TUBD1 recessive mutation on BTA19 (Bos taurus autosome 19), known to be associated with high calf mortality. A population of 3116 Fleckvieh and 392 Brown Swiss animals genotyped with the 54K SNP-chip was available for the analysis.SNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific mutations. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we compared – through cross-validation– five classification models , for the identification of carriers of the %, 0.4 % and 0.6 % in Fleckvieh, and 1.2 %, 0.9 % and 1.7 % in Brown Swiss. For the three models, the false positive rate was, respectively, 0.1 %, 0.1 % and 0.2 % in Fleckvieh, and 3.0 %, 2.4 % and 1.6 % in Brown Swiss; the false negative rate was 4.4 %, 7.6 %1.0 % in Fleckvieh, and 0.0 %, 0.1% and 0.8 % in Brown Swiss. MAG appeared to be more robust to sample size reduction: with 25 % of the data, the average error rate was 0.7 % and 2.2 % in Fleckvieh and Brown Swiss, compared to 2.1 % and 5.5 % with Lasso, and 2.6 % and 12.0 % with SVML.In general, the use of SNP genotypes proved to be very effective for the identification of mutation carriers. The best predictive models were Lasso, SVML and MAG, with an average error rate, respectively, of 0.2 The use of SNP genotypes is a very effective and efficient technique for the identification of mutation carriers in cattle populations. Very few misclassifications were observed, overall and both in the carriers and non-carriers classes. This indicates that this is a very reliable approach for potential applications in cattle breeding. CWC15 gene on BTA15 (haplotype JH1) in Jersey cattle ):y cattle .CWC15, or of the Weaver syndrome ) in Fleckvieh. Besides, better defined LD patterns and blocks around the mutation on BTA19 could be identified in Brown Swiss compared to Fleckvieh . From Sreliable . The LD Summarizing, sample size played in favour of the Fleckvieh dataset, while class ratio, genetic relatedness and linkage disequilibrium were more favourable in the Brown Swiss dataset. This helps explain the generally better predictive performances found in the Brown Swiss breed in this study.The error incurred when making predictions can always be decomposed into its variance and bias . The varFor binary classification problems, with the misclassification loss function, the variance and bias of the classification error can be estimated as follows : 9\docuWe estimated the variance and bias of the classification error given by the five models  used to identify mutation carriers in the two breeds. Results are in Table p) in the model (SNP) exceeded the number of observations (n) in the training set (p>n), unlike in Fleckvieh (n>p): this calls for stricter regularization in the Brown Swiss data, which would tip the bias-variance trade-off more towards the bias [As a proportion of the error, the bias was larger in Brown Swiss than in Fleckvieh, for all error types . In Brown Swiss, the number of parameters  is known to play a major role in determining the accuracy of genomic predictions (e.g. ); this wResults are reported in Table The size of the training population is confirmed to be a major factor behind the accuracy of genomic predictions, also in the identification of carriers of recessive mutations.r2) around TUBD1, and the genome-wide and “local” (around TUBD1) rescaled genomic relationships à la Van Raden were estimated. Around the mutation was again defined by taking SNPs from the first 20 Mbps on BTA19. The estimated LD around the mutation appears to increase with decreasing sample size; however, LD estimates are known to be biased upwards by small sample size in cattle [At each size-reduction step, the LD , three hundred and five Brown Swiss bulls typed for congenital spinal dysmyelination  were av% of the data were used to train the classifier, and the remaining 20 % to test it. Given the smaller dataset size , a 60/40 cross-validation scheme was used to tune the hyperparameters . The procedure was repeated 100 times to estimate prediction accuracy. With Lasso, SVML and MAG, respectively, TPA was 0.987 (±0.018), 0.978 (±0.016) and 0.983 (±0.016); TPR was 0.625 (±0.342), 0.435 (±0.356) and 0.611 (±0.341); and TNR was 0.997 (±0.007), 0.999 (±0.003) and 0.993 (±0.011).The three classification methods that gave the best results on the TUBD1 mutation were applied to the identification of carriers of the SDM mutation: Lasso, SVML and MAG. As in Fig. %, TPR was quite lower, in the range 0.435−0.625. Probably, this is related to the different frequency of carriers: 4.04 % (Fleckvieh) and 63.78 % (Brown Swiss) for TUBD1, as low as 2.62 % for SDM. Highly unbalanced data are expected to yield worse predictive performances. This was especially true in the case of SDM for which there were only eight carriers. The smaller sample size too, is likely to have played a role in the higher prediction error rates and, especially, the larger variability of estimates, as we showed when reducing the size of the TUBD1 dataset : whereas TPA and TNR were both very high and close to 100 % between TUBD1 and the BH2 haplotype [% between SDM and the BHD haplotype [Unlike the TUBD1 mutation, MAG did not (slightly) outperform Lasso and SVML in the minority class: using haplotypes did not appear to be as beneficial with the SDM mutation. This may be related to the different degree of concordance between the mutation and the associated haplotype: this was 99.2 aplotype , and 94.Several SNP-chips are available in cattle, with different marker densities and costs (see ). In thiFrom the 54 K SNP-chip data, the SNP on BTA19 corresponding to the BovineLD v2 Illumina BeadChip  were extracted and used for classification. Lasso and SVML, were used for classification, with the same experimental design as in Fig. With Lasso, TER, FNR and FPR were 0.0183 (±0.0151), 0.009 (±0.0065) and 0.0350 (±0.0287) in Brown Swiss, and 0.0111 (±0.0036), 0.0605 (±0.0309) and 0.0049 (±0.0037) in Fleckvieh. With SVML, TER, FNR and FPR were 0.0167 (±0.0146), 0.0109 (±0.0145) and 0.0257 (±0.0294) in Brown Swiss, and 0.0116 (±0.0042), 0.228 (±0.0959) and 0.0025 (±0.0023) in Fleckvieh. The computation time for one replicate of the model in the Brown Swiss and Fleckvieh datasets, respectively, was 3.51 and 5.58 s using Lasso, and 7.88 and 33.55 s using SVML.Overall, performances are comparable with the 54 SNP-chip: only the identification of carriers in Fleckvieh was notably less accurate with lower densitiy SNP data.TUBD1 mutation on BTA19.In predictive models, it can be of interest to find out which variables are more relevant for accurate predictions. When resampling strategies are adopted, the relevance of individual predictors can be indirectly derived from the frequency with which they appear in the different replicates of the predictive model. In the case of SNP-based models, the most predictive SNPs are likely to be linked to the target QTL/mutation. This approach was already presented by Biffani et al.  to trackTUBD1 mutation is located at 11 063 520 bps [The lasso penalised logistic regression model in Eq.  520 bps , which cp-values from a genome-wide association study (GWAS) for the binary trait carrier/non-carrier of the TUBD1 have been plotted in Fig. p-value in Fleckvieh than in Brown Swiss, probably due to the much larger sample size in the former.To provide a comparison, The results reported here and -previously- by Biffani et al.  show thaCWC15 gene in Jersey cattle), sometimes much looser . This may increase the uncertainty of identifying mutation carriers from SNP genotypes, and there is therefore value in directly predicting mutation rather than haplotype carriers.In cattle genetics, causal mutations or associated haplotypes for inherited disorders are being discovered at unprecedented rates (see USDA list: ). It is %, but larger proportions of misclassifications were observed in the minority class . In order to maximise prediction accuracy in all classes while keeping costs low, mixed genotyping strategies can be adopted by which most of the population is genotyped at low densities and only relatively few animals are genotyped with the 54 K chip: 54 K SNP genotypes can then be imputed back in all animals [In this paper, we showed that mutation carriers can be identified very accurately from SNP genotypes. Bulls and cows in breeding programmes are often routinely genotyped, e.g. for parentage verification, estimation of genomic breeding values, and it would therefore be rather straightforward and inexpensive to use already available genotype data to identify mutation carriers. Using the 54 K SNP-chip, the accuracy of prediction has been shown to be very high, close to 100 % in both breeds, with very few misclassifications also in the minority class. At lower SNP densities the overall accuracy was still impressively high, slightly lower than 99  animals . Mixed g animals ). To ass animals .R implementation. For larger datasets, though , scalability would certainly be an issue, and more efficient implementations of the algorithms, combined with computation strategies like parallel computing on multiple cores or distributed computing on a computer cloud/cluster should be adopted. A popular combination in machine learning and “big data” analysis is given by the scripting language Python within the Apache Spark framework for distributed computing [With the size of the data used in the present work , Lasso and SVML were quite efficient with an omputing .Once mutation carriers have been identified, this information can be used in breeding strategies to guide selection decisions and plan matings, with the objective of reducing the frequency of carriers in the population and avoiding mating of carriers. Mate allocation schemes to control inbreeding and recessive disorders have been already proposed in cattle , 55. BesTUBD1 mutation on BTA19, associated with reduced fertility in cows, was chosen as illustration. Together with MAG, Lasso-penalized logistic regression and Support Vector Machines with linear kernel gave the lowest error rates indicating the probable linear nature of this classification problem. Overall, the prediction accuracy was very high, close to 100 % in both breeds. Compared to single SNP genotypes, the use of haplotypes gave better accuracy in the minority class when the haplotype-mutation concordance was close to 100 % (BH2-TUBD1). The opposite was true for looser associations between haplotype and mutation (SDM). When using the low-density SNP-chip, the total error rate was still very low (∼1 %), but the proportion of misclassifications in the minority class tended to increase . This can however be compensated by optimised genotyping strategies combined with genotype imputation techniques, which could potentially make this a very effective and efficient tool for the identification of carriers of any mutation or haplotype of interest (both positive or negative) in Bos taurus. There is potential to build effective applications to be used by breeders and farmers (e.g. the Zanardi pipeline [This paper shows that SNP genotypes can be used very effectively to predict carriers of harmful recessive mutations in cattle populations. The pipeline ). The pr


