Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 https://doi.org/10.1186/s12711-020-0530-2 Ge n e t i c s Se l e c t i o n Ev o l u t i o n SOFTWARE Open Access SeqBreed: a python tool to evaluate genomic prediction in complex scenarios Miguel Pérez‑Enciso1,2* , Lino C. Ramírez‑Ayala1 and Laura M. Zingaretti1,3 Abstract Background: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3. Results: SeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data. Conclusions: SeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide associa‑ tion studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https​://githu​b.com/migue​ lpere​zenci​so/SeqBr​eed. Background about the genetic basis of complex traits. Thus, it is Genomic prediction (GP) is a method whereby DNA highly advisable to evaluate its potential advantages and polymorphism information is used to predict the breed- expected performance in advance. GP accuracy depends ing value of individuals for complex traits. The availabil- on a large number of factors. Several of these can be ity of high-throughput single nucleotide polymorphism controlled by the practitioner, to some extent, such as (SNP) genotyping in a cost-effective manner has led GP the number of SNPs, number of individuals, selection to become a standard tool in the analysis and improve- intensity, and the evaluation method. Other factors can- ment of complex traits [1]. GP has revolutionized breed- not be modified, such as linkage disequilibrium and are ing programs in plants and animals and, today, GP even unknown (genetic architecture). Although several methods are also widely used in human genetics or ecol- approximations of the accuracy of GP have been devel- ogy. Nevertheless, GP is more expensive than traditional oped, e.g. [2, 3], it remains difficult to analytically assess pedigree-based breeding. GP can be difficult to imple- the influence of these factors in practical scenarios across ment in practical scenarios, due in part to the difficulty generations. For this purpose, stochastic computer simu- of optimizing genotyping strategies and to uncertainty lation is the most reliable option. Although critical fac- tors such as the detailed genetic architecture of complex *Correspondence: miguel.perez@uab.es traits are unknown, the main genetic parameters are rea- 1 Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, sonably well known for most complex traits, such as her- 08193 Bellaterra, Barcelona, Spain itability and the distribution of genetic effects, which can Full list of author information is available at the end of the article © The Author(s) 2020. 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The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/publi​cdoma​in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 2 of 9 be approximated by a gamma distribution [4–6]. Thus, a SeqBreed is programmed in python3 using an object- simulation study can be performed to evaluate the effect oriented paradigm. The generic SeqBreed flowchart of the number of causal loci quantitative trait nucleotides is visualized in Fig. 1. As input, SeqBreed minimally (QTN) and of their location to assess the robustness of requires a genotype file from the founder base population predictions. in vcf [8] or plink-like format [9]. A typical SeqBreed run Here, we present a versatile python3 forward simu- consists of the following steps: lation tool, SeqBreed, to evaluate GP performance in generic scenarios and with any genetic architecture (i.e., 1. Upload founder sequence genotypes in vcf or plink number of QTN, their effects and location, and the num- format. The program automatically determines ber of traits). The purpose of SeqBreed is to generate ploidy and the number of chromosomes and SNPs. phenotype and genotype data of individuals under differ- 2. Specify genome characteristics. Sex-linked SNPs ent (genomic) selection strategies. SeqBreed is inspired and/or recombination rates can be specified. by a previous pSBVB fortran software program [7], but 3. Specify desired heritabilities and causal SNPs (QTN) the code has been rewritten in python3 and many new and their effects for every trait. Environmental vari- options have been added. Python can be much slower ances are inferred given founder genomes, QTN than compiled languages, but is much easier and friend- effects and heritabilities. lier to use, allowing direct interaction with the user to, 4. Offspring genomes and phenotypes are simulated by e.g., make plots or control selection and breeding deci- gene-dropping along a predetermined pedigree or by sions. In addition, many libraries in python, such as implementing selection. ‘numpy’ (https​://numpy​.org/) or ‘pandas’ (https​://panda​ s.pydat​a.org/), are wrappers on compiled languages, PCA plots or GWAS options are also implemented. such that careful programming significantly alleviates The main python classes are: the limited speed of native python. Thus, SeqBreed is much more versatile than pSBVB and incorporates many • Population: This class contains the main attributes new options, such as genome-wide association stud- for running selection