Congenital sensorineural deafness (CSD) is the most common type of deafness in Dalmatian dogs.To use results of CSD screening in Dalmatian dogs in the United Kingdom in genetic analysis and to determine any changes in the prevalence of CSD in this breed over time.A total of 8955 Dalmatian puppies undergoing hearing function screening using brainstem auditory evoked response (BAER) between July 1992 and February 2019.Results of BAER testing and pigmentation phenotypic data were linked to the UK Kennel Club Dalmatian pedigree database. Mixed model analysis was used to estimate variance parameters.The overall prevalence of CSD was 17.8% . Heritability of CSD was approximately 0.3 (across models) and significantly >0. Genetic correlations between CSD and blue irises (+0.6) and pigmented head patch (−0.86) were large in magnitude and significantly different form 0. Significant improving phenotypic and genetic trends were identified, likely as the result of selection against deafness, equivalent to avoiding breeding with the 4% to 5% of animals with the highest genetic risk of CSD.A decrease in the prevalence and genetic risk of CSD implies breeders have been selecting for hearing dogs. Selective breeding based on estimated breeding values (EBVs) can help further decrease the prevalence of CSD in Dalmatians in the future. AHTanimal health trustBAERbrainstem auditory evoked responseCSDcongenital sensorineural deafnessEBVsestimated breeding valuesh2heritabilityKCKennel ClubrAadditive genetic correlationUKUnited Kingdom1Congenital sensorineural deafness (CSD) is the most frequent type of deafness in dogs and results from loss of hearing receptors in the first 3 to 4 weeks after birth, and the condition is permanent. It can be objectively and noninvasively diagnosed by brainstem auditory evoked response (BAER) testing.The Dalmatian is the breed with the highest reported prevalence of CSD being as high as 30%  in the United States.The heritability estimates of CSD in Dalmatians ranged from 0.27 to 0.76 in studies conducted in the United States, Germany, and Switzerland.The magnitude of the range of heritability estimates indicates that estimated breeding values (EBVs) could improve the accuracy of selection. Where the narrow sense heritability is equal to 1, all phenotypic variation is additive genetic variance, and individuals' phenotypes reflect exactly their genetic liability. Conversely, when the heritability is 0, there is no additive genetic variance underlying phenotypic variance, and extant phenotypic variation is comprised of other influential factors, such as environment. Between these boundary values, some additive genetic variance impacting the trait exists, but is overlaid by variation from other, non‐genetic influences (the heritability indicating to what extent), and phenotypes thus are a mix of individual genetic liability (which is inherited by offspring) and individual nonadditive genetic and environmental influences (which are not). Therefore, the phenotype is an imperfect guide to underlying genetics, and selection using phenotypes will be prone to an increasing degree of inaccuracy at lower heritability. The calculation of EBVs uses pedigree data to determine the degree of relationship among all animals evaluated and, in conjunction with phenotypic data  from at least some individuals in the pedigree, to estimate the genetic liability for all individuals in the pedigree based on their own phenotype and those of all relatives. With sufficient data, EBVs will be a more accurate reflection of true underlying genetic liability or risk than phenotypes, and therefore a more accurate metric to use in selection.Our aim was to estimate the heritability of CSD and, from the resultant breeding values and results of BAER screening, determine whether any changes in the genetic liability and prevalence of CSD have occurred in UK Kennel Club (KC) registered Dalmatians over the past 20 to 30 years.2The study was approved by the institution's clinical research ethics committee. The institution's hearing clinic database was searched for purebred Dalmatian puppies that underwent BAER testing at 4 to 10 weeks of age between 29 June 1992 and 4 February 2019. The BAER testing was performed as part of a hearing‐screening program. Informed consent of the puppies' owners was obtained before BAER testing. Inclusion criteria were that BAER was carried out on all surviving puppies in the litter, phenotypic data were recorded, and UK KC registration details were available to link the phenotypic data to the KC pedigree database for quantitative genetic investigations.2.1Age, sex, presence of a pigmented coat patch or patches on the head or elsewhere on the body at birth, spot color, iris color, and hearing status based on BAER testing were recorded for each puppy. Spot color was categorized as black, liver, lemon, orange, or tricolor. Iris color was recorded for each eye as completely brown, completely blue, or partly blue. The hearing status of the puppies was classified (per ear) as “normal,” “deaf,” or “impaired/equivocal” , giving 9 bilateral classifications.2.2As previously reported by the authors,Stainless steel needle electrodes  were placed subdermally at 3 sites on the head. The ground electrode was located over the occipital protruberance, the reference electrode over the vertex, and the recording electrode just in front of the tragus of the tested ear. Rarefaction or alternating clicks, 0.1 ms in duration, were presented at 80 dBnHL through an unshielded audiometric headphone  held against the external ear opening. Before testing, puppies were allowed to become sleepy naturally, which, because of the minimally invasive nature of the test, enabled testing to be carried out without sedation or anesthesia. Data were acquired at a click rate of 20to 30/s and at least 512 responses were signal averaged to eliminate artifact. If a normal trace was absent at 80 dB, the test was repeated at 100 dB for the Sapphire 2ME and Synergy N‐EP, and 95 dB for the Synergy EDX. To exclude the contribution from a normal‐hearing contralateral ear, white noise, at 20 dB lower than the stimulus level, was delivered into the nonstimulated ear. Both ears were tested individually; first the right ear, followed by the left ear.2.3Phenotypic data records were linked to the UK KC Dalmatian pedigree database, using the unique KC registration number allocated to each registered individual or the parental KC numbers. Because not all puppies were registered with the KC before to the hearing test, some puppies and litters were identified by means of date of birth and their parents' KC registration numbers. The KC database includes registration number, registered name, sex, date of birth, sire registration name and number, and dam registration name and number. The pedigree data used in the analysis were the entire KC‐registered Dalmatian pedigree appended with puppies that had identifiable littermates or parents in the KC pedigree database.2.4σ2a A), c is the vector of litter effects, and e is the vector of residuals distributed with parameters  and , respectively. I is an identity matrix of the appropriate size, A is the additive genetic relationship matrix and σ2 denotes the variance of each of the respective random effects. Preliminary models indicated that the inclusion of litter as a random effect in the mixed model analysis of hearing status was highly significant , but that additional inclusion of dam as a random effect was not (P > .05). The fixed effects included in the univariate model were sex, year of test, number of blue eyes, and presence of pigmented head patch. Age (in weeks) at test and inbreeding coefficient were included as covariates.Mixed linear models using ASREML2.5An underlying, normally distributed liability of hearing status was assumed, and hearing status data (0/1/2) were transformed into the mean deviation of individuals with values exceeding truncation points based on the proportion of hearing, uni‐ and bilaterally deaf categories of data.2.6σ2) were replaced with the 2 × 2 variance/covariance matrix for both traits and the direct product operator.Bivariate analysis was performed with the dependent variables hearing status  and (a) the number of blue eyes  and (b) the presence of pigmented head patch  included in an animal + litter model, with fixed effects as previously described. The variances . The heritability (h2) is calculated as the proportion of the phenotypic variance explained by the additive genetic variance (σa2/σp2), and litter effect calculated as the proportion of phenotypic variance explained by litter variance (σlitt2/σp2). In bivariate analyses, the additive genetic variance of each trait and the covariances between each pair of traits are used to calculate the genetic correlation:rA is the additive genetic correlation, a and b denote the 2 traits in question, σA2 denotes the additive genetic variance of traits and σa,b)A, and rearrangement of the equation: R = i h2σP/L  can be used to determine i  was the y‐variable regressed on the years of birth for which complete data were available (1993‐2018) to detect any trend in CSD over time. The presence of any genetic trend was determined by regression of mean EBV of dogs born per year  on year of birth . Linear regressions