Cucumis sativus L.) fruit, and its molecular regulatory mechanism is poorly understood due to its environmental sensitivity. In the previous research, we obtained the hollow and the non-hollow materials of ecotype cucumbers of South China, which were not easily affected by the external environment through a systematic breeding method. In this study, first, we proposed to use the percentage of the hollow area as the criterion to compare the hollow characteristics between two materials, and to analyze the formation mechanism of early hollow trait from the perspective of cytology. The results showed that the hollow trait occurred in the early stage of fruit development, and formed with the opening of carpel ventral zipped bi-cell layer, which formed rapidly from 2 to 4 days, and then slowed to a constant rate from 14 to 16 days. Meanwhile, the different genetic populations were constructed using these materials, and fine mapping was performed by bulked segregant analysis (BSA) and kompetitive allele specific PCR (KASP) method. The Csa1G630860 (CsALMT2), encoding protein ALMT2, was determined as a candidate gene for regulating the hollow trait in fruit. Furthermore, the expression profile of CsALMT2 was analyzed by qRT-PCR and fluorescence in situ hybridization. The expression of CsALMT2 had obvious tissue specificity, and it was abundantly expressed in the ovule development zone inside the fruit. In the hollow material of cucumber fruit, the expression of CsALMT2 was significantly downregulated. The subcellular localization in tobacco leaves indicated that CsALMT2 was distributed on the plasma membrane. In conclusion, in this study, for the first time, we found the regulatory gene of hollow trait in cucumber fruit, which laid the foundation for subsequent research on the molecular mechanism of hollow trait formation in cucumber fruit, and made it possible to apply this gene in cucumber breeding.The hollow trait is crucial for commercial quality of cucumber ( Cucumis sativus L., 2n = 2x = 14) is one of the vegetable crops with important agricultural, economic, and nutritional value in the world [http://www.fao.org/faostat/zh/#data/QC; accessed on 8 March 2022). The biological basis of cucumber fruit development and morphogenesis has always been a hot spot for research. Recent researches have mainly focused on fruit shape, fruit size, fruit quality, pulp and ventricular development, and peel and spine development [Solanum lycopersicum) than in cucumber [Cucumber , and cucumber, and is closely related to abnormal ventricular development during the formation of ovary tissue [fw2.2 and fw3.2 that control cell division in carpels and peels [fas that encodes a transcription factor YABBY-like, which controls the size of tomato fruit by regulating the number of fruit ventricles [AFF gene. In this phenotype, after the ovary tissue changed from gel-like material to solid tissue, the hollow trait of tomato fruit was formed because the ventricle could not be filled [CsCLV3 acts as a repressor, while CsWUS functions as an activator for carpel number variation in cucumber. CsWUS can directly bind to the promoter of CsCLV3 and activate CsCLV3 expression, while CsCLV3 indirectly suppresses the activity of CsWUS. ACsFUL1 has been shown to positively contribute to an increase in carpel number by directly binding to the promoter of CsWUS and promoting its expression. Auxin participates in carpel number regulation through interaction of CsARF14 and CsWUS [The hollow trait exists in many plant fruits, such as tomato, y tissue ,8,9. They tissue ,11. The nd peels ,13 and tntricles , have bee filled . Cucumbee filled . Each vee filled . Previound CsWUS ,17.C. sativus var. sikkimensis) [mfh3.1 in material ”WI7088D” could reduce the hollow size of fruit, while the allele of mfh1.1 and mfh2.1 could increase the hollow size of fruit [CsGID1a has been reported to be closely related to locule formation in cucumber fruit. Silencing of cucumber CsGID1a might lead to abnormal development of carpels and ventricles and hollow trait in cucumber fruit, which would seriously affect the formation and development of seeds; however, the phenotype of hollow trait was significantly different from that of Sikkim cucumbers, which did not affect seed development [The hollow trait of cucumber fruit was first discovered by Hook 1876) in the mature fruit of Sikkim cucumber (76 in theimensis) . Long-teimensis) . Recent of fruit . The expelopment . TherefoDue to the lack of experimental materials with low environmental impact and the stable hollow trait of cucumber varieties, it is difficult to develop insight into the genetic and regulatory mechanisms of the hollow trait in cucumber fruit. In this study, the cucumber variety H7 with non-hollow fruit and cucumber variety H6 with hollow fruit were selected as experimental materials, and fine mapping was carried out combining phenotypic analysis, cytological observation, genetic analysis, MutMap, and kompetitive allele specific PCR (KASP); the expression characteristics and location of the gene controlling hollow trait in cucumber fruit were also analyzed. The results might be helpful to understand the molecular mechanism of hollow formation in cucumber fruit, and provide useful information for cucumber breeding.The hollow traits of 29 new varieties of open field cucumbers (12 North China ecotype cucumber varieties and 17 South China ecotype cucumber varieties) were observed during the implementation of the key R&D project of the 13th five-year plan. The results showed that none of the North China ecotype cucumber varieties had hollow fruit, while 15 of the 17 South China ecotype cucumber varieties had varying degrees of fruit hollowness. The proportion of varieties with hollow fruit accounted for 88.24% . 2 generation, indicating that there was no inevitable relationship between the hollow trait and the number of ventricles. From the autumn of 2018 to the spring of 2020 in Hunan, the hollow trait in H6 and H7 fruit was observed for four times in the greenhouse. The ratio of hollowness in cucumber fruit was 100% in H6, while it was 0% in H7. During fruit development, the change trends were the same in fruit cross-sectional areas, but the cross-sectional area of H6 was slightly larger  and H7 (non-hollow fruit) with stable hollow/non-hollow traits A were oby larger B. The hoy larger C. In ordy larger .The carpels of H6 and H7 fruits at different stages of early development were observed D. The ve1 (H6), P2 (H7), F1 (H6 × H7), and F2 (1 (H6) was mainly between 8.1% and 10.0%. The trait of non-hollow material parent P2 (H7) was stable, and the hollow area percentage was 0%. The average hollow area percentage of F1 was distributed between 4.1% and 6.0%. The separated population F2 had a single peak with a left bias normal distribution, and the single peak distribution ranged from 0.1% to 2.0%, suggesting that there might be a major gene which was affected by the environment , suggesting that the hollow trait of cucumber fruit was regulated by a single dominant gene in this population. Therefore, two pools were established to identify the causal gene using bulked segregant analysis (BSA) [In 1965, the quantitative geneticist Falconer formally defined the threshold. The definition of the threshold, as the point on the scale of liability above which all individuals are affected and below which no one is affected, provides a fixed point for comparing different incidences of different populations or groups . Using t2 B, it wasis (BSA) .2 hollow, and F2 non hollow) were used for genome resequencing, and a total of 23.9 GB of sequence data were obtained. The GC content of samples ranged from 38.91% to 39.59%, and the Q30 value of all samples was greater than 90.29%, which indicated the output and quality of the sequencing data could fully meet the analysis requirements  using the BWA software  and H7 (non-hollow fruit) as parents to verify whether the above regions were correct. Ten SNPs were mined based on the larger region containing the above region on chromosome 1. These SNPs were used to screen the recombinant individuals of the F2 generation population , and KASP was used for genotyping , and we named it CsALMT2 for its homology to AtALMT2 in Arabidopsis.We constructed the Fnotyping B. FirstlCsALMT2 genes in H6 and H7 were amplified and sequenced, respectively. The sequence comparison showed that CsALMT2 had four SNPs in the exon of H6 and H7, of which three were distributed on exon 1 and only one on exon 2. The sequences of the remaining three exons in H6 and H7 were completely identical. Further analysis revealed that only the transversion G82A (SNP28756924) on exon 1 of the four SNPs resulted in the amino acid changing from alanine to threonine (A28T) (C. melo), wax gourd (Benincasa hispida), and the sequenced reference genome varieties , suggesting that the amino acid changes at this position affected the protein function, and led to the hollow phenomenon of cucumber   A. CsALMTe (A28T) . In the cucumber . The seqcucumber . In addit, etc.) . These rCsALMT2 in H6 and H7 at different stages , an in situ hybridization technology using fluorescence signal detection probe, has been the most important technology in plant cytogenetics . The FISCsALMT2 into the vector. Subsequently, Agrobacterium tumefaciens carrying 35S: CsALMT2 GFP vector was transiently transferred to tobacco, cultured under weak light for two days, and then observed and photographed by a laser confocal microscope (Nikon C2-ER). The results showed that the expression intensity of ALMT2-GFP was weaker than that of the GFP control, but the GFP signals were distributed over the structures of cell membrane and plasma membrane, and possibly coincided with the chloroplast  and H7 (non-hollow fruit) with stable hollow traits. Based on this, in this study, we further improved the judgment standard for hollow trait. Wang et al., in a study on the hollow trait of Sikkim cucumber, introduced two standards, namely, immature fruit hollow size and mature fruit hollow size, to measure the degree of hollowness in fruit . In thisThe development of cucumber fruit is mainly due to changes in cell number and volume ,29,30. EArabidopsis ALMT2 homologous gene CsALMT2 (Csa1G630860) was finally identified as a candidate gene to regulate the hollow trait in fruit  and H7  with stable hollow/non-hollow trait were obtained in the seventh generation by systematic breeding method. The experiments were carried out in the greenhouse of the Hunan Vegetable Research Institute from August 2016 to June 2019.1 seeds were sown in the spring of 2020, the F2 was obtained by selfing at the flowering stage, then four genetic generations, i.e., P1, P2, F1, and F2, were constructed, the seeds of which were sown in the autumn of 2020. The sample sizes of lines in each population were 19 for P1, 20 for P2, 20 for F1, and 345 for F2. The