Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71–0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium, plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties.  Phaseolus vulgaris L.) is one of the most important grain legumes for direct human consumption  in many crops including dry beans  and Rhizoctonia  were also reported  populations (P. ultimum and P. irregulare)  .Fusarium root rot resistance in RILs populations. QTL were identified for F. solani  for . solani , Fusariuirostrum  and Fusasiliense . Even thThe availability of SNPs markers due to high-throughput genotyping technologies has allowed the development of molecular tools such as Genome-wide association studies (GWAS) and genomic prediction (GP) as support for breeding programs. GWAS aims to detect phenotype to genotype associations to identify major effect genes. GPs are based on a genetic model using genome wide markers simultaneously to estimate genomic breeding values based on the sum of all marker effects. GP is better suited to capture quantitative effects based on many minor genes .Phaseolus coccineus for general root rot in the field, and specifically for Pythium root rot in the greenhouse. The genetic basis of root rot response was evaluated by GWAS and GP to improve knowledge on the inheritance of Pythium root rot resistance and to develop molecular breeding tools that can be deployed in the breeding program.The objective of the study was to evaluate a panel of lines with interspecific introgressions from P. vulgaris × P. coccineus, [(SAB516 × (SAB258 × G35066) and (SAB514 × (SAB258 × G35464-5Q)], of which the majority were generated using the parent G35066 and only two using the parent G35464-5Q. F3 individuals were evaluated for root rot-related pathogens in 2014 at Popayan  and 12 of the best and 12 of the least resistant families were selected to generate a population for genetic analysis. Ten individual selections from each family were advanced at the CIAT-Palmira station , finally resulting in 198 F5 lines, coded as Root rot resistance Andean lines (RRR). Furthermore, 30 bulk-derived lines, coded as Root Rot Andean lines (RRA) were selected for their outstanding performance for root rot resistance  was evaluated for root rot traits . These lsistance . MLB49-8 results . AdditioPythium ultimum (Pyth_UGA) and Fusarium cuneirostrum (Fus_UGA) under greenhouse conditions at the CIAT-Kawanda research station (Uganda) as described by Rhizoctonia solani (Rhiz_USA) and Fusarium solani (Fus_USA) at North Dakota State University (United States) as described by Different data sets previously reported for other common bean root rot-related pathogens in the Andean Diversity Panel ADP were also used in this study. The ADP represents the genetic diversity of cultivated germplasm of the Andean genepool, containing landraces, breeding lines and varieties from public and private breeding programs . The ADPPythium myriotylum preserved on potato carrot agar was reactivated on potato dextrose agar at 24°C for 7 days and then, slants of the pathogen were increased on sterilized sorghum seeds. The inoculum was prepared following the method described by A pure colony of a pathogenic isolate of P. myriotylum, using rows of 10 seeds per genotype (60 genotypes per tray) following an augmented-block design. The lines MLB49-89A and GLP2 were used as resistant and susceptible controls on each tray. To favor the growth of the inoculum, the soil was watered regularly and the temperature inside the greenhouse was maintained between 24° (night) to 32°C (day). The evaluations of the root hypocotyl and stem were carried out at 7, 14, 21, and 28 days after planting. After the last evaluation, the plants were uprooted to score the root damage following the standard 1 to 9 visual scale developed at CIAT. In this scale, 1 = no visible disease symptoms. 3 = light discoloration either without necrotic lesions or with approximately 10% of the hypocotyl and root tissues covered with lesions, 5 = approximately 25% of the hypocotyl and root tissues covered with lesions but tissues remain firm with deterioration of the root system. Heavy discoloration symptoms may be evident, 7 = approximately 50% of the hypocotyl and root tissues covered with lesions combined with considerable softening, rotting and reduction of the root system. 9 = approximately 75% or more of the hypocotyl and root tissues affected with advanced stages of rotting combined with a severe reduction in the root system  was evaluated in an unreplicated seed multiplication trial. No destructive sampling was conducted in the field.A total of 216 genotypes, including 198 RR lines and 18 checks , were evaluated in a field with natural root rot infection at the CIAT-Popayán station . Two monTM PicoGreen® dsDNA Assay Kit , measured in a Synergy H1m . DNA quantity was determined by regression against a standard sample of known DNA concentration. The library for GBS was prepared at CIAT, following the protocol described by 1. The ADP accessions were previously genotyped with the same GBS protocol, and the data was retrieved from the ARS-Feed the Future Bean Research Team2.Genotype-by-sequencing (GBS) data for the RR population was generated as described by Phaseolus vulgaris reference genome of the accession G19833 (v2.1) (The GBS reads from the RR and ADP populations were mapped to the 3 (v2.1) . The SNP3 (v2.1) , followi3 (v2.1) . The fil3 (v2.1) , settingThe AUDPC data from four different evaluations in the greenhouse performed on the RR panel was modeled using the following formula:y is a vector of AUDPCs calculated from the four sequential evaluations of Pyth scores, iG is the effect of the ith genotype, jE is the effect of the jth evaluation, (GE)ij is the genotype-evaluation interaction term, (ET)jk is the effect of the kth tray nested within the jth evaluation, and εijk is the error term corresponding to yijk. In this model, the terms (GE)ij, (ET)ik were treated as random effects. The term iG  was treated either as fixed  or random . Additional models that did not include the evaluation term jE and its interactions from equation 1 were fitted for each evaluation separately in order to assess their individual variance components, heritabilities, and comparability of BLUEs. We assumed that every random term u and the residual ε adjusts to a normal distribution with mean 0 and independent variances u~Where The spatial arrangement of the experiment in the field was used to assign row and column coordinates to each plot. The PVg scores that were obtained from three separate evaluations were modeled using the following formula:y is a vector of PVg scores obtained from the field trial, iG is the effect of the ith genotype, jE is the effect of the jth evaluation, (GE)ij is the genotype-evaluation interaction term, (ER)jk is the effect of the kth replication nested within the jth evaluation, (ERc)jkl and (ERr)jkm are the effects of the lth column and mth row nested within the jkth replication, respectively. In this model, the terms (GE)ij, (ER)jk, (ERc)jkl, and (ERr)jkm were treated as random effects. The term iGwas treated either as fixed  or random . We assumed every random term u~Where p-value was adjusted following The NEPl and the individual PVg evaluations were modeled fitting a linear mixed model with spatial components using the functions “SpATS” and “PSANOVA” of the R package SpATS . The pheBLUP is the mean variance of a difference of two BLUPs of genotypic effects, Where υPythium. The root rot damage score RRD_field is the result of joining the BLUEs of NEPl and PVg traits, assuming that both variables are directly related to the resistance or susceptibility response to the disease in each genotype. These variables were standardized to have mean 0 and variance of 1 before combining them. NEPl was added as a negative value to PVg, so as to obtain a new variable that was positively correlated with the general standard Pyth score proposed by t-test.New variables were also calculated to obtain greater clarity about the general behavior of The association analyses for RR were performed using the multi-locus random-SNP-effect Mixed Linear Model tools (mrMLM v4.0), which includes six multi-locus methods  and Farmh = 0.5. These predictions were executed using the R package BGLR (v1.0.8) (Genomic prediction (GP) was assessed on each trait of the RR, ADP and extBALSIT panels separately by cross validation, partitioning each panel 50 times randomly into 70% training and 30% validation population. GPs were performed using single Gaussian kernel Bayesian Reproducing Kernel Hilbert Spaces (RKHS) regressions with a fixed bandwidth parameter (v1.0.8)  with 10,r2 thresholds of 0.3, 0.5, 0.7, 0.9, and 0.95  between SNP markers. For this, we used the option “-indep-pairwise” of PLINK (v1.90b6.9), with 50 kbp windows, a shift parameter of 5 kbp and maximum pairwise and 0.95 . The secIn order to simulate a breeding scenario of employing genomic predictions to select future populations, a pairwise cross-prediction scenario between root rot evaluations on the RR and ADP panels was performed. This scenario was divided in two separate cases. The first consisted in using the data from a given trait (training population) to predict every other trait in the same panel, producing 18 different combinations of training-validation datasets. This case was useful in the RR panel to validate the prediction performance that the greenhouse data can have on the field data, and vice versa. Similarly, this case was useful in the ADP to validate the prediction ability when using data from a root rot pathogen to predict the phenotypic response to another root rot pathogen. The second case used data from a given trait-panel to predict every other trait in a different panel, producing 24 different combinations of training-validation datasets. The second case was useful to explore the prediction ability across populations (ADP-RR), conditions (greenhouse-field) and causal agents of root rots.Pythium myriotylum root rot in the greenhouse. Symptoms included poor seedling establishment/damping-off (NEPl), poor development of aerial biomass (PVg), and root necrosis (Pyth). The phenotypic responses showed normal distributions for most traits in line with a quantitative nature of the disease response  the matrix filter that removed markers from repetitive regions of the genome  using 15,004 SNP markers. These markers were located mainly on the euchromatic regions of the genome due to the (i) use of the methylation-sensitive enzyme e genome . Howevere genome . The fire genome . These te genome . The bese genome . The bese genome  showed Pp < 1 × 10–5 in two or more models. A QTL located on chromosome 5 at 80,772 bp for PVg, RRD_field and RRD_all stood out explaining 6.86–30.61% of the phenotypic variation. Also, on the start of chromosome 8 four models