were performed using MATLAB.3A total of 8955 puppies met the inclusion criteria. These comprised 1225 unique litters from 780 unique dams and 375 unique sires. There were 4499 (50.2%) males and 4456 (49.8%) females. The distribution of age at time of testing is given in Table Of this overall data set, 7349 82.1%) puppies had normal hearing status, 1201 (13.4%) had unilateral CSD , 397 (4.4%) had bilateral CSD, and 8 (0.1%) had unilaterally impaired/equivocal hearing status (5 in the right ear and 3 in the left ear), which were excluded from analysis  puppies, 1 blue (completely) and 1 brown in 262 3%) puppies, bilaterally (completely) blue in 65 (0.7%) puppies, and at least 1 partially blue in 30 (0.3%) puppies . Smaller, but still significantly >0 (P < .001), litter variance effects on hearing status were estimated as 0.090 (SE 0.0123) and 0.089 (SE 0.0120) on the observed and liability scale, respectively. There was a small but significant effect (P < .01) of sex on hearing status on both scales, for example, of +0.041 (SE 0.0099) for females compared to males on the observed scale. There was a significant effect of age at test (in weeks) on hearing status (P < .01), largely because of the higher detected effect for dogs aged 8 and 9 weeks , with the patch negatively associated with deafness (−0.097 [SE 0.0165]) and 1 and 2 blue eyes positively associated with deafness; +0.330 [SE 0.0303] for 1 blue eye, and +0.481 [SE 0.0592] for 2 blue eyes . No significant association was found between hearing status and the remaining fixed effects or covariates.Estimates of heritability of hearing status (CSD in 0/1/2 ears) on the observed and liability scale were 0.305 (SE [standard error of the effect] 0.0404) and 0.271 (SE 0.0379), respectively, and were significantly >0  with number of blue eyes on the observed (liability) scale yielded estimates of heritability of 0.330 (SE 0.0411)  for hearing status and 0.172 (SE 0.0276)  for number of blue eyes; litter effect of 0.095 (SE 0.0124)  for hearing status and 0.038 (SE 0.0091)  for number of blue eyes. Bivariate analysis of hearing status with the presence of pigmented head patch on the observed (liability) scale yielded estimates of heritability of 0.343 (SE 0.0406)  for hearing status and 0.082 (SE 0.0181)  for hearing status and 0.039 (SE 0.0083)  for head patch.P < .001). Between hearing status and number of blue eyes, the genetic correlation estimated using the observed (liability) scale was +0.566 (SE 0.0875) . Between hearing status and presence of head patch, the genetic correlation estimated using the observed (liability) scale was −0.865 (SE 0.0663) . Estimates of correlation of litter effects were smaller in magnitude, but similar in direction: +0.360 (SE 0.1135) and +0.330 (SE 0.1085) between hearing status and number of blue eyes on the observed and liability scales, respectively, and −0.296 (SE 0.1125) and −0.326 (SE 0.1113) between hearing status and head patch on the observed and liability scale, respectively.Estimated genetic correlations among all traits were sizable and significantly different from 0 . This is equivalent to a decrease in prevalence of −0.0815  over the entire 26‐year period for which yearly data were complete (1993‐2018). For uni‐ and bilateral deafness individually, the regression of prevalence on year of birth also identified significant downward trends , equivalent to decreases in prevalence of 4.6% and 3.6% over the 26‐year period, respectively. Similar analyses of changes in prevalence of the traits determined as genetically related to CSD, presence of blue eyes and presence of patch, yielded statistically significant trends in the direction indicated by the genetic correlations: −0.0014 per year (P < .001), a decrease of −3.6% over the 26‐year period for the presence of blue eyes, and 0.0030 (P < .001) and increase of 7.8% over the 26‐year period in the presence of patch. The prevalence of total CSD , presence of blue eyes, and presence of a patch by year of birth are depicted in Figure The number of puppies with unilateral and bilateral CSD per year of birth, and the prevalence of unilateral, bilateral, and total CSD, per year from 1993 to 2018 are shown in Table P < .001; CI, −0.0042 to −0.0030), suggesting that genetic change is the driving force behind the decrease in the prevalence of CSD. This figure can be used as the observed response (R) to presumed selection. Rearranging the equation R = i h2σP/L  gives an estimate of i  on year of birth (1993‐2018) gives a coefficient similar in magnitude and also significantly different from 0; −0.0036 , which is only slightly lower than the prevalence  reported by the only other study on Dalmatians conducted in the UK, published in 1997.P < .001). If, as we expect, this trend is indicative of a general increase in participation in BAER testing, then it implies that any selection based on BAER testing will have been restricted to a minority of the breeding candidates in the overall population. This implies that the selection intensity applied by breeders actively participating in BAER testing exceeds that derived here .The heritability estimate of CSD in our study was moderate, at approximately 0.3 (across various models). This is large enough to elicit a response because of selection on phenotypes, as was observed and reported. The derived approximate selection intensity of 4% to 5% is consistent with excluding all bilaterally deaf dogs from breeding . However, this derived selection intensity is equivalent to excluding 4% to 5% of all potential breeding dogs , but it is unlikely that all selection decisions were influenced by BAER test results, as not all litters underwent or undergo BAER screening. To the best of authors’ knowledge, the Animla Health Trust (AHT) is the largest BAER testing center in the UK, and has been over the period these data encapsulate. The total number tested in this data set per year of birth , but with a presumed underlying continuous, normally distributed genetic risk, there will be considerable genetic variation in risk among dogs that have the same categorical phenotype . Even if all puppies were BAER tested and breeders then excluded all CSD dogs from breeding, doing so only would exert a selection intensity of <20% and still include many phenotypically unaffected but higher genetic risk animals. The routine provision of EBVs for CSD would have 2 benefits: (a) a continuous, quantitative metric of risk enabling discrimination between bilaterally hearing dogs with “high” and “low” genetic risk and (b) universality—they are available for all animals in the pedigree, even if they do not have a phenotype. Not only are EBVs a more accurate indicator of genetic risk, but because they are continuous , they can facilitate the application of a higher selection intensity. However, there are administrative and logistic obstacles to the routine calculation of EBVs .The heritability estimates of CSD given here are consistent with those reported for Dalmatians and other breeds both in the UK and in other countries, although the range is large: 0.27 Germany7, 0.57 https://www.thekennelclub.org.uk/services/public/breed/standard.aspx?id=4087). However, this is not the case in many other countries, including the United States. The change in prevalence of pigmented head patch observed here was larger than a correlated response would predict. This may be a consequence of some breeders selecting for (or easing selection against) pigmented patches on the understanding that it is genetically related to CSD, or it may simply be a result of random sampling of the non‐additive genetic factors affecting pigmented patches, which given the low heritability, will have greater proportional influence.In agreement with previous investigations,In conclusion, our study has provided further evidence that CSD as determined by BAER is moderately heritable (as has been previously reported on many occasions and in many breeds). However, our study also demonstrates, for the first time, improvement in terms of both a significantly decreasing trend in the prevalence of CSD over year of birth and a comparative decrease in the most recent estimate of the prevalence of CSD from baseline levels determined in UK KC‐registered Dalmatians born between 1992 and 1995. A corresponding decrease (improvement) in average genetic risk (EBVs) per year of birth indicates that this finding is most likely a consequence of selection against CSD. Genetic correlations between CSD and blue eyes, and CSD and pigmented head patches, imply that a correlated response in these traits would occur as a result of selection for a decrease in CSD prevalence, but that, in the case of pigmented head patches, this decrease would be very small. The regular calculation and public provision of EBVs for CSD from BAER testing would enable improvement in selection by (a) increasing the accuracy of selection (as compared to selection on phenotypes), and (b) by universal provision of a continuous, quantitative metric .T. L. is a full‐time employee of the Kennel Club. The other authors have no conflict of interest.Authors declare no off‐label use of antimicrobials.The study has been approved by the institution's Ethics committee.Authors declare human ethics approval was not needed for this study.


