DRO1, WOX11, and OsPID, were found to co-locate with the association loci. We further proved that the association results can facilitate the efficient identification of causal genes for root traits by the two case studies of Nal1 and OsJAZ1. These loci and their candidate causal genes provide an important basis for the genetic improvement of root traits and drought resistance.A variety of adverse conditions including drought stress severely affect rice production. Root system plays a critical role in drought avoidance, which is one of the major mechanisms of drought resistance. In this study, we adopted genome-wide association study (GWAS) to dissect the genetic basis controlling various root traits by using a natural population consisting of 529 representative rice accessions. A total of 413 suggestive associations, containing 143 significant associations, were identified for 21 root traits, such as maximum root length, root volume, and root dry weight under normal and drought stress conditions at the maturation stage. More than 80 percent of the suggestive loci were located in the region of reported QTLs for root traits, while about 20 percent of suggestive loci were novel loci detected in this study. Besides, 11 reported root-related genes, including  Nal1 and OsJAZ1, to demonstrate the high efficiency of the identification of genes in the control of root traits based on the association and candidate gene analyses. Our results would provide a foundation not only for elucidating the genetic and molecular basis of root development but also for improving the drought avoidance of rice.Drought stress is a key environmental factor that severely reduces crop yield all over the world. The root system plays a critical role in the drought avoidance of crops, but the genetic basis of the root system in soil conditions has seldom been investigated in rice. We analyzed the genetic control of 21 root traits via genome-wide association study (GWAS) of a natural population, and we identified 110 significant association loci containing many reported and unknown candidate genes related to root development. We presented the case studies of two genes,  Oryza sativa L.) is the staple food crop that feeds a large segment of the world’s population [Rice  has been assumed to be the candidate gene for this QTL [Sta1, a QTL controlling the stele transversal area in root of rice, was fine mapped to a 359-kb interval on chromosome 9 [(DEEPER ROOTING 1) DRO1 [DRO2 [DRO3 [qSOR1, a major QTL controlling the soil-surface rooting of rice in paddy fields, was mapped to an 812-kb interval on the long arm of chromosome 7 [qRL6.1 [qRL7 [DRO1 has been cloned, and DRO1 is involved in the regulation of deep rooting by affecting root growth angle [Traditionally, linkage mapping has been commonly employed to detect quantitative trait loci (QTL) for complex traits, including root architecture traits. In 1990s, linkage mapping of root traits in rice was conducted for the first time by using a population of 203 recombinant inbred lines (RILs) which were treated with drought stress at different stages, and five parameters of root morphology were investigated . Since tthis QTL . In anotthis QTL . Meanwhithis QTL . Sta1, amosome 9 . (DEEPER 1) DRO1 , DRO2 [1O1 [DRO2 , and DROO2 [DRO3 , three mmosome 7 ; qRL6.1  [qRL6.1  and qRL7The next-generation sequencing technology coupled with the growing number of genome sequences opens the opportunity to redesign genotyping strategies for more effective genetic mapping and genome analysis . In recejaponica rice accessions by using a hydroponic cultivation system at the seedling stage in rice [Several association studies have been conducted for root traits in rice and other crops. The genetic architecture of aluminum tolerance in rice was analyzed through GWAS and bi-parental linkage mapping of the relative root growth of the total root system . A GWAS  in rice . Another in rice . In maiz in rice . In barl in rice . HoweverNal1 and OsJAZ1, identified by association analysis, were confirmed to control the corresponding root traits by genetic experiments. These results revealed a complete genetic control of the root system in rice at the reproductive stage under both normal and drought stress conditions, which could be an important basis for the genetic improvement of root traits and drought resistance.In this study, the genetic architecture of root traits at the seed maturation stage under normal and drought stress conditions was investigated by GWAS using a panel of 529 rice accessions collected worldwide. We adopted a protocol for drought treatment by planting and stressing rice plants grown in individual polyvinyl chloride (PVC) tubes in which each genotype was stressed to the same extent at the same developmental stage [indica subpopulation (295 accessions) including ind I, ind II, and indica intermediate (ind), and japonica subpopulation (156 accessions) including tej, trj, and japonica intermediate (jap)  .Drought stress was applied at the booting stage, and the plants were recovered when all of the leaves became fully rolled. A total of 21 root related traits, such as maximum root length under normal (MRLN) and drought stress conditions (MRLD), volume of the deep roots (> 30 cm) under normal (RVDN) and drought stress conditions (RVDD), and dry weight of the deep roots (> 30 cm) under normal (RWDN) and drought stress conditions (RWDD), were measured at the seed maturation stage . These tr > 0.5), such as the pair of MRLD and MRLN (r = 0.63), RVTD and RVTN (r = 0.76), and the pair of RWTD and RWTN (r = 0.70). In order to examine the effect of drought stress treatment on root growth without the interference of correlation, we compared the three deep root rate traits, including deep root length rate (DLR), deep root volume rate (DVR), and deep root weight rate (DWR), under normal and drought stress conditions  and 6.03×10−8 (significant). In our study, a total of 413 suggestive associations with 373 lead SNPs were identified, and among them 143 associations (133 significant SNPs) exceeded the significant threshold , a large number of QTLs for root traits  in rice have been deposited [In the Tropgene DB (rice data)  . The detDEEPER ROOTING 1 (DRO1), a gene which increases the deep root ratio by controlling root growth angle [The th angle , was detWUSCHEL-related homeobox gene WOX11, a gene increasing drought resistance of rice by controlling root hair formation and root system development [OsAPY, another gene with a similar genetic effect of controlling root hair formation and root development [WOX11 on chromosome 7  [OsCKI1, regulating lateral root development in rice [OsRHL1, a gene controlling root hair formation and development [elopment , is loca1 (DGL1) , a gene  in rice , is locaelopment , is locaNal1 and OsJAZ1, were selected for functional confirmation as case studies.Among the closely associated candidate genes, there were several reported genes with no evidence for their functions in the control of root traits. Two of them, Nal1 has been reported to control leaf width [Nal1 was detected by GWAS for RWDD (dry weight of the deep roots under drought stress conditions), with the P-value of 3.12×10−7  and IRAT109. Sequence comparison identified one SNP and ten Indels in the promoter region  with a background of IRAT109  were significantly greater than in the NIL with the background of ZS97B  in root, leaf, and panicle  in a mixed linear model. Except for three associated SNPs in the promoter, the other three SNPs were in the intron or 3' UTR region. Haplotype analysis of the six associated SNPs revealed two major haplotypes , which has a role in the regulation of spikelet development in rice [http://ricexpro.dna.affrc.go.jp/) [A locus with a lead SNP sf0433137256 on chromosome 4 was identified for RWSN  with a 6.4×10−9  and for .58×10-8 . A repor in rice , is loca in rice , and it .go.jp/)   (P = 1.94×10−5), located in the promoter region, surpassed the Bonferroni-adjusted P-value  in a mixed linear model, and it was regarded as a suggestive association with the RWTN (P = 1.1×10−3)  . It was the RWTN . Meanwhithe RWTN , and Hap.1×10−3) .OsJAZ1 is functionally related to root traits, we checked the phenotype of OsJAZ1-overexpression (OsJAZ1-OE) plants. The OsJAZ1-OE plants exhibited significantly longer roots and more crown roots than the wild-type Zhonghua11 (ZH11) at the seedling stage  and the different japonica subpopulations   . Compariand trj)  and S8I,and trj) , but fewand trj) . It is nop codon . In the Arabidopsis, which has a primary root iteratively branching to generate several orders of lateral roots, the root system of cereal crops such as rice is more complex [Compared to many dicot species such as  complex . The com complex , 9. In tqFSR4 affects flag leaf width, spikelet number, and root volume in rice, and it has been fine mapped to a 38 kb region in which Nal1 was assumed to be the most likely candidate gene [Nal1 has been reported to be involved in auxin polar transport [PIN and CRL genes [qFSR4. We confirmed the function of Nal1 in the control of root traits by sequence and expression level comparison, genetic complementation, and haplotype analysis. Overexpression and RNA-interference lines of Nal1 resulted in significant changes in root volume and root weight under both normal and drought stress conditions compared to the control plants, demonstrating the authentic function of Nal1 in the control of root traits. The Nal1 allele from the japonica rice cultivar showed better performance in regulating adventitious root development at the seedling stage [japonica allele of Nal1 is superior when compared to the indica allele with respect to the control of root volume at the adult stage.The pleiotropic QTL ate gene . Nal1 haransport  and cellransport , and itsRL genes . In thisng stage , which iOsJAZ1, a candidate causal gene for the locus detected by RWSN and RWTN on chromosome 4, suggests that the association results could provide opportunities to identify novel genes or known genes with new functions in the control of root traits. OsJAZ1 has been reported to be involved in spikelet development in rice [Arabidopsis have been shown to regulate root development under JA treatment [OsJAZ1 was involved in root development with genetic evidence of OsJAZ1-overexpression lines at the seedling, tillering and reproductive stages. It would be very interesting to further reveal how OsJAZ1 regulates both the underground organ (root) and the reproductive organ (spikelet).Another case study for  in rice . Howeverreatment . Here, wBesides the tens of reported root-related QTLs or genes, many unreported loci or genes for root traits were detected in our association analysis. With the help of expression profiling databases and bioinformatic analysis, we could narrow down the potential candidate causal genes for root traits in the significant association loci. For example, 28 possible candidate genes with obvious drought-responsive and/or root-specific expression patterns were identified for some of the significant association loci . NeverthNal1 and OsJAZ1, demonstrate the feasibility of mining for candidate genes by GWAS. The association loci and causal genes identified in this study provide an important foundation for revealing the molecular mechanism of root development and genetic improvement of rice root or drought resistance in the future.In conclusion, our study provides a relatively