seedlings were raised in seedling tray and transplanted in the greenhouse. Genetic analysis of each population was carried out in the greenhouse.In the autumn of 2019, a hybrid combination H6 × H7 was created with cucumber H6 (hollow fruit) as the female parent and H7 (non-hollow fruit) as the male parent in the greenhouse of the Hunan Vegetable Research Institute. The FThe hollow traits of 29 new varieties of open field cucumber (12 North China ecotype cucumber varieties and 17 South China ecotype cucumber varieties) were investigated in the spring of 2019. ZhongNong38, ZhongNong48, ZhongNong106, 18C54, and 18C56 were provided by the Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences; YD109 and YD110 were provided by the Yangzhou University; HN1, HN2, HN3, and HN4 were provided by the Hunan Vegetable Research Institute; JYXM, JYXM2, and JYCQL2 were provided by the Beijing Yinong Vegetable Research Center; YueFeng, LiFeng2, LiFeng3, and ZaoQing8 were provided by the Vegetable Research Institute of Guangdong Academy of Agricultural Sciences; 15D036, 15D044, 15D088, 15D095, and YanBai were provided by the Chongqing Academy of Agricultural Sciences; ChuanCui13, ChuanLv11, ChuanLv15, ChuanCai19-1, ChuanCai19-2, and ChuanCai19-3 were provided by the Sichuan Academy of Agricultural Sciences. There were 40 plants in each variety, and we investigated whether there were more than three hollow fruits in each variety during the commercial fruit period.The hollow traits of the cross section in the middle segment of fruit at each stage of plant growth were investigated. The cross-sectional areas of the middle segment of all fruits were scanned with Deli altimeter . The total area and hollow area of each cross section were calculated using the Adobe Photoshop CS6 software. Finally, the percentage of hollow area was calculated with the statistical data of Microsoft Excel. The calculation formula was: Hollow area percentage =  × 100%.H6 and H7 (20 plants each) were planted in the greenhouse of the Hunan Vegetable Research Institute, and managed with conventional water and fertilizer. After 40 days of pollination, the number of hollow fruits and the total number of fruits of the two materials were recorded. We calculated the population hollow rate according to the formula: Population hollow rate =  × 100%. From the spring of 2018 to the spring of 2019, the population hollow rate of the two materials was investigated three times.We collected the fruits of H6 and H7 plants at different stages . The middle segment of fruit was cut and fixed in FAA  fixative (50%) for 24 h. We took out the tissue from the fixed solution, trimmed the target tissue with a scalpel in the fume hood, and put the trimmed tissue and the corresponding label in the dehydration box. Then, it was dehydrated and dipped in wax with gradient alcohol in the dehydrator. The wax-soaked tissue was embedded in the embedding machine. The trimmed wax block was put into the paraffin slicer for slicing, with a thickness of 4 μm. The slices floated in the warm water at 40 °C of the spreader to flatten the tissue, then the tissue was placed on a glass slide and baked in an oven at 60 °C. After the water was dried and the wax was roasted, it was taken out and stored at room temperature for standby. The slices were put into xylene I for 20 min, xylene II for 20 min, absolute ethanol I for 5 min, absolute ethanol II for 5 min, 75% alcohol for 5 min, and rinsed with tap water. The slices were put into aniline blue dye solution for 5–10 min, washed with tap water, and dried in an oven at 60 °C. The slices were put into xylene for 5 min and sealed with neutral gum. Finally, the upright optical microscope was used for microscopic examination and image analysis data were collected. Three samples were collected in each period and each sample was repeated three times.2 separated population were selected to construct the mutant DNA pool and the wild-type DNA pool, named F2 hollow and F2 non-hollow, respectively. DNA was extracted using the CTAB method [2 hollow and F2 non-hollow) were obtained. DNA samples were randomly broken into 350 bp fragments with crusher . The complete library was constructed by end repair, adding poly(A) tails, then adding sequencing connectors, followed by purification, PCR amplification, and other processes. After the library was constructed, we used Qubit 2.0  for preliminary quantification. The library was diluted to 1 ng/μL and the insertion size of the library was detected using Agilent 2100 . The effective concentration of the library was accurately quantified by Q-PCR (effective library concentration >2 nm). After the qualified library was constructed, it was sequenced by Illumina NovaSeq .Twenty-five hollow fruit plant leaves and 25 non-hollow fruit plant leaves from the FB method . Two parhttp://bio-bwa.sourceforge.net/; accessed on 29 January 2021) software (Version 0.7.12) was used to compare reads with 9930 cucumber reference genomes  [http://www.htslib.org/; accessed on 29 January 2021) software (Version v0.1.18) was used to find genome-wide SNP sites [2 mixed pools. The SNP index of the recessive pool minus the dominant pool was used for mapping.The BWA (ry 2021) . The SamNP sites . The mutNP sites . The SNP2 individual plants were used, including 259 plants that showed the hollow fruit phenotype and 87 plants that showed the non-hollow fruit phenotype. The SNP information of the target gene candidate region was detected on the LGC genomics typing platform at the Vegetable Center of the Beijing Academy of Agricultural and Forestry Sciences. The 100 bp DNA sequence upstream and downstream of each SNP site was intercepted as the KASP primer design template. A total of 3 primers (5′–3′ direction) were designed: Primer_Allele FAM, Primer_Allele HEX, and Primer_Common. The primer design information is shown in the For the KASP genotyping, 346 Fhttp://www.ebi.ac.uk/interpro/; accessed on 26 February 2022) online analysis software to predict the protein domain [http://bioinformatics.psb.ugent.be/webtools/plantcare/html/; accessed on 26 February 2022) online analysis software [We used the InterProScan  (2×), 10.0 μL; PCR Forward Primer (10 μM), 0.5 μL; PCR Reverse Primer (10 μM), 0.5 μL; ROX Reference Dye (50×), 0.4 μL; DNA template, 1.0 μL; ddH2O, 7.6 μL. The reaction was implemented in a fluorescence quantitative PCR instrument . The relative gene expression levels were analyzed according to the 2−∆∆Ct method. The internal normalization gene was CsActin.The middle segments in H6 and H7 fruit were collected at different stages . Eight types of tissue samples were collected from H7, including sprout, flower, leaf, root, stem, tendril, and fruit. The samples of each material were collected repeatedly for three times. A TaKaRa MiniBEST Plant RNA Extraction Kit  was used to extract the total RNA of the sample, and the test was carried out according to Protocol-II. The cDNA synthesis was performed using a TaKaRa PrimeScript RT reagent Kit with gDNA Eraser . After reverse transcription reaction, TB Green Premix Ex Taq II (Tli RNaseH Plus) (Takara Bio Inc.) was used for RT-PCR detection. The primer was ALMT2-F: TTGGACGAGGATTGAATAGG; ALMT2-R: GAGCGACAGCATAATAGGT. Csa1g630860 probe: 5′-CY3-AAGCCACAGUCGCCAUCGGUACUAUUUCCAG-CY3-3′) was added dropwise at a concentration of 6 ng/μL. For the hybridization reaction, the sample was placed in a 42 °C incubator for one night. The hybridization solution was washed off with 2 × SSC at 37 °C for 10 min, with 1 × SSC two times at 37 °C for 5 min each time, and then, washed with 0.5 × SSC at room temperature for 10 min. The slices were added with DAPI dye solution and incubated in the dark for 8 min. An anti-fluorescence quenching sealing agent was added to seal the slices after washing. The slices were observed under an upright fluorescence microscope  and images were collected. The nuclei stained by DAPI were blue under UV excitation, and positive expression was red-light labeled with corresponding fluorescein (Cy3).The 0 DAA paraffin section of 2.4 was put into xylene for 15 min, xylene for 15 min, absolute ethanol for 5 min, and absolute ethanol for 5 min. The slices were dried under natural conditions and soaked in DEPC water. Protease K (20 μg/mL) was added dropwise and digested at 37 °C for 20 min. The slices were washed with pure water, and then washed with PBS, three times, for 5 min each time. Prehybridizing solution was added dropwise and incubated at 37 °C for 1 h. The prehybridization solution was poured out and the hybridization solution containing probe  and CELLO version 2.5, subcellular localization predictor  were used to predict CsALMT2 subcellular localizations [The WoLF PSORT web server . The recombinant product was transformed into E. coli DH5α cells by heat shock method. Then, the vector sequencing results were analyzed. The constructed vector plasmid was transferred into Agrobacterium GV3101 and evenly plated on a kanamycin-containing plate. The single clones were picked and cultured in YEB liquid medium for 2 days in a shaker at 28 °C, and the cells were collected by centrifugation at 4000 rpm for 4 min. After removing the supernatant, the bacteria were resuspended in 10 mM MgCl2 containing 120 μM AS, and the OD600 was adjusted to about 0.6. The A. tumefaciens suspension was sucked up using a 1 mL syringe without a needle and pressed into the lower epidermis of tobacco leaves. Tobacco plants were cultured in low light for 2 days. After 3 days, the tobacco leaves in the injection area were taken to make slides, observed, and photographed under the laser confocal microscope. A. tumefaciens transformed with empty vector was used as a control.Primers for construction of the vector 1301-ALMT2-GFP were CZ-ALMT2-BglII-F: AACACGGGGGACTCTTGACCATGGAGATGGCTAATGAG and CZ-ALMT2-BglII-R: GCCCTTGCTCACCATATCTATAGTAACAACATGACAATG. The vector pCambia1301-JC-GFP was linearized by CsALMT2. Sequencing showed that the transversion G82A on exon 1 of CsALMT2 resulted in an amino acid changing from alanine to threonine (A28T). Further, we used qRT-PCR and FISH to analyze the expression characteristics. The expression level of CsALMT2 in H7 was significantly higher than that in H6, and showed tissue specificity that the ovule development zone inside the fruit had high expression. The CsALMT2 gene may be localized to the plasma membrane. The results of this study should help to understand the molecular mechanism of the formation of hollow trait in cucumber fruit, and be benefical to develop molecular markers for seedling screening of high-quality cucumber varieties with non-hollow fruit.Few studies have reported on hollow-related genes in cucumber fruit because of the environmental sensitivity of hollow trait and the unavailability of near-isogenic line materials. In this study, we used the non-hollow and hollow materials of cucumber fruit created by our research group and investigated them over multiple generations. The phenotypic analysis and cytological observations revealed that the hollow trait in cucumber fruit was formed in the early stage of fruit development with the opening of the carpel ventral zipper bi-cell layer. The genetic analysis showed that there may be a single dominant gene affected by the environment that controls the hollow trait in cucumber fruit. Through the BSA-seq and KASP genotyping analysis, the hollow gene of cucumber fruit was finely located, and the candidate gene was preliminarily determined as


Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings. In recent years, genome-wide association studies (GWAS) for longitudinal traits  have been carried out in cohorts, where multiple measurements have been collected from each individual -7. AlthoThere are methodological challenges associated with the genetic analysis of longitudinal traits for multiple variants. Most complex traits are typically controlled by multiple variants that interact with each other or environmental factors. It may be exceedingly difficult to model all candidate variants with epistasis effect and gene-time/environment interactions for longitudinal traits because genetic data are generally high-dimensional relative to the number of samples. Bayesian multiple quantitative trait loci (QTL) mapping methods -16 have Several statistical methods have been proposed for dealing with within-subject variation. For data collected at the same time points across all individuals, the measured values at each time point can be treated as one variable. The data can then be treated as multivariate outcomes and jointly analyzed -23. For In this paper, we develop a Bayesian variable selection method for longitudinal data where phenotypes are not measured at a fixed set of time points for all samples. It jointly models the main and pairwise interactions of all candidate genetic variants. We propose a novel grid-based approach to parsimoniously model each subject's covariance matrix as a function of a covariance matrix defined on a set of pre-selected time points where each observed time point is mapped to its two adjacent grid time points via linear interpolation. This approach thus deals only with a covariance matrix of a fixed dimension. The covariance matrix is then modeled nonparametrically using the modified Cholesky decomposition of Chen and Dunson , which f-6, which resulted in 6, 247, 288 SNPs.For our simulation studies, we utilized the whole-genome sequencing data from the 1000 Genome Project, which created a catalogue of common human variations using samples from people who provided open consent who declared themselves healthy. It ran between 2008 and 2015, generating a large public catalogue of human variations and genotype data. We randomly selected 400 out of 504 individuals of East Asian (EAS) ancestry from the 1000 Genome Project data (phase 3 version 5) and then removed single-nucleotide polymorphisms (SNPs) with a minor allele frequency <5% and p(Hardy-Weinberg equilibrium) <10n individuals where individual i has phenotypes measured at in time points  and p SNPs. Let p, the number of SNP-SNP interaction terms to pq, where q is the number of covariates in the model, including time. We define λ= T as the SNP positions associated with the above genetic effects, where γ=T for the selection of genetic effects to be included in (iγ=1) or excluded from (iγ=0) the model. The vector  determines the number and positions of SNPs. For the ith individual, tix denotes the in×q design matrix of time/environmental covariates, gix denotes the in×p design matrix of the p SNPs, ggix denotes the gtix denotes the in×pq design matrix of the SNP-time/SNP-environment interactions. We define the final design matrix as ix=.For a given trait, suppose we have γ, λ, and ix, we consider the following mixed model:Given iy=T is an in×1 phenotype vector of individual i; iμ=μ1in is an in×1 overall mean vector; Γ is a diagonal matrix with upper diagonal elements 1q  and lower diagonal elements ie is an in×1 vector of random errors with ie∼N. To model the correlation among repeated measurements of the same individual, we partition the observed time interval by k pre-specified grid points, t=T, and define iν as a k×1 vector of random effects at the grid time points with iν∼N where D is a k×k covariance matrix. Let P=diag where ip is defined as follows. If all subjects have k observations measured exactly on the k grid time points, then ip becomes an identity matrix. We apply an interpolation procedure  to any observation that does not fall on any of the k grid time points. For simplicity, we choose a linear interpolation here. When the jth measurement of individual i falls at time t, which is in between the grid points rt and tr+1 (rt≤ t ≤ tr+1), we set t=rt, we get ijp as ijp=aij1e1+...+ijkeka, where ijra is the rth element of ijp and re (1 ≤ r ≤ k) is a 1× k vector whose elements are all zero except the rth component, which equals 1. Note that ijra values can be non-zero due to the linear interpolation we employ here.where D, the covariance matrix of the random effects, by employing the modified Cholesky decomposition of Chen and Dunson  and then standardized to have mean 0 and variance 1, and ie∼N. We set σ2 =1. The true number of grid points was set to 3 , and ip was calculated from it by the linear interpolation as we described in the Methods section. We set δ= =  and ψ==. That is, iν∼N with diag(D)= and the lower triangle elements =. The prior distributions for the elements in δ were independent N+  and the prior distributions for the elements in ψ were independent N. For each simulated dataset, the MCMC algorithm ran for 4 × 105 iterations after discarding the first 1, 000 burn-in iterations. The remaining samples were further thinned for every 40 iterations, yielding 104 MCMC samples for the posterior analysis.To evaluate the performance of the proposed method, we conducted the following simulations. We first used 400 individuals and 1, 000 SNPs from the 1000 Genome Project data. The number of measurements for each individual ranged from 3 to 7 and the total number of observations was set to 2, 000. Six different setups (Setups 1‒6) were considered. We simulated the datasets containing 10 causal SNPs, which are randomly selected  with only main effects (Setup 1). For individual gjc  were varied to ensure that trait-heritability to 40%. To display time-dependent SNP effects for Setups 4 and 5, we compared the time-dependent curves of averaged phenotype values for three different genotypes  at the first causal SNP (with no SNP-time interaction) and 10th one (with SNP-time interaction). To further investigate the Bayesian mixed model, we analyzed additional datasets containing two SNP-SNP interactions (Setup 2), five SNP-SNP interactions (Setup 3), two SNP-time interactions (Setup 4), five SNP-time interactions (Setup 5), or ten SNP-time interactions (Setup 6). Specifically, we simulated data according to the following models: log(BF) for the combined main, epistatic effects, and SNP-time interactions of each SNP under the six setups were presented in The one-dimensional genome-wide profiles of 2To evaluate the performance of our Bayesian model, we further calculated the receiver operating characteristic (ROC) curves. For each setup, we conducted 100 simulations. The ROC curves with a false-positive rate less than 0.2 are presented in 4 iterations, Geweke's Z-scores [σ2, δ1, δ2, δ3, ψ21, ψ31 and ψ32 for each setup, showing that all chains moved around the true values for all parameters, indicating good convergence. We plotted the marginal posterior and prior densities of all parameters based on 10, 000 random draws . We simulated 100 datasets with 400 individuals and 1, 000 SNPs containing 10 causal SNPs  with only main effects. The causal SNPs were randomly assigned. The trait-heritability was set to 40%. The phenotype values were generated from the model: iax  are genotypes of the causal SNPs and it=T are the time points of the ith individual. We set = and =. We conducted another simulation to estimate the number of true grid points using the DIC  and simpiax are genotypes of the causal SNPs and it=)T are the time points of the ith individual. As in the previous simulations, we set the number of grid points to k = 3 and σ2 = 1, δ= = , ψ==. The trait-heritability was set to 40%. As the sample size decreased from 400 to 100, the true positive rates decreased in ROC curves, indicating that including more samples increased the true positive rates with fixed false positive rates. Next, we evaluated the effect of the number of SNPs (For a more detailed evaluation of our Bayesian method, we conducted the following simulations with 100 replications for each scenario. We first considered 400 individuals with three to seven time points, resulting in 2,000 observations, and decreased the sample size from 400 to 100 to assess the effect of sample size in ROC curves . The sim of SNPs . The sim of SNPs . The samWe developed a grid-based Bayesian mixed model for longitudinal genetic data with a built-in variable selection feature. The proposed Bayesian method modeled multiple candidate SNPs simultaneously and allowed SNP-SNP and SNP-time interactions, which enabled us to identify SNPs with time-varying effects. Such SNPs are of great scientific and medical interest. In addition, we proposed a new grid-based method to model the covariance structure nonparametrically. Not only is the proposed method parsimonious in estimating the covariance matrix, but also by employing a reasonable number of grid-points, it can flexibly approximate any type of covariance structure. The number of grid points was pre-set, but DIC and simplified BPIC can be used to select the optimal number.The simulation studies showed that the proposed Bayesian method using all time points outperformed the ordinary Bayesian method with one or all time points included. As expected, the proposed method that utilized the full data was more powerful than the corresponding univariate analysis method that only used a subset of the data. Furthermore, the proposed Bayesian method performed better than the ordinary Bayesian method because our method modeled the within-subject correlation. Further simulation studies showed that statistical power increased as the data had more samples, a smaller number of SNPs, a lower proportion of causal SNPs, and larger trait-heritability. For our simulation studies, we utilized data from the 1000 Genome Project. With only 400 independent samples of EAS ancestry, we restricted out analysis with up to 5, 000 SNPs. With a sufficient sample size, our method can be applied to all available SNPs. We are currently developing a parallel computing algorithm based on the message passing interface to execute multiple groups of SNPs simultaneously. This will make it feasible to apply our method to large-sample GWAS data.Another important issue to mention is Bayesian model identifiability. In the Bayesian community, there is a wide diversity of views on the identifiability issue. Lindley  remarked


