Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 , explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 , explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline. However, few associations with specific genetic variants were replicated and genetic polymorphisms explained only a small fraction of individual differences in antidepressant response. Other factors affecting the response to antidepressant drugs include the severity and type of depressive symptoms, prior exposure to adverse environment, and demographic factors. However, none of these provided differential prediction of alternative treatments outcomes with a clinically meaningful accuracy12.The reasons why some patients respond well to antidepressant medications but others do not benefit sufficiently from treatment are still poorly understood. Investigations of biologically related individuals from family studies12. However, it is unknown whether a combination of genetic and clinical variables can improve the prediction of treatment outcomes further. Here, for the first time, we aim to maximise prediction of outcomes of treatment with alternative antidepressants using a combination of genetic, demographic and clinical measurements in patients with major depressive disorder. We report on a statistical learning analysis using more than 500,000 common genetic variants and 139 demographic and clinical variables to optimize the prediction of remission during treatment with a serotonergic or noradrenergic antidepressant.The modest contributions of multiple clinical and genetic predictors suggest that a multivariate approach that combines genetic variants and clinical variables could improve the prediction of antidepressant treatment outcome. An initial application of statistical learning suggested that a combination of multiple clinical variables can improve the prediction over any single factorIn the training dataset of escitalopram-treated participants, 17 variables were selected including HRSD total score and item Somatic Symptoms - General, the symptom dimensions of loss of interest-activity and appetite, BDI item sleep, SCAN item fatigability and 11 genetic markers Tables .Table 1V2 0.37. In external validation, the same model predicted remission in the non-overlapping validation dataset with AUC 0.77 , sensitivity 0.69, specificity 0.71 and pseudo R2 0.30 , sensitivity 0.71, specificity 0.77 and pseudo R2 0.03, suggesting that prediction is drug-specific , sensitivity 0.46, specificity 0.67 and pseudo R; p value2 0.36. The model predicted remission in the non-overlapping validation dataset of nortriptyline-treated participants with an AUC 0.77 , sensitivity 0.68, specificity 0.87 and a pseudo R2 0.36 , sensitivity 0.29, specificity 0.52 and pseudo Rfic Fig. .The present results show that a combination of relatively few genetic and clinical variables can predict whether an individual with depression may reach remission with a specific antidepressant. The prediction models are parsimonious, based on only 17 and 20 variables, and the predictions are reproducible in non-overlapping validation datasets. These results demonstrate that a combination of genomic and clinical information in statistical learning framework has the potential to serve as a clinical decision support tool that may help select an antidepressant that an individual is more likely to benefit from.13. For the prediction of response to nortriptyline, the procedure selected only genetic variables. The selection of only genetic variables in the nortriptyline-treated group suggests that the information predictive of nortriptyline response was better captured by genetic variables than the information predictive of response to escitalopram. The genetic variants selected into the prediction models were distinct from those identified in univariate genome-wide association studies7. For example, the genetic variants that predicted remission with nortriptyline in the multivariate model did not include the variant rs2500535 in UST that was previously identified as significantly associated with response to this antidepressant in the same dataset7. These results demonstrate that a statistical learning framework uses a multidimensional pool of predictors in a way that is partially distinct from traditional univariate approaches and has the potential to build novel prediction models that are relevant to clinical outcomes and robust in generalisation.The prediction was largely antidepressant-specific. The models predicted remission in validation sample treated with the same antidepressant, but not in samples treated with the other antidepressant. The drug-specificity makes the multivariate prediction more useful and applicable to clinical decision making. While the prediction of remission with escitalopram was driven by a combination of clinical and genetic variables, the achievement of remission with nortriptyline was predicted from genetic variants only. The clinical variables that contributed to the prediction of remission with escitalopram overlapped with previously reported predictors. Our model suggested that patients who had low levels of interest and activity, sleep problems, somatic symptoms and severe depression were less likely to reach remission, reflecting previously identified associations with symptom profilesSGCZ gene, part of the sarcoglycan complex, a group of six proteins which bridge the inner cytoskeleton and the extra-cellular matrix, has been recently reported to be associated with major depression, schizophrenia and bipolar disorder14, as well as with alcohol and nicotine co-dependence15, and Parkinson’s disease16. The consistent down-regulation in major depression patients in three independent samples suggested that SCL25A37 may be used as a potential biomarker for major depression diagnosis17. This gene was also associated with fatigue18. The acid sensing ion channel (ACCN1) has been associated with response to lithium treatment in bipolar disorder19 and also associated with risk of autism20. The gene encoding the transmembrane protein 229 b has been associated with risk for Parkinson disease21 and with childhood obesity22. The gene TMEM170A encoding the transmembrane protein 170 A and the CFDP1, the craniofacial development protein 1, have been both associated with coronary risk disease23. The latter has been also associated with lung function24. Another variant identified in this work was located in the transmembrane protein 2 gene TMEM2, which has an essential role in coordination of myocardial and endocardial morphogenesis25. None of the selected genetic variations were located in genes previously associated with pharmacogenetics in depression treatment. However, it is a common finding in genomics that most predictive genetic variants are in locations other than the predicted candidate genes. This is responsible for the general failure of the candidate gene approach and it opens new ways for understanding pathogenesis and pharmacology. Surprising findings from genomic research in other disorders have open new ways of understanding and treating the disorders . Further functional characterization may provide potential targets for future therapeutic antidepressants.It is widely accepted that multiple genes/alleles are involved in determining response to antidepressants, some of which may not have been yet discovered. Interestingly, some of the genes containing variants that we reported as predictive of antidepressant treatment response have been recently identified as depression risk genes, as well as associated with bipolar disorder, schizophrenia and other brain diseases Tables  and 2. F26 our models had “acceptable discrimination” . The utility of biomarkers and prediction models in practice does not depend solely on their prediction accuracy, as reflected by the AUC, but also on clinical context, gravity of the predicted outcomes, cost and burden of the test. For example, a comparison among breast cancer prediction algorithms reported good performance for models having AUC’s below 0.727. The fact that genetic and clinical variables used in the present model can be obtained with high accuracy and low-cost measurements that do not burden participants suggest that such models may be useful in practice.The prediction was accurate enough to be clinically meaningful. Remission was predicted in validation data with an AUC of 0.77 in the escitalopram group and 0.77 in the nortriptyline group. Following the classification proposed by Hosmer & Lemeshowassociation of a single clinical or genetic variant with treatment response in any given test. Association analysis aims to test the effects of specific factors on the response. This approach will highlight the predictive variable that has the strongest relationship with outcome on its own. In contrast, our current report aims to achieve an optimized prediction of outcome with the use of all available predictor variables, thus following a substantially different aim. Statistical learning can be used to build a model that will predict treatment outcome for new (unseen) cases, with clinical utility in practice. While explanatory power provides information about the strength of an underlying causal relationship, it does not imply its predictive power. By capturing underlying complex patterns and relationships, predictive modeling can suggest improvements to existing explanatory models28.Most of our previous work reporting on GENDEP applied analytical methods from the traditional inferential statistical framework, based on the assessment of 30. The longitudinal study design of GENDEP allowed the follow-up of patients and the prospective assessment of symptom change, this being the most appropriate approach to establish cause-effect relations and avoid inconsistencies in data collection. The study was specifically designed to assess remission as the primary outcome, with patients being followed for 12 weeks. All patients had four or more depression severity measurements, with more than eighty percent of the sample having eight or more depression measurements, enough time to observe a clinical trend that could lead to clinical remission. However, interpretation of the present results has to take into account several limitations. First, while a wealth of information was available in the GENDEP dataset, not all relevant predictors were