comprehensive analysis of the genetic architecture of root traits in rice. We employed a GWAS with 529 rice accessions for root traits at the seed maturation stage under normal and drought stress conditions, and 225 of 264 loci identified by GWAS overlapped with reported root related QTLs. Many known root-related genes were located in the significant association loci. Importantly, case studies of two genes, A total of 529 rice accessions including 202 from the China Core Collection and 327 from the World Core Collection were used for the association analysis . This paFor GWAS of root traits, 529 rice accessions were grown in PVC tubes  described by Yue et al [A total of 21 root related traits were phenotyped in this study. The root traits were measured at the seed maturation stage of the plants. To measure these traits, the plastic bag containing the soil and roots was pulled out from the PVC tube and laid out on a soil-washing table with a 2-mm sieve. After removing the plastic bag, the soil was washed away carefully and the length of the longest root was scored as the maximum root length (in centimeters). The washed roots which were free from the soil were shown in P-value thresholds of the entire population were respectively 1.21×10−06 and 6.03×10−08. The linkage disequilibrium (LD) statistic r2 was calculated by Plink based on haplotype frequencies. More detailed information about our association analysis was referenced in the recent study [Nal1 and OsJAZ1 was performed with TASSEL version 5 [r2 values between the pairs of SNPs multiplied by 100 .A total of 529 accessions including nine subpopulations were collected to construct this association panel. For the 21 traits used for GWAS, we adopted a mixed-model approach using the factored spectrally transformed linear mixed models (FaST-LMM) program, with 4,358,600 SNP across the entire rice genome . The suggestive and significant ersion 5 . LD plothttp://www.gramene.org/). The physical region of each association locus was defined as 200 kb around each lead SNP. An overlapping locus was claimed if the physical region of the association locus is overlapped with the physical region of any reported QTL for root traits.The physical region of root-related QTLs was determined by the physical position of the left border marker and the right border marker, which were obtained by searching markers information in the GRAMENE database . The haplotypes were classified based on all of the SNPs with an MAF > 0.05 in a candidate gene. The haplotypes containing at least ten rice accessions were used for comparative analysis. One-way ANOVA and Student’s t-test was employed to compare the differences in root traits among the haplotypes [The genotypes of plotypes .qFSR4 was narrowed down to a region of approximately 38-kb flanked by markers FSR-75 and FSR-78 [Nal1ZS97B (qZS) while the plant with homozygous genotype as IRAT109 was used as NIL Nal1IRAT109 (qIR) in our study. For the complementation test of Nal1, a 2,059-bp Nal1 promoter fragment was amplified from the NIL qIR with the addition of restriction sites for KpnI and EcoRI, and the 1,749-bp Nal1 full CDS fragment was amplified from the cDNA of the qIR line with the addition of a restriction site for KpnI at both ends. The two fragments were cloned into the binary vector pCAMBIA2301 to generate the transformation plasmid for the complementation test. The resulting transformation plasmid was introduced into NIL qZS by Agrobacterium-mediated transformation [The QTL d FSR-78 , and theormation . The copNal1 overexpression transgenic plants, the full-length cDNA of Nal1 was amplified from the japonica cv. Nipponbare. The sequence-confirmed PCR fragment was ligated into pCAMBIA1301U which was digested with KpnI, based on the Gibson assembly principle [japonica cv. Zhonghua11 by Agrobacterium-mediated transformation [For the rinciple . The conormation .OsJAZ1 overexpression transgenic plants, the full-length cDNA of OsJAZ1 was cut from a clone from the full length cDNA library of the indica cv. Minghui63, and cloned into the pCAMBIA1301H vector driven by the OsLEA3-1 promoter LEAP [japonica cv. Zhonghua11 by Agrobacterium-mediated transformation [For the ormation .Nal1 interference transgenic plants, the 454-bp length cDNA of Nal1 was amplified from the japonica cv. Nipponbare by RT-PCR. The sequence-confirmed PCR fragment was recombined into the pANDA vector [japonica cv. Zhonghua11 by Agrobacterium-mediated transformation [For the A vector , by the ormation .Nal1 in different tissues at different stages, seeds of the NILs qZS and qIR were germinated on normal 1/2 strength MS medium in Petri dishes. Before germination, rice seeds were surface-sterilized in 75% ethanol for 5 min, followed by a 10 min incubation with 0.15% HgCl2, and then washed four to five times with sterile water. After germination, the seedlings were transplanted to 10 cm high sterile plastic box with a 5 cm deep normal 1/2 strength MS medium in it. The roots and leaves of qZS and qIR seedlings were sampled at the two-leaf stage, then the qZS and qIR seedlings were transplanted in the field. Flag leaves, the second leaves, and the third leaves of qZS and qIR were sampled at the heading stage. Panicles of qZS and qIR were sampled before heading and after heading.To investigate the transcript levels of OsJAZ1 and the root phenotype of OsJAZ1 overexpression and wild-type plants Zhonghua11 (ZH11), the positive transgenic plants were selected by germinating on 1/2 strength MS medium containing 25 mg/L hygromycin B (Roche). The wild-type seeds were germinated on normal 1/2 strength MS medium. Before germination, the rice seeds were surface-sterilized in 75% ethanol for 5 min, followed by a 10 min incubation with 0.15% HgCl2, and then washed four to five times with sterile water. After germination, the overexpression seedlings were transplanted to 10 cm × 10 cm square plastic Petri dishes with a 0.5 cm deep normal 1/2 strength MS medium in it , with control plants in the same dish . Root length, seedling length, and crown root number of the overexpression and wild-type plants were investigated at the two-leaf stage. The roots and leaves of overexpression and wild-type plants were sampled after phenotype investigation. The seedlings were transplanted to pots with sandy soil in it until the tillering stage, at which point the seedlings were carefully washed out from the sandy soil and the roots were cut for root weight measurement after air drying. Then, the seedlings were transplanted to PVC tubes (each with one overexpression plant and one wild-type plant), and the root phenotype of the overexpression and wild-type plants were investigated at the seed maturation stage using the same method as mentioned above.To investigate the transcript levels of qFSR4 and complementary plants, the seeds of qZS, qIR, COM4, and NC were sown in the nursery field and the seedlings were transplanted to PVC tubes at the four-leaf stage. Each tube contained two plants, one qZS plant and one qIR plant, or one COM4 plant and one NC plant. Each group was repeated ten times (tubes) for normal growth and drought stress treatment, respectively. Leaf width was measured for three flag leaves of each plant at the heading stage, and the panicles were harvested for counting spikelet and seed numbers after maturation. The root traits were measured by carefully washing soil away using the method described above.To investigate the phenotype of the NILs of Nal1 overexpression and RNA-interference plants, the seeds of transgenic positive plants (OE3-16(+), OE6-3(+), Ri12-11(+), and Ri13-6(+)) and negative controls (OE3-19(-), OE6-13(-), Ri12-5(-), and Ri13-15(-)) were sown in the nursery field, leaves of each line were sampled at the four-leaf stage for investigating the transcript levels of Nal1. Then the seedlings were transplanted to PVC tubes. Each tube contained two plants (overexpression or RNAi plant and corresponding control). Each group was repeated ten times (tubes) for normal growth and drought stress treatment, respectively. Drought stress treatment was applied at the booting stage as described above. The traits of root volume and root weight were investigated as described above.To investigate the root phenotype of the Ubiquitin gene was used as the internal control. The relative expression level was determined as reported previously [Total RNA was extracted using Trizol reagent (Invitrogen). The first-strand cDNA was reverse transcribed using M-MLV reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. Quantitative PCR was conducted on a 7500 Real-Time PCR System (Applied Biosystems) using SYBR Premix ExTaq (TaKaRa) according to the manufacturer’s instructions. The rice eviously .S1 Fig(TIF)Click here for additional data file.S2 FigThe correlation coefficients are shown in the lower left part of the figure. The correlation coefficients are indicated by the color  and size of the circles in the top right part of the figure.(TIF)Click here for additional data file.S3 FigThe vertical long bars represent chromosomes and black dots represent centromeres. Scale bar, 5 million base pair.(TIF)Click here for additional data file.S4 FigThe vertical long bars represent chromosomes, the short black bars represent the QTLs for root weight, and the asterisks represent the association loci for root weight. The numbers corresponding to asterisks represent the lead SNPs for the association loci. Scale bar, 5 million base pair.(TIF)Click here for additional data file.S5 FigThe vertical long bars represent chromosomes, the short black bars represent the QTLs for root volume, and the asterisks represent the association loci for root volume. The numbers corresponding to the asterisks represent the lead SNPs for the association loci. Scale bar, 5 million base pair.(TIF)Click here for additional data file.S6 FigThe vertical long bars represent chromosomes, the short black bars represent the QTLs for root length, and the asterisks represent the association loci for root length. The numbers corresponding to the asterisks represent the lead SNPs for the association loci. Scale bar, 5 million base pair.(TIF)Click here for additional data file.S7 FigThe vertical long bars represent chromosomes, the short black bars represent the QTLs for deep root rate, and the asterisks represent the association loci for deep root rate. The numbers corresponding to the asterisks represent the lead SNPs for the association loci. Scale bar, 5 million base pair.(TIF)Click here for additional data file.S8 Fig10P-values from a genome-wide scan were plotted against the position of the SNPs on each of 12 chromosomes, and the horizontal grey dashed line indicates the suggestive threshold (P = 1.21×10−6). For the quantile-quantile plots, the horizontal axis indicates the -log10-transformed expected P-values, and the vertical axis indicates the -log10-transformed observed P-values.Manhattan plots (left) and quantile-quantile plots (right) are presented for (A) MRLN, (B) MRLD, (C) MRLR, (D) DRLN, (E) DRLD, (F) RVSN, (G) DVRN, (H) RVSD, (I) RVTD, (J) RWSD, (K) DWRN, (L) RWTD, and (M) DWRD. For the Manhattan plots, -log(TIF)Click here for additional data file.S9 FigP < 0.01, Student’s t-test.Flag leaf width (A) and spikelet number per panicle (B) of the complementary lines and NILs. The data represent the mean ± SE (n = 10). **(TIF)Click here for additional data file.S10 FigNal1-overexpression (OE3-16(+)) plants and the segregated negative-transgenic control (OE3-19(-)) at the seed maturation stage in PVC tubes under normal (A) and drought stress (B) conditions.  Visual root phenotypes of the Nal1-RNAi (Ri13-6(+)) plants and the segregated negative-transgenic control (Ri13-15(-)) at the seed maturation stage in PVC tubes under normal (C) and drought stress (D) conditions. Visual root phenotypes of the (TIF)Click here for additional data file.S11 FigOsJAZ1 in root is indicated by a dashed box.Data from the RiceXPro database. The expression level of (TIF)Click here for additional data file.S12 FigOsJAZ1-OE plants and wild-type ZH11. (B) Seedling length, root length, and crown root number of the OsJAZ1-OE plants and wild-type ZH11. The data represent the mean ± SE (n = 15). **P < 0.01, Student’s t-test.(A) Visual phenotype of (TIF)Click here for additional data file.S13 Fig(A) Planting arrangement in the PVC tubes. (B) Rice plants in the PVC tubes. (C) Photo of the roots washed from PVC tubes. (D) Flow chart of the root trait investigation.(TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 Table(XLS)Click here for additional data file.S5 Table(XLS)Click here for additional data file.S6 Table(XLS)Click here for additional data file.S7 Table(XLS)Click here for additional data file.S8 Table(XLS)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableThe expression in root was regarded as "high" when its Cy3 signal intensity in root tissue in the RiceXPro database was higher than 10,000, otherwise it was regarded as "normal".(XLS)Click here for additional data file.S11 Table(XLS)Click here for additional data file.