RXFP2 gene. Low-density genotypes of these animals were imputed to the Illumina Ovine high-density (600k) chip and the 1.78-kb insertion polymorphism in RXFP2 was included in the imputation process to whole-genome sequence. We evaluated the mode of inheritance and validated models by a fivefold cross-validation and across- and between-family prediction.In horned sheep breeds, breeding for polledness has been of interest for decades. The objective of this study was to improve prediction of the horned and polled phenotypes using horn scores classified as polled, scurs, knobs or horns. Derived phenotypes polled/non-polled (P/NP) and horned/non-horned (H/NH) were used to test four different strategies for prediction in 4001 purebred Merino sheep. These strategies include the use of single ‘single nucleotide polymorphism’ (SNP) genotypes, multiple-SNP haplotypes, genome-wide and chromosome-wide genomic best linear unbiased prediction and information from imputed sequence variants from the region including the The most significant SNPs for prediction of P/NP and H/NH were OAR10_29546872.1 and OAR10_29458450, respectively, located on chromosome 10 close to the 1.78-kb insertion at 29.5 Mb. The mode of inheritance included an additive effect and a sex-dependent effect for dominance for P/NP and a sex-dependent additive and dominance effect for H/NH. Models with the highest prediction accuracies for H/NH used either single SNPs or 3-SNP haplotypes and included a polygenic effect estimated based on traditional pedigree relationships. Prediction accuracies for H/NH were 0.323 for females and 0.725 for males. For predicting P/NP, the best models were the same as for H/NH but included a genomic relationship matrix with accuracies of 0.713 for females and 0.620 for males.GG of SNP OAR10_29458450 or TT of SNP OAR10_29546872.1 since all sheep with these genotypes will be non-horned.Our results show that prediction accuracy is high using a single SNP, but does not reach 1 since the causative mutation is not genotyped. Incomplete penetrance or allelic heterogeneity, which can influence expression of the phenotype, may explain why prediction accuracy did not approach 1 with any of the genetic models tested here. Nevertheless, a breeding program to eradicate horns from Merino sheep can be effective by selecting genotypes The online version of this article (10.1186/s12711-018-0398-6) contains supplementary material, which is available to authorized users. Breeding for polled animals is a long-standing issue in horned species such as cattle, sheep and goats –3 and anFOXL2 and RXFP2 transcripts in horn buds has been reported in cattle [FOXL2 and a non-coding RNA [Ovis aries) [RXFP2 gene causes a polled phenotype in sheep. However, Lühken et al. [In cattle, sheep and goats, several studies have attempted to describe the genetic mechanism that underlies the horned phenotype. In cattle, the most common mutation for the polled phenotype is mapped to the proximal end of bovine chromosome 1, where different genomic variants are described as causative  in different European breeds , 5. Alson cattle , which ading RNA . A quants aries) . Wiedemas aries)  providedn et al.  showed tThe mode of inheritance of polledness is complex since expression of this phenotype differs between sexes and because no single-locus model with complete penetrance can explain the phenotypic variation both within and across breeds. Moreover, this issue is complicated by the existence of intermediate phenotypes such as knobs and scurs. A knob is a hard bony lump at the horn site which may have a horny cap, generally less than 2.5 cm long, and a scur is a horny growth, irregular in shape and smaller than a true horn . In addiRXFP2 gene are not present on the Illumina Ovine 50k chip or the OvineHD 600k chip, but four such SNPs have been included in the Illumina 15k Ovine array that was released in 2016.Breeding strategies have been developed to increase polledness in several sheep breeding programs. Selection on polledness in both males and females (without the presence of genomic data) has been successful and resulted in polled breeds such as the polled Merino and Poll Dorset. With the availability of genomic data, it should be possible to optimize selection for polledness. A clear difference in allele frequency between polled and horned breeds is observed for single nucleotide polymorphisms (SNPs) that are closely linked to the 1.78-kb insertion on OAR10 . CurrentRXFP2 region.Currently, prediction accuracy of the horned phenotype is not equal to 1, because the predictive SNP is not in full linkage disequilibrium (LD) with the 1.78-kb insertion, or penetrance is incomplete, or because of epistasis, i.e. interaction between two or more genes. Recently, whole-genome sequence data of 726 European sheep from various breeds have become available. Thus, imputation of the 1.78-kb insertion into the Merino population could provide a better predictor than the current SNPs used for prediction. To further investigate the genetic architecture of the polled and horned phenotypes, we compared models that fit the effects of all SNPs as random effects with the same a priori distribution  and the effects of the most predictive SNPs as fixed effects, in order to estimate the variance explained by all SNPs genome wide, conditional on the genotypes of the most predictive SNPs in the RXFP2 region imputed to sequence level.In this study, we evaluated various strategies for predicting horned or polled phenotypes in 4001 purebred Merino sheep, which include the use of single-SNP genotypes, multiple-SNP haplotypes, genome-wide and chromosome-wide GBLUP and information from structural variants in the Data were extracted from two research datasets known as the Information Nucleus Flock (INF ) and theThe phenotypes polled, scurs, knobs or horns were recorded. Analysis was performed on two binomial traits that were classified as polled/non-polled (P/NP) and horned/non-horned (H/NH). Table 2 > 600) was strong, and if the minor allele frequency (MAF) was lower than 0.01. In addition, only autosomal SNPs were used for imputation. Finally, 11,377, 48,559 and 510,174 SNPs remained for the 12k, 50k and HD SNP chips, respectively. The 12k genotypes were imputed up to 50k using the Beagle software v3.2 [Of the 4001 animals, 3708 were genotyped with the OvineSNP50k BeadChip and the remaining 293 were genotyped with the Illumina-Ovine 12k SNP chip. Four hundred and forty-five animals were also genotyped with the Illumina Ovine HD (600k) . Quality controls for each SNP chip were the same: SNP genotype records were removed if the call rate was less than 90%, if the GC  score was less than 0.6, if deviation from Hardy–Weinberg equilibrium (χare v3.2  and the are v3.2 . For botare v3.2  and was Four different methods were applied to predict the P/NP and H/NH phenotypes.In method (1), we used a single-SNP genotype to predict the phenotype. To select the best SNP, we ran an association study for all SNPs on OAR10 by fitting each SNP as a covariate. Each single-SNP genotype was either fitted as a single predictor variable (base model), or was fitted jointly with a polygenic effect. This polygenic effect was assumed to be normally distributed with a covariance structure that was proportional to either the numeral relationships expected based on pedigree information or a structure described by a genomic relationship matrix (GRM). To account for the binomial nature of the response variables, we used logistic regression with the statistical package ASReml v4 .RXFP2 gene. Haplotypes were formed after phasing the genotyping data using EAGLE v1.0 [In method (2), we used multiple SNPs to predict the phenotype. A haplotype was formed using the most significant SNPs from the single-SNP GWAS using either three, five or ten SNPs. The three haplotypes covered a region of 5, 55 or 125 kb both up- and downstream of the 1.78-kb insertion in the GLE v1.0 . Each anMethod (3) applies a GBLUP analysis using a GRM  based onIn method (4), we tried to identify the 1.78-kb insertion described by Wiedemar and Drögemüller . The refPindel can detect whether an individual carries no, one or two insertions, while Breakdancer only detects the presence of the insertion. Seventy-four animals, which were concordant between Breakdancer and Pindel, were used for imputing the 1.78-kb insertion in the population of 4001 Merino sheep using EAGLE v1.0 for phasing  and MiniZ is the incidence matrix relating observations to an individuals’ polygenic effects, We compared various models to investigate the mode of inheritance (effect of dominance and sex) of the polled and horned phenotypes fitting a single SNP (fitted as random effect):Models were compared based on Akaike’s information criterion (AIC ). We comA fivefold cross-validation was repeated five times to compare prediction accuracy of the five models. Prediction accuracy was defined as the Pearson correlation between the predicted phenotype in the test set and the binomial traits P/NP or H/NH. The mean of accuracy and the standard deviation across folds are reported.P/NP and H/NH may not represent fully monogenic traits and we wanted to further investigate the inheritance pattern of these traits. Therefore, we applied another validation strategy that contrasted the use of family information versus no family information in predicting phenotype. The dataset was split by using two different approaches. First, within-family prediction was estimated by using phenotypic data from approximately half of the animals in each half-sib family to predict the phenotypes of the other half of the animals (intra-family comparison). Second, across-family prediction was estimated by using phenotypic data from approximately half of the families to predict the phenotypes of the animals in the other half of the families (inter-family comparison). Sires with more than 13 offspring were selected for family validation. One hundred and thirty-eight sire families and 3671 sheep were used for this validation. The mean of accuracy (Pearson’s correlation) and standard error across a fivefold validation are reported.RXFP2 region is significantly associated with the phenotypes and identified highly significant SNPs at about 29.5 Mb on OAR10  = 126), which differed from the most significant SNP for horned, i.e. OAR10_29458450  = 51), although both SNPs are close to each other  and in high LD (r2 = 0.985). Both SNPs always ranked as number 1 or 2 in terms of significance level, and were used to test the accuracy of prediction for the single SNP analyses. The region around the 1.78-kb insertion also contained other significantly associated SNPs, e.g. in the region between 29 and 30 Mb, the average − log10 across all 228 SNPs was equal to 24 for P/NP and 11 for H/NH. Note that, in general, p-values less significant for the H/NH phenotype because the frequency of horned phenotypes in the dataset was low, especially in females  did not increase prediction accuracies substantially, compared to single-SNP prediction models. For example, prediction accuracies for P/NP in males using three, five or ten SNP-haplotypes and using a GRM were equal to 0.714, 0.716 and 0.700, respectively, compared to 0.713 when using a single-SNP haplotype. Creating a haplotype from more than five SNPs even decreased prediction accuracy (not significant).G + D model based on SNPs of OAR10 was used compared to 0.642 when the single SNP model was used. The