measured. For example, history of maltreatment in childhood has been shown to predict outcome of treatment with antidepressants31, but information on childhood maltreatment is not available in GENDEP. Second, since GENDEP only included individuals of white European ancestry without family history of bipolar disorder, the results may not generalize to individuals of other ethnicities or those with family history of bipolar disorder. Third, GENDEP only included two antidepressant drugs distinct in their mechanisms of action. Similar prediction of outcomes with other antidepressants, with neurostimulation and psychological treatments will require investigation in large and richly assessed samples of individuals treated with different modalities. Fourth, the GENDEP study was used as an exploratory dataset to build and test the predictive models. The clinical application of these models will require a comparison of outcomes between individuals whose treatment is selected according to a prediction model with those whose treatment is selected by chance or according to the judgement of the treating physician.GENDEP has several strengths that make it suitable for prediction modeling. It is a randomised controlled trial that allows optimal comparison between treatments and the development of treatment-specific predictorsIn conclusion, the present results demonstrate that a combination of a relatively small number of clinical and genetic variables can meaningfully and robustly predict remission with escitalopram and nortriptyline antidepressants among individuals with major depressive disorder. Statistical learning methods may be used to derive similar models for individuals treated with various antidepressants and other treatment modalities to map the opportunities for individualized indications for treatments.https://gist.github.com/raqini/669c38a6329aa2231268770200519d64.The models are available online at 32. The two antidepressants were selected as representatives of different classes of antidepressants (SRI and TCA) that differ in their pharmacodynamics (serotonergic vs. primarily noradrenergic reuptake inhibition) and pharmacokinetics (distinct primary metabolizing enzymes). Genetic data for GENDEP participants were obtained in two phases. Firstly, 706 individuals were genotyped7. In a second phase, 105 more individuals were genotyped building a total sample of 811 individuals that were partially randomized to escitalopram and nortriptyline. Since our hypotheses concerned differential prediction and participants non-randomly allocated differed on some clinical characteristics2, we restricted the present analyses to the randomly allocated participants (n = 430). Randomisation has been shown to be crucial to avoid systematic confounding effect that might prevent predictive models from properly generalizing to other samples33. The participants were recruited from nine European centers and diagnosed with ICD-10/DSM-IV current depressive episode of at least moderate severity with the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) interview34. Because of the genetic character of the study, the recruitment was restricted to individuals of white European parentage. Patients with personal or family history of bipolar disorder or schizophrenia and those with current substance dependence were excluded. They were treated for 12 weeks according to a protocol that guided dose adjustments according to response and tolerability, with 10 to 30 mg of escitalopram or 50 to 200 mg of nortriptyline daily. We randomly separated the participants into a training sample  and a validation sample  , Dr T Eaton (Co-Chair), Dr J Bearn, Professor T Craig, Professor A Farmer, Dr N Fear, Mr R Maddox, Mrs J Bostock, Dr V Kumari, Dr M Leese, Dr V Mouratoglou, Professor Sir Michael Rutter, Mr G Smith, Dr D Taylor, Dr U Ettinger, Mr J Watkins, Dr V Ng, Dr D Freeman and Dr T Joyce. All participants signed a written informed consent. All experiments were performed in accordance with relevant guidelines and regulations. The GENDEP study was registered at ISRCTN03693000  on 27th September 2007. Participant characteristics are described in Supplementary Table We investigated treatment outcomes in 430 adults with major depressive disorder who were randomly allocated to receive either escitalopram, a selective serotonin reuptake inhibitor (SRI), or nortriptyline, a second-generation tricyclic antidepressant (TCA) that acts primarily as a norepinephrine reuptake inhibitor, and completed at least 4 weeks of treatment with the allocated antidepressant as part of the Genome-based Therapeutic Drugs for Depression (GENDEP)36 at the last available measurement after 4–12 weeks of treatment.The outcome was remission, defined as scoring 7 points or less on the 17-item Hamilton Rating Scale for Depression (HRSD)37, HRSD36 and the Beck Depression Inventory (BDI)38. Study interviewers collected information on gender, age, age at depression onset, body mass index (BMI), smoking (yes or not), years of education, marital status, occupation, and children (yes or not). The number of stressful life events in the 6 months previous to the interview was reported with the Brief List of threatening Events questionnaire (BLEQ)39. Medication information was recorded including the use of antidepressant at the time of recruitment, number of prior antidepressant trials and the types of antidepressants tried . Missing data were imputed by a bagged tree nonparametric method that allows inclusion of all cases without causing bias under a broad range of assumptions about missing data mechanisms40. Categorical data were rounded to plausible values after imputation41. In total, we included 139 clinical and demographic predictors 42 and genotyped using the Illumina Human610-quad bead chip . This chip assays more than 610,000 single nucleotide polymorphisms (SNPs) and copy number variant markers selected to provide a comprehensive coverage across populations, and captures the majority of known common variation in the human genome, based on HapMap (release 23). Of the 550,337 SNPs with a minor allele frequency >0.01, a total of 539,391 (98%) were at least 99% complete and retained for analyses. The 430 participants presented no sex mismatches, no ambiguous genotypic sex and no outliers on heterozygosity. One individual in each of six pairs of related individuals (three first- and three second-degree pairs of relatives) was retained for further analyses. No population structure outliers were detected. The 430 individuals had a mean genotyping completeness of 99.82%. Using the IMPUTE v2 program43, we imputed missing SNPs data up to the 1000genomes (build 37). Quality control procedures and imputation are described in detail in Supplementary materials. Variants showing linkage disequilibrium (LD) over 0.8 were excluded from analysis. A total of 524871 common genetic variants were analysed.DNA was extracted from blood samples collected in ethylenediaminetetraacetic acid35. Within the training data set we performed 5-fold cross-validations to select informative variables and derive a statistical learning model to predict remission separately for escitalopram and for nortriptyline. The two resulting models  were then externally validated in the validation dataset, a set of participants treated with the same drug that was not used in any way in the model derivation  and validation dataset  (i.e. the probability of a variable to be non-informative with regard to remission prediction given its CAT score) for each potential predictor. We retained predictors that had a LFDR smaller than 0.8 more times than not across the 100 rounds.In training data, we performed variable selection in 20 repetitions of a 5-fold cross-validation, 100 rounds in total. In each round, we left out one fifth of the training dataset and, in the remaining four-fifths of the training dataset, we estimated a Correlation-Adjusted T (CAT) score  with a cross-drug analysis .2 coefficient. AUC49 can be interpreted as the probability that a classifier can identify (discriminate) a remitter when a remitter and a non-remitter cases are selected at random. The maximum value for the AUC is 1.0, thereby indicating a  perfect discrimination . An AUC value of 0.5 indicates no discriminative value . The Nagelkerke pseudo R2 approximates the proportion of outcome variance explained by the model.We indexed the accuracy of prediction with the Area Under the Curve (AUC) of a Receiver Operating Curve (ROC), sensitivity, specificity and Nagelkerke pseudo R50, sda45, glmnet51 and pROC52 libraries from R 3.2.3 statistical software53.We used caretThe data that support the findings of this study are available from the corresponding author on reasonable request. Data were used under license for the current study, and so are not publicly available.Supplementary material