experiments and is a container ies (GWAS) and principal component analysis (PCA). for Individual objects. It includes methods to add Most importantly, it allows automatic implementation new individuals generated by mating two parents of standard genomic selection procedures. Usage details or randomly shuffling founder genomes in order to and the main features of SeqBreed are described in the increase the number of base population animals (see following and in the accompanying GitHub site https​:// [10]). It also prints basic population data and sum- githu​b.com/migue​lpere​zenci​so/SeqBr​eed. mary plots. • Individual: It allows generation, manipulation, and Implementation printing of the genotypes and phenotypes of individ- Outline uals. Internally, an individual’s genome is represented Broadly, SeqBreed takes genotype/sequence data from by contiguous non recombining blocks rather than a founder population and simulates phenotypes accord- by the list of all SNP alleles, which allows dramatic ing to a predetermined genetic architecture. Offspring savings in memory and increases in efficiency (see genomes and phenotypes can be simulated under selec- Figure 1 in Pérez-Enciso et al. [10]). tion or random drift. By default, selection is simulated • Genome: All genome characteristics are stored and across a predetermined number of generations and selec- can be accessed by methods in this class. It speci- tion intensities. SeqBreed offers extensive flexibility to fies ploidy, number and class of chromosomes, and the user. For example, accuracy of GP with several SNP recombination rates or SNP positions. arrays can be simultaneously compared using the same • GFounder: SeqBreed requires as minimum input the data; offspring of specific pairs of parents can be gener- genotypes of the so-called ‘founder population’, which ated; and dihaploid offspring can be simulated. SeqBreed comprises the parents of the rest of the individuals to can be run using scripts or interactively, where the user be generated. This class stores these genotypes and can, say, obtain plots for each generation or generate automatically retrieves main genome features such genotype data of a given set of individuals. Examples of as SNP positions, number of chromosomes, etc. Ini- the program’s usage are in the GitHub’s jupyter notebook tial genotypes can be filtered by minimum allele fre- https​://githu​b.com/migue​lpere​zenci​so/SeqBr​e ed/blob/ quency (MAF). maste​r/SeqBr​eed_tutor​ial.ipynb​ and in the python script • QTN: This class determines the genetic architecture https​://githu​b.com/migue​lpere​zenci​so/SeqBr​e ed/blob/ for each trait simulated. It has methods to determine maste​r/main.py. the environmental variance given a desired heritabil- Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 3 of 9 QTNs location and effects Founder genotypes Recombination map Input files Founder population (G,y) Working Data Output Gene dropping Pedigree Genotypes PCA, GWAS, Phenotypes QTN plots Population (G,y) Text files Pedigree variance EBVs … SNP arrays Mating New Selected Parents Offspring Selection intensity Fig. 1 Outline of the SeqBreed pipeline. Inputs are shown in red squares, dashed border rectangles represent optional input, internal data are in blue rounded squares, main operations are indicated in blue, and outputs are in green circles; G and y refer to genotypes and phenotypes, respectively. The program starts with an optional gene dropping step following an input pedigree. No selection is performed at this stage. The bottom loop represents selection, where new offspring are generated based on the genotypes of selected parents. A list of SNPs in the genotyping array must be determined when using GBLUP and BLUP. A new cycle starts when these new offspring are added to the existing population. Plots can be performed at several stages ity, and to plot variance components for QTN. In its Specifying genome features and genetic architecture current version, SeqBreed allows for dominance and By default, SeqBreed assumes that all loci are autosomal additive actions, but not epistasis. and a recombination rate of 1 cM = 1 Mb throughout the • Chip: This class is basically a container for the list genome. It includes options to specify sex or mitochon- of SNPs that are included in a genotyping array. It drial chromosomes, and local and sex specific recombi- allows easy comparison of different genotyping strat- nation maps. A pseudo-autosomal region (PAR) is not egies in genomic selection. accommodated for sex chromosomes, i.e., the whole Y chromosome is assumed to be non-recombining. A Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 4 of 9 mitochondrial chromosome is a non-recombining chro- where nQTN is the number of QTN, aj is the additive mosome that is transmitted maternally. SeqBreed allows effect of the j-th QTN, that is, half the expected differ- for autopolyploidy of any level, which is automatically ence between homozygous genotypes, with γij taking the detected from vcf files. Accurate modeling of meiosis in values − 1, 0 and 1 for homozygous, heterozygous, and polyploids is notoriously difficult [11, 12] and SeqBreed alternative homozygous genotypes, respectively, dj is the implements a simplified algorithm: dominance effect of the j-th QTN, with δij taking the value 1 if the genotype is heterozygous and 0 otherwise. 