The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013–2014 and 2014–2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values. GS uses this marker data in two different ways: by using markers to model relationships between individuals or by estimating the effects of each marker on the trait of interest, some of which are presumed to be in linkage disequilibrium (LD) with relevant quantitative trait loci (QTL). The predictor of an individual phenotype is the genomic estimated breeding value (GEBV) obtained as the sum of all corresponding marker effects of the individual. GEBVs are then used to rank and select genotypes, without phenotypic data, for the next generation of breeding.3Genomic selection (GS) was originally proposed in animal breeding4–7 due to the increase in genetic gains that it provides compared with conventional methods. In a broad context, increases in selection response via GS are related to: (i) prediction of GEBV of untested individuals that do not have phenotypic records; (ii) shortening the breeding cycle;8 and (iii) improving the precision of estimates of genetic effects.4 However, other potential benefits can be obtained with the incorporation of GS into the breeding strategy, such as: (i) increasing selection intensity by having larger populations for prediction; (ii) reducing testing effort by eliminating partially or completely the establishment of some field experiments; (iii) better planning of crosses by more effective control of inbreeding and relatedness;9 (iv) predicting hard-to-measure traits using correlated traits as predictors;10 and (v) controlled reduction of genetic diversity in the short term.9Genomic selection has been studied or applied in several agricultural crops, trees and animal breeding programs,12 Differences between methods are usually small in empirical studies;5 however, some differences have been reported, mostly associated with differing genetic architectures among traits.7A variety of statistical methods to predict GEBV have been implemented to deal with the problem of a large number of genetic markers and a limited amount of phenotypic data, including parametric, Bayesian and non-parametric methods, which differ in their assumptions about the underlying genetic model.13 apple,14 sugar beet15 and tomato.16 None have reported GS in strawberry (Fragaria×ananassa). Owing to the challenges presented by the allo-octoploid genetic constitution of cultivated strawberry, there has been a lack of genome-wide, high-throughput genotyping capability until the recent development of the Affymetrix Axiom IStraw90 SNP array17 and the Axiom IStraw35 array . With these resources in place, it is now possible to envision marker-assisted approaches to breeding for complex traits in strawberry. While certain traits in strawberry, particularly disease resistance, are controlled by major genes located to a single subgenome,19–21 other traits are controlled by alleles across multiple subgenomes.22 In the University of Florida strawberry breeding program, which develops cultivars for annualized winter and early-spring production systems, the genetic architectures of several complex yield and fruit quality traits23 have been described, for which no significant QTL or only a few minor-effect QTLs have been detected.18At present, only a small number of studies have reported either the study or the application of GS in horticultural crops, including grapevine,The objective of this research was to experimentally evaluate different GS approaches for octoploid strawberry and to define a future breeding strategy for its operational implementation, using the University of Florida strawberry breeding program as an example. The focus of this study was on the use of datasets from different trials and years to perform true validations, in which a model is trained in a given trial/year and applied in a test population from a separate trial/year. A secondary objective was to estimate linkage disequilibrium in the germplasm tested in order to inform interpretations of GS results and GS implementation.The strawberry genotypes included in this study were a representative selection of parents, current named cultivars and advanced selections of the strawberry breeding program at the University of Florida, Institute of Food and Agricultural Sciences. Four clonally replicated field trials corresponding to stage 1 , and stage 2  were established at the Gulf Coast Research and Education Center in Balm, Florida, USA, during the 2013–2014  and 2014–2015  seasons. There were 62 full-sib families arising from 22 parents in a circular mating design in trial T1/2013 and 60 full-sib families arising from 31 parents in a circular mating design in trial T1/2014. However, the total number of families tested in these trials was 76 and 70, respectively, as they included other selected crosses and control checks. The advanced selections from T2/2013 originated from 122 crosses among 86 parents, and those from T2/2014 originated from 109 crosses among 64 parents. Several advanced selections and control checks were common among pairs of trials, and there were many common ancestors among the genotypes evaluated .Germinated seedlings from the mating designs  were transported to a summer nursery near Monte Vista, Colorado, USA, for further clonal propagation by runners. Before establishment, all individual plants were weighed such that transplant weight (g) could be used as a covariate for a given trait. In contrast, runners and crowns from the advanced selections  were propagated by Crown Nursery near Macdoel, California, USA, for clonal propagation and transplants were not weighed. Thus, T1 and T2 trials differed in propagation source.For each trial, clonally propagated genotypes were arranged in a randomized complete block design. T1 trials from both seasons had three clonal replicates per individual and three raised beds per replicate block, with each bed subdivided into eight subplots, each with a common control to account for environmental variation along the bed. T2 trials had five clonal replicates per individual, one raised bed per replicate block and seven to nine subplots. In all trials genotypes were established as single-plant plots. The total number of individuals tested in the field for each trial is presented in Data for all traits was gathered on an individual plant basis. Fruit harvests were made weekly from the last week in November to the middle of March in both seasons. A total of six yield and fruit quality traits were evaluated in each trial. All ripe fruit were harvested and counted. Early marketable yield  was obtained as the marketable weight of all fruit harvested until the end of January. Total marketable yield  was calculated as the weight of all marketable fruit harvested until the middle of March. Average fruit weight  was estimated as the TMY divided by the number of marketable fruit. Total unmarketable (cull) fruit  was evaluated as the proportion of total fruit that were unmarketable due to small size (<10 g), disease, malformation and any other quality defect. Soluble solids content  was calculated as the average of five measurements in each season. One ripe fruit per plant was hand-squeezed onto the prism of a handheld digital refractometer. Not all plants had ripe fruit at all harvest dates due to different ripening patterns.A reference population of 1628 individuals, out of a total of 1834 genotypes, belonging to the four tests were genotyped with the Axiom IStraw90 SNP array. A total of 28 828 high-quality markers were segregating in these individuals. Quality control was implemented in which those markers with minor allele frequencies (MAF) <5% and with missing marker data >5% were eliminated. Hence, a total of 17 479 markers were available for further statistical/genetic analysis. Missing values for each of the markers (0.38%) were imputed based on the average allele frequency.The following generic linear model was fitted to each of the traits and trials separately using the raw data:μ is the overall mean; w is a covariate of the initial weight of a transplant, and β is its associated coefficient; r is a fixed replication effect; b(r) is the random effect of bed within replicate, with p(br) is the random effect of plot within bed, with a is the random additive effect of genotype, where f is the family random effect, with c(f) is the random effect of clone within family, with e is the residual term, with A, of dimension 3951×3951, is the numerator relationship calculated from pedigree information consisting of all tested individuals, parents and their ancestors, 1 is a vector of ones, X and Z are incidence matrices, and I is an identity matrix.where, h2) and broad-sense heritability (H2) for the above raw data analyses were calculated for the above fitted model using the expressions: ad-hoc narrow r corresponding to the harmonic mean of the number of replicates of each genotype in each trial. The covariate of initial weight w was only measured and included in trials T1/2013 and T1/2014 for the traits where it was significant , and some of the other designs terms b(r) and p(br) were considered according to the design layout. Note that both term b(r) and p(br) were included to assist with control of spatial variability.Narrow-sense heritability  for each of the traits, a model was fitted that combined the raw data from each pair of tests as:μ is the overall mean; t is the fixed effect of trial; w(t) is the covariate of initial weight of a plant within a trial; r(t) is a fixed effect of replicate within a trial; b(rt) is the random effect of bed within replicate and trial, with p(brt) is the random effect of plot within a bed, replicate and trial, with a is the random additive effect of a genotype, where ta is the random interaction effect of genotype with trial, with f