We presented a risk assessment model to distinguish between type 1 diabetes (T1D) affected and unaffected siblings using only three single nucleotide polymorphism (SNP) genotypes. In addition we calculated the heritability from genome-wide identity-by-descent (IBD) sharing between full siblings. We analyzed 1,253 pairs of affected individuals and their unaffected siblings  from the T1D Genetics Consortium (T1DGC), applying a logistic regression to analyze the area under the receiver operator characteristic (ROC) curve (AUC). To calculate the heritability of T1D we used the Haseman-Elston regression analysis of the squared difference between the phenotypes of the pairs of siblings on the estimate of their genome-wide IBD proportion. The model with only 3 SNPs achieving an AUC of 0.75 in both datasets outperformed the model using the presence of the high-risk DR3/4 HLA genotype, namely AUC of 0.60. The heritability on the liability scale of T1D was approximately from 0.53 to 0.92, close to the results obtained from twin studies, ranging from 0.4 to 0.88. One of the main reasons for disease gene identification is to provide the ability to identify people who are at risk of disease. Thus, a central question for the field is whether validated marker data can be used to discriminate effectively between cases and controls. However, even markers with replicated highly significant odds ratios may be poor classifiers and most variants identified so far confer only small increments in risk and still explain only a small proportion of phenotypic diversity . T1D is The association of T1D with alleles at HLA loci, especially the HLA class II genes DR and DQ, is well validated . The higIn this study, we presented a cost-effective predictive model that could distinguish T1D status in siblings from multiplex families. Our model can be conducted at birth for early prediction and prevention. Our 3-SNP model can not only prevent mortality, but also decrease morbidity and public health costs.We used subjects from the Type 1 Diabetes Genetics Consortium (T1DGC) . A subjeprobands. Next, sibs were selected from each family and were paired together with the probands to create 1,253 pairs of proband-sib. Then we randomly split the 1,253 pairs into two datasets, namely, a “discovery” dataset of 750 pairs and a “validation” dataset of 503 pairs, subject to the equal proportion of case vs control in each dataset.For each family, we randomly selected an affected subject to form a dataset, namely, rs2854275, rs3104413 and rs9273363 that could rapidly define the HLA-DR and HLA-DQ types relevant to T1D . We useP = yes on the basis of continuous or categorical independents of an observation G = , formally:We used a logistic regression model to construct risk prediction models . This mepredictor G is a 9-dimension vector, including the 3 SNPs genotyped from a proband, the corresponding 3 SNPs genotyped from a sib and 3 binary indicators showing whether or not a genotype from the proband is equal to the corresponding genotype from the sib.Logistic regression uses an approach called maximum likelihood estimation to estimate regression coefficients. In this case, the We measured the discriminative accuracy of the predictive models using receiver-operator curve (ROC) analyses ,15. The n sib pairs, of the squared difference between the phenotypes Pi1 and Pi2 of the ith pair of siblings on the estimate of their genome-wide IBD proportion Heritability of disease traits is formally defined as the proportion of phenotypic variance in a population attributable to additive genetic factors . Traditiu is random effect from T1D samples’s regions. The fixed effect coefficient β is estimated using the lme4 package [Because T1D subjects were recruited from different families across worldwide ,3 the eq package  after deP is the proportion of case vs. control and T = Φ-1(1-K).To account for ascertainment that generates a much higher proportion of cases in our analyzed samples than in the population, the estimate heritability scale as ,20:hl2=IBD probabilities were calculated using Plink . Fig 1 sh2L of T1D ranging from 0.53 to 0.92 depends on different settings of the T1D prevalence K. Our heritability estimates using sib-pair methods are closed to the results obtained from twin studies, ranging from 0.4 to 0.88 [We used a non-parametric bootstrap method  to calcuThe AUC and the corresponding 95% CIs for the sib-pair logistic regression model obtained in the discovery and validation sets are shown in T (the logit score in this study). When disease is rare like T1D, the threshold should be selected toward lower left portion of the ROC curve where the sensitivity is small but the specificity is high [In the past 15 years, the genetics of common human diseases has been transformed by GWAS. These studies have been a powerful approach to the identification of genes involved in these complex diseases and led to developing predictive genetic tests. The tests using SNPs to predict an individual’s future risk of disease are one of the most appealing early disease prediction methods. Such tests can be conducted at birth and, by use of appropriate prevention strategies, prevent individuals from contracting a disease. These tests have the potential to be the cornerstone of epidemiology and are anticipated to have a large impact on health care (see further reviews in refs –25). It  is high . For exaEven though preventing or curing type 1 diabetes at risk subjects remain elusive despite effortless and substantial investments in industrialized countries ,27, diabh2). As the heritability estimates for T1D explain ~90% of the phenotypic variance the GWAS-based predictions can be significantly improved by incorporating many other factors. These include invoking rare variants, structural variants, interaction between genes and environment factors, non-linear interaction between genes and genes, family history conditional on genotype at known loci and signals in non-coding regions [Genetic factors play a significant role in T1D disease, as indicated by the proportion of explained variance Click here for additional data file.S2 FileR program for calculating t1d h2 using Haseman-Elston regression analysis.(R)Click here for additional data file.S3 FilePlink IBD probabilities for all sib-pairs studied in this project.(TXT)Click here for additional data file.S4 FilePlink IBD probabilities for non-affected vs non-affected sib pairs.(TXT)Click here for additional data file.S5 FilePlink IBD probabilities for non-affected vs affected sib pairs.(TXT)Click here for additional data file.S6 FilePlink IBD probabilities for affected vs affected sib pairs.(TXT)Click here for additional data file.


GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing ( By analysing the genomic variation between individuals and populations, polymorphisms can be used to identify genotypes, and connect them to phenotypes. Since the advent of high‐throughput sequencing technologies, the abundant and heritable single nucleotide polymorphisms (SNPs) have emerged as the most widely used genotyping markers. These can be used for linkage mapping, analysis of quantitative trait loci (QTL), association studies, marker‐assisted selection (MAS) and genomic selection (GS) in crops. Moreover, their generally low mutation rate means they can be used for genetic diagnostics and germplasm identification. The versatility of SNPs has also led to their widespread use in phylogenetics and phylogeography  approach using restriction enzymes to decrease genome complexity before sequencing  and exome sequencing represent important alternative reduced‐representation approaches. Both of these approaches allow more selective sequencing, enabling focus on protein‐coding regions. Although coding sequences may amount to only 1%–2% of the genome Pennisi, , these s, et al.  found maet al., et al., An important advantage of RNA‐seq is that no prior genomic information is required . Although exome sequencing is thus substantially more expensive than RNA‐seq, costs vary widely depending on sequencing coverage, choice of library preparation, location and sequencing provider. Compared to GBS, RNA‐seq and exome sequencing have the advantage that most transcripts and exons can be annotated using existing databases, which provides a functional context for SNPs. Most SNPs yielded by GBS, on the other hand, lie outside coding regions and are not easily annotated. Nevertheless, the many biases in RNA‐seq and the high cost and prior requirements of exome sequencing are the reasons that GBS, and particularly RRS, has become an increasingly popular genotyping method in diverse fields of biology  follows a six‐step protocol . New ap et al., .et al., et al., The three main pitfalls of RRS are allele dropout, PCR duplicates and variance in coverage. In allele dropout, polymorphisms in the restriction enzyme recognition site prevent cutting and can thus lead to erroneous genotyping , SNPs and genotypes are called using low‐coverage genomic reads, typically <1×, to make genotyping large populations viable . Long‐read sequencing platforms such as Oxford Nanopore technologies and Pacific Biosciences single‐molecule real‐time sequencing can achieve reads >10 kb. Observed error rates on both platforms, however, have been higher than in conventional short‐read sequencing at ~15%  use ancestral recombination events to identify the genetic loci underlying traits at high resolution. By employing association panels consisting of diverse genotypes, GWAS is able to pinpoint candidate genes precisely when linkage disequilibrium is relatively low, overcoming the limitations of less exact methods such as QTL mapping. Although commercial SNP arrays have been widely used for GWAS in crops such as rice, maize and soybean  varieties, which detected 512 loci associated with 47 agronomic traits  to the genetic linkage map, scaffolds can be anchored and ordered. In this way, linkage maps produced with high‐resolution GBS can be used to validate and fix assemblies  has been genotyped and mapped using ddRAD, which helped anchor an unmapped 120 Mbp and identify misjoined scaffolds  stem blight resistance  for the available genotypes. Finally, a breeding population can be created from selected individuals and used without further phenotyping  produce summary reports, while others such as PRINSEQ  is a collection of command line tools providing quality reports and read trimming. A limitation of many quality controls tools is that quality assessments are produced individually for each sample and therefore require additional analysis for easy comparison. Recently developed tools such as MultiQC , tomato (http://solgenomics.net) and wheat . These community databases increase accessibility of research data and help drive community‐based storage and analysis solutions.Increasing data protection and efficient access are also important for the management of genotyping data. Data management systems such as iRODS (Rule‐Oriented Data management systems) can help simplify data replication and allow adjustable levels of metadata to enhance accessibility (Chiang GBS represents a powerful suite of genotyping approaches with wide‐ranging applications and the potential to accelerate plant breeding programmes. However, these approaches vary in their costs per marker data point, in the types of data produced and in errors and potential biases. When selecting a method, researchers must therefore consider genome size, population structure, prior genomic information available and their varying influence on the different methods. The choice of software tools should be made consciously with respect to the specific genome and the analytical goals. While RRS approaches remain cost‐effective, WGR methods, possibly in combination with long‐read sequencing, will increase in use as sequencing costs continue to drop.


