if its P-value < 0.05 or 0.01 . The type I error rates of M* and I* are smaller than the nominal significance levels because of the conservative nature of the BON.For GRS, in addition to the type I error rates under 10 Besides ADABF, GRS-M and GRS-I, we also evaluated the power performance of single marker analysis while controlling the family-wise error rate (FWER) at 5% using the BON, or controlling the FDR at 5% using the Benjamini–Hochberg approach (BH) . AlthougD = 20 or 50 in Regarding the performance of pinpointing true SNP × E, we compared the sensitivity and PPV of our ADABF with BON and BH. Sensitivity is defined as the total number of true findings over the total number of SNP × E in the 1000 simulationreplications, i.e. 20000 or 50000  answered ‘yes’ to alcohol drinking, whereas 14 779 subjects answered ‘no’. A total of 12 subjects did not respond to this question and were regarded as missing values. Smoking has been described in the section of ‘Numerical experiments and simulations’. Both alcohol drinking and smoking are binary Es. To obtain more reliable BP , 70, twoRegarding single marker analysis, for each of the 601 531 autosomal SNPs, we regressed DBP, SBP or HYP (yes versus no) on SNP, the E , SNP × E, while adjusting for age, gender, BMI and the 1st seven PCs. After adjusting for the 1st seven PCs, PLINK reported very low genomic inflation factors that were based on the median of the Chi-square statistics, i.e. h the BH .P-value thresholds used for the GRS, the Bonferroni-corrected significance level to control the FWER at 5% should be We then performed the GRS tests using the subset of SNPs that were nearly independent , i.e. ths on DBP .P-value thresholds (P < .05) are then analyzed using ADABF. In the analysis of marginal associations, DBP, SBP and HYP were separately regressed on each of the 143 574 SNPs while adjusting for age, gender, BMI and the 1st seven PCs. Linear regression models were used for analyses of DBP and SBP, whereas logistic regression models were fitted for HYP. In total, 7652, 7508 and 7474 SNPs passed the screening test for DBP, SBP and HYP, respectively. 5 resampling replicates in the ADABF approach. A P-value < 0.05 or 0.01 is sufficient to reject 0H of no polygenic SNP × E interactions [P-values than GRS-M. Additionally, ADABF identified SNP × alcohol interactions on HYP.Finally, as described in the screening stage of the ‘Methods’ section, SNPs passing the screening at the desired significance level , rs62065089 × alcohol interaction on SBP (resampling FDR = .4%) and rs79990035 × smoking interaction on DBP (resampling FDR = 1.1%) through the resampling procedure in ADABF. These three SNPs can also be identified by BON/BH when controlling the FWER/FDR at 5% given the ∼7600 SNPs that passed the pruning and screening stages. Because we have removed SNPs in high LD, these ∼7600 SNPs are nearly independent of each other and therefore the resampling FDR and BH lead to the same results. Despite this, performing ADABF is still worthwhile. As shown by P-value <0.05 or 0.01 is sufficient to reject 0H of no polygenic effects [5 replicates is sufficient for a real data analysis. ADABF takes ∼2.5 h to analyze SNP × E on a continuous phenotype based on a Linux platform with a Dell Intel Xeon E5–2690 2.9 GHz processor and 8 GB of memory. Approximately 3.4 h are required for the analysis of a binary phenotype, because fitting a logistic regression takes more computation time than fitting a linear regression.For a polygenic test, a  effects . TherefoGenetic effects can differ between subjects depending on their lifestyle factors or environmental exposure because of G × E . Therefoet al. [Most complex diseases are polygenic , including obesity , 74, HYPet al.  construcet al. , and theP-value threshold for the ‘optimal’ subset of SNPs is unknown, many studies constructed a panel of GRSs under various P-value thresholds [P-value thresholds evaluated [P-value threshold to select SNPs is not an issue in the ‘GRS-marginal-internal approach’ and ‘GRS-interaction-training approach’. Hüls et al. select SNPs according to a multivariate elastic net regression [When external information is unavailable, the weights for a GRS have to be determined internally. Because the ‘best’ resholds , 84, 85.valuated . We here compare our ADABF with the GRS-M and GRS-I tests. Regarding the power to detect polygenic–environment interactions, ADABF is the most powerful test in the absence