accuracies obtained with the G + D model were lower for H/NH in males compared to those obtained with single-SNP or multiple-SNP haplotypes (0.597 for OAR10 vs. 0.714 using single-SNP haplotype). In general, the G + D model based on all chromosomes resulted in lower prediction accuracies than fitting only the SNPs on OAR10.Method (3) used the sum of two GRM (matrix Method 54) used the imputed insertion for prediction. This insertion was fitted as a single SNP  and modeled according to the model with the best mode of inheritance [Table  used theA + D model . Although prediction accuracies in across- and within-family comparisons were quite similar, standard errors (SE) on the estimated correlations were higher for the across-family predictions. For example, within-family prediction for females for P/NP using pedigree information had a SE of only 0.013 compared to 0.10 for across-family prediction. Fitting a single SNP greatly reduced the SE of the correlation to values of 0.01 and 0.004 for across- and within-family prediction.The results for across- and within-family prediction are in Table RXFP2 gene (at position 29456047 through 29457881 on OAR10). OAR10_29546872.1 has been used by the Australian Sheep CRC since 2012 as the main predictive SNP for the polled phenotype in Merino sheep , with OAR10_29389966_X.1 , indicating the importance of explicitly fitting highly significant SNPs close to the known causative mutation. Applying the GBLUP method, which shrinks all SNP effects equally, will result in a lower prediction accuracy in the presence of a large QTL. Similar results were described by Lee et al.  who analaccuracy , 37. Alsaccuracy .GG genotype of SNP OAR10_29458450, and almost perfect in females . Although selection against horns can be successful, other forms of horns will still exist such as knobs and scurs.For the prediction of both horned and polled phenotypes, including a polygenic effect in the model achieved better prediction accuracies compared to using only a single SNP, with little difference between modeling the polygenic covariance structure via pedigree-based relationships versus genomic relationships. The variance explained by models including a polygenic effect was larger than that explained by models without pedigree information. For the horned trait, variance components of the model indicated that 70% of the phenotypic variance was explained by a single SNP, and this figure increased to 78% when pedigree was also included. Heritability estimated for H/NH on the underlying scale (using the numerator relationship matrix) without fitting the significant SNP was equal to 0.40 and dropped to 0.06 when fitting the significant SNP as a fixed effect (i.e. it was excluded from the genetic variance). For the polled trait, 51% of the phenotypic variance was explained by a single SNP and 70% of the phenotypic variance when pedigree was also included. Heritability estimated for P/NP on the underlying scale (using the numerator relationship matrix) without fitting the significant SNP was equal to 0.60 and dropped to 0.13 when fitting the significant SNP as a fixed effect. These results indicate that the single SNP does not explain all the genetic variance. However, prediction of non-horned sheep is 100% accurate in males when selecting for the RXFP2 region was further investigated via within- and across-family validation. Polygenic traits such as milk production or growth tend to show a pattern in which across-family prediction is lower than within-family prediction. Assuming that non-genetic effects (e.g. common environment) are correctly taken into account and that other effects due to population structure such as genetic groups are accounted for, within-family prediction uses both linkage and LD information for prediction, whereas across-family prediction can only depend on LD [RXFP2 region when sheep with knobs and scurs were tested against horned sheep in the GWAS . An important factor for a successful breeding program is to ensure accurate phenotype recording. Although the trait seems simple, classifying between horns, scurs, knobs or polled is still a challenge especially with the interference due to castration of males. Clear guidelines for such recording are necessary to improve the overall prediction of horned and polled phenotypes. Recently, a new SNP chip (15k) was developed, which includes SNPs present in the 1.78-kb insertion. Currently, the number of phenotypes and genotypes is not sufficient to evaluate the accuracy of prediction based on these SNPs, although the expectation is that these SNPs could increase the prediction accuracy even more.The mode of inheritance for polled and horned phenotypes is sex-dependent. Horned vs. non-horned and polled vs. non-polled in males both show an additive and dominance effect, whereas non-horned is dominant. Prediction of horned females is difficult since this phenotype is rare and no genetic model is clearly favourable, while polled vs. non-polled shows a similar mode of inheritance in females and males .RFXP2 region were not detected. Nevertheless, a breeding program aimed at eredicating horns from Merino sheep by selection on genotype GG of SNP OAR10_29458450 or genotype TT of SNP OAR10_29546872.1  could be successful.Prediction of polled and horned phenotypes when using a single SNP gives an accuracy of ~ 0.7. Prediction accuracy is not 100% since the causative mutation was not genotyped, and the model of inheritance is not completely known yet, e.g. incomplete penetrance, allelic heterogeneity or other  interactions can cause variability of prediction within genotypes. Neither the models including more SNPs by forming haplotypes, nor those fitting a GRM and dominance matrix based on all SNPs (whole genome and OAR10 only) resulted in higher prediction accuracies. Addition of pedigree information via a numerator relationship matrix or a GRM to a single SNP model did result in an increased accuracy, but only in a slight one. However, interaction with or effects of genes outside the Additional file 1: Figure S1. Genome-wide association study for polled and horned corrected for the most significant SNP (OAR10_29458450 or OAR10_29546872.1). Description: (a) females, horned/non-horned, (b) males horned/non-horned, (c) females polled/non-polled and (d) males polled/non-polled. The x-axis indicates the genomic location of the SNPs and each chromosome is color-coded. The y-axis shows the −log10 of the association statistics for each SNP.Additional file 2: Figure S2. Genome-wide association study for horned vs knobs and scurred sheep. Description: (a) GWAS not corrected for the most significant SNP OAR10_29458450 and (b) GWAS corrected for the most significant SNP OAR10_29458450. The x-axis indicates the genomic location of the SNPs and each chromosome is color-coded. The y-axis shows the −log10 of the association statistics for each SNP.


Plasmodium vivax contributes to almost half of all malaria cases outside Africa, it has been relatively neglected compared to the more deadly P. falciparum. It is known that P. vivax populations possess high genetic diversity, differing geographically potentially due to different vector species, host genetics and environmental factors.Although P. vivax isolates spanning 10 countries across 4 continents. Using population genetic methods we identified hotspots of selection pressure, including the previously reported MRP1 and DHPS genes, both putative drug resistance loci. Extra copies and deletions in the promoter region of another drug resistance candidate, MDR1 gene, and duplications in the Duffy binding protein gene (PvDBP) potentially involved in erythrocyte invasion, were also identified. For surveillance applications, continental-informative markers were found in putative drug resistance loci, and we show that organellar polymorphisms could classify P. vivax populations across continents and differentiate between Plasmodia spp.We analysed the high-quality genomic data for 46 P. vivax populations can be used to elucidate potential drug resistance and invasion mechanisms, as well as facilitate the molecular barcoding of the parasite for surveillance applications.This study has shown that genomic diversity that lies within and between  Plasmodium vivax malaria parasite is the second most virulent malaria species after P. falciparum. Geographically, it is found throughout Asia, South and Central America, Oceania, Middle East and some parts of Africa, with nearly 2.85 billion people at risk of infection . Although P. vivax contributes to almost half of all malaria cases outside Africa, as it kills infrequently and is not amenable to continuous in vitro culture, it has been relatively neglected compared to the more deadly P. falciparum. However, as P. vivax drug-resistant strains emerge and spread and fatality rates increase, the need to implement better control and elimination strategies is becoming urgent. Many of the interventions used for controlling P. falciparum malaria are not as effective against P. vivax. Consequently, P. vivax has become the dominant malaria parasite in several countries where P. falciparum transmission has been successfully reduced. Hence, control and elimination of P. vivax malaria calls for additional interventions, notably against the dormant liver stage of the parasite. However, gaps in our knowledge of P. vivax epidemiology and biology may compromise its control. Genomic research can contribute greatly to enhancing our understanding of P. vivax basic biology and evolutionary history, supporting the development and surveillance of new interventions.The P. vivax genome sequence , several population genetic studies, based on microsatellite data and more recently using whole genomes, have shown that this parasite is more polymorphic than P. falciparum,5,6,7. P. vivax populations harbour high genetic diversity, even on small spatial scales, and can differ extensively between locations due to vector species, host genetics and environmental factors ,5,6. Genetic variation enables the parasite to overcome host immune responses and antimalarial drugs to establish persistent infections and increase transmission. Genomic studies in natural populations of P. vivax can pinpoint genetic regions that are under selective pressure, including those associated with resistance to antimalarial drugs. Such studies can also contribute to the identification of vaccine targets. Moreover, global genomic studies can assist with identifying sets of polymorphism private to populations, allowing the monitoring of gene flow over space and time, and the tracking of imported infections. By developing a molecular barcode of individual parasites it will also be possible to distinguish recrudescent from re-infections.Since the first characterisation of the , 11, 12. The advancement of whole genome sequencing technologies has opened up opportunities to obtain a comprehensive picture of the epidemiology and structural variation of P. vivax. There is now the ability to perform genome-wide analysis of the various populations without the need for in vitro culture and overcoming difficulties with low parasitaemias and high human DNA contamination . Recent studies using genome-wide SNPs highlighted that signals of natural selection suggest that P. vivax is evolving in response