The expression of facial asymmetries has been recurrently related with poverty and/or disadvantaged socioeconomic status. Departing from the developmental instability theory, previous approaches attempted to test the statistical relationship between the stress experienced by individuals grown in poor conditions and an increase in facial and corporal asymmetry. Here we aim to further evaluate such hypothesis on a large sample of admixed Latin Americans individuals by exploring if low socioeconomic status individuals tend to exhibit greater facial fluctuating asymmetry values. To do so, we implement Procrustes analysis of variance and Hierarchical Linear Modelling (HLM) to estimate potential associations between facial fluctuating asymmetry values and socioeconomic status. We report significant relationships between facial fluctuating asymmetry values and age, sex, and genetic ancestry, while socioeconomic status failed to exhibit any strong statistical relationship with facial asymmetry. These results are persistent after the effect of heterozygosity (a proxy for genetic ancestry) is controlled in the model. Our results indicate that, at least on the studied sample, there is no relationship between socioeconomic stress (as intended as low socioeconomic status) and facial asymmetries. Fluctuating asymmetry (FA) on bilateral human phenotypic attributes has been proposed as a proxy of development instability –10. ThisUndoubtedly, a large core of publications identified negative consequences related to growing and developing on low SES contexts, and a wide spectrum of outcomes, such as asthma, infections, ,28 and dIn this context, we postulate that before establishing hypothesis based on the assumption that fluctuating asymmetry is a proxy of developmental instability, some important factors deserve attention. For instance, it should be noted that some degree of directional or fluctuating facial asymmetry is normal  at the population level, even among healthy individuals see –41. AlsoConsidering all the above, the question arises if there is some detectable effect of SES inequality on the patterns of FA. In this context, the establishment of human facial features or expressions as biological adaptations requires a rigorous review of current knowledge on the “normal” variation patterns, which is a basic preliminary step to test any evolutionary hypothesis .Historically, the region has been marked by continuous, differential and diverse intra and intercontinental migration events . Such miIn this paper, we aim to test if, besides the potential effects of genetic admixture on it, FFA scores are linked to multivariate SES inequality on a sample of urban individuals belonging to five admixed Latin American populations. Note that, instead of developing a classic approach aimed to test if extreme poor conditions trigger some degree of FA, we focus on the “normal” observed range of SES variations on modern, urban Latin-American populations.As part of the CANDELA initiative ,52, we rAll covariates were taken as continuous variables (sex was assessed taking the discriminant function axis between sexes). For tabulation of the http://www.photomodeler.com/ Eos Systems Inc, Vancouver, Canada). As described elsewhere [Facial shape was recorded following scientific photographic protocols described in detail in references  and 52]52]. UponFrom a blood sample taken from each volunteer we obtained genomic data involving 730,525 SNPs (single nucleotide polymorphisms) ,52. AfteDue to its statistical properties, fluctuating asymmetry can be easily confounded with measurement errors . Thus, aData repeatability was estimated on the basis of replicated data belonging to 200 individuals taken from the five countries and both sexes. The mean square values resulting from a Procrustes ANOVA performed on this data subset can be considered as a variance score that can be compared to the FA and DA variances. If the used protocol guarantees data repeatibillity, then the magnitude of the error term would be considerably lower than the individual, DA and FA effects .We characterized the asymmetric component of shape variation in the facial phenotype using Procrustes ANOVA and MANOVA designs , followiDeparting from the intrinsic complexity and multifactorial, non linear nature of the SES  we approThe relationship between FFA scores and SES was explored using a Hierarchical Linear Model (HLM) implemented in SPSS v. 12. Thus, individuals were “nested” within sub-populations  in order to circumvent any among-country effect on the SES variables, and to evaluate significant effects of the FFA scores by country and sex. Age, sex, BMI, melanin index, heterozygosity, genetic ancestry and SES variables where considered as main effects in the model. On a previous paper on the same populations  we reporThe ANOVA approach compute on the distances to the centroid proposed by  providesThe Procrustes ANOVA repeatability essay, performed on the 200 replicates, indicated that the error term provided the lowest mean square values in relation to variation due to sex, individual, DA, and FA . Note thConsidering the intra/inter-observer error and repeatability patterns described above, and the relatively large size of the faces studied here, these margins of error and repeatability were considered acceptable.The Hierarchical Linear Model (HLM) performed on both, heterozygosity corrected and uncorrected individual FFA scores provided a significant dependence of FFA scores on age, sex, BMI, melanin index and heterozygosity (for uncorrected data). Specifically, both corrected and uncorrected FFA increases with age in both sexes. Additionally, independently of the heterozygosity effect, FFA scores increases with BMI in both sexes, showing a marked effect in males see .WI resumes 24.44% of the total variance in the sample, and the positive values of this PC are characterized by the possession of TV, video, bathroom, car and radio. PC2WI explains a 9.97% of the total variance and is related to FMS, education and possession of freezer. The two significant principal components, in terms of its association to FFA in the HLM model explain collectively a 14% of the total variation. Specifically, PC3WI explains a 7.72% of the total variance and is mainly explained by fridge and education in the positive values versus having a vacuum and bike in the negative scores. Finally, PC4 WI accounts for 6.30% of the total variance and is related to having a dishwasher in the positive axis and a motorcycle for the negative quadrant (see WI). Considering both, the small variance explained by PC3WI and PC4WI added to the fact that these PCs do not seem to discriminate among high and low wealth status, but on the pattern of acquisition or possession of specific goods , we cannot state that there is a relationship among increased levels of asymmetry and low socioeconomic status (We have detected a moderated association with PC3 (p = 0.028) and PC4 (p = 0.036) of the WI . The PC1c status . This geMatrix graphs displaying the pairwise comparisons of FFA values and SES shows non-association among these variables and even a pattern of decreasing FFA as SES values increase . Some ofRegarding among-country variations, Peru showed the higher FFA values of the sample and Colombia the lower ones in males, whereas the highest FFA values in females were found in Chile Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


