Phaseolus vulgaris L.), for geographic regions where production is threatened by drought and high temperature stress, is challenging because of the complex genetic nature of these traits. Large scale SNP data sets for the two major gene pools of bean, Andean and Middle American, were developed by mapping multiple pools of genotype-by-sequencing reads and identifying over 200k SNPs for each gene pool against the most recent assembly of the P. vulgaris genome sequence. Moderately sized Bean Abiotic Stress Evaluation (BASE) panels, consisting of genotypes appropriate for production in Central America and Africa, were assembled. Phylogenetic analyses demonstrated the BASE populations represented broad genetic diversity for the appropriate races within the two gene pools. Joint mixed linear model genome-wide association studies with data from multiple locations discovered genetic factors associated with four production traits in both heat and drought stress environments using the BASE panels. Pleiotropic genetic factors were discovered using a multi-trait mixed model analysis. SNPs within or near candidate genes associated with hormone signaling, epigenetic regulation, and ROS detoxification under stress conditions were identified and can be used as genetic markers in dry bean breeding programs.The genetic improvement of economically important production traits of dry bean ( Phaseolus vulgaris L.) is the most important and affordable food legume for over 80 million poor people in regions of Latin America, the Caribbean, and Eastern and Southern Africa. Major consumers of common bean in these countries often live on less than two U.S. dollars per day, where beans are grown primarily by smallholder farmers on less than two hectares (http://phaseolusgenes.bioinformatics.ucdavis.edu/) are enabling the discovery of genetic factors associated with the abiotic stress response. The first dense genotyping tool was the 6k Illumina Infinium SNP assay  was initiated to understand the genetics and physiological mechanisms of the response of dry beans under abiotic stress environments. One component of the project was to develop appropriate sized populations that can be managed by research teams with limited resources yet large enough to discover genetic factors of moderate to large effects. The CRIB project designed new MA and Andean germplasm panels that are specifically adapted to the climate challenged regions of Central America and Africa. Because of resource constraints for field research in these target regions, the panels were designed to be modest in size (n∼120 lines). Here we report on the development of these moderate-sized panels and the results obtained by combining SNP genotyping data of these panels with those of the MDP and ADP to generate large SNP marker collections for each gene pool. This report also describes the utility of those SNP collections and the new association panels to map genetic factors controlling important production related traits under heat and drought conditions. The utility of multi-trait mixed model (MTMM) GWAS analysis (Wild Middle American (MA) and Andean common bean gene pools evolved from an ancestral population ∼110,000 years ago (Phaseolus acutifolius) genotypes form the BASE germplasm collection were evaluated in three separate panels (Table S1). The BASE_120 panel consists of 93 genotypes from the MA gene pool, 22 genotypes from the Andean gene pool, and four tepary bean (Phaseolus acutifolius) genotypes. The BASE_Meso panel consists of 119 genotypes primarily from Race Mesoamerica within the Middle American gene pool. The BASE_Andean panel contained 124 genotypes. The genotypes forming these panels were obtained from breeding programs at CIAT, Colorado State University, USA; Zamorano University, Honduras; USDA-ARS, Prosser, Washington; USDA-ARS, Puerto Rico; and the University of Puerto Rico. Whereas the BASE_120 and BASE_Meso consist primarily of cultivars and advanced breeding lines, the BASE-Andean contains a large portion of landraces from East Africa.Several common bean breeding programs that are partners in this project have been developing cultivars and lines for tolerance to a variety of abiotic stresses. A flexible system for the evaluation of these lines under different abiotic environments is designated here as the Bean Abiotic Stress Evaluation (BASE) approach. Overall, a total of 155 genotypes from the MA gene pool, 147 Andean genotypes, and 5 tepary bean . The GATK Unified Genotyper v3.3 (The libraries were sequenced (read length = 230 nt) at the HudsonAlpha Institute for Biotechnology using Illumina HiSeq 2500 Sequencing System. Sequencing barcodes were removed and low-quality sequences were trimmed. Only processed reads with a quality score ≥ 20 and a minimum trimmed length of 180bp were used for mapping. BWA-MEM Li 2013), and Samtools  phylogenic tree using the SNPhylo pipeline (ng MEGA7 .-1), days to flowering (DTF) recorded when 50% of the plants had at least one open blossom, Macrophomina phaseolina disease severity scored from 1 to 9  and Nacaome, Honduras (HN). For Juana Diaz, the panels were grown in separate drought and heat experiments, using a lattice design with three replications in 2014, an RCBD design with three replications in 2015, and an RCBD design with six replications under drought and four replications under heat in 2016. All plots were three m in length and row spacing was 0.76 m. The data from Nacaome, HN used a randomized complete block design with three replications of the BASE populations conducted under heat during the dry seasons of 2015 and 2016. The experimental lines were planted on raised 1.5 m wide beds with two rows spaced 0.6 m apart. Row length was 2.5 m. Phenotypic data collected included seed yield . This transformation generates individual phenotypic data that is relative to the overall performance of the population at a specific location under a specific stress condition. In this way, we can pool the Z data across locations or stresses to discover common factors affecting the trait. The association between each quantitative trait and the genome-wide SNPs was analyzed with the GAPIT R package  as descrCandidate genes were selected within a ±50 kb interval of the peak SNP within a GWAS peak region. The predicted functional effect of each SNP was obtained from a SNPeff database developed for all SNPs using snpEFF.jar with “build-gff3” . Individual MA and Andean haplotype maps (HapMap) were developed after final SNP filtering and imputation. The MA HapMap contained 205,293SNPs, and the Andean HapMap consisted of 260,670 SNPs. The range of SNPs per Mb is consistent across both and heterochromatic of the and Andean SNP data sets (Table S3). For the MA SNP collection, there are 1.79x SNPs in the heterochromatic region compare to the euchromatic region, while that ratio for the Andean SNP collection is 1.51x. The un-imputed HapMap data for each gene pool can be found in S4 and S5 text files. Given the large number of genotypes in each of the two HapMaps, researchers can now design experiments to capture phenotypic data from all or a subset of the genotypes in the HapMap populations and then perform GWAS analyses with a very large SNP dataset to discover important genetic factors controlling traits of interest. The genotypes used to develop the HapMaps represent many of genotypes used for production purposes over the last 50 years in the USA and elsewhere. These HapMaps are a major output from the USAID CRIB project and will be an important genetic resource well beyond the end of the project.To maximize the number of SNPs for the haplotype maps, sequencing reads from multiple GBS libraries consisting of individuals with either MA or Andean parentage were pooled. These HapMaps were based on 381,092,199 GBS reads across 469 MA genotypes and 280,085,901 GBS reads across 325 Andean genotypes. These reads averaged 201bp and were mapped against version 2.1 of the To develop a full characterization of the genotypes used to generate the SNP datasets, an initial ML tree with 807 MA and Andean common bean genotypes along with a few tepary bean genotypes, was constructed with 5,637 common SNPs with LD < 0.1 . Two disOut of the 102,878 SNPs shared by the two gene pools, a reduced set of 1,882 SNPs with pairwise LD values less than 0.1 were chosen for a Bayesian structure analysis with genotypes used for the BASE populations. The optimum number of subpopulations was k = 2  and corr), including G13654, G2377, G23829, SAB_6292, SEQ_11, 754_3 and 379_PI_203934, were grouped with BASE_Meso genotypes despite being selected as members of the BASE_Andean panel. Tepary 22 fell in between the two clusters.The 1,882 SNPs were also used to develop a bifurcated ML phylogenic tree that demonstrated the two populations were clustered into two separate clades . The BASFigure 2BThe populations developed for this project were deliberately of a smaller size since not all project partners had the necessary resources to manage replicated field trials for large populations. Theory suggests that larger population sizes can uncover either large or small effect size genetic factors while smaller size populations tend to discover only effects of larger size . With thP-value cutoff of -log10(P) = 4.1.Populations such as those used here that are small and pre-selected for abiotic stress tolerance will also exhibit high LD. Therefore it is important to determine the effective number of genomic regions in that population and using that number when performing a conservative cut-off value test such as Bonferroni. Here we applied the simpleM algorithm . Often the response of two traits, or a single trait scored in two environments are correlated, and the goal of discovering genetic effects associated with these two situations is a goal of quantitative genetics. Recently, multi-trait mixed models (MTMM) statistical methods have been developed to uncover common genetic effects that act in a pleiotropic manner on two correlated traits . The peak SNP for yield  is located on the distal end of chromosome Pv03 and explains 14% of the variation in yield (Table S2). This peak SNP is located in gene model Phvul.003G187400. Orthologs of this gene model are associated with plants response to heat stress . Three other SNPs  defined other individual loci, and each accounted for more than 7% of the variation, and along with the peak SNP, these four SNPs collectively accounted for 20.2% of the variation in yield under heat stress (Table S2).Yield is the primary target for genetic improvement, and an important genetic goal is to understand the response of yield to a specific stress across locations. As mentioned above, both Nacome, HN and Juana Diaz, PR are high heat stress environments. The effect of heat stress on yield and DTF was assessed on the BASE_Meso population genotypes evaluated in PR in 2015 and 2016, and HN in 2016 (Table S2). Since the three locations were considered different environments with potentially different heat stress conditions, the phenotypic data were transformed using the Z transformation, and the data were combined into a single MLM GWAS analysis . The peaP = 9.02E-06) and accounted for 9.9% of the variation. At the proximal end of this interval, gene model Phvul.003G181900 is located. The highest level of expression for this gene was noted in flower buds relative to other developmental and anatomical tissues . Three SNPs are located in this gene model, and these SNPs are in high LD relative to the peak SNP located 36kb distal to Phvul.003G181900. DTF is often a major factor in yield performance. That trend was observed here with a Pearson correlation of r=-0.35 between the two traits. To consider that correlation in terms of the strong DTF genetic effect discovered in this analysis, the allelic effects of the major SNP for DTF  on yield was evaluated. For DTF at the same location and years, the major QTL peak from the joint MLM analysis was located in the Pv03:40.46-40.50 Mb interval . The peaM. phaseolina infection data were collected on the BASE_120 population grown in PR in 2014 under heat stress and in 2015 under drought stress. The data were standardized using the Z transformation. The individual GWAS results for the two years were consistent, and the same major locus was discovered at Pv04/4.64-4.84 Mb . Additional SNPs were located at Pv04:25,282,114 bp (P = 5.04E-5) and Pv10: 32,029,428 bp (P = 5.04E-5) in the combined analysis. Collectively, these three SNPs accounted for 17.6% of the