to antimalarial drugs and is adapting to regional differences in the human host and the mosquito vector ,7. Several other whole genome sequence studies have been published , 15, 16, covering 10 countries. Using these and other data, we explore the genetic diversity within and between continents, identify signatures of drug pressure and molecular barcodes that could be useful for determining the source of infections and monitoring parasite populations.Highly polymorphic microsatellites have been the preferred method of genetic analysis, revealing high levels of diversity and highlighting interesting genotypic patterns and geographical clustering across global populations P. vivax samples were gathered from multiple sources, and included reference strains ), field and clinical isolates (Cambodia (n = 3) [13], Thailand (n = 39) [13], Madagascar (n = 3) ,17, Colombia (n = 8) [14] and Peru (n = 11) ,15) and clinical samples from travellers (to Papua Indonesia (n = 2) [13], India (n = 2) [13], and Papua New Guinea  [13]). All sequencing data for non-reference strains were generated using Illumina paired end technologies (read lengths ≥75bp). The raw sequence data were mapped to the Sal-1 reference genome (version 10.0) using bwa-mem with default parameters. SNPs  were identified using the samtools software suite (samtools.sourceforge.net) with high quality scores . Genotypes were called using ratios of coverage, where the minimal heterozygous calls still present after filtering were converted to the majority genotype if the coverage ratio was 80:20 or greater ,19. SNPs were retained if they were biallelic, had low genotype missingness (<10%) and heterozygous (<0.4%) calls. SNPs in regions of extreme coverage and very low coverage were excluded, as well as in non-unique regions (using a k-mer approach with length of 54 bp) and highly polymorphic VIR genes. Two samples were found to have P. vivax and P. falciparum co-infections (ERR020124 and SRR828528), and were excluded from population genetic analysis. Isolates were retained if they had at least average 10-fold genome-wide coverage, and at most 10% missing genotype calls. The final high quality dataset consisted of 46 (62.2%) isolates  and 219,288 SNPs, and used as the basis of population genetic analyses. FreeC software  was used to identify regions of the genome with a significant increase or decrease in read coverage identifying potential copy number variants (CNVs) after accounting for GC bias through coverage normalization. Regions identified as CNVs were inspected visually and assessed using de novo assembly methods .Publicly available whole genome sequence data for 74 π) with the R package "pegas". An in-house R script was used to compute the allele frequency-based Tajima's D test  to identify genes under balancing selection in the individual populations , this method was chosen over the dN/dS approach given the latter being not fit for analysis on individual populations . To detect signals of directional selection, the integrated Haplotype Score (iHS) approach  was applied to individual populations supported by a principal component analysis (PCA). This approach used the most frequent allele where mixed calls where found so the haplotype analysis will be based on the most abundant strain in each sample .P-values for iHS were computed from standardised values based on a 2-tailed conversion from a Gaussian distribution . The Salvador-I being the reference and oldest sample was used as ancestral haplotype. Multiplicity of infection was estimated using a novel method of counting the unique haplotypes formed by polymorphism on paired sequencing reads . For comparisons between populations, we first applied PCA and neighbourhood joining tree clustering based on a matrix of pairwise identity by state values. These analyses were followed by applying the cross population long-range haplotype method  and the population differentiation metric FST. P-values for the Rsb estimates were calculated using a Gaussian approximation . A significance threshold of P < 0.001 was established for both iHS and Rsb using bootstrap- and permutation-based simulation approaches ,19. We used the ranked FST statistics to identify the informative polymorphism for the barcoding of populations and driving the clustering observed in the PCA. Linkage disequilibrium (LD) was assessed in the two populations with the largest sample sizes (Thailand and South America) using the r2 and D’ metrics , calculated for pairs of SNPs with different physical separation up to 2 kbp using a sliding window approach. The SNPs were annotated and effects of variants on genes (such as amino acid changes) were predicted using snpEFF software . The R statistical package was used to analyse SNP data, including implementation of selection analyses using the “rehh” library.Genetic diversity was estimated using the average pairwise nucleotide diversity (S2 Table). Although 60% of the annotated reference genome is coding , approximately half the SNPs in the isolates were found in genic regions  . The proportion of non-synonymous sites is consistent with those found in other Plasmodium species, with 52% of coding SNP sites being non-synonymous in the nuclear genome, 36% in the mitochondrion and 56% in the apicoplast. The differences in these genomes suggest they may be subject to differential selective pressure . The majority of SNPs are rare, with nearly half of the mutations (45%) being observed in single samples  as seen in other Plasmodium populations . There was some evidence of polyclonality in 22 samples .The genomic coverage in the nuclear genome was high , and in keeping with multiple organellar copies, the mitochondria and apicoplast coverage was 30-fold and 1.8 fold greater than the nuclear coverage. The density of SNPs in the nuclear genome  was greater than in the mitochondrial  and apicoplast genomes   in Cambodian and Thai isolates . Several MDR1 variants have previously been reported, some considered putative chloroquine- and mefloquine-resistance alleles ,30–35. At the MDR1 locus, we observed either a duplication of ~35kb , a major deletion in the promoter region of the gene , or a combination of both structural variants, including two copies, one with the deletion in the promoter and another copy with a complete promoter ; as confirmed by the increase or decrease in coverage and accumulation of split reads in the regions where a break in the coverage occurs. The known duplication in the Duffy binding protein PvDBP  in Malagasy  was confirmed in one of the two Madagascan isolates (SRR828416). The PvDBP gene is potentially involved in erythrocyte invasion, and the duplication was also observed in thirteen Thai isolates. A further duplication was observed in Pv-fam-e , a gene linked to P. vivax selectivity for young erythrocytes and/or immune evasion .Analysis of structural variants and copy number variants was limited to Thai, Cambodian and Madagascan isolates, which had high and uniform genomic coverage. CNVs were located less than 1 kbp distance from the π) was calculated by gene and chromosome. There was little difference across the chromosomes with mean 11.1x10-4 (range 6.0 x 10−4 to 19.0x10-4), which is consistent with other studies with similar sample size  as well as larger datasets when restricted to high quality SNPs (1.5x10-3) , 7. LD decays rapidly for non-rare polymorphism  within a few hundred base pairs, and reaches a baseline within 500bp in South American and Thai nuclear genomes . Like P. falciparum, there is a high correlation between non-rare SNPs  in the mitochondrial and apicoplast genomes supporting the inference that the organelles are co-inherited and supporting the view that these SNPs have potential utility for barcoding .The average polymorphism  and its promoter region in Thailand, and a region surrounding the MRP2 gene . Several surface proteins were identified in both populations, including the MSP7 and MSP3.1, which are thought to be under selection pressure due to their role in erythrocyte invasion and strong vaccine candidates and have been identified before by other studies using sanger sequencing  . In addition, some helicases showed strong signals of selection (PVX_088190 and PVX_111220) which were also detected in the same study  reinforcing the method used. Furthermore, we identified in South America a proximal region of selection  described elsewhere .To examine evidence for signatures of positive natural selection we calculated the . This observation could indicate a population expansion in the recent past, where as a population grows in size, the frequency of rare alleles also increases . The Tajima’s D method was applied to genes with at least five SNPs in the two main populations  and South America 3,549 (70.0%) genes). The majority of Tajima’s D values were negative , reinforcing the presence of an excess of low frequency and singleton polymorphisms, potentially due to population expansion in the recent past or purifying selection. For Thailand, we identified 398 (8.5%) genes with positive Tajima’s D values, of which 14 were in excess of 2.5 and potentially under balancing selection (Table 3). Similarly, for South America, of the 1,260 (35.5%) values that were positive, 12 were in excess of 2.5 (Table 3). The loci under potential balancing selection in both populations encode proteins with predominantly roles surface proteins (e.g. MSPs) and antigens. The majority of the 26 genes identified in this study have had positive indices of balancing selection in previous studies , 38, or have orthologues in P. falciparum.The allele frequency spectrum of different classes of nucleotide sites all show an excess of rare alleles, with coding, non-synonymous, synonymous and intergenic sites more skewed than expected under a Wright-Fisher model of constant population size S4 Fig) revealed clustering by continent, in keeping with similar P. falciparum analyses , 19. The across population long-range haplotype method (Rsb implementation) was applied to compare Thailand to the South American population, to identify regions potentially under recent directional selection (Table 4). We detected several loci including at multidrug resistance-associated protein MRP1 (PVX_097025), and the CCR4-associated factor 1  located within 20kb of DHPS (associated with resistance to sulfadoxine ). Five non-synonymous mutations were identified in the DHPS gene , with evidence that the P383A has driven toward fixation across all geographical regions. Except for mutation in codon 616, all the others have been previously reported –42. The DHFR gene, associated with resistance to pyrimethamine (part of the SP drug combination), exhibited elevated Rsb (>3). Seven non-synonymous mutations were identified, including the previously described S58R and S117N –44 that were fixed across populations, and F57I/L and T61M –46 that were absent from South America (S3 Table). No evidence was observed of a hard sweep around the MDR1 copy number gene. However, nine non-synonymous SNP mutations were identified, five of which have been reported previously. These included the fixed alleles T958M and M908L, F1076L at high frequency across populations, and G698S and S513R absent from South America –44. There was no evidence of a sweep around the P. vivax orthologues of