The increasing availability of whole-genome sequence data is expected to increase the accuracy of genomic prediction. However, results from simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to high-density (HD) single nucleotide polymorphism (SNP) chip genotypes. In addition, the sheer number of variants makes analysis of all variants and accurate estimation of all effects computationally challenging. Our objective was to find a strategy to approximate the analysis of whole-sequence data with a Bayesian variable selection model. Using a simulated dataset, we applied a Bayes R hybrid model to analyse whole-sequence data, test the effect of dropping a proportion of variants during the analysis, and test how the analysis can be split into separate analyses per chromosome to reduce the elapsed computing time. We also investigated the effect of imputation errors on prediction accuracy. Subsequently, we applied the approach to a dataset that contained imputed sequences and records for production and fertility traits for 38,492 Holstein, Jersey, Australian Red and crossbred bulls and cows.With the simulated dataset, we found that prediction accuracy was highly increased for a breed that was not represented in the training population for sequence data compared to HD SNP data. Either dropping part of the variants during the analysis or splitting the analysis into separate analyses per chromosome decreased accuracy compared to analysing whole-sequence data. First, dropping variants from each chromosome and reanalysing the retained variants together resulted in an accuracy similar to that obtained when analysing whole-sequence data. Adding imputation errors decreased prediction accuracy, especially for errors in the validation population. With real data, using sequence variants resulted in accuracies that were similar to those obtained with the HD SNPs.We present an efficient approach to approximate analysis of whole-sequence data with a Bayesian variable selection model. The lack of increase in prediction accuracy when applied to real data could be due to imputation errors, which demonstrates the importance of developing more accurate methods of imputation or directly genotyping sequence variants that have a major effect in the prediction equation.The online version of this article (doi:10.1186/s12711-017-0347-9) contains supplementary material, which is available to authorized users. The increasing availability of whole-sequence data, which should contain causative mutations for complex traits, is expected to increase the accuracy of genomic prediction and to aid in the identification of these causative mutations. There are two advantages of using sequence data over single nucleotide polymorphism (SNP) chip genotypes. First, if the SNP chip does not explain all of the genetic variance explained by the sequence, prediction accuracy will be limited regardless of the prediction method used. Second, if there is no single SNP that is in complete linkage disequilibrium (LD) with a quantitative trait locus (QTL), prediction accuracy using SNP chip genotypes will decrease. In particular, the latter influences Bayesian prediction methods, which work best when they identify a single SNP with a large effect. Both of these reasons concern the LD between causative mutations and SNPs. In dairy cattle, LD is extensive within a breed but the phase of LD varies between breeds , which iDrosophila [However, results from both simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to SNP chip genotypes. The large number of variants makes analysis of all sequence variants and accurate estimation of all effects computationally challenging. Furthermore, the higher rate of genotype errors due to imputation errors in sequence data compared to SNP chip data , may limosophila , substanOn the contrary, other studies show that preselecting sequence variants can lead to an increase in bias and, thus, an increase in accuracy is not evident. Calus et al.  used splp value below a certain threshold. Therefore, a model that analyses multiple SNPs simultaneously may be more efficient in identifying sequence variants that increase prediction accuracy than a GWAS, which tests SNPs one at a time. Bayesian variable selection models are effective for the identification of causative mutations [While promising results were obtained by selecting variants based on a GWAS , it requutations . Howeverutations  developeWhile the Bayes R hybrid model decreases computing time substantially compared to Bayes R , estimatOur objective was to find a strategy to approximate multi-breed and across-breed prediction, by analysing whole-sequence data with a Bayesian variable selection model. First, we used a simulated dataset that consisted of a filtered set of whole-genome sequence variants to test the accuracy of the Bayes R hybrid model. We also considered the effect of dropping variants with little or no effect during the analysis and tested how the analysis can be split into chromosomes to reduce the elapsed computing time. Furthermore, we investigated the effect of imputation errors on the prediction accuracy. Subsequently, we applied the tested approach to a dataset that contains imputed sequences and records for production and fertility traits for a large number of Holstein, Jersey, Australian Red and crossbred bulls and cows.For this study, we used two datasets: a small dataset, with a reduced number of variants and simulated phenotypes, to speed up initial comparisons of different scenarios and a second dataset to test the scenarios in practice, which contained a much larger number of sequence variants and individuals, with daughter trait deviations (DTD) for bulls and trait deviations (TD) for cows for milk, fat, protein and fertility.The simulated dataset was the AUS-Sim set that is described in more detail by Macleod et al. . This daAll individuals were genotyped with the Illumina BovineSNP50 chip , or custQTL were randomly sampled from all SEQ variants. In total, 4000 causative mutations were simulated, of which 3485, 500 and 15 were categorised as having small, medium and large effects on the trait. Effects were sampled from three normal distributions, with a mean of 0 and variances of 0.0001To investigate the effect of imputation errors on prediction accuracy, errors were added to the SEQ variants for the reference population, the validation population or both populations. For each allele, the probability of an error  and Interbull.2 > 0.9). For LD pruning, variants were divided into four groups based on their functional annotations: NSC variants, REG variants, variants on the HD chip and all other variants. Annotations for the sequence variants were collated using the NGS-SNP software [2 higher than 0.9 with a NSC variant, HD variants with an r2 higher than 0.9 with a NSC variant or a REG variant and other variants with an r2 higher than 0.9 with a NSC variant. After filtering based on MAF and LD, 4812,745 variants were retained for further analysis.The second dataset contained daughter trait deviations (DTD) or trait deviations (TD) for milk, fat, protein and fertility for 38,540 animals. Animals were genotyped with the Illumina BovineSNP50 chip  and impusoftware . LD prunThe dataset was split up into a reference population with Holstein and Jersey bulls born before 2005, and Holstein, Jersey and crossbred cows, and a validation population with Holstein and Jersey bulls born in 2005 and after, and Australian Red bulls and cows. Animals in the reference population that had sons in the validation population were removed from the dataset. Furthermore, seven animals were removed from the dataset because their sequence differed for less than 10,000 variants from another individual in the dataset. Because of the presence of crossbred individuals, a principal component analysis (PCA) was used to divide the Holstein and Jersey animals in five different clusters, as shown in Figure S1  module to estimate The accuracy of prediction was defined as the correlation of the predicted breeding value with the TD (cows) or DTD (bulls) between validation animals.To speed up the analysis, it is possible to drop some of the variants during the different stages of analysis (e.g. after the EM step or after a certain number of MCMC iterations). Variants were ranked based on their posterior inclusion probability (PIP) to be included in any of the distributions with a non-zero variance, and the variants with the lowest PIP were dropped in order to drop the desired proportion of variants. After dropping, the mixing