Brassica napus L.) cultivated in laboratory and field environments. Root morphology was screened using a high-throughput hydroponic growth system with two divergent nitrogen supplies. The panel showed an important diversity for biomass production and root morphological traits. Differences in root and shoot dry biomasses and lateral root length were mainly explained by the genotype, and differences in primary root length by nitrogen nutrition. The cultivars were tested in a pluriannual field trial. The field variation for yield and seed quality traits attributed to the genotype was more important than the year or the genotype × year interaction effects. The total root length measured at the seedling stage could predict the proportion of nitrogen taken up from the field and reallocated to seed organs, a component of the nitrogen use efficiency. The genetic interrelationship between cultivars, established with simple sequence repeat markers, indicated a very narrow genetic base. Positive correlations were found between the genetic distance measures, root morphological traits during nitrogen depletion and yield components. This study illustrates a root phenotyping screen in the laboratory with a proof of concept evaluation in the field. The results could assist future genetic improvements in oilseed rape for desirable root characteristics to reduce nutrient losses in the environment.The root system is responsible for soil resources acquisition. Hence, optimizing crop root characteristics has considerable implications for agricultural production. This study evaluated a panel of twenty-eight European modern cultivars of oilseed rape ( Nitrogen (N) is the nutrient required in the greatest amount for plant growth and the most determining one for crop yield . From thArabidopsis thaliana, a dual effect of nitrate on lateral root (LR) development is described by: (i) a systemic inhibition of uniformly elevated nitrate concentrations occurring on LR elongation at the post-emergence developmental stage and (ii) a localized stimulation of nitrate-rich patches triggering LR elongation of N-deficient plants, known as the foraging capacity  is the second most important oilseed crop worldwide after soybean, and the first one in Europe  to explore the natural variation for root morphology at the seedling stage with two contrasting N supplies, and to identify root traits accounting for most of the variation (ii) to examine yield components in a pluriannual field trial (iii) to compare seedling with adult plant traits and to assess the predictiveness of laboratory observations for field performance and (iv) to evaluate relationships between genetic distances based on molecular markers and distances computed from phenotypic data.Brassica napus L.), registered in the European catalogs of plant species and varieties for less than 10 years, was assembled (A panel of twenty-eight cultivars of winter oilseed rape (ssembled . Seeds wssembled . These gssembled . The mea–2 s–1)/8 h darkness and the relative humidity 70%. Two days after germination, six seedlings of uniform size were placed onto one blue paper sheet (20 cm × 30 cm), covered with a black microperforated rigid plate  to overshadow the root organs. The mounts  were placed in containers filled with 10 L of nutrient solution. After 4 days, root and shoot organs were separated, and the root systems scanned at 300 dpi (HP Photosmart C4100). Images were analyzed with the RootNav software (3)2 + 2.4 mM CaCl] and the N+ solution 5.0 mM nitrate [2.5 mM Ca(NO3)2].A pouch and wick hydroponic system was used for phenotyping root morphology of seedlings . The seesoftware  to extrasoftware . EventuaThe field trials were conducted over four growing seasons (2015–2019) at the CARAH experimental farm in Ath, Belgium . Annual rainfall is typically 863 mm, spread evenly over the year, and annual average temperature is 10.4°C (reference period 1980–2010). The four-year-period was characterized by less important rainfall (ranging from 362 mm in 2018 to 533 mm in 2016) and higher temperature (ranging from 10.8°C in 2016 to 11.6°C in 2018) than average. The silt-loam soil is classified as Luvisol with a favorable drainage.–2. The culture received growth regulator and was protected against weeds, pests and diseases, as required. Each year, microplots of 1.5 m × 12 m in size were sown following a randomized complete block design with four replicates. Sowing dates were between August 28th and September 8th over the four-year-period. The field conditions were not N-limiting. Plots were fertilized with ammonium nitrate after winter, at vegetation stage BBCH 31–32. The fertilizer amount was adjusted on a yearly basis , according to N absorbed in plant aerial biomass and mineral N in soil samples (0–90 cm profile) after winter, and using the predictive balance sheet method commonly used for the main arable crops , based on pigment fluorescence  of the control varieties was less than 10% (between July 15th–30th over the 4-year period). To avoid side-effects, only the central parts of the microplots (1.5 m × 9 m) were harvested with a combine harvester . Seed yield (SY) and seed quality traits are listed in rescence . Within rescence . These cGenomic DNA was extracted from the cotyledons of two individuals per genotype, germinated in greenhouse conditions, using the DNeasy Plant Mini Kit following manufacturer’s protocol . The DNA samples were quantified with the ND-3300 NanoDrop spectrofluorometer .TM 500 LIZTM dye Size Standard (Applied Biosystems), then denatured at 95°C for 3 min and quenched on ice. The denatured samples were run on the SeqStudioTM Genetic Analyzer System (Applied Biosystems), following standard run module parameters. Estimations of PCR product lengths were determined using GeneMapperTM Software 6.0 (Applied Biosystems).Seventeen simple sequence repeat (SSR) markers were selected from the literature followed by in-depth internal testing based on the following criteria: (i) homogeneous repartition on the chromosomes; (ii) optimal amplification and resolution; (iii) capability to detect high rates of polymorphism; (iv) adequacy of observed fragment sizes with those reported in selected literature ; and (v)Pi2, where Pi is the frequency of the ith allele detected in the subset, according to the Nei’s statistic (Polymorphism Information Content (PIC) was calculated for each marker as PIC = 1−Σtatistic . Allelictatistic . Genotyptatistic  from a dA two-way analysis of variance (ANOVA) was used to isolate the genotypic effect from the environment/nutrition effect in the laboratory experiment and from the year effect in field trials, as well as their interaction and residual effects. Furthermore, a principal component analysis (PCA) with laboratory and field traits was performed for capturing traits influencing the most the observed variability. Both ANOVA and PCA were executed using R software  with FacThe Mantel test was used to investigate the relations between genetic dissimilarities matrix, computed from genotyping analysis, and trait dissimilarities matrices calculated as Euclidean distances from laboratory and field assays. Data were computed using ade4 package on R software at significance level α = 0.05 (parameter: 9999 permutations) .PR, +30.5%), the length of primary root zone 4 , the sum of lateral root lengths , the mean length of lateral roots , the total root length , and the specific root length  were more important, the densities of lateral roots in zone 1  and in zone 2  were less important, whereas the lengths of primary root zone 2 and 3  and the number of lateral roots (NLR) did not change during N− compared to N+ conditions (P < 0.01). Root morphology greatly varied among genotypes. For instance, the percentage differences between the two most extreme genotypes were in the range of 28% (Angus vs. ES Mambo) and 58% (ES Navigo vs. DK Exentiel) for LPR, and of 139%  and 140% (DK Exclamation vs. ES Mambo) for ΣLLR, respectively, at N− and N+  and ΣLLR .A panel of 28 winter oilseed rape cultivars was grown hydroponically to measure biomass production and root morphological traits , in respnditions , 2C. All− and N+ , 2A,B. T− and N+ . A largeZ4, was largely dependent on the environment (67%), while LZ2 and LZ3 were more reliant on the genotype . The remaining traits were also depending on the genotype but generally to a lesser extent. Overall, the interaction (genotype × environment) effect was weak.A global analysis of variance (ANOVA) assessed the effect of (i) the genotype/cultivar, (ii) the environment/nutrition, (iii) the interaction between the genotype and the environment, and (iv) the residual in the variation of phenotypic traits. The biomass traits  were predominantly influenced by the genotype , while the R:S ratio by the environment (41%) . The lenLR, LZ2, MLLR) and R had mainly loads on PC1 (63%), while other traits  on PC2 (61%) and MSW on PC3 (18%).A principal component analysis (PCA) captured the variation in phenotypic traits across the 28 cultivars and the two N treatments . The thr–1 in 2007, max 6.65 t ha–1 in 2015; unpublished data) and of other surveys  to normalize the data. The field performance of these genotypes during the 4-year trial is given in  surveys . On aver surveys .The data set of the pluriannual field trial was examined with an ANOVA considering the following effects: (i) genotype, (ii) year, (iii) interaction between genotype and year, and (iv) residuals . The perLR, ΣLLR, MLLR, DLR-Z1) were positively correlated to each other during both N conditions. These traits were positively correlated with LPR at N−, and negatively correlated with LZ4 at N+. The MSW was correlated to R and S in both environments and only to some length parameters  at N−. In field conditions, FLO was negatively correlated with H. All yield traits were implicitly correlated with SY, while the ProtConc was negatively correlated with OilConc and SY. The nitrogen balance index (NBI), an optical index measured at flowering, was negatively correlated with the seed humidity at harvest (H).Correlations were established firstly between all traits measured in one culture environment, and secondly between all traits measured in laboratory and field environments . Only trLR, DLR-Z1, R) measured under N− were positively correlated with ProtConc, while ΣLLR and MLLR under N+ were positively correlated with SY. Some other root morphological traits  measured during both N treatments were positively correlated with the seed N uptake (SNU). The responsiveness to N depletion of some traits related to lateral roots  was positively correlated with ProtConc, and the responsiveness of S negatively with FLO. No significant (P > 0.05) correlation was found between TSW and seedling root traits during both N treatments.Some root related traits  were observed between genetic dissimilarities and distances among root morphological traits like S (r = 0.24), R+S (r = 0.19), LPR (r = 0.33), LZ2 (r = 0.33), LZ4; (r = 0.30), and SRL (r = 0.26) measured in N− conditions. Significant correlations were also found between genetic dissimilarities and distances among field traits like SY; (r = 0.35), ProtConc (r = 0.20), GLS (r = 0.17), OilY (r = 0.33), SNU (r = 0.30), and FLO (r = 0.21).Euclidean distances matrices between cultivars were first calculated for each of the traits measured in hydroponic or field conditions and then, correlated to the genetic dissimilarity matrix computed from the genotyping data, using a Mantel test . SignifiThere is a growing awareness among crop breeders concerning research on root being neglected compared to shoot and reproductive organs. Optimizing root morphology is an important strategy for increasing water and nutrient uptake and coping with soil fertility problems . This stLR), while other ones on the N nutrition . Several authors share the foundation that a profuse crop root system exploring a large volume of soil would limit N leaching  and of lateral roots responded distinctly to N availability  at N+, point out different strategies deployed to modulate horizontal or vertical expansions of the root system. The N responsiveness of LPR and LZ4  was negatively correlated with S biomass responsiveness   . This inThe cultivars were tested in a pluri-annual field trial for determining some NUE and yield components. The N taken up by roots and utilized for producing seeds (SNU) was considered as a proxy for NUE. The data showed the genotype effect was overall the most important and that genotype × year interaction generally greater than the year effect . Indeed,LR, MLLR) measured in hydroponics during N+ and in-field SY  measured under both N treatments were positively correlated with SNU, signifying that root phenotypes could be considered for screening NUE components. Measurements of root system morphology and total biomass at harvest  should be considered in future field trials to better evaluate the total N uptake and the reallocation to seed organs, but this is hardly achievable in field conditions. The 4-year trial was marked with a severe rain deficit, in such conditions root traits may also be important for water absorption and maintaining yield under drought conditions  were correlated positively with FLO and negatively with H . However, ProtConc was positively correlated with root traits measured during N−  and N+ (LPR) conditions.Breeding efforts to select modern varieties achieving great oil yield could possibly be the reason for which SY and OilConc traits are intricated , 2017. TLR  was positively correlated with ProtConc  were negatively and positively correlated with FLO, respectively (Z4 during N depletion were flowering early.The N responsiveness of ΣLProtConc , meaningectively . This inThese results corroborate recent reports on oilseed rape, but also on other crops like maize and wheat, in which root traits observed at a seedling stage could predict field performance .B. napus gene pool could be adopted to characterize the allelic variation. Such traits could be incorporated in superior cultivars by genomic and marker-assisted selection strategies.The assessment of the genetic diversity among the hybrid varieties with SSR markers indicated a rather narrow genetic basis . Four maThis report with a small panel of modern winter oilseed rape cultivars compared root phenotypes, field harvest and NUE components. Similar investigation sought to evaluate genotypic variation for root systems and NUE were successfully conducted in core sets of other crop species . To streBelow-ground phenotyping of the root organ can be tedious in breeding programs. Root traits measured in laboratory culture were to a certain degree predictive of field performance. Such high-throughput screen could be applied to a larger mapping population to identify genes and alleles shaping root morphology, for selection targets in breeding programs to use soil resources more efficiently. This exploratory work is supporting possible genetic improvements for the root morphology of modern oilseed rape cultivars.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.CH and JL contributed to the conceptualization and funding acquisition. JL contributed to the methodology, field and laboratory investigations, formal analysis, and writing-original draft. MS contributed to the genotyping investigation. CH, MS, and JL contributed to the writing-review and editing. 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.


