Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods  becomes unfeasibly long.Here, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies.The results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations. Whole genome sequence data is available for an increasing number of species. In some cases enough individuals have been sequenced to serve as a reference panel for imputation of individuals that have been genotyped with SNP arrays to whole genome sequence variant genotypes. A good example of such a reference set is the 1000 bull genomes project which includes 234 bulls with whole-genome sequencing data and 28.3 million genotyped sequence variants , better As the resulting data sets will be extremely large , the algorithms used to derive genomic predictions must be computationally efficient. Ideally, they should also implement a non-linear model at the level of the SNP effects, including the possibility of excluding some SNPs from the model, as such models have been demonstrated to give higher accuracies of genomic predictions for some traits with high-density genotype data , 6. AlthAnother advantage of non-linear models such as BayesR, is the application of QTL mapping , 13, 14.To take advantage of the accuracy superiority of MCMC nonlinear models but improve their time-efficiency, a hybrid scheme (termed HyB_BR) was proposed by Wang et al. . This scWith the aim of investigating whether HyB_BR gave comparable accuracies to BayesR with MCMC for genomic prediction and precision of QTL mapping with whole genome sequence data, we implemented HyB_BR on a large subset of imputed whole-genome sequence data with 994,019 variants in 16,214 cattle. The genotype data came from the imputed sequence variants in or close to gene coding regions and some SNP from the 600 K Bovine HD SNP genotypes. The HyB_BR algorithm was evaluated on this data set with three criteria: 1) computational performance (speed) compared to a full MCMC implementation, 2) prediction accuracy for a range of complex traits with different genetic architecture. The traits included fat yield, milk yield, protein yield, fat percent, protein percent, fertility and heat tolerance and 3) the precision of HyB_BR for QTL mapping of milk production traits, fertility and heat tolerance.Two types of genomic data, 600 K Bovine HD SNP array, and imputed sequence data were used in this study. As described by Kemper et al. (2015) , all genFor the Sequence data set (termed SEQ), the sequences of 136 Holstein and 27 Jersey bulls from the 1000 Bulls Genome Project . In totahttp://datagene.com.au/index.php). A summary of the phenotypes is shown in Table Protein, fat and milk yields and, fat and protein percent are key traits in dairy cattle breeding. Phenotypes that were pre-corrected for fixed effects  were used in this study, these are known as trait deviations (TDs) and daughter trait deviations (DTDs) for cows and bulls respectively. TDs and DTDs are provided by DataGene , which is the organisation responsible for providing genetic evaluations to the Australian dairy industry.  which is the number of days between consecutive calving, For fertility, the number of bulls and cows in the reference set, i.e. with genotypes and fertility phenotypes was around 15,190. The validation set includes Holstein bulls (youngest cohort born after 2004) and Jersey bulls (youngest cohort born after 2005).As weather becomes warmer and less predictable, there is growing interest in developing genomic breeding values for heat tolerance . In AustThe input parameters for HyB_BR were estimated from the data with ASReml4  and inclGBLUP assumes all marker effects follow a normal distribution with the same additive genetic variance. The overall model of GBLUP is:Where,y = vector of n phenotypes.β = vector of b fixed effects, following uninformative priors.u = vector of q random genetic effects  captured by the SNP, with G is the q x q genomic similarity matrix between pairs of individuals constructed as described by . The conditional distribution of SNP effect on the proportion parameter Pr is:Pr= the vector of proportion parameter, which defines the proportion SNPs in each of four normal distributions. The prior of Pr is drawn from Dirichlet distribution Pr ~ Dirichlet(α), with Z is the standardised (for mean and variance) n × m genotype matrix.To implement the BayesR model, and arrive at posterior estimates of parameters, Gibbs sampling has been used, as described by Kemper et al. (2015) . On the The HyB_BR model  incorporAs described in Wang et al. 2016) [ [16], thi follows the same prior assumption as BayesR with Zi being the standardized genotype for SNP i. 2) to correct the prediction errors generated by all other SNPs, HyB_BR introduces the genetic values u, whereby a correction based on the prediction error variance (PEV) is introduced to account for the effects of all the other SNP with a GBLUP model as detailed by Wang et al. [gi , Pr  , β , u , v, p(θ| y) ∝ f(y| θ)p(θ), where f(y| θ) is the likelihood function based on model (3) and p(θ) is the prior density function for the parameter sets θ. Based on the derived marginal posterior distribution p(θ| y), the expectation- maximization steps are implemented to estimate each parameter while “integrating out” the other parameters detailed by Wang et al. (2016). The process of the EM module is presented in pseudo code in Fig. Assumptions in the model are 1) each SNP effect gg et al. . Then ung), Proportion parameter (Pr), the variance for each SNP (X), the pedigree based relationship matrix (A), the genomic relationship matrix (G), the error matrix (E), and index matrix for polygenic effects (W). Similar to emBayesR [g and Pr are set as g = 0.01 and Pr = {0.5, 0.487, 0.01, 0.003}, while n×3 matrix X is a design matrix, allocating the phenotypes to fixed effects. In our case, matrix X is set up with first column being the mean, the second and third columns defining the breeds (Holstein and Jersey) and sex (bulls and cows) of the cattle. The pedigree relationship matrix, A, is built using the lower symmetrical matrix Ped detailed by Henderson [G is constructed using the equation G = ZsZs′/n, Zs is the standardized Z matrix with E is constructed according to the equation defined by Garrick et al. [As shown in Fig. emBayesR , the staenderson ; while tO(mn). For the calculation of O(m2n). This calculation accounts for 40% of the total computational time of EM module. Since the calculation is independent for each SNP, we parallelize the operations by chromosomes, which reduce total running time by approximately 30%.The EM steps require the time complexity q the iteration number, the parameter estimates from the EM are used as starting points of parameter values in the MCMC iterations. The steps of MCMC iterations iteration were detailed by Kemper et al. (2015) [P is greater than 0.90, the SNP effects will be set as zero. Previous investigation showed that 4000 MCMC iterations were required by HyB_BR for both 600 K SNP panel and imputed sequence data to maximize accuracy of genomic prediction across all the traits [Once the EM has converged using the criterion (. (2015)  suggestee traits .To compare the computational cost between BayesR and HyB_BR and how this changed with an increasing number of individuals in the reference set, we divided the data Table  into thrIn all three reference sets, the speed advantage of HyB_BR compared with BayesR was investigated. Then the accuracy of genomic prediction from BayesR, HyB_BR and GBLUP was compared in the full data (including the sequence variants).In addition, the precision of mapping QTL from the three methods was compared.For both 600 K and SEQ data sets, HyB_BR was more than 10 times faster than BayesR, Fig. These timings were recorded on a server with Intel E5–2680 2.7GHz processors and 384GB of 1333 MHz RAM.For the milk production and fertility traits, the combined Holstein and Jersey reference sets were used to predict three validation sets including Holstein bulls Table , Jersey When predicting the Holstein validation bull data, BayesR and HyB_BR performed equally well. Compared with GBLUP, BayesR and HyB_BR had a small but consistent accuracy improvement for the milk production traits except protein%. For fat% trait, BayesR and HyB_BR gave a 5% improvement in accuracy compared with GBLUP. However, for protein% and fertility there was no difference between the methods. With the Jersey validation set, the accuracy superiority of HyB_BR and BayesR over GBLUP was greater; for example for fat percent, BayesR and HyB_BR gave a 10% higher accuracy than GBLUP. HyB_BR and BayesR also gave regression coefficients (DTD on GEBV) closer to one than GBLUP for most traits.In addition, when incorporating the polygenic effects into the prediction model, a small but consistent accuracy improvement was observed for milk production traits, Table When predicting Australian red bulls and cows using the combined Holstein and Jersey reference set (across breed prediction), both HyB_BR and BayesR had a considerable accuracy advantage (up to 12% increase) over GBLUP for all the traits Table . CompareThe accuracy of genomic prediction for the heat tolerance traits was similar for GBLUP, BayesR, and HyB_BR, Table Compared with 600 K SNP panels, the impact of sequence data (SEQ) on the prediction accuracy of GBLUP, BayesR, and HyB_BR was dependent on the trait and validation population Fig. . For theTo compare the genetic architecture of the traits using whole genome sequence data, the number of SNPs in each of four distributions  was detected by BayesR and HyB_BR. These genes haveb previously been reported to be associated with calving traits , 40.As there is a significant unfavourable correlation between milk production and heat tolerance, at least for the traits we have used for heat tolerance (decline in milk production with increasing heat stress) , mutatioEscherichia coli of human or other animals under heat-shock response. Variants in SERPINE2 and CACNA1D  have been reported to impact the sweating rate and respiration rate of dairy cattle [In total, we found fourteen novel variants Table  in our sy cattle . DYRK3  implemented for Bayesian models have tended to reduce the accuracy compared with