proportions at the time of dropping were added to the prior for the rest of the analysis, to account for the dropped variants.d), variants selected based on their PIP from each chromosome (CHR) reanalysed with all chromosomes together (S_KEPT_Dd), and variants selected by CHR and all HD variants reanalysed with all chromosomes together (S + HD_KEPT + HD_Dd). As a comparison to the S_FULL_D0 scenario, we analysed all HD genotypes (HD_FULL_D0). In the S_FULL_Dd scenarios, the sequence variants were analysed simultaneously with d = 0, 0.25, 0.5, 0.7 or 0.9 as the target proportion of variants dropped during the analysis. Variants were dropped after the EM step, after 200 MCMC iterations, or after 10,000 MCMC iterations.Using the simulated dataset, we tested several strategies to analyse sequence data, which are summarized in Table d with d = 0, 0.7 or 0.9, the sequence variants were split up and analysed per chromosome. The effects of variants that were estimated during HD_FULL_D0 were used to correct the DTD and TD for all other chromosomes except the chromosome that was analysed. After analysing all the chromosomes, the estimated effects of variants of all the chromosomes were used to estimate a genome-wide breeding value.In scenarios S_CHR_Dd directly to compute breeding values assumes that effects are estimated independently between chromosomes. Therefore, in scenarios S_KEPT_Dd with d = 0.7 or 0.9, variants that were retained in the model by S_CHR_Dd were reanalysed in a genome-wide analysis to re-estimate effects of variants and GEBV.Using the variant effects estimated by S_CHR_Dd, with d = 0.7 or 0.9, was the same as S_KEPT_Dd¸ except that in addition to the variants that were retained in the model for the analyses per chromosome, the HD variants were added into the model.The approach used in scenarios S_KEPT + HD_Dd and S_CHR_Dd, the prior for the number of variants per distribution was d and S_KEPT + HD_Dd, this was set to the posterior estimate of the number of variants per distribution obtained by S_FULL_Dd.For scenarios HD_FULL_D0, S_FULL_DUsing the real dataset, we compared scenarios HD_FULL_D0, S_CHR_D0.9, S_KEPT_D0.9 and S_KEPT + HD_D0.9. For HD_FULL_D0, the prior for the number of variant per distribution was No ethical approval was required for this study.The accuracy and bias of the different strategies are in Figs. Splitting up the analyses per chromosome and analysing all chromosomes in parallel decreased the computing time from 55 h for S_FULL_D0 to between 1.9 and 4.5 h per chromosome. However, the accuracy was lower than that obtained by S_FULL_D0. The reduction in accuracy was up to 0.04 in Australian Red and 0.01 in Red Holstein. Combining the process of dropping 70 or 90% of the variants with splitting up the analysis per chromosome did not decrease accuracy furthermore.d scenarios, reanalysing the variants that were kept in the model in a genome-wide analysis in the S_KEPT_Dd scenarios resulted in accuracies that were similar or only slightly lower than those obtained with S_FULL_Dd. Adding the HD variants in the S_KEPT + HD_Dd scenarios resulted in accuracies that were similar to those obtained with S_FULL_Dd.Contrary to the S_CHR_DTable d scenarios, i.e. 0.65 and 0.63 for S_KEPT + HD_D0.7 and S_KEPT + HD_D0.9, respectively.Table d scenarios.Table Figures The accuracy and bias of the scenarios tested with real data are in Figs. The number of variants assigned to each of the four distributions is in Table Table We focus the discussion on two points, i.e. (1) on the ability to reduce the computing time needed for analysis of whole-genome sequence data by using an EM-MCMC hybrid approach, dropping some variants from the analysis and processing chromosomes in parallel, and (2) on the reasons why genome sequence data may or may not result in higher accuracies than HD SNP genotypes.The simulated datasets were previously analysed by Macleod et al. , using BWe tested a new option of the hybrid model, which drops a proportion of the variants during the analysis to decrease the required computing time even more. The dropping of variants was tested at different stages of the analysis, and the proportion of variants that were dropped varied. While dropping variants reduced computing time, it resulted in a decrease in accuracy. The decrease in accuracy became smaller as fewer variants were dropped, and when variants were dropped after a large number of MCMC iterations. However, the goal of dropping variants is to reduce computing time, and the gain in computing time is smaller when fewer variants are dropped. Running the full MCMC chain before dropping any variants resulted in an accuracy that was similar to that in the analyses that did not drop any variants. However, if the analysis was run first for 10,000 iterations before dropping the variants, and subsequently run for another 10,000 iterations with the dropped variants computing time increased rather than decreased compared to analysing all the variants for 10,000 iterations without dropping any variants. Therefore, if the goal is to increase the speed of the analysis, it is better to use all the variants. However, if it is necessary to select variants that are associated with the trait, the results of the hybrid model can be used to select variants, but a large number of MCMC iterations is advisable. We note that dropping variants from the analysis can lead to bias. We prevented this by recording the mixing proportions for the four distributions immediately before any SNPs were dropped and adding this to the prior.d. Our approach is somewhat similar to that tested by Calus et al. [d scenarios compared to S_FULL_Dd, although we tried to address this issue by pruning out variants in high LD with each other. Furthermore, the dataset used by Calus et al. [d in the simulation. However, this required to drop a large number of variants, which resulted in a decrease in accuracy even for the S_FULL_Dd scenarios. The vast majority of variants that were dropped would probably have very small effects, and therefore were not likely to be linked to major QTL. They could, however, be used to explain part of the polygenic effects. Therefore, we added the HD variants to the analysis, which further increased the accuracy.Analysing a few millions of sequence variants simultaneously is computationally challenging and would take a long time to complete. Therefore, we tested if it is possible to split up the analysis per chromosome. However, using the effects of SNPs estimated per chromosome directly to estimate breeding values resulted in a decreased accuracy compared to S_FULL_Ds et al. . Calus es et al.  split ups et al.  observeds et al.  containes et al. . Our apps et al. , in thatUsing the simulated data, analysis of sequence data resulted in a higher accuracy than analysis of HD SNP genotypes, i.e. there was a large advantage of S_FULL_D0 over HD_FULL_D0, and consequently, the accuracy of any scenario using sequence data was higher than HD_FULL_D0, even for the scenarios with an accuracy lower than that of S_FULL_D0. For the Red Holstein validation population, the advantage of S_FULL_D0 over HD_FULL_D0 was much smaller than for the Australian Red validation population. This is likely because the Red Holstein is much more closely related to the Holstein individuals in the reference populations. Because LD is conserved over much shorter distances across breeds than within breeds, sequence data is thought to be especially beneficial for multi-breed and across-breed prediction .There are two reasons for the use of sequence data resulting in higher accuracy: it might capture more of the genetic variance and it might include QTL with large effects when there are no HD SNPs in complete LD with these QTL. However, the variance not explained by SNPs , right graph breeds based on the pedigree .Additional file 2: Table S1. Correlation and concordance between true and imputed sequence genotypes for variants on chromosomes 1, 5, 20 and 25.