Bos taurus ARS-UCD1.2 assembly (bosTau9) for gene ontology analysis. In summary, the GWAS identified 52 SNPs associated  with milk production, including NUB1 and SLC24A2, which were previously described as related to milk production traits in cattle. The results suggest that SNPs associated mainly with NUB1 and SLC24A2 could be useful to understand milk production in Girolando and used as predictive markers for selecting genetic predisposition for milk yield in Girolando.Milk production phenotypes are the main focus of genetic selection in dairy herds, and although there are many genes identified as related to the biology of these traits in pure breeds, little is known about crossbreed animals. This study aimed to identify potential genes associated with the 305-day milk yield in 337 crossbreed Gir × Holstein (Girolando) animals. Milk production records were genotyped for 45,613 single-nucleotide polymorphisms (SNPs). This dataset was used for a genome-wide association study (GWAS) using the 305-day milk yield adjusted for the fixed effects of herd and year and linear and quadratic effects of age at calving (in days) and calving factor averaged per animal. Genes within the significant SNPs were retrieved from the  Bos indicus) and Holstein (Bos taurus), has been selected as a suitable breed for dairy farms due to its high adaptability, milk production, and reproductive efficiency in tropical systems  to identify polymorphic chromosomal regions involved in complex traits . GWAS alDespite the large number of genes previously described as related to the complex trait of milk production in pure breeds, such as Holstein and Jersey cows, very little has been reported in crossbreeds . Still, Therefore, the current study aimed to identify SNP markers associated with the 305-day milk yield in Girolando bred and raised tropically in Brazilian dairy farms.The study was conducted with data available with the Empresa Brasileira de Pesquisa Agropecuária–Gado de Leite (EMBRAPA Dairy Cattle), and thus, it is exempt from local ethical committee reviews. The Brazilian Association of Girolando Breeders collected phenotypic data and biological samples for animal genetic evaluation, and their members owned the participating herds and voluntarily consented to have their animals included in the study following best practices of veterinary care.® BovineSNP50 v2 Genotyping BeadChip assay according to the manufacturer’s protocol. Individuals that presented a call rate lower than 90% or that shared identically-by-state over 95% of their alleles with another sample were excluded. Only autosomal SNPs with unique genomic coordinates were analyzed, and markers were removed from the dataset if they did not present a minimum call rate of 90%, minor allele frequency of at least 2%, and Fisher’s exact test p value for Hardy–Weinberg equilibrium greater than 1 × 10–20. These procedures were performed using customized scripts in R v.3.1.1 (available at1) and the GenABEL v1.8 package  were available for the genotyped animals, comprising 832 observations. All records were pre-filtered for a minimum lactation period of 30 days and physiological interruption of lactation. First-lactation cows and records of interrupted lactations due to non-physiological reasons  were excluded from the data. The phenotypic data were adjusted for the fixed effects of herd and year and linear and quadratic effects of age at calving (in days) and days in milk. The total lactation (kg) was adjusted to 305 days by the following formula: (lactation length/305) × milk yield . The resIn order to map putative quantitative trait loci (QTLs), associations between markers and phenotypes were tested using the following single-marker linear regression model:y is the column vector of phenotypes, 1n is a vector of 1 s, μ is the overall mean, b is a column vector of unobserved allele substitution effects, x is a vector of genotypes  relating observations in y to vector b, and e is the column vector of random residual effects, assumed p < 10–4 and p < 10–5, respectively. The expected false discovery rate (FDR) was computed as:where k is the total number of markers being tested, α is the significance level adopted, and s is the number of markers declared significant at α. These procedures were performed using customized scripts in R v.3.1.1 [available at (see text footnote 1)] and the GenABEL v1.8 package [1].where via the linear model:Ideally, the variance explained by causal quantitative trait nucleotides (QTNs) can be estimated K is the kernel matrix of additive genetic relationships between pairs of individuals at QTNs, and g=Kc, which results in where K is computed from high-density SNP data. Different relationship matrices based on specific SNP sets, such as markers clustered by chromosomes, can also be computed to partition the additive genetic variance onto variance due to groups of markers [3]. Here, in order to estimate the variance due to putative major QTLs and remaining polygenic effects, we partitioned the additive genetic variance between genome-wide significant and non-significant markers by fitting, as described by As QTNs are not known in practice, genome-wide markers are often used as proxies to indirectly capture the effects of causal loci, and matrix BGLR v1.0.3 package in R [4]. Normal priors were assigned to random effects, and a flat prior was assigned to the overall mean. Variance components were assumed a priori scaled inverse chi-square distributed with ν = 5 degrees of freedom and scale parameter S = var(y)(1−R2)(ν + 2), where R2 is the proportion of phenotypic variance a priori assigned to the random effects. We assumed R2 = 0.5 for the residual variance, and the unexplained variance was equally assigned to the remaining variance components. A single Markov chain with a length of 100,000 iterations was used. The burn-in period was set at 10,000 iterations and the thinning interval at 100 iterations.where subscripts QTL and G represent significant and non-significant genome-wide SNPs. Model parameters were estimated using the Gibbs sampling algorithm implemented in the 2.For Gene Ontology analysis, genes within significant SNPs were considered; if the SNP was located in the intergenic region , we selected the closest gene from the marker according to the ARS-UCD1.2 reference genome . Gene naFrom an initial set of 404 cows, 54,609 SNPs were collected. However, at best, 337 animals and 45,622 markers passed all filtering criteria. The average (± SD) number of parities observed per animal, age at calving (in days), and days in milk were 2.47 ± 1.47, 1,874.0 ± 758.72, and 254.3 ± 70.6, respectively. The averages (± SD) for minimum and maximum milk yields were 4,563.0 ± 2,280.83 kg and 316.0 ± 13.88 kg, respectively.p = 10–4 and FDR 8.77%), indicating 52 significant SNPs. When the threshold was set to p = 10–5, seven significant markers remained (FDR 6.52%). Significant associations were detected in 18 out of the 29 bovine autosomes. Thirty-five of the 52 SNPs identified via the GWAS in Girolando cows are associated with previously described genes of the bovine genome  are associated with four genes in the physical map of the bovine genome, namely, Solute Carrier Family 24 Member 2 (SLC24A2 and BTA8), Negative Regulator of Ubiquitin-Like Protein 1 (NUB1 and BTA4), KH RNA Binding Domain Containing, Signal Transduction Associated 3 (KHDRBS3 and BTA14), and Membrane-Associated Ring-CH-Type Finger 10 (MARCHF10 and BTA19).–4 and 10–5) and the remaining markers. The top-scoring seven SNPs explained 14.4% of the adjusted phenotypes alone, whereas all 52 top-scoring SNPs together explained 28.7% of the variance in the adjusted phenotypes. Altogether, genome-wide SNPs could explain as much as 49% of the phenotypic variance.Considering that bovine parity is a quantitative trait, it is very likely that the genes identified in association with 305-day milk yield in Girolando account for a small fraction of the total genetic variance for milk yield. Nevertheless, the findings reported here are essential in the context of the biology and functional characterization of milk production in Girolando, the most common industrial dairy crossbreed in Brazil.SLC24A2 gene encodes member 2 of the solute carrier family 24 of transporter proteins. The SLC24A family comprises six mammalian protein subtypes that exchange cellular calcium and potassium with extracellular sodium  that severely restricts the efficient delivery of antitumor drugs to cranial glioma tissues , CRV, and Zoetis. Thus, legal and privacy restrictions prevent data from becoming publicly available. However, requests to access these datasets should be directed to Dr. MS at Ethical review and approval was not required for the animal study because the present study was exempt of the local ethical committee evaluation as genomic DNA was extracted from stored hair of animals from commercial herds. Written informed consent was obtained from the owners for the participation of their animals in this study.ACr, DS, and ACa wrote the manuscript. ApC, CS, MS, and JG conceived the study. YU, ACa, and MS contributed to data production and quality control. FR, DS, and YU performed data analysis. LM and LF contributed to the interpretation of the results. ApC and MS provided samples or funded part of the analyses. All authors read, made corrections, contributed, 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.


Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible. Miscanthus, a C4 perennial grass that is a phylogenetically close relative to sugarcane (Saccharum officinarum), has great potential for combustion and lignocellulosic ethanol biofuel production, as well as abundant genetic diversity for climatic adaptation (Miscanthus as a bioenergy crop have focused on the nothospecies M. × giganteus (M×g), an interspecific cross between M. sinensis  and M. sacchariflorus   to train a GS model for predicting breeding values in interspecific M×g breeding material. Previous work has shown that the degree of relatedness between individuals in the training set and the breeding material has a major influence on GS prediction accuracies, where highest accuracies tend to be achieved when the individuals in both sets are highly related . Thus, t2 population derived from an Msa × Msi cross, as well as 50 simulated interspecific diploid F2 populations derived from different sets of diploid parents that were randomly selected from Msi and Msa diversity panels. The latter simulation study was conducted to mitigate the drawback of having only one interspecific diploid F2 population available for analysis. Our specific objectives were to (i) evaluate the impact of population structure in Msi and Msa diversity panels on GS prediction accuracy and (ii) quantify the advantages and disadvantages of using both Msi and Msa panels as a training set for fitting GS models.In summary, the development of optimal training sets will enable a propitious utilization of the genetic diversity in Msi and Msa for predicting GEBVs of traits related to biofuel capacity and winter hardiness in M×g breeding populations. This could enhance current efforts to incorporate more genetic diversity into M×g clones by applying GS as effectively and efficiently as possible. Therefore, the purpose of this study was to explore the influence of Msi and Msa training set composition on the ability of GS models to predict breeding values of M×g clones. We considered a prediction set consisting of a diploid F2 population , and then simulated traits were used to assess the ability of the Msi and Msa panels to train GS models for accurate prediction of GEBVs in all 51 F2 populations.We used both observed and simulated genotypic and trait data to investigate the ability of an Msi diversity panel , compressed circumference (Ccirc), culm length (CmL), diameter of basal internode (DBI), number of days to first heading (HD1), and dry biomass yield (Yld), were obtained during the third year of three panels grown at replicated field trials. Each panel was planted as a randomized complete block design at multiple locations, with genotypes propagated vegetatively as clonal replicates. Due to import restrictions and differential survival of genotypes, not all genotypes were able to be evaluated at all locations.The first panel evaluated was a diversity panel of Msi, previously described by The second panel evaluated a diversity panel of Msa. These trials took place at three temperate locations, including HU (four blocks), UI (four blocks), and KNU (one block). Trials were planted in 2015 and evaluated in 2017. In total 356 diploid and 242 tetraploid genotypes of Msa were evaluated, including the six diploid and two tetraploid genotypes that were also evaluated in the Msi trials. Population structure and genetic diversity of the genotypes within the diversity panel have been described by 2 population derived from Msa ‘Robustus’ × Msi ‘Cosmopolitan Revert’ . This population was evaluated in a single field trial with four blocks planted at UI in 2015 and evaluated in 2017. This population has been previously described by et al. (2017). ‘Cosmopolitan Revert’ was also evaluated in the Msi diversity panel trial, and ‘Robustus’ in both the Msi and Msa diversity panels. Trait data were obtained for 216 F2 progeny.Lastly, the third panel was a diploid Fi.e., each combination of location, year, and panel) with the Across all three panels and locations, least-squared means (LSmeans) of the six evaluated traits for each clonal genotype were estimated using the emmeans R package . Briefly, in each population, TASSEL5 GBSv2 was used to identify unique tags and their read depth within each taxon. Bowtie2 (Miscanthus sinensis v7.1 reference available from DOE-JGI at https://phytozome.jgi.doe.gov/. A Python script using TagDigger (mixed.solve function in the rrBLUP R package (A custom pipeline using TASSEL5 GBSv2 ( Bowtie2  was thenagDigger  was then package .https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12472); such a filtering step was essential because a genetic map was needed to create the 50 simulated interspecific F2 populations. Upon completion, a total of 356 markers across 19 linkage groups were available for the simulation study.A second filtering procedure was conducted to obtain markers for the simulation study. This procedure used the same markers segregating in the three panels, but the genotypes called by polyRAD were discretized to allow for the quantification of dominance effects, and hence assess the importance of dominance in the downstream GS analysis. Another round of filtering was conducted to remove markers that were not anchored to a genetic map created for the 09F2 population  of the genomic markers  model e.g., in . In brie penalty . This mo penalty . For a gWe evaluated two strategies to quantify the influence of population structure on the performance of GS on the observed traits within the Msi and Msa diversity panels, which are summarized in The CDmean procedure has been previously described . In brieWe next assessed the ability of the Msi and Msa panels to obtain accurate GEBVs of the observed trait LSMeans in the 09F2 population. Seven different configurations  underlying these traits. The genetic architectures  of the simulated traits varied according to five different scenarios presented in runif function in R. Two scenarios (Scenarios 4 and 5) considered the impact of unequal QTN effect sizes between the two different species on prediction accuracies. Finally, the impact of different QTNs segregating within the diversity panels were assessed by selecting QTNs based on the following: (i) the same set of QTNs were selected for both diversity panels (Scenario 1), (ii), different QTNs were selected within each diversity panel , and (iii) QTNs were selected from each panel such that 50% of them were the same while 50% were different (Scenario 2). For each F2 population, all segregating QTNs from both diversity panels contributed to the genetic signal of the simulated trait.A custom script in the R programming language similar to the one described in 2 populations were similar to those described for evaluating GS performance in the 09F2 observed trait data. In particular, the RR-BLUP GS models were trained using the same seven configurations of Msi and Msa diversity panel individuals described in 2 populations.The approaches used to assess the prediction accuracy across the 51 Fhttps://github.com/marcbios/MsiMsa. Supplemental material available at figshare: https://doi.org/10.25387/g3.12357563.R code, sequence data, phenotypic data, and individuals selected by CDmean during analysis are available at We compared the distribution and heritabilities of the LSmeans of six observed traits measured in the Msi, Msa, and 09F2 panels. With the exception of Bcirc, the range of observed values of a given trait tended to be similar across the three panels . The distributions of HD1 was distinctively trimodal in the Msa panel, while all remaining distributions were generally unimodal. Finally, all six traits were less heritable in the 09F2 population than in the two diversity panels . This could suggest that only a subset of the genes comprising the genetic architecture of these evaluated traits were segregating in the 09F2 population.The results of the PCA conducted on the genome-wide markers within each diversity panel underscore previous findings  that subFor each of the observed traits in each diversity panel, the prediction accuracies of the PCA model were similar, albeit less than those from the corresponding RR-BLUP model . This suThe prediction accuracies from the models fitted using 200 individuals selected from the CDMean procedure and models fitted using the random samples of 200 individuals were consistently less than models fitted from all individuals in the training set. However, the prediction accuracies from the model trained with 200 individuals selected from the CDMean procedure were uniformly higher than those trained from the random sample of 200 individuals. This suggests that individuals selected from the CDMean procedure could be better at predicting total genetic merit than an equally-sized random set of individuals, although the best prediction accuracies can be expected when all individuals in the training set are used.Regardless of which subset of individuals from the Msi and Msa diversity panels were used to train the GS model  unaccounted population structure can predict breeding values nearly as well as an RR-BLUP model within each of the panels, (ii) there are differences in the predictive ability of the diversity panels and such differences are likely to be trait-dependent, and (iii) the combination of GEBVs independently estimated from Msi and Msa resulted in increased prediction accuracies for the simulated data, but not for the observed trait data.The exploitation of Msi and Msa diversity for genomic prediction of M×g clones will not reach its full potential until the characteristics of an optimal training set for a target trait are identified. To this end, we evaluated the degree to which GS prediction accuracies of six biofuel-related traits within Msi and Msa diversity panels were influenced by subpopulation structure, and then we assessed the usefulness of including individuals from both of these panels to train GS models that predict breeding values in an interspecific diploid M×g FOur analysis of the observed traits suggests that subpopulation structure contributed substantially to the prediction accuracies within each panel , and thiAnother area of future research is to explore the predictive ability of GS models that explicitly quantify different