identified QTL for field root rot traits. However, only one model reported a single significant marker associated with Pyth, showing no overlap with field traits, which would be expected for a major resistance gene. Data for 100 seed weight (100SdW) from the unreplicated seed multiplication trial was evaluated as a control trait for which QTL are usually identified. Five out of seven models identified QTL for this trait at the start of chromosome 7 (100SdW7.1 and 100SdW7.2), presenting the highest LOD identified in 4 out of 7 models. In addition, six models identified other QTL in chromosomes 6 (100SdW6.1) and 8 (100SdW8.1) with lower significance. Identification of strong QTL signals for 100SdW confirmed that this data set is suitable for GWAS; interestingly, not all GWAS models identified these QTL. Taken together, no major resistance genes were found, hence, the results suggest a quantitative inheritance for the observed heritable root rot disease tolerance.GWAS was performed testing 7 different algorithms to identify QTL for root rot disease traits and their associated markers . These aPythium and Fusarium trials performed in Uganda  models was evaluated using the RKHS model for all traits within each RR trial. In addition, we tested how GP performs in a qualitatively inherited biotic stress trait. GP accuracies of root rot disease traits ranged from 0.7 to 0.8 . The oven Uganda , and 0.2h Dakota . We alsoh Dakota . The GP To evaluate how GP can predict performance across different evaluations and traits in the RR population, we used RRD_field data to train a GP model and the greenhouse Pyth data as validation set, and vice versa. The average prediction accuracies were similar, field to greenhouse 0.56 and greenhouse to field 0.54, providing a subtle improvement over the phenotypic correlations of 0.48 . AccuracPythium, Fusarium (Fus), and Rhizoctonia (Rhiz) root rot evaluated in the Andean Diversity Panel (ADP)  . Cross vel (ADP) . EvaluatF. solani root rot screening , so the initial goal of this study was to identify major resistance genes, based on introgressions from P. coccineus , in line with the high heritabilities of these traits. GP accuracies of 0.52 and 0.41 were reported in common bean for two types of soybean cyst nematode , tagging genes with major effects. Genomic selection, on the other hand, has recently been promoted as a genetic tool to predict quantitative traits, yield and yield-related traits being the most investigated. In this work we have seen good GP accuracies for a quantitatively inherited disease trait, hence we extended this analysis to the well characterized qualitative ALS resistance in common bean. We saw that the GP model captures more genetic variation than GWAS in the clear presence of a major resistance gene, which was reported to explain 8.6–31.4% of variation . In maizrom GWAS . Using mrom GWAS . This suFusarium response data from Uganda. Thus, the observed genetic tolerance must be a feature regulated by several genes leading to disease tolerance without being based on a specific resistance gene. Also for ALS, where resistance depends on a major gene, a quantitative model explained more variance, suggesting that other genes have an additional effect on the disease response. GP may capture better the unexplained variance not accounted for by major genes. Cross predictability to other germplasms like the ADP panel and other traits such as Fusarium response, indicate that we are looking at a general quantitative tolerance mechanism available in tropical Andean germplasm. This should be further characterized and explored to evaluate if breeding can generate acceptable tolerance levels for farmers following this method.The correlation between field and greenhouse data is not based on a major resistance gene, as one might have expected for a disease resistance. This leaves the question what type of genetically controlled mechanism is conferring the trait correlations. Evaluations of agronomic plant performance in the field and visual scoring disease damage in the greenhouse are very different traits, and environmental conditions are contrasting between the hot greenhouses at Palmira on the one hand, and the Popayan field station at ∼700 m higher altitude on the other. Hence, the similarity in traits cannot just be attributed to adaptation. Also, we observe a strong genetic correlation with Pythium and Fusarium response. Data suggests a general quantitative resistance toward root rot disease present in tropical germplasm. The interactions between various root rot pathogens in bean disease development need further investigation. Applying GS looks promising for semi-quantitative or even qualitative traits, such as disease traits and more. This method needs to be evaluated with other quantitative and qualitative traits, and employed in breeding.Soil borne diseases are a growing threat for bean production, a situation likely to be aggravated by climate change. Hence this trait is of high importance for breeding. Some level of genetic resistance or tolerance was found in this panel, showing good correlations of emergence and vigor observed in field trials with greenhouse data, which should aid selection. Genetic studies revealed no major resistance genes. Instead, genomic predictions performed well, with more potential than GWAS/MAS for these disease traits. We found significant predictive ability across the ADP and the RR panel and also across root rot response data for https://doi.org/10.7910/DVN/SVA5CJ.The SNP marker matrix, the raw and modeled phenotypic data of the RR panel used in this study are available for download at Harvard Dataverse: BR, GM, and SB conceived the study. HFB, VA, and LMD conducted the experiments in the field and greenhouse. LMD, JA, and DA-S performed phenotypic and genotypic data analysis, GWAS, genomic predictions, and interpretation of results. CM provided the ADP data from Uganda. BR assisted in the experimental setup and data analysis. LMD, DA-S, and BR drafted the manuscript. All authors contributed to the article and approved the submitted version.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 contend that social science variables are the product of multiple partly heritable traits. Genetic associations with socio-economic status (SES) may differ across populations, but this is a consequence of the intermediary traits associated with SES differences also varying. Furthermore, genetic data allows the social scientist to make causal statements regarding the aetiology and consequences of SES.We argue that, just as a PGS captures the aggregate effect of each individual single nucleotide polymorphism (SNP) used in its construction, each SNP from a GWAS conducted on education, captures the aggregate effect of each heritable trait associated to differences in education. This process, referred to as vertical pleiotropy  describes incidences where phenotype A (intelligence for example) is associated with phenotype B (education) and so a genetic variant found to be associated with phenotype A will also be associated with phenotype B .gr = 0.72, SE = 0.09) , health E = 0.03), ADHD (r = 0.03) , and neuE = 0.02) show conE = 0.02) found thVertical pleiotropy also illustrates why some PGS are population specific. When applied to education, a PGS would be population specific insofar as the heritable traits underlying educational attainment are not universal. An example of this was provided by Some of the heritable traits underlying differences in education may indeed be population specific, as indicated by population specific genetic effects on education . HoweverFinally, Applied to social science variables, MR has helped to understand the causes and consequences of SES differences where intelligence has been putatively shown to be a causal factor for income  and educIn conclusion, PGS and GWAS conducted on social science traits capture the partly heritable traits that likely contribute to some of the variance of SES. Such associations are as authentic as those that act in biological pathways influencing disease traits, the difference being that, for social science traits, SNP associations are at the start of a phenotypic pathway beginning at molecular genetic inheritance and ending at phenotypic consequence. This pathway can differ between populations, but it is a strength of the molecular genetic design that MR can be applied to examine which heritable traits are causally linked to SES differences across and between cultures.


The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities. Enhancing agricultural production efficiency by reducing yield gaps while also breeding more stress-resilient cultivars is the next challenge for plant breeders . The mosPlant breeding was established as a science in the beginning of the 20th century, when new insights about the genetic basis of phenotypic variation for quantitative traits were integrated with the foundational theories of inheritance mechanisms and crop hybridization elucidated by Mendel and Darwin, respectively . Since tPlant phenotyping has always been paramount for genetic improvement. Recent advances in proximal remote sensing, paired with new sensors and computer science applications, has enabled cost-effective high-throughput phenotyping (HTP) and dissection of novel traits . HTP proTraits that are expressed repeatedly or continuously over the lifetime of an individual can be defined as longitudinal traits , infinitCurrent HTP platforms, also referred to as “phenomic” tools, include a variety of methodologies that use remote sensing to obtain non-destructive phenotypic measurements, either in controlled environments or in the field . The mosThe spectral signatures of plants change during the life cycle, giving rise to genotype-time-specific phenotypes. For instance, during senescence, there is an increase of reflectance in the red region caused by a loss of chlorophyll . For fieThe genetic control of longitudinal traits captured with HTP was recently reported for different crops. In a greenhouse, In the field, Field-based HTP for roots has seen less progress than HTP of above-ground traits due to the difficulty of below-ground imaging . ShoveloPlant growth and development are characterized by several phenotypic changes, which can only be studied by monitoring repeated phenotypes over time . HTP plaMany of the complexities of plant growth are commonly represented using non-linear growth models that account for temporal variation in growth, capturing age and size-dependent growth . UsuallyArabidopsis thaliana  function is a type of non-asymptotic, non-linear growth model that does not produce an S-shaped curve . They arthaliana .Linear models, such as orthogonal polynomials and spline functions, are also used to fit longitudinal traits . The useOrthogonal polynomials are particularly popular for