of SNP main effects  and C. WW) will change the magnitude of a BF and can lead to a different inference. For example, a BF between 10 and 100 is regarded as a ‘strong’ evidence against 0H, whereas a BF larger than 100 is deemed as a ‘decisive’ evidence against 0H [W) [W was set at .12 = 0.01 or 0.32 = 0.09, the ADABF results were very close to those obtained from W = 0.22 = 0.04  . When W 2 = 0.04  and 2.When multiple SNPs interact with E but their effect sizes are small, we may obtain a significant ADABF test result; however, no individual SNP × E can be identified by controlling the resampling FDR at 5%. SNP × alcohol on HYP shown in P-value < 0.05 or 0.01, no multiple hypothesis correction is required), we conclude that G × E interactions exist and the E can modify the genetic effect on the phenotype. However, each SNP × E test may not be able to reach a sufficient power that can withstand the multiple hypothesis correction. In this situation, GRS-M can help to identify whether synergistic or antagonistic interactions exist between risk alleles and E, according to the sign of Gene × alcohol and gene × smoking interactions on BP have been found in Caucasians , 87 and cument}s C indicatacylphosphatase 2 (ACYP2) gene. Cheng et al. recently also found an ACYP2 × smoking interaction related to susceptibility to ischemic stroke (IS) in a Han Chinese population [ACYP2 × smoking interaction on BP warrants further investigation.We found the rs79990035 × smoking interaction on DBP (resampling FDR = 1.1%). The SNP rs79990035 is located in the pulation . TherefoSome G × E have been discovered by constructing a GRS based on external GWAS results , 29–31. The scientific community usually constructs a GRS and tests the interaction between this score and an E. However, until now, little has been known about the performance of the GRS for detecting G × E. Moreover, appropriate external weights required for a GRS are not always available.We explored a powerful polygenic approach for detecting G × E when external weights are not available, by comparing our ‘ADABF’ with the GRS-M and GRS-I effects.ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when SNP main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.bby086_SuppClick here for additional data file.


Oryza glaberrima, O. barthii, and O. longistaminata) and an Asian rice (O. sativa), which are conserved at the AfricaRice gene bank; and (iii) develop species- and sub-species (ecotype)-specific diagnostic SNP markers for rapid and low-cost quality control (QC) analysis. Genotyping error estimated from 15 accessions, each replicated from 2 to 16 times, varied from 0.2 to 3.1%, with an overall average of 0.8%. Using a total of 3134 accessions genotyped with 31,739 SNPs, the proportion of misclassified samples was 3.1% (97 of the 3134 accessions). Excluding the 97 misclassified accessions, we identified a total of 332 diagnostic SNPs that clearly discriminated the three indigenous African species complex from Asian rice (156 SNPs), O. longistaminata accessions from both O. barthii and O. glaberrima (131 SNPs), and O. sativa spp. indica from O. sativa spp. japonica (45 SNPs). Using chromosomal position, minor allele frequency, and polymorphic information content as selection criteria, we recommended a subset of 24 to 36 of the 332 diagnostic SNPs for routine QC genotyping, which would be highly useful in determining the genetic identity of each species and correct human errors during routine gene bank operations.Species misclassification (misidentification) and handling errors have been frequently reported in various plant species conserved at diverse gene banks, which could restrict use of germplasm for correct purpose. The objectives of the present study were to (i) determine the extent of genotyping error (reproducibility) on DArTseq-based single-nucleotide polymorphisms (SNPs); (ii) determine the proportion of misclassified accessions across 3134 samples representing three African rice species complex (The online version of this article (10.1007/s11032-018-0885-z) contains supplementary material, which is available to authorized users. Between 2012 and 2016, CGIAR gene banks distributed over half a million accessions for research and breeding purposes across the world. The Africa Rice Center (AfricaRice) and the International Rice Research Institute (IRRI) are the CGIAR centers, which conserve rice (Oryza L.) germplasm. Africa harbors a huge diversity