marker effects within each subpopulation, as described in The CDmean procedure had a tendency to sample heavily from the subpopulation clusters located at the extremes of the PC biplots made for both the Msi and Msa individuals . Relative to the maize data analyzed by A key limitation of our study is that we used only 5,140 markers common to all three panels to evaluate the ability of the GS models to predict breeding values of the observed traits. Given the rapid LD decay rate that we observed in both the Msi and Msa diversity panels , we conclude that the markers we used for GS did not provide sufficient density to tag enough causal variants; consequently, the observed prediction accuracies might be lower than expected with a denser marker set. This conclusion is in part justified given that prediction accuracies tended to be higher when 2 populations.Extremely low and often negative prediction accuracies were obtained for the observed traits in the 09F2 panel when either one or both of the Msi or Msa panels were used to train the GS models . Our ana2 populations motivated our evaluation of prediction accuracies across 50 simulated interspecific populations, each of which have one randomly selected Msi parent and one randomly selected diploid Msa parent. Although these prediction accuracies were from simulated traits, they nevertheless provided additional insight into the ability of diversity panels to predict breeding values in interspecific crosses. For instance, we observed similar ranges of prediction accuracies regardless of the amount of non-additive genetic signals underlying the simulated traits. We also observed slightly higher prediction accuracies when the QTNs were common between Msi and Msa. These results suggest that prediction accuracies might be robust to variations in genetic architectures within and between panels, especially if predictions are made based on the sum of GEBVs fitted separately within the two diversity panels.The lack of availability of such multiple FWe noticed several major differences between GS prediction accuracies in the observed and simulated traits. Most importantly, prediction accuracies were noticeably lower in the observed traits than the simulated traits. It is likely that this result arose because the marker set used lacked the density to sufficiently tag the causal mutations underlying the observed traits (discussed previously). Another important difference was that for the simulated traits, adding the GEBVs together from the GS models trained separately in the panels were consistently yielded the highest prediction accuracies; meanwhile for the observed traits, this strategy never yielded the highest prediction accuracies. One hypothesis explaining this discrepancy was that the size of the genetic effects, and possibly the genetic architectures, of the observed traits substantially differed between the panels. This hypothesis is supported by the prediction accuracies observed in the simulation settings where one of the diversity panels contained the large-effect QTNs, while the other contained only small-effect QTN .2 families. To fully assess whether or not our results for the six observed traits are generalizable to interspecific M×g material, multiple F2 families are needed. This important shortcoming motivated us to simulate multiple F2 populations, where each population was derived from randomly selected Msi and diploid Msa parents. Although not as useful as actual F2 populations, the simulation study provided additional preliminary insight into the paradigm of breeding interspecific M×g breeding material. As shown in 2 populations whenever we added the GEBVs from the model trained in the Msi panel to those trained in the Msa panel. This suggests that a potential breeding strategy could be to produce interspecific F2 populations using Msi and Msa parents that are selected from GS models trained within their respective species. Preliminary simulation results suggest that such a strategy could yield prediction accuracies that are equivalent (after controlling for sample size differences between the F2 populations and the diversity panels) to those from GS models trained in multiple interspecific F2 populations, each with different parents . However, these preliminary results from simulated data need to be confirmed using real trait data on actual interspecific F2 populations. Such a follow-up study on real data will help determine if the gains in prediction accuracy from using these F2 populations are substantial enough to justify the additional time and resources needed to develop these populations.Thus, the inferences we made for the six observed traits are limited because we were unable to assess prediction accuracy for these traits across multiple F2 germplasm that we analyzed consisted of diploid individuals, and thus our inferences on the prospects of GS in M×g are most directly applicable to diploid F2 M×g material. Because Msa exists in diploid and tetraploid forms, there can be diploid, triploid, or tetraploid M×g, all of which have exhibited strong hybrid vigor  and which might be more advantageous to introgress directly from Msi or Msa. This can feed back into the generation of triploid and tetraploid Mxg if we were to create more complex hybrids, for example hybrids derived from fertile hybrid or introgressed individuals . Therefore, one logical extension of the work presented here would to use and modify approaches from other studies  is anticipated to be an effective use of GS for developing germplasm pools that produce exceptional parental genotypes for cultivar development. Moreover, population improvement via GS within diploid and tetraploid F2 or backcross M×g populations will provide a full range of interspecific genomic compositions to test and from which to develop cultivars that are diploid, triploid or tetraploid. Thus, the work presented in this paper should serve as only the beginning of more complex future studies that evaluate the effective use of Msi, Msa, and M×g germplasm that is currently being developed for training the most efficient GS models for rapid genetic gain in M×g. Indeed, our findings suggest that more research is needed into addressing the question of optimal training set composition.In any case, GS is expected to benefit breeding in 2 population is not an ideal strategy for increasing the genetic diversity of M×g. One potential reason for the low prediction accuracies we observed in the 09F2 population is the inherent difficulty in obtaining an ideal training set for an interspecific F2 cross. When analyzing the performance of GS in multiple maize breeding populations, 2 populations (We ultimately conclude that using Msi and Msa diversity panels to train GS models for prediction of breeding values in an interspecific diploid Fulations . We ther


Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels  provided higher prediction accuracy than genotypes  for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response . Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus , respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the  Until recently, breeding values were predicted using mixed model methodology and pedigree relationships between individuals . In human genetics, precision medicine refers to the goal of predicting the probability of developing a particular disease given an individual’s own genome  in addition to genotypes for the same samples. This has enabled integrating different types of data to uncover genotype-phenotype relationships using a systems genetics approach  non-redundant information about phenotypes (Drosophila melanogaster Genetic Reference Panel (DGRP) ] and e is an n-vector of random residual effects [e ∼ N]. G is the additive genomic relationship matrix (GRM) built using genetic variants according to the formula W is the matrix of centered and standardized genotypes for all the lines and p is the number of variants; I is the identity matrix.The data were analyzed using several different models. The baseline model was the Genomic Best Linear Unbiased Predictor (GBLUP). This is a linear mixed model where the covariance among lines is modeled using their realized relationships based on DNA marker loci ] and e is an n-vector of random residual effects [e ∼ N]. T is the additive TRM built according to the formula Z is the matrix of centered and standardized expression levels for all the lines and m is the number of genes; I is the identity matrix.To evaluate the performance of transcriptomic data for predicting complex traits, we used the Transcriptomic Best Linear Unbiased Predictor (TBLUP). This is very similar to GBLUP, but the GRM is substituted with a transcriptomic relationship matrix (TRM), which evaluates the similarity among lines based on gene expression levels  effects [g×t ∼ N where # is the Hadamard product].Finally, a fourth model, GTIBLUP, including three variance components associated with GRM, TRM, and the interaction of the two called IRM, respectively, was fitted. This model is e.g., in GBLUP, the proportion of variance explained by g is equal to r, between true and predicted phenotypes, averaged over folds and replicates.To avoid overfitting in the prediction analysis, 30 replicates of fivefold cross-validation were used; variance components were estimated in the training set using the ‘regress’ R package v. 1.3-15 , and then used to predict phenotypes in the test set. The proportion of variance explained in the training set by each component was calculated as the ratio of the variance explained by each component over the total phenotypic variance, estimated by REML  is a single decision tree (independent of the others) grown on a bootstrap sample of the original data using only a subset of transcripts at each node, and X is the matrix of expression levels for all the lines. The algorithm implemented in the ‘randomForest’ R package v. 4.6.14  were selected and used to build a trait-specific TRM. The trait-specific TRM was fitted in the TBLUP model to estimate variance components in the training set, to be used to predict phenotypes in the test set.To try and enrich the TBLUP model for genes associated with the trait of interest and to eliminate noise from unassociated genes, variable