the falciparum chloroquine related CRT  or GTP cyclohydrolase I folate pathway  genes. No common non-synonymous mutations were identified within the CRT gene, while 7 low frequency non-synonymous SNPs where identified in the GTPCH locus.Both a principal component and a neighbourhood joining tree analysis  gene expressed in the surface of the ookinete stage during the mosquito part of the life cycle, pv47 (PVX_083240) and pv48/45 (PVX_083235) involved in the transmission of the parasite. There are continental-specific pv47 and pv48/45 SNPs (and haplotypes) as previously found , 48, consistent with the presence of different species of mosquito in each the regions , resembling a similar pattern found in P. falciparum.The P. vivax could ultimately assist with surveillance and disease control. Previous work  has described a 42 SNP barcode to classify geographically P. vivax across 7 countries. Across the 46 isolates analysed here, we found 3 SNPs in the barcode to be either non-segregating or not passing quality control filtering. Use of the remaining 39 SNPs led to imperfect clustering by continent . Application of the FST population differentiation metric identified SNPs driving the observed differences between Thailand, South America and other populations (S5 Table). These SNPs occurred in drug resistance loci, including MRP1 (PVX_097025), DHPS (PVX_123230) and UBP1 (PVX_081540) , and in close proximity (e.g. PVX_089960 within 8kb of DHFR). Population differentiation due to genetic diversity in drug resistant loci is also observed in P. falciparum,19.The development of molecular barcode for . Genotyping of organellar markers would benefit from greater copy number and coverage as well as highly conserved sequences . Eight markers across five apicoplast genes could differentiate Thai and Southeast Asian samples from the other isolates, and two non-genic markers were found to be exclusive to South America . No informative mitochondrial markers were identified . Further, as the organelle genomes are known to be highly conserved between Plasmodia species, when comparing a set of P. falciparum geographical markers  to P. vivax sequences, we found evidence of positions close in the sequence. Two of the samples (ERR020124 and SRR828528) had a high density of mixed calls in the organellar genomes, in this case, a signature of P. falciparum overlaying onto P. vivax . In general, this density signature is indicative of a co-infection of P. vivax with another Plasmodium spp. By comparing the sequencing reads to the Plasmodium knowlesi reference genome , there was no evidence of any vivax and knowlesi co-infections. However, the presence of a unique triallelic SNP reinforces the potential for an organellar inter-plasmodia species barcode .Previous work has proposed the mitochondria and apicoplast organellar genomes as candidate regions for a barcode P. vivax populations using whole genome data, but with low sample sizes. Recently, two papers using a combined collection of over 400 isolates from 17 countries described major genomic diversity in Plasmodium vivax, 7. Here we analysed a complementary collection of 46 high quality isolates spanning 10 countries across 4 continents in order to position them within the context of this new work. As expected we confirmed that P. vivax genomic diversity is greater compared to P. falciparum, and even at a relatively low sample size, the samples clustered geographically. We reveal a wider genomic distance between South American and Southeast Asian continents than observed between P. falciparum African and Southeast Asian populations , 18, 19, highlighted by the greater and more uniform distribution of SNPs with a high FST across the genome. Hotspots of selection pressure were identified, including the previously reported MRP1, DHPS and other putative drug resistance genes, as well as several loci related with the mosquito stage of the parasite life cycle. The latter observation is consistent with recent work ,7 and the presence of different Anopheles species across continents. We identified structural variants, including extra copies and deletions in the promoter region of the MDR1 gene, a locus associated with multiple antimalarial drugs . We also confirmed the duplication in the Duffy binding protein gene (PvDBP) in a Madagascan sample, and detected it in Thai isolates. This duplication has been found in parasites from several regions in Africa, South America and Asia ,37. Many of these locations are areas where Duffy-negative individuals make up >45% of the population. However other regions like Cambodia do not present Duffy-negative individuals . It has been theorized that the duplication allows the parasite to infect Duffy negative individuals , however more research is needed in this area.Several studies have previously described the genomic diversity of P. vivax isolates, and together with antigen genotyping identify mixed infections and extent of transmission, used as the basis of genetic epidemiology. In comparison, whole genome sequencing provides a higher specificity in the application of geographical clustering . While other studies have focused on creating a barcode using the nuclear genome , we also considered organelle genomes (mitochondrion and apicoplast), which are more stable over time, do not undergo recombination and are co-inherited . The analysis revealed organellar markers that are potentially Southeast Asian and South American specific, and others that highlighted the presence of multi-species mixed infections. The sequencing of large numbers of isolates, beyond currently published samples sizes, will be required to establish robust intra- and inter-species organellar-based barcode. Such large-scale datasets across multiple regions will also serve to identify the high genomic diversity that lies within and between P. vivax populations, which could be exploited for biological insights, including elucidating drug resistance and invasion mechanisms, and ultimately measures of disease control.Microsatellite genotyping has been used previously to cluster geographically P. vivax populations can be used to elucidate potential drug resistance and invasion mechanisms, as well as facilitate the molecular barcoding of the parasite for surveillance applications.This study has shown that genomic diversity that lies within and between S1 FigMDR1 gene (chromosome 10) in the Thailand population; (i)Structural variants located around the  a sample without a copy number variant or deletion (even coverage), (ii) a major deletion in the promoter region of the gene (n = 7); (iii) duplication of ~35kb ; and (iv) a combination of both structural variants (ii) and (iii), including two copies, one with the deletion in the promoter and another copy with a complete promoter . The horizontal dashed line is average chromosomal coverage and the red outline encloses the promoter region of the MDR1 gene.(TIFF)Click here for additional data file.S2 FigiHS*) a)Intra-population evidence of directional selective pressure ( Thailand b) South America. *iHS integrated haplotype score; see Table 1and Table 2for a summary of the hits.(TIFF)Click here for additional data file.S3 Fig(PNG)Click here for additional data file.S4 Fig|Rsb| > 3 (P<0.003); Red line represents a human GWAS cut-off; see Table 4for a summary of the hits.Blue line: (PNG)Click here for additional data file.S5 Fig* SNPs and genotypes are shown in S4 Table(PNG)Click here for additional data file.S6 Fig(PNG)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 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Arachis hypogaea; 2n = 4x = 40) is a nutritious food and a good source of vitamins, minerals, and healthy fats. Expansion of genetic and genomic resources for genetic enhancement of cultivated peanut has gained momentum from the sequenced genomes of the diploid ancestors of cultivated peanut. To facilitate high-throughput genotyping of Arachis species, 20 genotypes were re-sequenced and genome-wide single nucleotide polymorphisms (SNPs) were selected to develop a large-scale SNP genotyping array. For flexibility in genotyping applications, SNPs polymorphic between tetraploid and diploid species were included for use in cultivated and interspecific populations. A set of 384 accessions was used to test the array resulting in 54 564 markers that produced high-quality polymorphic clusters between diploid species, 47 116 polymorphic markers between cultivated and interspecific hybrids, and 15 897 polymorphic markers within A. hypogaea germplasm. An additional 1193 markers were identified that illuminated genomic regions exhibiting tetrasomic recombination. Furthermore, a set of elite cultivars that make up the pedigree of US runner germplasm were genotyped and used to identify genomic regions that have undergone positive selection. These observations provide key insights on the inclusion of new genetic diversity in cultivated peanut and will inform the development of high-resolution mapping populations. Due to its efficiency, scope, and flexibility, the newly developed SNP array will be very useful for further genetic and breeding applications in Arachis.Peanut ( Genome-wide single nucleotide polymorphisms (SNPs), identified among diploid and tetraploid Arachis species and used to construct an Affymetrix 60K SNP array, provided insight into diversity across US runner-type peanut cultivars and the US mini - core collection. The frequency of fixed marker polymorphisms and identification of preferentially selected haplotypes will inform future peanut breeding to enhance diversity. Arachis hypogaea; also known as groundnut) is an especially important nutritional resource for the poor and malnourished. With the advent of the genome sequences from the ancestors of peanut A. duranensis and A. ipaensis, peanut now has become “the orphan legume whose time has come”  has lagged behind other polyploids. Polyploid species for which high-density SNP arrays have already been developed include cotton . Allopolyploid A. hypogaea is the only cultivated polyploid Arachis species, and one of only two polyploid species in the botanical section Arachis . Furthermore, A. hypogaea is most likely derived from only one hybridization event and so within-species genetic diversity is narrow  and A. ipaensis (B) genomes, respectively. The set of A. hypogaea genotypes used was selected to include important parents of research community RIL populations, segregating for disease resistance, biotic stress tolerance, pod characteristics, and yield, varieties identified as being aflatoxin resistant and drought tolerant, as well as globally important cultivars and parental genotypes important in Israel and India  and chromosome 6 (NC3033)  . This inaAaGbGb. In this case, in the RIL population, one individual has undergone tetrasomic recombination  population, and one can see how many novel recombination events occurred after six cycles of breeding. A. hypogaea, indicates that these alleles are in general rare, as over 50% of them have an allele frequency below 0.05  were fixed among the four main ancestors: Basse, Spanish 18-38, Dixie Giant, and Small White Spanish. The other ancestors donating significantly to runner germplasm, PI203396, Jenkins Jumbo, and Virginia Bunch 67, contributed an additional 1022, 230, and 142 