Plukenetia volubilis L. from small isolated subpopulations in the Peruvian Amazon. Samples were collected in nine localities from the region of San Martin. Analysis was done in eight datasets with a genotyping error from 0 to 5%. Using eleven primer combinations, 102 to 275 markers were obtained according to the dataset. It was found that it is only possible to obtain the most reliable and robust results through a multiple-level filtering process. Genotyping error and software set up influence both the estimation of population structure and genetic diversity, where in our case population number (K) varied between 2–9 depending on the dataset and statistical method used. Surprisingly, discrepancies in K number were caused more by statistical approaches than by genotyping errors themselves. However, for estimation of genetic diversity, the degree of genotyping error was critical because descriptive parameters  varied substantially (by at least 25%). Due to low gene flow, P. volubilis mostly consists of small isolated subpopulations (ΦPT = 0.252–0.323) with some degree of admixture given by socio-economic connectivity among the sites; a direct link between the genetic and geographic distances was not confirmed. The study illustrates the successful application of AFLP to infer genetic structure in non-model plants.An analysis of the population structure and genetic diversity for any organism often depends on one or more molecular marker techniques. Nonetheless, these techniques are not absolutely reliable because of various sources of errors arising during the genotyping process. Thus, a complex analysis of genotyping error was carried out with the AFLP method in 169 samples of the oil seed plant  Plukenetia volubilis L. (sacha inchi) is a climbing, perennial, oleaginous plant of the Euphorbiaceae family which grows in the tropical jungles of the Americas at altitudes between 200 and 1500 m ))P. volubDue to the many steps during laboratory work and subsequent data analysis, there are multiple sources of genotyping error that can have a great impact on the results of population studies . Over thP. volubilis was assessed, and finally (iv) the relationship between the spatial and genetic structure and the landscape setting was explored.In the present study, the general steps leading to improving the precision of AFLP analysis are discussed. Firstly, (i) the minimization of genotyping error at multiple levels. Secondly, (ii) different error rate datasets were created, with the aim of analyzing their impact on the estimation of population structure and genetic diversity. Then, (iii) the population structure of Plukenetia volubilis cultivation and breeding in PARI.This study was not carried out in national parks or other protected areas of land or sea or on private land. Sampling was conducted under the auspice of Danter Cachique Huansi (co-author) from the Peruvian Amazon Research Institute (PARI), Tarapoto, Peru as a responsible person for Specific permission was not required for this location and activity. We confirm that the field study did not involve endangered or protected species.P. volubilis plants collected from nine different localities in the San Martin Region in the Peruvian Amazon   were tested on 12 samples originating from all studied locations. Finally, 12 primer sets were chosen for the analysis of all samples, but due to poor signal quality one PS was excluded before data filtering, thus 11 PS remained . The fin rfu150) . ExploraTM polymer. Separation conditions were an injection time of 5 s at 10 kV and a running voltage of 13 kV.Prior to capillary electrophoresis separation, four differently labelled products of selective PCR were poolplexed in the ratio 0.8 (6FAM): 0.9 (VIC): 1.4 (NED): 1.9 (PET) and then diluted with water 1:1. One microliter of pooled amplicons was mixed with 12 μl of Hi-Di formamide (Life Technologies) and 0.2 μl of size standard GeneScan LIZ600® (Life Technologies). PCR products were loaded on an ABI PRISM 310 genetic analyzer (Life Technologies) and separated in a 36-cm long capillary with POP-4http://www.antp.be/software/renamer). The freeware program Peak ScannerTM v1.0 (Life Technologies) was used for signal detection and sizing with set parameters: light peak smoothing, range 50–600 bp, and minimum peak heights 50 rfu. The other parameters remained as the default settings. The limit of 50 rfu was based on testing of instrumental noise in 24 samples containing only pure water both for a new capillary (<100 runs) and a used capillary (>600 runs). The number of noisy peaks was counted for three minimum peak thresholds, i.e. 50, 100 and 150 rfu.For greater clarity and easier data manipulation in downstream analyses, the names of *.fsa files were shortened in the program Ant Renamer v2.10.0 created by A. Potten  for the reproducibility test. The only condition was that at least one sample per locality must be included. All replicates were treated in the same way as the original samples, i.e. independent DNA isolation, PCR amplification and capillary electrophoresis separation were done .Detection of size homoplasy was done by a linear regression model according to Vekemans et al.  togetherGenotyping quality is also affected by the software and selection criteria settings used . For thiThe raw data of each chosen primer set with peaks of 50 rfu  and highRawGeno v2.0 was used for binning with same the parameters as for the previous four datasets, with the exception that there was no reproducibility testing. The next step was exporting the raw data with rfu values in an AFLPScore compatible format. This was accomplished using an R script published by Lambert et al. . The resAn estimation of the number of populations, the assignment of individuals to a population and their characterization was performed using several mathematically different methods. Four techniques were used: multidimensional scaling (MDS), AMOVA-based K-means clustering, and Bayesian clustering . Individual assignment to a population was verified via an assignment test.Distance matrices based on Dice, Jaccard and Simple Matching coefficients were created in DARwin v5.0.158  and used6 Markov chain Monte Carlo (MCMC) iterations with 1000 repeats. The optimal number of clusters (K) was determined by pseudo-F statistics and the Bayesian Information Criterion (BIC).The method was developed by Mermains  and impl6 MCMC iterations after a burn-in period of 105 iterations and 10 replicate runs, performed for each K value (1–9). The last step comprised 100 replicate runs for the chosen K and only runs with the highest probability were used , the large greedy algorithm with 106 random input orders was used for the final runs. The final results were plotted using DISTRUCT v1.1 [The calculation of ΔK and averaging lnP(D) values over 10 runs (100 for chosen K) was performed using STRUCTURE HARVESTER v0.6.94  in the c v1.1.2. . DependiUCT v1.1 .0.1 (~0.0084) in the studied datasets  for every population was tested by 9999 random permutations.The level of interpopulation differentiation was analyzed by analysis of molecular variance (AMOVA) in GenAlEx v6.501 [PT pairwise values  verified the assumption about normal distribution by Shapiro-Wilk W test (p = 0.08623–0.78834) and homoscedasticity of variance by Levene’s omnibus test . An appropriate temperature profile was chosen according to the two-way ANOVA results, revealing statistically significant differences both in the temperature profile  and the rfu value , where the average number of bins was 26.36 for T58 vs. 45.88 for T64 and 39.86 for rfuThe analysis was performed for both the concatenated matrices of eight datasets and for individual combinations within every dataset. The results in the form of correlation coefficients r and p-values are listed in In RawGeno datasets, 25 AFLP markers were obtained per combination for the all-100 dataset, 18 markers for the all-150 dataset, 14 markers for the rep-100 dataset and 11 markers for the rep-150 dataset. The total number of markers, on average, was 19.15, 13.29, 10.67 and 8.26% from the number of bins of raw data.The “Error” datasets (error-2-5) had 12, 13, 17 and 24 as the average number of markers per combination, which gives a yield of 9.39, 9.61, 12.57 and 18.32% from the initial number of bins. In terms of the error rate, it was found that the error rate per locus ranged from 0 to 75% according to the mismatch error rate  and between 0 and 100% according to the Jaccard error rate  for the primary dataset. After filtering, the average error rate was 5.98% (median 6.25%) for the mismatch error rate and 33.08% (median 6.9%) for the Jaccard error rate. It was also found that 191 out of 572 bins (33%) from the primary dataset did not appear in replicated samples and thus were not tested for the error rate.Testing three distance matrices with MDS showed that both the Dice and Jaccard coefficients 0.2146–0.2339) seemed to perform better than the simple match coefficient (0.2264–0.2405) according to the stress parameter. A two-dimensional model was selected because, even though the three-dimensional model exhibited better values of the stress parameter, it did not exhibit any improvement in biological interpretation against the two-dimensional model . MDS ind6–0.2339 Assigning the individuals to populations according to the sample collection site based on a re-allocating procedure showed that 37 individuals were not assigned to any population and 19 were re-allocated incorrectly in dataset error-2, i.e. 56 individuals (36.6%) in total. In dataset error-3, this number decreased to 26 unassigned individuals and again 19 assigned incorrectly, i.e. 45 individuals (29.41%) in total. The remaining datasets showed that a stable plateau of unassigned individuals ranging among 19–21 and 15–18 samples were misassigned, i.e. 23.42% on average. The lowest rate of correct assignment was typical for the SCR , PAC (23–47%) and PUC (23–41%) subpopulations in which the highest level of admixture was also detected.PT, FST, PLP5%). Inbreeding coefficient (FIS) varied from 0.0065 to 