Elaeis guineensis (Eg) is suffering severely from bud rod disease, restricting the potential cultivation areas. Therefore, breeding companies have started to work with interspecific Elaeis oleifera × Eg (Eo × Eg) hybrids which are tolerant to this disease. We performed association studies between candidate gene (CG) single nucleotide polymorphisms (SNP) and six production and 19 oil quality traits in 198 accessions of interspecific oil palm hybrids from five different origins. For this purpose, barcoded amplicons of initially 167 CG were produced from each genotype and sequenced with Ion Torrent. After sequence cleaning 115 SNP remained targeting 62 CG. The influence of the origins on the different traits was analyzed and a genetic diversity study was performed. Two generalized linear models (GLM) with principle component analysis (PCA) or structure (Q) matrixes as covariates and two mixed linear models (MLM) which included in addition a Kinship (K) matrix were applied for association mapping using GAPIT. False discovery rate (FDR) multiple testing corrections were applied in order to avoid Type I errors. However, with FDR adjusted p values no significant associations between SNP and traits were detected. If using unadjusted p values below 0.05, seven of the studied CG showed potential associations with production traits, while 23 CG may influence different quality traits. Under these conditions the current approach and the detected candidate genes could be exploited for selecting genotypes with superior CG alleles in Marker Assisted Selection systems.Oil palm production is gaining importance in Central and South America. However, the main species  East-Asian countries address most of the oil palm production. Actually, Indonesia, Malaysia, and Thailand together produce almost 90% of the palm oil worldwide. Latin-American countries have started climbing positions in production few years ago, since Asian countries suffer lack of space due to increased oil demand and restricted cultivation areas . ColombiElaeis guineensis (Eg) is suffering from bud rot disease “Pudrición de Cogollo” in these countries [Elaeis oleifera and Eg (Eo × Eg) [2 100 g−1, Eg palms showed 52 g I2 100 g–1 and Eo palms an average of 77.4 g I2 100 g−1. However, the main oil palm species ountries ,5 leadinEo × Eg) . These hEo × Eg) ,8. CadenEo × Eg)  reportedElaeis species. Therefore, in the current study a broader collection of Eo × Eg hybrids was analyzed for different traits, divided in two big groups; production and quality traits. Production traits cover agronomic performance in terms of bunch number, bunch weight, and bunch yield and the oil contents in mesocarp and bunch. The analyzed oil quality traits considered different components of lipids and tocols, as well as carotenoids. Even though these last two represent only minor components, they are of nutritional importance [Many breeding and seed companies have started breeding programs to get elite hybrid palms. Marker-assisted selection has emerged as a useful technology for this purpose, particularly for traits controlled by multiple genes, such as those related to oil quality and oil quantity. However, until now only a few studies have been published on this topic. Montoya et al.  identifiportance . The quap < 0.001 as well as 16 quality traits. The SSS triglyceride (SSS), Delta compound (Delta), and Gamma compound (Gamma) traits did not reveal significant differences between origins.Saphiro–Wilk tests revealed 16 traits which were not normally distributed. They are marked with “*” in The results of the Tukey post hoc tests are presented in Three separate amplicon libraries were constructed with a total of 167 candidate genes. The first library was constructed from 56 candidate genes and yielded over 13.9 million raw reads. The second library from 55 CG produced around 9.2 million raw reads and the third library from 56 CG generated around 9.6 million raw reads. This total number of 32.7 million reads was reduced to 9.8 million clean reads after the filtering steps. Approximately 83% of the reads mapped to the Eg reference genome. The Snakemake-capture workflow identified initially 12,200 potential SNP. However, after the mentioned filtering steps, only 115 potential SNP remained for the following analyses. The average observed (Ho) and expected heterozygosity (He) were 0.61 and 0.37, respectively. Bartlett’s test revealed a significant difference between expected and observed heterozygosity. The fixation indices (Fst) values revealed no discriminant differentiation between populations as can be seen in The remaining 115 SNP belong to 62 of the 167 initial CG used in the study and four of them showed multi locus mapping at two loci. SNP numbers for each candidate gene varied between one and four. The remaining CG are shown in p values of each model were drawn as a Quantile-Quantile (QQ) plot for each trait. 2) of the CG data points from the diagonal of the QQ plot was applied for determining the best fitting model for each trait, even though in several cases the differences in the values for alternative models are very small. The results are shown in After running Association Mapping using GAPIT, expected and observed p values < 0.05 between CG SNP and traits are displayed, as well as the genome location of the significant SNP, the applied model, the significance level of the association, the explained variance, and the effect of the marker. The significant SNP which belong to a particular CG were grouped.SNP belonging to a total of seven CG influenced significantly six production traits. Three CG revealed significant effects on two different production traits, while the other four CG influenced only one trait each, leading to a total of 10 significant associations for production traits. The BW trait was influenced by three different CG, OildM and OilB by two CG and BN, BY, and OilfM by only one CG. The explained variances by the model ranged from 8.9% to over 26% for the different CG. p values. Alph3 did not show any association with any of the studied CG SNP. The explained variances by the models ranged from 6.1% to over 28% of the total variance. For nine CG more than one SNP showed associations with different traits. Poly-Un showed associations with six of the studied CG and the Car trait revealed associations with five CG. Four potential associations were observed for the Delta, Delta3 compound (Delta3), Mono-Un, OA, SSS, and Toc traits and three associations for Alpha compound (Alpha), Gamma3 compound (Gamma3), IV, Toc3, Tocph, and UUU. SUU revealed two potential associations and Gamma, Sat and SUS showed only one potential association. It is also worth to notice, that five of the CG—EgNAC, PKP-ALPHA, SEQUI, LIPOIC, and TO1—showed also potential effects on different production traits. However, considering FDR adjusted p values, all detected associations are not significant anymore.For quality traits SNP belonging to a total of 23 CG showed potential significant associations with 18 out of the 19 quality traits using unadjusted Eg had higher CO2 fixation capacities, which are positively correlated with an increase in oil contents [guineensis palms. In our study we also found higher bunch weights (BW) in all origins involving Taisha and a higher bunch yield (BY) in the Taisha × Avros (Oleoflores) accessions. However, Arias et al. [The analyses of production traits revealed larger differences between Coarí × La Mé genotypes and the other four origins where Taisha was involved. The Coarí × La Mé origin presented on average a higher oil to bunch (OilB) percentage and higher oil percentages in fresh and dry mesocarp . Peláez et al.  observedcontents . On the contents  as “Oleis et al.  studied s et al.  are loweConsidering analyses of quality traits, some studies are available from Montoya et al. , Singh eWe performed also a detailed study for tocols contents which are composed of tocotrienols and tocopherols. These components represent different forms of vitamin E and can be found in oil palm as beneficial phytonutrients . Both, tWe used the Ion Torrent Personal Genome Machine (PGM) sequencing platform for convenience, based on previous experiences in other studies and ease of access. Similar studies using the PGM platform were also performed by other authors ,26.Eg var. pisifera genome sequence as reference. The decision to use this genome relied on the fact that actually no reference genome exists for Eo even though Singh et al. [Elaeis species could be used as reference for mapping the sequence reads. Mapping of the sequenced reads were performed using the published h et al.  publisheh et al.  analyzedElaeis species. The high number of SNP was reduced drastically after the filtering steps and only 115 potential markers remained. The 62 targeted CG included two CG with multi-locus characteristics , since they mapped to different chromosomes on the genome. These results suggest that the corresponding CG primers were specific for gene families rather than for individual CG. In our analysis 83% of the reads could be mapped onto the reference genome and 12,200 SNP were identified initially. According to Singh et al.  73% of tRandom seed samples were received descending from multiple crosses made by Oleflores and RGS. However, nothing was known about the population structure a priori. Therefore, we performed some global genetic analyses. The Ho = 0.561 was significantly higher than the He = 0.37 in the accessions of all five origins. This high Ho value is in accordance with Arias et al.  who evalAccording to Johnson and Shaw  the highElaeis species, which are so important in Latin-American regions, have been studied far less so far. Actually, only some QTL studies have been performed in order to improve the crop [Many studies have been published for the important oil palm crop Eg with the objective of crop improvement. However, the hybrids between the the crop ,11,31,32p values was visualized in a QQ plot for each trait. Several authors have used these QQ plots to determine the best fitting models visually [In total four different models were used for association mapping. Two GLM models with population structure (GLM_Q) and principal component analysis (GLM_PCA) as covariates and two MLM models where in addition a K matrix between individuals was included . After the analysis, the coincidence of observed and expected visually ,34,35. 