fitting biological curves because they have much lower correlations among their coefficients and provide estimates of the covariance matrices that tend to be more robust over a variety of data sets . LegendrSpline functions offer a more flexible alternative for modeling longitudinal traits compared to orthogonal polynomials . SplinesSpline functions have recently been used to model longitudinal traits in crops. For instance, haulm senescence was assessed at several points during the growing season in a diploid potato mapping population and fitted using P-splines . MontesiPlant breeding is mostly based on the selection of new genetically superior cultivars from a large set of candidates. Simple arithmetic means of the phenotypic values, or Best Linear Unbiased Estimation , were used for selection prior to the development of Best Linear Unbiased Prediction BLUP; . BLUPs aAlthough rarely used, pedigree data is an easy and inexpensive source of information for plant breeders to leverage the relationship between individuals for a more accurate estimation of breeding values. Pedigree-based BLUPs have been successfully used in various crops , and havThe inclusion of genomic information provides more accurate estimates of genetic relatedness among genotypes, especially with regards to the Mendelian sampling effects . GenomicFor longitudinal traits, one can calculate BLUPs for each time point separately as individual traits with unique phenotypes; however, these approaches do not directly investigate and compare trends over time . This maIndividual measurements recorded over time can be treated as repeated records of the same trait. This is known as the repeatability model. There are two critical assumptions implicit in this method: (1) the variances of different measurements within the same genotype  are always equal, regardless of the time interval between records; and (2) the genetic correlations between all measurements are equal to one, i.e., measurements at different time points are all influenced by the same genes . In thisOne of the simplest methods is the repeated-measurements analysis of variance (ANOVA) using a split-plot in time design, which treats the genotypes as a whole-plot unit and genotypes at particular times as a sub-plot unit . It is iIn the context of mixed models, specifying the random and fixed effects in the model will depend on the study objectives, data structure, and the assumptions that can be made. Usually, time is considered as a fixed effect because it is not randomized in an experiment. Simple repeatability models have been used to calculate BLUPs and BLUEs of longitudinal traits derived from HTP for genomic prediction, such as in wheat .Often, HTP platforms are used to generate phenotypes of plants in different “ages” or development stages, with the mean and variance of the phenotypes between measurements/assay dates changing over time. Thus, the assumption is that the genetic control of longitudinal traits will be different over time, characterizing the longitudinal records/phenotypes as different traits. A common approach to analyze longitudinal traits in this scenario is a multi-trait analysis that considers each time point as a different dependent variable .Multivariate analysis of variance (MANOVA) is an extension of ANOVA, mentioned earlier, that avoids the covariance structure problems raised in repeated-measures ANOVA. However, it still requires equality in covariance among the groups being compared and balanced data over time. In addition, MANOVA assumes a multivariate normal distribution. Alternative methods have been proposed to overcome these restrictions , but MANIn the BLUP context, multiple-trait mixed models were first implemented by There are some disadvantages of multiple-trait mixed models. For instance, high-dimensional longitudinal data  can lead to over-parameterized models with high computational requirements . There iCanonical transformation of phenotypic records is a common procedure to eliminate autocorrelation among traits through eigenvalue decomposition . A set oThe principal component analysis (PCA) and factor analysis (FA) methods are often more appropriate to reduce dimensionality for a large number of traits. FA identifies common factors, called latent variables, associated with the correlations between variables . On the As longitudinal traits are, by definition, taken along a time trajectory, the whole data set can be represented by parameters describing the shape of the trajectory curve . These parameters can become the new dependent variables or integrated covariance structures in MTM ; howeverA common property of longitudinal traits is that the covariance between repeated measures depends on the interval of time between them. In other words, measurements collected closer in time will be more correlated than measures collected farther apart. First-order autoregressive analysis (AR-1) is an appealing method for modeling covariance structure for phenotypes measured over time . It assuNote that so far, we are assuming homogenous variance over time. There are also covariance structures to handle heterogeneous variance, such as the first-order ante-dependence structure . Thus, LThe RRMs were first introduced in animal breeding to overcome over-parameterized models in MTM and they have been used extensively since then . In summgij is the jth repeated record of genotype i ; bqg is the qth fixed regression coefficient for the gth group; ari is the rth random regression coefficient for the additive genetic effect of the ith genotype; psi is the sth random regression coefficient for permanent environmental effect of the ith genotype; egij is the residual effect; and zqg, zri and zsi are the covariates related to the function used to describe time , assuming the same function  with possible different orders Q, R, and S   .Random regression models have been shown to be the most effective choice to genetically evaluate longitudinal traits in numerous livestock breeding programs  population of selection candidates consisting of individuals that were genotyped but not phenotyped . TherefoUnder the longitudinal framework of GS, the prediction of temporal breeding values enables targeted selection on specific periods in the growing season or selection of individuals that exhibit desirable trait trajectories. In addition, the longitudinal trait can be used as secondary traits to improve the genomic selection of economic endpoint traits such as yields . Campbel1 have shown that the accuracy of GS is greatly improved by incorporating HTP longitudinal data from the so-called secondary traits measured with UAV  and do not take advantage of genetic covariance among traits or phenotypic records collected at different time points . Howeverwith UAV , 2019, awith UAV . In addiwith UAV . TherefoGiven the capability of HTP to collect multiple temporal traits at the same time, multiple-trait RRMs (MTRRMs) can be powerful tools for joint genomic prediction of several longitudinal traits . In addiIn animal breeding and multiple-stage plant breeding analysis, it is common to use deregressed genetic values as the pseudo-phenotypes for genomic predictions. One of the main goals in genomic research is to predict the phenotypic variation using genotypes, by identifying genetic variants. The development of an organism is the result of an interacting network of genes and environmental factors . Unlike As an alternative, Setaria were also revealed using this approach , based on RRMs, which integrates GWAS and mathematical models describing biological processes. In summary, fGWAS estimates the mean for different SNP effects for each genotype and time point and then performs hypothesis testing to determine whether the SNP has any additive or dominant effect during the time course. The main drawback of this method is that it only performs a single-locus analysis. Later, fGWAS by estimating the time-dependent population mean and the SNP effects separately, instead of fitting them directly. They also extended the model to capture the time-varying polygenic effect of complex traits by treating SNPs as covariates (fGWAS-C) or factors (fGWAS-F). However, their method was shown to be computationally inefficient due to the high dimensionality of the mixed model equations compared to other models. Subsequently, To the best of our knowledge, To capitalize on advances in phenotyping and molecular technologies, greater progress is needed in developing ways in which breeders can manipulate systems to understand the relationships between genotype and phenotype. The underlying biological changes due to environmental systems and/or over time can be captured with longitudinal data. The major challenge is synthesizing the various layers of information together in a meaningful manner to understand the downstream effects of developmental stress and implications for breeding .Non-additive genetic effects may significantly contribute to the total genetic variation of complex traits. Prediction models that include dominance effects represent an important component of breeding programs that focus on crossbred populations, hybrid production, and vegetatively-propagated species . There iThe temporal dynamics of longitudinal traits lead to interactions that change the phenotype over time. This may be because the gene-gene and gene-environment interactions (GxE) are time- or age-dependent and need to be properly modeled . In thisThe rapid advances in “-omics” technologies enable the generation of large-scale “-omics” datasets for many crop species, providing new opportunities to investigate and improve complex traits. The different approaches described in this review offer valuable tools to combine phenomics and genomics data to reveal the underlying genetic basis of longitudinal traits. However, one current challenge is integrating additional “-omics” technologies  to provide a holistic multi-omics approach to study biological mechanisms and their response to environmental stresses for important agronomic traits.Brassica rapa. The combination of multi-omics approaches also seems promising to elucidate senescence processes in model and crop plants  is a powerful and highly flexible class of machine learning algorithms based on representation-learning methods that incorporates multiple levels in a non-linear hierarchical learner . EssentiDeep learning has demonstrated its utility in different fields of biological sciences, such as disease diagnosis , multi-oWithin the classes of DL, recurrent neural networks (RNN) are designed for sequential or time-series data  and may FM, KR, and LB conceptualized the manuscript. FM wrote the manuscript. HO, LB, and KR critically revised and improved the manuscript. JV edited section 3 “Modeling Longitudinal Traits.” All authors read 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.