is the random family effect, with tf is the random interaction effect of family with trial, with c(f) is the random effect of clone within family, with tc(f) is the random interaction effect of trial with clone within family, with e is the residual term, with D corresponds to diagonal matrices that estimates a different variance component for each of the trials, and all other terms were previously defined. The G×E interaction due to both the interaction of additive effects between pairs of trials and the interaction of additive effects between pairs of trials in different seasons was estimated using Type-B genetic correlations,24 calculated as: where, yadj, for each genotype to be used in the genomic prediction approaches, the following model was fitted for each of the trials and traits:In order to obtain an adjusted phenotypic mean prediction, g which corresponds to the total genetic value  and here it was assumed to be a fixed effect.where all terms were random effects and were previously defined with the exception of 25 which estimates variance components based on residual maximum likelihood using the raw information available from the field trials. In all cases, residuals were checked to verify assumptions, and no important departures from normality were observed.All of the previous models were fitted using ASReml v. 3.0,11 Bayes C,12 Bayes Ridge Regression, BRR,26 Bayesian LASSO, BL,27 Bayesian Reproducing Kernel Hilbert Spaces, RKHS28 and Genomic BLUP, GBLUP.29 The Bayesian methods address the problem of small number of observations (n) and a large number of parameters (p) to be estimated (n<<p) by restricting the size of the regression coefficients via shrinkage or regularization.11 The package BGLR30 implemented in the package R version 3.1.1 that maps ‘marker genotypes αI’.32 In this method, the observed numerator relationship matrix (Ag) is replaced by the kernel matrix (K), using a Gaussian prior evaluated by the square Euclidean distance between markers.34 The bandwidth parameter h, and the residual variance indexed by a scale and degrees of freedom parameters were set using default values.32BGLR also implements RKHS regression which was proposed for the prediction of genetic values by GianolaAg, was obtained using all 17 479 markers with no imputation for missing values. This matrix was calculated by using the equations described by Yang et al.,35 and later an inverse of this matrix was generated implementing bending due to the presence of non-positive eigenvalues.36 Markers quality control, generation of the Ag matrix and its inverse were all performed with the software GenoMatrix.37 This matrix was later used, based on the adjusted phenotypic values, to fit a simple animal/individual model of the form: y=1μ+Za+e, where 38 as implemented for the statistical package R version 3.1.1.31In GBLUP, an observed numerator relationships matrix, The assessment of the different GS methods, for each of the traits and trials, was done by calculating the predicted ability (PA) and the prediction accuracy (PACC) of the genomic estimated breeding values (GEBV). PA was estimated as the correlation between the adjusted phenotypic value and the GEBV, ad hoc heritability described earlier. For PACC comparisons, a PBLUP analysis was performed, that replaces the genomic relationship matrix (Ag) from the marker data with its traditional counterpart (A) obtained from pedigree information.The PACC of GEBV, or the correlation between the true genomic breeding value and the predicted breeding value,39 was used to create a high-density SNP linkage map of 14 332 SNP markers using a FL_08-10×12.115-10 mapping population comprised of 165 progeny . Because of stringent mapping parameters and in order to minimize gaps between SNPs, a few linkage groups (LG) were subdivided into two groups. Orientation and subgenome specificity of each LG were assigned according to van Dijk et al.40 In order to evaluate the contribution of LD and genetic relationships on the accuracy and efficiency of GS methods, LD parameters were calculated for the entire T2/2013 and T2/2014 population sets. Common SNPs between the mapped markers and the 17 479 SNPs chosen for the genomic analysis were extracted. A common set of 4841 SNPs were distributed in 28 LGs and analyzed for extent of LD. Pairwise LD (r2) for each LG was estimated using the R package LDheatmap.41 The genomic relationship matrix derived from each LG was estimated using GenoMatrix software37 and used for the estimation of LD corrected for relatedness 42JoinMap 4.1 softwareParent selection efficiency was used to experimentally evaluate the performance of PBLUP, GBLUP and Bayes B versus indirect selection in a true validation context. This consisted of calculating the ratio of genetic gain when selecting the top 5 and 10% genotypes obtained by using incomplete information versus complete information for selection. The incomplete information scenario consisted of using a GS model trained in trial T2/2013 to predict genetic values in trial T2/2014, using only marker data from T2/2014. This represents a typical GS strategy in which phenotypes have not yet been obtained. In the complete information scenario the predicted genetic values from the trial T2/2014 were calculated using both the phenotypic and marker information from T2/2014 by fitting a GBLUP model. This represents best available analysis practice given that both phenotypic and marker data T2/2014 were available for ranking and selection. In theory, a perfect GS model would achieve 100% of the genetic gain (100% selection efficiency) achieved in the complete information scenario.h2) were found , and the lowest levels were detected for TMY and EMY . Broad-sense heritability (H2) averages were high (0.62 for AWT) to moderate (0.38–0.47) for the remaining traits. Non-additive effects varied on average across trials and years between 0.20 for EMY to 0.27 for TMY, however, there was a high range of variability of non-additive genetic effects between different combinations of traits and trials.A summary of the phenotypic data are presented for each of the trials and traits . In genere found . HoweverType-B additive genetic correlation estimates showed a wide range of G×E interactions, with no interaction for AWT and SSC, low to moderate for TC and high for EMY and TMY . The corTrue validations, obtained by fitting a GS model from one trial and evaluating the model in a different trial, resulted in a wide range of PA varying from 0.08 to 0.59 . Even thOf special interest are the models built for T1/2013 and T2/2013 to predict T2/2014, due to their higher PA estimates compared with the other two models presented in Results of the fivefold cross-validation within each trial for all GS methods are included in the Prediction accuracies calculated from the true validation models had average values ranging from 0.37 to 0.75 across the GS methods with largest average values for the traits SSC and TMY, and lowest for TC . The prer2) was 0.26 and LD corrected for relatedness r2) was 0.26 and LD corrected for relatedness r2) declined significantly after correcting for relatedness r2 and r2 decreased at a slower rate than r2) was >0.2 until 10 cM, however, LD r2, A set of 4841 SNPs, common between 17 479 polymorphic SNPs chosen for genomic selection analysis and 14 332 genetically mapped SNPs, were distributed in 28 LGs . The ovess (rv2) . For eacor LG 6A . The r2, T2/2013 .The efficiency of parent selection for each trait, expressed as a function of genetic gains, and obtained by performing selection of the best 5 and 10% of genotypes using the complete information scenario , against the incomplete information scenario (genetic predictions from a model fitted with T2/2013 but predicting genotypic performance in T2/2014 using markers only) is presented in 8 sugarcane43 and bread wheat.44The main objective of the present study was to evaluate GS methods in octoploid strawberry, with the practical aim of incorporating the GS in strawberry breeding, using the University of Florida strawberry breeding program as an example. The magnitudes of PA, PACC and selection efficiency obtained for five complex traits indicate that genomic selection is a promising tool for genetic improvement of quantitative traits in strawberry. In particular, high prediction accuracies and selection efficiencies from true validations, using independent trials from two consecutive years, strongly support the potential utility of GS in a practical strawberry breeding context. In general, our PA estimates are consistent in magnitude with other studies using true validation, as is the case in apple,46 Furthermore, based on cross-validation, RKHS or GBLUP would appear to be the best choices for the application of GS. In contrast, for true validations Bayes B gave the highest average PA and the highest average selection efficiency, and performed particularly well for the lowest heritability traits (EMY and TMY) for which genetic gain is at a premium. The cross-validation PA estimates of this study are within the range of other plant and tree studies, as reported for tomato,16 bread wheat44 and Maritime pine.47 PACC estimates of SSC using cross validation reported for tomato,16 sugarcane43 and sugar beet48 are smaller than our estimates for the same trait using true validation. However, larger estimates of PACC for SSC were reported in an apple study (PA=0.86) compared with the present study, possibly due to higher LD (r2=0.32) in the studied population.14For cross-validations within training populations, predictive abilities were higher than for true validations across trials/years, apparently due to overfitting. Several studies have shown that the prediction accuracy decreases when cross-validation within the same training population is compared with true validation based on independent trials.49 have shown small to moderate improvements in prediction accuracy and bias reduction. In addition, a recent study of GS on rice hybrids using univariate and multivariate models indicated that, in single trait models, PA is improved for some traits by including dominance effects.50As the models evaluated in this study are based on prediction of additive genetic effects, parent selection and selection of cross-combinations are of the most immediate relevance. However, strawberry cultivars are deployed as clones, and the prediction of non-additive genetic effects would be of great value in the future for predicting clonal performance. Indeed, the high proportions of non-additive variance for all traits under study  indicate51 In a simulation study in Asian rice, RKHS performed better than GBLUP, Bayesian methods and other non-parametric methods when traits had high heritability, presence of epistasis and were controlled by a large number of QTLs.52 In a simulation study comparing PBLUP, Bayes B and GBLUP under different genetic models , Bayes B showed higher accuracy of breeding values for the QTL and rare variant models and similar accuracy for the infinitesimal model.53 In a simulation study in barley, under a high-density marker scenario, two Bayes B models with different priors (π) had better prediction accuracies when the trait was controlled by less than 20 QTL, than RR-BLUP and a BLUP model based on the marker relationship matrix.54 Moreover, the authors concluded that the BLUP models mainly capture genetic relationships, whereas the Bayesian models captured both genetic