HOXB13 and a SINE element located in the promotor of HOXB13 as promising candidates. These results enable more precise breeding towards shorter tails, improve animal welfare by amplification of ancestral alleles and contribute to a better understanding of differential embryonic development.Docking the tails of lambs in long-tailed sheep breeds is a common practice worldwide. But this practice is associated with pain. Breeding for a shorter tail could offer an alternative. Therefore, this study aimed to analyze the natural tail length variation in the Merinolandschaf and to identify causal alleles for the short tail phenotype segregating within long-tailed breeds. We used SNP-based association analysis and haplotype-based mapping in 362 genotyped (Illumina OvineSNP50) and phenotyped Merinolandschaf lambs. Genome-wide significant regions were capture sequenced in 48 lambs and comparatively analyzed in various long and short-tailed sheep breeds and wild sheep subspecies. Here we show a SNP located in the first exon of  HOXB13 associated with short tail length is identified in Merino sheep, which may be a target for safely selecting shorter tails and improving sheep welfare.Using SNP-association analysis and genetic mapping, a SNP and an insertion in and close to  It is proposed that the Asiatic mouflon , which was common in the same area, was the wild ancestor. The Asiatic mouflon, like other wild sheep subspecies, is a short-tailed hair sheep. Accordingly, the first domesticated sheep were also short-tailed hair sheep, kept mainly for their meat2. The systematic production and processing of wool did not occur until several millennia later, leading to the “Secondary Product Revolution”3 and the worldwide replacement of hair sheep by wool sheep. Long-term selection for fine wool fibers culminated in the economically most important and widespread group of sheep breeds, the Merinos. All Merinos are characterized by long tails and the common occurrence of fine wool and a long tail led to the frequent opinion that these phenotypes are also genetically coupled or the result of the same artificial selection4. This assumption could not be proven directly, however, in today’s sheep husbandry systems the long woolly tail comes with several problems, e.g., the accumulation of dags in the tail area, which predisposes for flystrike5. Therefore, most lambs of long-tailed breeds worldwide are docked shortly after birth4. With the increasing importance of animal welfare in our society and subsequent restrictions and prohibitions of practices that cause pain and suffering to animals, tail docking has come under scrutiny. In Scandinavia, tail docking without a veterinary indication has already been made illegal6 and in the Netherlands, exclusions from the docking ban have only been granted for three long-tailed English breeds under the condition of an effective breeding program for shorter tails7. In Austria, tail docking in lambs is allowed until the age of 7 days, provided the operation is done by a veterinarian or another qualified person and an analgesic for intra- and post-operative pain-relief is given8. The German Animal Welfare Act currently still allows tail docking without anesthesia for lambs under eight days of age9, but future amendments will probably seek to eliminate exceptions to the amputation ban10.Sedentary human communities began sheep management as early as 10,000–11,000 BP in an area stretching from central Anatolia to northwestern Iran12. Therefore, this breeding could be seen as a “back to the roots” program.These developments clearly show that a long-term non-invasive alternative for the painful practice of tail docking is urgently needed. Here, a genetic solution offers itself. A high ethical acceptance of genetic breeding for a shorter tail could be expected as all wild sheep subspecies and thus also the ancestor of today’s domestic sheep have naturally short tails13 or 0.77 in Finnsheep14. James et al.15 suggest that the inheritance of TL in Australian Merino depends on a small number of interacting genes of large effects, in which short tail genes show a possible dominance. The presence of various short-tailed Nordic breeds offers a possibility of genetic reduction of TL by introgression of the desired genetic variants from short-tailed breeds. Scobie and O’Connell16 crossed short-tailed Finnsheep with long-tailed Cheviot sheep and observed that an increased proportion of Finnsheep genes led to a proportional reduction in TL. However, this option is unpopular with breeders, as crossing is associated with the loss of breed-specific traits and a possible decline in previously achieved breeding progress in important production traits17. In extreme cases, it could also introduce undesirable traits/conditions in the population. For example, Jordan18 described fully or partially hindquarter paralysis in the No-tail lambs from the cross of Siberian fat-rumped sheep with other sheep breeds such as Hampshire sheep and Rambouillet sheep. This condition is attributed to the appearance of a lethal gene, known as “sidewheeler”. The majority of the lambs with this condition starved to death during early years of their life. Post-mortem examination of these lambs revealed the abnormal termination of the spinal cord.The genetic basis for shorter tail breeding efforts is provided by the medium to high heritability of tail length (TL) in different sheep breeds, e.g., 0.58 in Merinos19 reported for Romney sheep that breeding for the short-tail phenotype possibly reduced the viability of embryos that were homozygous carriers of some putative short-tail alleles. In Merinos, James et al.15 observed increased incidences of rear-end defects. Zhi et al.20 discovered a c.G334T mutation in the T gene in the native Chinese Hulunbuir breed and showed that the T allele leads to the extreme short-tailed phenotype, i.e., tailless animals with exposed anus. To prove the causality of this mutation, they genotyped 120 short-tailed Hulunbuir sheep. The observed frequencies of the genotypes  are consistent with the embryonic lethality due to the T/T genotypes in T gene20. On the other hand, Han et al.21 claim that the nonsynonymous c.334 G > T mutation is predominantly or exclusively homozygous in Chinese fat-rumped and Hulunbuir sheep. Moreover, this genotype also segregates in other breeds and is always associated with the synonymous c.333 G > C mutation. Despite these two contradictory statements by Zhi et al.20 and Han et al.21, both studies concluded that an association between the (extremely) short tail phenotype and the c.334 G > T mutation is likely. A comparable association between short tails and embryonic lethality or malformations has been demonstrated in various breeds of dogs and cats too24. These undesirable negative side effects discouraged and slowed down active breeding programs against overlong tails in the economically most important wool sheep breeds. Moreover, there have been no successful genetic mapping studies in long-tailed Merino sheep breeds and the possible relationship between the short tail phenotype and embryonic viability or hind end malformations has, to the best of our knowledge, never been investigated on a genetic basis in Merinos.As an alternative, breeders in Australia and New Zealand attempted genetic shortening of the tail by phenotypic selection within individual breeds. However, CarterTo investigate the phenotypic and additive-genetic variance in TL in the Merinolandschaf, which belongs to the economically most important long-tailed Merino breed group worldwide;To map the position of the major quantitative trait locus (QTL) affecting TL;To detect and confirm causal candidate genes by sequencing and genotyping;To determine the distribution of ancestral and derived alleles in a wide range of domestic sheep breeds with different TLs as well as in different wild sheep subspecies;To contribute to the understanding of the relationship between genotype and phenotype during embryonic patterning and early development;To put causal alleles in the evolutionary context of sheep species.The aim of the present study was therefore:Together, these objectives will enable more efficient breeding towards the ancestral phenotype and thus improve animal welfare in sheep production without negative side effects.GCTA-GREML analysis revealed SNP-based heritability of 0.992 (standard error of 0.12), meaning that a very high proportion of the TL variance in Merinolandschaf breed is explained by genome-wide SNP markers. Despite this very high heritability, i.e., close to 1, the initial association analysis  as shown in Supplementary Fig. LRTmax = 19.356 corresponding to P = 1.08 × 10−5 (Bonferoni corrected: P = 1.22 × 10−2).The haplotype-based cLDLA mapping (cLDLA Model 3) resulted in two genome-wide significant QTLs associated with TL in Merinolandschaf Fig. . The mosLRTmax on OAR11 between positions 37,000,925 bp and 37,521,490 bp and for the maximum value on OAR2 between positions 93,441,900 bp and 96,402,884 bp. These intervals were then considered in the UCSC Genome Browser Oar_v4.0/oviAri4 Assembly, which resulted in the list of genes summarized in Table HOXB13  as the closest and most prominent candidate, lying only 179 Kb proximal to the LRTmax.Applying the 2-LOD criterion, the corresponding confidence interval (CI) was set for the LRT values far below the genome-wide significance and thus, were not investigated further.Additional chromosome-wide significant peaks were observed on OAR2, OAR3, OAR10, OAR14, and OAR17. However, these peaks show ASReml25 to estimate variance components, fixed and random effects affecting TL in Merinolandschaf breed. Here, we analyzed in more detail the estimated effects at loci with the most significant association, i.e., at loci showing LRTmax values. We sorted all 362 lambs according to the random additive-genetic QTL effects (vector q) estimated at LRTmax. Together with the QTL effects, we simultaneously considered all input , withers height (WH), maternal and paternal haplotypes at 40-SNP window with LRTmax in interval between SNP 20 and 21) and output data  that contributed to LRTmax. Visual inspection of this table and regression analysis allowed us to select 48 lambs for targeted capture sequencing. Supplementary Figs. In the previous step of cLDLA, we used lm in R26 estimated the adjusted coefficient of determination of R2 = 0.58 for the LRTmax on OAR11 and only R2 = 0.15 for the LRTmax on OAR2 when using TL as the dependent and diplotype effect as the independent variable. Adding, age, sex, BW, and WH as additional independent variables yield R2 = 0.78 for QTL on OAR11 and R2 = 0.45 for QTL on OAR2  as a nonsynonymous point mutation within the first exon of HOXB13 (relative position 23). The point mutation results in a p.