Paramoeba perurans, which colonizes the gill tissues and causes distress for the host. AGD can cause high morbidity and mortalities in salmonid and non-salmonid fish species. To understand the genetic basis of AGD and improve health status of farmed A. salmon, a population of ~ 6,100 individuals belonging to 150 full-sib families was monitored for development of AGD in the sea of Ireland. The population was followed for two rounds of AGD infections, and fish were gill scored to identify severity of disease in first  and the second  infection with freshwater treatment after the first gill-scoring. A subset of this gill-scored population  from 119 full-sib families were genotyped with 57,184 SNPs using custom-made Affymetrix SNP-chip. GWAS analyses were performed which resulted in five significantly associated SNP variants distributed over chromosome 1, 2 and 5. Three candidate genes; c4, tnxb and slc44a4 were found within QTL region of chromosome 2. The tnxb and c4 genes are known to be a part of innate immune system, and may play a role in resistance to AGD. The gain in prediction accuracy obtained by involving genomic information was 9–17% higher than using traditional pedigree information.Amoebic gill disease (AGD) is a parasitic disease caused by the amoeba  Paramoeba perurans, which colonizes gill tissue2 and ultimately causes inappetence, respiratory distress and cardiovascular compromise4. Attachment of amoebae to the gill initiates a localized host cellular response, including hyperplasia and hypertrophy of the gill epithelium and lamellar fusion5. This pathological condition can cause high production losses in multiple salmonid and non-salmonid fish species4. Although AGD can occur year-round, it is most prevalent in warmer water and high salinity with increased frequency and severity6. The existence and severity of disease at farmed facilities is evaluated by random sampling of fish (~10–15) and investigation and/or scoring of gills for pathological conditions caused by amoeba as described by Taylor et al.7. The detection for the specific type of pathogen/amoeba and the pathogen load (measure of disease severity) can also be determined using quantitative PCR (qPCR) where load of pathogen is determined using threshold cycle (Ct) values10. However, studies have shown that the severity of AGD established using Taylor scoring system vs. Ct values using qPCR have high genetic (close to unity) and phenotypic (0.81) correlations revealing that both are potentially the same trait10.Amoebic gill disease (AGD) is caused by a parasite Salmo salar) of Tasmania, and cleaning of amoeba from the gills requires freshwater treatments which costs ~10–20% of the total production cost4. The treatment is also a welfare issue for the fish due to the physiological stress caused by freshwater bath.Amoebic gill disease has been a major problem over many years in farmed Atlantic salmon , Norway and the Faroe Islands caused major AGD outbreaks on farmed Atlantic salmon11, and AGD became the largest infectious health problem for the salmon industry in Ireland, Scotland and France those years12.In Northern Atlantic, the presence of 13, and since then amoeba has been regularly reported every year on the southwest coast and further north14 in Norway.AGD is a rising threat for Norwegian salmon with first documented occurrence in 200615. However, reported heritability estimates for AGD score in Tasmanian population showed higher range with estimates of 0.10 to 0.4817, with lower heritability estimates obtained from the first infection and the higher estimates for the subsequent infections. Tasmanian research has shown that the resistance against first and later subsequent infections are different traits with poor genetic correlations  and/or genomic selection (GS) using molecular markers that are directly or indirectly linked to variation in causal loci could provide potent tools to overcome these challenges which may increase both selection accuracy as well as selection intensity as this allow also for within family selection21. In addition, identification and subsequent fine-mapping of QTL regions should allow for the pinpointing of genes that underlie such traits. Significant association between genetic markers and quantitative traits of economic importance have been reported in Atlantic salmon24.Norwegian Atlantic salmon populations from the two breeding companies  have shown genetic variation for resistance against AGD both in field 27 as well as in cellular-adhesion (e.g. CCAAT/enhancer binding protein beta)25 were reported to be of importance for playing role in trait variation.A few studies have been conducted on detection of QTL for resistance against AGD, where results have shown that the genetic architecture for AGD resistance trait is polygenic in nature28 and provides opportunity to rank individuals within and across families. The feasibility of GS depends on the availability of a high-quality SNP genotyping platform and on extensive trait records collected in the reference populations. It has already been a widely used approach in many livestock and aquaculture species33 due to relative reduction in genotypic and sequencing costs which is primarily applied for the improvement of traits of economic and welfare importance (e.g. disease resistance).Availability and popularity of advanced GS by which breeding values of individuals are predicted using statistical methods has become a method of choice in recent era. The GS involves genotypic data for genome-wide distributed single nucleotide polymorphism (SNP) markersThe aim of the current study was to identify the genetic basis of host resistance to AGD by performing genome-wide association analysis (GWAS), and compare accuracies of genomic vs. pedigree-based predictions.The population used originated from Marine Harvest (MH) breeding nucleus in Ireland which was developed with a cross of parents mated in 1:2 male to female ratio. The starting population had 150 full sib families with 40 full-sibs per family. Families were communally reared from the eyed egg stage, and the tagging was performed at an average body weight of ~45 g. At tagging, fin clip samples were collected and preserved in 100% ethanol for further DNA extraction and genotyping (~50 K SNP chip). Pedigree was constructed using a panel of 65 SNP markers.P. perurans was confirmed by PCR. The monitoring for the development of AGD in the test cage was done by regular gill-scoring of a small number (~10–15) of fish per week. Gills were scored from 0 to 5 as described by Taylor et. al.7 with intensity of infection increasing with ascending order of score level. Major scoring for AGD phenotypes (first infection) was done on 29th July 2014 when the estimated average gill-score in the sea net-cage passed score 2. Fish were haphazardly picked from the cage and uniquely gill scored by one of the three scorers . The sampling continued until the daylight was no longer appropriate for gill scoring which resulted in 3,663 gill-scored fish. The day after the fish were treated with fresh water to kill the amoeba, after which the fish were allowed to develop another round of a natural AGD infection (second infection). Scoring for second infection was also done by the same three scorers on 12th of September 2014, which resulted in 3,511 gill-scored fish.A population of 6,100 fish at an average weight of 61 g were placed in a sea net-cage on 24. April 2014 at the South West farm of Marine Harvest Ireland, where AGD is a common problem. Fish population was allowed to develop AGD from the natural concentration of amoeba in sea. In June 2014, AGD was reported at the farm holding the fish and presence of From the 3,663 gill-scored fish during first infection, 1,190 fish (10 sibs per family) were randomly selected from 119 full-sib families for further processing. Genomic DNA was extracted from the fin clips using a commercial kit , following the manufacturer’s instructions. Fish were genotyped using a 57 K axiom Affymetrix SNP Genotyping Array (NOFSAL2). After genotyping, we were left with a total of 1334 (193 Parents + 1141 Progeny) individuals from 119 full-sib families of 6–10 offspring per family and the rest either failed to genotype or filtered out during genotype calls due to the poor genotype quality.34, and SNPs were excluded with minor allele frequency (MAF) lower than 5%, missing rate higher than 15%, and Mendelian errors. Finally, approximately 54 K SNPs were retained for analyses.Genotypic data was filtered using the Plink softwareAnalyses were performed for both first and second infection but the second infection did not show any genetic variation for this dataset of 1,141 phenotypes. Hence, following analyses were continued with gill-scores obtained in first infection only.The applied model also included a fixed effect of scorer and an additional random effect common to full-sibs other than additive genetics. However, both effects were found to be non-significant (P > 0.05) and was therefore omitted from the model.35.36 as Genome wide association analysis was performed using the following linear mixed animal model implemented in GCTA program with “–mlma-loco” and “–reml” functions37 for multiple testing  of SNPs were considered genome wide significant when they exceeded the Bonferroni thresholdQuantile-quantile (q-q plot) plot with distribution of observed vs. expected p-values was checked, and the Inflation factor  was calculated using following equationVariances explained by the top significant SNP(s) were estimated using following two approaches (direct and indirect).38). Therefore, the proportion of the of genetic  were estimated as For the indirect approach, the proportion of the genetic or phenotypic variance explained by the genome-wide significant SNP(s) was estimated using the model: 39 program. Pedigree-based breeding values were computed by replacing the G matrix with the numerator relationship matrix (A). Pedigree breeding values were obtained with the dataset consisting of phenotypic records from only the genotyped (PBLUP_I) or from all the phenotyped (PBLUP_II) animals. Similarly, genomic breeding values were computed using records from only the genotyped animals (GBLUP) or a combined relationship matrix that uses all genotyped and phenotyped (ssGBLUP) animals. Whereas the G matrix was used for the GBLUP analysis, the realized relationship matrix (H) replaces G. The inverse of the H matrix  was constructed as follows:15 and was fixed in all analysis.Pedigree as well as genomic breeding values (PEBVs vs. GEBVs) were computed using full  or reduced  datasets. The full dataset contained phenotypic records on all the recorded animals , while the reduced dataset  included phenotypic records on only the genotyped individuals which is a subset of the full data. Breeding values were estimated by applying the same model as described under the “GWAS” section of materials and methods, except that the marker effect  and the remaining animals were used as training dataset. This procedure was replicated 50 times and for each replicate, accuracy of predictions were computed as:et al. 201915.the correlation  are known to play a role in the immune system41.Potential genes with effect on AGD were searched around ±20 Kb region of the genome or chromosome-wide significant SNPs using salmon genome databaseNP Table . Both gePedigree and genomic-based prediction accuracies for amoebic gill disease are given in Table pes n=1,11 of onlyAlthough animals were used in this work, no direct experiments were performed on them. Permission was taken to use the site  for farming and production, and fish were moved to the site after obtaining the approval. Health status of fish was monitored regularly, and in-case of any outbreak fish were recorded and treated as part of routine practices. The studied population faced natural outbreak of AGD twice, and the recording of phenotype(s) is a routine procedure of breeding companies. The collection of tissue samples was carried out by highly skilled and experienced personnel from the breeding company. Tagging of fish, and the sampling the fin clips is not considered as an experimental intervention in the EU. Hence, no approval from the ethics committee was necessary according to local legislation.Amoebic gill disease (AGD) is an increasing