Sparus aurata) belongs to a group of teleost which has high importance in Mediterranean aquaculture industry. However, industrial production is increasingly compromised by an elevated outbreak of diseases in sea cages, especially a disease caused by monogeneans parasite Sparicotyle chrysophrii. This parasite mainly colonizes gill tissues of host and causes considerable economical losses with mortality and reduction in growth. The aim of current study was to explore the genetics of host resistance against S. chrysophrii and investigate the potential for genomic selection to possibly accelerate genetic progress. To achieve the desired goals, a test population derived from the breeding nucleus of Andromeda Group was produced. This experimental population was established by crossing of parents mated in partial factorial crosses of ∼8 × 8 using 58 sires and 62 dams. The progeny obtained from this mating design was challenged with S. chrysophrii using a controllable cohabitation infection model. At the end of the challenge, fish were recorded for parasite count, and all the recorded fish were tissue sampled for genotyping by sequencing using 2b-RAD methodology. The initial  and the final body weight  of the fish were also recorded. The results obtained through the analysis of phenotypic records (n = 615) and the genotypic data  revealed that the resistance against this parasite is lowly heritable (h2 = 0.147 with pedigree and 0.137 with genomic information). We observed moderately favorable genetic correlation (Rg = −0.549 to −0.807) between production traits  and parasite count, which signals a possibility of indirect selection. A locus at linkage group 17 was identified that surpassed chromosome-wide Bonferroni threshold which explained 22.68% of the total genetic variance, and might be playing role in producing genetic variation. The accuracy of prediction was improved by 8% with genomic information compared to pedigree.Gilthead sea bream ( Sparus aurata) is an economically important farmed fish species, specifically to the Mediterranean aquaculture industry with an annual production of ∼ 160,563 metric tons (Sparicotyle chrysophrii (S. chrysophrii), is a common monogeneans parasite which infests both wild and cultured gilthead seabream and instigates lethal epizootics in sea cages and is considered as one of the main diseases which threatens Mediterranean aquaculture. This parasite mainly colonizes gill tissues of the host and causes considerable economical losses with mortality ranging from 10 to 30% ] and their associations with the traits of economic importance could prove very helpful to overcome unfavorable correlations among traits. Moreover, selection response in animal breeding programs especially for the traits which are difficult to improve  by traditional selection can benefit from genome-wide distributed genetic markers and the knowledge about genetic markers linked to genes affecting quantitative traits . The genSingle nucleotide polymorphisms are popular molecular genetic markers widely used in aquaculture and livestock research . RestricMoreover, there have been recent developments in genomic resources and tools for gilthead sea bream including the availability of a reference genome assembly  and SNPsS. chrysophrii by estimating the role of genetic variation explaining resistance to this gill parasite, generate a linkage map, perform a GWAS to detect QTL, and finally compare accuracies of predictions based on genomic and pedigree data.The aim of current study was to explore genetic basis of host resistance to N = 2,000) were randomly sampled from each tank and kept until they reached the average body weight of 16 g. The descendent fish taken from this mating design had approximately the same size.All the fish used originated from the breeding nucleus of the Andromeda Group which was developed with a cross of parents mated in ∼8 × 8 partial factorial crossbreeding design using 58 sires and 62 dams. The brooders of this species are known to be fluent spawners under natural photoperiod without any need of hormonal and/or light regimes. Parents were allowed to perform natural mass spawning events in eight different broodstock tanks. All the parents were tissue sampled for later parentage assignment and genomic analysis. The collection of fertilized eggs was performed in four consecutive days with equal number of eggs collected in each day from each of the eight tanks. The collected eggs were pooled each day to avoid any environmental variances. For this specific experiment, an average of around 250 fingerlings per tank  located within an inland cement tank (50 m3). The remaining fish were kept in identical conditions in an adjacent cement tank and constituted the control group. One week later, 250 gilthead sea bream (50 ± 13 g) naturally infected with S. chrysophrii (infection confirmed by stereoscopy but level not counted) were introduced into the cement tank harboring the recipient fish and were left to swim freely outside of (around and beneath) the net for the whole experiment’s duration. With this design, donor fish were not interfering with the feeding environment of the recipient fish. Fish tanks were continuously supplied with aerated flow-through sea water. All fish were hand-fed a commercial diet at a daily rate of 2–2.5% of body weight. The whole experiment lasted for 10 weeks from the time when donor fish were introduced in the system until the final recording of gill parasites. The water temperature during the experimental period ranged from 27 to 20°C . At the beginning of the experiment, fish were weekly monitored (random sample of 10 fish) and subjected to stereoscopical examination until it was confirmed that the recipient fish had received the parasite. This sampling ended after 2 weeks at the second sampling event when the observed prevalence was found to be 100%.A challenge test was performed following the controllable cohabitation infection model developed by n = 807) along with the control fish  were individually weighed. The weighing of control fish was performed to evaluate the impact of infection vs. infection free environment on the growth. All recipient fish were slaughtered and subjected to stereoscopical examination (described above) for total counting of adult parasites of their 2 external gill arches. At the end of challenge test we had recordings on parasite count (PC), initial and final body weights , and the specific growth rate (SGR) within the window (∼ 68 days) of the challenge test which was calculated using BW1 and BW2 information. The formula used for the calculations of SGR is given below.At the end of the experiment, all surviving recipients  for further genomic work.AlfI  enzyme. The digestion step was followed by ligation of