0.0718  according to median depending on population and dataset . We can demonstrate this need by comparison our “RawGeno” datasets all-100 and all-150 against the rep-100 and rep-150 dataset. These datasets differ by retained/removed untested bins thus total number of markers (here 121/72). Nonetheless, the software gives the same value of error rate for both datasets, although there is higher uncertainty about data quality in all-100 and all-150 datasets.Moreover, in a number of published studies, the error rate is just reported without an explanation of how it was calculated . Thus, iST. The recommended values of this parameter were over 1 bp and ideally 2 bp or more. We chose a compromise value of 1.5 bp, leading to the elimination of bin oversplitting and a reduction in technical homoplasy in the ideal case.There is another source of error prevailing in AFLP analysis called homoplasy , which cThe second type of homoplasy, size homoplasy  is causeThe multiple datasets approach covering various levels of the error rate, the influence of software choice, and the parameter settings were included as well as other aspects connected with the error rate, such as homoplasy. The purpose was not a comparison of programs or their algorithms but rather to show the limitations of our study. It is difficult to choose objectively the best dataset because we did not detect some kind of "break point" as in the case of Zhang and Hare study , but theFirstly, the "Error" datasets gave more consistent results for various kinds of tests than "RawGeno" datasets and, secondly, a lower number of markers as a result of too stringent selection criteria limit the power of some statistical approaches to obtain the best estimation of true population structure. For a conservative approach with mismatch error rate, we suggest using RawGeno only for binning purpose, thus without a reproducibility test, then  do some kind of marker preliminary screening using not too stringent criteria for error rate estimator, and finish the analysis for marker choice in some software such as AFLPScore  or scanAP. volubilis, several statistical methods were employed. The first technique (MDS) possesses some advantages in comparison with more popular Bayesian clustering methods [In order to understand the population structure of  methods . For the methods . However methods . Three-dP. volubilis, the final effect of selection for seed size is similar to non-random mating and the preferred parameter should be BIC.The other method combining AMOVA with the K-means hierarchical clustering method revealed very different estimations for the number of clusters, depending on the summary statistics used (pseudo-F or BIC). The result of pseudo-F was the same as in MDS, i.e. two clusters, but the BIC criterion showed nine clusters . ClusterThe last comparative method was a model approach based on the Bayesian clustering algorithm. The “standard” type of analysis, corresponding to the original methodology proposed by authors of program STRUCTURE , showed P. volubilis cultivation and the size of the plots. Therefore, the imbalance in the K estimation can be attributed to an unsuitable number of markers.The “hierarchical” variant of analysis in STRUCTURE proposed by Coulon et al. , showed A different situation occurred in the “RawGeno” datasets where the estimation of K ranged from K = 5  to K = 6  and K = 8 (rep-150). There were perhaps the most apparent differences between “RawGeno” datasets. Hierarchical analysis at each step typically showed an estimation according to ΔK = 2 or 3 , when th2. On the basis of the available information we suppose strong anthropogenic influence determining the magnitude of several evolutionary forces like selection, genetic drift and gene flow on population structure of P. volubilis.A comparison of clustering methods indicates with high probability the existence of nine genetically differentiated clusters, which largely coincide with the locations of sample collection. Only MDS or parameter pseudo-F in the AMOVA-based K means a clustering method preferred two clusters. It raised the question what was the cause of high genetic differentiation into subpopulations occurring in the relatively small area about 70 kmP. volubilis has been cultivated. Farmers cultivating this crop do not purposely carry out cross-breeding and open pollination occurs. However, from the obtained seeds, the biggest ones are selected as a basis for the next generation of plants. From the breeding perspective, this is not such an efficient method  and it is slower than other methods (e.g. pedigree or recurrent selection). Nevertheless, selection for a certain phenotype is happening as well as on genes or control regions influencing this phenotype. Simultaneously, genetic hitchhiking of associated regions is occurring and leads to the fixation of alleles positively influencing required trait(s) and alleles of neutral loci close to these genes which supports structuring into local subpopulations, as indicated by the ΦPT or FST values  method was able to clearly distinguish only plants from Dos de Mayo. This locality is poorly accessible, the most distant from the other localities, not currently inhabited, and plants growing there are therefore isolated. We do not know how much time has passed since the last villager leaved this place, but it was undoubtedly long enough for elimination of gene flow through anthropogenic transfer of seeds mixture. This example could be used as another indirect evidence of strong influence on population structure by human being. Another insight into observed population structure and connectivity between the sites could provide analysis both spatial and genetic data.P. volubilis, rather than a demonstration of isolation by distance (IBD), differentiation can be expected through reduced gene flow between subpopulations due to barriers such as the rainforest or mountains. However, no spatially oriented test showed a statistically significant result  each subpopulation according to He etc. , but between the subpopulations varied more widely (0.5–7%). When we omit the technical issues connected with AFLP like parameter estimation based on dominant type of markers, genotyping error, homoplasy, coverage of different genome part, and frequent violation of assumption about linkage equilibrium [A further insight into the subpopulation structure is provided by an estimation of Filibrium , then poilibrium , and thuilibrium . MoreoveThe comparison of eight datasets showed that it is better to perform variant multiple data filtering by combining several different approaches. Despite the understandable differences, the “Error” datasets , showed more consistent results than “RawGeno” datasets . The estimation of clusters was independent of reproducibility of the underlying dataset. The exception was hierarchical analysis in STRUCTURE where, thanks to the gradual reduction in the number of individuals and markers, the background noise was predominant over the structural signal in datasets with higher error rates. However, noticeable differences were found in the genetic diversity estimation between datasets. Data quality considerably influenced estimates of genetic diversity, which is a concern for conservation genetics.P. volubilis represents a traditional Peruvian oil crop for human nutrition. Recently, a growing interest in this crop has been noted within the food and pharmaceutical industry. Breeding of sacha inchi allows for improvements in the required characteristics, but for the successful creation of varieties it is necessary to learn about its genetic diversity and variability. Our study showed that, due to long-term phenotypic selection and low gene flow, P. volubilis exists as very isolated subpopulations corresponding with the sampling localities. The degree of differentiation between the subpopulations is probably due more to socio-economic connectivity among the sites than to biological connection, because seeds are transported over different distances. Using the AFLP technique and appropriate filtering data and estimation of error rate can be successfully applied for genetic structure estimation. For the first stage of the breeding process, it would be interesting to create a collection of samples originating from neighboring localities. However, questions remain regarding the degree of variability within loci affected by selection for the desired phenotype. Answers could be provided by association studies aiming to identify these loci.S1 Text(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableThe correlation coefficient and p-value for each primer set and concatenated matrix are shown. Statistically significant results are highlighted.(DOCX)Click here for additional data file.S5 TableDetection of SH based on a linear regression model with different criteria for the minimum fragment size . The correlation coefficient and p-value for each dataset are shown. Statistically significant results are highlighted.(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 TableThe best clustering according to pseudo-F  and BIC  is highlighted.(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 TableThe correlation coefficient and p-value are shown.(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 TablePT values. Statistically significant results are highlighted.The genetic distance matrix was based on Φ(DOCX)Click here for additional data file.S1 FigThe number of retained runs is written in brackets together with the K value and the type of STRUCTURE analysis . Please note the different scales of the Ln(P)D axes.(TIF)Click here for additional data file.S2 FigEstimation of the K number according to ΔK for each round of hierarchical analysis and the number of individuals within the cluster is shown. Red colored clusters represent clusters which could not be divided any further.(TIF)Click here for additional data file.S3 FigThe calculated r value (blue line) is shown with upper (U) and lower (L) bounds of the 95% confidence interval (red lines).(TIF)Click here for additional data file.