of both cultivated and wild rice species, representing six of the ten known genome types  and two cultivated species (O. glaberrima and O. sativa)  gene banks safeguard some of the most widely used collections of crops and trees in the world, which is critical for attaining global development goals to end hunger and improve food and nutrition security. The CGIAR centers have a gene bank platform that enables them to conserve and make available germplasm under the International Plant Treaty and distribute to the global community  platform  in the number of polymorphic SNPs from 16,532 in the 2223 accessions to 3834 in the remaining 2179 accessions. The 44 outlier accessions may possess rare alleles, which might make them very different from most of the O. glaberrima accessions. Outlier accessions might also have resulted from natural allele introgressions into O. glaberrima from another Oryza species  plant identification while collecting the accessions in their natural habitats due to inadequate taxonomic expertise and (ii) routine gene bank operations, including germplasm acquisition, conservation, regeneration/multiplication, DNA preparation, and/or genotyping. Proper taxonomic classification and identification of germplasm prior to seed banking is critically important but remains a great challenge for gene bank managers due to heavy dependence on morphological characters that are less accurate in cases where there is limited phenotypic diversity, trait ambiguity, and their variability due to genotype-by-environment interactions  and O. sativa (23), with 33 simple sequence repeat (SSR) markers and reported 10 SSRs that displayed different allelic profiles between the two species. Zhao et al.  markers of which 57 of the markers were found to be highly subspecies-specific between O. sativa spp. indica and O. sativa spp. japonica. Chin et al.  they were recommended based on very small sample size, (ii) all authors used agarose gels for fragment separation that not only has poor resolution in discriminating genotypes that differ by small allele sizes but also the method is tedious and very low throughput; and (iii) some of the markers are dominant and do not discriminate heterozygous and homozygous loci.Misclassification (misidentification) has been reported in several species, including i et al.  screenedn et al.  genotypeo et al.  genotypen et al.  screenedO. sativa spp. indica genotypes . It also suggested a panel of 24 SNPs for global QC genotyping in rice. However, the detailed methodology used in selecting the two SNP panels for QC genotyping and their relevance to serve as species and subspecies (ecotype) discriminatory marker set are not currently available. Therefore, the objectives of the present study were to (a) determine the extent of genotyping error (reproducibility) in DArTseq and the proportion of misclassified accessions across four rice species  and (b) develop a set of species- and sub-species (ecotype)-specific diagnostic SNP markers for low-cost QC genotyping to minimize errors during routine gene bank operations.The availability of next-generation sequencing-based genotyping technologies, such as genotyping by sequencing (GBS) , O. barthii (51), O. glaberrima (2422), and O. sativa (558) and genotypes derived from interspecific crosses between O. glaberrima and O. sativa (83). O. longistaminata was represented by smaller sample size due to limitation on the number of available collections at the AfricaRice gene bank. Thirty-seven accessions were used twice from original and regenerated seed lots to assess the level of human errors during routine gene bank operations. The interspecific genotypes, commonly referred as New Rice for Africa (NERICA), genetically resemble their recurrent O. sativa parents. Genomic DNA was extracted from a single plant per sample from 3-week-old seedlings grown in a screenhouse. The detailed methodology used for DNA extraction, SNP genotyping using DArTseq™, and imputation were described previously , of which 82.3% of the markers  were mapped to the 12 rice chromosomes, while the remaining 17.7% were not assigned into any of the chromosomes.The two initial sets of germplasm used in the present study are summarized in Supplementary Table www.xlstat.com) using the scatter plot option and species/ecotype as a categorical variable. Accessions belonging to the same species/ecotype tend to cluster more closely together than those from other species/ecotype