new marker polymorphisms, respectively. These introductions came into the runner germplasm at cycles three (Jenkins Jumbo), four (PI203396), and five (Virginia Bunch 67). Cultivars released in cycles one, two, and three were fixed for 83% of markers. From new introductions, the amount of fixation decreased in cycles four to eight . If looked at as pairwise shared identity between cultivars, many of the retained polymorphic markers have low minor allele frequencies. Cultivars released in cycles one, two, and three share on average 92% of alleles, up from 84% in the ancestors. The shared identity increases in cycles four, five, and six to 93% and then decreases slightly to 91% in cycles seven and eight. So, although there is increased polymorphism among the released cultivars in terms of total alleles, there has been an overall reduction in pairwise diversity. It is possible that the available genetic diversity was lost due to genetic drift as a consequence of small population sizes and inbreeding. To investigate whether the allele fixation observed in the historical breeding cycles can be attributed to more than what would be expected due to genetic drift, a simulation was carried out that recreated the historical pedigree with no selection of progeny as released cultivars and random mating of cycle progeny where no ancestor was used as a parent . low 0.05 . DespiteA. cardenasii-derived alien alleles into the germplasm . The first runner-type peanut released was Early Runner in 1952 by W.A. Carver  were used with three different selection intensities , and each combination was simulated 553 700 times (100 simulations per marker) and the 99th percentile of the distribution for all number of tests from 0 to 45 is shown where a test is a polymorphic parent between the two sample parents  but, taking into account probable linkage disequilibrium, there were only 267 unique genomic regions (4.82%) . The freE1  and FAD2B (B09), genes that have mutations that when combined confer a high oleic acid ratio  and the top eight haplotypes ranked by frequency were assessed for frequency in the eight ancestors, cultivars released in cycles four, five, and six, and cultivars released in cycles seven and eight. In addition, the haplotype diversity (π) was assessed as the pairwise average nucleotide diversity across the 20 marker haplotypes and normalized to the diversity in the mini core collection by taking the log of πpopulation/πmini core, which shows a decrease in diversity as a negative value.As described above, genetic diversity in terms of SNP markers has decreased more than can be attributed to genetic drift during modern runner-type peanut breeding. The 111 mini core genotypes that estimate the breadth of diversity in The haplotype frequencies paint an encouraging picture compared with SNP marker diversity . The culA. hypogaea accessions. The initial validation of SWEEP-filtered SNPs was done using RNA-sequencing data, which as a strategy for genome reduction is less sensitive to false positives stemming from repetitive DNA in non-genic regions. Coding sequence, however, is highly conserved and contains less polymorphism among genotypes. For large-scale SNP discovery in peanut, the genomic sequence needed to be assayed. The accessions used for SNP discovery were on this array, allowing for unprecedented SNP validation in peanut. Although 33% of tetraploid SNPs were validated on the array, this was more than double the accuracy previously achieved. However, a vast improvement in methodology is still needed to apply next-generation sequencing to sequencing-based genotyping methods, and validated SNPs will provide a training set to improve our methods. We have 14 233 SNPs identified with re-sequencing data and validated on the array, and 28 425 false-positive SNPs identified in the same dataset. This allows deciphering the differences between true SNPs and false positives in real peanut data on a large scale and will provide the key insights needed to train models to achieve higher accuracy.Discovery of high-quality SNPs while minimizing false positives has been challenging in peanut due to genome complexity. Large-scale validation of SNPs identified from next-generation sequence data has seen error rates between 86% and 93.8% . A pipelArachis species, was to address the needs of all researchers working in Arachis genetics, breeding, and improvement. The future of peanut breeding is likely to rely heavily on the inclusion of disease and stress tolerance alleles from wild species, and the genotyping of interspecific hybrid populations will be crucial to map, identify, and transfer these desirable alleles  and three elite cultivars that have been and could potentially be used as recurrent parents in interspecific populations . The number of polymorphic markers on the array will be very useful for genotyping these populations with a range of 9924 and 29 748 polymorphic markers between these cultivars and the interspecific hybrids. This is an increase in markers for mapping by 268%–800% from 3693 SSR markers in a consensus map integrated from 13 published maps . The same tetrasomic alleles are present in PI 203396, a subspecies hypogaea accession collected in Brazil. PI 203396 was an important parent for TSWV resistance in runner germplasm and is an ancestor of many current cultivars . The change in cultivars had an immediate, dramatic effect on losses due to TSWV, dropping from more than 40 million to less than 5 million in only 3 years , will usher in a new era of productivity for peanut research and improvement that will affect global food security for years to come.Here, we report deployment of the first large-scale SNP genotyping array for A group of 20 cultivated genotypes was selected for whole-genome re-sequencing, including 10 parents of RIL populations  and 10 gA. duranensis and A. ipaensis; A. duranensis genome and 96.9% over the A. ipaensis genome. The SNP calling between all the genotypes was based on the reference genomes, and filtering of homologous SNPs was carried out following the SWEEP Prime version program , and 24 validated trait-linked SNPs were submitted for design of the Affymetrix SNP array, called Axiom_Arachis array . All A/T6:8 RIL populations with the parents Tifrunner × NC3033 and Florida 07 × GP-NC WS 16 (SPT06-06) : A. batizocoi Krapov. & W.C. Greg. × A. stenosperma; A. gregoryii C.E. Simpson, Krapov. & Valls × A. stenosperma, and A. duranensis × A. ipaensis were also included. 6 plots grown in the field in Tifton, GA. DNA from diploid species was extracted from leaf tissue. All DNAs were extracted using a QIAGEN DNAeasy Plant mini kit and quantified using picogreen.Three hundred and eighty-four samples were genotyped on the Axiom_Arachis array, including 109 accessions of the USDA mini core collection, 64 tetraploid genotypes representing the history of US runner market-type breeding, the 20 parents re-sequenced for SNP selection plus Tifrunner, and important lines harboring traits of interest related to aflatoxin contamination, drought tolerance, and disease resistance, and selections from two FPT06-06) . Eleven All tetraploid lines genotyped on the array were analyzed using Axiom Analysis Suite v.1.1.0.616 using default polyploid threshold configurations. All SNPs that were placed into the categories PolyHighResolution, NoMinorHom, OTV, and Other were further curated manually. Markers where more than one third of the genotypes were scored as heterozygous were evaluated first. Next, the clustering for unexpected clusters using criteria similar to P values were corrected for multiple testing using a Benjamini-Hochberg correction to test for a false discovery rate of 0.1.Using an updated pedigree of runner market-type cultivars  first puSeven ancestors contributed all of the marker polymorphisms in runner-type cultivars grown in the southeastern United States: Basse, Spanish 18-38, Dixie Giant, Small White Spanish, Jenkins Jumbo, Virginia Bunch 67, and PI203396. All of the marker polymorphisms from these ancestors were present in the 18 modern cultivars assayed. This gave us the ability to trace polymorphisms specific to each ancestor. Specific marker polymorphisms from each ancestor were identified by selecting those where all other ancestors were fixed for the opposite nucleotide. Markers where any of the ancestors had a heterozygous call were discounted. Tetrasomic genotype scores on chromosome A04 from PI203396 were also disregarded because they were only retained in Tifrunner. Using custom scripts, marker polymorphisms from each ancestor in the 18 cultivars were tracked, and the percentage of available unique alleles for each breeding cycle was calculated .A. duranensis and A. ipaensis pseudomolecules. Changes of phase between two markers were recorded at each crossing cycle within the specific path. The crossing cycles here do not correspond to the pedigree cycles but instead to crossing cycles within the breeding path. For example, in one path originating with the cross Dixie Giant × Small White Spanish, Southern Runner is the progeny of the fourth cross from the original and so is assigned to cycle four. In another path originating with the cross PI203396 × Florunner, however, Southern Runner is assigned to cycle one or the progeny of the first cross from the ancestor. Wilcox rank sum tests were carried out in R v.3.2.3 using the wilcox.test function.Eleven breeding paths were assembled when genotyping information for at least one parent at each cross was available. One parent was enough to determine if there was a phase change in the selected progeny between two markers. The real opportunity for a phase change by knowing which markers were polymorphic between the two parents was not taken into account. This is because, in a breeding situation, the actual alleles of the parents being crossed are not known. The process measures the actual recombination occurring after crossing and selection when breeders are only making crossing decisions based on the phenotypes of the parents. Markers were tested for each path in overlapping pairs ordered based on their putative position relative to the United States Agency for International Development (USAID) ; the US-Israel Binational Agricultural R&D Fund (BARD) project, IS-4540-12 ; the Agriculture and Food Research Initiative competitive grant 2012-85117-19435 of the USDA National Institute of Food and Agriculture ; the Peanut Foundation , and Bill & Melinda Gates Foundation (grant no. OPP1114827) funded Tropical Legumes III project (R.K.V.).This project was funded by the Feed the Future Innovation Lab for Collaborative Research on Peanut Productivity and Mycotoxin Control (Peanut and Mycotoxin Innovation Lab), supported by funding from the P.O.-A., R.K.V., and S.A.J. conceptualized the research; Y.C., C.C., D.J.B., S.C.M.L.-B., J.V., N.A.B., R.H., M.B., T.G.I., and C.C.H. provided genetic resources and data; J.C., Y.C., C.C., G.A., M.K.P., S.N.N., A.C., D.J.B., S.C.M.L.-B., and B.A. performed experiments, conducted data analysis, and curated data; J.C. and J.V. contributed to the methodology; J.C. wrote the original draft and was responsible for data visualization; P.O.-A., S.A.J., R.K.V., Y.C., M.K.P., D.J.B., S.C.M.L.-B., C.C., and J.C. revised the manuscript; P.O.-A. administered the project.