MCMC.In this paper, we have demonstrated that HyB_BR  could beHyB_BR implements an EM algorithm to quickly converge for estimates of SNP effects and other parameters, followed by a limited number of MCMC iterations to optimise the posterior estimation for SNP effects. When applied to whole genome sequence data, our results indicated HyB_BR had similar accuracy of genomic prediction and precision of QTL mapping to BayesR implemented with full MCMC, but with 10 fold less computational time required. Furthermore, compared with the prediction accuracy on 600 K SNP panels, we have demonstrated that using sequence data improved the accuracy of genomic prediction for some of the traits, and particularly in multi-breed evaluations, if a breed was not included in the reference population.O(mn2). In comparison with BayesR, HyB_BR has the same number of basic operations. But after the EM converges  [The key improvement for computational efficiency was that HyB_BR reduced the iteration times. BayesR required a huge number of MCMC iterations, which was dependent on the size of the data. For example, on the whole genome sequence data with 16,214 animals and almost 1 million variants, 40,000 iterations with first 20,000 as burn-in were required. For each MCMC iteration, the basis operation times were . (2015) ), HyB_BRIn addition to the computational time, the prediction accuracy of HyB_BR for multi-breed prediction and across-breed prediction was very similar to BayesR for a range of traits with various genetic architectures, shown in Tables For the genetic architecture identification of milk production traits, there was one notable difference between BayesR and HyB_BR: In comparison with BayesR, HyB_BR does not shrink variants with small effects   has prevThe computational advantage of HyB_BR makes it attractive for implementation of genomic prediction in many applications. However, there are still two limitations: 1) the speed-up scheme of HyB_BR defines the fixed threshold for different traits and various densities of genomic data, which could hinder its flexibility for practical applications; 2) when the size of the data increases dramatically to 30 million variants on millions of animals, which is possible in the near future, HyB_BR is still not computationally efficient enough. Therefore, a flexible and more efficient speed-up scheme will play an important role to further improve the computational performance of HyB_BR.A hybrid scheme of Expectation-Maximisation algorithm and MCMC sampling was implemented on whole-genome sequence data for simultaneous genomic prediction, inference of genetic architecture inference and causal mutation identification. The accuracy of HyB_BR for multi-breed and across breed prediction for all traits was very similar to the results from BayesR (implemented with full MCMC) while requiring only 1/10 of the total running time of BayesR. HyB_BR could identify some well-known mutations (e.g. DGAT1) with the highest posterior probability, which demonstrated the value of the method for QTL mapping of complex traits. The advantage of using sequence data and HyB_BR was greatest for multi-breed and across breed predictions.


Recent focus on studying rare variants makes imputation accuracy of rare variants an important issue. Many approaches have been proposed to increase imputation accuracy among rare variants, from reference panel selection to combinations of existing methods to multistage analyses. We aimed to bring the strengths of these new approaches together with our proposed two-stage imputation for family data.Our imputation methods were tested on the region from 46.75Mb to 49.25Mb on chromosome 3. We did quality control based on the proportion of missing genotypes per variant and individual, leaving 495 individuals with 761 genome-wide association studies (GWAS) variants only, 45 with 14,077 sequence variants only, and 419 with both GWAS and sequencing data. All data were prephased using SHAPEIT2 with a duo hidden Markov model algorithm prior to performing imputation. Imputations were performed 100 times, each time masking the sequence data for 1 individual and imputing it from the GWAS data. We used well-imputed genotypes, defined as a probability of greater than 0.9, above 2 different minor allele frequency cutoffs—0.01 and 0.05—from Impute2 as input for Merlin, and compared these results to Impute2 and Merlin separately. The imputed results were evaluated using correlation measurement and the imputation quality score.Our method improved imputation accuracy, measured by imputation quality score, for variants with minor allele frequency between 0.01 and 0.40, but failed to improve accuracy for variants with minor allele frequency less than 0.01 when we used a minor allele frequency cutoff of 0.01 for the Impute2 results. In contrast, our 2-stage approach with a minor allele frequency cutoff of 0.05 performed the worst of all methods for variants with minor allele frequency between 0.01 and 0.40.This method gave promising results, but may be further improved by changing the inclusion criteria of Impute2 variants. More analyses are needed on a larger region with different inclusion thresholds to assess the accuracy of this approach. However, recently Saad et al  and KreiOur approach combined the strengths of Saad et al and Kreiner-Møller et al. We performed a 2-stage imputation, implementing Impute2 and Merlin sequentially, to test the hypothesis that increasing the density of genotypes in a sequenced reference panel using a population-based imputation before performing a family-based imputation would lead to higher imputation accuracy in a related genome-wide association studies (GWAS) study panel.N = 45) from the GWAS samples, and limited our analysis to the 46.75 Mb to 49.25 Mb region on chromosome 3. This yielded 914 people with GWAS data and 761 Illumina variants. For the sequenced data, we removed any variant with more than 10 % missing data, leaving 14,077 sequenced variants. All sequenced individuals had less than 5 % missing data. Thus, all 959 individuals were included in the analyses: 495 with GWAS only, 45 with sequencing only, and 419 with both GWAS and sequencing.Our sample consisted of 959 Mexican Americans from 20 families. All 959 subjects were genotyped on the Illumina platform, and 464 of these individuals were also sequenced. We removed all SNPs with more than 5 % missing data and all individuals with more than 5 % missing data  . For ther2 less than 0.2 implemented in PLINK 1.9, which yielded 91 variants in approximate LE. The mean pairwise r2 for the 91 variants was 0.0252 and the median was 0.0014. To get the clustered markers and haplotype frequencies, we searched for GWAS markers for which r2 is larger than 0.2 and defined the clusters, including each identified pair and intervening markers, which were implemented in Merlin with the–rsq and –cfreq options. The imputations were conducted with all GWAS variants (dense markers), pruned GWAS variants in LE and dense markers with predefined haplotypes, separately. Table Because the algorithm used in Merlin depends on markers being in linkage equilibrium (LE), we also compared the family-based imputation qualities by using sparse markers, dense markers, or the haplotype-block approach   from the observed proportion of agreement. In cases where the expected agreement is high, such as with variants with low MAFs, the second term in the numerator is higher, thus lowering the Kappa statistic. Lin et al extended this idea to incorporate the uncertainty of imputation by using the posterior probabilities of all 3 genotypes instead of the best-guess genotype, thus allowing the cells in Table Among 100 individuals that we selected, the number of imputed polymorphic sequence variants is 6726. The accuracy assessments with IQS and correlation were conducted within the 100 individuals and polymorphic variants. However, different imputation strategies yield different numbers of polymorphic variants with meaningful IQS or correlation Table . This isGenerally, our proposed 2-step imputation method performed better than only using population-based imputation with Impute2 or only using family-based imputation with Merlin for the variants with a MAF larger than 0.1 and less than 0.4 Figs. . With deOur combined method with a MAF cutoff of 0.01 performed better than either Merlin or Impute2 alone for variants with MAFs between 0.01 and 0.4, and our combined method with a MAF cutoff of 0.05 performed better than either Merlin or Impute2 alone for variants with MAFs >0.05. Because the performance suffers below our MAF cutoffs, this suggests that we should not filter Impute2 results by MAF at all, but filter only by best-guess genotype probability.One potential limitation of this study is that families with more sequence data were more likely to be selected in our set of 100 individuals. We would expect higher imputation accuracy in these families, as there were more individuals included in the reference panels for imputation. More work needs to be done to determine exactly how much the number and relationships of sequenced family members available affect imputation quality. This was beyond the scope of our project, but may be useful in helping investigators choose which family members to sequence.It is unclear from these results whether the sequential nature of the imputation increases accuracy. In the future, we should compare our method to a method combining independent results from Merlin and Impute2, both based on best-guess genotype probability and Saad et al’s proposed vote strategy . FurtherOur 2-stage method with a MAF inclusion cutoff of 0.01 for Impute2 results achieved better IQSs than either Impute2 or Merlin alone, and similar correlation values, for variants with MAFs between 0.01 and 0.4. This method could be further improved by including all Impute2 imputed genotypes above a certain quality threshold regardless of MAF. Other probability thresholds should be tested, and this 2-stage method should be compared to results using Merlin and Impute2 independently to examine whether the sequential nature of the procedure increases accuracy above and beyond the increase obtained by combining population- and family-based methods.