(Thr8Ser) substitution. All sequenced Merino lambs from the long-tailed group are homozygous for this missense variant (G/G). In the short-tailed group, 4 lambs are homozygous G/G, 6 are heterozygous C/G and 15 are homozygous C/C on that position. The Ensembl Variant Effect Predictor29 predicted a SIFT score of 0.54 and classified the mutation as so-called “tolerated” missense variant.Capture Sequencing was carried out at a mean depth between 0.41 and 2.45 on the target region of OAR2 and between 1.01 and 2.39 on the target region of OAR11. This coverage is much lower than intended and most possibly caused by competition with whole-genome sequencing (WGS) performed on the same sequencing lane. However, applying default settings of BLAST30 was used to align the amino acid sequence of the mutant HOXB13 protein against the amino-acid sequence of wild-type HOXB13 protein of different mammals including all the extant wild sheep species shown in Supplementary Table 31. On the other side out of 16 investigated Asiatic mouflon 3 were heterozygous C/G and 2 homozygous G/G for the point mutation. However, it is worth mentioning here that one Asiatic mouflon (G/G) and the two ancient WGS have low coverage at this locus.In the next step, protein Smoove32, Delly33, and visual examination with Jbrowse. Using the strict threshold criteria, i.e., SV not present in a pooled sample of short tail sheep but present in a pooled sample of long tail sheep, we identified 27 and 32 SVs from Smoove and Delly approaches, respectively. However, it is noteworthy that the captured sequencing had highly non-uniform and relatively low coverage, therefore, these approaches might have missed many true positives and included high number of false positives. In fact, the visual examination of these regions also indicated so . Visual examination of the region upstream of the first exon of HOXB13 gene on OAR11 revealed only one cluster of soft-clipped reads at positions 37,524,996  on both sides. Therefore, we hypothesized that this insertion could be a part of short interspersed nuclear elements (SINE) repeat family. Next, we searched for this sequence in DFAM repeat database and identified it as belonging to OviAri-1.113 SINE family.To investigate the soft-clipped regions further, we carried out Sanger sequencing of five samples, based on the previously described candidate SNPs. The analysis of the Sanger sequencing data Fig.  and subsHOXB13 are ancestral alleles, whereas the 167 bp insertion and base G indicate derived alleles.To confirm the association between the detected variants and TL, we performed genotyping of these two candidates in the entire mapping population and used genotypes in the GWAS and cLDLA Model 2, 4, and 5 Table . The PCRAll lambs homozygous for ancestral alleles could be classified as short-tailed with mean tail-length of 24.1 cm (±1.34) and mean QTL effect of −2.92 cm (±0.71). On the other side lambs homozygous for derived alleles classified as long-tailed as well as short-tailed. Consequently, lambs with derived homozygous genotype show higher average TL of 31.5 cm and 4.15 times higher standard deviation of TL (±5.56). Lambs with heterozygous genotype show average TL of 25.7 and 2.73 times higher standard deviation (±3.66). Table P = 6.2 × 10−12) locus, while cLDLA revealed a slightly altered significance (LRTmax = 29.112) at the slightly altered position 37,311,842 bp. However, in the initial mapping LRTmax was 179 Kb away from HOXB13 and the Model 4 of cLDLA placed LRTmax between HOXB13 and HOXB9  allows us to model important causal candidates and thus improve the mapping of residual variance (if present) in the mapping population. To investigate the presence of additional loci affecting TL in long-tailed Merino sheep, we modeled the genotypes at the candidate insertion and SNP as fixed effects, i.e., lambs with homozygous ancestral genotype were classified as class 1, heterozygous as class 2, and homozygous derived as class 3, while the other input data, parameters, and model did not change. We first estimated the SNP-based heritability of TL after correcting for the most significant causal candidates. The heritability decreased from h2 = 0.992 to 0.898. However, as shown in Fig. The above results point to h2 = 0.992). Despite the relatively small number (362) of phenotyped and genotyped animals, heritability was estimated with a relatively low standard error (SE = 0.12). According to Visscher et al.36, the SE depends only on the sample size and became below 0.1 by using more than ~3000 independent individuals. Phenotypic correlation in related individuals is often due to a shared environment rather than SNP-associated genetic effects, leading to inflated estimates of heritability38. Since all the lambs studied here are from only two different farms, the actual heritability is most likely lower than the calculated one, even when relatedness and population structure are taken into account.The present study aimed to identify the genes or variants responsible for the natural variability of TL in the Merinolandschaf breed. The results presented here suggest that the inheritance of TL depends largely on additive-genetic variance almost without environment effects . However, in studies such as this, where phenotypes are not routinely collected, the number of phenotyped animals may be limited or can only be expanded at great expense. On the other hand, the number of markers can also be a limiting factor in many species, e.g., there is only 50 K chip for domestic goats. The alternative solution could be to apply a mapping method that uses more information from the current design. Due to time and cost constraints, we opted for haplotype-based mapping and obtained a highly significant (P = 5.71 × 10−8) and fine mapped QTL on OAR11  as well as another genome-wide significant result (P = 1.08 × 10−5) with less sharp mapping . This shows that variance component analysis in the haplotype-based mixed linear model can be more successful than methods such as GWAS when the number of phenotyped individuals and genotyped markers is not large enough for GWAS.The GWAS (MLMA), i.e., the standard method for mapping loci associated with complex traits and diseases, showed no significant and even no suggestive association in the design with 362 animals and 45,114 markers. Typically, GWAST gene, also known as the brachyury gene, as a candidate causal gene. In Hulunbuir short-tailed sheep, a c.G334T mutation in T gene is the main cause of the extreme short-tail phenotype20. Mice that are heterozygous for mutations in the T gene have a short tail and homozygous embryos die in the middle of the gestation41. In Manx cats, the short-tailed phenotype is caused by naturally occurring mutations in T gene23, in particular by three 1-bp deletions. The T gene has also been associated with the short-tailed phenotype in various dog breeds43. Additionally, ANKRD11, ACVR2B, and SFRP2 were detected as plausible candidate genes that could contribute to the reduction in TL in particular dog breeds42 and the somite segmentation-related gene HES744 in Asian domestic cats.Previous research on TL in domesticated animals, including sheep, mainly pointed to the T gene region . Furthermore, neither ANKRD11 , ACVR2B  or SFRP2  showed a significant increase in the LRT value. HES7 is located on OAR11  but is 10 Mb away from the QTL CI and was therefore not considered as a candidate gene for TL in the Merinolandschaf breed. The CI on OAR11 includes the complete ovine HOXB cluster and seven additional genes  indicates this gene as the most likely gene causing the large effect. The literature search for plausible candidates for genes at OAR2 (Table P < 0.05), 29 ontologies were significantly enriched, but we saw no plausible link to TL.In this study, we could not detect any increase in the LRT curve in the LRTmax of both regions. Again, the visual inspections, as well as linear regression analyses, confirmed QTL on OAR2 as inconclusive. This is evident from adjusted R2 = 0.58 and only 0.15 for TL fitted to QTL of OAR11 and OAR2, respectively.To select the most suitable lambs for capture sequencing of CIs of QTLs on OAR11 and OAR2, we mutually considered haplotypes, phenotypes, fixed effects, and random effects at Ovis gmelini and Ovis aries. The 167 bp insertion in the promotor region is also derived but more recent and segregates exclusively in domestic sheep and predominantly in long-tailed sheep breeds. This insertion occurred in the haplotype with the older missense allele G and both derived alleles segregate as a block. So, we did not observe the insertion without allele G, but we did observe the older allele G without insertion. The presence of the insertion in long-tailed sheep breeds and its location in the promotor region of HOXB13 increases the probability of the insertion to be the causative variant for the long tail phenotype. Most likely because of its location the insertion modulates the promoter activity of HOXB13 and leads to a longer tail by reducing the expression of the HOXB13 gene. Indeed, a recent study by Li et al.35 also detected an insertion of 169 bp close to the 5’ UTR of HOXB13 at position 37,525,005 (ARS-UI_Ramb_v2.0) using the graph-assembly-based method on the Pac-bio sequencing data of 13 different sheep breeds including Merino. Therefore, it is likely this study and Li et al.35 both identified the same insertion. The small differences in length and position of this insertion can be attributed to sequencing error due to the presence of long homopolymer of “T” base in the identified SINE repeat element. Second generation short reads sequencing technology coupled with assembly errors can make finding promising structural candidate variants difficult and thus visual examination of the targeted region should be considered. However, long-read sequencing technology like PacBio and ONT have already proven to be effective in identifying such SVs.Consistent with the clues in favor of OAR11 discussed above, capture sequencing revealed one plausible point mutation and one SINE insertion in the QTL region. All Merinolandschaf lambs and their fathers show a complete linkage disequilibrium between these two variants. This is not surprising because these mutations are only 132 base pairs away from each other and recombination in such a short segment should be extremely rare. We further investigated this linkage in some typical long-tailed and short-tailed breeds (Supplementary Table rs413316737) in the first exon of HOXB13  is included on the OvineHD array as SNP marker oar3_OAR11_37337253. This offers the possibility to check the allele distribution in available open-source genotypes  or identity of the rs413316737 and/or insertion with the causative locus. Additionally, it