Our results suggest that the effects of genes shaping seasonal timing depended on annual spring temperature. Such environment-dependent effects are expected for any phenotypically plastic trait. Taking these effects into account will thus improve the success of detecting genes involved in phenotypically plastic traits, thereby leading to a better understanding of their evolutionary potential.Understanding how genes shape phenotypes is essential to assess the evolutionary potential of a trait. Identifying the genes underlying quantitative behavioral or life-history traits has, however, proven to be a major challenge. The majority of these traits are phenotypically plastic and different parts of the genome can be involved in shaping the trait under different environmental conditions. These variable genotype-phenotype associations could be one explanation for the limited success of genome-wide association studies in such traits. We here use avian seasonal timing of breeding, a trait that is highly plastic in response to spring temperature, to explore effects of such genotype-by-environment interactions in genome-wide association studies. We genotyped 2045 great tit females for 384081 single nucleotide polymorphisms (SNPs) and recorded their egg-laying dates in the wild. When testing for associations between SNPs and egg-laying dates, no SNP reached genome-wide significance. We then explored whether SNP effects were modified by annual spring temperature by formally testing for an interaction between SNP effect and temperature. The models including the SNP Environmental change, as, e.g., global warming, will inevitably lead to novel selection pressures, especially in phenological traits, as, for example, timing of breeding, migration or hibernation , and in Due to the advancement in molecular genetics, it is now possible to map the loci that underlie traits of interest in natural populations of ‘ecological model species’ and this has moved the research field of ecological genetics into the genomics era . So far,In phenotypically plastic traits the same genotype can produce different phenotypes, and these different phenotypes can hence not be the product of genomic sequence variation alone but must be caused by environmentally induced differential gene expression or post-translational mechanisms . This meBreeding time, i.e., the timing of egg-laying, is a highly plastic trait in birds. Depending on spring temperature breeding time can vary by weeks from 1 year to the next . BesidesParus major) has been studied in our long-term study populations on the ‘Veluwe’ area near Arnhem  since the 1950s. Great tits are small, hole-breeding passerine birds that readily accept nest boxes, which has made them a ‘model system’ in behavioral and evolutionary ecology. In our study populations nest boxes are supplied in abundance so that almost the entire population breeds in boxes and can be monitored. Nest boxes were checked weekly in spring and the dates when the first egg of a clutch was laid (from now on: egg-laying date) were back-calculated from the number of recorded eggs assuming one egg is laid per day. All nestlings were banded with standard aluminum bird-bands and blood sampled when seven to 10-days-old. Adult great tits were caught when feeding their chicks with spring-traps in the nest box, identified from their bands, and blood-sampled. Whole blood samples were stored in either 1 ml Cell Lysis Solution  or Queens buffer . SNP calling was done following the Affymetrix® best practices workflow by using the Axiom® Analysis Suite 1.1. Eight individuals with dish quality control value of <0.82 were discarded. Dish quality control is an Affymetrix-specific QC measure and we used the default threshold to exclude individual samples. The recommended SNP group  consisted of 537174 SNPs while 73796 SNPs were discarded. In addition to the SNPs that did not pass the quality control steps, an additional 234 SNPs were removed because they were duplicates or the genomic position was missing . Altogether 536940 SNPs passed initial quality control and 2303 individuals were included for downstream analyses.DNA was extracted from blood samples by using the FavorPrep 96-Well Genomic DNA Extraction Kit (Favorgen Biotech corp.). DNA quality and DNA concentration were measured on a Nanodrop 2000 (Thermo Scientific). A total of 2311 female great tits were genotyped using a custom made AffymetrixThe GenABEL package v1.8-0  implemenIn PLINK 1.07  we calcu1). We then calculated the 33%iles and grouped the first, second and third 33%ile into the ‘cold,’ ‘intermediate’ and ‘warm’ category, respectively. The ‘cold’ category comprised 1074 observed egg-laying date of 941 individuals in 6 years, the ‘intermediate’ category 770 observed egg-laying dates of 651 individuals in 6 years and the ‘warm’ category 1615 observed egg-laying dates of 1310 individuals in 5 years. Note that the total number of individuals in our analysis (2045) is not the sum of the individuals breeding in cold, intermediate and warm years, since the same individual can be included in more than one category. The average (and standard deviation in brackets) of the temperature during the period March 11 to April 20 was 6.2°C (0.96°C) for the ‘cold’ category, 7.8°C (0.27°C) for the ‘intermediate’ category, and 9.4°C (0.66°C) for the ‘warm’ category, respectively. Our model to test for SNP effect and their interaction with temperature was:Individual egg-laying dates were corrected for among-year and among-area variation prior to analyses by centering to the annual mean egg-laying date separately per area. To test whether SNP effects differed depending on spring temperature we divided the years in our data set into three categories according to their spring temperature. For every year we calculated the average daily mean temperature for the period March 11 to April 20. Average temperature during this period correlates best with annual mean egg-laying dates for our study population . The usei,jy of observation j of individual i is a function of the population mean μ, its age (two-level factor: first year breeder or older), its SNP genotype, the interaction between SNP genotype and temperature class temp, its ‘permanent environment’  effect ipe, its breeding values ia and the residual effect ie. μ, age, the SNP genotype and temperature class are fixed effects and ipe and ia random effects. The variance-covariance matrix for a is derived from the expected covariance between individuals due to their additive genetic effects. Fitting the additive genetic variance-covariance matrix accounts for the fact that individuals in our study population are related.where the phenotype All GWA analyses were run in ASReml-R 3.0 . Due to ∗environment interactions and can lead to inflation of p-values as has been recognized in the field of psychiatric genetics  between all SNP loci into account has been suggested . We calc∗area as fixed effects. These analyses were also run in ASReml-R 3.0.Although previous work in of the study populations analyzed here showed that egg-laying dates are heritable in general  and also2 = 9.39, p = 0.002; χ2 = 41.9, p < 0.001; χ2 = 57.5, p < 0.001, Table 1).The heritability of egg-laying date varied among temperature classes. In line with earlier results  it was lFigure 1). When we tested whether SNP effects differed between cold, intermediate and warm springs, without fitting heterogeneous residuals, two SNPs reached genome-wide significance and one SNP was close to genome-wide significance . The two SNPs that reached genome-wide significance and the SNP that almost reached genome-wide significance were, however, still among the 10 most significant SNPs when heterogeneous residuals were fitted (Supplementary Table TG) gene, the heparan sulfate-glucosamine 3-sulfotransferase 5 (HS3ST5) gene and the teneurin transmembrane protein 4 (TENM4) gene, respectively (Table 2). For illustration, we plotted mean egg-laying dates against genotypes and spring temperature for these three SNPs . In cold springs the birds laid about 10 days later than in warm springs. The maximum difference in egg-laying dates between SNP genotypes was 0.94 days in cold springs, and 0.77 days in warm springs but 2.46 days in intermediate springs. In all three SNPs no genotype bred consistently earlier under all spring temperatures.When all years were analyzed together, no SNP was associated with year- and area-centered egg-laying date at the genome-wide significance level .To compare model fit of the models with and without SNPFigure 4) although no SNP reached significance after multiple testing correction . This result shows that including the effects that the environment has on genotype-phenotype relationship can improve our understanding of this relationship. This has been realized previously in some research fields, as, e.g., animal and plant breeding or psychiatrical genetics  large number of tests are performed, as for example in GWAS. The inflation of p-values due to heteroscedasticity is likely more problematic when SNP∗environment interactions are fitted but could theoretically also occur when only SNP effects are tested. It is normally assumed that variants at a locus affect the mean but they may also affect the variance of the trait  gene. Thyroglobulin is a precursor for thyroid hormones which form an integral part of the Hypothalamus–pituitary–thyroid axis, which has an important role in controlling general metabolism (TG has been linked to age of puberty (HS3ST5) gene, which is involved in heparan sulfate biosynthesis and glycosaminoglycan metabolism. Heparan sulfate is involved in cell-signaling and has various essential functions in development and homeostasis . Teneurins are a highly conserved gene family that function as cell surface signal molecules and transcriptional regulators (TENM4 has been shown to play a role in neural development in chicken (Among the 10 most significant SNPs whose effects on breeding time differed depending on spring temperature in the year the phenotype was expressed, three were associated with genes of known function. One SNP is located within the thyroglobulin (tabolism . In bull puberty  and thyr puberty . Anothereostasis . Like fo vessels . The thigulators . TENM4 h chicken .∗environment interactions.Genome-wide association studies have worked well in certain fields, e.g., in case-control studies of human diseases  but idenMost traits are phenotypically plastic  and the PG and MV compiled the phenotypic data. VL compiled the genomic data. PG and VL conducted all analyses. AM organized the DNA samples and extracted the DNA. PG, VL, KvO, and MV developed this research, PG wrote the manuscript with contributions from VL and all co-authors commented on it.