threat for the Atlantic salmon industry and understanding genetic basis of AGD and the application of advanced selection methods could lead to robust and sustainable salmon production. Current study aimed at detection of QTL(s) for AGD resistance as well as determine consistency in accuracy of genomic vs. pedigree based prediction methods.15. The observed genomic heritability of ~11.5% (for AGD score at first infection) obtained from our analysis was found to be lower than the previously reported estimates by Lillehammer et al.15 with 20% and 11% in field and challenge conditions respectively, and a field test based heritability of 14% (first infection) reported by Kube et al.16. The reported heritability estimates on AGD scores recorded at the third infection in challenge conditions fall within medium to higher level with estimates ranging from 0.24 to 0.488. The difference in estimates could have been due to multiple reasons e.g. difference in adopted methodology for the estimations, population differences, infection types (first vs. subsequent later infections) used as phenotype which might activate different immune responses, e.g. first infection should activate innate while subsequent later infection should be mainly pursued by acquired immune system, and/or the total number of markers used in this study perhaps could not explain the total genetic variance.The model applied in our statistical analyses did not include scorers as fixed effect because it was found to be non- significant (P > 0.05) for this dataset. However, scoring system for determining the severity of AGD is subjective, and it is therefore recommended to include scorer-effect in the model as it might have a significant effect as seen in some other experimentset al. and Boison et al.9. This disagreement could likely be due to the differences in infection conditions , infection type or time of recording (first vs. subsequent infections), and/or differences in populations. The studied populations in both Robledo et al. and Boison et al.9 were challenge tested and had a trait in common where amoebic load was recorded using qPCR based et al. and Boison et al.9 also did not show concordance in any of the detected QTLs which further highlights the complexity of this trait, and perhaps indicates the cruciality of factors i.e. environment, time and type of recordings and genetic background of populations.The detected significant QTLs at chromosomes Ssa01, Ssa02 and Ssa05 for the AGD scores after first infectiondid not show concordance with the detected QTLs in previous studies of Robledo 42. Large impact of these SNPs on genetic variation does not necessarily mean that the SNPs are causative mutations, but that these SNPs explain an important amount of the QTL variation, either directly or through LD with the causative mutations.The top three most significant SNPs of GWAS analysis showed a favorable effect on the trait with negative α values which represent a reduction in AGD score Table , while s40, and interestingly they were positioned in homeologous block, 2p-5q_1 of both chromosomes (Ssa02 and Ssa05). However, when their positions were checked towards a recently developed linkage map (unpublished data), all three significant SNPs of Ssa02 and Ssa05 were found to be closely located on the same chromosome Ssa02 within a distance of 0.62 cM. The LD pattern and linkage map suggest that the top significant SNPs of Ssa02 and Ssa05 are located at the same chromosome which is discordant with the physical map and could be due to assembly error or complicated long-range linkage disequilibria explained by Koch et al.43. Manhattan plot for all chromosomes and the distribution of P-values for the SNPs on Ssa02 were replotted after correcting positions of significant markers  which provided relatively better shape of the QTL peak.It is interesting to note that the significant SNPs on Ssa02 are located within ~92 Kb region  and show high LD of 0.77, but the SNPs “AX-88137791” and “AX-87970438” located on chromosome Ssa02 and Ssa05, respectively showed complete LD of 1.0. The LD information among the significant SNPs in Figure S1.4 and Table S1.1 gives strong impression of co-segregation pattern for the SNPs on Ssa02 and Ssa05. We checked if SNPs were belonging to the homeologous regions of Ssa02 and Ssa05tnxb.b and c4 which have been reported to be involved in the immune response  of the highest and upstream (~71.5 Kb distant) to the second highest SNPs of Ssa02. Gene C4 is known to be central to the activation of both the classical and the lectin pathways of complement system that enhances the ability of antibodies and phagocytic cells to clear pathogens from an organism45. The functional properties of these candidate genes (tnxb.b & C4) with their role in immune system and in cellular-adhesion agrees with transcriptomics results of previous studies where immune and cellular-adhesion functionality genes were detected to be differentially expressed25. Available annotated information on both the genes (tnxb.b & C4) signifies the impact with strong signal that the QTL of Ssa02 could directly and/or indirectly be linked with variation in expression level and/or function of these gene(s), which ultimately cause variation in AGD score. However, further studies on QTL validations as well as advanced assays like in situ hybridization technique to detect localization of gene expression, differential expression or sequencing of selected genes in susceptible and resistance fish might lead to a better understanding of the biological mechanism/pathways in response to this pathogen.The region of ±20 Kb surrounding significant SNPs on Ssa02 include genes se Table , and may47 for parasite and pathogen resistance traits, as well as for production traits in livestock species50. The advantage of GBLUP over PBLUP is because realized genomic-based relatedness between animals deviate from pedigree-based relationship coefficients.Overall, we observed 9.3% increase in accuracy with genomic information depending on the method used to calculate the accuracies. Similar trend of higher accuracies using genomic vs. pedigree-based information has been reported in Atlantic salmon52 and cattle53.Our results showed that single-step methodology (ssGBLUP), which take advantage of pedigree, phenotypic and genomic information simultaneously, gave higher prediction accuracies compared to PBLUP and/or GBLUP, which used only pedigree or genomic information. Similar results obtained with ssGBLUP were reported in salmonidsc4, tnxb, and slc44a4 were found within nearly 20 Kb flanking region of the detected loci on chromosome 2. The tnxb and c4 genes are known to be a part of innate immune system, which may be involved in resistance to AGD. Genomic prediction using SNP based genotypic data  improved prediction accracy with 9–17% over the pedigree-based predictions which highlights both the potential and importance of genomic selection in commercial breeding programs.A SNP array based genotyping of a population of Atlantic salmon which was field recorded for resistance to AGD revealed one genome-wide significant and the two suggestive QTLs distributed over chromosome 1, 2 and 5. Three candidate genes; Supplementary file 1.


Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.Genomics and high throughput phenomics have the potential to revolutionize the field of wheat ( Agronomically important traits are often controlled by a large number of small effect quantitative trait locus (QTLs), which have been challenging to take advantage of in plant breeding . In receOriginally proposed by Genomic selection models rely on accurate phenotypic information which has been the main driver for increasing genetic gain in classical breeding approaches. Prediction accuracy of GS models depends upon the quality of phenotypic data collected on the training population . HoweverThe basis of spectral radiometry is to measure electromagnetic energy at varying wavelengths interacting with different plant parts. Plant cells, tissues, and metabolites have a light specific reflectance, absorption, and transmittance of photons . The pheCombining information from SRI and GS to identify lines which have higher genetic potential for grain yield and end-use quality traits have the potential for use in wheat breeding. There are various physiological processes affecting grain yield and grain protein content (GPC) in wheat, and SRI provides indirect information about them . TraditiThese findings show that incorporation of secondary traits helps improve the performance of GS models. Grain yield and GPC are two traits very important in hard red spring wheat breeding, yet difficult to select for due to their well-known negative correlation. Furthermore, these traits are correlated to various SRI and can be used for indirect selection. Thus, we wanted to evaluate the (1) comparison between univariate and multivariate GS models for predicting GPC and grain yield in wheat; (2) identification of the best growth stage for collecting spectral information in wheat breeding in the Pacific Northwest (PNW) for each trait; (3) selection of the best SRI for incorporation into GS models; and (4) assessment of GS model performance for within and across environments predictions for both traits.F2 plants from each family were planted in greenhouse in Bozeman, MT in 2010. Selections were performed from the F2 to F5 generation to select early flowering plants and photoperiod insensitive genotypes. Furthermore, plants having height greater than the median height were discarded, as most of the landraces were homozygous for Rht alleles. Complete details about the population development is referred to The nested association mapping (NAM) population used in this study consists of 32 spring wheat accessions from the USDA-ARS National Small Grains Collection, each crossed to common cultivar “Berkut” to create 32 half-sib families . Berkut –1) was obtained from grain weight per plot with a Wintersteiger Nursery Master combine . A Perten DA 7,000 NIR analyzer  was used to determine the percentage of GPC. A handheld CROPSCAN multi-spectral radiometer  was used to obtain spectral reflectance at Feekes growth stages 10.1 (heading) and 11.1 (grain filling) (Grain yield (thafilling) . These tfilling) , 2018b. filling) .The augmented complete block design (ACBD) model implemented in the R program was used to calculate adjusted means for all phenotypic data collected under field conditions between 2014 and 2016 . Best liij is the primary trait, u is the mean effect, Blocki represents effect of the ith block, Checkj denotes effect of the repeated checks on each block, Genj represents un-replicated genotypes, and eij is the standard normal error. Broad sense heritability for all traits is obtained from the ACBD model treating genotypic effects as random and using the formula Where YH2 is the broad-sense heritability, σ2g and σ2e are the genotypic and error variance components.Where Narrow sense heritability for both primary and secondary traits were calculated with the modelN, where G is the genomic relationship matrix and σ2a is the additive genetic variance, and e ∼ N, where I is the identity matrix and σ2e is the residual variance. Narrow sense heritability for primary and secondary traits was calculated using the formula h2 = Where Y is the BLUP of the genotype for each trait, X is an incidence matrix for the fixed effect (b), Z is also an incidence matrix corresponding to random genetic effect (g), and e is the standard error. Variance and covariance were based on the assumptions that g ∼ rg) was calculated with the bivariate model, which is represented asGenetic correlation  and secondary trait (one of the SRI), separately, and b, g, and e are the fixed effects, random genetic effect, and residual for each trait, respectively. Variance components were calculated assuming N, where H is the genetic variance-covariance matrix and G is genomic relationship matrix, and N, where I is identify matrix, and R is the residual variance covariance matrix. Genetic correlation was calculated asWhere ycov is the covariance between primary and secondary trait, and var(A), and var(B) represents the genetic variance of the primary and secondary trait, individually , g(xi) is a function relating the predictors to the phenotypes, and