Previous studies have shown that the SH mutation affects the fruit ability to synthesize appropriate amounts of indol-3-acetic acid (IAA), which orchestrates the ripening processes through the activation of system 2 ethylene pathway. Allelic variation in a TC microsatellite located within the first intron of PpYUC11-like (a YUCCA-like auxin-biosynthesis gene) has been recently proposed as the causal mutation of the SH phenotype.Texture is one of the most important fruit quality attributes. In peach, stony hard (SH) is a recessive monogenic trait  and SH accessions provided an expression patterns overview of the annotated transcripts within the hd locus, confirming the absence of PpYUC11-like expression in SH fruits. To explore further possible associations between genomic variants at the hd locus and the SH phenotype, re-sequencing data of the SH accession ‘D41–62’ were compared with several SH and non-SH accessions with different genetic backgrounds. A further step of validation was provided through the evaluation of variant-trait association in two bi-parental F2 populations issued from the SH accession ‘D41–62’ and a panel of advanced breeding selections, showing perfect co-segregation of the PpYUC11-like intron TC20 allele and the SH phenotype.The simple genetic determinism of the SH trait has been clarified through genome-wide association and LD analyses in a diverse set of accessions, restricting the PpYUC11-like locus as the genetic determinant of the SH trait, opening interesting perspectives at both biological and applied research level.In this study, we provide a multi-level validation of the genetic control of the SH trait through the integration of genome-wide association mapping, transcriptome analysis and whole-genome resequencing data for SH and non-SH accessions, and marker-trait association in a panel of advanced breeding selections and segregating progenies. Collectively, our data confirm with high confidence the role of allelic variation at The online version of this article (10.1186/s12870-018-1293-6) contains supplementary material, which is available to authorized users. DR12 (an Auxin Response Factor) in tomato induces modification of pectin structure [.Texture is an important fruit quality trait, affecting consumers’ degree of liking and marketability. During ripening, fruits undergo progressive modifications of textural characteristics, caused by structural rearrangement of cell wall polymers and change in turgor pressure, finally leading to fruit softening . In climtructure . Applicatructure . In appltructure .P. persica L. Batsch) an interesting model species for the dissection of the genetic determinism of fruit texture and softening behaviour [M/m) located on chromosome 4 [M locus and involving the presence/absence of two endo-PG isoform (endoPGM and endoPGF), has been recently demonstrated as the genetic basis of the recessive non-melting flesh (NMF) trait [The availability of genetic and genomic resources, and the wide phenotypic variability for textural characteristic, make peach  trait . NMF peaF) trait .hd locus. SH fruits produce null or very low ethylene amounts during ripening [PpACS1 [M/m locus status is phenotypically revealed only by cold temperature exposure (below 10 °C) of SH fruits or by the treatment with ethylene or its precursor 1-aminocyclopropane-1-carboxylate synthase (ACC) [PpACS1 expression in SH fruits, indicating that proper IAA levels are required for activating ethylene biosynthesis [PpYUC11-like (ppa008176) is strongly correlated with IAA levels at late stages of ripening, while being undetectable in SH fruits [PpYUC11-like expression and IAA levels are correlated with PpACS1 activation, ethylene biosynthesis and firmness reduction. Molecular analysis identified allelic variation in repeat number at a TC microsatellite located in the first intron of PpYUC11-like gene: the TC20 allele is homozygous in SH accessions and heterozygous or absent in MF ones. Interestingly, in the heterozygous ‘Goldhoney3’ accession, the allele carrying the TC20 variant is not transcribed, further supporting its role in conferring the SH phenotype [Stony-hard (SH) flesh is a monogenic recessive trait firstly reported by Yoshida  and reguripening , maintai [PpACS1 . SH textse (ACC) , 19. In se (ACC) . Exogenoynthesis . Among tH fruits . This geH fruits . In MF fhenotype .In spite of the physiological and molecular evidences about the role of this allelic variant in conferring the SH phenotype, a more in-depth validation at genomic level and in a broader genetic background is still lacking. In this work, a comprehensive validation of the causal sequence variation conferring the SH trait is reported by the integration of genome-wide association mapping, transcriptome analysis, whole-genome re-sequencing and variant-trait association approaches in a panel of peach accessions, breeding selections and segregating progenies.p-values under null hypothesis, through quantile-quantile (QQ)-plot inspection and considering statistical power against False-Discovery Rate (FDR). A conservative threshold for assessing SNP significance was calculated based on Bonferroni correction for a type I error rate of 0.01. Intra-chromosomal LD patterns were measured and visualized using HAPLOVIEW v4.2 [The panel used for GWAS was established by including a total of 87 accessions, of which 63 MF, 12 NMF and 12 SH  using DA-meter instrument and firmness decay using a digital Andilog penetrometer equipped with 8-mm plunger  and visualized using heatmap R package. Differentially expressed genes (DEGs) were identified through non-parametric t-test equivalent using nparcomp package, considering the two accessions of each group as biological replicate.RNA-Seq libraries were constructed from samples collected from two different cultivars for each flesh texture phenotype (MF and SH) at Stage III  and Stage IV  of fruit development. Maturity degree was established by monitoring the It method, using Actin (Prupe.6G163400) as reference gene [For qPCR analyses, fruit samples of the four accessions used for RNAseq analyses were collected during SII to SIV stages of development. RNA was extracted from three biological replicates using RNeasy Plant kit (Qiagen) and cDNA synthesized using Superscript III reverse transcriptase (Thermo Fisher Scientific), according to manufacturers’ protocols. Primers for qPCR analysis are listed in Additional file nce gene .mpileup tool. For variants identification, after duplicate removal and reads indexing with PICARD, a joint-calling approach was performed using Haplotype Caller algorithm in GATK, following Best Practice guidelines. Sequences for predicted peach gene models were retrieved from the Phytozome v12.1 database [Whole-genome sequence (WGS) library of the accession ‘D41–62’ was prepared by the Genomics Platform of Parco Tecnologico Padano  with the Illumina Truseq DNA Nano sample prep kit  following manufacturer’s protocol and sequenced on the Hiseq2000 with paired-end sequencing module using the Truseq SBS kit v3. FASTQ files were obtained with the Illumina CASAVA Pipeline. WGS libraries from the other accessions were retrieved from SRA archive . Seedlings of two SH segregating F2 populations, BO10040 and BO10039  were grown at the experimental farm ‘Mario Neri’ of ‘CRPV  - ASTRA’ in Imola  and managed according to standard cultural practices. The two progenies were obtained from self-pollination of the heterozygous selections ‘BO0501406’ and ‘BO060260 issued from the SH parent ‘D41–62′ crossed with a MF selection [Breeding selections were derived from the SH donor parents ‘Yumyeong’, ‘IFF331’, ‘QinWang’ and ‘Hakuto’ back-crossed with different MF accessions and located at the experimental farm of CREA . Fruit phenotype of all selections  was confirmed by measuring post-harvest ethylene emission. Five fruits of each accession were weighted and individually placed into 1,7 l hermetically sealed glass jars. After a 24-h storage at room temperature (22 ± 1 °C), ethylene concentration in the sealed jar head-space was measured using the Ethylene SPY ES100 instrument  and expressed in ppm of ethylene per kilogram of fresh weight per day (ppm *kgPpYUC11-like). Forward and reverse primer sequences were 5’-CTATCTGGTATATAAGCTGAAACG-3′ and 5’-CTTGCATGAGGTACTTGGCAC-3′, respectively. The expected amplicon length ranges between 95 base pair for the homozygous TC20 repeat to 113 for the TC29 [20 repeat fragment from all others, irrespective of the number of repeats  through a simple agarose gel-based assay. Amplicons were amplified in 10 μl Go-Taq reactions using the following thermal profile: 95 °C for 2 min, 35 cycles of 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 30 s, with a final extension at 72o C for 3 min. PCR products were directly scored through a high-resolution 3% agarose gel-electrophoresis  stained on ethidium-bromide. Following electrophoresis, representative amplicons were excised, purified and directly sequenced to confirm the expected number of repeats.Total genomic DNA was extracted from leaf tissues by a modified CTAB protocol and quantified using Qubit (ThermoFisher). Based on previous reference peach transcript annotation V1.0, primers were designed at the flanks of the TC dinucleotide microsatellite on intron I of the ppa008176m gene  or non-stony hard (non-SH) phenotypes  and several individuals with various degree of admixture, in agreement with the well-known pattern of peach domestication and dispersal . The texture phenotypes were coded as binary trait assigning 0–1 to NMF and M accessions, respectively  