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The spreading/bunch trait was shown to be controlled by a single gene with no cytoplasmic effect. That gene was named Bunch1 and was significantly correlated with pod yield per plant, time to maturation and the ratio of “dead-end” pods. Subsequently, bulked segregant analysis was performed on 52 completely bunch, and 47 completely spreading F3 families. In order to facilitate the process of SNP detection and candidate-gene analysis, the transcriptome was used instead of genomic DNA. Young leaves were sampled and bulked. Reads from Illumina sequencing were aligned against the peanut reference transcriptome and the diploid genomes. Inter-varietal SNPs were detected, scored and quality-filtered. Thirty-four candidate SNPs were found to have a bulk frequency ratio value >10 and 6 of those SNPs were found to be located in the genomic region of linkage group B5. Three best hits from that over-represented region were further analyzed in the segregating population. The trait locus was found to be located in a ~1.1 Mbp segment between markers M875  and M255 . The method was validated using a population of recombinant inbreed lines of the same cross and a new DNA SNP-array. This study demonstrates the relatively straight-forward utilization of bulk segregant analysis for trait fine-mapping in the low polymeric and heterozygous germplasm of cultivated peanut and provides a baseline for candidate gene discovery and map-based cloning of Bunch1.The growth habit of lateral shoots  is an important descriptive and agronomic character of peanut. Yet, both the inheritance of branching habit and the genetic mechanism that controls it in this crop remain unclear. In addition, the low degree of polymorphism among cultivated peanut varieties hinders fine-mapping of this and other traits in non-homozygous genetic structures. Here, we combined high-throughput sequencing with a well-defined genetic system to study these issues in peanut. Initially, segregating F Arachis hypogaea L.) is an economically significant crop grown throughout the world. It is the second-most important cultivated grain legume and the fourth largest edible oilseed crop  is one of the most important descriptive characteristics of peanut Pittman, . The wilct Table . The desct Table .2 progeny of crosses between plants exhibiting different growth habits. A fundamental set of experiments conducted by Ashri  due to the allopolyploid nature of the peanut genome. This is particularly true in cases of genetic populations that are based upon a cross between closely related parental genotypes, which are occasionally needed for better genetic dissection of specific traits.Like many polyploid species, cultivated peanut has experienced a genetic bottleneck, which, together with the effects of domestication, has greatly narrowed its genetic diversity and limited DNA polymorphism among subsequently derived 2 populations derived from a reciprocal cross between very closely-related Virginia marketing-type cultivars were analyzed. Against this particular background, the spreading/bunch trait, a well-known characteristic of the Virginia varieties, was found to be controlled by a single gene with no cytoplasmic effect. Subsequently, a combination of bulked segregant analysis and deep sequencing was developed to facilitate the SNP detection process and the fine-mapping of this gene. The processes were validated using a RIL population derived from the same cross and a new SNP array . In 2013, 314, and 252 F2 individuals from reciprocal Hanoch × Harari and Harari × Hanoch crosses, respectively, were grown under field conditions. The plot consisted of two rows, 75 cm apart, with 40 cm spacing between plants within each row. The experimental plots were sown alongside commercial plots under full irrigation. All agricultural practices were carried out according to local growing protocols as described previously  at 80 days after sowing. Plant maturity index was determined based on three random plants in the homozygous F3 families, as the number of fully matured pods out of the total number of pods at 140 days after sowing. To validate the bulk segregant analysis, 94 RILs (F6:F8), which originated from the same Hanoch × Harari cross by single seed descent, were analyzed. In 2016, 16 randomly-selected plants from each RIL were grown under field conditions and the growth habit of each plant was recorded 70 days after sowing.Growth habit was recorded at 80 days after sowing. At the end of the season, pods were harvested on an individual-plant basis. For each sample, the total pod yield, net pod yield , number of pods, total seed weight, “dead-end” ratio (relative number of pods with the remote seed aborted) and seed ratio (net seed weight per plant divided by the net pod weight per plant) traits were recorded as well. From each population, approximately 150 F3 families that were found to be homozygous for the spreading or bunch growth habit. In total, 52 completely bunch and 47 completely spreading families were sampled. Young leaves were collected from all 16 individuals in each family. In each phenotypic group (spreading/bunch), all tissues from the families were bulked for the RNA extraction. Working on the RNA level was preferable to working on the DNA genomic level due to the large and relatively complex peanut genome and also facilitated the detection of candidate genes. Samples were taken of each of the ground tissues (400 mg each) and were used for RNA extraction using the hot-borate method, as described by Brand and Hovav  including: (1) trimming read-end nucleotides with quality scores <30 using fastq_quality_trimmer and (2) removing reads with less than 70% base pairs with quality score ≤ 30 using fastq_quality_filter. The sequences were mapped against the 4X tetraploid peanut transcript assembly reference (http://www.peanutbase.org/) and against two Arachis diploid genomes . The same leaves that were used for the RNA study were used for DNA extraction, but the DNA analysis was conducted on a single-plant basis instead of with bulk samples. To validate the three best SNP markers, the following primers were used: M35: F-TCTCTCTCTCTCACAGTCAC; R-CTTGCCGGCAAATAGAGCAT. M255: F--CAGATATGCAAGGCCTAACT; R-TGCCAGAGCAAGGAACATGT. M875: F-CCATCTGCAGTGAGAGTCAA; R-GTGATTCCTGCGTTCAAGTC. These primers were also used for further mapping of the trait in 182 F4 individuals derived from one F3 family segregating for the branching habit trait.For further validation of the SNPs, DNA was collected from the parental lines and 20 Fhttp://www.affymetrix.com). The chip array calls were subjected to cluster-quality filtering, carried out according to Affymetrix guidelines, and additional filtering to select only SNPs that were polymorphic between the parental lines and segregated in a 1:1 ratio in the RILs.The fine-mapping of the branching habit gene carried out using the bulk-segregant approach was further validated by a custom Affymetrix Axiom SNP array  = 0.0022] effect on early maturation. On the other hand, the BUNCH1 phenotype (spreading) was significantly associated with higher total pod weight and a greater number of pods per plant.In addition to growth habit, other traits with agronomic importance were examined in the segregating populations. The associations between the bunch1 on the peanut genome, a bulk segregant analysis was performed. For that analysis, 52 completely bunch and 47 completely spreading F3 families were bulked RNA was extracted from each bulk and converted into two libraries suitable for Illumina sequencing. After a cleaning procedure, 72 million reads per library (on average) were aligned to a 4X transcript assembly (peanutbase.org) that contains 120,364 peanut transcripts from both the A and B genomes . With about 98% of reads mapped to the reference assembly, the expression levels of 117,957 peanut genes were measured.In order to map Pipelines for the SNP discovery and the analysis of bulk frequency ratio were constructed according to the general scheme that was previously suggested for polyploid wheat  were selected  and M255 .SNP marker M875 and other two SNPs from the same genomic location that had high bulk frequency ratios  were further analyzed Figure . SamplesBunch1 gene was obtained using a new Affymetrix Axiom SNP array  co-segregated with the phenotype of Bunch1 . All significant SNP markers were located in one region at linkage 5B, indicating once again that a single locus is controlling the branching habit trait in this background. These SNPs were located in very close proximity to the three SNPs that were derived from the bulk segregant analysis  and Prostrate Growth 1 (PROG1), have also been identified in rice  such as Arachis hypogaea and fine-mapped a major gene that controls this trait. The bunch1 gene was mapped to a relatively small genomic region that includes ~70 ORFs for gene models. Interestingly, BlastX analysis showed that none of the above-mentioned genes that control the growth angle in either monocots or dicots were present in the peanut genome or mapped in approximation to bunch1. The genetic controller of bunch1 may therefore be novel. Several candidate genes involved in plant hormone metabolism and light reception are located within that region and have been identified as possibly controlling bunch1. One of these may be a FAR1-Related sequence (B05:146200756.146203528) that encodes a family of proteins that are essential for phytochrome A-controlled far-red responses in Arabidopsis , many flowers are too far from the ground and cannot reach to the soil. For that manner, only the pods that are close to the root will develop. However, those pods that reach the soil develop in a more synchronized manner among the bunch types than among the spreading types. This promotes uniform maturation, better pod filling and, eventually, fewer dead-end pods. Indeed, many of the dead-end pods in the spreading types (like cv. Hanoch) are from distal parts of the branches, where pods develop late in the season and do not fully mature. In a subset of the RIL population, the bunch phenotype of the branching gene is significantly correlated with greater resistance to white mold . Therefore, it is suggested that the phenotype of the Bunch1 gene has an important agricultural role in Virginia-type peanuts.The bunch1 gene is relatively common within Israeli peanut breeding germplasm. Crosses between cv. Hanoch (spreading) and cv. Hillah/Shulamit (bunch) resulted in a 3:1 spreading:bunch ratio (data not shown), tracing the origin of this trait back to the early 1970s. Crosses between cv. Harari (bunch) and a runner-type peanut line GK-7-Ol (spreading) also resulted in a 3:1 ratio (data not shown), indicating that the single-gene model for this trait is not confined to the Virginia-type germplasm. Yet, in crosses with Valencia-type peanut germplasm (plants with an erect branching habit), this system of single-gene inheritance was not found andthe branching habit was therefore relatively hard to classify. We conclude that the bunch1 phenotype is confined to the A. hypogaea ssp. hypogaea gene pool. However, other allelic variations of this gene may exist in the A. hypogaea ssb. fastigiata germplasm, since some of the bunch-habit Virginia-type lines in Israel  have A. fastigiata origins. Moreover, the branching habit trait of peanut was analyzed in a previous study by Fonceka et al. (A. ipaensis /A. duranensis and a Spanish-type cultivar (A. hypogaea ssb. fastigiata var vulgaris) with an erect growth habit. There was one significant QTL for the branching habit at the same location as Bunch1 (at the end of linkage group B5), explaining 16.2 of the total variation of the trait in the population. It is very likely that the reported QTL for branching habit and the locus of Bunch1 are the same. This demonstrates once again that the origin of the Bunch1 phenotype may be beyond the A. hypogaea ssp. hypogaea genetic background.As we explored the segregation patterns of several other crosses between Virginia-type related cultivars with different branching habits, we noticed that this model of the a et al. , which i1 hybrids  and the selection of homozygous spreading families in early breeding generations. The fine-mapping of this trait also provides a baseline for the cloning of Bunch1, presumably one of the first map-based positional cloning ventures in current genetic research of peanut.We have demonstrated the relatively straight-forward and easy utilization of bulk segregant analysis for the fine-mapping of a monogenic trait in the low-polymorphic and heterozygous germplasm of cultivated peanut. Also, although the bunch/spreading trait is very easy to classify, there may be some new uses for these new markers in peanut breeding, particularly in the validation of successful FGK and YB were responsible for the molecular work; AF was responsible for the bioinformatic analysis; AP was responsible for RIL population analysis; IH conducted the field trials; RH managed the study and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.