ei is the residual term. Each GS model tries to lower the residuals or certain loss function. The GEBVs estimated from the models are equal to g(xi).Where The traditional least square regression for predicting a trait and their residual sum of the square is represented asRidge regression is very similar to least square regression, except the coefficients of the equations are estimated using minimizing the ridge regression coefficient estimated asWhere λ is known as the tuning parameter. We can see that ridge regression fit the data by lowering the RSS and selecting small coefficients for λ in GS is assuming that marker effects are drawn from a normal distribution centered on zero and solving the mixed linear model equation of 2e/σ2u where σ2e is the residual variance and σ2u is marker effect variance, as small σ2u will result in shrinking of marker effects strongly toward zero, showing that large λ has an equivalent impact too. Here in this study, we used the rrBLUP package for performing GS using shrinkage capacity of the ridge regression; and this model is equivalent to the traditional BLUP models, and hence all the formulas are represented as mixed model equations.Another way of solving for The genome-wide marker effects for GPC and grain yield were estimated using ridge regression best linear unbiased prediction (rrBLUP) including SRI in the model . PredictN and e is the residual error with e ∼ N. The solution for mixed equation can be written asWhere y is the N × 1 vector of BLUEs for the phenotypic trait, μ is the overall mean, Z is an N × M matrix linking markers to the genotypes, μ is the vector of normally distributed random marker effects as μ∼ 2e/σ2u. The rrBLUP model was used in this study, as it has the capability of dealing with “large p and small n” with penalized regression and high stability with correlated markers  univariate-single trait GS (Uni-GS) model, (2) models using SRI as predictors only, (3) GS model with SRI as a phenotypic covariate, and (4) multivariate GS model where primary and secondary traits were fitted for each environment. The SRI collected at heading and grain filling stages were fitted separately in the model to identify the best stage for data collection for GPC and grain yield. The models used for the analysis are(1.)Uni-GS used to calculate the GEBVs and represented asN and e is the residual error with e ∼ N. This equation was solved to obtain the GEBVs for all the lines, treating markers as independent variables.Where y is an N × 1 vector of BLUEs for GPC and grain yield for each line, μ is the overall mean, Z is an N × M matrix assigning markers to genotypes and u is a 1 × N matrix of normally distributed marker predictor effects as u ∼ (2.)The univariate model fitting SRI as predictors (SRIr). Only SRI information was used for prediction and the model is represented asX is the design matrix for the fixed effect components (SRI) and β is the vector of fixed effect coefficients. Other terms are defined previously. This equation was solved using SRI information as independent variables in the model.Where (3.)In a covariate GS model, SRI were fitted as fixed effects (GS + SRI). The equation of the model is represented asN and e is the residual error with e ∼ N. In this case, both markers and SRI information was used as independent variables in the model for obtaining GEBVs for all the lines.Where X and Z are the design matrix associating the fixed effects (SRI) and random effects (markers), β is the vector of fixed effect coefficient of each SRI, u is a vector of normally distributed random marker effects as u∼ (4.)Lastly, a multi-variate GS model was used containing primary and secondary traits in the model (Multivariate GS). The equation of the model is represented as1 to n represents the vector of BLUEs for the primary (GPC and grain yield) and secondary traits (SRI) y, X is a design matrix of fixed effects which simplifies to a vector of 1 for each trait representing the mean only as only markers were entered in the model, Z represents the random effect design matrix, N where G is the genomic relationship matrix and H is the variance-covariance matrix, and ∈n1… represents the standard normal error, distributed as ∼ N, where I is identify matrix, and R is the residual variance covariance matrix. The covariance matrix H and R were assumed unstructured for the variance estimation. In this multivariate equation, markers are used as independent variables while SRI and primary traits (grain yield or GPC) are used as dependent variables for predicting the GEBVs for all the lines.Where n is the number of traits , yThe GS models were developed separately for each environment using a subset of the population as the training set to estimate each marker effect. After assessing the marker effects, GEBVs were calculated for the whole population. GS model accuracy is defined as a correlation between GEBVs of predicted individuals and actual phenotypes. GS was performed with five-fold cross-validation by including 80% of the lines in the training model and predicting the GEBVs values of the remaining 20% of the lines under each environment. One replicate consisted of five model iterations where the population was split into five groups, and the testing set was rotated between each group. For accuracy assessment, two 50 replication sets were performed.Independent validations were performed by training the GS model using the 2014 environment and predicting the GEBVs for remaining two environments. Similarly, the model trained on the 2015 environment was used for predicting GEBVs for the 2016 environment. These validations represent the scenario to predict the performance of a line before planting them in the field for further observations. Furthermore, SRI data were also included in the independent validation GS models. These GS models represent a scenario in the breeding program where SRI have been collected, and the plants are not harvested; therefore, lines will be selected primarily on predicted values.Average grain yield ranged from 1.7 to 2.4 t/ha across the three environments with 2016 having the highest and 2015 being the lowest yielding. Average GPC ranged from 12.2 to 14.4% with 2014 being the highest, and 2015 being the lowest . Broad-sIn contrast to heritability, correlation between primary traits (grain yield and GPC) and secondary traits varied significantly across growth stages and environments , 5. ThisFor the three environments, GS prediction ability ranged between 0.07 and 0.58 for grain yield using four different models . There wp < 0.05) improvement compared to the grain filling stage. The maximum prediction accuracy for GPC was observed during the 2014 environment (0.64), followed by the 2015 (0.58) and 2016 (0.56) environments using multivariate GS models  improvement in prediction accuracies for both traits, which can be attributed to the genetic correlation between primary and secondary traits used in this study. These results are strengthened by GS studies in animals and other crop plants, where improvements in predictions were observed with the inclusion of secondary correlated traits  improvement for predicting GPC during 2014 and 2015 environment compared to multiple SRI in the model. GNDVI was the best SRI which resulted in the highest increase in GPC prediction accuracy for all three environments, suggesting that there is some strong relationship between GNDVI and GPC. GNDVI measures reflection in the near infra-red and green region of the electromagnetic spectrum , validating our results for inclusion of the single most influential SRI in the GS models. In this context, due to issues associated with inclusion of large number of SRI in the GS models, it is advisable to select the best SRI for making predictions. In this study, we concluded that GNDVI and NWI result in the highest improvement in the prediction accuracy for GPC and grain yield, respectively, and should be used in selecting improved lines. Furthermore, these indices have higher genetic correlation and heritability for inclusion in the GS models and will ultimately translate to increase the genetic gain in wheat breeding.Another advantage of including individual SRI compared to multiple SRI, is computational time and convergence problems in the mixed model outcomes. There is no advantage of including a large number of SRI in the GS models if they do not increase the prediction accuracy. p < 0.05) decrease in across environment prediction accuracy compared to cross-validation prediction accuracy. This is due to the different environmental conditions in training and testing environments compared to the common environment for the cross-validation predictions. Furthermore, univariate GS models are not able to model the genotype by environment interactions and provide an opportunity for inclusion of secondary correlated traits or genotype by environment interactions into the GS models to explain such variations. The other reason for the decrease in prediction accuracy is that both these traits are polygenic. Hence, training of a population in one environment results in the prediction of marker effect under that environment only, completely ignoring non-genetic variations (We also applied across cycle predictions providing realistic scenarios for the use of GS in plant breeding programs by testing the performance of lines in untested environments. There was a significant (riations . When thThe highest independent prediction accuracy for grain yield was observed for the 2016 environment when the GS model was trained on the 2015 environment. This is because the genotypic variance explained for grain yield was highest for the 2015 environment compared to the other two environments . Higher Previous studies by We did not observe any improvement in independent prediction accuracies for GPC in 2016, which can be attributed to the negative correlation observed between GPC and SRI in 2016 compared to positive correlation in other environments. This suggests that GS selection models used for across cycle predictions are problematic if the secondary correlated traits are affected by environmental conditions and do not account for unpredictable genotype by environment interactions. These findings provide evidence that increased prediction in the untested environment is governed by the secondary correlated traits.The approach used for across cycles predictions in this study resembles a breeding program where SRI has been collected, and plots are not yet harvested to get information for grain yield and GPC. This allows the breeders to make the selection based on these predicted GEBVs by incorporation of secondary traits in the multi-trait GS models. Previously, it has been shown that these secondary correlated traits have the potential for indirect selection in the PNW for spring and winter wheat breeding ,b. This Our study demonstrates the improvement in GS prediction accuracies for grain yield and GPC in wheat with the inclusion of secondary correlated traits in the models and identifies the most effective SRI and plant growth stage for secondary data collection. We observed a vital role of secondary traits for improving the predictions for both within and across cycle predictions. On average, there was an improvement in prediction accuracies of 20% for grain yield and 12% for GPC. Moreover, we observed that secondary traits have the potential to improve independent validations, showing their capabilities to accommodate for different environmental effects in the models. This study shows the potential of combining genomics and HTP for improving selection in wheat breeding programs and can be transferable to other plant breeding programs. Inclusion of HTP and GS in a plant breeding program will ultimately improve the genetic gain by increasing the selection accuracy and reducing the cycle time.The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.KS analyzed the data, conceptualized the idea, and drafted the manuscript. PM assisted in genotyping curation and edited the manuscript. ML collected the phenotypic data. MP edited the manuscript, conducted field trials, and obtained the funding for the project. AC edited the manuscript, conducted field trials, and obtained the funding for the project. 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.