2) of the CG data points from the diagonal of the QQ plot which represents an objective method for determining the best fitting model for each trait. When looking to the example of a QQ plot for carotenes contents in In our study the mixed effects models fitted better for most of our traits, while only a few traits were found to have better associations with GLM models where the K matrix was not taken into account. These findings are in accordance with those of Wang et al. , Nigro ep values from GAPIT is highly stringent, leading to the loss of the detected significant associations using unadjusted p values. A p value of 0.05 was set as threshold for identifying potential CG with potential significant influence on a trait as also in other studies with similar approaches [As pointed out by Gao et al.  the outpproaches ,39,40,41p values, LIPOIC revealed potential associations with one production traits  and six quality traits  It represents a lipoyl synthase gene, responsible for the synthesis of lipoic acid a universal antioxidant under oxidative stress conditions. This gene is required for cell growth, mitochondrial activity, and coordination of fuel metabolism and uses multiple mitochondrial 2-ketoacid dehydrogenase complexes [If not considering FDR adjusted omplexes  for the omplexes . It is komplexes . p values, also TO1 may influence the production traits BN and BW and one quality trait Gamma3. This CG represents a gamma-tocopherol methyltransferase which catalyzes the conversion of gamma-tocopherol into alpha-tocopherol. In Arabidopsis the overexpression of this enzyme resulted in more than 80-fold increase of α-tocopherol at the expense of γ-tocopherol without changing the total tocopherol contents [Using unadjusted contents . C. verbenacea and Cannabis sativa L. [The candidate gene SEQUI showed potential influence with one production trait, BW, and six quality traits; Toc, Toc3, Gamma3, SSS, IV, and Poly-Un. It is an alpha-humulene synthase transcript related to zerumbone biosynthesis. This compound is known as an essential oil of ativa L. ,47 and hativa L.  and antiativa L. . Finally, EgNAC showed that it could be associated with seven quality traits and one production trait. NAC transcription factors have been studied widely in different crops. They are known to regulate different plant functions in plants, such as fruit ripening in tomato , variatiThese findings indicate that many significant candidate genes could be involved in complex biological pathways, but there is still a lot of information missing. Fully understanding these metabolic pathways can help to discover the precise role of these genes influencing particular characters and can be a good starting point to obtain higher yielding oil palm varieties with increased oil contents. Association mapping results could be exploited in potential downstream applications by selecting genotypes with superior alleles of different significant candidate genes in Marker Assisted Selection systems.Production traits are the most interesting characters from a commercial point of view. However, quality traits are becoming more and more important in recent years. Breeding Companies look for high quality oil properties in order to satisfy customer’s preferences. Components such as high levels of unsaturated acids, high carotene contents, or high amount of tocols are becoming more and more important traits for taking into account. Our association mapping approach and whole understanding of the function of these detected candidate genes could help to obtain improved palms with these desired qualities. In our study we only considered partial amplicons from a reduced number of candidate genes, limiting the scope of our approach. Further studies should be conducted in the future to improve the results, considering other molecular tools such as whole genome resequencing, transcriptome sequencing, or bait sequencing in order to increase the number of targets.A broader collection of 198 Eo × Eg F1 genotypes from five different origins were evaluated in the Energy and Palma plantation in San Lorenzo . These consisted of 40 hybrid genotypes from Coari × La Mé origin , 75 accessions from Taisha × Avros , 37 genotypes from Taisha × Avros , 21 genotypes from Taisha × Yangambi , and 25 genotypes from Taisha × Ekona .Partial amplicons from 167 CG related to oil production and oil quality were used for the analysis. These CG were identified randomly by in silico mining using different sources: (i) literature searches related to known genes from oil palm or other species with proven influence on the trait of interest, (ii) relevant patent sequences in oil palm and other species, (iii) exploration of relevant metabolic pathways such as palm oil biosynthesis for potentially useful enzymes, and (iv) analyses of published QTL and co-located transcripts with a relevant biological meaning. Amplicon primers for these CG were designed only in exons, but not in adjacent regulatory regions . The CG Eo × Eg genotypes were planted in 2010 and phenotypic data recording started in 2014. In total, six production traits and 19 quality traits were studied. The phenotypic raw data are shown in The evaluated production traits were bunch number ), bunch weight ), bunch yield ), oil percentage in fresh mesocarp ), oil percentage in dry mesocarp ), and oil percentage in the bunch ). BN and BW data were collected over four years and cumulative data were used for the analysis. OildM data was determined by Soxhlet extractions. OilfM and OilB were calculated according to García and Nañez  as modifiodine/g was measured using the AOCS Official Da 15-48 method [The analyzed oil quality traits considered different components of lipids and tocols, as well as carotenoids. Lipid components included percentages of oleic acid (OA), of saturated acids (Sat), mono-unsaturated acids (Mono-Un), and poly-unsaturated acids (Poly-Un) and were measured using the AOCS Official Ce-1h-05  method. 8 method  and the 8 method . The nom8 method  and are 8 method . Saphiro–Wilk tests were applied in order to check for non-normal distributed data. The traits that showed a significant deviation were normalized by z-score correction and the normalized data were further used for ANOVA analyses. ANOVA analyses of the different traits and origins were performed in order to see how the origin of the different accessions affects oil production and quality. Separation of means for traits with significant differences was performed using a Tukey post hoc test. All analyses were performed using R language. DNA extractions were performed from young leaflet tissue samples using the Analytik JenaLife extraction kit  according to the manufacturer instructions. ® .All PCR primers were designed in exons of the CG by blasting the CG against the oil palm genome sequence from MPOB  and usinThree amplicon libraries were constructed with a total of 167 CG in the mentioned plant materials. First and second libraries were constructed with 55 CG each, while the third had 57 CG. The CG for each library were chosen randomly. The library number in which a particular CG was included is indicated in For the first multiplex PCR reaction fusion primers were used which were composed of a universal part  and a part common to the CG of interest. These primers produced 120–300 bp amplicons. For each library several multiplex reactions were performed. For selecting appropriate primers for these multiplex reactions, each primer pair was tested with all others for Self-Dimers and Cross Primer Dimers formation using Thermo Fisher Multiple Primer Analyzer . Sets ofA total of 20 ng of each genomic DNA, Invitrogen™ Platinum™ SuperFi™ PCR Master Mix , and 0.16 µM primer-mix were used per 25 µL amplification reaction. The PCR conditions were as follows: 98 °C denaturation for 30 s, followed by 30 cycles of 98 °C for 10 s, 58 °C for 30 s, 72 °C for 60 s, and a final elongation step of 72 °C for 5 min. PCR reactions were performed in a Thermal Cycler ABI 2720 . Amplification products were visualized as described and the PCR products were purified using Agencourt AMPure XP .All purified multiplex PCR products of a specific genotype were combined in one pool and used in a second PCR reaction to barcode each genotype. For this purpose, fusion primers were designed which were composed of one part complementary to the universal part of UniA and UniB, a genotype specific MID part, the key part (ACGT) to calibrate the sequencing machine and the specific key sequences A and B used by the sequencing platform. All primers as well as the forward and reverse MID sequences are shown in 2 genotypes can be discriminated. The genotype specific combinations of the MID sequences with UniA and UniB sequences, respectively, allow to identify unambiguously each genotype. By using a combination of forward and reverse MID a large number of genotypes can be barcoded with a relatively small number of primers. With 2n MID primers nFor each barcoding reaction, a 25 µL reaction volume was prepared containing 1 µL of the purified PCR product, 0.2 µM forward and reverse barcoding primer, and Invitrogen™ Platinum™ SuperFi™ PCR Master Mix. PCR reactions and visualization were performed as described before in the first step.PCR products of each barcoded genotype were individually quantified with a Qubit 2.0 device, using the Qubit dsDNA HS assay . Equal concentrations of genotype specific PCR products were mixed in one tube.Each pool was purified with columns using the GeneRead Size Selection Kit . The quality of the libraries was verified on an Agilent 2100 Bioanalyzer using DNA Chips with HS DNA Kit reagents according to the manufacturer’s protocol (Agilent Technologies). The libraries were sent for sequencing to the Center for Applied Medical Research , using the Ion Torrent PGM. Emulsion PCR was performed with Ion PGM™ Template OT2 400 Kit according to the manufacturer’s protocol. All libraries were sequenced using the 318 Chip v2 with the Ion PGM™ Sequencing 400 Kit. Sequencing was performed unidirectionally. http://southgreen.cirad.fr/ [Analyses of the obtained sequences were performed using the South Green Bioinformatics Platform irad.fr/ , which pE. guineensis pisifera genome sequence [The Fastq files of the three libraries were combined and processed together, since all genotypes had the same MID combination in the three libraries. In order to obtain clean amplicon sequences, trimming and demutliplexing steps were performed. First, each genotype was identified by the combination of MIDs in each read. Sequences were separated in genotype specific files. For this purpose the public “demultiplex.py” Python script  was usedsequence  was usedp value < 0.05) was that the population is in equilibrium and pairing occurs randomly. fastStructure software v1.0 [The SNP of the obtained Variant Calling Format (VCF) file were filtered using VCFtools software v4.2 . Markersare v1.0  was applp values also FDR using the method of Benjamini and Hochberg [p values. Association studies were performed on a single marker basis using GAPIT v 3.0  in R envHochberg  adjustedp values of each model were visualized separately for each trait in a QQ plot, in order to get a first impression on the fitting of different alternative models. In addition, an equation was developed to measure the average square distance (d2) of the CG data points from the diagonal of the QQ plot for each model:oP and eP are the expected and observed –log(p) values, respectively and n the number of CG data points. The model with the smallest d2 value was considered as the best fitting model for our data.The resulting observed and expected