and FarmCPU algorithm adjusted for population structure  . The application of mixed models adjusted for kinship and population structure matrices (MLM + K + Q) allowed to detect a highly significant signal on chromosome 6 composed of two closely spaced SNPs , close to the Bonferroni threshold  and SNP_IGA_534275  , predicted to encode two separated transcripts on the reverse strand: Prupe.6G157400 (from II to IV exon) and Prupe.6G157500 (I exon), both spanning the region from 14,091,690 to 14,095,013 base pair, and located at about 1.1 Mb from the most significantly associated marker (SNP_IGA_538171)  and two cinnamoyl alcohol dehydrogenase CAD9-like (Prupe.6G161800 and Prupe.6G162000), putatively involved in cell-wall metabolism, as well as several putative kinases (among which an RLK1 and a CLAVATA1-like proteins) having an array of biological functions.Linkage disequilibrium (LD) analysis of the regions surrounding the SNP_IGA_538171 tagged by the array revealed an extended LD block , which delimits the identified 2RKPM values), a few transcripts present within hd locus showed a differential expression pattern between the two texture types  expression in SH fruits, in contrast to the melting ones, where it was up-regulated at ripening  and Prupe.6G163500 (similar to DNA mismatch repair protein MSH3) only expressed in SH fruits , a key enzyme of ethylene biosynthesis, endo-PGM, the main polygalacturonase regulating the melting process, and a GH3.3-like, an auxin-conjugating enzyme, all down-regulated in SH fruits (data not shown). The same trend characterized also several genes implicated in auxin metabolism, perception and signalling pathways.An overview of the expression patterns of annotated transcripts within the selected interval was obtained from fruit transcriptome data. Genes differentially expressed between SH and non-SH fruits were identified through the comparison of two melting flesh, ‘Bolero’ and ‘Redhaven’ and two SH accessions, ‘BO05030081’ (a selection issued from 'D41–62') and ‘IFF331’, along SIII and SIV stages of ripening, when the two types of texture begin to differentiate. As depicted by the heat-map  accessions with different genetic origins. According to the genetic inheritance of SH trait, putatively associated variants had to be: i) homozygous and shared between SH genotypes; ii) heterozygous in non-SH genotypes carry on the recessive hd allele (e.g. ‘Okubo’ and ‘Okitsu’); iii) absent in non-SH genotypes. In line with the multiple evidences supporting the PpYUC11-like gene as the causative locus of SH phenotype, several polymorphisms conserved among SH accessions were identified in nearby regions. In addition to the already reported partial deletion of TC repeat within the intron microsatellite, other interesting variants were represented by two INDELs (a 26-bp insertion and a 20-bp deletion) respectively located in the promoter region  and 5’UTR  of the gene. However, the 5’UTR variant is only present in the SH accession ‘Jing Yu’ but not in ‘D41–62’ or ‘Yumyeong’, whereas the upstream INDEL was identified in other non-SH accessions  were also identified in upstream and/or downstream regulatory regions of differentially expressed genes Prupe.6G159800 and Prupe.6G160300  and Prupe.6G159300 , although their involvement in SH trait can be excluded, being these genes not expressed in fruit tissues.Further insights about the associations between genomic variations at the PpYUC11-like. Nonetheless, a further step of validation was provided through the evaluation of variant-trait association in bi-parental F2 segregating populations and advanced breeding selections. Two segregating progenies were considered: a total of 24 individuals out of 89 were phenotyped as SH based on firmness measurements   alleles, all SH individuals were homozygous for the TC20 allele (hd/hd) and MF individuals homozygous for the TC24 allele (Hd/Hd) or heterozygous ; in contrast ethylene production ranged from 13 to 94 ppm *Kg− 1 *day− 1 within the control MF accessions, which were either homozygous or heterozygous for the TC24 allele (Hd/Hd or Hd/hd)  Fig. . Also, td) Table .Fig. 5Fra PpYUC11-like gene has been proposed as the candidate determinant of SH trait, in the light of molecular and functional evidences [Understanding the genetic basis of peach textural characteristic is an essential step for the exploitation of the natural phenotypic variability and as a model for other soft fruit species. Among the various types of peach textures, SH type has received remarkable interest, both at biological and applicative research levels , 26. A svidences .hd locus to an interval of about 1.9 Mbp in the centromeric regions of chromosome 6. Gene mining across the target genomic interval, coupled with fruit transcriptome data from MF and SH accessions, allowed to prioritize a limited number of differentially regulated transcripts, among which PpYUC11-like and a few others with no previous evidence of a function in ripening-related phenomena. Comparison of resequencing data of the SH peach ‘D41–62’ with several SH and non-SH, MF and NMF accessions of different genetic origins, provided an accurate overview of genomic variation present at hd locus. This layer of information was essential for identifying variants putatively affecting regulatory and/or coding regions. Sequencing data clearly prioritized the intron TC20 microsatellite allele on PpYUC11-like, although a discrete number of SH-specific variants were also identified in regulatory regions of other differentially expressed genes within the mapped interval. While the involvement of such variants in SH trait cannot be fully excluded, they most probably reflect strong local LD around the SH locus, arising from the low recombination events in pericentromeric regions [20 allelic variant was evaluated in a broader genetic background, consisting of several advanced selections and two bi-parental progenies derived from the SH ‘D41–62’ parent. The perfect co-segregation adds a further level of validation, which, together with genomic, physiological and molecular evidences, allows to confirm with high confidence the role of allelic variations at PpYUC11-like as the genetic determinant of the SH phenotype.In the present work, the genetic architecture of the SH trait has been clarified through genome-wide association and linkage disequilibrium analyses in a panel of accessions with different textures, delimiting the  regions . The segPpYUC11-like opens interesting perspectives. At biological level, a key role for auxin in the regulation of peach ripening has been long demonstrated, as well as its intense interplay with ethylene [PpYUC11-like or in other genes of the YUCCA family (such as the ripening-related PpYUC10-like), may represent interesting targets for future studies in peach as well as in other soft fruit species.Although ultimate proof of the causal link between the candidate gene and the SH trait may require genetic engineering approaches, the body of evidence around ethylene . The SH ethylene . ConsidePpYUC11-like locus as the genetic determinant of the SH trait, opening interesting perspectives at both biological and applied research level.In this study, we provide a multi-level validation of the genetic control of the SH trait through the integration of genome-wide association mapping, transcriptome analysis and whole-genome resequencing data for SH and non-SH accessions, and marker-trait association in a panel of advanced breeding selections and segregating progenies. Collectively, data confirm with high confidence the role of allelic variation at Additional file 1:Table S1. List of analyzed peach accessions and respective texture phenotype. (DOCX 19 kb)Additional file 2:Figure S1. Evolution of flesh firmness during ripening of four peach accessions with a stony hard (‘BO05030081’ and ‘IFF331’) or non-SH/MF  texture, as measured through a penetration-based test. (BMP 4181 kb)Additional file 3:Table S2. List of primers used for quantitative PCR analyses. (DOCX 12 kb)Additional file 4:Table S3. SRA accession number of assembled Illumina Whole-Genome libraries. (DOCX 13 kb)Additional file 5:Figure S2. A) Histogram summarizing the frequency of stony hard (SH)/non-stony hard (non-SH) and melting (MF)/non-melting (NMF) phenotypes in a panel of 87 accessions used for GWAS; B) Genetic structure plot of the analysed panel for the optimal number of a priori genetic clusters (K = 3), with ancestry proportion on the Y-axis. The red, green and light blue bars indicate the subpopulations I (breeding-derived), subpopulation II  and subpopulation III , respectively. (BMP 1255 kb)Additional file 6:Figure S3. Manhattan and quantile-quantile (QQ) plots of the -log10p-values estimated for MF/NMF trait in a panel of 87 accessions using FarmCPU algorithm adjusted for population structure (left panel). Red horizontal indicates significant SNPs passing the Bonferroni-adjusted threshold based on the effective number of independent tests. (BMP 1220 kb)Additional file 7:Table S4. List of candidate genes identified on the chromosome 6 region comprised between SNP_IGA_652659  and SNP_IGA_534275 . (DOCX 17 kb)Additional file 8:File S1. Full list of variants annotations and effects  from whole genome sequencing assembly of 19 accessions within the hd locus on chromosome 6. Candidate variant on PpYUC11-like gene is highlighted in black; variants associated to DEGs in yellow; high-impact variants predicted by SNPEff in red. See text for variants filtering criteria. (XLSX 41 kb)Additional file 9:Table S5. Phenotypic evaluation for SH trait and allelic status at YUC11 TC microsatellites of the two F2 segregating progenies BO10040 and BO10039 (issued from the SH parent ‘D4162’). (DOCX 16 kb)