1. For each chromosome id, homologs are randomly In the case of polyploids: paired. nQTN nQTN 2. Within each pair of homologs, cross-over events are   gi = ηij aj + ϕij dj , generated as for diploids, i.e., no interaction between j=1 j=1 homologous chromosomes is modeled and the num- ber of cross-over events is simulated following a Pois- where ηij is the number of copies of the alternative allele son distribution with a rate equal to chromosome (coded as 1) minus half the ploidy for the j-th QTN length in Morgans. and the i -th individual, and aj is therefore the expected 3. Sex chromosomes are modeled with a maximum change in phenotype per copy of allele ‘1’ at the j-th ploidy of 2. QTN. In polyploids, technically as many dominance coef- ficients as ploidy levels (h) minus two can be defined, Therefore, our algorithm does not fully model the which is not practical. As in pSBVB [7], we define ϕij as interaction of preferential pairing of homologous chro- the minimum number of copies of allele ‘1’ such that mosomes and double reduction arising from multivalent the expected phenotype is d (see Figure 1 in Zingaretti formation [13]. For the purposes of this software (i.e. et al. [7]). SeqBreed uses ϕij = 1, that is, all heterozygous comparison of GP strategies over a limited number of individuals have the same genotype value as the com- generations), it is unlikely that this approximation has a plete homozygous ‘1’. SeqBreed computes genotypic val- dramatic effect. ues for each individual and simulate phenotypes from SeqBreed allows the simulation of any number of yi = µ + gi + ei , where µ is a constant and e is a normal phenotypic traits, regardless of ploidy. For each trait, deviate e ∼ N (0, Var(e)). broad-sense heritability must be specified. There are For multiple traits, the user needs to specify additive three options to specify the number of QTN and their and dominant QTN effects separately for each trait. This effects (https​://githu​b.com/migue​lpere​zenci​so/SeqBr​ is done via an external text file, where additive and domi- eed#3-speci​f ying​-genet​ic-archi​tectu​re): (i) a random nant QTN effects are specified for each trait (option 3 in number of QTN positions are sampled genome-wide https​://githu​b.com/migue​lpere​zenci​so/SeqBr​eed#3-speci​ and additive effects are sampled from a gamma distri- fying​-genet​ic-archi​tectu​re). There is no specific assump- bution Ŵ (shape = 0.2 and scale = 5), as suggested by tion on genetic correlations between traits. To simulate Caballero et al. [5]; (ii) the positions of the QTN are no pleiotropy, QTN for each trait have zero effects for specified in a file and additive effects are sampled from all other traits. Note that this does not prevent a non- a gamma distribution; and (iii) QTN positions and addi- zero genetic correlation arising from linkage disequilib- tive and dominant effects for each trait are specified rium. The program does not automatically adjust desired in an external file. By default, QTN are not removed genetic correlations and, thus, different QTN values may from the sequence data to perform genomic evalua- need to be tested to fit desired correlations. Environmen- tion. To remove QTN from evaluation, a SNP chip can tal correlations are always zero. be defined that excludes the QTN. Options (i) and (ii) It is typically difficult to find real sequence data to gen- can only be used with one trait and without dominance. erate a reasonably sized founder population. To accom- SeqBreed adjusts the environmental variance Var(e) to modate this, SeqBreed can generate ‘dummy’ founder retrieve the desired   broad-sense  2 heritabilities (H ) from 2 individuals by randomly combining recombinant hap- Var(e) = Var g × 1 − H /H , where Var g is the 2 lotypes. This can be done in two ways, either by gener- variance of the genotypic values of individuals in the ating a random pedigree and simulating a new founder founder population. The genotypic value for individual i individual by gene-dropping along this pedigree, or by is defined as: directly simulating a number of recombining breakpoints nQTN nQTN and assigning random founder genotypes to each block gi =  γij aj +  δij dj , between recombination breakpoints (https​://githu​b.com/ migue​ l pere​ z enci​ s o/SeqBr​ e ed/blob/maste​ r /READM​ j=1 j=1 E.md#breed​ing-popul​ation​). Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 5 of 9 Gene dropping and selection implementation are predicted using the chosen evaluation method and Seqbreed can be run along a predetermined pedigree marker information. By default, the data from all indi- or using a combination of options (several examples are viduals across the current and previous generations are provided in the GitHub site). It is also possible to gener- used, but this can be changed by specifying the subset ate new individuals interactively, including dihaploids. To of individuals to be used. Second, a function is used to speed up computations and avoid unnecessary memory generate offspring from selected parents. This function usage, only recombination breaks and ancestor haplotype requires specifying the candidates for selection (allowing ids are stored for each individual (see Figure 1 in [14]). for continuous or discrete generations), selection inten- SeqBreed allows computing estimated breeding val- sity, family size, and either assortative or random mating ues using several GP methods. It also allows several lists between selected parents. Hierarchical mating between of SNPs (SNP chips) to be defined, such that GP per- females and males is employed by default (https​://githu​ formance can be easily compared across chips. From a b.com/migue​lpere​zenci​so/SeqBr​e ed#7-imple​menti​ng- methodological point of view, most GP implementations selec​tion). Assortative and random mating schemes are are based on penalized linear methods (e.g., de los Cam- implemented; more sophisticated mating schemes, such pos et al. [15]). SeqBreed includes some of the most pop- as those based on optimal contributions [19, 20], have to ular GP options, including pedigree BLUP [16], GBLUP be specified manually by modifying the function ‘Return- [17], and single-step GBLUP [18]. Only single trait GP NewPed’ in the selection module (https​://githu​b.com/ algorithms are implemented so far. Mass selection is also migue​lpere​zenci​so/SeqBr​eed/blob/maste​r/src/selec​tion. implemented. For GBLUP and single-step GBLUP, the py). genomic relationship matrix G is obtained using Van- Raden [17] as: Visualization A novel feature of SeqBreed, as compared to our previ- XX′ ous software pSBVB, is the capability of graphical out- G = nSNP  , 2 j=1 pj 1 − pj puts. Figure 2 illustrates some of the plots that can be performed automatically. Figure 2a shows the results of where X is a N × nSNP matrix containing genotypes the QTN.plot() function, which plots the individual QTN (coded 0,1,2 deviated from the mean) for SNPs on the variance as a function of MAF, the histogram of QTN chip, pj is the allele frequency of the j-th SNP, N is the variances, and the cumulative variance when QTN are number of individuals, and nSNP is the number of SNPs. sorted by MAF. This is performed for each phenotype To avoid potential singularity problems, diagonal ele- and for both additive and dominance variances, based ments of G are multiplied by 1.05. SeqBreed requires on allele substitution effects α = a + d(1 − 2p), where p that heritabilities to be used in BLUP or GBLUP are pro- is the minimum allele frequency, and assuming complete vided (i.e., they are not estimated). The program allows equilibrium. In addition, PCA plots using all sequence or the incorporation of other custom GP methods based custom defined SNP sets (Fig. 2b) are available, as well on a user python function or by exporting SNP data and as GWAS plots showing p-values or false discovery rate phenotypes from SeqBreed, running a genetic evaluation, (FDR) values (Fig. 2c). Genotype and phenotype data can externally and then importing the resulting estimated also be exported in text files. breeding values. Selection can be automatically configured and run, Usage and examples as documented in the GitHub examples (https​://githu​ The basic functioning of SeqBreed is illustrated by the b.com/migue​lpere​zenci​so/SeqBr​eed). Running a selec- main.py script that is available at https​://githu​b.com/ tion scheme requires specifying the number of genera- migue​lpere​zenci​so/SeqBr​eed/blob/maste​r/main.py. This tions, the numbers of females and males to be selected, script, or its equivalent jupyter notebook (SeqBreed_ and the number of offspring per female. SeqBreed splits tutorial.ipynb), shows the basic commands to run Seq- the selection process in three steps, which allows a fine Breed and import the required modules. First, SeqBreed control over the breeding program. First, breeding values modules are imported as: from SeqBreed import genome as gg from SeqBreed.selection import selection as sel Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 6 of 9 Fig. 2 Example plots produced by SeqBreed. a Contribution of each QTN to total variance. Top, individual QTN variances as a function of minimum allele frequency (MAF); middle, histogram of QTN variances; bottom, cumulative variance when QTN are sorted by MAF. In blue, additive variances; in red, dominance variances. The figure shows a fully additive phenotype, thus dominance variances are zero. b Principal component analysis plot; individuals of different generations are in different colors. c Genome-wide association study showing false discovery rate values (− log10 scale). SNPs on different chromosomes are represented in alternate colors Founder population genotypes are uploaded from the information about SNP positions, which are used in the vcf file, using the command: next step. gbase = gg.GFounder(vcfFile=vcffile, snpFile=seqfile) which generates a GFounder object that contains Next, the main genome features are specified. The fol- founder genotypes, vcffile is the file containing genotypes