Heterodera glycines Ichinohe, has been one of the most devastating pathogens affecting soybean production. In the United States alone, SCN damage accounted for more than $1 billion loss annually. With a narrow genetic background of the currently available SCN-resistant commercial cultivars, high risk of resistance breakdown can occur. The objectives of this study were to conduct a genome-wide association study (GWAS) to identify QTL, SNP markers, and candidate genes associated with soybean leaf chlorophyll content tolerance to SCN infection, and to carry out a genomic selection (GS) study for the chlorophyll content tolerance.Soybean cyst nematode (SCN), A total of 172 soybean genotypes were evaluated for the effect of SCN HG Type 1.2.3.5.6.7 (race 4) on soybean leaf chlorophyll. The soybean lines were genotyped using a total of 4089 filtered and high-quality SNPs. Results showed that (1) a large variation in SCN tolerance based on leaf chlorophyll content indices (CCI); (2) a total of 22, 14, and 16 SNPs associated with CCI of non-SCN-infected plants, SCN-infected plants, and reduction of CCI SCN, respectively; (3) a new locus of chlorophyll content tolerance to SCN mapped on chromosome 3; (4) candidate genes encoding for Leucine-rich repeat protein, plant hormone signaling molecules, and biomolecule transporters; and (5) an average GS accuracy ranging from 0.31 to 0.46 with all SNPs and varying from 0.55 to 0.76 when GWAS-derived SNP markers were used across five models. This study demonstrated the potential of using genome-wide selection to breed chlorophyll-content-tolerant soybean for managing SCN.In this study, soybean accessions with higher CCI under SCN infestation, and molecular markers associated with chlorophyll content related to SCN were identified. In addition, a total of 15 candidate genes associated with chlorophyll content tolerance to SCN in soybean were also identified. These candidate genes will lead to a better understanding of the molecular mechanisms that control chlorophyll content tolerance to SCN in soybean. Genomic selection analysis of chlorophyll content tolerance to SCN showed that using significant SNPs obtained from GWAS could provide better GS accuracy. To the best of our knowledge, this is the first report of QTL associated with chlorophyll content tolerance to soybean cyst nematode (SCN) in soybean.Glycine max (L.) Merr.] is one of the most important legumes worldwide by providing oil and being a source of vegetable protein. Developing soybean-derived biofuel has been recently increasing, with an estimated value exceeding $35 billion in the United States (www.soystats.com). Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is an important pest with total annual yield losses about $1.5 billion in the U.S. alone Data consisted of CCI without SCN, CCI of plants in soil infested with SCN, and percentage reduction in CCI. Percentage reduction in CCI was obtained as following:Descriptive statistics were generated using the option ‘Tabulate’ of JMP Genomics®7 . Data were visualized through combined violin and boxplots using the packages ‘ggplot2’, ‘labeling’ and ‘gridExtra’ of R 3.3.0. Data were analyzed using PROC GLM of SAS®. 9.4. The statistical model for the analysis was the following.ij denoted the response of the ith genotype at the jth replication, Gi represented the effect of the ith genotype (assumed to have fixed effect), and εij was the experimental error associated with the ijth observation.Yhttps://www.soybase.org/snps/download.php) were used in the genotyping. SNP data having more than 10% missing data, more than 20% heterozygous SNPs, and minor allele frequency less than 4% were removed from the analysis. After SNP filtering, a total of 4089 high-quality SNPs were used for further analysis.DNA was extracted from young leaves of each accession using DNeasy 96 Plant Kit . The DNA samples were genotyped using an Illumina GoldenGate SNP assay. A total of 4252 SNPs obtained from the Soy6K SNP Infinium Chips (GWAS was performed using a Fixed and Random Model Circulating Probability Unification (FarmCPU) in R software as previously described . FarmCPUi represented the phenotypic data obtained from the ith individual, Mij’s denoted the pseudo QTNs, bj’s were the effect of the jth pseudo QTN, Sik denoted the kth SNP corresponding to the ith individual, and ei was the random error for the ith observation such that ei ~ N.(b)where yi was the phenotype corresponding to the ith individual, ui denoted the total genetic effect (random effect) for the ith individual with a variance-covariance matrix defined as 2Kσ2a, σ2a was an unkown genetic variance and K was the Kinship generated from the pseudo QTNs, and ei was the residual such that ei ~ N. Estimate of the variance-covariance matrix was computed using a Singular Value Decomposition (SVD) of the pseudo QTNs based upon the FaST-LMM  algorithm.where ywww.soybase.org).Significant SNPs (LOD > 2.0)  postulatSNP selection accuracy and selection efficiency were computed based on the formulas previously developed . The topGenomic selection was conducted using all 4089 SNPs and the SNPs showing association (LOD > 2.0)  with theN, W was the incidence matrix relating the genotype to the phenotype, G was the genetic matrix, and ε was the random error. The solution for the rrBLUP equation was defined by β^=(ZTZ + Iλ)−1ZTy with Z = WG. The ridge parameter was described as λ = σ2e/σ2β with σ2e being the residual variance and σ2β the marker effect variance. rrBLUP was performed using the ‘rrBLUP’ package of R [The rrBLUP model was y = WGβ + ε  where y age of R .r = Xrβ + Zrμr + εr where the ‘r’ subscript referred to the genotypes involved in the reference panel, yr was the vector phenotype, β was the genetic effect being assumed to be fixed, Xr was the incidence matrix relating β to yr, μr denoted the polygene random additive effect with μr ~N where K was the Kinship matrix and σ2a the additive genetic variance, εr was the random error with εr ~N where I was an identity matrix and σ2e was the residual variance.The gBLUP model was yrr was the variance-covariance matrix for the reference group, Kii represented the variance-covariance matrix for the inference group, and Kir = (Kri)’ denoted the covariance matrix between individuals from the reference and inference groups, respectively.The Kinship matrix was divided into reference and inference panel as described below.i denoted the polygene effect in the inference group, and Kir, Krr, and μr were previously described. gBLUP was performed using GAPIT [The predicted genetic effect in the inference panel was obtained using the following formula .\documeng GAPIT .Bayesian LASSO was a modified version of LASSO regression. In Bayesian LASSO, posteriors related to the genetic and residual variances were Exponential and Multivariate Normal, respectively. The statistical model was described as following .j(λ/2)exp.{− λ|gj|} with λ~Unif being the λ prior, and the posterior distribution of ε|σ2e~MVN with σ2e~Inv-χ2(4) being the prior distribution for σ2e. Bayesian LASSO was done in R using the package ‘BGLR’ [y = μ + Xg + ε where y was the vector phenotype, μ denoted the overall mean, X represented the SNP matrix, g was the vector of random effect due to SNPs, ε represented the vector of random residuals, the posterior distribution of g was defined by g|λ~∏e ‘BGLR’  with bure ‘BGLR’ .F^rfB(x)) was the mean outcomes obtained from B trees defined by {T}1B. Therefore, the prediction function was described as following.Random forest regression was based upon on unpruned tree decision . In randF^=(1/B)* Σb T with b = 1….B and ψb denoted the bth Random Forest tree defined by the split variables, cutpoints at each node, and the terminal node. Random Forest regression was established in R using the package ‘randomForest’ [mForest’ . A totalmForest’ .Support Vector Machines (SVMs) have been recently widely used in genomic selection-related studies. This is a kernel-based supervised machine learning approach with a regression equation described as following .j βj Kh  being the kernel generating function. In this study, a Gaussian kernel was used. SVMs regression was performed in R using the package ‘kernlab’ [y = f(X|β) + ε with f(X|β) = Σkernlab’ .A five-fold cross validation was performed for the genomic selection study . The assAdditional file 1: Table S1. Descriptive statistics for leaf chlorophyll content indices (CCI) of plants grown in soils without the soybean cyst nematode (SCN), CCI of plants in soils infested with SCN, and reduction in CCI by SCN.Additional file 2: Table S2. Genomic selection accuracy for leaf chlorophyll content indices (CCI) without the soybean cyst nematode (SCN) infestation, CCI of the SCN-infested plants, and reduction in CCI by SCN.