demonstrates the power of GWAS when the design includes causal variants or markers with population-wide LD. On the other hand, the inclusion of candidates as markers in haplotype-based mapping leads only to partial change, i.e., the mapping significance remains about the same, but the mapping peak is 9-times closer to the most plausible candidate gene. Even more conclusive is the impact of these mutations as a fixed effect in the model, because the correction for the true causal variant should cancel out the LRT peak on OAR11 and if this explains a full additive-genetic variance, the heritability should also be reduced towards zero. Indeed, this model erase the LRT peak on OAR11 . In mammals, there are 39 Hox genes organized in four clusters and 13 paralogous groups47. There is functional redundancy among the paralogous Hox genes48 and the paralog alleles can compensate for each other to a certain degree49. Therefore, the loss of function of one Hox gene from a cluster is usually compensated by the functionality of an intact paralogue, and only the loss of function of several paralogs results in more severe consequences in axial structuring47. There is indirect evidence that HOXB650 and HOXB851 may influence TL and embryonic viability. However, capture sequencing finds no genetic variants associated with TL in these two candidate genes. Therefore, these candidates are not discussed further.45, which is mainly achieved through regulation of proliferation and apoptosis activity in the posterior embryonic regions52. In mice, loss-of-function mutations in Hoxb13 lead to overgrowth of the spinal cord and caudal vertebrae in homozygous mice. These animals consequently show longer and thicker tails while viability and fertility remain unaffected52. Premature expression of genes of the Hox13 paralogous group, on the other hand, negatively influences the extension of the caudal axis and results in a truncated phenotype51. Kingsley et al.53 also suggested that Hoxd13 plays a role in the development of natural TL variation and found out that long-tailed forest mice express less Hoxd13 in the embryonic tail bud than short-tailed prairie mice and (therefore) show better climbing performance. Among all candidate genes from the Hox family, HOXB13 is thus the best functional candidate gene. Moreover, Li et al.35 used previously published RNA-Seq data of sheep colon, performed luciferase reporter assays, and indicated an association of lower expression of HOXB13 with insertion in its promotor region. This is in the line with above findings in mice. However, due to complete linkage, we cannot exclude the cooperative causality of the insertion and missense mutation in exon 1. The HOXB13 is expressed in the prostate of adult humans and is intensively studied as a candidate biomarker for the prognosis of prostate cancer . Studies show that missense mutations in the coding sequence of HOXB13 can change the affinity55 or half-life56 of heterodimer between HOXB13 and, e.g., MEIS1 proteins. However, it was not within the scope of this study to investigate the affinity between different transcription factors in growing sheep embryos or cell lines. It is well known that the SINE insertion alone can alter gene expression in multiple ways57, but we propose to test the hypothesis of a possible causality of the combination of altered expression and altered amino acid sequence.As vertebrate embryos develop progressively from head to tail, the HOX13 paralogous group has been proposed to control axis termination19 and Merino15 sheep. These attempts failed due to evidence of reduced viability of presumably homozygous short-tailed Romney embryos and by increased incidences of rear-end defects in short-tailed Merinos. However, these observations contradict the fact that the short tail is the ancestral trait and that old Nordic short-tailed breeds like Romanov and Finnsheep are very viable and highly fertile. Therefore, we assume that these early experiments were not carried out with animals that had shorter tails due to ancestral genetic variants, but due to some recent deleterious variants. James et al.15 mentioned that the TLs of the four sires measured before mating were all less than 5 cm. This is very short for an adult ram, even much shorter than the tail of short-tailed adult Romanov rams (~20 cm). Also, Romney sheep used for experimental mating by Carter19 were described as “tailless”. Therefore, short or tailless phenotypes described in initial sheep breeding attempts19 are comparable with deleterious mutations described for certain dog and cat breeds43 rather than to some ancestral alleles. The causal candidates for short tails detected in this study are highly frequent or fixed in some very viable and highly fertile breeds , HOXC , and HOXD  gene clusters show no signals in the cLDLA analyses without (Model 3) and with (Model 5) candidate mutations as a fixed effect  of heterozygous lambs could be classified as short-tailed, while homozygous derived lambs are mostly 71.4%) but not always long-tailed  236 Merinolandschaf lambs with very short 104) or long (132) tails were selected for phenotyping and sampling from 2293 visually inspected lambs on a farm in Lower Bavaria with no custom of tail docking, (ii) 102 random male lambs from the same farm were phenotyped and sampled, i.e., without pre-selection by visual inspection of tail-length and (iii) 24 Merinolandschaf lambs with short (19) or long (5) tails were selected from 102 visually inspected lambs from the sheep flock of the teaching and research farm “Oberer Hardthof” at the Justus Liebig University of Giessen (JLU). Figure  or long The collection of blood samples for this study was approved by the ethics committee of the Veterinary Faculty of LMU Munich. All blood samples were taken by experienced and qualified veterinarians and under a permit from the Government of Upper Bavaria (permit number: 55.2-1-54-2532.0-47-2016), or the Regional Council of Gießen, Hassia (KTV number: 19 c 20 15 h 02 Gi 19/1 KTV 22/2020).https://www.ncbi.nlm.nih.gov/assembly/GCF_000298735.2) and the positions of all markers or sequences in this work correspond to the Oar_4.0 reference genome unless otherwise stated. The chip contains 54,241 SNPs almost evenly distributed across the genome with an average marker spacing of 50.9 Kb64. Not all of these 54,241 SNPs were used for mapping. We filtered SNPs according to the following exclusion criteria: (i) unsuccessful genotyping in >5% of the animals, (ii) frequent paternity conflicts in animals with known paternity, (iii) unknown position in the reference genome, (iv) minor allele frequency (MAF) of >0.025 and (v) localization on a sex chromosome since the analyses were exclusively carried out on autosomes. As a result, 45,114 markers remained in the marker set for the mapping analyses. Haplotype phasing and imputation were conducted using a Hidden Markov Model implemented in Beagle version 5.065. To improve the accuracy of haplotyping and imputation, genotype and pedigree information from ~5100 additional animals genotyped with the OvineSNP50 BeadChip but not phenotyped were added to the haplotyping design66.All 362 phenotyped lambs and 22 sires were genotyped using Illumina’s OvineSNP50 BeadChip  according to the manufacturer’s specifications. All physical marker positions were determined on the ovine reference genome assembly Oar_4.0 , to estimate the proportion of variance explained by all SNPs (the SNP-based heritability)67. The sex, age, BW, and WH of the lambs at phenotyping were modeled as fixed effects.First, we tested the heritability of the TL in the pure Merinolandschaf breed. For this purpose we used the software GCTA v1.93.268. The model is described below:y is the vector of TLs (cm), ß is a vector of fixed effects including the mean, sex, age, BW in kg, and WH in cm at tail phenotyping (BW and WH data were both standardized and centered), a is the vector of the additive effect (fixed) of the candidate SNP to be tested for association, u is the vector of polygenic effect (random or accumulated) of all markers, excluding those on the chromosome which contains the candidate SNP and e is a vector of residuals. X, Za, and Zu are the incidence matrices for ß, a, and u, respectively.To map a putative TL locus, we performed MLMA analyses with a leave-one-chromosome-out approach as implemented in the software P < 1/N and the genome-wide significance threshold at P < 0.05/N, according to Bonferroni method, N stands for the number of markers69. For the initial MLMA-analysis (Model 1) we considered 45,114 markers resulting in the suggestive P value at 2.22 × 10−5 and genome-wide at 1.11 × 10−6.The suggestive significance threshold was set at A second MLMA (Model 2) analysis included one additional locus, which we detected during this research, as a consequence, we considered 45,115 markers. The suggestive and genome-wide significance threshold remained the same.70, which have been successfully used for binary and quantitative trait mapping in previous studies74.Parallel with SNP-based association analyses using MLMA, we performed multiple haplotype-based cLDLA analyses75. The inverse of the UAR matrix was then included in the variance component analysis. To account for linkage disequilibrium in the form of local haplotype relationships, the Locus IBD (LocIBD) was estimated according to the method of Meuwissen and Goddard76 using sliding windows of 40 SNPs. For each window, we estimated LocIBD in the middle, i.e., between SNPs 20 and 21, based on the flanking marker haplotypes. Following the method for additive-genetic relationship matrices by Lee and Van der Werf77, the matrix of LocIBD-values was converted into a diplotype relationship matrix (DRM).To correct for familial relationships and population stratification, unified additive relationships (UARs) were estimated between all animals on a genome-wide levelASReml25 according to the method of Meuwissen et al.70. ASReml estimated the maximum likelihood, variance components and fixed and random effects simultaneously by considering the genome-wide UAR matrix as well as the locus-specific (DRM) relationships matrices in the following mixed linear model:y is again the vector of TLs and β is the vector of fixed effects . The vector u is the vector of random polygenic effects , q is the vector of random additive-genetic QTL effects  and e is the vector of random residual effects . It is assumed that u, q, and e are not correlated. X, Z1, and Z2 are incidence matrices linking the observed values with the fixed and random QTL effects.Variance component analyses were carried out with the program ASReml were then used in a likelihood ratio test statistic (LRT). The LRT values follow a χ2 distribution with one degree of freedom78 and were calculated as:L0) is the logarithm of the likelihood estimated by ASReml for the model without QTL effects and