Wolbachia bacteria infect most insects and other arthropods, making these bacteria some of the most common endosymbionts in nature. Despite their global prevalence, it remains mostly unknown how Wolbachia influence host physiology and behavior to proliferate. We demonstrate pervasive effects of Wolbachia on Drosophila temperature preference. Most hosts infected with A-group Wolbachia prefer cooler temperatures, whereas the one host species infected with divergent B-group Wolbachia prefers warmer temperatures, relative to uninfected genotypes. Changes to host temperature preference generally do not alter Wolbachia abundance in host tissues, but for some A-group strains, adult males have increased Wolbachia titer when shifted to a cooler temperature. This suggests that Wolbachia-induced changes to host behavior may promote bacterial replication. Our results help elucidate the impact of endosymbionts on their hosts amid the global Wolbachia pandemic.Microbes infect a diversity of species, influencing the performance and fitness of their hosts. Maternally transmitted  Wolbachia, a bacterial endosymbiont known to alter host reproduction, nutrient acquisition, and virus susceptibility. Here, we broadly test the hypothesis that Wolbachia modifies host behavior by assessing the effects of eight different Wolbachia strains on the temperature preference of six Drosophila melanogaster subgroup species. Four of the seven host genotypes infected with A-group Wolbachia strains  prefer significantly cooler temperatures relative to uninfected genotypes. Contrastingly, when infected with divergent B-group wMau, Drosophila mauritiana prefers a warmer temperature. For most strains, changes to host temperature preference do not alter Wolbachia titer. However, males infected with wSh and wTei tend to experience an increase in titer when shifted to a cooler temperature for 24 h, suggesting that Wolbachia-induced changes to host behavior may promote bacterial replication. Our results indicate that Wolbachia modifications to host temperature preference are likely widespread, which has important implications for insect thermoregulation and physiology. Understanding the fitness consequences of these Wolbachia effects is crucial for predicting evolutionary outcomes of host-symbiont interactions, including how Wolbachia spreads to become common.Heritable symbionts can modify a range of ecologically important host traits, including behavior. About half of all insect species are infected with maternally transmitted  Heritable symbionts have diverse ecological effects on their hosts. In insects, microbial symbionts influence host reproduction  , 2, acqu3–10–15–21–Wolbachia bacteria are the most common endosymbionts in nature, infecting the cells of about half of all insect species, as well as other arthropods  have important implications for insects, because ectothermic performance and fitness explicitly depend on temperature . It is still unknown whether observed changes to pT increase or decrease Wolbachia titer  and one B-group Wolbachia-infected genotype (wMau in Drosophila mauritiana), which diverged from A-group strains 6 to 46 million years ago  prefer a significantly cooler pT than uninfected flies of the same host genotype. In contrast, D. mauritiana infected with B-group wMau have a significantly warmer pT. Unlike previous reports  of their Drosophila host species  and a Poisson error structure , wHa-infected D. simulans , wMau-infected D. mauritiana , wSh-infected D. sechellia , and wTei-infected D. teissieri   mean temperature of 21.72°C ± 1.02°C (±standard error [SE]) compared to 23.12°C ± 1.02°C for uninfected flies, wHa-infected D. simulans preferred an LS mean of 23.56°C ± 1.01°C compared to the uninfected mean of 24.89°C ± 1.01°C, wSh-infected D. sechellia preferred an LS mean of 23.32°C ± 1.01°C compared to the uninfected mean of 23.98°C ± 1.01°C, and wTei-infected D. teissieri preferred an LS mean of 22.7°C ± 1.01°C compared to the uninfected mean of 23.7°C ± 1.01°C. In contrast, D. mauritana infected with B-group wMau preferred a warmer LS mean temperature of 21.15°C ± 1.01°C compared to the uninfected mean of 19.67°C ± 1.02°C.We used a thermal gradient apparatus to test whether eight different tructure . Wolbach= 0.004) . These r= 0.004) . Of the ed flies : wRi-inf10.1128/mBio.01768-20.1FIG S1FIG S1, DOCX file, 0.4 MB.The thermal gradient apparatus is composed of a 44 × 13× 1 cm aluminum plate and a 1-cm-high removable Plexiglas lid. The thermal gradient is subdivided into seven 10 × 6 cm sections  and wHa-infected D. simulans  , such that older flies tended to prefer cooler temperatures. Finally, we found that the run order each day had a significant effect on pT for the wMelCS-D. melanogaster  and the wMau-D. mauritiana genotypes . In both instances, flies assayed earlier in the day tended to prefer cooler temperatures. This is consistent with prior findings that the pT of D. melanogaster increases from morning to evening due to a circadian clock  was specifically shown to have increasing pT throughout the day  .In addition to = 0.009) . For botn status . For thean clock . In factstrains) . Circadistrains) . We alsoWolbachia preferred cooler temperatures, whereas the one species infected with B-group Wolbachia preferred a warmer temperature. We conducted a phylogenomic analysis to test whether closely related Wolbachia strains exhibit similar effects on host pT. We generated a Wolbachia phylogram and used the change in LS mean pT of each host genotype to test for phylogenetic signal , whereas a λ value of 0 indicates that character evolution occurs independently of phylogenetic relationships (P = 0.203). Simulations suggest that a much larger number of Wolbachia strains are required to statistically distinguish λ ≈ 0.8 from zero , whereas the N = 50 tree had a λ estimate that does not overlap with zero . Unfortunately, far fewer strains exist in laboratory culture, precluding such an analysis. Nevertheless, our finding that most A-group Wolbachia decreased host pT and the one B-group strain increased host pT hints that divergent Wolbachia may have contrasting effects on host behavior.Notably, hosts infected with A-group c signal . A Pagelionships , 67. Ourrom zero . A simul10.1128/mBio.01768-20.2FIG S2Wolbachia phylogram . For simulated trees, character evolution was simulated with our λ estimate of 0.778 using the “sim.bdtree” and “sim.char” functions in the geiger R package  have a large number of near-zero λ values arising by random chance, which increases the uncertainty of parameter estimation. Indeed, small phylogenies are likely to generate near-zero λ values by chance, not necessarily because the phylogeny is unimportant for trait evolution , bootstrapped estimates of λ cluster around the true λ value fitted to the original phylogeny. Download FIG S2, DOCX file, 0.1 MB.Distribution of maximum likelihood estimates of λ from 1,000 bootstrap replicates. The bootstrap analysis for our hylogram  is shown package . For eacvolution . As the Copyright © 2020 Hague et al.2020Hague et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the pT of infected flies represents a host-induced behavior to reduce Wolbachia titer and ameliorate the negative effects of infection  to a cool temperature should reduce Wolbachia titer in host bodies , whereas shifting D. mauritiana infected with wMau to a warm temperature should reduce Wolbachia titer . We tested whether shifting infected hosts toward their pT increases or decreases Wolbachia titer  or males . Similarly, for wHa-infected D. simulans, titer did not differ between the temperature treatments for females  or males . We also observed no significant difference in titer between temperature treatments for wMau-infected D. mauritiana females  or males . For wSh-infected D. sechellia, we detected no difference in Wolbachia titer between females from each temperature treatment ; however, we found that males significantly differed in titer between cold and warm treatments . Male D. sechellia shifted to 18°C had a higher median relative Wolbachia density (0.16) than males shifted to 25°C (0.11). This pattern suggests that shifting infected males toward their pT increases Wolbachia titer. We found a similar result for wTei-infected D. teissieri. While we detected no difference in Wolbachia titer between the treatments for females , males differed significantly in titer between the cold and warm treatments . As with D. sechellia, male D. teissieri shifted to 18°C had a higher median relative Wolbachia density (3.36) than males shifted to 25°C (2.98). Importantly, the wSh and wTei results suggest that males shifted to a colder temperature experience an increase in titer; however, these titer increases are not significant at a threshold of P = 0.005 after a Bonferroni correction for multiple tests.For Wolbachia may generally influence host thermoregulatory behavior. Five of the eight Wolbachia strains we assayed had a significant effect on host pT: wRi in D. simulans, wHa in D. simulans, wMau in D. mauritiana, wSh in D. sechellia, and wTei in D. teissieri. In contrast to past reports . Temperature is considered a major ecological factor limiting the distribution of Drosophila  is required to test whether A- and B-group Wolbachia effects on pT exhibit phylogenetic signal (see Wolbachia increase pT. The only other B-group strains that infect hosts in the D. melanogaster subgroup (wNo and wSn) almost always occur as coinfections with other Wolbachia   and likely spread recently via introgression (wHa in D. simulans (157.1) and wSh in D. sechellia (0.2) differ by nearly 3 orders of magnitude  and wHa (24.97°C) genotypes was significantly different . This was also true for the mean pT of the uninfected wMelCS (27.9°C) and wMel (24.3°C) D. melanogaster genotypes . Prior work has similarly found that pT of D. melanogaster varies in North America along a latitudinal cline  , yet wTe. yakuba . Similargression , 88, yetagnitude . Host baal cline . Indeed,ia titer , maternasmission , componesmission –93, and mission ––96.Wolbachia does not influence D. melanogasterpT, in contrast to past reports (pT of wMelCS-infected D. melanogaster (25.06°C versus 25.78°C for uninfected flies), and Truitt and colleagues  reduced D. melanogasterpT by nearly 4°C. The effect size reported by Truitt et al. , whereas prior analyses did not . Our results are consistent with a hypothesis of parasite manipulation, in which Wolbachia alters host behavior to seek environmental conditions that promote Wolbachia growth  to reduce Wolbachia titer and mitigate the negative effects of infection  reduce wAlbB titer in mosquitoes at the larval stage, but then titer rebounds in adulthood when fourth instar larvae are shifted to warmer conditions (>21°C)  . Temperaenotypes . For exasquitoes , 61. In tibility , 94, 117tibility , 110, antibility –120. CleWolbachia bacteria induce changes to host pT and that short shifts in temperature can increase titer in some Wolbachia-infected males. Behavioral changes like these are likely to have fundamental consequences for host physiology and thermoregulation. Wolbachia also modifies a range of other ecologically important host traits in Drosophila species, including reproduction , all our Wolbachia-infected genotypes were naturally sampled to form isofemale lines, such that single gravid females were collected from the field and placed individually in vials. wMelCS is found only at low frequency in global populations of D. melanogaster  was kindly provided to us by Michael Turelli. All lines