observed variation. The A allele at the peak SNP was associated with lower disease incidence in the two trials. This SNP peak is located in one of the two major clusters of malectin/receptor-like kinase genes in the common bean genome.Field -4.84 Mb , 4B. The) was located at Pv09:17,981,113 (P = 1.08E-6) and accounted for 12.7% of the phenotypic variation (Table S2). This peak QTL region is located in a cluster of chitinase genes. For drought, the strongest SNP peak was located in the heterochromatic region between Pv08 18.4-21.5 Mb  and accounted for 7.0% of the variation. This interval was also detected in the combined analysis .SPAD readings are a general indicator of greenness of the plant. Given proper flowering conditions, this trait can be an indicator of yield potential. A MLM GWAS analysis was performed in separate heat and drought environments in PR in 2016 using the BASE_Meso population. Again, because the trials were under two stress conditions, Z transform data were evaluated. This trait appeared to be under different controls under the two conditions. The major SNP peak under heat  was loca-21.5 Mb  and accoanalysis .r = 0.96), while the environmental correlations were not  > 5.0 were only observed for DTM. For the full MTMM model, eight genomic regions passed the –log10(P) > 5.0 threshold. Only one region, Pv11:47.1 Mb, was found to have a common effect that exceed the Bonferroni threshold  is ideal for this purpose because phenotypic values are scaled relative to the variation at the location. By pooling standard score data across locations, a full data set is utilized and a more accurate measure of the effect of specific genetic physical positions can be assessed. The usefulness of this approach was demonstrated when we compared standard DTF data pooled across locations and Macrophomina data pooled across stresses. For both traits, the pooled data identified the same significant peak SNP regions that were observed in the individual analyses with the untransformed data. MTMM methods are another way of maximizing the data that is collected  effects or SNPs that condition a common response in both locations. This begins with a determination of the genetic correlation of the response in the two locations. For DTF, this correlation was high (r = 0.96) and very significant , and withttps://phytozome.jgi.doe.gov/). The peak QTL region for DTF in the joint MLM analysis under heat stress in HN and PR is located on Pv03 at 40.48-40.50 Mb. At this position, three SNPs are located in Phvul.003G181900, an ortholog of the Arabidopsis BIM1 gene. In Arabidopsis, BIM1 functions in the brassinosteroid pathway to regulate flowering through its interaction with SPL8 to promote anthesis  from Phvul.003G239000 at Pv03:47.36 Mb. This gene is the bean homolog of Arabidopsis HOS15, a gene associated with histone deacetylation and epigenetic control of flowering . DUF538 proteins are putative chlorophyll hydrolyzing enzymes that function in the ROS detoxification system when the plant is exposed to heat and drought stress , and ROS detoxification enhances heat and drought stress tolerance . A previous study on switchgrass showed that a DUF538 domain protein was significantly up-regulated in leaves under high heat conditions while expression was very low under normal conditions (Li olerance . BecauseLeaf senescence and the associated loss of greening is a result of multiple stresses on the plant including excessive heat (M. phaseolina in the MLM analysis under heat and drought stress are located at positions Pv04:4,639,929 bp and Pv04: 4,639,994. These SNPs are located within a cluster of seven Malectin/receptor-like protein kinase genes. Another large cluster of Malectin/receptor-like protein kinase genes is located on Pv08. This Pv08 cluster contains multiple paralogs of the bean COK-4 gene, and one of the bean paralogs was recently shown to rescue Arabidopsis mutant FER lines susceptible to Pseudomonas syringae (M. phaseolina resistance.The two peak SNPs for response to In general, these GWAS results demonstrate that significant factors with relative high effects can be discovered using moderate size populations along with high-density SNP data sets using single and multi-trait analyses. These analyses will discover polymorphisms within genes with strong candidate credentials, and these SNP polymorphisms can be used as selection tools for traits important for high crop productivity. This makes it now possible for groups of bean researchers with modest resources to use the panels and SNP data sets developed here to search for genetic factors and polymorphisms that would be useful for improvement in their breeding programs.


Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another , then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure , and do not arise due to SNPs influencing the two traits through independent pathways . The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR. Of fundamental importance to medical and social sciences is being able to elucidate how one phenotype (the exposure) causally relates to another (the outcome). Mendelian randomization (MR) is a method that strengthens causal inference by using natural genetic variation to mimic a randomized controlled trial (RCT) ,2 . A data frame of SNP-exposure and SNP-outcome association results is termed a mary set . These data can be obtained simply from published summary data from genome-wide association studies, which are often freely available and non-disclosive about study participants. Typically, the instruments for an exposure are readily available through publications, the GWAS catalog , or othe MR analyses that use only summary data have been called summary MR (SMR)  and two-Individual level data—here the SNPs, exposure phenotype and outcome phenotype are all measured in the same sample. Ideally the SNPs that are to be used as instruments have been identified from an external source. Individual level data are valuable because it can be used to perform some sensitivity analyses that cannot be done with summary data, e.g. the use of interactions  characteristics of an individual . While iGiven that the majority of measurable phenotypes have a heritable component , pleiotrFrom a statistical perspective, MR returns a ‘positive result’ if a SNP known to influence the hypothesized exposure also influences the hypothesized outcome. This is the precise definition of pleiotropy. In the interests of reliable causal inference, what is of crucial importance is divining the mode of pleiotropic action: does the SNP influence the outcome because the exposure influences the outcome? This is the mechanism assumed in MR and will be referred to as vertical pleiotropy in this review, but it has also been termed mediated pleiotropy , type IIThere are two alternative mechanisms by which a SNP could associate with two phenotypes. First, a SNP could influence the outcome through a pathway other than the exposure. In this review, we will refer to such an effect as horizontal pleiotropy, though it has also been called biological pleiotropy , Type I Within the context of a single MR analysis (i.e. one exposure on one outcome) a single genetic variant could simultaneously exhibit different modes of pleiotropy .Suppose we have a single genetic instrument for the exposure. This is a common scenario especially for ‘omic’ variables, such as gene expression , DNA mets-effect . An estis-effect   to yield a more precise estimate. The weights used in the standard inverse variance weighted (IVW) meta-analysis (first-order weights), make two assumptions. Firstly, that the SNP-exposure and SNP-outcome association estimates are uncorrelated (so that covariance terms can be ignored) and secondly that the SNP-exposure association is measured with infinite precision [the NO Measurement Error in the exposure (NOME) assumption]. In practice the NOME assumption is always violated because the SNP-exposure association standard errors are always non-zero, but when the NOME assumption is strongly violated , the IVW estimate will suffer from regression dilution bias towards the null. The magnitude of this dilution is inversely proportional to F. First-order weights are also traditionally used by Cochran’s Q statistic to test for the presence of heterogeneity, which is used to infer the presence of horizontal pleiotropy in MR. In this case, strong NOME violation leads to Cohran’s Q detecting heterogeneity too often when in fact no pleiotropy is present   will be the same across SNPs, making their individual causal ratio estimates homogeneous. The more SNPs that satisfy this expectation, the less likely it is that the SNP-outcome associations are arising simply because of horizontal pleiotropy  . It is iInvariably we know we can test whether the instrumenting SNPs associate with the outcome, but inferring why that association is present is difficult. Much of the recent method development in MR, which we will now go on to describe, has focused on modelling the Wald ratios from multiple instrumenting SNPs in an attempt to separate the vertical pleiotropic pathway  from any other influences.Because the IVW estimate is essentially a weighted average of the Wald ratios obtained from each SNP, if any of the SNPs exhibit horizontal pleiotropy (i.e. influencing the outcome through a pathway other than the exposure) then the causal effect estimate is liable to be biased. Thus, in principle the IVW estimate is said to have a 0% ‘breakdown level’ because it is not guaranteed to tolerate any SNPs violating the third IV assumption (exclusion restriction assumption). A tool used extensively in meta-analysis is to assess the heterogeneity between studies is Cochran’s Q statistic , and it The outcome of interest is a binary variable (e.g. a disease status), and the SNP-outcome associations are measured on the odds ratio scale. Heterogeneity in this case is due to the non-collapsibility of the odds ratio as a summary measure, meaning that each SNP is estimating a slightly different causal parameter ;The samples used to estimate the SNP-exposure and SNP-outcome associations are not homogeneous e.g. a difference in the distribution of a covariate confounding the exposure-outcome relationship across samples could induce heterogeneity ;The SNP-exposure and SNP-outcome relationships are not correctly specified—i.e. in the two-sample setting the causal relationship between the exposure and the outcome is different in each of the samples ,54.Though not the subject of this review it is important to note that there are many other factors that could induce heterogeneity among the causal ratio estimates of a set of SNPs, in the total absence of pleiotropy. For example, heterogeneity could arise because (but not limited to):Heterogeneity is therefore a sign that either the modelling assumptions are wrong, or the IV assumptions are violated.Suppose that all the SNPs exhibit horizontal pleiotropy, such that each SNP influences both the exposure and also the outcome through another pathway. In this scenario, we can model the SNP-outcome effect as being the influence of the SNP on the outcome through the exposure, but in addition each SNP is also allowed a random positive or negative effect on the outcome through some other pathway. Here, it is assumed that on an average the random effects have zero mean and are uncorrelated with the SNP-exposure effect  Appendi.While there is often concern that horizontal pleiotropy will induce false positive causal associations, it can also reduce the true positive rate. In the universal pleiotropy model described earlier, the horizontal pleiotropy introduces noise to the causal association which means that statistical power will be reduced.directional. In this scenario, the IVW estimate will be biased.In the case of balanced horizontal pleiotropy, it is assumed that the random effects have zero mean, which will lead to the IVW estimate being unbiased. However, an alternative possibility is that the random effect does not have zero mean, and that the average random effect is A simple approach to account for this bias is to use MR-Egger regression ,57, whicThere are three important factors to consider when using standard MR-Egger regression. First, it is required that the SNP-exposure estimates are oriented to be positive, and the SNP-outcome effects are flipped accordingly. This is done so that the SNP-exposure association reflects the `weight’ it receives in the analysis. The need to perform re-orientation has