selection was performed by repurposing the approach of k genes  were randomly sampled and used for prediction in a similar way to the TWAS-selected genes.As a control, prediction using only randomly sampled genes was performed for the three traits. At each round of cross-validation, et al. 2014). Genes were then mapped to Gene Ontology (GO) terms using the R package ‘org.Dm.eg.db’ v. 3.5.0 , notGOg is an n-vector of random line genomic effects associated with all the remaining variants , and all the other parameters are as defined above. This model was fitted for all GO terms including at least 5 genes, resulting in 2,605 GO terms.For SNP-based prediction, a GO-GBLUP model was fitted: GOt is an n-vector of random line transcriptomic effects associated with genes pertaining to a specific GO term (through a GO-specific TRM built using genes in a specific GO), notGOt is an n-vector of random line transcriptomic effects associated with all the remaining genes , and all the other parameters are as defined above. This model was fitted for all GO terms including at least 5 genes that were present in our expression data, resulting in 2,346 and 2,287 GO terms for females and males, respectively.For expression-based prediction, a GO-TBLUP model was fitted: In order to evaluate whether genome-level and transcriptome-level GO terms contribute overlapping information, the following combined model (GO-GTBLUP) was fitted: e.g., in GO-GBLUP, the mean proportion of variance explained by GOg is equal to mean .All the statistical analyses were performed using Microsoft R Open v. 3.4.3 (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE117850. All quantitative trait data and genome sequence variant data are available at http://dgrp2.gnets.ncsu.edu. The code used for the analyses is available at https://github.com/morgantelab/multiomic-prediction-dgrp. Supplemental material available at figshare: https://doi.org/10.25387/g3.13133342.All DGRP lines are available from the Bloomington Drosophila Stock Center . All raw and processed RNA-Seq data are available at the NCBI Gene Expression Omnibus  of the phenotypic variability for starvation resistance in females . The performance of TBLUP was similar to GBLUP for this trait, explaining 73 ± 2% (90 ± 1%) of the phenotypic variability females  . HoweverWe then fitted a joint genetic and transcriptomic model (GTBLUP) to evaluate the relative variance explained from the genome and the transcriptome. The performance of this model was relatively similar to TBLUP alone for starvation resistance in females, with the transcriptome explaining 69 ± 2% of the phenotypic variance and only 9 ± 2% being explained by the genome. For male starvation resistance, the model explained nearly all of the phenotypic variance, with 17 ± 1% of the variation explained by the genomic and 80 ± 1% by the transcriptome contributions . For staFinally, we fitted a model considering the genome, transcriptome and their interaction (GTIBLUP). For female starvation resistance, the performance of this model was very similar to that of GTBLUP. The transcriptome explained 69 ± 2%, the genome explained 9 ± 2% of the phenotypic variance, and negligible variance was explained by their interaction. For male starvation resistance, nearly all phenotypic variance was explained, with 11 ± 1% coming from the genome, 77 ± 1% from the transcriptome and 10 ± 1% from their interaction . For femr = 0.07 ± 0.01 (r = 0.15 ± 0.01) in females . The TBLUP model had greater than twice the prediction accuracy of GBLUP for starvation resistance, with r = 0.28 ± 0.01 (r = 0.38 ± 0.01) in females . The predictive abilities of the GTBLUP and GTIBLUP models were similar to TBLUP for starvation resistance (r = 0.15 ± 0.01 (r = 0.14 ± 0.01) in females . The TBLUP model had prediction accuracies of r = 0.12 ± 0.01 and r = 0.11 ± 0.01 in females and males, respectively. The GTBLUP and GTIBLUP models has similar prediction accuracies to TBLUP in females , whereas in males they had the lowest prediction accuracies  . Prediction accuracies for all four models for chill coma recovery were all less than zero .Since the transcriptome contributes significantly to trait phenotypic variance in the training set in most cases, we next evaluated the predictive ability of the four models in the test set. For starvation resistance, the predictive abilities of each of the models was greater in males than females. The GBLUP model had the lowest prediction accuracy – sistance . In contr = 0.33 ± 0.01) had a greater prediction accuracy than TBLUP; whereas for male starvation resistance, the reverse was true: TBLUP had greater prediction accuracy than Random Forest (r = 0.31 ± 0.01) (r = 0.10 ± 0.01) were similar in females; but the prediction accuracy of the Random Forest model (r = 0.15 ± 0.01) was greater than TBLUP for male startle response . Both models had no predictive ability for chill coma recovery in either sex . Thus, there was no consistent difference in the performance of Random Forest compared to the TBLUP model.To assess whether non-parametric methods could perform better than linear models by capturing potential interactions among genes, we fitted Random Forest models to the data. For female starvation resistance, Random Forest ( ± 0.01) . For stap-vales . Again, the results of these analyses varied by trait and sex. For female starvation resistance, the most stringent p-value threshold (10−6) significantly improved prediction accuracy (r = 0.37 ± 0.01) over using all highly expressed genes and gave the best accuracy overall. However, using all transcripts provided the highest prediction accuracy for male starvation resistance, where selecting transcripts always resulted in lower accuracy ; lowering this threshold below P < 10−2 significantly decreased prediction accuracy . Prediction accuracy for female chill coma recovery was again zero for all models; for male chill coma recovery, the model selecting genes with P < 10−4 gave the best, albeit very low, predictive ability (r = 0.07 ± 0.01) .We next combined mapping and prediction into a single pipeline in an attempt to enrich the TBLUP model for genes associated with the trait of interest (TWAS-TBLUP). For each trait and sex combination, we included transcripts associated with the trait in the training set with increasingly stringent accuracy . For femp-value thresholds (P < 10−5 and P < 10−6), were reduced by two and fivefold, respectively, from the TWAS-TBLUP analysis . Predictive abilities for chill coma recovery were zero or below for all models in females, and the randomly selected genes did not yield any positive prediction accuracies in male, in contrast to the true TWAS-TBLUP analysis, where significant prediction accuracies were found for transcript association p-values < 10−4 . We conclude from these results that the TWAS-TBLUP strategy was not effective for the three traits analyzed.We also performed a control analysis in which we randomly selected 5000, 1000, 500, 50 and 5 transcripts with which to perform TBLUP prediction. For starvation resistance, decreasing the number of randomly selected genes monotonically decreased the prediction accuracy relative to the model including all transcripts, as expected . In partanalysis , 5. Howeanalysis . This isWe hypothesized that the lack of success of the TWAS-TBLUP model might have been due to the small size of the DGRP, providing low power to map genes  affecting the traits. We therefore assessed whether prediction accuracy could be improved by grouping variants and genes according to GO categories, and whether some GO terms are particularly predictive of the traits. First, we fitted the GO-GBLUP model proposed by Sik2, Sik3, Akh, AkhR) have previously been associated with starvation resistance (Table S2). In particular, AkhR, which is shared by both GO terms, was among the top associations with starvation resistance in both females and males in the DGRP (PDZ-GEF (Table S2), which was also among the top associations with starvation resistance in males in the DGRP  and GO:0007266 and GO:0035025 in males  (Table S2). None of the top predictive GO terms for the same trait are in common between males and females. Interestingly, the most predictive SNP-based GO terms and the most predictive transcript-based GO terms were distinct.We then developed the GO-TBLUP model and fitted it to our data. The results showed a very similar pattern to those of GO-GBLUP – most GO terms had very similar predictive abilities to TBLUP , yet some GO terms provided much higher accuracies  to affect the traits analyzed. However, the most predictive GO terms in GO-GBLUP and the most predictive GO terms in GO-TBLUP were not the same. This suggests that while the genome and transcriptome as a whole may contribute redundant information, this is not true when gene ontology information is incorporated. This observation also suggests that it may be possible to build a trait-specific model with the most predictive SNP-based GO terms and the most predictive gene-based GO terms to improve the overall prediction accuracy. However, to be able to do so, it is necessary to develop a procedure to select the most predictive GO terms without bias in the training set, and more research is needed in that area.i.e., fewer effects to estimate) and biological  advantages.Epistasis is a common hallmark of the genetic architecture of Drosophila quantitative traits (In summary, this study has confirmed that using transcriptomic data to predict quantitative trait phenotypes is promising for some traits. Our work, together with other studies (