relationships and marker-QTL associations based on LD.On average the Bayes B method had higher PA and PACC than the other methods for all traits and trials in the present study, and was superior to GBLUP. This was particularly the case for the yield traits EMY and TMY. However, the difference of PACC estimates for AWT and SSC between Bayes B and GBLUP were very small, indicating that the methods perform equivalently for these traits in the T2/2013 and T2/2014 populations. For some trials and traits, RKHS gave similar results to Bayes B. RKHS is a method that has shown better results that other methods in some studies, and its superiority is attributed to the capture of some non-additive effects.r2 and46 For comparison, the genome-wide estimates of LD for T2/2013 (r2=0.26 and r2=0.011),47 but slightly less than apple .14 Although some studies have been designed to partition observed prediction accuracy into the portion due to LD and the portion due to genetic relatedness,54–57 the present study was not designed to allow such a partitioning.These previous studies suggest that the moderate difference (0–15%) in PACC for Bayes B over GBLUP in the present study may be due to the better capture of QTLs in LD with markers, particularly for the yield traits EMY and TMY . On the mong LGs , the ave7 Both T1/2013 and T2/2013 showed similar average PA across all traits when predicting T2/2014 despite the fact that both shared a similar number of parents, 29 and 40, respectively, with T2/2014 but had different population sizes (647 vs 244). This discrepancy might be explained by the fact that the T1 populations are seedling populations from mating designs of about 60 crosses, plus controls and other specific complementary crosses, and T2 is derived from the T1 trials, plus advanced selections from previous T2 trials with lower genetic diversity than T2. This disparity between trials reduces prediction ability in T1 trials due to more genotypes from different crosses and increases PA in T2 due to a higher ratio of genetic relationships between T2/2013 and T2/2014.From a breeding standpoint, it is noteworthy that predictive abilities for the true validation from T2/2013 to T2/2014 were nearly as high on average as those from T1/2013 to T2/2014. The predictions from both T1/2013 and T2/2013 to T2/2014 are important for selecting parents because T2 populations contain the parent pool for future crossing. Besides, it is expected that the PA and PACC will increase as the population size of the T2 training populations is augmented by combining marker data from successive cycles of breeding. Several authors have shown the impact of increasing the size of the training population and improving the genetic relationships between training and testing populations on the estimation of prediction accuracy.The T2 trials also have added practical value in that these trials are essential for phenotypic testing of advanced selection performance as clones. On the other hand, the T1 trials are comprised mainly of unselected seedlings, and their main value lies in estimation of genetic parameters and parental breeding values. Thus, these results suggest that a cost savings could be obtained by eliminating yearly T1 replicated trials and focusing efforts on T2 replicated trials for both phenotypic testing of clones and training GS models for future parent selection. This is what we propose for the University of Florida strawberry breeding program . By focuIn summary, where both phenotypic and marker data are available in strawberry breeding populations, GS models should allow more precise breeding value estimates compared with models utilizing only pedigree information. In addition, with appropriate statistical models such as Bayes B, parent selection efficiencies near to and even >50% can be obtained for several commercial traits when phenotypic information is not yet available. For some parents predicted early to be top performers, this methodology may allow a reduction of the breeding cycle from 3 to 2 years, with the goal of increasing genetic gains over time for several complex yield and fruit quality traits. The results also suggest that replicated advanced selection trials have potential as training populations for predicting parental performance in the next cycle of breeding. In the future, we plan to expand this study to the prediction of clonal performance, as well as the potential of GS for seedling selection within full-sib families, should a reduction in genotyping costs make this approach economically feasible in strawberry.


Millions of genetic variants have been identified by population-scale sequencing projects, but subsets of these variants are needed for routine genomic predictions or genotyping arrays. Methods for selecting sequence variants were compared using simulated sequence genotypes and real July 2015 data from the 1000 Bull Genomes Project.Candidate sequence variants for 444 Holstein animals were combined with high-density (HD) imputed genotypes for 26,970 progeny-tested Holstein bulls. Test 1 included single nucleotide polymorphisms (SNPs) for 481,904 candidate sequence variants. Test 2 also included 249,966 insertions-deletions (InDels). After merging sequence variants with 312,614 HD SNPs and editing steps, Tests 1 and 2 included 762,588 and 1,003,453 variants, respectively. Imputation quality from findhap software was assessed with 404 of the sequenced animals in the reference population and 40 randomly chosen animals for validation. Their sequence genotypes were reduced to the subset of genotypes that were in common with HD genotypes and then imputed back to sequence. Predictions were tested for 33 traits using 2015 data of 3983 US validation bulls with daughters that were first phenotyped after August 2011.The average percentage of correctly imputed variants across all chromosomes was 97.2 for Test 1 and 97.0 for Test 2. Total time required to prepare, edit, impute, and estimate the effects of sequence variants for 27,235 bulls was about 1 week using less than 33 threads. Many sequence variants had larger estimated effects than nearby HD SNPs, but prediction reliability improved only by 0.6 percentage points in Test 1 when sequence SNPs were added to HD SNPs and by 0.4 percentage points in Test 2 when sequence SNPs and InDels were included. However, selecting the 16,648 candidate SNPs with the largest estimated effects and adding them to the 60,671 SNPs used in routine evaluations improved reliabilities by 2.7 percentage points.Reliabilities for genomic predictions improved when selected sequence variants were added; gains were similar for simulated and real data for the same population, and larger than previous gains obtained by adding HD SNPs. With many genotyped animals, many data sources, and millions of variants, computing strategies must efficiently balance costs of imputation, selection, and prediction to obtain subsets of markers that provide the highest accuracy.The online version of this article (doi:10.1186/s12711-017-0307-4) contains supplementary material, which is available to authorized users. Accuracy of genomic predictions can be improved by using more variants, including variants that are pre-selected for their effect, located near genes or within genes, predicted to affect gene function, or known to be causal. Past analyses often gave equal weight to evenly spaced markers, whereas new analyses can focus on potential quantitative trait loci (QTL) or preselected variants that are more closely linked to QTL. Nearly 40 million variants have been identified from whole-genome sequence (WGS) data for over 1500 bulls, and several strategies to impute these variants to additional animals and use them in genetic evaluation for economic traits show potential . For exaImputing, selecting, and predicting effects for millions of variants and many thousands of individuals require efficient computation. Computational costs, which are proportional to the number of variants multiplied by the number of individuals, could exceed the marginal benefits from adding more variants. Variants within or near genes should improve the reliability of predictions, and direct use of causal variants is preferred to using linked markers. Strategies to choose variants for inclusion on genotyping arrays of different densities or in routine predictions were developed and compared using simulated data for Holstein bulls. Here, we first examined simulated data and then real sequence genotypes from the 1000 Bull Genomes Project [The goals of this study were to (1) compare the reliability of prediction from sequence, array, and combined data as well as different types of variants, (2) test the methods first on simulated data before applying them to real sequence data imputed for a large reference population, and (3) investigate editing, imputation, and computing strategies that are efficient for even larger genotyped populations.Our simulation was designed to closely mimic an actual large-scale sequencing project for cattle, in which a subset of ancestor bulls had WGS data, another subset of ancestor bulls had high-density (HD) SNP-array genotypes, and most bulls had medium-density genotypes. Sequence variants were simulated for 26,984 Holstein bulls in the US reference population in December 2014 using a pedigree file that included 112,905 animals, and the sequences were then reduced to mimic the actual available array genotypes. Among these animals, the 1000 bulls that had the most daughters had genotypes observed for 30 million sequence variants, and 773, 24,863 and 343 other bulls that had fewer daughters were genotyped with 600,000 (600 k), 60,000 (60 k) and 12,000 SNPs, respectively. Each simulated chip was an evenly spaced subset of the previous chip and the sequence variants. The 30 million variant sites were randomly located across 30 chromosomes each 100 million bases long, and all variants had two alleles. The genotypes were simulated using genosim software [Editing reduced the list of variants to 8.4 million by removing SNPs with a minor allele frequency (MAF) lower than 0.01 and a level of LD less than 0.95 with any remaining neighbouring SNP, but all 0.5 million variants that were within or near the 10,000 (10 k) simulated QTL were retained. The QTL were located randomly across the genome, and the 25 variants on either side were retained. No actual genes were simulated, only the QTL and other variants. If any of the 350 variants on either side of a specific marker were correlated i.e. with an |r| higher than 0.95, editing based on LD retained one variant and removed all others that had an |r| higher than 0.95 with that variant. The 600 k SNPs were all retained to improve imputation, and the 505,210 SNPs that were within 2500 bases of a true QTL were retained to mimic bioinformatic selection using gene positions. The selected SNPs were imputed for all bulls. Strategies were compared to choose the most significant variants or those with the largest estimated variances or effect sizes for five independent traits using individual regressions on each variant or multiple regression on all variants.