Expression quantitative trait loci (eQTL) provide insight on transcription regulation and illuminate the molecular basis of phenotypic outcomes. High-throughput RNA sequencing (RNA-seq) is becoming a popular technique to measure gene expression abundance. Traditional eQTL mapping methods for microarray expression data often assume the expression data follow a normal distribution. As a result, for RNA-seq data, total read count measurements can be normalized by normal quantile transformation in order to fit the data using a linear regression. Other approaches model the total read counts using a negative binomial regression. While these methods work well for common variants , an extension of existing methodology is needed to accommodate a collection of rare variants in RNA-seq data. Here, we examine 2 approaches that are direct applications of existing methodology and apply these approaches to RNAseq studies: 1) collapsing the rare variants in the region and using either negative binomial regression or Poisson regression and 2) using the normalized read counts with the Sequence Kernel Association Test (SKAT), the burden test for SKAT (SKAT-Burden), or an optimal combination of these two tests (SKAT-O). We evaluated these approaches via simulation studies under numerous scenarios and applied these approaches to the 1,000 Genomes Project. Expression quantitative trait loci (eQTL) studies provide insight on transcription regulation and have the potential to illuminate the molecular basis of phenotypic outcomes.  As a resTraditional eQTL mapping methods for microarray expression data often assume the expression data follow a normal distribution and involve application of linear regression or equivalent approaches for eQTL mapping.  To meet These methods for eQTL mapping in RNA-seq data readily accommodate common variants  > 5%), but were not specifically designed for a collection of rare variants. Several methods have been proposed for testing the association between sequence data with rare variants and a normally distributed or dichotomous outcome. Most methods for analyzing rare variants fall into two categories: burden tests and variance-based tests. Burden tests collapse rare variants within a region and use an indicator function, sum, or proportion as the genetic variable in an association test. , 12] Mo Mo12] MoWhile these methods are used in a wide variety of settings, most of the association methods for rare variants are for normally distributed or dichotomous phenotypes rather than count data like RNA-seq data. Here, we examine 2 extensions of existing methodology to analyze rare variants in RNA-seq data: 1) collapsing the rare variants in the region and using either negative binomial regression or Poisson regression and 2) using the normalized read counts with the Sequence Kernel Association Test (SKAT), the burden test for SKAT (SKAT-Burden), or an optimal combination of these two tests (SKAT-O). We evaluated these 2 approaches via simulation studies under numerous scenarios. We then applied these approaches to the 1000 Genomes Project Consortium data to determine if the genes that show strong differentiation between closely related populations are acting on expression of nearby genes.J rare variants in a gene indexed by j = 1, …, J. Let xij = 0, 1, and 2 for 0, 1, and 2 copies of the disease allele, respectively, for subject i and SNP j. Let ti be the total number of reads mapped to this gene for subject i and yi be the normalized read count. Let Ci be a vector of k covariates for subject i, where i = 1,.., n.Consider a collection of ti by a Poisson distribution or a negative binomial distribution, depending on whether there is significant over dispersion for the Poisson distribution. αxj comesj, wj, is close to zero, then variant j makes only a small contribution to variance-component score statistic Q. It is recommended to set the weight for variant j from a beta distribution such that j is the sample minor-allele frequency for variant j in the data. The prespecified parameters a1 and a2 can vary. For example, to allow rare variants to have a larger effect, one can set 0 < a1 ≤ 1 and a2 ≥ 1. The default for the SKAT software in R is to set a1 = 1 and a2 = 25 because this increases the weight of rare variants while still putting nonzero weights for uncommon variants with MAF 1%–5%.  associated with lactose toleranceFADS cluster [chromosome 11] that may be associated with dietary fatSLC24A5 [chromosome 15] associated with skin pigmentationHERC2 [chromosome 15] associated with eye colorThe study also found several potentially novel selection signals including:TRBV9 [chromosome 7]PRICKLE4 [chromosome 6]TRBV9 gene and we excluded this gene from our analyses. Using the total read counts for 87 subjects with African ancestry from Yoruba and 266 subjects with European ancestry from England, Scotland, Italy, and Utah residents with Northern and Western European ancestry, we applied the above approahces to determine if the rare variants in these genes are associated with the overall expression levels for these genes in these 2 populations.Given these results, we wanted to determine if rare variants within these 6 genes demonstrated association with expression of the respective gene. There was very sparse coverage of the LCT region on chromosome 2 were significantly associated with overall expression in the subjects with African Ancestry using the SKAT-Burden  and SKAT-O  approaches. This same region was marginally associated in the European Ancestry group using the SKAT-Burden  and SKAT-O  approaches. Based on the simulation studies, the SKAT methods  using the normalized read counts were the only methods that maintained the type 1 error rate in all scenarios and were thus the recommended approaches. These proposed methods were able to detect an association of the rare variants in the LCT region with overall expression. However, since both groups had a relatively small sample size (n = 87 for the subjects with African ancestry and n = 266 for the subjects with European ancestry), further study of this region is needed.As seen in PRICKLE4, FADS, SLC24A5, HERC2) were significantly associated with the overall expression using any method other than the Poisson regressions. Both of the Poisson regression approaches had an inflated type 1 error in Scenarios A.2 and B.2 in our simulation studies and were, therefore, not recommended. These analyses demonstrate the potential for substantially disparate conclusions depending on the method used and highlight the advantage of using the SKAT methods with normalized read counts to detect eQTLs of rare variant associations in RNA-seq data.None of the other regions  methods using the normalized read counts were the only methods that maintained the type 1 error rate in all scenarios for all sample sizes. Therefore, we recommend using SKAT with normalized read counts over the other approaches considered here. The data analysis further supported this recommendation. The 2 methods that found a significant association of rare variants in the For our simulation studies, we generated the rare variant data such that rare variants in the region acted in the same direction. If rare variants in the region were not directionally consistent, this could be an issue for the negative binomial regressions which collapsed rare variants in the region. However, this would not be an issue for the SKAT approaches, which further supports our recommendation.We considered 2 approaches: (1) collapsing the rare variants in the region and using negative binomial regression or Poisson regression and (2) using the normalized read counts with SKAT, SKAT-O, or SKAT-Burden. Given that SKAT is based on generalized linear mixed models, another approach would be to extend SKAT for negative binomial traits.S1 Fileμ = 50, 100, 500 and n = 15, 30, 50, 100, 500 subjects for scenarios A.1, A.2, B.1, B.2).The file contains the results for all 60 plots of the 540 simulation scenarios considered Click here for additional data file.