recently been relaxed with a modification of the original MR-Egger model based on Radial regression . Second,Finally, both the IVW and MR-Egger frameworks are dependent on the so-called InSIDE assumption (Instrument Strength Independent of Direct Effect). This justifies treating pleiotropy as a random effect. Furthermore, the MR-Egger assumptions are in fact a subset of the IVW assumptions, because the former relaxes the additional assumption that the average pleiotropic effect is zero. If the InSIDE assumption is violated and the SNP-exposure effects are correlated with the horizontal pleiotropic effects, then bias will be incurred. InSIDE violation is very likely when a sizable proportion of the horizontal pleiotropy operates through a confounder of the exposure-outcome relationship .maximum breakdown level of 100%  and calculate the weighted mode of the Wald ratio estimates . The majcis-acting variant might account for >50% of the weight even when there are many SNP effects from elsewhere in the genome (trans-effects)  ,66,67.trans-effects but only one cis-effect, the cis-effect is likely closer to the biology of the molecular trait due to its genomic proximity. By contrast in order for the trans SNP to exert an influence on the molecular trait it is presumed that it must go through several pathways, opening the possibility that those pathways influence the outcome independently of the original exposure.One issue with outlier removal (or down-weighting) is that it is at some level a form of cherry picking—generally the standard error of the causal effect estimate will be reduced after removing those SNPs that appear to deviate from the majority. There are also good examples where the SNP that might appear to be the outlier is in fact the most biologically reliable. For example, for ‘omic variables where there are potentially many CRP gene is likely to have a more direct effect on CRP levels themselves , consistent with previous analyses (see Appendix 2 for R code on how to obtain these results in MR-Base)  levels may fall into this category. Many of the SNPs that could be used to instrument CRP are from upstream inflammatory pathways, while the variant in the promotor region of the emselves . If inflP on CHD ,70 is li  . By conttruments , indicattruments ,73 . These methods attempt to arrive at unbiased estimates without incorporating additional knowledge of the potential alternative pathways in which SNPs might be operating. But often one can hypothesize what those pathways might be and include them explicitly in the analysis.Multivariable MR  attemptsnegative control outcomes) is to test if the exposure associates with outcomes that should be impossible, by conducting MR with the instruments that will be used in the focal analysis. If an association is obtained this would indicate that the instruments were in some way invalid . For example, suppose we want to estimate the influence of alcohol intake on blood pressure. If the instrument for alcohol intake is valid, there should be a SNP-outcome association only among individuals who drink alcohol. However, if there is found to be an association from a sample of individuals who do not drink alcohol then the SNP-outcome association must be arising through a pathway other than the hypothesized exposure, thus proving a violation in at least one of the assumptions   was deveIn practice there are very few epidemiological examples, including the alcohol example, with a perfect no-relevance point ,91. MRGxhypothesis-driven analysis (i.e. a particular exposure is being tested against a particular outcome) a common strategy    applied . (2017)  put forwmeta learning (summary sets (Box The mixture of experts (MoE) is a machine learning framework in which data can be fed to several different methods (‘experts’), and then the most reliable among them is selected . The MR-learning . First, sets Box  are prodA rich and diverse statistical toolkit is emerging that attempts to distil horizontal pleiotropic effects from vertical pleiotropic effects, in order to improve the reliability of causal inference. In MendelianRandomization (gsmr (mrrobust (Alongside method development, it is now crucial that codebases are maintained in which statistical methods can be deposited and easily applied to arbitrary data. The MR-Base platform integrates an R package with a database, enabling automated causal inference through summary data across a wide range of methods . Other smization  and gsmron (gsmr  and in Smrrobust . The MR-mrrobust  databasemrrobust . The nexmrrobust ,18.


ESR1, FGF2, FGFR2, GLI2, IQGAP3, PGR, PRLR, RREB1, BTRC, and TGFBR2.Using GWAS to identify candidate genes associated with cattle morphology traits at a functional level is challenging. The main difficulty of identifying candidate genes and gene interactions associated with such complex traits is the long-range linkage disequilibrium (LD) phenomenon reported widely in dairy cattle. Systems biology approaches, such as combining the Association Weight Matrix (AWM) with a Partial Correlation in an Information Theory (PCIT) algorithm, can assist in overcoming this LD. Used in a multi-breed and multi-phenotype context, the AWM-PCIT could aid in identifying udder traits candidate genes and gene networks with regulatory and functional significance. This study aims to use the AWM-PCIT algorithm as a post-GWAS analysis tool with the goal of identifying candidate genes underlying udder morphology. We used data from 78,440 dairy cows from three breeds and with own phenotypes for five udder morphology traits, five production traits, somatic cell score and clinical mastitis. Cows were genotyped with medium (50k) or low-density (7 to 10k) chips and imputed to 50k. We performed a within breed and trait GWAS. The GWAS showed 9,830 significant SNP across the genome (p < 0.05). Five thousand and ten SNP did not map a gene, and 4,820 SNP were within 10-kb of a gene. After accounting for 1SNP:1gene, 3,651 SNP were within 10-kb of a gene (set1), and 2,673 significant SNP were further than 10-kb of a gene (set2). The two SNP sets formed 6,324 SNP matrix, which was fitted in an AWM-PCIT considering udder depth/ development as the key trait resulting in 1,013 genes associated with udder morphology, mastitis and production phenotypes. The AWM-PCIT detected ten potential candidate genes for udder related traits:  Genetic architecture of complex phenotypes in cattle includes many loci affecting a given trait . Most ofDespite the limitations of the chip density, previous studies have shown the usefulness of the AWM to identify candidate genes in cattle, e.g. –10 and cIn this study, we report results based on GWAS analysis for mammary conformation, milk production and health phenotypes for 78,440 dairy cows. A multi-step validation by combining the results of single SNP, single phenotype, in a multiple-breed context using the AWM-PCIT algorithm was performed. The aim was to identify the genes associated with mammary conformation and health phenotypes in Holstein, Montbeliarde, and Normande breeds, accounting for milk production as supportive traits. We further explored gene networks with the main gene ontology domains including biological processes, cellular component, and molecular functions.2. CM events are declared by the farmer and recorded by the technician at each test-day. The analysis trait is defined by 1 if the cow has at least one clinical mastitis event in the lactation, and 0 if no there is no event. SCS and CM are repeated over lactations. The model for obtaining yield deviations was different according to the trait and all models included the genetic additive effect. The model for SCS and CM (recorded in the first three lactations), included the effect of herd x year, age at calving x parity x year, month of calving x parity x year, preceding days dry (for later parities), and a permanent environmental effect. All models assumed heterogeneous variances depending on herd-year. Each cow had one record for each trait. UDD can be either udder depth (Holstein and Normande) or udder development (Montbeliarde), but treated as the same trait because they have similar definitions. Depending on the trait, the number of animals with phenotypes ranged from 7,671 to 11,965 in Normande, 13,879 to 20,141 in Montbeliarde, and 32,491 to 46,732 in Holstein , technical quality assessed by their very low rate of Mendelian mismatch between parents and progeny and known position of genome assembly. We imputed cows genotyped from BovineLD v.2 BeadChip to 50k to obtain a genotype without missing information. This step was performed within breed in the conventional pipeline for genomic selection [All cows were genotyped using Illumina BovineSNP50 BeadChip (50k) or Illumina BovineLD v.2 BeadChip. We used the UMD3.1 assembly of the bovine genome  for SNP election . We impuelection , and refy is a vector of yield deviations, μ is a mean; u is a vector of random additive polygenic effects and is G is genomic relationship matrix based on all cows with phenotypes per breed and all autosomes. Z is incidence matrix relating phenotypes y to u, wi is a vector of genotypes for SNP i, si is the effect of SNP i, and e is a vector of random residual effects. We calculated the relationship between two individuals’ j and k as xij being the number of alleles for individual j and SNP i and pi is the observed allelic frequency, and, w was 43,800. We applied a genome-wide Bonferroni correction on all 43,800 tests to account for multiple testing.GWAS was done within breed with the Mixed Linear Model Association (MLMA) method as implemented in GCTA . Becauseth SNP on the tth trait. This allows exploration of trait correlations column-wise, and gene/SNP interactions row-wise. We then calculate the SNP-based correlations and compare the SNP-based and genetic correlations, the latter being calculated as pedigree-based restricted maximum likelihood (REML), established in the same cows’ populations. We then use the AWM SNP matrix as the input for the PCIT algorithm [We used the Association Weight Matrix (AWM) procedure to identify candidate genes per breed . The AWMlgorithm  and for lgorithm . FinallyCollectively for 78,440 cows, imputation resulted in a genotype density of 43,800 SNP. Of these, 38,827, 38,109, and 40,810 SNP had a MAF greater than 0.1 in Montbeliarde, Normande, and Holstein. Distribution of allele frequencies (MAF) of imputed genotypes was almost uniformly distributed across the MAF classes .We performed GWAS for real and imputed SNP and yield deviations (YD) for 12 traits: five udder conformation traits, five milk production phenotypes, somatic cell score and clinical mastitis . Fig 2 pSOAT1 gene, p = 3.47x10-15, Effect size = -0.185(0.02), MAF = 0.08) and rs108972236 , MAF = 0.20). The two lead SNP in Holstein were rs41641987 , MAF = 0.04) and rs110651226 , MAF = 0.45).Significant SNP associated with the key trait were evident for Holstein and Montbeliarde, and the most significant SNP that mapped a gene for the key trait is presented per chromosome in GF2 gene, Effect size = -0.12(0.01), p = 4.95x10-12, MAF = 0.073, Holstein), BTA20 , p = 3.16x10-16, MAF = 0.033, Holstein), and, BTA26 , p = 6.89x10-4, MAF = 0.068, Montbeliarde).We observed SNP associated with FUA and FTP in all breeds. The most significant of these signals were in Holstein and Montbeliarde and they include: BTA17 , pedigree-based genetic correlations , and SNP correlations  are presented in Mammary morphology genetic correlations to production traits ranged from positive for fore udder attachment (FUA) and milk yield (MY) in Montbeliarde (0.42) and Normande (0.28) to medium negative for udder depth or development (UDD) and protein yield (PROT) in Normande (-0.56) and front teat placement (FTP) and fat yield (FAT) in Holstein (-0.33). SNP correlations were numerically different compared to genetic correlations; however, the correlation was generally in the same direction. For instance, the genetic correlation between fore udder attachment (FUA) and udder balance (UB) was 0.40, 0.63 and 0.39, whereas, SNP correlations for these two traits was 0.59, 0.16 and 0.16 for Montbeliarde, Normande and Holstein breeds, respectively. There were moderate to zero genetic correlations between milk yield (MY) and FUA for Montbeliarde (0.42), Normande (0.28), and Holstein (0), and low SNP correlation (Montbeliarde (0.10), Normande (0.16) and Holstein (0.16)) between MY and udder depth or development (UDD). Holstein breed had a highly positive genetic (0.49) and SNP correlation (0.59) between front teat placement (FTP) and clinical mastitis (CM), a trend that was not evident in other two breeds. However, Montbeliarde breed showed a strong SNP correlation between FTP and CM (0.33) and between UDD and CM (0.18). Other trends evident from genetic correlations were between FUA and PROT for Montbeliarde (0.39) and Normande (0.26), FUA and CM for Montbeliarde (0.19) and a moderate genetic correlation between FTP and CM for Holstein (0.49). Genetic and SNP correlations were also comparable between FTP and PROT for all breeds, with genetic/SNP correlation being from medium in Montbeliarde (0.21 / 0.12) and Holstein (-0.34 / -0.1) to low in Normande (-0.12 / -0.1). However, the trend deviated between udder balance (UB) and fat percent (FAT %) with minimal genetic correlation in Normande (0.22) and no genetic correlation in Montbeliarde and Holstein but with moderate SNP correlations in Montbeliarde (-0.41), Normande (0.38) and Holstein (0.38). In general, genetic correlations are in the range of usual values, with high correlations between milk, fat and protein, a moderately negative correlation between production and type traits, moderately positive correlations between conformation traits, and low correlations otherwise. However, though there were deviations between genetic and SNP correlations in some of the traits, most traits correlations were in the same direction thus drawing plausibility of SNP correlated traits.