(|q|−2) such that the largest effect contributed 3 to 13% of the genetic variance, the largest 10 effects contributed 20 to 34%, the largest 100 contributed 57 to 63%, and the largest 1000 contributed 90 to 93%. Actual traits may be controlled by QTL with smaller effects, however, most actual traits had at least one QTL as large as those simulated here [Breeding values for five independent traits were simulated by summing across effects of the 10 k QTL. The five traits were not true replicates because the QTL locations did not vary, only the effects, mimicking quantitative inheritance where each QTL may affect most traits very little but some traits more. A heavy-tailed distribution was generated from normally-distributed effects (q) raised to the power of 2.7After imputing the 8.4 million edited variants for all bulls, the variants with the largest effect estimated by genomic prediction or the most significant variants from genome-wide association (GWA) analysis were selected. The oldest 17,896 bulls were used as the reference population, and true breeding values (TBV) of the 9088 younger bulls were used to validate predictions from the selected variants. In all tests, the phenotypes used for estimating effects and selecting variants were only from the truncated reference data so that validation phenotypes were independent and tests should be unbiased. Many of the reference bulls and a few validation bulls had sequence data included in the 1000 Bull Genomes Project and used for variant discovery, which might bias estimates of allele frequency, but should not bias the phenotypic effects.p is the allele frequency and α is the allele substitution effect. All three methods were compared. Selecting the variants that contribute the most variance has more theoretical appeal and results in variants with higher MAF, which could also contribute to improve imputation accuracy. Using the nonlinear Bayes A algorithm of VanRaden et al. [Variants can be selected based on the highest significance test, largest absolute effect, or largest genetic variance contributed by the locus, which is computed as mentclasspt{miniman et al. , 15 and Genomic predictions from 60 or 600 k SNPs were compared with predictions from additional markers selected also using Bayes A multiple regression. To mimic the selection process used to design the GeneSeek HD version 1 chip , the topSelection based on sequence variants should improve accuracy more than selection on HD SNPs, but the previously genotyped SNPs must be retained during imputation because sequence variants are not available for most animals. Genomic predictions included the 600 k SNPs plus 500 k sequence variants near QTL totalling 1.1 million variants, which was similar to the analysis of real data by Hayes et al. . The varSNP and insertion-deletion (InDel) calls (sequence variants) from run 5 of the 1000 Bull Genomes Project  were relThe HD genotypes of 2394 Holsteins mainly from North America, Italy, and Great Britain were used to impute genotypes of 590,363 other Holsteins that had genotypes obtained mainly by using SNP chips with 50,000 or fewer SNPs. The imputed HD genotypes of bulls used in this study were a subset of those animals. The original 777,962 HD SNPs were reduced to 312,614 by removing highly correlated markers with an |r| higher than 0.95 and by further editing before imputation with findhap (version 3) [Variants with a MAF lower than 0.01, incorrect map locations, an excess of heterozygotes, or low correlations (|r| < 0.95) between sequence and HD genotypes for the same variant were removed. A few hundred sequence variants were removed in specific regions that were known to be mapped incorrectly in the UMD3.1 bovine reference assembly. Most map issues had been previously detected by using small sets of SNPs that were lowly correlated to adjacent sets within windows that had excessive total numbers of haplotypes .After merging sequence and HD data, Mendelian conflicts between parents and progeny were set to missing for 0.01% of the genotypes. The percentage of conflicts was expected to be small because both the HD and sequence genotypes had been previously edited. About 1% of the HD imputed genotypes were unknown in the findhap output, and their allele frequencies were substituted when used in genomic prediction. All HD SNPs that were also in the sequence data were retained except in cases when the absolute correlation between HD SNPs was lower than 0.95. This editing step removed less than 1000 (0.3%) of the HD SNPs because a similar edit had previously been applied before imputation .p(1 − p). After these edits, only 3,148,506 variants remained.Three different sets of variants were imputed to test the use of candidate SNPs (Test 1), candidate SNPs and InDels (Test 2), and candidate SNPs, InDels, and intergenic and intronic variants (Test 3). Predictions and QTL discovery using Test 3 data will be reported separately. The initial edits for sequence genotypes used in Tests 1 and 2 were revised in Test 3 because imputation accuracy decreased when millions of intergenic and intronic variants were included. The new edits for Test 3 computed statistics across all samples to improve imputation accuracy instead of editing each animal individually. The VCF file contains three genotype probabilities from Beagle, and the editing done for Tests 1 and 2 simply retained any genotype with a probability higher than 0.98. The new edits were based on an individual probability higher than 0.95, and after processing all animals, a second edit deleted any variant that had more than 5% missing genotypes for low frequency variants (MAF < 0.10) or more than MAF/2 missing genotypes for more common variants. Thus, variants with MAF = 0.50 were not used if more than 25% of the called genotypes had a probability below 95%. The third new edit for Test 3 checked for Hardy–Weinberg equilibrium and deleted variants that had 1.5 times more heterozygotes than the expected fraction of 2Quality and orientation of calls were examined using 179 bulls that had both sequence and HD genotypes. After reversing the orientation of the HD SNPs to match sequence data and keeping the sequence instead of the HD genotype for animals that had both, the two datasets were combined, resulting in 27,235 animals. Quality of imputation was assessed by keeping 404 of the sequenced animals in the reference population and randomly choosing 40 animals as a test set. Their sequence genotypes were reduced to the subset of genotypes that were in common with HD genotypes and then imputed back to sequence. The percentage of imputed genotypes that matched the original genotypes was the simple measure of sequence imputation accuracy.Genomic predictions were computed using deregressed evaluations from August 2011 for 33 traits and 19,575 bulls. Predictions were tested using 2015 data of 3983 bulls with daughters that were first phenotyped after August 2011. Reliabilities were estimated from the squared correlations of predictions with the deregressed evaluations, divided by their reliabilities to account for error variance, and adding the difference between observed and expected reliability of parent average to account for selection . Regressftp://ftp.ensembl.org/pub/release-79/gtf/bos_taurus). Test 2 also included any InDels that were located within genes or within the regions 2 kb upstream and 1 kb downstream of genes. Imputed data of Test 3 were used only for GWA because genomic predictions converged too slowly with more than 3 million variants, and the GWA results from real data will be reported separately. Additional file Test 1 combined 481,904 candidate sequence SNPs with HD genotypes for 312,614 markers and a total of 762,588 variants. The candidate variants included 107,471 variants in exons, 9422 in splice sites, 35,242 in untranslated regions at the beginning and end of genes, 254,907 within a 2-kb region upstream, and 74,862 within a 1-kb region downstream, for a total of 481,904 candidate variants based on the Ensembl gene annotation [A subset of variants was selected for potential use in routine genomic prediction by applying methods similar to those used previously to select the HD SNPs with the largest effects in the national evaluation  except tEdits for MAF and LD removed 3.4 and 18.4 million variants, respectively, from the simulated WGS variants from our 1000 bull founder population, which reduced the variant list from 30 million initial simulated variants to 8.4 million that included the 600 k array SNPs and the 505,210 genic variants. For the 26.6 million variants with a MAF higher than 0.01, the maximum absolute correlation with any of the 350 variants on either side was on average equal to 0.96. High correlations improve imputation and also indicate that most QTL can be efficiently traced by nearby markers.Average reliability of prediction was equal to 28.4% based on the simulated parent average, 77.8 and 80.1% based on the 60 and 600 k chips, respectively, 79.2% based on the markers selected by GWA from the 600 k chip, and 87.2% based on only the 10 k imputed true QTL with no weight on the markers  and BTA22 and a minimum of 94.9% for BTA15 and 95.0% for BTA4  compared with using HD SNPs only . This higher accuracy has an annual national value of about $3 million because each year 3.3 million of the 9.2 million US dairy cows are replaced. These annual gains are permanent and will accumulate. The initial cost of generating the US sequence data for the 88 dairy bulls contributed to the 1000 Bull Genomes Project was $132,000 at current estimates of reagent costs (assuming a cost of ~$1500 per sample). The return on investment from this research is high and greatly increased because of data sharing.New animals will be directly genotyped for the selected variants and thus could have slightly higher reliability gains than in these tests that use imputed data, but most reference animals will still have imputed data. Re-genotyping old animals with the new arrays might be less expensive than additional sequencing to improve accuracy of imputation.Variant selection is needed because routine genomic predictions cannot impute and include all of the millions of sequence variants for all animals. Significant gains in reliability are possible if the true QTL can be identified or if bioinformatic methods can choose the regions that are more likely to contain causative variants. Because individual QTL have such small effects, large reference populations are needed with phenotypes for the relevant traits and observed or imputed genotypes for the QTL or closely linked variants. Testing many individual traits gives more power because the effect of each QTL may be detectable only for a few traits, but these same QTL may have smaller effects on several correlated traits. Assigning more prior variance to the QTL or to the newly selected variants can improve reliability when estimating effects, but the SNPs from previous arrays must be retained during imputation because genotypes of previous animals include only the SNPs and not the new variants.Computation becomes a limiting factor as reference populations and target populations grow in size. Total computing time was only a few days with up to 1000 sequences and 30,000 reference bulls, but more than 150,000 reference cows and 800,000 young animals were not included. Multiple regressions used for genomic prediction were more accurate than GWA for selecting variants but required much more computation time. Imputation allows many more sequence variants to be tested, selected, and included in routine predictions to increase their reliability. For both the simulated and real data, gains from selecting and including candidate sequence variants were larger than from selecting HD SNPs.