Assignments are made after consideration of the likelihoods of all possible first‐, second‐ and third‐degree relationships between the focal individuals, as well as the traditional alternative of being unrelated. This careful exploration of the local likelihood surface is implemented in a fast, heuristic hill‐climbing algorithm. Distinction between the various categories of second‐degree relatives is possible when likelihoods are calculated conditional on at least one parent of each focal individual. Performance was tested on simulated data sets with realistic genotyping error rate and missingness, based on three different large pedigrees (N = 1000–2000). This included a complex pedigree with overlapping generations, occasional close inbreeding and some unknown birth years. Parentage assignment was highly accurate down to about 100 independent SNPs (error rate <0.1%) and fast (<1 min) as most pairs can be excluded from being parent–offspring based on opposite homozygosity. For full pedigree reconstruction, 40% of parents were assumed nongenotyped. Reconstruction resulted in low error rates (<0.3%), high assignment rates (>99%) in limited computation time  when at least 200 independent SNPs were used. In three empirical data sets, relatedness estimated from the inferred pedigree was strongly correlated to genomic relatedness.Data on hundreds or thousands of single nucleotide polymorphisms (SNPs) provide detailed information about the relationships between individuals, but currently few tools can turn this information into a multigenerational pedigree. I present the  Pedigrees have many uses in a wide variety of fields, ranging from animal breeding and human genealogy to wildlife genetics and ethology. Parentage assignment remains essential for unbiased estimation of trait heritabilities, as even though pairwise relatedness coefficients can now be estimated more precisely directly from genomic data than from a pedigree 3/2 = 166 375 possible trio genotypes, and alternative tactics have been developed siblings sharing the same, nongenotyped parent. This is often performed using ony Wang , and canet al. et al. Most pedigree reconstruction methods can be grouped into three broad categories: exclusion methods, relatedness‐based methods and likelihood‐based methods, which are of increasing power, but have increasing computational cost as a trade‐off. The first simply excludes all candidate parents which do not share at least one allele with the focal individual at each marker locus, and has been used with both microsatellites .Methods in the second category estimate pairwise relatedness Thompson . In systet al. cervus  are considerably more powerful  for each individual in turn  assigns grandparents to sibships and singletons and (3b) identifies avuncular links between sibships , great tits (Parus major) and domestic pigs (Sus scrofa). I show that several hundred independent SNPs with high minor allele frequency are sufficient to obtain a high assignment rate (>99%) and low error rate (<0.1%).Here, I present an algorithm that compares likelihoods for seven different relationship alternatives, including their inbred derivatives, speeded up by steps to exclude unlikely relatives. It 1) assigns parents, (2) clusters sibling groups across multiple cohorts,  and should be provided together with sex and birth year information for the majority of genotyped individuals.The input format for sequoia is called, first a check for duplicate identities and genotypes is performed to avoid downstream problems. Next, several iterations of parentage assignment are performed, until the total likelihood  an individual cannot be its own ancestor; (ii) ancestors are born prior to their descendants, or either or both have an unknown birth year; and (iii) the two parents of an individual are of opposite sex, or either one is of unknown sex .et al. TOH = 3 + εL is used to avoid exclusion of true PO pairs, where L is the number of loci and ε the per‐locus genotyping error rate. Typically some pairs of non‐PO close relatives will be nonexcluded, particularly full‐sibling (FS) pairs  pairs  is used to again avoid exclusion of true relatives.A second filtering step for parentage assignment, and the only filtering step for the other stages, consists of calculating the log‐likelihood ratio LH0–LH6). If the focal relationship R (here PO) has a higher likelihood than the most likely alternative relationship (denoted by ∨  for brevity), by a user‐defined margin Tassign, an assignment is made  is the probability of observing genotype X at locus l in individual A, conditional only on its parents DA and SA in pedigree P. It is assumed a set of SNPs is used which are unlinked and in low linkage disequilibrium, such that a simple multiplication over all loci provides a good approximation of the total likelihood.The quantity that is maximized is the total likelihood P can be broken down into a genotyping error term Pε, a Mendelian inheritance term PM  and a pPε) is a function of A's actual genotype x and the genotyping error rate ε, which is assumed constant across loci. Details of the genotyping error model are given in Methods in Appendix PM) is the probability that individual A inherited actual genotype x from its parents DA and SA, conditional on their actual genotypes y and z. This probability can take values of 0, 1/4, 1/2 and 1. As SNP genotypes can only take three possible values , the likelihood components Pε and PM can be calculated once at initiation and stored in look‐up tables, for increased computational efficiency. In contrast, the parental genotype probabilities PP (the third term) are continuously updated. They give the probability that A's parents carry actual genotypes y and z and come in three different flavours, denoted by a superscripted prefix:The first term  takes the standard values when assuming Hardy–Weinberg equilibrium of ql2, 2ql(1 − ql) and (1 − ql)2, that is unknown parents are assumed a random draw from the population. When DA is a known genotyped individual, the probabilities for all possible actual genotypes y are calculated conditional on DA's observed genotype Y and its parents, if any. Using that P(A|B) = P(B|A)P(A)/P(B) (Bayes’ theorem) and dropping subscripts l for brevity,DDA and SDA are grandparents of A. When DA is not genotyped at a particular locus, the term Pε(DA = Y|DA = y) is omitted from Eqn gPP(DA = y) becomes dependent on the grand‐parental genotypes only. When both DDA and SDA are unknown, gPP(DA = y) reduces further to hPP(DA = y|ql). The probability dPP for dummy parents is defined in the section ‘Sibship likelihoods’  under any relationship configuration. Single‐locus likelihoods are illustrated in Fig. A and B, when neither individual has any parent yet assigned. In this case, second‐degree relatives  cannot be distinguished from each other. However, when one can condition on the genotype of a parent or dummy parent of each individual, such a distinction can be made. Details on these likelihood equations, and those for inbred relationships, are given in Appendix ΛHS/U∗>Tfilter, followed by calculation of LH0–LH6 for the pair. These pairs are clustered into sibships using likelihoods calculated over the pair and all putative siblings. Assignments are made when  >Tassign. Subsequently during each iteration, all sibships of the same type are considered for merging to minimize erroneous splitting of true sibships, and all individuals who lack a parent of type k are considered for addition to each sibship of type k to maximize assignment rate  in Eqn Ai, to avoid double counting. Most often, the joined likelihood over A and all directly connected sibships is calculated, as PP(Si = yi) will be a function of the presumed genotype of DA, and therefore, the PP(Si = yi)'s of different connected sibships are nonindependent  as the grandparent of sibship A, when DB is more likely to be the grandparent of A1,A2,…,AnA than related in any of the alternative ways listed in Table Lastly, in each iteration, grandparents are assigned, in a process similar to parentage assignment. This includes potential assignments of the dummy parent of one sibship  or not (0) given the age difference between the two individuals. After parentage assignment, the empirical age distribution of fathers and mothers and between maternal and paternal siblings is used as prior to assist subsequent sibship clustering  and their parents, totalling 1998 individuals.et al. masterbayes and colony , medium run length, weak sibship size priors of 1.0, and with sibship scaling.6.1 Wang , using ifranz  was calculated as the fraction of the total number of individuals (founders + nonfounders) with an incorrectly assigned parent. A sibship parent, say dummy father, was deemed correct if the majority (>50%) of inferred paternal siblings (PS) were true PS. For both erroneous merging and erroneous nonmerging, the error count equalled the size of the smaller of the two sibships.The assignment rate (AR) for the simulated data sets was calculated as the number of individuals with a correctly inferred parent, divided by PO/∨ showed a clearer divide between true PO pairs and non‐PO pairs than did ΛPO/U , in a fraction of the time. franz is somewhat slower than sequoia, but the difference is negligible compared to for example masterbayes  and error rates low (ER < 0.1%) when at least 100 SNPs are used  and low ER (<0.1%) when at least 200 SNPs were used with ony Fig. . Even atsequoia cannot differentiate between GG or FA, which would require that one parent is already assigned to each individual. AR for parentage assignment (e.g. franz) is necessarily limited by the number of PO pairs where both are genotyped, while the upper limit for colony is determined by the number of dummy individuals (=number of sibships), to which it does not assign parents.The