Theobroma cacao. Cacao cultivation is highly relevant for small-holder farmers throughout the tropics, yet its productivity remains limited by low yields and widespread pathogens. A panel of 148 improved cacao clones was assembled based on productivity and disease resistance, and phenotypic single-tree replicated clonal evaluation was performed for 8 years. Using high-density markers, the diversity of clones was expressed relative to 10 known ancestral cacao populations, and significant effects of ancestry were observed in productivity and disease resistance. Genome-wide association (GWA) was performed, and six markers were significantly associated with frosty pod disease resistance. In addition, genomic selection was performed, and consistent with the observed extensive linkage disequilibrium, high predictive ability was observed at low marker densities for all traits. Finally, quantitative trait locus mapping and differential expression analysis of two cultivars with contrasting disease phenotypes were performed to identify genes underlying frosty pod disease resistance, identifying a significant quantitative trait locus and 35 differentially expressed genes using two independent differential expression analyses. These results indicate that in breeding populations of heterozygous and recently admixed individuals, mapping approaches can be used for low complexity traits like pod color cacao, or in other species single gene disease resistance, however genomic selection for quantitative traits remains highly effective relative to mapping. Our results can help guide the breeding process for sustainable improved cacao productivity.Chocolate is a highly valued and palatable confectionery product. Chocolate is primarily made from the processed seeds of the tree species  Theobroma cacao is an important crop for tropical small-holder farmers. Cacao's fermented and dried seeds are used as raw material for a variety of products, particularly chocolate. Worldwide, low yields and high disease pressure are two major challenges that limit cacao production, resulting in increased deforestation and increased use of chemical pesticides, which has raised concerns regarding practices required for sustainable crop production  and genomic prediction. GWA allows for the identification of markers in linkage disequilibrium (LD) with polymorphisms that cause phenotypic changes and can be used for quantitative trait dissection in diverse populations  to select individuals with improved productivity and resistance to frosty and black pod diseases and 2) to compare the performance of GWA and genomic prediction to characterize the genetic basis and to develop predictive models for those same phenotypes among high-yield cacao selections. A sample of 148 clones was assembled and evaluated in a multi-year trial with 25 plants per clone (Methods). The sample contained 35 representative individuals from the germplasm collection and 121 superior clones, representing crosses between 29 diverse parents .Understanding the representation of diverse ancestral groups within the breeding germplasm is useful for breeding efforts. The selected clones were therefore characterized relative to cacao ancestral populations (Methods). The most represented genetic groups in the trial were Amelonado and Nacional, with an average proportion ancestry across clones of 0.27 Figure . MeanwhiPrincipal Component Analysis  was estimated both between and within chromosomes. Genetic correlations can exist between markers in different chromosomes, especially after recent admixture. In our population, most of the markers across chromosomes segregated independently; however, 0.13 of all pairwise comparisons between markers across chromosomes displayed R2 values > 0.1  is an important production trait, and heritability was 0.44 for the pod index, with a mean of 29 pods per kilogram of fermented dry beans. The top clones based on yield had good pod index values, with a mean of 22, a minimum of 15 and a maximum of 31 pods per kilogram of fermented dry beans.A major objective of this work was to identify clones that combine high productivity with low incidence of black and frosty pod disease. The back-transformed adjusted clone means were estimated for all traits using mixed linear models  was located adjacent to the TcMYB113 gene on chromosome 4 at position 20,879,148, , and we found a significant association of markers with two traits, pod color and frosty pod count, at the 0.05 significance threshold Figure . For podIn genomic selection, breeding values can be predicted using markers due to their LD with causative polymorphisms. Given the high LD observed in the study population, we were interested in establishing the effect of marker density on predictive ability. We observed consistent predictive abilities within traits across marker densities, with modest increases relative to increased numbers of SNPs after 100 markers Figure . The traDifferential gene expression can be used to detect genes with divergent expression relative to specific conditions, and several methods have been developed to statistically model and detect the genes with significant differences  at the La Lola farm, located at sea level in Costa Rica. In the QTL mapping population, located in the same country at the La Montana site (600 m above sea level), disease pressure was significantly lower, and susceptible controls showed only sporadic symptoms of disease during the trial. Interestingly, although both sites are in relative proximity, the greatest environmental difference is altitude, which could affect the distribution of the pathogen. For black pod, evaluated at La Lola, the disease pressure was lower, and the transformed count and proportion of pods with disease showed lower heritability than the proportion of pods with frosty pod.When considering the fitted values for both diseases and yield, of the top 10% of the clones with the lowest frosty pod disease proportion, only CATIE R5, CATIE R6, and CATIE R58 were also among the highest yielding clones. For black pod, the equivalent set included clones CATIE R6 and CATIE R73. The highest-yielding clone was CATIE R92; however, CATIE R6 ranked among the top 10% for yield, as well as for frosty pod and black pod disease resistance, despite having a slightly lower yield than CATIE R92. Both clones have annotated UF 273 as a common parent; CATIE R 92 and CATIE R6 have POUND 7 and PA 169 as the other parent respectively. Clones UF 273 Type 1 and UF 273 Type 2 are very closely related, they belong to the UF series developed by the United Fruit company and both are moderately susceptible to black pod but resistant to frosty pod disease. Clone POUND 7 was collected by F. J. Pound in 1942 along the Nanay river, a tributary of the Amazon River in Peru, and it complements the disease traits of UF 273 clones, displaying resistance to black pod and susceptibility to frosty pod. Finally, clone PA 169 was collected in Peru in the Parinari district of the Loreto province, at Rio Marañon. Similar to UF 273 Type 1, this clone displays resistance to frosty pod. These results confirm the limited number of allele donors used in breeding for genetic resistance to frosty and black pod diseases.In addition to productivity and disease resistance, we characterized the effect of rootstock choice on the productivity of the scion. Significant rootstock effects have been documented in many other tree crops  planted with 5-m spacing, and permanent shade was provided by the legume Gliricidia sepium at 7.5-m spacing. Border cacao plants were set up around the field, within the field around two rivers, and around a road contained within the field. Plants that died during the experiment were replaced with new grafts to provide an even canopy structure; however, these individuals were excluded from the analyses.Yield evaluation was conducted at a location with a high endemic prevalence of black pod and frosty pod diseases. The trial was established by the CATIE Cacao Breeding Program in June–July 2006 in a 4-hectare area at the La Lola farm. This farm is on the Atlantic Coast of Costa Rica, located 40 meters above sea level at 10° 06′ N latitude and 83° 23′ W longitude. Most of the farm's soil (69%) consists of silty clay, and the remainder comprises 21% coarse sand and 10% sandy clay. The evaluation was performed in an agroforestry system with natural rainfall, application of 150 g of granular fertilizer formula 18-5-15 every 3 months, and manual pruning performed with a broad blade once or twice a year. The plants were planted in a completely randomized design, with 2.5 m spacing between plants. Temporal shade was provided by banana plants (http://www.lgcgenomics.com). After quality control (>80% sites), 3,733 individual plants contained a genotypic profile for 90 SNP markers. Using the 15 k SNP Chip as a reference, a 6.88% off-type rate was observed, with 90% of clones having three or fewer off-type plants. Off-type plants were removed from the analyses.For genotyping, the parallel objectives were to characterize the presence of off-types in the field and to generate high-density markers for the true-to-type individuals. Off-types are a widespread problem in cacao breeding programs due to human sample error and biological phenomena such as rootstock escape. For each clone, two leaves were collected from three mature plants on the field. DNA was extracted from all leaves using