Diagnostic codes within electronic health record systems can vary widely in accuracy. It has been noted that the number of instances of a particular diagnostic code monotonically increases with the accuracy of disease phenotype classification. As a growing number of health system databases become linked with genomic data, it is critically important to understand the effect of this misclassification on the power of genetic association studies. Here, I investigate the impact of this diagnostic code misclassification on the power of genetic association studies with the aim to better inform experimental designs using health informatics data. The trade-off between (i) reduced misclassification rates from utilizing additional instances of a diagnostic code per individual and (ii) the resulting smaller sample size is explored, and general rules are presented to improve experimental designs. Clearly, a wealth of important clinical information is contained within large electronic health record (EHR) systems. Such information can be an invaluable resource for measuring disease prevalence  and diseIn a general setting, the effect of phenotypic misclassification on statistical power of genetic association studies has been previously explored –14. EdwaConsidering ICD data with misclassification, the type I and type II error rates for genomic association studies were recently thoroughly explored by Duan et al. . The Duax instances of a particular ICD code. Consider a design where individuals with ambiguous numbers of instances (i) of the code  are excluded from the analysis. Further consider a comparison of well-defined cases  against a large, fixed set of controls. With regard to the genetics, restrict the methods to biallelic markers with minor alleles segregating in the population at a frequency of at least 1% single-nucleotide polymorphisms (SNPs). Define the alleles at a SNP contributing to the susceptibility of the disease as A1 and A2. Let the relative risk of the minor allele, A2, be R, such that R = P(A2 | cases)[P(A2 | controls)]−1. Let the frequency of A2 in the general population be q. Accordingly, 1 − q is the frequency of A1. Define nx as the number of cases obtained from the definition of having at least x instances of the ICD code being evaluated. Set the number of controls as m, such that m ≫ nx. Assume that the A2 frequency in controls is approximately q. Model the decrease in the misclassification proportion within cases as x increases with a monotonic function f(x), such that the expected number of truly positive cases is nx[1 − f(x)]. The form of f(x) may vary considerably for different ICD codes. Lastly, let α be the statistical threshold for determining a positive finding in analyses where p value < α. The statistical test of genetic association considered is the binomial test of proportions which evaluates the null hypothesis of no correlation between the frequency of A2 and the disease status.For a large genetic association scan of using ICD data, define a simple disease classification scheme such that cases are those individuals with x and the resulting experimental design. Under the model specified above, the power to detect association at an autosomal SNP, 1 − β, is calculated by the approximation as follows:Φ is the standard Gaussian cumulative distribution function, z is the inverse standard Gaussian score, N = 4nxm/nx + m, and q and s are the A2 frequencies in controls and cases, respectively. Using Bayes' theorem, the expected frequency of A2 within cases under the misclassification model is given byStatistical power will be used to evaluate the impact of increasing f(x):δ is the parameter that can be estimated for each ICD code. Similarly consider the following form for nx as a function of nx=1 to model the reduction in the number of cases defined by using increasing numbers of instances of an ICD code:ε is the parameter that captures the rate of decline in case numbers as the definition for case status becomes more stringent with the use of larger numbers of ICD code instances and can also be estimated for each ICD code. The machinery is now in place for the calculation of statistical power to detect disease association at a genetic marker using data from linked ICD coding systems.To model the decrease in the misclassification rate with increasing numbers of ICD code instances, consider the simple decay function for x which maximizes power to detect genetic association, one can numerically solve the following differential equation for x:The above model is used to conduct an exploration of the impact of ICD code definitions on power. To obtain a value of The solution to  can be sx that solves this continuous equation can be used to optimize the power for a given set of parameters. To exemplify the use of this approach, let m = 10,000, nx=1 = 400, R = 2, q = 0.20, δ = 0.15, and ε = 0.15. Call this set of parameters the baseline model. x = 7.2265 solves the differential equation. Therefore, using seven instances of an ICD code will yield the optimal design weighing the trade-off between the case sample size and the misclassification. For that set of parameters, The closest integer value to the value of nx=1), the calculations were performed as the nx=1 varied from 100 to 800. Visual inspection shows the peak of power at approximately 7 instances. To investigate the power curves, varying the baseline number of cases (δ and ε parameters on the power curves, the calculations were performed fixing the other parameters. Figures Next, to determine the role of the Genetic data linked to longitudinal electronic health records can serve as a very useful tool in modern disease genetics. However, misclassification present in ICD coding systems can severely hamper large-scale screens using those codes for the purpose of genetic association studies. This work has described a simple approach to better understand the impact of misclassification present in EHR systems for the purpose of optimizing experimental designs that screen numerous ICD codes in genetic association studies. Under the mathematical models considered, the methods offer an approach to select the number of instances of an ICD code for the purpose of defining cases and obtaining an optimal experimental design for the identification of genetic markers. Additional work is needed in this area to improve disease classification schemes for genetic association studies as well as for other investigations.


These kinds of data sets require powerful, fast and versatile methods able to discover molecular Quantitative Trait Loci (molQTL). Here we propose such a solution, QTLtools, a modular framework that contains multiple new and well-established methods to prepare the data, to discover proximal and distal molQTLs and, finally, to integrate them with GWAS variants and functional annotations of the genome. We demonstrate its utility by performing a complete expression QTL study in a few easy-to-perform steps. QTLtools is open source and available at  Analysis of molecular quantitative trait loci (molQTL) can help interpret genome-wide association studies and requires efficient approaches to correct for multiple testing. This study describes a bioinformatics toolkit called QTLtool that can handle large data sets and quickly perform multiple types of molQTL analyses. To increase the explanatory power of genome-wide association studies (GWAS), many genetic studies now routinely combine genetic information with one or multiple molecular phenotypes such as gene expression128QTLtools is a modular framework designed to accommodate new analysis modules as they are being developed by our group or the scientific community. In its current state, QTLtools performs multiple key tasks  such as To control the quality of the sequence data, QTLtools proposes two complementary approaches. First, it can measure the proportions of reads (i) mapping to the reference genome and (ii) falling within an annotation of interest , such asTo quantify gene expression, QTLtools counts the number of sequencing reads overlapping a set of genomic features  listed in a given annotation file . We quancis-acting) molQTLs. To do so, it extends the QTL mapping method introduced by FastQTLP values for multiple testing  learn the number of independent signals per phenotype, (ii) determine the best candidate variant per signal and (iii) assign all significant hits to the independent signal they relate to (Methods section). We applied this conditional analysis on Geuvadis and discovered that ∼38% of the significant genes have actually more than one eQTL  molQTLs. The first method we implemented relies on permuting all phenotypes together to draw from the null distribution of associations while preserving the correlation structure within genotype and phenotype data intact (Methods section). By repeating this permutation scheme multiple times , we can obtain an empirically calibrated Quantile–Quantile plot that properly shows signal enrichment , we also designed an approximation of this process that gives reasonably close FDR estimates while being multiple orders of magnitude faster . Given that the whole genome is effectively tested for each phenotype, we quickly build a null distribution of associations for a single phenotype by permutations. We then use this null distribution to adjust each nominal P value for the number of variants being tested and then use standard FDR methodsP values to correct for the multiple phenotypes being tested. In practice, this approach can be seen as an extension of the mapping strategy we use in cis for trans analysis, and gives FDR estimates that are close to those obtained with the full permutation pass  pinpoint the same underlying functional variant. To do so, it uses regulatory trait concordanceAll functionality described above has been implemented in C++ for high performance and in a modular way to facilitate future implementation of additional functionalities by the community. In practice, this allows all the experiments described above to be run in a relatively short time ; the fulPopulation scale studies combining genetic variation and molecular phenotypes have become a standard to detect molecular QTLs. This requires multiple computational steps to go from the raw sequence and genotype data to collections of molecular QTLs. So far, this can be done using multiple tools that are often hard to combine and/or adapt to the amount of data involved. We propose in this paper, QTLtools, a software package that integrates all functionalities required to easily and rapidly perform this task. It includes multiple new and powerful statistical methods to prepare and control the quality of the data, to map proximal and distal QTLs and to integrate those with GWAS results and functional annotations. It also offers a unique framework for the community to develop further additional methods or alternative to the ones already included, so that molecular QTL analysis can be more seamless among laboratories. By its integrative design and efficient implementation, QTLtools dramatically decreases the time needed to set up and run the various analysis pipelines traditionally needed by molecular QTL studies, freeing researchers to spend more effort on the interpretation and validation of their results.cis , resulting in millions of nominal P values. Due to the large number of tests performed per molecular phenotype, multiple testing has to be accounted for to assess the significance of any discovered candidate molQTL. A first naive solution to this problem is to correct the nominal P values for the number of tested variants using the Bonferroni method. However, due to the specific and highly variable nature of each genomic region being tested in terms of allele frequency and linkage disequilibrium, the Bonferroni method usually proves to be overly stringent and results in many false negatives. To overcome this issue, a commonly