combination of parentage assignment, sibship clustering and grandparent assignment resulted in reconstruction of 99% of parent–offspring links in Pedigree II when at least 20% of parental genotypes was treated as known Fig. . When sisequoia were low when at least 200 SNPs were used (0.1%–0.3%) and were undetectably low for colony  was more strongly correlated to rgrm than rped, FRANz  showed a similar pattern across data sets and pedigrees  and assignment rates high (>99%). As for any software, performance in real data sets will be somewhat lower, but results in three empirical data sets are favourable compared to existing pedigrees and parentage assignment only.sequoia and most other methods is that a high likelihood solution is found in a handful of iterations, rather than the tens of thousands of iterations typical of MCMC approaches. sequoia's sequential, heuristic method requires a conservative approach to assignments, which results in lower AR than colony under identical conditions. There is also some loss of accuracy, but this can be overcome using a few hundred extra SNPs. When less than approximately 200 independent, high frequency SNPs are available, due to a small genome size or for budgetary reasons, the methods initially developed for a dozen or so microsatellite markers still perform best. For limited marker numbers, Mendelian noise can be substantial, and as a result, the true configuration may not be amongst those with the highest partial likelihood, violating a core assumption underlying sequoia. The true pedigree will typically still have the highest global likelihood, which can be more easily found by MCMC or simulated annealing algorithms such as colony, than by a hill‐climbing algorithm such as sequoia.The main difference in approach between sequoia performs intermediately between opposite‐homozygosity‐based exclusion and franz  versus paternal HS and maternally related as HA or CC, for example  and in absence of parental genotypes, it is impossible to distinguish between maternal and paternal HS. Programs incorporating prior information on observation‐based parents are then preferred, such as colony  and includes a pedigree and life‐history file for Pedigree II, a user manual, and the Fortran source code. The red deer SNP data are available from github.com/JiscaH/Manuscripts‐Papers, the great tit data were retrieved from datadryad.org/resource/doi:10.5061/dryad.5t32v (UK part), and the pig data were from http://www.g3journal.org/content/suppl/2012/04/06/2.4.429.DC1.The Appendix S1 Supporting Methods and Results.Click here for additional data file.Appendix S2 Sequoia R vignette.Click here for additional data file.


Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools. Medicago sativa L) is the first most-important forage legume in the world, because of its high biomass yield (BY) and good nutritional quality. To meet the future demand of quantity and quality, the main objectives in alfalfa breeding programs are biomass related agronomic traits and nutrition value related quality traits. Though yield and quality of alfalfa have been improved by phenotypic selection, the genetic gain are relatively low compared to other crops, owing to many reasons, such as low heritability, complex genetic architecture, and high genotype-environment interaction , known as genetic merit of an individual which cannot be measured directly, is always the key issue in plant breeding programs. However, accurately estimated breeding value (EBV) is impossible to be achieved in complex traits by using phenotypic data alone. To improve the accuracy of prediction, incorporating information of genetic markers, known as marker-assisted selection (MAS), is an optional strategy. The superiority of MAS than phenotypic selection is determined by the percentage of the genetic variance accounted for by the QTLs associated with the markers . UnfortuDue to the decreased cost of high-throughput genotyping methods, huge amount of genomic information of many non-model plants has been produced. Utilization of genotypic information in plant breeding has become a highly prioritized research area in recent years. Since dense genetic markers covering whole genome are available in many species, a new method for estimating breeding value, namely the genomic selection (GS) or genomic prediction (GP), showed a great potential for enhancing the accuracy of GP of BV . It is aAs mentioned above, GP can significantly improve the accuracy of estimation of breeding value. Therefore, it attracts a great interest of plant breeders worldwide. Traits being targeted in plant breeding programs are either difficult or costly to be measured. Additionally, the targeted traits  in plant breeding are mostly quantitative traits, which are controlled by multiple genes and generally sensitive to environmental variables. Phenotypic selection, neglecting the underlying biological processes and the interactions between genes and environments, cannot make a significant genetic gain in a short time frame. Considering the genetic architecture of the quantitative traits, MAS is also not the best choice. GP, following its assumption, is thus an ideal tool to be used in the plant breeding programs. Many methods have been adopted for GP or GS. Bayesian methods and GBLUP, however, are those being frequently used. Bayesian methods exhibited more advances than GBLUP in terms of prediction accuracy following a simulation study . No mattThe alfalfa materials used in this study were consisted of 322 genotypes representing 75 tetraploid alfalfa accessions under the experimental designs as described in Table 1), including 15 quality and 10 agronomic traits, were measured for all genotypes. All the plants were harvested at early flowering stage and prepared to measure the 15 quality traits using a FOSS 5000 scanning monochromator . The 15 quality traits included three fiber-related traits, four digestibility-related traits, and eight nutrition component traits being measured following the procedures described in our previous studies  and Qubit® DNA Assay Kit in Qubit® 2.0 Flurometer , respectively. DNA was digested by MseI [New England Biolabs (NEB)] restriction enzyme. The reduced representation libraries were constructed for individual genotypes according to published GBS protocol . DNA degradation and contamination were monitored on 1% agarose gels. DNA purity and concentration were checked using the NanoPhotometerprotocol  and sequprotocol  was usedAfter SNP calling, NPUTE was used to impute the GBS data .Three regression methods with different prior assumptions of the distribution of marker effects were used to estimate SNP effects, namely the BayesA , BayesB iy is the EBV of one trait, μ is the overall mean, m is the number of markers, ijZ is the jth SNP genotype of plant i, αj is iistheresidualerrorwithanassumednormaldistributionN. SNP effects were estimated based on the training population using this equation. The GEBV for plant i in the validation population was predicted by summing up SNP effects over all loci. Predictive accuracy was measured as the correlation between the EBVs and GEBVs. Random sampling training and validation sets were repeated 10 times and the mean of correlations was calculated to measure the GP accuracy. All Bayes programs were run in BGLR package in R environment.1 The number of Burn-in was 10000, thin was 20, and the total number of iteration was 30,000. Other priors of parameters were assigned following where, Supplementary Figure S1.Since our previous works have described the phenotypic variations of some fiber-related traits  and crudTable 1. The predictabilities drawn from the ten-fold cross validation varied across different traits. SN had the lowest predictability (0.0021) but FH had the highest predictability (0.6485). Some quality traits such as crude protein (CP), RUP, and ADL exhibited relatively low prediction accuracies (< 0.1) while the remaining quality traits such as fat, K, and Ca showed low to moderate predictabilities (0.11-0.34). Agronomic traits hold similar patterns except three traits that had relatively high predictabilities with FH to be the highest (0.65), followed by FD (0.52), and PR (0.51). Other traits, such as LS, PB, and BY displayed moderate predictabilities (0.24-0.32). Similar to BayesA method, BayesB and BayesCπ methods did not reveal any significant difference from each other in terms of the predictabilities of all quality and agronomic traits . The predictabilities among the three Bayesian methods are shown in Figure 1. From the bar-plotting, only minor differences were observed among the three methods for all 25 traits, it was therefore hard to determine which method was the best.Sequencing of the GBS libraries yielded approximately 184.59 million raw reads and 178.2 million clean reads in all 322 alfalfa genotypes. After imputation, 44,757 high quality SNPs were obtained and used for GP. The results of prediction accuracies of three Bayesian methods are shown in Since GS was proposed by Figure 1 shows that these three Bayesian methods demonstrated very similar prediction accuracies across all 25 traits, irrespective of their different assumptions. BayesA, BayesB, and BayesCπ identified six, five, and four quality traits as well as three, four, and three agronomic traits having the highest accuracies, respectively.Several methods, such as random regression BLUP, Bayesian methods and GBLUP, were employed to estimate GP and GS. Some simulation studies on different species suggested Bayesian methods to be superior than GBLUP in terms of the prediction accuracy . CompareBesides the methods of GP discussed above, there are other factors affecting the prediction accuracies. One of them is the population composition and structure. Therefore, EBVs were directly used as the response variable to GP rather than phenotypes in the study. Since EBVs were corrected for non-genetic effects, it can be readily captured by SNPs using the Bayes methods. Methods of imputation for SNP genotypes are also important .Compared to previous studies, there were some differences in the accuracies of prediction for both agronomic and quality traits. For example, All the 15 quality traits had relatively low prediction accuracies due probably to their low heritabilities  determinThe present study was an attempt to predict alfalfa GEBVs of 25 important traits associated with BY and forage quality using three Bayesian statistical methods. Overall, they all exhibited similar predictabilities. Some traits possessed relatively high prediction accuracies . Therefore, it is feasible to apply GS on these traits in alfalfa breeding programs. While GS/GP may be poorly effective for other traits such as ADL, crude protein, and RUP with low prediction accuracies.ZW designed the experiments. HZ, XW, and GL phenotyped the traits. CJ and FZ analyzed the data and drafted the manuscript. ZW and JH revised the manuscript. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We sought to investigate whether genetic effects on response to TNF inhibitors (TNFi) in rheumatoid arthritis (RA) could be localised by considering known genetic susceptibility loci for relevant traits and to evaluate the usefulness of these genetic loci for stratifying drug response.We studied the relation of TNFi response, quantified by change in swollen joint counts (CD40 locus (p=0.0004) and an inverse association between We detected a statistically significant association between CD40 pathways could be relevant for targeting drug therapy.The association with the CD39 trait is difficult to interpret because patients with RA are often prescribed TNFi after failing to respond to methotrexate. The CD39 and  To date, no strong associations of individual genetic loci with response to tumour necrosis factor inhibitors (TNFi) in rheumatoid arthritis (RA) have been identified, despite recent large efforts based on conventional genome-wide association studies and a crowdsourcing initiative.We introduced a new methodological approach for localising genetic effects by using genotypic risk scores based on known genetic loci for related traits and likely biomarkers.We identified two genetic loci strongly associated with TNFi response in RA and demonstrated that the genetic determinants of TNFi response are different to the known susceptibility loci for RA.CD40 and CD39 and their corresponding pathways could be relevant for targeting drug therapy in RA.Measurements of expression of Our new methodological approach could be useful for localising genetic effects in traits for which assembling large sample sizes is not feasible, such as drug response.Biologic therapies have transformed the outlook for rheumatoid arthritis (RA). However, for the most commonly used class of agent, tumour necrosis factor inhibitors (TNFi), there is substantial variability in response to treatment among patients with RA.Genome-wide association studies (GWAS) of response to TNFi have shown that common single nucleotide polymorphisms (SNPs) explain an estimated 40% and 50% of the variance of change in swollen joint counts (SJC) and erythrocyte sedimentation rate (ESR), respectively; however, no strong associations with individual SNPs have been detected.It has been suggested that improving prediction of complex clinical outcomes may be possible by incorporating information about the genetics of relevant traits in the prediction model.In the current study, we incorporated available genetic information on susceptibility to RA,We used a sample of 2938 individuals of European ancestry for whom complete clinical and GWAS data were available. This sample comprised individuals from a pre-existing international collaboration formed to study the genetics of response to TNFi agentset alet al.In RA, response to treatment is quantified by change in the Disease Activity Score (DAS), which depends on four measurements: ESR, SJC, tender joint count (TJC) and patient global health assessment rated on a visual analogue scale (GHVAS). Previous work has shown that only the SJC and ESR measurements have evidence of non-zero heritability10.1136/annrheumdis-2018-214877.supp1Supplementary datahttps://pm2.phs.ed.ac.uk/genoscores/) to compute genotypic risk scores for the intermediate traits. GENOSCORES is a database of published SNP to trait associations from a large number of well-powered GWAS, including GWAS of disease traits, biomarkers, gene expression and methylation. The database is accompanied by a software package implemented in R that can be used to compute genotypic risk scores and run downstream statistical analyses in cohorts with SNP data.We used the GENOSCORES platform , cohort , genotyping array and the 10 first principal components computed from the genotypic data of the full data set. We used individuals with complete measurements in the statistical analyses of each TNFi response outcome see .We used two measures to quantify improvement in prediction: the difference in log-likelihood between a model with clinical covariates and genotypic scores and a model with clinical covariates only ); and the per cent of residual variance explained by the genotypic scores. Both measures were computed on the testing data from 10-fold cross-validation on the full data set.For readers who prefer a frequentist interpretation, the asymptotic equivalence of model choice by cross-validation and Akaike’s information criterion (AIC)For models with a test log-likelihood difference of at least two nats, we further examined the univariate associations between genotypic scores and the TNFi response outcomes. We used the full data set to test the univariate association and included the same clinical covariates as in the multivariate prediction, in addition to a score.For genotypic scores significantly associated with TNFi response at the Bonferroni-corrected p value threshold, we compared the estimated effects among groups receiving different TNFi agents. We considered etanercept, adalimumab and infliximab, where a large enough sample size was available. Additionally, we tested if the associations held when we adjusted for additional covariates: anticitrullinated protein antibodies (ACPA) status and smoking status. These covariates have been reported to influence TNFi response but were only available for a third of the samples, and thus were not included in the full models.A diagram of the statistical analysis pipeline is given in In the following sections, we focus on the results for the primary TNFi response outcomes. The results for secondary outcomes are discussed in the online supplementary results see .Prediction of response to TNFi as quantified by on model . The tesCD40 locus had the highest explanatory power for both response phenotypes  and a cis-acting mQTL score for methylation of CD40 in whole blood (correlation=−0.70). The strongest association between response to TNFi and genotypic scores at the CD40 locus was with the score for RA  had suggestive associations with ENTPD1 locus passed the p value threshold corrected for the number of immune cell trait scores tested (470) and two response phenotypes. This score was correlated with the cis-acting eQTL score for ENTPD1 (correlation=0.65). Higher score for ‘CD39 on CD4 T cells’ at the ENTPD1 locus was associated with worse TNFi response as quantified by ENTPD1 locus  responses.Higher RA risk at the CD40 expression in whole blood.CD40 expression is associated with elevated TNF-CD40 and CD40L transcripts are increased in the disease tissue in both early and established disease,CD40 risk allele respond better to TNFi therapies.The risk allele associated with RA is associated with elevated Overall, if there are genetic loci that predict TNFi response, these are mostly different to the known RA risk loci. However, we note that patients who receive TNFi therapy are likely to have more severe disease and to have failed on other treatments. It is therefore possible that our study sample has been selected with respect to genetic load for RA, thus limiting the heterogeneity in genetic RA risk profiles compared with a sample of newly diagnosed cases.ENTPD1 gene which encodes CD39. In stratified analyses, the inverse association with response was stronger in the groups receiving TNFi concomitantly with MTX or another non-biologic DMARD compared with the group receiving TNFi monotherapy and was stronger in the group receiving infliximab compared with the groups receiving adalimumab or etanercept. The CIs of the estimated effects among all groups overlapped. This effect on response to TNFi agents is in the opposite direction to the association reported between low expression of CD39 on regulatory T cells and resistance to MTX in RA.The genotypic score for the expression of the ectonucleotidase CD39 on CD4 T cells was inversely associated with TNFi response. The SNPs contributing to this score are in the ENTPD1 expression or genotype may be useful in the choice of MTX, TNFi agent or concomitant treatment as first-line therapy for patients who need a DMARD.Interpreting the association between drug response and the CD39 trait is difficult both epidemiologically and mechanistically. The reasons for this being that in the UK and most European countries TNFi are usually prescribed only after patients have had a poor response to MTX and, unless of intolerance, always in combination with MTX. Therefore, these patients are likely to represent a selected group; this, together with the almost universal use of combination MTX/TNFi therapy, hinders progress in dissecting the potential mechanisms responsible for the divergence. Nonetheless, as RA is a highly heterogeneous disease, it is plausible to speculate that