-16). Among the AWM-PCIT, genes ten were transcription factors (TF) directly associated with the terms “mammary gland development”, “mammary gland duct morphogenesis", mammary gland alveolus development”, “tissue development”, and, “epithelial tube morphogenesis”. These ten TF include GLI2 (BTA2:72.98Mb), IQGAP3 (BTA3:14.31Mb), PGR (BTA4:62.53Mb), ESR1 (BTA9:89.97Mb), FGF2 (BTA17:35.23Mb), PRLR (BTA20:39.13Mb), TGFBR2 (BTA22:5.14Mb), RREB1 (BTA23:47.90Mb), BTRC (BTA26:22.06Mb), and, FGFR2 (BTA26:41.82Mb). -12) and “system development” (p = 5.38x10-5). Top GO term for the second cluster was “mammary gland epithelium development” (p = 5.16x10-16) while “regulation of intracellular signal transduction” was the least significant term in this cluster (p = 4x10-4). There was an enrichment for “neuropathic pain-signaling in dorsal horn neurons pathway” (p = 2.57x10-8), “G-protein coupled receptor signaling” (p = 1.07x10-7) as well as the “CREB signaling in neurons” (p = 7.24x10-7). Other pathways detected were “cAMP-mediated signaling” (p = 2.88x10-5), “synaptic long-term depression (p = 8.71x10-7), “axonal guidance signaling” (p = 1.15x10-6), and “synaptic long-term potentiation” (p = 1.58 x10-5).We considered main ontology domains including, biological processes, cellular component, and molecular functions. We observed 39 gene clusters  and Tabl−3), whereas, for biological processes, “dendrite development” and “putrescine biosynthetic” process were most represented (P<10−3) while “postsynaptic density” and “presynaptic membrane” were top GO terms for cellular components.Pathway enrichments detected (S3 File) included “Calcium: cation antiporter activity” and the “Calcium-activated Potassium channel activity” for molecular functions  was previously associated with milk production traits in Finnish Ayrshire dairy cows [FGF2 gene (Fibroblast growth factor 2) to fat yield and percentage and somatic cell score in US Holstein. Coleman-Krnacik et al. [FGF2 gene in the bovine mammary gland and uterine endometrium (UE). In the mammary gland, the FGF2 gene may play a role in development and reorganization of the mammary gland, while in UE, FGF2 gene is mainly expressed throughout estrous cycle and early pregnancy. BTRC gene (Beta-Transducing Repeat Containing E3 Ubiquitin Protein Ligase) is an F-box protein involved in Wnt/β-Catenin signaling pathway and indirectly activates nuclear factor kappa-B (NF-kB)[Previously, Fortes et al.  and Ramairy cows . Wang etiry cows  reportedk et al.  reportedB (NF-kB). Raven eB (NF-kB). The AWMB (NF-kB). Fortes B (NF-kB) reportedThe AWM was assessed for gene ontology (GO). The top GO terms were Calcium cation antiporter and Calcium-activated Potassium channel activity. As reported by Paulsen et al. , the forThe central pathways that showed enrichment were “Neuropathic pain-signaling in dorsal horn neurons pathway," and, “CREB signaling pathway." Neuropathic pain is the pain after nerve injury whereas the dorsal horn  pathways have been shown to offer substantial overlap with spinal projections from adjacent mammary glands in model organisms . Reflex BTRC, ESR1, FGF2, FGFR2, GLI2, IQGAP3, PGR, PRLR, RREB1, and, TGFBR2. These TF are homonymous with genes encoding them. This top cluster is directly involved in mammary gland development, regulation of response to a stimulus, gland development, epithelium development, tissue development, animal organ development, system development and multicellular organism development. This was partly in agreement to works reported by Yang et al. [Fontanesi et al.  reportedg et al.  on QTL aESR1, FGF2, GLI2, PGR and BTRC), and provided new candidate genes for udder morphology. This experiment may be replicated using whole genome sequence data and other independent datasets.Our study suggests the usefulness of system-based approaches to identify candidate genes from interacting gene networks in a multi-breed context. We achieved this by exploiting associations between correlated traits. The reluctant inclusion of intergenic SNP leaves the possibility that the AWM approach was not capturing significant SNP such as trans-activators. Nonetheless, the AWM proved to be more efficient for integrating related complex traits and analyzing thousands of SNP and therefore appropriate for the analysis of these complex traits. When applied to our dataset, it predicted gene interactions that are consistent with the known biology of udder morphology and health captured known TF Click here for additional data file.S1 Table(XZ)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


The Average r2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective population size of 43 was estimated for the population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon population. Furthermore, based on the contemporary Ne, a new mate allocation strategy is suggested to increase the effective population size.The estimation of linkage disequilibrium between molecular markers within a population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon breeding population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the breeding population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as  Oncorhynchus kisutch) is one of the six Pacific salmon species found in North American and Asian watersheds  and high-throughput genotyping technologies, it has become possible to perform artificial selection through the use of genomic estimated breeding values (GEBVs). By using dense molecular markers from the whole genome, genomic selection (GS) can be used in broodstock enhancement . This mer2 and |D′|, both ranging from 0 to 1. When |D′| < 1, it indicates the occurrence of historical recombination between loci, while |D′| = 1 indicates no recombination. The r2 statistic represents the correlation between genotypes from molecular marker pairs. This latter parameter is preferred over |D′| because |D′| tends to be overestimated in small samples sizes and when low-frequency alleles are used    and in A mykiss) .The first step to calculate the number of molecular markers necessary for genomic selection and association mapping is to estimate the extent and decline of LD within a population. To date, there have been no studies aimed to characterize the levels and extent of LD in coho salmon. The current work aimed to evaluate the effective population size and the extent of linkage disequilibrium, at the genomic and chromosome level, on a breeding coho salmon population using a 200K high-density SNP chip array.best linear unbiased prediction (BLUP), harvest weight had been selected over eight generations in this population. For LD estimations, a total of 64 animals (33 sires and 31 dams), corresponding to the parents of 33 families from a 2012-spawning year class, were selected. The mean relatedness among individuals (0.07) was estimated using Plink v1.09 . Using nk v1.09 . For spenk v1.09 , 2016. Sp < 3.8e−7, Minor Allele Frequency (MAF) ≤ 0.05 and genotyping call rate < 0.95. Fish with genotyping call rates <0.95 were excluded from further analyses. Subsequent analyses were assessed using the SNPs markers which passed all quality control (QC) criteria.Genomic DNA was extracted from fin clips from the 64 individuals. Genotyping was carried out using a 200K Affymetrix Axiom® myDesign Custom Array developed for coho salmon by the EPIC4 genome consortiumr2) statistic which is less sensitive to allelic frequencies , between SNPs pairs on each chromosome.The LD between each pair of genetic markers was estimated using Pearson’s squared correlation coefficient  was estimated using NeEstimator v2.01  was estimated based on the following formula proposed by Effective number of chromosome segments (r2) refers to the average linkage disequilibrium over all pairwise combination of SNPs within each chromosome. Thus, the total eM within the population is the sum of each individual eM.where mean, and a minimum and maximum value of 0.22 and 0.27, respectively .r2 values for each coho salmon chromosome. All of the 93,502 SNPs placed onto chromosomes and which passed quality control were included in this analysis. These markers encompassed 1,684.62 Mb of the genome, representing ~71.1% of the total genome size . The molecular marker density per chromosome per Mb, ranged from 48.31 to 66 with a mean of 55.99. In general, SNPs were uniformly distributed along the 30 chromosomes. The number of SNPs on each chromosome ranged from 1,954 on Okis16 to 4,846 on Okis04, which is in agreement with Okis16 and Okis04 being the shortest and longest chromosome, respectively. The overall mean linkage disequilibrium (measured as r2) among SNP pairs was 0.10 ± 0.14. The global median was lower at 0.05. Low average LD among adjacent SNPs along the 30 chromosomes was observed in the current population, with values ranging from 0.07 to 0.15  among all SNP pairs within each chromosome, we estimated that at least 74K SNPs markers would be needed for an efficient GWAs or GS analysis in the current farmed coho salmon population.Considering the estimated linkage disequilibrium  within a population, at a physical distance up to 10 Mb . Filtere low MAF , low MAF low MAF . Thus, tMe) . However, this approach may produce an underestimated Me, leading to an overestimation on the EBVs accuracy (r2)  . Due to dictions . Based oaccuracy . Thus, wacy (r2)  which isacy (r2) , the SNPThe variation in the average and standard deviation in the LD among chromosomes found in the current study , is partr2 values estimated in our study were higher than those estimated in a wild Finnish Atlantic salmon population, with values ranging from 0.015 to 0.037 (r2) values up to 0.67 for SNPs closer than 1 kb  suggested to retain the evolutionary potential and sustainability of a breeding program , which agree exactly with the beginning of the breeding program of this farmed population . This va program . Thus, aIn the current study, we performed an LD analysis with 64 coho salmon genotyped with 93,502 SNPs. We showed the feasibility to estimate LD and infer the effective population size based on the observed LD using data from a high-density SNP array. Furthermore, based on the current effective population size, a new mate allocation strategy that allows to increase it is suggested.Coho salmon individuals and sampling procedures were approved by the Comité de Bioética Animal from the Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile (Certificate N08-2015).AB performed DNA extraction, LD and Ne analysis, and wrote the initial version of the manuscript. KC contributed on the data analysis and discussion. GY contributed with LD analysis and discussion. AJ performed DNA extraction. JPL contributed with study design. BK, JSL, and ER developed the chip array. WD contributed with writing and discussion. JMY conceived and designed the study, supervised work of AB, and contributed to the analysis, discussion, and writing. All authors have reviewed and approved the 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.