FTO) gene with body composition phenotypes, obesity, dietary intake, and physical activity in European, East Asian, and African populations. However, the impact of the rs1421085 variant has not been sufficiently tested in ethnic populations (such as Arabs) with high levels of obesity. Further, there is a lack of studies identifying biomarkers that interact with FTO. Therefore, we investigated the association of rs1421085 with obesity and body composition traits and metabolic biomarkers in Arab population. We genotyped rs1421085 SNP in 278 Arab individuals, where multiple biomarkers relating to obesity, inflammation, and other metabolic pathways were quantified. We performed genetic association tests under additive mode of inheritance using linear regression models and found association of rs1421085_C allele with higher levels of body weight, soft lean mass (SLM), and total body water. Examination (using linear regression models under dominant mode of inheritance) of correlation among biomarkers and interaction with genotypes at the variant revealed that measures of these three body composition traits were found mediated by interaction between carrier genotypes (TC+CC) and measures of ghrelin, ApoA1, and ApoB48. Lean body mass (LBM), to which SLM contributes, is an important determinant of physical strength and is a focal point in studies on sarcopenia. Low LBM is known to be associated with higher risk of cardiometabolic disorders. Thus, the finding on the FTO variant as a genetic determinant of SLM via interaction with ghrelin, ApoA1, and ApoB48 is important.Association studies have implicated single nucleotide polymorphisms (SNPs), particularly rs1421085, from the fat mass and obesity-associated ( FTO) gene is a well-established obesity-susceptibility locus. Early genome-wide association studies (GWAS) associated FTO genetic variants with obesity traits in Europeans (FTO variants with type 2 diabetes mellitus (T2DM), independent of body mass index (BMI), has been demonstrated in East and South Asians . The prevalence of obesity is most noticeable in the Gulf region, where nine countries ranked highest in the obesity statistics among adults aged ≥18 years. Despite its high prevalence in the Gulf region, the Arab population is under-represented in global studies of obesity. In this study, we evaluated the influence of the FTO rs1421085 variant on various anthropometric and body composition measures and obesity, inflammatory, and cardiovascular biomarkers in a cohort of Kuwait Arab subjects.According to the most recent World Health Organization report, worldwide prevalence of obesity has tripled since 1975  and baseline characteristics, and vital signs such as height, weight, and waist circumference (WC) were recorded during enrolment. Details on medications taken by the participants for lowering lipid levels and diabetes were collected and used in correction procedures with the association statistics. All participants signed the consent form before participating in the study.® kit . Quant-iT™ PicoGreen® dsDNA Assay Kit  and Epoch Microplate Spectrophotometer (BioTek Instruments) were used to quantify DNA; absorbance values at 260–280 nm were checked for adherence to an optical density range of 1.8–2.1.Collection of blood samples and measurement of vital signs were performed in accordance with established institutional guidelines. DNA was extracted using the Gentra PuregeneThe plasma levels of 46 biomarkers listed in Body composition of the participants was measured using Bio-impedancemetry Body Composition Analyzer IOI 353 . Total body water (TBW) is the summation of body water that exists inside the cell membrane (intracellular) and water outside the cell membrane . Soft lean mass (SLM) is the addition of TBW and proteins in the body and is made up of skeletal and smooth muscle . Lean bo® Genotyping Assay on ABI 7500 Real-Time PCR System from Applied Biosystems  was used to perform candidate SNP genotyping. Next, 10 ng of DNA, 5× FIREPol® Master Mix , and 1 µl of 20× TaqMan® SNP Genotyping Assay constituted each polymerase chain reaction sample, and thermal cycling conditions were set at 60°C for 1 min and 95°C for 15 min followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. Sanger sequencing, using the BigDye™ Terminator v3.1 Cycle Sequencing on an Applied Biosystems 3730xl DNA Analyzer, was performed for selected cases of homozygotes and heterozygotes to validate genotypes determined by the above techniques.The TaqManG2  to assume values in the range of 0.001–0.05 in step of 0.005. We considered the population (mean ± standard deviation) of the quantitative trait and of the environment term (interaction trait) in these calculations.We used the Quanto software tool  to calculate the power of the study cohort and its ability to delineate quantitative trait variability at a given power (which we set at 80%). We considered the underlying genetic model to be based on additive mode of inheritance; we performed the power calculation considering “gene only” contributions with the underlying genetic model as based on additive mode of inheritance (AA versus AB versus BB) or under “gene-environment (GxE)” contributions  with theFTO variant. We considered any quantitative trait values < Q1- 1.5 * IQR or any value > Q3+1.5 * IQR as an outlier and excluded it from further statistical analysis.We used PLINK (version1.9) to assess quality and statistical association of the rs1421085 SNP. We calculated minor allele frequency (MAF) and Hardy–Weinberg equilibrium for the https://sites.google.com/site/qutsgel/software/matspd-local-version). The analysis pointed to 23 independent traits.A total of 60 traits and biomarkers  , generated using the max(T) permutation procedure available in PLINK, based on 10,000 permutations.We performed allele-based statistical association tests for rs1421085 with all 60 traits using linear regression adjusting for regular corrections toward age and sex. We also adjusted for diabetes medication and lipid-lowering medication. Correction for multiple testing was assessed by adjusting the P-value threshold for the 23 independent traits (0.05/effective number of independent traits), which was (0.05/23 = 0.0021). We also examined empirical P-values , corrected for age/sex, as well as interactions of such correlations with other traits (example: SLM ~ genotypes+Age+sex+ApoA1*ApoB48) were evaluated using multivariable linear regression tests. Summary statistics on the relationships among the traits/biomarkers included percentage variation in response variable (r2), estimates denoting standardised beta-coefficients, standard error, and significance of test (P) for the genotype distributions. P-value threshold was set at 0.05.The quality control procedures for checking outliers in trait measurements and missing information on traits led to varying number of samples for different traits, with the maximum size of 270 for BMI.2 and mean WC (99.36 ± 13.36 cm); 48% of the subjects were obese and 41% were diabetic. Mean HbA1c (6.31 ± 1.3%), low-density lipoprotein (3.13 ± 0.97 mmol/L), high-density lipoprotein (HDL) (1.2 ± 0.32 mmol/L), total cholesterol (5.02 ± 1.09 mmol/L), and triglyceride (1.22 ± 0.6 mmol/L) were normal or near optimal. Of the 278 subjects, 104 were taking medication for diabetes and 91 for lowering lipids.The summary statistics on the considered 60 traits and biomarkers of the 278 participants are summarised in FTO rs1421085 variant, the cohort exhibited significant differences  in the mean values for weight, BMI, SLM, TBW, leptin, eotaxin, TNFα, MMP-9, MIP1α, IFNγ, and several interleukins (see After partitioning based on the genotypes (TT vs TC+CC) at the FTO variant and the phenotype traits, derived from allele-based association tests based on additive models corrected for age and sex, are listed in emp-value ≤ 0.05 were observed for these three association signals when the tests were further corrected for medication  when the cohort was split as per the genotype distributions (TC+CC vs TT)  with an interaction term involving “obesity status” or “diabetes status” (while correcting for age and sex) and examined the P-values  associated with the FTO variant, we analyzed the remaining traits and biomarkers of the 60 that we began with using the linear regression model. We found that with TC+CC genotypes (regressed against reference TT genotype), the P-values for interactions of SLM and TBW with ghrelin, ApoA1, and ApoB48 were significant, but for weight, only an interaction with ApoA1 was significant  with non-anthropometric traits such as TGL, HDL, FPG, HbA1c, insulin, PBF(%), leptin, ApoC3, glucagon, adiponectin, adipsin, ApoB48, and ghrelin , rs9939609, rs1121980, rs8050136, rs3751812, rs17817449, and rs8050135. The study variant is in strong linkage disequilibrium with all the other above-listed variants (R2 > 0.90) except for rs8050135 (R2 = 0.0).Most notable genetic variants from FTO SNPs and biomarkers show differing data in literature. For example, FTO risk allele, rs9939609, which has a strong linkage disequilibrium with rs1421085, was associated with increased CRP level but had no impact on IL-1, IL-6, and TNFα. However, rs9939609 was associated with increased TNFα in Mexican women with gestational diabetes  at the FTO variant upregulate expression of FTO in skeletal muscle , which is a major component of SLM; it is known that an upregulation of FTO can result in increased food intake leading to obesity  have been linked to lean mass  was significantly associated with PBF, while our study did not find a significant association. This could be attributed to differences in the cohort studied; the study of Antonio et al. compared non-athletes and athletes, who are likely to show more significant differences in PBF  and results of power calculation support the notion that the sample size is adequate to support the association of the FTO variant, rs1421085, is a genetic factor for the body composition traits of TBW, SLM, and body weight in Kuwaiti Arabs. Our findings support an association between the FTO variant and plasma levels of ghrelin, ApoA1, and ApoB48.In conclusion, we report that the jehad.abubakr@dasmaninstitute.org.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher. Requests for access to data may be addressed to Dr JA at The studies involving human participants were reviewed and approved by Ethical Review Committee of Dasman Diabetes Institute. The patients/participants provided their written informed consent to participate in this study.JA, FA-M, and TAT are responsible for the study design. MA-F and AM performed participants recruitment and collection of samples and phenotype. FA and MM performed genotyping experiments. PH carried out data analysis and contributed to interpreting the results. TAT, MA-F, and PH prepared the manuscript. Manuscript was critically reviewed and approved by FA-M. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.