different cellular and molecular networks may be involved in driving diverse immune/inflammatory responses in different patients to different drugs. For example, as mentioned above, the poor response to MTX has been associated with low CD39 expression by regulatory T cells, while increased CD39 expression has been reported to be important in the expansion of Th17 cells driven by IL-6 and TGF-β via Stat3 and Gfi-1 transcription factors.Using previously reported associations between transcripts and TNFi response to select relevant genes, we have shown evidence that eQTL scores for these genes contain information that predicts TNFi response, even though the proportion of variance explained was low and no single genes associated with response could be identified.Improved genomic prediction of treatment response requires measuring response more precisely to capture the molecular in addition to the clinical phenotype. The detected associations between genotypic scores and TNFi response were not the same for the two measures of response—change in SJC and change in ESR—suggesting that the two measures reflect different aspects of disease activity affected by TNFi. To derive refined measures of drug response, large data sets with multiple inflammatory biomarkers, joint imaging and clinical variables before and after treatment are needed.10.1136/annrheumdis-2018-214877.supp2Supplementary data10.1136/annrheumdis-2018-214877.supp3Supplementary data10.1136/annrheumdis-2018-214877.supp4Supplementary data10.1136/annrheumdis-2018-214877.supp5Supplementary data10.1136/annrheumdis-2018-214877.supp6Supplementary data10.1136/annrheumdis-2018-214877.supp7Supplementary data


Eucalyptus nitens breeding population focused on improvement for solid wood production. A high-density SNP chip (EUChip60K) was used to genotype 691 individuals in the breeding population, which represented two seed orchards with different selection histories. Phenotypic records for growth and form traits at age six, and for wood quality traits at age seven were available to build genomic prediction models using GBLUP, which were compared to the traditional pedigree-based alternative using BLUP. GBLUP demonstrated that breeding value accuracy would be improved and substantial increases in genetic gains towards solid wood production would be achieved. Cross-validation within and across two different seed orchards indicated that genomic predictions would likely benefit in terms of higher predictive accuracy from increasing the size of the training data sets through higher relatedness and better utilization of LDGenomic selection is expected to enhance the genetic improvement of forest tree species by providing more accurate estimates of breeding values through marker-based relationship matrices compared with pedigree-based methodologies. When adequately robust genomic prediction models are available, an additional increase in genetic gains can be made possible with the shortening of the breeding cycle through elimination of the progeny testing phase and early selection of parental candidates. The potential of genomic selection was investigated in an advanced  Eucalyptus nitens (Deane & Maiden), is the most important commercial eucalypt species in New Zealand, with an advanced breeding programme moving towards its fourth generation  breeding population, therefore, some inaccuracy in breeding values is expected due to unknown paternal contribution and plausible pedigree errors. Precisely recorded pedigrees and known ancestors of the breeding population individuals are prerequisites for accurate genetic evaluation and consequently, efficient breeding programme management and boosted genetic gain in forest tree species.Shining gum, Genomic selection was proposed as a tool to predict individual breeding values on the basis of information from high-density genetic marker panels through multiple regression models  was estimated in terms of status number as following Ɵ is group co-ancestry of individuals  individuals per family used for genomic prediction analysis for which genomic information and phenotypic trait records were available. The Waiouru seed orchard was represented by 431 genotyped individuals with effective population size of 133.2, while the Tinkers seed orchard was represented by 236 genotyped individuals with effective population size of 64.7. The effective population size . The marker data were filtered for genTrain score > 0.5, GenCall > 0.15, minor allele frequency > 0.01, SNP call rate > 0.6 and pairwise LD in terms of a composite estimate (r2 < 0.9), with 12,236 SNPs selected to train genomic prediction models. The missing data were imputed through expectation-maximisation algorithm implemented in “rrBLUP” package   following A is the average numerator relationship matrix  is set nested within replication following e following X and Z1, Z2 and Z3 are incidence matrices assigning fixed and random effects to measurements in vector y  from breeding cycles and selections based only on genetic markers. It was estimated as follows:The full efficiency of genomic selection was investigated by comparing genetic gains per generation using pedigree-based estimated breeding values (BLUP) with genetic gains using genomic marker-based estimated breeding values (GBLUP). Both of these individual based estimated genetic gains included the availability of phenotypic records for all traits. The mean value of BLUP and GBLUP breeding values of the 20% of selected individuals was the measurement for the estimated genetic gain.Pedigree-based analysis showed low to moderate within seed orchard heritabilities in both seed orchards Table . PedigreAcross seed orchard heritability estimates were generally higher using marker-based breeding values than pedigree-based with only a few exceptions Table . RecondiMarker-based breeding values had consistently higher accuracies than pedigree-based breeding values. Generally, accuracies of breeding values were higher for both seed orchards when using marker-based rather than pedigree-based estimated breeding values. However, there were some inconsistencies, with the Tinkers seed orchard showing lower genomic breeding value accuracies for radial wood shrinkage, DBH and height than for the same traits using pedigree-based models, reflecting the pattern in heritability.The cross-validation analysis showed no, or very low, predictive accuracy for breeding values for one seed orchard when based on a model trained in an alternative seed orchard, using pedigree-based or marker-based models Table . Within Generally estimated absolute genetic gains per generation were as expected higher when using GBLUP breeding values rather than BLUP breeding values Table . The estThe benefits of using information from genomic markers in the genetic evaluation were apparent in the current study, where both within seed orchard and across seed orchard estimations supported the use of GBLUP over BLUP predictions. The reduced number of SNPs after filtering still provided sufficient genomic information to perform efficient marker-based predictions, which improved breeding value accuracies when compared with pedigree-based evaluations. A gain in accuracy of breeding values would likely be even more substantial when increasing the size of training population by additional genotyping. Grattapaglia and Resende  estimateForest tree breeding programmes are generally in the very early stages compared to animal and crop breeding programmes with faster generation turnover  plays a significant role in forestry tree breeding (Li et al. Estimated absolute genetic gains based on GBLUP breeding values per generation indicate that genomic selection would significantly improve the efficiency of selection for solid wood properties. The major benefit of genomic selection in accelerating the rate of genetic improvement would be derived from the ability to shorten the generation interval through the very early selection at the seedling stage (Resende et al. E. nitens breeding population. A question remains as to how well prediction models perform after several breeding cycles (Resende et al. E. nitens breeding population is required to find the best possible methodology, including additional data infusions of wider breeding population that will reflect better the future selection population than in the current study. We expect further data infusions to result in considerably higher predictive accuracy of genomic breeding values compared to pedigree-based methods. Increasing the training population size as well as applying different statistical methods that can account efficiently for the accuracy due to LD may give further confidence in implementing GEBVs in the breeding programme (Habier et al. The potential to make faster selections by skipping progeny testing should be pursued in this E. nitens was possible by implementing genomic marker-based prediction compared to pedigree-based prediction. The greatest improvement in genetic parameters was obtained for tangential air-dry wood shrinkage and growth strain, which are the key traits in selection for solid wood production in eucalypts. Wood shrinkage traits had moderate heritabilities, which mainly increased further with genomic prediction.This study showed that a significant improvement in breeding value accuracy and genetic gains for selection of wood properties in Results from cross-validation analysis implied that further infusions of additional seed-orchard material into the training data would be useful to increase the efficiency of genomics in the selection, regarding breeding value accuracy and predictive accuracy. Further analysis, including more progeny trial sites to investigate the transferability of these models across generations and environments is recommended.Data available from Dryad: 10.5061/dryad.pf58510.