log(L1p) the model with QTL effects at the center of the window p.The variance components and likelihood estimated with 79. This resulted in a corrected P value of <4.44× 10−5 , and a corresponding LRT value with genome-wide significance is equal to 16.67.To obtain a significance threshold, a Bonferroni correction was carried out to account for multiple testing due to the 40-SNP sliding windowsβ is the vector of µ, sex, age, BW, WH, and candidate locus effects. A comprehensive overview of the different models is provided in Table LRTmax) that exceeded the genome-wide significance threshold, the 2-LOD (logarithm of the odds) criterion was used to determine the associated CIs80. Closely located LRT peaks were assumed to belong to the same QTL as described by Müller et al.74.In addition to Model 3 described above, we performed two further genome-wide cLDLA analyses, with both analyses using candidate locus genotypes for the same set of animals. Model 4 contains only one additional locus and is therefore comparable to Model 2 of MLMA. In Model 5, the genotypes of the same candidate locus are considered as a fixed effect, i.e., R package qqman81.The results of the analyses were presented as Manhatten plots produced by the LRTmax were compared with a map of annotated genes in the UCSC Genome Browser Oar_v4.0/oviAri4 Assembly (https://genome.ucsc.edu/cgi-bin/hgGateway) using the “RefSeq Genes” track. We refer to the assembly of Oar_v3.1 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000298735.1), the Mus musculus Assembly (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.27) and the Homo sapiens Assembly (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39) as well to consider genes encompassing the CIs. Next, gene set enrichment analyses were carried out with the software Enrichr 83.The CIs of the Genomic regions where the LRT curve reached the maximum values above the genome-wide significance threshold (in Model 3) were sequenced using targeted capture sequencing on 48 selected lambs (23 long-tailed and 25 short-tailed). To minimize the risk of missing some causal variations that are close but outside the CI, we increased the candidate regions on both sides by ~300 Kb. This resulted in the captured regions on the sheep chromosome 2 (OAR2) from 93,200,000 to 96,700,000 bp and on chromosome 11 (OAR11) from 36,600,000 to 37,900,000 bp. Genomic DNA was extracted from the blood samples using the ReliaPrep™ Blood gDNA Miniprep System.WGS libraries were prepared from 250 ng of genomic DNA by tagmentation with the NexteraFlex kit from Illumina. Subsequently, the libraries were dual-barcoded and amplified by PCR, purified with SPRI beads and pooled in equimolar amounts. The pooled libraries were enriched for the target regions by hybridization to an Agilent capture array with 244k oligo spots. The oligo probes were selected from the repeat-masked DNA sequence as all possible 60mers that do not overlap with repeat-masked bases and that are staggered in 15 nt tiling steps concerning their neighbors. After 65 hours of hybridization in the presence of cot-I sheep DNA and adapter blocking oligos at 65 °C the capture array was washed and the captured library molecules were eluted at 95 °C for 10 min in a volume of 500 µl DNA-grade water. The enriched libraries were then amplified by PCR, analyzed on the Bioanalyzer and sequenced in 2 × 110 bp paired-end mode on a P2 flowcell of a NextSeq1000 sequencer from Illumina.Sickle84 was used to trim the adaptors and filter low-quality sequences of the raw reads in the FASTQ files. With FastQC85, we assessed the quality parameters of the filtered sequencing data. The filtered reads were mapped to the sheep reference genome OAR_v4.0 using the default parameters of BWA-MEM86 alignment tool. To convert the SAM files into coordinate sorted BAM files and to remove the duplicated reads, Samtools87 and Picard88 were used. Base quality recalibration and indel realignment were done with GATK27. SNPs and small indels calling was performed with GATK HaplotypeCaller27 using: (i) 37 wild sheep genomes, (ii) 16 domestic sheep genomes (representing 8 samples of short-tailed breeds and long-tailed breeds each) and (iii) two sequence pools, representing short and long-tailed group of the 48 Merinolandschaf lambs. These 48 lambs were sequenced individually (see above), then reads were pooled to increase the sequencing coverage of both groups.GATK short variants, the criteria described in the Supplementary methods were applied. To detect structural variants (SV) we used SMOOVE (LUMPY)32 and DELLY33 with default parameters in the pooled reads of short- and long-tailed lambs.The wild and domestic sheep genomes downloaded from NCBI sequence read archive are shown in Supplementary Table JBrowse28, focusing on the region near the candidate gene detected here.To ensure that we do not miss any candidate variant, we performed a visual examination of the captured regions using Primer389 were TTTAAAACGCTTTGGATT (forward) and CACTCGGCAGGAGTAGTA (reverse). The used restriction enzyme was BsrI. It recognizes the mutant sequence TGAC/CN, where the G is the variable base and the “/” presents the site where cutting is performed. DNA amplification was performed with 35 cycles. The total reaction mixture was 15.0 µl containing 3.0 µl 5× buffer, 1.5 µl dNTPs (10 mM), 0.6 µl of 10 µM forward and reverse primers, respectively, 1.0 µl DNA (15 ng/µl), 0.07 µl GoTaq®G2 DNA Polymerase  and distilled water.For one detected candidate SNP, we performed genotyping by PCR-RFLP and electrophoresis on a 2% agarose gel on all 362 sampled lambs, and there 17 confirmed fathers. The PCR primer sequences designed by BsrI, 3.0 µl DNA (PCR product), 2 µl Cut Smart Buffer, and distilled water for a total reaction volume of 20 µl. The reaction mixture was afterward incubated for 3 h at 65 °C. In the final step, we separated the DNA fragments by size and visualized them by GelRed™-stained agarose gel electrophoresis. Only sequences harboring the derived allele (SNP G) were cut, the two resulting fragments had a length of 120 bp and 259 bp. The sequence with the ancestral allele (SNP C) retained its length of 379 bp.We used 1.5 U of the enzyme Primer389 were tested .Two lambs, which are homozygous for the SV, one lamb, which is homozygous for the ancestral allele, and two heterozygous lambs were resequenced using Sanger sequencing with the above-mentioned best working primers. The amplicons were sequenced using the cycle sequencing technology (dideoxy chain termination/cycle sequencing) on ABI 3730XL sequencing machines . The sequenced data were analyzed using Further information on research design is available in the Supplementary InformationReporting Summary


A set of 3,380 SNPs distributed across the cacao genome were interrogated on the EMBRAPA multi-species 65k Infinium chip. The 90 cacao clones showed considerable variation in genome-wide SNP homozygosity (mean 0.727± 0.182) and 19 of them with homozygosity ≥90%. By assessing the increase in homozygosity across two generations of self-pollinations, SNP data revealed the wide variability in homozygosity within and between S1 and S2 families. Even in small families (<10 sibs), individuals were identified with up to ~1.5 standard deviations above the family mean homozygosity. From baseline homozygosities of 0.476 and 0.454, offspring with homozygosities of 0.862 and 0.879 were recovered for clones TSH-1188 and CCN-51 respectively, in only two generations of selfing (81–93% increase). SNP marker assisted monitoring and selection of inbred individuals can be a practical tool to optimize and accelerate the development of inbred lines of outbred tree species. This approach will allow a faster and more accurate exploitation of hybrid breeding strategies in cacao improvement programs and potentially in other perennial fruit and forest trees.Cacao is a globally important crop with a long history of domestication and selective breeding. Despite the increased use of elite clones by cacao farmers, worldwide plantations are established mainly using hybrid progeny material derived from heterozygous parents, therefore displaying high tree-to-tree variability. The deliberate development of hybrids from advanced inbred lines produced by successive generations of self-pollination has not yet been fully considered in cacao breeding. This is largely due to the self-incompatibility of the species, the long generation cycles (3–5 years) and the extensive trial areas needed to accomplish the endeavor. We propose a simple and accessible approach to develop inbred lines based on accelerating the buildup of homozygosity based on regular selfing assisted by genome-wide SNP genotyping. In this study we genotyped 90 clones from the Brazilian CEPEC´s germplasm collection and 49 inbred offspring of six S Theobroma cacao L.) is a predominantly allogamous tropical tree species [Cacao , Ecuador, Malaysia, Indonesia, among others. However, despite the many benefits of cloning, there are also some drawbacks . First, Hybrid cacao varieties are planted in most producer countries in the world, including those in West Africa, where around 70% of the world cocoa beans are produced . Besides2/tree).The use of inbred lines as parents of hybrids could minimize the problems associated with both, clonal propagation and interclonal hybrid variability, by consolidating the advantages of both deployment methods, i.e., seed propagation and capture of hybrid vigor. This was attempted since the beginning of hybrid breeding in cacao by searching for naturally inbred selections from local populations  –13, usinGenome-wide DNA marker data provides a simple and accurate proxy of the genome-wide level of homozygosity of an individual plant produced by self-pollination by simply counting the proportion of marker loci that, once heterozygous in the self-pollinated parent, segregated to homozygosity in the inbred offspring. Individuals with higher levels of attained homozygosity would be prioritized to carry out the subsequent generation of self-pollination, fast-tracking the production of inbred lines when compared to simple random selection of offsprings. Despite this obvious application of DNA marker data to accelerate the buildup of inbreeding in outbred plants, reports to this end have been rare, using only low-resolution microsatellite genotyping in papaya  and cassWith the dramatic reduction of sequencing and genotyping costs in recent years, cacao has experienced significant advances in genomic resources and knowledge . Two cac1 and S2 individuals within and across families. Furthermore, the genome-wide levels of homozygosity in a set of germplasm accessions of the Cacao Research Center (CEPEC/CEPLAC) in Brazil were estimated to potentially select which clones to prioritize for the development of inbred lines, as well as to drive pairwise clone combinations to generate hybrid