The analysis of large genomic data is hampered by issues such as a small number of observations and a large number of predictive variables , high dimensionality or highly correlated data structures. Machine learning methods are renowned for dealing with these problems. To date machine learning methods have been applied in Genome-Wide Association Studies for identification of candidate genes, epistasis detection, gene network pathway analyses and genomic prediction of phenotypic values. However, the utility of two machine learning methods, Gradient Boosting Machine (GBM) and Extreme Gradient Boosting Method (XgBoost), in identifying a subset of SNP makers for genomic prediction of breeding values has never been explored before. In this study, using 38,082 SNP markers and body weight phenotypes from 2,093 Brahman cattle , we examined the efficiency of three machine learning methods, namely Random Forests (RF), GBM and XgBoost, in (a) the identification of top 400, 1,000, and 3,000 ranked SNPs; (b) using the subsets of SNPs to construct genomic relationship matrices (GRMs) for the estimation of genomic breeding values (GEBVs). For comparison purposes, we also calculated the GEBVs from (1) 400, 1,000, and 3,000 SNPs that were randomly selected and evenly spaced across the genome, and (2) from all the SNPs. We found that RF and especially GBM are efficient methods in identifying a subset of SNPs with direct links to candidate genes affecting the growth trait. In comparison to the estimate of prediction accuracy of GEBVs from using all SNPs (0.43), the 3,000 top SNPs identified by RF (0.42) and GBM (0.46) had similar values to those of the whole SNP panel. The performance of the subsets of SNPs from RF and GBM was substantially better than that of evenly spaced subsets across the genome (0.18–0.29). Of the three methods, RF and GBM consistently outperformed the XgBoost in genomic prediction accuracy. A SNP dataset consisting of 40,184 SNP markers from 2,093 tropical Brahman cattle was used for the study. The animals consisted of 1,097 Brahman bulls  and 996 Brahman cows (referred to as “cow population”). The bull population varying from 373 to 509 days old, came from 57 contemporary groups  and were measured for live weight (the average weight being 308.64 kg (± 38.85 kg) with the range from 180 to 430 kg, Barwick et al., Animal Care and Use Committee approval was not obtained for this study because historical data was used and no animals were handled as part of the study. Analysis was performed on phenotypic data and DNA samples that had been collected previously as part of the Australian Cooperative Research Centre for Beef Genetic Technologies  is used to predict a target phenotype (yi). The prediction value is a continuous variable. The fundamental part of a supervised learning method is about how to make the prediction yi given Xi. Normally it involves the identification of an objective function and optimizing it. The objective function usually comprises two parts—training loss function and regularization term , then optimizes it using a cross validation method , maximum tree depth  and minimum samples per leaf. For regression, a SNP VIM value GBM produces is the relative influence. It is a maximal estimated improvement in MSE over a constant fit over all iterative trees , learning rate . Most importantly, XgBoost can improve prediction errors by applying a more regularized model formalization to control over-fitting problems . The larger the value, the more important the SNP is. The detailed description of fundamental differences between XgBoost and GBM algorithms are given in the guide for XgBoost  that need to be predetermined in XgBoost, including 3 general, 12 booster and 3 task parameters. A SNP VIM value that XgBoost produces is the “Gain” value , 2*sqrt(M), or 0.1*M, where M is the total number of SNPs . The minimum values of the parameters were determined when the average MSE value of all SNPs reached a stable status in which increasing Ntree and other parameter values no longer changed the average MSE value. Then these parameters were used for the subsequent analyses. The R program library randomForest  and the number of markers at each sampling event (mtry) to form a tree. To determine the minimum requirement for these parameters, we systematically examined the impacts of a range of Ntree and mtry values on the average population MSE value of all SNPs using the bull population. These included Ntree = 500, 1,000, 1,500, 2,000, 2,500, … 5,000 , and mtry = 1, sqrt(shr (for GBM) and eta (for XgBoost) values, in which we examined a range of values for Ntree = 500, 1,000, 1,500, 2,000, 2,500, …, 5,000  and the learning rate shr for GBM or eta for XgBoost = 0.01, 0.04, 0.07, or 0.1 respectively. Again, we used the error rate curve to determine the minimum parameters required. The minimum parameters were reached when the average population MSE value reached a consistent status. That is, the value where increasing input parameters did not change the MSE trend.For GBM and XgBoost, we applied the R libraries gbm , or eta (a step size shrinkage for XgBoost) were determined, they were used for the final run of individual machine learning methods. Based on the SNP VIM values from RF (%IncMSE), GBM (relative importance) and XgBoost (Gain), all SNPs were ranked from the most important to the least important ones. The top 400, 1,000, 3,000 SNPs as well as all SNPs with the positive VIM values were then identified from each method. These values are chosen largely due to the fact that in practice commercial companies for DNA genotyping are always carried out using the multiplexes of 96 or 384 wells.Once the minimal values for the parameters - Ntree, mtry, Bos Taurus (for organism), statistical overrepresentation test , PANTHER GO-Slim biological process (annotation data set) and Fisher's Exact with FDR multiple test correction (test type). In addition, we also applied the UCSC's liftOver tool (minMatch = 0.1)  with the positive VIM values, using the program PANTHER  of individual animals was validated with the cow population. To quantify the effects of the top 400, 1,000 and 3,000 SNPs on the live weight phenotype, we applied a linear mixed genomic model to estimate the genetic variance explained by each subset of selected SNPs. The model is as follows:X(nxb) refers to a design matrix, b(bx1) represents a vector of fixed effects consisting of contemporary groups and age, and n is the number of animals. Z(nxn) is an incidence matrix, a(nx1) refers to a vector of random SNP additive effects, and e(nx1) is a vector of errors. In the model, we assume the random effects a and e follow a normal distribution, with mean zero and variance GRM  and I(nxn)m refers to the number of SNPs, W(nxm) is a matrix containing all additive contributions from m SNPs of n animals. For a given jth animal at kth SNP locus, the additive contribution Wjk of the SNP with three genotypes AA, AB, and BB, is calculated as 2-2pjk, 1-2pjk, and -2pjk, respectively. Here, pjk is the allele frequencies for allele B. The software Remlf90  were randomly split into 5 equal-size groups and each group with about 199 animals (20% of the population) was in turn assigned with missing phenotypic values and used as the validation set. The accuracy of genomic prediction was calculated as the correlation between the predicted GEBVs of the animals with no phenotypic values and the corrected phenotypes of the animals, divided by a square root of the heritability value. The corrected phenotypes were derived after adjusting the original phenotypes for the fixed effects of contemporary group and age . The accuracy reported in the study was the average of the accuracies of genomic prediction from 5-fold groups.For comparison purposes, we also calculated the accuracies of genomic prediction from all the SNPs , the SNPs with positive VIM values from each machine learning method, as well as 400, 1,000, and 3,000 SNPs that were selected to be evenly spaced across the genome.The results from an initial examination of the combination of various parameters in individual methods using the bull population are shown in Figure M)  being the default value suggested by RF method) or 2*sqrt(M). Using 10% of total markers , as expected, single marker (mtry = 1) analysis Figure  producedshr (GBM) or eta (XgBoost), the MSE value became very stable. Therefore, we chose Ntree = 2,000 and shr = 0.1 and eta = 0.1 for subsequent GBM and XgBoost analyses respectively. The reason for choosing the learning rate of 0.1 for shr and eta, instead of a much smaller value, is that the smaller the value the longer the program takes to run. In addition, Friedman  for GWAS in which the analysis generally provides the parameter estimates such as individual SNP allele substitution effect and a corresponding significance The Venn diagram Figure  generateBMPER  on BTA4. A literature search found that BMPER played vital roles in adipocyte differentiation, fat development and energy balance in humans and mice . The evidence was much stronger for the genes near all the SNPs with positive VIM values, involving the growth pathways of development process  and system development .Tables Ps Table  or the SP-value = 1.22*10−4), canonical Wnt receptor signaling pathway involved in positive regulation of endothelial cell migration , positive regulation of cell-cell adhesion , low density lipoprotein particle mediated signaling  and cellular response to lipoprotein particle stimulus .When converting the genome positions of all of the positive SNPs identified by RF to the human coordinates and checking these against known human biological processes using the GREAT program, we found that there were 16 association terms in our SNP dataset, including AMP catabolic process  of live weight in the cow population for a subset of 400, 1,000, 3,000 SNPs and the SNPs with positive VIM values identified by RF, GBM or XgBoost respectively. The same estimates are also given for the evenly spaced 400, 1,000, 3,000 and all the 38,082 SNPs in Table Table 2 estimates (0.11–0.14 with standard error of 0.046–0.053) from the top 3,000 and the SNPs with positive VIM values from RF  and GBM  were very close to the value of using all SNPs  with those from three machine learning methods Table , all subWhen comparing the computational time (in terms of seconds) each method had taken to complete an analysis Figure , it is oGenomic prediction and selection is one of post-genome-era applications that revolutionize genetic improvement programs. Low-density SNP panels can offer a cost effective solution for broad spectra applications of genomic selection