irrespective of the algorithms used for computing genetic distance matrices and the multivariate method used for analyzing genetic relationships.All statistical analyses were performed after filtering the SNP data of the two sets of germplasm using a minor allele frequency (MAF) of 0.01 in TASSEL v.5.2.43 software , O. glaberrima (2), and O. sativa (4), whereas those misclassified accessions from O. longistaminata were similar either to O. glaberrima (1) or O. barthii (3). The 64 misclassified O. glaberrima accessions were clustered with the lowland O. sativa (44) and upland O. sativa (22) accessions , O. longistaminata (1), and O. sativa (4).After filtering the SNPs using a minor allele frequency of 1%, nearly 74% of the markers  in the first dataset were polymorphic across the 117 template control DNA samples. Genotyping errors across the multiple DNA samples of each of the 15 accessions varied from 0.2 to 3.1%, with an overall average of 0.8%. Genotyping error between pairs of DNA samples of the same accession exceeded 1% only in three  of the 15 accessions Fig. , which aO. sativa (O. sativa spp. indica and japonica and interspecifics) and two ecologies (lowland and upland). The number of diagnostic SNPs identified between pairs of species or groups varied from none to 5640. We first searched for diagnostic SNPs between indigenous African species complex  as one group and the Asian rice  as the second group and identified 156 diagnostic SNPs (Supplementary Table O. sativa groups (O. sativa spp. indica and japonica or lowland and upland ecologies). We found that all O. longistaminata accessions differed from O. barthii and O. glaberrima at 649 and 141 SNPs, respectively, of which 131 SNPs were common in both comparisons . No diagnostic SNP was identified between O. barthii and O. glaberrima. Since no marker was found to be diagnostic between O. glaberrima and O. barthii, the 131 SNPs should be sufficient to serve as diagnostic markers between O. longistaminata and O. glaberrima/O. barthii accessions. One hundred and ten of the 131 were mapped across the 12 rice chromosomes, with each chromosome consisting of 4 to 29 diagnostic SNPs. Sets of 30 and 45 SNPs discriminating O. sativa accessions to lowland or upland ecologies and O. sativa spp. indica or japonica, respectively, were identified . The 30 diagnostic SNPs were common across the two ecologies and the two ecotypes, whereas 15 SNPs were diagnostic only between O. sativa spp. indica and japonica. No diagnostic SNP was found between NERICA and O. sativa spp. indica and japonica, which is expected due to the high genetic similarity of NERICAs with their recurrent O. sativa parents with a clear population structure corresponding either to the lowland or upland ecology  varying from 0.005 to 0.223 and from 0.004 to 0.487, respectively. Supplementary Fig. O. glaberrima and O. sativa , which were supposed to be O. sativa; however, the proportion of misclassification in their study seems over 20%, as there were several O. barthii accessions that were clustered with O. glaberrima in both neighbor-joining phylogenetic analysis and PCA. Buso et al.  18% of the 180 lines were misclassified based on species; (ii) 30% of the 76 suspected samples were misclassified; (iii) 9% of the randomly selected samples were misclassified; (iv) several individuals were found to be the product of interspecific hybridization events; and (vi) SNP markers proved to be effective at confirming species identity. Using 53 phenotypic descriptors, approximately 21% of 3156 yam accessions were found misidentified  . The actual cost per sample varies with the total number of samples to be genotyped. To utilize such services, users can choose 24 of the 36 SNPs  background, which is evident from the low proportion of O. glaberrima genome estimated using microsatellite markers  and between upland NERICA and upland O. sativa spp. japonica (6.6%) using SNP markers  diagnostic SNPs that we recommended for routine QC genotyping of at least rice germplasm that are high in demand for distribution to users. About 24 diagnostic SNPs should be sufficient for routine QC genotyping, which includes a set of 8 diagnostic SNPs to discriminate the three African species complex from the Supplementary Table S1(XLSX 188 kb)Supplementary Table S2(DOCX 14 kb)Supplementary Table S3(XLSX 74 kb)Supplementary Table S4(XLSX 355 kb)Supplementary Fig. S1(DOCX 59 kb)Supplementary Fig. 2(DOCX 73 kb)