a ZR-96 Plant/Seed DNA Kit (Zymo Research) and genotyped using Fluidigm. Using a majority rule, the most frequent clonal type was selected for high-density genotyping. To generate a large number of markers for likely true-to-type individuals, the most frequent clonal type was genotyped using a high-density SNP microarray . Three leaves from each tree were collected again, and genotyping was performed by LGC Genomics using Kompetitive Allele Specific PCR (KASP™)  produced per tree per year, μ is the overall mean, Bi is the random effect of blocks, Yj is the random effect of year, Bi * Yj is the random block-by-year interaction, Ck is the random clone effect, and ϵijk is the residual error, with The data for all years was analyzed together for the true-to-type plants using a mixed linear model. The model used in the data analysis was:yij is the number of healthy pods, Bi is the main effect of block, Rj is the main effect of the rootstock and ϵij is the residual error, with In addition to the covariates above, data were available for 61% of the plants to match each scion with the maternal source of the corresponding rootstock. Therefore, an analysis of variance table was generated , and higher densities consisted of random SNPs in increments of 500 up to 10,000 SNPs. Prediction accuracy was estimated by performing 5-fold cross validation 50 times with GAPIT software, using the method proposed by VanRaden to estimate kinship matrices. To estimate the upper limit of the prediction accuracies, heritability was estimated as the proportion of variance explained by relatedness  and RNA-SeQC  method and used to select genes with significant expression differences  counts were used as input for gene expression differential analysis with the Bioconductor Limma package  was 4.2 for the trait analyzed therefore LOD score values >4.2 were considered significant for frosty pod percentages.The mapping populations consist of the previously reported (Brown et al., JR analyzed data and wrote the manuscript. WP-M conceived the experiment, analyzed data and wrote the manuscript. AA-L collected and analyzed data. AM-Q collected and analyzed data. NH analyzed data and wrote the manuscript. GM analyzed data and wrote the manuscript. DL generated, analyzed data and wrote the manuscript. HvB analyzed data and wrote the manuscript. DK conceived the experiment, generated data and wrote the manuscript. LP conceived the experiment, analyzed data. AK conceived the experiment, generated data and wrote the manuscript. JM conceived the experiment, analyzed data and wrote the manuscriptThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Recent years have seen an increase in studies that associate genomic loci with behavioral variation both within and across animal species. Ryan York compiles and analyzes over 1,000 of these loci, finding that the genetic... Drosophila Genetics Reference Panel indicate that courtship and feeding behaviors have significantly greater genetic contributions and that, in general, behavioral traits overlap little in individual base pairs but increasingly interact at the levels of genes and traits. These results provide evidence that different types of behavior are associated with variable genetic bases and suggest that, across animal evolution, the genetic landscape of behavior is more rugged, yet predictable, than previously thought.Although most animal behaviors are associated with some form of heritable genetic variation, we do not yet understand how genes sculpt behavior across evolution, either directly or indirectly. To address this, I here compile a data set comprised of over 1000 genomic loci representing a spectrum of behavioral variation across animal taxa. Comparative analyses reveal that courtship and feeding behaviors are associated with genomic regions of significantly greater effect than other traits, on average threefold greater than other behaviors. Investigations of whole-genome sequencing and phenotypic data for 87 behavioral traits from the  NEARLY all behaviors are associated with some form of heritable genetic variation . This inDrosophila species . These analyses provide insight into the genetic architecture of behavior across animals and the interplay between specific behavioral traits and their genetic influence through evolutionary history.Here, using reports associating behavioral variation with the genes for specific traits across diverse species, I assemble a comparative behavior genetics resource composed of 1007 significant genomic loci from 114 QTL studies conducted in 30 species across five taxonomic classes. These data exploit the wealth of QTL mapping efforts that have worked to identify genomic regions associated with behavioral variation over the past several decades . With the.g., PUBMED) with the keywords “QTL,” “behavior,” “quantitative trait locus,” and “behavioral.” I analyzed the results and collected QTL for each relevant publication identified. To gather as many relevant QTL as possible over time, I expanded the search to include more specific terms relating to behaviors and categories of interest and to those referenced in previously identified papers. I filtered for loci reported as significant by the original authors, resulting in 1007 QTL from 115 studies. For each locus, I recorded the reported effect size (percent phenotypic variation explained), significance measure, genomic location, sample size, and the number of loci reported overall. QTL studies often report other measures in addition to those that I collected . While it would be desirable to compare some of these across behaviors and taxonomic groups, I found that, within the studies assayed, the reporting of measures other than those I collected was very inconsistent and allowed for only extremely restricted comparisons. Since the measure used to report significance varied across studies, I converted all LOD scores to Log P-values in R . In our classification of these, I attempted to strike a balance between breadth (to increase the tractability of our comparisons) and biological specificity. To do so, I required that a category be represented in at least two species or populations and that the classification match either that reported by the original authors or a reasonable division as reported by the animal behavior literature. The classification of a range of biological traits into broader categories is, of course, difficult and can repeatedly tempt debate; accordingly, this is discussed at length in I next classified behaviors following the six groups used in the meta-analysis of mouse QTL studies done by I used the phylogenetic relationships reported in Ruff/quail and chicken: Quail and chicken: Nine-spined and three-spined stickleback: Stickleback and teleost: Cave fish and teleost divergence: Laupala cricket and insect divergence: Wax moth and insects: Pea aphids and insects: Peromyscus and mice/rats: Apis: Solenopsis and Sheep and cows: White fish and teleosts: et al. 2006) using five chains for each model. In total, seven models comprising all combinations of the random effects were tested and then ranked based on the deviance information criterion (DIC) outputted by MCMCglmm. The results of this comparison are presented in Supplemental Material, Table S1 in File S1. The best-ranked model from these analyses was also tested using linear mixed effects regression in the lme4 package . Raw data for phenotypic measures were downloaded from the following sources:I downloaded the DGRP freeze 2.0 variant calls and plink files from the Starvation resistance: Startle response: Chill coma recovery time: Startle response under oxidative stress: Negative geotaxis under oxidative stress: Olfactory behavior : Courtship behavior: Olfactory behavior (multiple measures): Aggressive behavior: Food intake: Alcohol sensitivity: Morphology: I compiled the raw data into two tables for use in genome-wide analyses of SNP variation: one composed of the 87 behavioral traits obtained and another of the 26 morphological traits. For traits in which multiple measurements were reported I calculated the mean trait measurement and used this for subsequent analyses. I classified traits into behavioral categories in the same fashion as for the evolutionary QTL analyses.P-value from GREML was < 0.05, resulting in 20 traits. I first employed genome-wide complex trait analysis (GCTA) to survey genomic heritability across the 87 behavioral traits -significant SNPs, I compiled a list of associated SNPs for each trait and built a separate genotype relatedness matrix for each by extracting just those SNPs from the plink bed files. I then reran GREML for each trait using the corresponding genotype relatedness matrix and testing only for the SNPs that it contained. As above, I then filtered for traits in which the reported P-value < 5 × 10−6. SNPs associated with multiple traits were identified and plotted using a binary heatmap with the heatmap2 function in R. Genes associated with multiple SNPs were identified using the variant annotation file available on the DGRP website.The plink and phenotype files from the GCTA analyses were used to conduct separate GWAS for each trait. I used plink v1.90 (File S1).I next assayed relationships between SNPs and multiple traits using the effect sizes (β) in the *.qassoc files outputted by plink. To do so, I compiled a matrix of the effect sizes for all traits at each of the 25,919 significant SNPs (Table S4). This matrix could then be directly queried for comparison of the effect sizes associated with a certain set of SNPs across traits of interest. To