progenies with higher levels of heterozygosity and potentially heterosis.In this study we show that genome-wide SNP marker data can be efficiently used to assess the increase in homozygosity across two generations of self-fertilizations, revealing a wide variability in the attained homozygosity among S1 families were produced with 16 to 53 individuals per family (average = 32.8 trees/family), and eight S2 families, with 26 to 33 individuals per family (average = 30.1 trees/family). The S1 and S2 families were generated by selfing So and S1 plants, respectively, by manual (protected) pollination, as usually done in cacao. Among those 90 clones, four are widely used in CEPEC´s breeding program, because of their high yield and/or resistance to diseases: CCN-51, PS-13.19, TSH-1188 and SIAL-169. Six inbred families were genotyped in this study to evaluate the feasibility of the proposed approach of assessing the within-family variability in homozygosity and identify more homozygous offspring. Two S1 families were obtained by selfing cacao clones PS-13.19 and SIAL-169; four S2 families, three of them obtained by selfing three different S1 offsprings of clone TSH-1188; and one family from an S1 offspring of clone CCN-51  . PT-1501 was established using a spacing of 3.0 x 1.5 m, in June/2015, under a randomized complete block design, with 3 blocks and 21 plants per plot, and 102 progenies involving sources of resistance to moniliasis . In some plots of that trial, the 1,261 S1 and S2 individuals were planted. To assess the prospects of the approach proposed in this study, between 6 to 9 inbred plants per family showing good performance were genotyped , all analyses were done in terms of the observed and expected homozygosities, and not in terms of heterozygosities as usually done in diversity studies. For each individual plant the number of homozygous (AA and BB) and heterozygous (AB) SNPs genotypes were counted and the observed homozygosity estimated as the total proportion of homozygous SNPs over the total number of genotyped SNPs. For each inbred S1 or S2 family, the mean (μ) and standard deviation (σ) of the observed homozygosity were calculated. Individual offspring homozygosities were normalized by the standard deviation (σ) to allow ranking and identifying the S1 and S2 individuals with higher proportions of homozygosity within each family. A chi-square test (α = 0.05) was used to test the null hypothesis of no difference between the observed and expected counts of the three SNP genotypic classes  for each offspring individual. Expected genotypic counts for the inbred generation were obtained based on a simple Mendelian model assuming no selection. In other words, from the genotypic counts  in the self-pollinated parent, the number of homozygous SNPs (AA and BB) counts are expected to increase by 25% each, and the number of heterozygous SNP counts to decrease by 50% in each generation. A significant chi-square would indicate either a less than expected or higher than expected homozygosity from Mendelian expectations, providing a statistical assessment of the deviation due to sampling effects and/or selection against inbreeding in each individual offspring.A parentage test using the SNP marker data was carried out on all S2 families (family C) the S1_1 parent  between the observed and Mendelian model predicted homozygosity was also estimated for each family, as a way to quantify the deviation from the predicted inbreeding.The average expected homozygosity in each inbred family was estimated in an analogous manner, i.e., based on the observed homozygosity of the self-pollinated parent. In other words, the homozygosity is expected to increase by 50% of what was the observed homozygosity in the previous generation. For one of the S CP-255,  was miss1 and S2 individuals was calculated as a preliminary tool to choose what crosses among them would yield hybrids with the highest proportions of SNP loci in heterozygosity. Genetic distances were estimated using the genetic distance measure of Smouse and Peakall [1 and S2 individuals, were built using MegaX [A matrix of genetic distances based on the SNP data among all 90 clones and the S Peakall  between  Peakall . UPGMA dng MegaX  to visuaRaw SNP data exported from GenomeStudio 2.0 with GenCall>0.15 were submitted to additional quality controls. Only SNPs that passed the following Illumina recommended multi-variable metrics criteria were retained: (i) genotype clusters separation > 0.3; (ii) mean normalized intensity (R) value of the heterozygote cluster >0.2; (iii) mean normalized theta of the heterozygote cluster between 0.2 and 0.8; (iv) >99% reproducibility across the replicated samples and >99% correct inheritance between generations in the inbred families. Only SNPs with call frequency ≥95% and MAF≥ 0.01 for samples with call rate ≥87% were retained for further analyses. Out of the 3,412 SNPs present on the chip, data for 3,380 SNPs were ultimately retained after quality control. The full data set is provided . AlthougThe average observed homozygosity for this group of 90 clones was 0.727±0.182 , while t1 and S2 inbred individuals studied (see below)  . UPGMA dd CCN-16 .2 individuals supposedly derived from clone TSH-1188 were excluded from the study based on the observation of several hundred SNP genotypes incompatible with parent-offspring inheritance. For all remaining 49 confirmed inbred plants, 31 S2 and 18 S1 plants, estimates of individual  was around 0.5 ranging from 0.454 for CCN-51 to 0.491 for PS-13.19. Given the very high homozygosity of clone SIAL-169, its S1 inbred family showed little variation in homozygosity with the average, minimum and maximum values close to the homozygosity of the original SIAL-169 parent. As expected, for the other five families, the range and the average homozygosity in the four S2 families was higher than the homozygosity seen in the S1 family. This S2 to S1 difference in homozygosity was smaller for the three S2 families of clone TSH-1188 and larger for the S2 family of clone CCN-51. While the average homozygosity was 0.867 in the S2 family of CCN-51, and between 0.753 and 0.786 in the three S2 families of clone TSH-1188, it was 0.715 in the S1 family of clone PS-13.19. The higher average homozygosity of the S2 family of CCN-51 (0.867) is explained by the higher homozygosity (0.741) already seen in the S1 plant (CCN-51_S1_1) when compared to the S1 plants of clone TSH-1188. Overall, no significant difference was seen between the observed and expected family mean homozygosity  and the deviations (RMSD) between the Mendelian model predicted and the observed homozygosity were small, varying from 0.3% for the S1 family of SIAL-169, to a maximum of 6% for the S1 family of PS-13.19 . Clone Clone SCA-12 was the third case of misplacement. It belongs to the Contamana group and was expected to cluster with its highly related clones SCA-6, SCA-5 and SCA-19. The genetic profiles of clone SCA-12 and a few other clones were kindly provided by Dr. Osman Gutierrez (USDA-ARS) as part of a study that established an optimized set of SNPs genotyped by the Agriseq™ genotyping-by sequencing technology . The prowww.icgd.rdg.ac.uk/). SNP data profiles for a few markers are downloadable. However, profiles for a much larger number of SNPs would be needed to allow effective data comparison. Concerted international efforts in this respect would represent an important advancement for the conservation, utilization and international exchange of cacao germplasm.The assessment of the correct germplasm identity of CEPEC´s collection is currently the focus of an ongoing project. Nevertheless, to be able to check the correct clonal identities, public databases of SNP profiles for trustworthy reference samples, using widely adopted SNP sets are necessary. An initial effort exists through the International Cocoa Germplasm Database at the University of Reading  with small range of movement. Furthermore, semi-wild populations were established from small seed samples of the germplasm present in the source region [Despite the general conception that cacao is self-incompatible based on the favorable conditions for allogamy, the literature is rich on the fact that cacao may nonetheless present a high level of homozygosity even in wild and semi-domesticated populations. Up to 96% of self-fertilization in clones otherwise considered as self-incompatible, was reported . This mie region , followee region  and GWASe region , 34. TheClones with high genome-wide homozygosity characterized in our study would be priority candidates to be assessed for general and specific combining abilities. They could be tested as parents of hybrids aiming to reduce variability in operational plantations and potentially exploit hybrid vigor in a more systematic way. A number of studies have shown a strong influence of dominance variance on cacao yield, supporting the dominance hypothesis for heterosis , 62–65. 1 and S2 individuals  or vice versa that only have 50% probability. Among the clones sampled in this study some are already widely used as parents of hybrids in Latin America. Given that breeders are frequently faced with an unfeasible large number of possible crosses, the data provided in this study could help establish some priorities.SNP heterozygosity in the hybrid could be used as a proxy to model heterosis for traits in which dominance variance is known to be important, as done in domestic animals –69. The ividuals , and moror yield . The genor yield  attribut1 versus an S2 generation. One possibility is that more variation would be expected in the S1 generation as more genetic load would still be present for natural selection to operate. Unfortunately, we only sampled two S1 families, and the one derived by selfing clone SIAL-169 actually did not provide information, as the parental clone itself was already highly homozygous. Nevertheless, the range in the standardized proportions of homozygosity in the other S1 family (PS-13.19) was not different than in the four S2 families sampled  had a significantly lower than expected homozygosity, as indicated by the highly significant chi-squares  and CCN51 (0.454) are essentially equivalent Click here for additional data file.S2 FileData are presented in the Illumina AB format where the alleles A or T at the SNP correspond to the allele code “A” and alleles G or C at the SNP correspond to allele code “B”.(XLSX)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File1 and S2 offspring from clones TSH-1188, CCN-51, PS-13.19 and SIAL-169 based on 3,380 SNPs.Matrix with overlapping heat map of the genetic distances among all 90 cacao clones and the 49 S(XLSX)Click here for additional data file.S6 File(A) Dendrogram for the 90 clones only and (B) dendrogram for all 139 plants  based on a matrix of genetic distances estimated with 3,380 SNPs.(PDF)Click here for additional data file.