adopted approach is to analyse thousands of permuted data sets for each phenotype to empirically characterize the null distribution of associations . Then, we can easily assess how likely an observed association obtained in the nominal pass originates from the null, resulting in an adjusted P value. In practice, thousands of permutations are required in this context and therefore fast methods able to absorb such substantial computational loads in reasonable running times are needed. FastQTL has recently emerged as a good candidate for this task by proposing a fast and efficient permutation scheme in which the null distribution of associations for a phenotype is modelled using a beta distributionP values at any significance level in short running times. In the original FastQTL paper, it has been shown that running 1,000 permutations gives accurate adjusted P values while being ∼17 times faster than when implementing the standard permutation scheme with MatrixeQTL running on a BLAS-optimized R versioncis-window of 1 Mb. And since the eQTL mapping is quick and easy, we repeated the whole-mapping pass multiple times across multiple conditions. Specifically, we repeated the whole-Geuvadis analysis across multiple missing data proportion filters  contains M phenotypes  and that the corresponding cis-window G contains L genetic variants, QTLtools proceeds as follows:MxL possible variant-phenotype pairs are tested using linear regressions. The pair with the smallest nominal P value is stored as best candidate QTL for this group of phenotype.All P using the same random number sequence. As a result, the inner correlation structure within both P and G remains completely unchanged, while the correlation in between P and G is broken.Permute simultaneously all phenotypes in P and G by scanning all MxL possible variant-phenotype pairs in the permuted data set and by retaining the best association.Draw from the null distribution of association between G and P by repeating the steps (2) and (3) as many times as needed .Build empirically the null distribution of association between Fit a beta distribution on this empirically defined null distribution using expectation-maximizationP value of the best pair obtained in step (1) using the fitted beta distribution.Adjust the nominal P value of association for each.Repeat step (1) to (6) for all groups of phenotypes to get a candidate QTL together with an adjusted R/q valueP values.Determine all significant QTLs at a given FDR  using a FDR procedure such as Storey–Tibshirani implemented in It is common that some kinds of molecular phenotypes may belong to higher order biological entities. For instance, a given gene often contains multiple exons. Similarly, nearby regulatory elements may cooperate within some module structures such as VCMMxL tests we performed. Alternatively to this extended permutation scheme, we also implemented an approach based on dimensionality reduction. This has been previously used to discover QTLs for VCMs from single-ChIP-seq peak quantificationsM phenotypes and the loadings on the first PC are used as a quantification vector for the entire group of phenotypes. We can then perform the standard mapping approach implemented in QTLtools to discover a QTL for P. We applied these two approaches on Geuvadis to discover gene-level eQTL from exonic quantifications and compared them with the standard gene-level quantifications. We find that the largest number of eQTL is obtained with the extended permutation scheme and the smallest with the PCA-based approach; the gene-level quantifications lying in between. The boost provided by the extended permutation scheme is really appreciable since we get an additional set of 1,019 eQTLs that the gene-level quantification is unable to discover , we get an estimate of the effective number of independent tests corresponding to the actual discover . Of notediscover . In addidiscover . DespiteInitialization. We determine a nominal P value threshold of significance on a per-phenotype basis. To do so, we first perform a permutation pass as described above which gives us an adjusted P value per phenotype (or group of phenotypes) together with its most likely beta parameter values. Next, we determine the adjusted P value threshold corresponding to the targeted FDR level  and feed the beta quantile function  with it to get a specific nominal P value threshold for each phenotype. Here, the beta quantile function allows us to use the Beta distribution in a reversed way: from adjusted P value to nominal P value. Note that the resulting nominal P value thresholds vary from one phenotype to the other depending on the complexity of the cis regions being tested and the effective number of independent tests they encapsulate.Forward pass. We next learn the number of independent signals per phenotype using stepwise regressions with forward variable selection. More specifically, we start from the original phenotype quantifications and search for the variant in cis with the strongest association. When the corresponding nominal P value of association is below the threshold defined in step (1), we store the variant as additional and independent discovery and residualize its genotypes out from the phenotype quantifications. We then repeat these two steps until no more significant discovery is made: this immediately gives us the number of independent molQTLs together with a best candidate variant for each.Backward pass. Finally, we try to assign nearby genetic variants to the various independent signals we discovered in step (2). To do so, we define a linear regression model that contains all candidate QTLs discovered so far in the forward pass: P=Q1+…+Qi+…+QR where R is the number of independent signals and {Q1, …, Qi, …, QR} are the corresponding best molQTL candidates. Then, we test all possible hypotheses by fitting this model Rx(L-R) times each time fixing { Q1, …, Qi−1, Qi+1,…, QR } and setting Qi as another variant in cis . We then end up with a vector of R nominal P values for each variant in cis which allows us to determine the signal the variant belongs to by simply finding the smallest P value in this vector and comparing it to the significance threshold obtained in step (1).The two mapping approaches above only report a single candidate QTL per phenotype or group of phenotypes. In some cases, this limitation may reduce significantly the number of discoveries. For example, it is relatively frequent that expression for a given gene is affected by multiple proximal eQTLstrans-QTL). This presents multiple computational and statistical challenges related to multiple testing, computational feasibility and confounding factors such as read misalignment, gene homology or incorrect gene location. In the context of this work; we only address two particular problems: how to correct for multiple testing and how to perform this analysis in reasonable running times. We solved this problem by testing all possible phenotype-variant pairs for association excluding all those in cis  using linear regressions with high computational performance as we do for cis mapping. In practice, we manage to perform ∼1.3 M linear regressions per second for 358 individuals on an AMD Opteron(tm) Processor 6,174 at 2.2 GHz. To minimize the RAM usage, the phenotype data is stored in memory and the genotype data streamed as we move along the genome and tested against all phenotypes at once. To minimize the size of the output files, we only report detailed information for associations below a given threshold ; all those above are simply binned to have an idea of the overall P value distribution. Once the nominal pass done, we correct for multiple testing using one of these two approaches:Full permutation scheme. We permute all phenotypes using the same random number sequence to preserve the correlation structure unchanged. By doing so, the only association we actually break in the data is between the genotype and the phenotype data. Then, we proceed with a standard association scan identical to the one used in the nominal pass. In practice, we repeat this for 100 permutations of the phenotype data. Then, we can proceed with FDR correction by ranking all the nominal P values in increasing order and by counting how many P values in the permuted data sets are smaller. This immediately gives an FDR estimate: if we have 500 P values in the permuted data sets being smaller than the 100th smallest nominal P value, we can then assume that the FDR for the 100 first associations is around 5% (=500/(100 × 100)).Approximate permutation scheme. To enable fast screening in trans, we also designed an approximation of the method described just above based on what we already do in cis. To make it possible, we assume that the phenotypes are independent and normally distributed (which can be enforced in practice). Then, we draw from the null by permuting only one randomly chosen phenotype, testing for associations with all variants in trans and storing the smallest P value. When we repeat this many times , we effectively build a null distribution of the strongest associations for a single phenotype. We then make it continuous by fitting a beta distribution as we do in cis and use it to adjust every nominal P value coming from the initial pass for the number of variants being tested. To correct for the number of phenotypes being tested, we estimate FDR  again as we do in cis; that is onto the best adjusted P values per phenotype (one per phenotype). As a by-product, this also gives an adjusted P value threshold that we finally use to identify all phenotype-variant pairs that are whole-genome significant. In our experiments, this approach gives similar results to the full permutation scheme both in term of FDR estimates and number of discoveries  between molQTLs and functional annotations. Then, we permute randomly many times  the lists of functional annotations across the phenotypes  and for each permuted data set, we count how often the Y molQTLs do overlap the newly assigned functional annotations: this gives the expected overlap frequency fexp(Y) between molQTLs and functional annotations. By doing this permutation scheme, we keep unchanged the distribution of functional annotations and molQTLs around molecular phenotypes. Now that we have the observed and expected overlap frequencies, we use a fisher test to assess how fobs(Y) and fexp(Y) differ. This gives an odd ratio estimate and a tow-sided P value which basically tells us first if there is enrichment or depletion and second how significant this is. Then, we typically plot these two quantities on a scatter plot with the x axis and y axis being the odd ratio and the significance of the enrichment/depletion, respectively . Then, we split this window into small bins (default 1 kb) and count the number of functional annotations overlapping each bin. This produces an annotation count per bin that can be then plotted to see if there is any peak or depletion around the molQTLs . Complemectively . In our http://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/processed/ (RNA-seq data) and ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ (for DNA-seq data)The Geuvadis RNA-seq data corresponds exactly to what has been generated in the original Geuvadis study, so please consult the https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3657/.The histone modification data set contains ChIP-seq across for 3 histone modifications across 47 European samples: H3K4me1, H3K4me3 and H3K27ac that are known to usually tag enhancers, promoters and active regions. Please consult this paperhttps://qtltools.github.io/qtltools/.QTLtools is open source and available for download at How to cite this article: Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun.8, 15452 doi: 10.1038/ncomms15452 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Table, Supplementary Notes and Supplementary References