programs if subsets of SNPs with biological relevance can be effectively identified to provide high accuracy of genomic prediction of breeding values. While the concept of using low density SNP panels associated with a phenotype as a cost-effective solution for genomic selection has been explored in a number of studies  enrichment analysis clearly indicate that the machine learning methods are efficient methods in identifying a subset of SNPs  with direct links to candidate genes affecting the growth trait. These results could largely contribute to the fact that the machine learning methods captured complex SNP-SNP interactions and non-linear relationships. Therefore, they produced much smaller residual variance, hence, resulted in an increased genetic variance and heritability values.m.” A goal of a learning algorithm is to minimize both statistical bias and variance. In RF each individual decision tree that is formed with mtry SNPs is renowned to be prone to an overfitting prediction error, caused by a high variance and a low bias of an individual tree. However, by using a large number of un-pruned decision trees  to form a forest, the prediction error can be reduced through reducing the variance component  evaluation that prediction error variances are proportional to the diagonal elements of the inverse of the relationship matrix  and MAF  can systematically impact the variable importance measures used by both RF and GBM  population, heritability of a trait of interest, relationship between training and validation animals , length of chromosomes (in Morgans) and the effective population size Goddard, . There aThere is no doubt that there are other machine learning methods that can be used for high dimension reduction and efficient selection of subsets of SNPs for low-density SNP panels  using the machine learning methods as a pre-screening tool (or a high-dimension reduction tool) to identify biologically relevant variants from large genome sequence variants of a large population, and then apply subsets for detailed investigation of gene functions or pathways or genomic prediction of future generations; (2) Building large reference populations by initially genotyping a large SNP panel on part of a population, and then choosing subsets of SNPs to genotype the rest of a population for future genomic selection.In this study, using the live weight from Brahman cattle and 38,083 SNPs, we demonstrated that two machine learning methods—RF and GBM, are efficient in identifying potential candidate genes for the growth trait. Using at least 3,000 SNPs with positive VIM values identified by RF and especially GBM achieved the similar estimates of heritability and genomic prediction accuracy of breeding values as those of using all SNPs. The subsets of SNPs  selected by the RF and GBM significantly outperformed those SNPs evenly spaced across the genome. The superiority of GBM performance comes at the expense of longer computational time.http://www.beefcrc.com/) and are co-owned with Meat and Livestock Australia. The data can be made available subject to the agreement of the owners. Requests to access the raw dataset should be directed to YL (yutao. li@csiro.au).The raw data supporting the conclusions of this manuscript, containing 40,184 SNP genotypes and body weight phenotypes from 2,093 Brahman cattle, are part of the Australia Beef CRC project (BL performed the data analysis using the machine learning methods in the bull population and provided the information to the manuscript. NZ and Y-GW were involved in the initial programing of RF and GBM methods. AG and AR provided valuable input on the project and assist improving the manuscript. YL provided crucial concepts, supervised the project, conducted the genomic prediction in the cow population and drafted the manuscript. All authors 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 confirmed the ultra-thin ReS2 nanosheets with ≈1–4 layers on the surface of the CB (ReS2@CB) by using analytical techniques of field emission scanning electron microscopy (FESEM) and high-resolution transmission electron microscopy (HRTEM). The ReS2@CB nanocomposite showed high specific capacities of 760, 667, 600, 525, and 473 mAh/g at the current densities of 0.1 (0.23 C), 0.2 (0.46 C), 0.3 (0.7 C), 0.5 (1.15 C) and 1.0 A/g (2.3 C), respectively, in conjunction with its excellent cycling performance  after 100 cycles. Such LIB performance is greatly higher than pure CB and ReS2 powder samples. These results could be due to the following reasons: (1) the low-cost CB serves as a supporter enabling the formation of ≈1–4 layered nanosheets of ReS2, thus avoiding its agglomeration; (2) the CB enhances the electrical conductivity of the ReS2@CB nanocomposite; (3) the ultra-thin (1–4 layers) ReS2 nanosheets with imperfect structure can function as increasing the number of active sites for reaction of Li+ ions with electrolytes. The outstanding performance and unique structural characteristics of the ReS2@CB anodes make them promising candidates for the ever-increasing development of advanced LIBs.ReS TMSs poand WS2) . The aboand WS2) ,10,11. 2 has recently been investigated for its enhanced Li+ ion transport kinetics for practical LIB applications because of its unique properties of a weak S–S interlayer binding strength and a large interlayer distance [2 at the higher current density has hindered its practical application. To overcome this limitation, one of the most legitimate strategies is to synthesize nanocomposites of ReS2 with a variety of morphologies  combined with the nanocarbon-based materials  the ReS2 nanosheets’ sufficient space for reaction with electrolytes; (3) small size (≈20 nm), ultra-thin thickness (≈1–4 layers) and defect structure of the ReS2 nanosheets to accelerate the Li+ ion transport and buffer against mechanical stress induced by volume variation during the cycling process. With these unique physico-chemical characteristics, as-synthesized ReS2@CB nanocomposites shows high specific capacities of 760, 667, 600, 525, and 473 mAh/g at current densities of 0.1 (0.23 C), 0.2 (0.46 C), 0.3 (0.7 C), 0.5 (1.15 C), and 1.0 A/g (2.3 C), respectively, along with its excellent capacity retention of 91.4% with a high capacity of 432 mAh/g at 1.0 A/g (2.3 C) after 100 cycles. Such excellent rate capability and superior cycling stability could open up new, challenging prospects in this ever-increasing development of TMS-based anode materials for advanced LIBs.Herein, to address this issue, we, for the first time, fabricated ReSncluding ,18: (1) 2@CB through a simple hydrothermal synthesis process [4ReO4, 0.8 g of CS(NH2)2, 0.35 g HONH3Cl, and 20 mg of CB were dissolved into 30 mL of deionized (DI) water and then sonicated for 30 minutes to make a homogeneous solution using a sonicator . Afterwards, the solution was transferred to a 50 mL Teflon-lined stainless-steel autoclave in air atmosphere; subsequently, the autoclave was pressurized at 180 °C for 30 hours in electrical furnace . After synthesis, we purified the as-synthesized ReS2@CB particulate sample via centrifugation at 7000 rpm  with ethanol and deionized (DI) water for three times, respectively. For comparison, ReS2 powder was prepared with the same process without adding CB. Finally, the purified ReS2@CB and ReS2 were dried at 80 °C overnight in a drying oven. 2@CB structure.We fabricated ReS process . In typi2@CB and ReS2 samples were identified using an X-ray diffractometer (XRD) . The morphological and microstructural features of the samples were analysed using field emission scanning electron microscopy (FESEM)  and high-resolution transmission electron microscope (HR-TEM) . The chemical composition, electronic states, and purity of the ReS2@CB and ReS2 samples were investigated using X-ray photoelectron spectroscopy (XPS) . The specific surface area and pore volume of the ReS2@CB and CB were measured using Brunauer–Emmett–Teller (BET) equipment . We used Horvath–Kawazoe (HK) and Barrett–Joyner–Halenda (BJH) methods to acquire micropore and meso-/macro-pore size distributions, respectively.The crystal structures of the as-synthesized ReS2@CB and ReS2 powder) (80 wt%), Carbon Black (CB)  (10 wt%) as a conducting agent, and polyvinylidene difluoride (PVDF)  (10 wt%) as a binder in the solvent of n-methyl-2-pyrrolidone . For the CB anode sample, the weight ratio of CB and PVDF was 8:2. Subsequently, the slurry was uniformly pasted onto a copper foil as a current collector to produce the anode samples, which were then dried at 80 °C in a drying oven overnight. In a dry room, the coin-type of cells were assembled with the ReS2@CB, ReS2 powder, and CB as working electrodes; a monolayer polypropylene (PP) film  as a separator; 1 M LiPF6 in ethylene carbonate (EC):dimethyl carbonate (DMC) (volume ratio of 1:1) as an electrolyte ; and lithium foil as counter and reference electrodes. With these assembled cells, the galvanostatic charge and discharge cycling test was carried out within a voltage window of 0.01 to 3 V versus Li+/Li by using a multi-channel battery testing unit . The cyclic voltammetry (CV) for the ReS2@CB anode sample was conducted using a multi-channel potentiostat  in the same voltage window at scan rates of 0.1, 0.2, 0.4, 0.6, 0.8, 1.0 mV/s. Electrochemical impedance spectroscopy (EIS) was performed on the electrochemical workstation (Bio Logic Science instrument-VSP) in the frequency range from 100 kHz to 0.1 Hz with an alternative current (AC) voltage amplitude of 5.0 mV. The electrode slurry was fabricated by mixing active materials  with a smooth surface  [2 was successfully grown on CB through the hydrothermal process without any side products. The broad peak appearing at 14.5° corresponds to the ReS2 (002) plane, suggesting its low crystallinity [2 nanoparticles on CB. Random space between the interlayers of the ReS2 was produced in the composite as shown in 2, which is agreement with the standard JCPDS card (No. 89-0341). The d-spacing (0.61 nm) for the (002) plane of the ReS2@CB sample also proved the vertical orientation of the ReS2 nanosheets on CB, which was consistent with the FESEM images shown in 2 layers. These defect structures may be beneficial for realizing a high capacity in the LIB performance as they can provide more active sites for Li+ ion accommodation, thus enhancing LIB performance [2@CB was 39.4 m2/g according to the N2 adsorption–desorption isotherms (see 89-0341) , thus prallinity . Consistformance . Alternaerms see . 2@CB nanocomposite sample using XPS. 7/2 and Re 4f6/2 orbitals are located at 41.9 and 44.2 eV [2 product  [+ ion into ReS2@CB [4+ to Re0 in the Re region embedded in a Li2S phase [2S [The ReSformance . We usedformance a. The cu< x < 1) , which w ReS2@CB ,11. The 2S phase ,29. Durihase [2S ,30. 