assess the overall structure of this data set, I used Spearman’s rank correlations to test the associations between all possible trait pairs. The results of this test were visualized using the clustering functionality of heatmap2 in R  to the observed D:Directionality in the relationships between trait pairs was tested by first obtaining pairwise rank correlations for each trait pair in which both traits were associated with more than three significant SNPs (60 traits). For traits x and y, spseudo = c;for  {pxx = sample, replace = F);Bxy = sample, replace = F);Byy = sample, replace = F);Byx = sample, replace = F);p_x_cor = corp_y_cor = cord = abs(p_x_cor-p_y_cor)pD = abs(1-d)pseudo[trial] = pD}p_value = sum(D > pD)/1000P-values were adjusted using Bonferroni correction.The resulting File S2. DGRP GWAS and behavioral data are available through either the original publications or the DGRP website (http://dgrp2.gnets.ncsu.edu/).QTL data used for the meta-analysis are included File S2). The species examined represent over 500 million years of evolutionary divergence over a broad spectrum of phylogenetic data , genomic locus, and study sample size. I focused the analyses on the reported effect sizes to allow comparison of the genomic architecture of traits across studies similar to previous meta-analyses of behavioral QTL in mice and flies . My null hypothesis was that individual categories would likely reflect the overall distribution seen across the data set, consistent with previous observations that QTL have relatively similar effect sizes across mouse and fly phenotypes (n = 124) explained significantly more phenotypic variance than all other behaviors combined  and had a mean effect size three times larger than found in all other categories (n = 11) also explained significantly more phenotypic variance than all other behaviors combined (P = 6.8 × 10−13), while emotion and social behaviors explained significantly less .I next asked whether genetic architecture might vary across enotypes . Surpristegories . Loci asP = 4.5 × 10−5). To control for this quantitatively, I estimated phylogenetic divergence and generation times between the crosses used in each of the 115 studies . There was a positive correlation between evolutionary divergence and effect size . I also considered sample size, a well-known source of bias for which there was a negative correlation with effect size .Given the heterogeneity of the experiments analyzed here, in addition to inborn issues of QTL mapping such as the Beavis effect , I next vs. intraspecific crossing schemes, I employed both Bayesian and frequentist mixed models to test the relationship between QTL effect sizes and behavioral traits. Using the R . The best ranked of these Bayesian models used sample size and generation time as random effects (DIC = 4462.879). Notably, adding inter- vs. intraspecific crossing schemes as a random effect reduced the explanatory power of the model (Table S1 in File S1). Analyzing posterior means of the best fit model showed that courtship behavior had a significant influence on effect size  = 0.739–39.806; b = 20.131, SE = 5.985, P < 0.001) in addition to feeding .In light of the potential confounding influences of sample size, evolutionary relationships, and inter- ng the R  package ng the R , I compaP < 0.0005). None of the other traits reached significance (Table S3 in File S1).Variation in the number of studies measuring a given behavioral trait was also explored as a possible confounding influence. For example, a specific behavior’s significance may be driven by a small number of studies with extremely large effect sizes. To explore this possibility, the observed mean effect sizes for each behavioral category were compared to null distributions obtained by permuting all other effect sizes 10,000 times without replacement. The mean effect sizes for courtship and feeding behaviors were both significantly greater than expected when compared to these permuted distributions . The majority of these traits were enriched for involvement in courtship and feeding: 30% (6/20) were associated with courtship and 50% (10/20) were either involved in olfactory behavior or feeding. Notably, for a number of these traits, the vast majority of phenotypic variation could be explained by genome-wide SNPs, including preference for the food odorant ethyl acetate (99.99 ± 38.05%) and courtship transition 9 (89.38 ± 50.03%).I first used GCTA to survey the extent to which the 87 behavioral traits varied in genomic heritability attributable to all autosomal SNPs . At this threshold, I found 25,919 SNPs (Table S4). Recent studies have demonstrated the susceptibility of moderately sized next-generation mapping panels such as the DGRP to effect size overinflation via the Beavis effect . This suggests that, while the Beavis effect is likely present given the inborn issues with genetic panels like the DGRP, the overall effect may be distributed across the studies and should be taken as a caveat when interpreting observed patterns in the data.The previous analyses of QTL effect sizes suggested that the genomic architectures of courtship and feeding traits may be simpler and of higher effect. To test whether or not this was the case among the DGRP lines, I performed a separate GWAS for each trait across all lines with available phenotypic data and filtered for SNPs with a nominal s effect , thus caP < 5 × 10−6 from the GWAS . This test is more conservative compared to genome-wide GCTA since it uses just the fraction of genomic variants that are significantly associated with each individual trait. After GCTA, I found 16 behavioral traits that passed the P-value threshold of P < 0.05. Half of these significant traits were courtship behaviors, including the top four traits with the most variation explained by GWAS SNPs . The number of GWAS-significant SNPs for these 16 traits varied substantially and was positively correlated with the amount of phenotypic variance explained . For traits with more SNPs, significant portions of the variance could be accounted for. For example, 665 SNPs could account for 63.52 ± 8.42% of variation in courtship wing movement, 828 accounted for 68.64 ± 6.69% of genital-licking behavior, and 8013 accounted for 78.45 ± 5.97% of courtship approach behavior. The results from both GCTA tests in the DGRP lines support the hypothesis that, at the genetic level, courtship and feeding-related behaviors are associated with more heritable architectures of large effect, even within less-diverged natural populations.I reran GCTA for each trait using only SNPs identified at File S1 and Table S5). SNPs found to be associated with morphology and behavior at P < 1 × 10−5 were distributed across the D. melanogaster genome, 80 of which were associated with both behavioral and morphological traits  . I explol traits .File S1).With this list of variants, I queried which individual SNPs were associated with multiple behavioral categories. I identified 169 SNPs associated with at least two behavioral measures. These variants largely overlapped within behavioral categories rather than between categories, suggesting that, at the level of individual SNPs, these traits may have largely independent genetic architectures among the DGRP lines . Many ofFile S1. In general, I found extensive correlations between behavioral traits, suggesting widespread pleiotropic genetic effects. I also observed several large clusters of highly correlated traits, suggesting a higher-level structure for phenotypic variation based on trait interactions . Analyses of genetic correlations using whole-genome SNP information via bivariate GREML paralleled this observation of structured trait correlations . The existence of these apparent clusters suggests that, while behavioral categories in the DGRP overlap little in genomic architecture at the individual variant level, there may be common molecular pathways through which different behavioral traits are altered in a correlated fashion.I then assessed the extent to which behaviorally associated variants may act pleiotropically at the trait level, using the list of 25,919 variants associated with behavior. With this, I correlated the effect sizes of trait-associated SNPs with the effect sizes of those same variants across all other traits. The results of this analysis are summarized in the clustered heatmap in Figure S3 in vs. scenarios where a genotype exerts an effect on a trait through a second, intermediate trait (summarized as 1←G→P2P compared to 1→P2G→P) .I conducted pairwise tests of each trait at which GWAS variants at the P = 8.6 × 10−8; supplemental methods in File S1).Trait pairs identified as significant showed an uneven distribution of potential directional effect between behavior and morphology, with the largest amount occurring between pairs of behavioral traits . Figure Drosophila, though with little evidence of a molecular basis for this effect (1→P2G→P) and general (1←G→P2P) pleiotropic effects between traits in the DGRP, supporting the notion that the early stages of behavioral diversification involve the role of genes that can affect multiple types of traits. Furthermore, I observe that while variation in behavior across trait categories is associated with nonoverlapping variants, these may occur in common genes and molecular pathways with pleiotropic effects, reflecting suggestions of the existence of phenotypic “hotspots” that are recurrently used by evolution to sculpt phenotypes (The connection between male courtship behaviors and body size has long been recognized in laboratory strains of s effect . In geneenotypes .Taken together, these results suggest that behavioral traits may respond to evolutionary processes with greater variation than previously appreciated. For example, researchers may now anticipate that assaying a courtship ritual will likely yield a higher genetic effect than, say, variation in a personality trait. These insights are supported by observations that behavioral categories vary in their heritability and genomic architecture during even the earliest stages of diversification within populations. Furthermore, such behaviors are associated with a small number of highly pleiotropic genes and these traits interact, indicating that there are identifiable molecular and phenotypic patterns that govern behavior.These findings suggest several important caveats and prospects for future behavior genetic studies. First, QTL mapping methods possess inherent limitations in detecting the complete genetic architecture of certain traits. For example, QTL studies are often insensitive to the detection of loci with opposing effects on the trait of interest, thus potentially masking important genetic effects from the researcher’s analysis . While twww.genetics.org/lookup/suppl:/doi:10.1534/genetics.118.300712/-/DC1.Supplemental material is available: online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