were maintained on standard cornmeal medium prior to experiments  and a second set of primers for the arthropod-specific 28S rDNA that served as a positive control . We then reconstituted the gut microbiome of the tetracycline-cleared flies by rearing them on food where infected males of the same genotype had fed and defecated for the prior 48 h. Tetracycline-cleared flies were given at least three more generations before we conducted experiments to avoid detrimental effects of the antibiotic treatment on mitochondrial function  of each genotype using a thermal gradient apparatus adapted from previous studies , ranging from a mean of 34°C at the warmest end (section 1) to 17°C at the coldest end (section 7). Mean temperatures at the center point of each section across all experiments are reported in All sections , and we 10.1128/mBio.01768-20.7TABLE S4Table S4, DOCX file, 0.01 MB.Mean temperature and standard error for each section of the custom-built thermal gradient apparatus, across all 347 experimental replicates in this study. Download Copyright © 2020 Hague et al.2020Hague et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The following protocol for our assay was adapted from previous experiments , 132. TrpT data using generalized linear mixed models (GLMMs) and a Poisson error structure in R , and the run order of each replicate over the course of the day  as fixed effects. The replicate identifier (ID) of each run was included as a random effect. We then assessed the significance of fixed effects using an analysis of deviance with chi-squared tests. The pT data for some genotypes more closely approximated a normal distribution  with Wald’s chi-squared tests. The LMMs produced qualitatively similar results to the GLMMs, so only results from the GLMMs are presented in the main text.For each genotype, we analyzed the ure in R  with the package . We treation see , so we cpT of Drosophila species reported in previous experiments  where temperatures averaged about 17°C . Given teriments , 55, 131eriments . A similgradient . Thus, wgradient ; howeverts on Tp . We conc10.1128/mBio.01768-20.3FIG S3pT) for uninfected and infected flies of each genotype when the coldest section of the thermal gradient (section 7) is included. Individual points are jittered to show overlap. Download FIG S3, DOCX file, 0.5 MB.Box plots showing temperature preference . Statistically significant fixed effects at P < 0.05 are shown in bold text with asterisks. Download Table S5, DOCX file, 0.02 MB.Results and sample sizes from the GLMM and LMM analyses of Copyright © 2020 Hague et al.2020Hague et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Wolbachia strains included in this study. Hosts infected with A-group Wolbachia  preferred cooler temperatures, whereas D. mauritiana infected with B-group wMau preferred a warmer temperature. Therefore, we used a Wolbachia phylogram to test whether these Wolbachia effects on host pT exhibit phylogenetic signal. We obtained Wolbachia sequences from publicly available genome assemblies, which included wRi   is singly infected with wSh using qPCR primers described below, which can distinguish between A-group and B-group Wolbachia. Finally, we sequenced our wMelCS- and wMel-infected D. melanogaster genotypes  to compare the sequence similarity of our variants of these strains to those used in the prior assay of pT by Truitt et al.  . Importawith wSn . We conft et al. , 97.Tissue samples for genomic DNA were extracted using a DNeasy Blood & Tissue kit (Qiagen). DNA quantity was tested on a Nanodrop (Implen), and total DNA was quantified by Qubit fluorometric quantitation (Invitrogen). DNA was cleaned using Agencourt AMPure XP beads  following the manufacturer’s instructions, and eluted in 50 μl of 1× TE (Tris-EDTA) buffer for shearing. DNA was sheared using a Covaris E220 Focused Ultrasonicator (Covaris Inc.) to a target size of 400 bp. We prepared libraries using NEBNext Ultra II DNA Library Prep with Sample Purification beads (New England BioLabs). Final fragment sizes and concentrations were confirmed using a TapeStation 2200 system (Agilent). We indexed samples using NEBNext Multiplex Oligos for Illumina (Index Primers Set 3 and Index Primers Set 4), and 10 μl of each sample was shipped to Novogene  for sequencing using Illumina HiSeq 4000, generating paired-end 150 bp reads.K values of 71, 81, and 91 were used, and scaffolds with the best nucleotide BLAST matches to known Wolbachia sequences with E values less than 10−10 were extracted as the draft Wolbachia assemblies. For each genotype, we chose the assembly with the highest N50 and the fewest scaffolds  mean pT for infected and uninfected flies from the GLMMs and then used the change in LS mean pT as a continuous character to calculate the maximum likelihood value of Pagel’s lambda (λ)  and our λ estimate of 0.778 using the “sim.bdtree” and “sim.char” functions in the geiger R package (We used the resulting phylogram to test whether mbda (λ) . We usedphytools . We alsools  or warm (25°C) incubator for 24 h. Flies were separated by sex and maintained in groups of 40 in individual food vials throughout the course of the experiment. Following 24 h of the cold/warm temperature treatment, flies were frozen in a −80°C freezer for subsequent analysis of Wolbachia titer.We tested whether shifting infected hosts toward their Wolbachia titer in flies shifted to 18°C versus 25°C. Flies from each temperature treatment were homogenized together in groups of 10. The final samples included six biological replicates for each sex and temperature treatment. DNA was extracted using a DNeasy Blood & Tissue kit (Qiagen). Preliminary analyses indicated that our extractions contained DNA quantities that are well within the recommended range for PowerUp SYBR green Master Mix (Thermo Fisher Scientific) used in our qPCRs. We used a Stratagene Mx3000P (Agilent Technologies) to amplify Drosophila- and Wolbachia-specific loci. In order to quantify the titers of the five different Wolbachia strains, we utilized multiple combinations of Drosophila and Wolbachia qPCR primers (Ct) value of three technical replicates for each sample. We estimated relative Wolbachia density as Ct = Cthost – WolbachiaCt .Genome assemblies are deposited on GenBank (BioProject accession no. PRJNA658309). All other data are available on Dryad (


Genome-wide association studies (GWAS) are used to detect genetic variants that explain common human diseases in populations. The initial GWAS achieved notoriety by successfully identifying thousands of genes associated with a variety of genetic disorders. However, these identified genes have been most successful in establishing individual associations with Mendelian diseases and explaining only a small portion of the heritability. Complex diseases are likely better explained by multiple interacting genetic and environmental variants. Such non-linear, non-additive gene-gene interaction effects, i.e., epistasis, render traditional one-gene-at-a-time analysis methods ineffective for GWAS. Instead, powerful machine learning algorithms that can detect and characterize high-order interactions among multiple genetic variants are needed.The focus of this Special Topic Issue is to examine the novel design and application of machine learning algorithms in detecting interacting genetic variants for GWAS in six included articles.Liu et al. proposed a deep-learning framework using convolutional neural networks to predict the quantitative traits from single nucleotide polymorphisms (SNPs) and to investigate genotypic contributions to the trait using saliency maps. The authors evaluated the performance of the proposed approach using both simulation and experimental soybean datasets. The results showed that deep learning modeling can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than well-established statistical methods. The authors claim their approach effectively and efficiently identifies significant SNPs and SNP combinations associated with GWAS data.Zhang et al. presented circLGB, a machine learning-based framework to discriminate circRNA from other lncRNAs. This approach combined commonly used sequence-derived features and three new ones; adenosine to inosine (A-to-I) deamination, A-to-I density, and internal ribosome entry site. circLGB categorizes circRNAs by utilizing a LightGBM classifier with feature selection. In addition, the authors apply circMRT, another ensemble machine learning framework to systematically predict the regulatory information for circRNA, including their interactions with microRNA, RNA binding protein, and transcriptional regulation. Feature sets including sequence-based features, graph features, genome context, and regulatory information features were modeled in circMRT. Experiments on publicly available datasets and lab generated ones showed that the proposed algorithms outperform the available state-of-the-art methods.Nicholls et al., the authors discussed the landscape of ML applications in GWAS by following three components: selected models, input features, and output model performance. The authors focused particularly on the prioritization of complex disease-associated loci and explored the contributions ML has made toward reaching the GWAS end-game with consequent wide-ranging translational impact.In a review article by Leem et al. have proposed a permutation method for GWAS, i.e., ENhanced Permutation tests via multiple Pruning (ENPP). ENPP prunes the features in each permutation round if they were determined to be non-significant. Their simulation study showed that the ENPP method could remove about 50% of the features, at the first permutation round, and by the 100th permutation round, 98% of the features were removed. Only 7.4% of the compute time was required, compared to the original unpruned permutation approach. In addition, they applied this approach to a real data set of ~300 K SNPs, to find the association with a non-normal distributed phenotype.Arabnejad et al. designed a machine learning algorithm, i.e., Nearest-neighbor Projected-Distance Regression (NPDR), in order to detect complex multivariate effects for GWAS. NPDR used a regression formalism that allowed statistical significance testing and efficient control for multiple testing. In addition, the regression formalism provided a mechanism for NPDR to adjust for population structure, which was applied to GWAS data of Systemic Lupus Erythematosus (SLE). The authors also tested NPDR on benchmark simulated genetic variant data with epistatic effects, main effects, imbalanced data for case-control design, and continuous outcomes. NPDR identified potential epistatic and other effects that influence the complex SLE disorder.Ni et al., ~300 K stomach tissue-specific eSNPs with gastric cancer (GC) risk in three GWAS datasets were investigated. The authors conducted a gene-based analysis to calculate the cumulative effect of eSNPs through a sequence kernel association combined test and Sherlock integrative analysis. At the SNP-level, they identified two novel variants associated with GC risk. Gene-based analyses identified 2 novel susceptibility genes for GC which were significantly overexpressed in GC tissues than in their adjacent tissues and the high expression level of these two genes was associated with an unfavorable prognosis of GC patients. Co-expression genes with these two novel genes in normal stomach tissues were significantly enriched in several cancer-related pathways.Lastly, in the article by All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.