2 and Li. 2@CB in the initial four cycles. During the first discharge, the significant voltage plateaus at around 0.7 V are observed, which corresponds well to the cathodic peak in the CV curves and in the subsequent charge, where the clear plateau at ~2.3 V agrees well with the anodic peak during the CV scan, which is consistent with the previous reports of ReS2-based anodes [2 is a semiconducting material with a band gap of 1.35 eV and suffers from the volume expansion/contraction to cause detrimental pulverization problems. To overcome this limitation, CB is incorporated with ReS2 to facilitate electrical conductivity and guard the volume variation during charge and discharge cycles [2@CB compared to those of pure CB and ReS2 samples. For the ReS2@CB, the specific capacity decreased from 840 mAh/g for the first cycle to 761 mAh/g for the fifth cycle, whereas coulombic efficiency increases from 60 to 93%, meaning enhanced capacity reversibility with cycling. The initial capacity loss was mainly attributed to irreversible processes such as the inevitable formation of a solid electrolyte interphase (SEI) film on the electrode surface and side-reactions between Li+ ion and the active material [2@CB nanocomposite shows a high specific capacity of 760, 667, 600, 525, and 473 mAh/g at the current densities of 0.1, 0.2, 0.3, 0.5, and 1.0 A/g, respectively; these values were higher than those of CB (82 mAh/g @ 1A/g) and ReS2 powder (13 mAh/g @ 1A/g) as shown in 2@CB, CB, and ReS2 powder at 1 A/g. Such excellent cycling stability with a high specific capacity of 432 mAh/g for ReS2@CB anode sample was closely attributed to the CB, as well as the vertical orientation with a defect property which offered high electrical conducting pathways and accelerated Li+ ion diffusion rate, thus improving the kinetic efficiency of Li-storage [2 anode sample shows a negligible capacity of ~3 mAh/g at 1 A/g, confirming the crucial role of CB to enhance LIB performance. Furthermore, the pure CB anode sample exhibited a specific capacity of ~92 mAh/g after 100 cycles. According to these results, we can interpret that the higher specific capacities stemmed from the crucial role of the incorporated CB to promote electrical conducting pathways for ReS2, buffer against a large volume change of ReS2 during cycling and enable the ultrathin thickness of the ReS2@CB to expose more active sites, thereby improving Li+ ion kinetics.All the curves were well-superimposed, thus showing the reversible conversion reaction between ReSd anodes . ReS2 ise cycles . This prmaterial ,28. The 2@CB and ReS2 powder samples (see 2@CB (139 Ω) in the high-medium frequency region was smaller than that of the ReS2 powder (460 Ω), which indicates that ReS2@CB possessed the lower contact and charge-transfer resistances (Rct) and the slope of the ReS2@CB in the low frequency region is also higher than the ReS2 powder. These EIS results prove that the ReS2@CB architecture showed a high electrical conductivity of the overall electrode and enhanced the Li+ ion diffusion of the ReS2 anode during cycling process. Electrochemical impedance spectroscopy (EIS) measurement were performed with our samples to evaluate the charge transfer resistance of the ReSples see a. Signif2@CB at the different sweep rates from 0.2 to 1.0 mV/s  suggest the more favored capacitive kinetics of the ReS2@CB electrode, which could be due to the ultra-thinness and imperfect crystallinity of the ReS2@CB. Compared with the published values, our data show the superior specific capacity and cycling performance at 1 A/g as shown in To understand the deep insight into the improved electrochemical performance, we investigated the capacitive effect of the battery system by testing CV curves of the ReS1.0 mV/s b. The ca process . As disp2@CB anode structure with ultra-thinness (~1–4 layers) and low-degree crystallinity through the hydrothermal process. When employed in the LIB performance, the synthesized ReS2@CB (473 mAh/g @ 1 A/g) showed a higher rate capability and higher capacity than the CB (82 mAh/g @ 1 A/g) and ReS2 powder (13 mAh/g @ 1 A/g), as well as an excellent cycling retention of 91.4% (@ 1 A/g), even after 100 cycles. The improved electrochemical performance of our ReS2@CB nanocomposite anode was closely attributed to the decisive roles of CB on the enhanced electrical conductivity of ReS2 and the ultrathin nanosheets with a defect structure to facilitate the Li+ ion diffusion rate, thus improving the kinetic efficiency of the redox reaction during the cycling process. Furthermore, with these unique physico-chemical properties, we confirmed that the investigation on the ReS2@CB anode would open up a new avenue of pursuing high-performance TMS-based anodes for advanced LIBs.In summary, we synthesized the ReS


Cucumis sativus L.) is an economically important vegetable crop worldwide, but it is sensitive to low temperatures. Cucumber seedlings exposed to long-term low temperature stress (LT), i.e., below 20°C during the day, and 8°C at night, exhibit leaf yellowing, accelerated senescence, and reduced yield, therefore posing a threat to cucumber production. Studying the underlying mechanisms involved in LT tolerance in cucumber seedlings, and developing germplasm with improved LT-tolerance could provide fundamental solutions to the problem. In this study, an F2 population was generated from two parental lines, “CG104” (LT-tolerant inbred line) and “CG37” (LT-sensitive inbred line), to identify loci that are responsible for LT tolerance in cucumber seedlings. Replicated phenotypic analysis of the F2-derived F3 family using a low-temperature injury index (LTII) suggested that the LT tolerance of cucumber seedlings is controlled by multiple genes. A genetic map of 990.8 cM was constructed, with an average interval between markers of 5.2 cM. One quantitative trait loci (QTL) named qLTT5.1 on chromosome 5, and two QTLs named qLTT6.1 and qLTT6.2 on chromosome 6 were detected. Among them, qLTT6.2 accounted for 26.8 and 24.1% of the phenotypic variation in two different experiments. Single-nucleotide polymorphism (SNP) variations within the region of qLTT6.2 were analyzed using two contrasting in silico bulks generated from the cucumber core germplasm. Result showed that 214 SNPs were distributed within the 42-kb interval, containing three candidate genes. Real-time quantitative reverse transcription PCR and sequence analysis suggested that two genes Csa6G445210, an auxin response factor, and Csa6G445230, an ethylene-responsive transmembrane protein, might be candidate genes responsible for LT tolerance in cucumber seedlings. This study furthers the understanding of the molecular mechanism underlying LT tolerance in cucumber seedlings, and provides new markers for molecular breeding.Cucumber ( Cucumis sativus L.) is an economically important vegetable crop. Global production reached 80 million metric tons in 2016, with a steady increase in production since 1998  mapping of LT tolerance in cucumber seedlings. A few studies were carried out under short-term extreme LT . When a in silico BSA ” was reported  and F1' (“CG37” x “CG104”) populations. The F1 (“CG104” x “CG37”) was self-pollinated to produce an F2 population of 189 individuals. A total of 189 F2:3 families were derived from F2 populations accordingly. Moreover, 10 core germplasms with high LT-sensitive or LT-resistant phenotype , Chinese Academy of Agricultural Sciences (CAAS).Two cucumber inbred lines, “CG104” (LT-tolerant) and “CG37” (LT-sensitive) were used in this study. “CG37” and “CG104” were crossed to generate an Fhenotype Table S1henotype  were use1 and F2-derived F3 families were exposed to 15–17°C for 2 weeks, and the LT injury was classified into six grades, based on the degree of yellowing and dryness of the cotyledons and the first true leaf  in early spring, respectively Table S20+1×S1+3×S3+5×S5+7×S7+9×S9)/N×9. S0–S9 indicates the number of plants corresponding to each grade. N indicates the total number of plants. Three replicates were set for each treatment, and eight plants of each replicate were investigated. The LTII in two experiments was evaluated by two people, respectively.A low-temperature injury index (LTII) was used as an indicator to indicate the LT tolerance of each plant. The formula used for the calculation of LTII refers to A modified CTAB (cetyltrimethylammonium ammonium bromide) procedure  was appl2 individuals. QTL analysis was performed with the R/QTL software package (http://www.rqtl.org/). Using individual plant data, a whole genome scan was performed to map the QTLs with composite interval mapping (CIM) procedures (P < 0.05) for declaring the presence of QTLs were determined using 1,000 permutations. For each detected QTL, a 2-LOD support interval was calculated and defined by left and right markers. The QTL naming format is the abbreviation of the character , chromosome (Chr.) number and locus number.Polymorphic primers were used for genotyping Focedures . Genome-in silico BSA strategy. The LT tolerance of 87 sequenced core germplasm (CG) seedlings was previously evaluated in our lab  and five highly LT-sensitive CG lines  were selected to generate LT-resistant and LT-sensitive bulks (https://www.ncbi.nlm.nih.gov/sra/?term=SRA056480). The detailed accession number of each CG line was listed in qLTT6.2. region  were searched . The qRT-PCR was performed using SYBR Premix Ex Taq™ (Tli RNaseH Plus)  in Roche Diagnostics with Light Cycler 480 System, and PCR amplification was conducted following the instructions. Actin1 (Csa3G806800) was used as the reference gene for the normalization of gene expression values  was synthesized. The qPCR primers were designed with Primer Premier 6.0  and 250–500 ng of DNA. The PCR conditions were as follows: 94°C for 5 min, 30 cycles of , 72°C for 10 min, and a final incubation at 4°C for use. The PCR products were then sequenced by Sangon Biotech . The sequence of the candidate genes of the parental lines were analyzed using SeqMan, and the amino acid sequences were analyzed using the MEGA 6.0 software (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/), and the protein sequences of candidate genes were predicted by SMART software  and the SNP mutation sites at the candidate genes. Specific SNP markers at mutation sites were designed , sensitive (b), and hybrid (ab), respectively. Then, the genotype and LTII value of each F2 individual were compared.Fdesigned Table S5All tests for significant differences between “CG104” and “CG37” were done using