A central goal in the biomedical and biological sciences is to link variation in quantitative traits to locations along the genome (single nucleotide polymorphisms). Sequencing technology has rapidly advanced in recent decades, along with the statistical methodology to analyze genetic data. Two classes of association mapping methods exist: those that account for the evolutionary relatedness among individuals, and those that ignore the evolutionary relationships among individuals. While the former methods more fully use implicit information in the data, the latter methods are more flexible in the types of data they can handle. This study presents a comparison of the 2 types of association mapping methods when they are applied to simulated data. Linking variation in quantitative traits to locations along the genome (single nucleotide polymorphisms [SNPs]) is a central goal in the biomedical and biological sciences. The rapid growth of sequencing technology in recent decades has given researchers the ability to sequence genomes in a time-efficient and cost-efficient way. Development of statistical methods appropriate to search for associations among SNPs and quantitative traits (or phenotypes) has been quite active, often with complex data sets . These clocalization.The search for SNPs associated with variation in a quantitative trait under study is often referred to as quantitative trait mapping (QTM). There are 2 specific goals of QTM: detection of SNPs associated with variation in quantitative trait(s) and localization of SNPs associated with variation in quantitative trait(s). In detection, the goal is to determine if any SNP in the region of the truly associated SNP is statistically significant. However, when analyzing hundreds of thousands of SNPs, the chance of detecting the exact causal locus is very small. As such, preferred methods will detect SNPs that are nearer to the causal SNP. This goal is referred to as t-test and those in McClurg et al. [Some association mapping methods  analyzephylogenetic tree. For example, in the phylogenetic tree in Fig. Uneven relatedness among randomly sampled individuals can be attributed to the evolutionary history that exists within each SNP. Each SNP has an evolutionary history that can be represented by a bifurcating tree called a Consider the evolutionary history of a SNP associated with a trait. If 2 observations share a large portion of their evolutionary history  were analyzed.In this study, both a non–tree-based method and a tree-based method are used to directly compare performance of the 2 classes of methods. The analyzed data contains simulated phenotypes on 1943 unrelated individuals . Five geTNN, LEPR, GSN, TCIRG1, and FLT3). Locations of genes were determined using GeneCards Version 3 [The data analyzed was provided by the Genetics Analysis Workshop 19 . Beagle ersion 3  and dataersion 3 . SNPs lat-test and the likelihood score statistic (LSS) approach [t-test, at each SNP, the chromosomal observations were partitioned according to SNP state , and a pooled t-test was performed using these 2 groups. It should be noted that the number of observations is twice the number of individuals in the study.The classical 2-sample approach  were stuk clusters defined by the (k − 1) earliest splits in the tree. Each cluster of observations has a mean trait value (μi). The covariance among 2 observations is proportional to the length of their shared lineages in the tree. The method assumes the trait values follow a multivariate normal distribution with a mean structure , and covariance structure, V(Θ), defined by the estimated clustered tree. A constant variance, σ2, is assumed for all trait values. The LSS is based on the Bayesian information criterion and is defined to be the maximum penalized likelihood of the parameters given the data and the estimated tree. The likelihood is penalized by the number of parameters used in the calculation of the likelihood and the maximum is taken over the number of clusters . At the ith SNP, the score is defined to be:For the LSS approach , at eachμ and σ2, respectively. The LSS treats each allele as an observation, so that the number of observations, n, is twice the number of individuals under study.Here, t-test using chromosomes as observations as in Sasieni [p-value was taken to be the proportion of permuted data sets producing test statistic values more extreme than the test statistic calculated using the observed data. To address localization, the average LSS and t-statistic values are shown at each SNP for comparison to the absolute true effect size at each SNP analyzed. The performance of the phylogenetic method (LSS) and the pooled 2-sample t-test were compared in the case of each goal.We have 2 goals to address—detection of the gene and localization of the causal loci—and we compare the performance of a tree-based method (the LSS) to a non–tree-based method  in termt-test and the LSS (with a maximum of k = 15 clusters) were used to analyze systolic blood pressure using 5 genes from the 200 simulated phenotype data sets. All loci in all replicates chose far fewer than k = 15 clusters. We note that the alleles t-test does not account for phenotypic correlation between repeated measures across chromosomes. Four of these genes  ranked in the top 9 genes in terms of total percentage variation in simulated systolic blood pressure (SBP). TNN and LEPR had effect sizes as large in magnitude as 10.89 and 11.99, respectively. We also analyzed genes accounting for a smaller percentage of the total variation in SBP, including FLT3, TCIRG1, and GSN, which had effect sizes as large in magnitude as 3.89, 3.38, and 0.76, respectively. Detection and localization performance was compared across the non–tree-based t-test and the tree-based LSS approach.Both the pooled TNN and LEPR , much lower power was achieved by both methods, and neither method uniquely outperformed the other. In addition, 200 simulated Q1 trait data sets (data sets including a trait simulated without any direct genotype–phenotype association) were also analyzed to assess the type I error of each method. The LSS showed error rates lower than 0.05, whereas the t-test showed error rates above 0.05 in the case of all 5 genes  and a more recently proposed tree-based approach (the LSS). The results highlight the differences between the considered methods with respect to both the detection and the localization analyses. In the detection analysis, the methods showed similar power, but differed slightly in type I error rates. However, allelic tests can be biased [t-test. In a separate simulation study, we used an empirically derived critical value from the Q1 (null) trait data to make decisions about detection. In this case, the powers of detection were similar (within 2.5 %) of the results shown in Table t-test had similar trends in estimating the location of the truly causal loci. In the case of a smaller effect size (GSN), the LSS outperformed the t-test, which may point to an advantage of using additional implicit information in genomic data sets to search for weaker genetic signals rather than excluding this information.In this study, genomic data were analyzed using both a classical non–tree-based approach (the 2-sample e biased , and thiThe study performed here was advantageous in that the simulated phenotypes were far more complex  than mosEven though the rapid advancement of sequencing technology has produced large quantities of genomic data to analyze, improvements upon QTM methods continue to be developed. Non–tree-based methods exist to analyze complex data sets, but such methods tend to find large genetic effects. Currently, tree-based methods are able to analyze only basic genetic data sets, but more fully use implicit information in the genetic data to estimate the evolutionary relatedness among observations. These methods may improve upon QTM performance of non–tree-based methods, and could be extended so they are able to analyze more complex scenarios, such as inclusion of external covariates. Doing so would address a current limitation of tree-based association mapping methods and could improve the performance of methods in explaining variation in quantitative traits under study.