lowing command creates a Genome object that assumes and seqfile is generated by the program and contains that the ‘X’ chromosome is the sex X chromosome, while SNPs on the chromosome named ‘MT’ are mitochondrial. # seqfile is a file obtained in previous step gfeatures = gg.Genome(snpFile=seqfile, mapFile=mapfile, ploidy=gbase.ploidy, XChr='X', MTChr='MT') Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 7 of 9 Genetic architecture can be specified in different We illustrate the software with sequence data from the ways. The simplest is to generate nqtn QTN randomly Drosophila genome reference panel (DGRP, [21]), parsed distributed along the genome with effects sampled and filtered as explained in [22], and genotype data from from a gamma distribution, where h2 is the desired tetraploid potato [23], parsed as described in [7]. Data heritability. and scripts are in https​://githu​b.com/migue​lpere​zenci​so/ # 10 QTNs are simulated, h2 of the trait is 0.7 qtn = gg.QTNs(h2=[0.7], genome=gfeatures, nqtn=10) # environmental variances are computed qtn.get_var(gfeatures, gbase) Selection is implemented in cycles, the number of gen- SeqBr​eed/tree/maste​r/DGRP and in https​://githu​b.com/ erations, the numbers of males and females selected, and migue​lpere​zenci​so/SeqBr​eed/tree/maste​r/POTAT​O for family size must be specified. The following is an example the Drosophila and potato examples, respectively. The with GBLUP selection, random mating, and continuous DGRP scripts illustrate the specific recombination map generations. of Drosophila, where males do not recombine, as shown ngen = 5 # no. of selection generations nsel = [5, 10] # no. of males and females selected noffspring = 10 # no. offspring per female # selection cycles for t in range(ngen): # STEP 0: generate marker data for evaluation, stored in X matrix # pop is a Population object containing individual genomes and phenotypes # chip0 is a Chip object containing SNPs to be used in genomic evaluation X = gg.do_X(pop.inds, gfeatures, gbase, chip0) # STEP 1: estimate breeding values using criterion GBLUP, assuming h2=0.3 # criterion can take values 'random', 'phenotype', 'blup', 'gblup' or 'sstep' sel.doEbv(pop, criterion='gblup', X=X, h2=0.3, nh=gfeatures.ploidy) # STEP 2: pedigree with offspring of selected individuals # mating can be ‘assortative’ or ‘random’ # generation indicates that individuals from generation onwards are considered as selection candidates newPed = sel.ReturnNewPed(pop, nsel, famsize=noffspring, mating='random', generation=0) # STEP 3: generates new offspring (this function adds QTN genotypes, true bvs and phenotypes) # New individuals are added to current Population pop.addPed(newPed, gfeatures, qtn, gbase) Pérez‑Enciso et al. Genet Sel Evol (2020) 52:7 Page 8 of 9 in the ‘dgrp.map’ file. The example provided in GitHub SEV-2015-0533. LCRA is funded by “Don Carlos Antonio López” Graduate program (BECAL) from Paraguay. consists of a small experiment to compare genomic and mass selection. Plots in the jupyter notebook are imple- Availability of data and materials mented to track phenotypic changes by generation. The https​://githu​b.com/migue​lpere​zenci​so/SeqBr​eed Availability and requirements: Project name: SeqBreed. Project home page: potato scripts illustrate how to generate an F2 cross https​://githu​b.com/migue​lpere​zenci​so/SeqBr​eed. Operating systems: Tested between extreme lines and to perform a GWAS experi- in linux and mac. It should also run in windows python. Programming lan‑ ment in polyploids. GWAS results using PCA corrected guage: Python. License: GNU GPLv3. Any restrictions to use by non-academics: None. phenotypes are also shown. Ethics approval and consent to participate Conclusions and future developments Not applicable. Several other programs have been developed for simi- Consent for publication lar purposes as SeqBreed, including our own pSBVB [7], Not applicable. AlphaSim [24] and its successor AlphaSimR (https​:// Competing interests alpha​genes ​.rosli​n.ed.ac.uk/wp/softw​are-2/alpha​simr/), The authors declare that they have no competing interests. PedigreeSim [13], simuPOP [25], and QMSim [26]. How- ever, SeqBreed offers a unique combination of features Author details 1 Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, for simulation of GP of complex traits, including built- 08193 Bellaterra, Barcelona, Spain. 2 ICREA, Passeig de Lluís Companys 23, in implementation of several GP methods, the possibil- 08010 Barcelona, Spain. 3 Universidad Nacional de Villa María, IAPBCyA-IAPCH ity of simulating polyploid genomes, and several options Villa María, Córdoba, Argentina. to specify QTN and SNP arrays. It also allows new indi- Received: 27 August 2019 Accepted: 29 January 2020 viduals to be generated interactively and provides graphi- cal plots of results. It is easy to use, easy to install, and software options are illustrated with several examples in the GitHub site. Given the interactive nature of python References and its graphical features, SeqBreed is especially suited 1. Meuwissen T, Hayes B, Goddard M. 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