one-way ANOVA in the R environment .1 and F2:3 populations, 3-week-old seedlings were exposed to 15–17°C for 2 weeks in March 2017 and April 2017, respectively. The LTII was used to indicate the LT tolerance of each plant. Results showed that after 2 weeks of LT treatment, cotyledons and the first true leaf of “CG37” showed severe withering and yellowing, and the LTII score of “CG37” in greenhouse and plastic greenhouse were 59.1 and 57.7 respectively. However, “CG104” had no symptoms and the LTII were 14.9 and 8.7 respectively. The LTII of the F1 hybrids were 26.4 and 21.7, respectively, with the symptoms more inclined toward “CG104.” Frequency distribution of the LTII among F2:3 families obeyed a normal distribution  were shown in qLTT5.1 on Chr.5, qLTT6.1 and qLTT6.2 on Chr.6 were repeatedly detected  in two experiments  from the two experiments and the genetic map constructed were used for QTL mapping. Details of each QTL detected, including map location, peak location, peak logarithm of odds (LOD) support value, confidence intervals, and percentages of total phenotypic variances explained . Csa6M445230 has seven base pair substitutions within the CDS. Two substitutions were synonymous resulting in no amino acid change, while the rest were non-synonymous . The Asp257 resulted in changes in the transmembrane helix , which is a transcription factor that controls the expression of auxin responsive genes. Csa6M445230 encodes a transmembrane protein, ethylene-insensitive 2 (EIN2). To further predict and analyze the function of these two predicted amino acids, the sequences analyzed by BLAST against the NCBI database, and phylogenetic analyses were performed with their orthologues in seven other species  and temperature (12 or 4°C), and different evaluation standards . qLTT6.1 identified in our study, was only 233 kb away from SSR07248 identified in the previous study  to a 50 kb region, and finally identified the candidate gene Csgl3 for the trait. In our study, the phenotypic data obtained in two replicated experiments were consistent, and the genotype sequencing depth reached 30 X, therefore this method was employed to narrow the target region. Based on the sequence data and LT-tolerance performance of CG, the genomic region of qLTT6.2 was delimited to a 42-kb region, which only contained three candidate genes. Gene sequence alignments revealed that there were amino acid substitutions in both Csa6G445210 and Csa6G445230. Furthermore, allelic diversity of the candidate gene region in F2 individuals showed that the two markers based on the non-synonymous SNP sites within Csa6G445210 and Csa6G445230 were both associated with the phenotypes of F2 individuals. Therefore, our study demonstrated that the in silico BSA approach can accurately identify SNPs linked to LT tolerance trait in cucumber.“nt genes . With thCsa6G445210 and Csa6G445230 that might be involved in LT-stress response in cucumber seedlings were identified. Csa6G445230 encodes EIN2 which is a key regulator in ethylene signaling pathway, and Csa6G445210 encodes ARF which is responsive to auxin. The role of ethylene in abiotic stress resistance including cold responses was established , to improve plant cold tolerance  and prevents the ARF-mediated expression of downstream genes . All SNPs within the 42 kb target region can be found in The accession numbers of the ten core germplasm lines used in our study can be found in WW and SW conducted the experiments and analyzed the data. SD, WW and KB analyzed the data and drafted the manuscript. ZS helped collect the data. HM helped analyze the data. XG and SZ designed the experiments.This research was supported by National Key Research and Development Program of China (2018YFD1000800), the Earmarked Fund for Modern Agro-industry Technology Research System (CARS-25), Science and Technology Innovation Program of the Chinese Academy of Agricultural Science (CAAS-ASTIP-IVFCAAS), Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, P.R. China, and Central Public-Interest Scientific Institution Basal Research Fund (No.Y2017PT52).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.


Tilletia caries and T. foetida) has been considered as a major disease in wheat (Triticum aestivum) following rust (Puccinia spp.) in the Near East and is economically important in the Great Plains, USA. Despite the fact that it can be easily controlled using seed treatment with fungicides, fungicides often cannot or may not be used in organic and low-input fields. Planting common bunt resistant genotypes is an alternative.Common bunt  contains supplementary material, which is available to authorized users. Tilletia caries (D.C.) Tul. (=T. tritici) and T. foetida  Liro (=T. laevis) can cause huge losses in wheat grain yield. Infected plants with common bunt usually produce low grain yield with low quality compared with healthy plants. The reduction in the yield and its quality in the infected plants occurs due to the replacement of grains with bunt balls spores  - 25 °C[day] and grown using an increasing long day  to maturity at which time when they were harvested and scored [http://hprcc.unl.edu/). In each experiment, a winter susceptible line (Heines VII) were included to verify the effectiveness of the inoculation.The two tested nurseries  were tested in the field. The experiments were conducted in season 2015/2016 at two locations Mead and Lincoln, Nebraska, USA. The experimental design was replicated augmented incomplete block design with three replications and five incomplete blocks each. Two checks ‘Goodstreak’ and ‘Freeman’ were included three times in each block (15 times in each replication). The inoculated seeds of each genotype were sown in a one-meter long row at depth 5 cm. Each row was planted in a group of four with 30 cm between rows. The planting date was October 14th, 2015 and the soil temperature was 18 °C and 17 °C at 10 cm depth at Lincoln and Mead, respectively , average chlorophyll content from five flag leaves , and pl. Data of the different traits were collected using field book Android application [The level of resistance was determined using the following scale: Percentage of infected heads 0.0% = very resistant, 0.1–5.0% = resistant, 5.1–10.0% = moderately resistant, 10.1–30.0% = moderately susceptible, 30.1–50.0% = susceptible, 50.1–100.0% = very susceptible . Data oflication . In eachTo improve normality of the common bunt resistance data, the data were transformed using arcsine root square method using Excel 2013 as it was estimated as a percentage. Shapiro-Wilk normality test was used to confirm the improved normality of the transformed data compared with the original data. For all the other studied traits, data from Lincoln and Mead experiments were combined and analyzed using SAS Version 9 . The anaWhere Y is the observation of genotype, L is location, R(L) is replication nested within locations, Iblock (R) is Iblock nested within replication, Pcol (Iblock) is the number of columns nested within Iblock, Prow is the row number, E is Entry and LxE is location x Entry interaction.2) was calculated across locations using the following formula:The graphical presentation of box plots for all studied traits was created using R package ‘ggplot2’  and the PstI and MspI were used to digest the extracted DNA [DNA was extracted from all the 330 tested genotypes  using BioSprint 96 DNA Plant Kits  from 2 to 3 leaves of two-week-old seedlings. Two restriction enzymes, cted DNA . The refcted DNA . The genSNP markers data from both nurseries with a total number of 318 unique genotypes  were used to analyze population structure using the Bayesian model-based software program STRUCTURE 3.4 . The bur2) was calculated using TASSEL 5.0 [2) among the significant SNPs was calculated by TASSEL 5.0 and illustrated using Excel 2013.Genome-wide association analysis was performed to test marker-trait association for all studied traits in the 318 genotypes . The transformed data of the common bunt resistance was used in GWAS. The GWAS between the SNP markers and all traits was carried out using TASSEL 5.0 software . Two metSSEL 5.0 . The resSSEL 5.0 . For theFor common bunt resistance, an additional genome-wide association study was done using Settlement of MLM Under Progressively Exclusive Relationship (SUPER) method by GAPIT-R package . SUPER mhttp://www.wheat-expression.com/).Significant SNPs were inspected as to whether they are in genes identified and annotated in the reference genome assembly (IWGSC Ref Seq v1.0) to further explain the GWAS results. Functional annotation of the genes having significant SNPs was retrieved from the genome annotations provided by IWGSC and examined for their association with disease resistance. For additional understanding of the GWAS results, the gene expression in the different developing stages of wheat was compared based on the wheat expression database  and the transformed data using arcsine root square (b). (PDF 217 kb)Additional file 2:Figure S2. Scatter plot represents the correlation between the percentage of common bunt infected heads in the tested genotypes at the two locations (Mead and Lincoln). (PDF 30 kb)Additional file 3:Figure S3. The distribution of a) days to heading and b) plant height under common bunt infection. In the upper part of the figure, histogram represents the frequency of the studied traits as an average of both locations (Mead and Lincoln). In the lower part box plot comparing between the values of the studied traits in the resistance and susceptible genotypes as an average of both locations. (PDF 90 kb)Additional file 4:Table S1. Association analysis of common bunt resistance based on TASSEL and SUPER identified 123 significant SNPs. Table S2. List of gene models underlying significant SNPs based on IWGSC and TAGC databases. (XLSX 39 kb)Additional file 5:Figure S4. a) Manhattan plot displaying SNP marker-trait association identified for plant height in GWAS using 318 winter wheat lines. Redline is significance threshold of 5% Bonferroni correction and blue line is significance threshold of 5% FDR. Chromosomes with names written in red are carrying SNPs significantly associated with plant height. b) Quantile-Quantile (QQ) plot used to evaluate the performance of the mixed linear model used for of GWAS for plant height using mixed linear model (MLM + Q-matrix). (PDF 99 kb)Additional file 6:Figure S5. The distribution of the SNPs across a) the different wheat chromosomes and b) the different wheat genomes. Black charts representing the number of SNPs used in the present study while red charts represent the number of SNPs used in previous studies (Lara 2017). (PDF 14 kb)