library-specific adaptors, sample specific barcoding, and the enrichment of 2b-RAD fragments using PCR amplifications. The individual specific libraries were then pooled using two different multiplexing strategies for parents (64 libraries per pool) and offspring (128 libraries per pool) with an objective to have higher depth of sequencing in parents. The pooled libraries were quality controlled using Agilent 2100 Bioanalyzer, and finally sequencing of pooled libraries was performed on an Illumina NextSeq500 platform  with 50 base single-end sequencing .The genomic DNA was extracted from the collected tissue samples of ∼ 20 mg. The genomic DNA extraction method and library preparation protocol are detailed in The genotyping from the individuals specific raw sequence reads was performed using following four steps,(i)Adapter trimming and quality filtering was performed using custom developed scripts from the 2bRAD pipeline v2.0 . Poor qu(ii)de novo reference sequence was performed by clustering of quality sequence reads of parents, which was accomplished with the use of Perl based scripts together with CD-HIT program (The development of  program .(iii)V = 0.7.13-r1126; Individual specific quality reads were then aligned to the developed reference sequence using bwa samse ((iv)The detection of reliable putative SNPs in the population was carried out using genotype quality (≥20), read depth (≥5), minor allele count (≥50) in the population at a particular site, and finally, minimum of 40 individuals were required to have genotype calls at the particular locus to fulfill mentioned criteria.The SNP that passed the above-mentioned criteria were considered as a putative SNP for further analyses.The genotype data was further filtered to obtain a panel of highly informative markers for parentage assignments. Hence, the SNP genotype data was pruned for both parents and offspring using minor allele frequency criteria (MAF ≥ 0.35). The filtering criteria retained 916 highly informative SNPs which were then used to construct the pedigree with the likelihood ratio method implemented in CERVUS version 3.0 . The assP < 0.001), and the Hardy–Weinberg equilibrium exact test (P < 1.0 × 10–7). Filtering of individuals was performed using individual specific genotyping rate (≥70%). An additional criterion was used for building linkage only where individuals or families were removed if they had less than five full-sibs per family. This criterion was used to include informative families with sufficient information on recombination, and to avoid computational problems in building the map.The data was further pruned for the genetic linkage analysis, both at the marker and the individual levels. The filtering of markers was performed on, locus specific genotyping rate (≥70%), deviation from expected Mendelian segregation (Linkage mapping was performed using Lep-Map v2  with theP ≤ 0.001) effect on both SGR and BW2. The BW1 was not used as a covariate for BW2 to avoid genetic parameter deviations for BW2 due to potential collinearity between traits (BW1 and BW2) but it was used as a covariate for SGR, which should adjust for the same starting weight for all the fish and then obtain genetic variation for SGR. The raw data for PC presented a positively skewed distribution. Thus, to normalize this distribution a log transformation was performed on count values, i.e.,A generalized linear model was used in R to test the effects of recorded traits and to obtain initial evaluation of models. The growth traits  did not show any significant effect on PC and therefore were not used as covariates. The initial body weight (BW1) showed a significant .The heritabilities and genetic correlations for LPC, SGR, and BW2 were estimated using ASReml 4.0  with genyi is a vector of ‘n’ records on trait i , μ, b, and u are overall mean, vector of fixed effects of BW1 applied on SGR only, and the vector of additive genetic effects, respectively. The vector of breeding values is assumed to follow eitherwhere u∼MVN,oru∼MVN, where u = G = genomic relationship matrix, A = pedigree relationship matrix, X = incidence matrix for fixed effects, Z = incidence matrix for additive effects, and e is the vector of random residuals with e∼MVN, where P, Nsnp, and pi are the matrix of centralized genotypes, total number of SNP markers, and the allele frequency of the reference allele, respectively. The narrow sense heritability was computed as the ratio of additive genetic variance to total phenotypic variance and genetic correlations were obtained from the above described bivariate analyses implemented in ASReml 4.0  except that the marker effects were also computed. The linear mixed model included random polygenic effects which accounted for family relationships (covariance between relatives) in the GWAS analysis. The marker – trait associations were performed using GCTA program  with theP-value of P ≤ 2.29 × 10–06 with –log10(P) = 5.63, and if they surpassed P-value of P ≤ 5.74 × 10–05 with –log10(P) = 4.24 then they were graded as chromosome-wide significant. The significant values (chromosome and/or genome-wide) were computed as described in The SNPs were considered as genome wide significant when they exceed the Bonferroni threshold for multiple testing 10 for each SNP, and the expected –log10 from a theoretical distribution was plotted. The inflation factor  was calculated using 2 are obtained from significance-based P-values, and 0.456 is chi-square expected under the null hypothesis.The q–q plot with the distribution of the observed –logG matrix used in this model was constructed with all other SNPs except the SNP(s) used as fixed effect in model (1).The proportional contribution of the chromosome and/or genome-wide significant SNP(s) to the total genetic variance was computed in ASReml 4.0  as the rn = 21,773) left after filtering in genotyping process.Breeding values were computed for LPC trait with univariate model (as described under the genome-wide association study) to quantify and compare accuracy of predictions obtained through pedigree (PBLUP) vs. genomic (GBLUP) information based BLUP models. The genomic relationship matrix was computed using all the quality markers . The mean accuracy of 20 replicates was computed as correlation (rcorr) of the estimated breeding value (pedigree/genomic) with the true phenotype, which were scaled by the square root of the heritability as G[P]EBV, y, and h2 are correlation coefficient, breeding values estimated using genomic or pedigree information, partially masked phenotypes, and genomic or pedigree based heritability estimates, respectively.A cross validation scheme was designed where ∼18% of the total 549 individuals were randomly masked. The random masking retained 449 individuals with information on LPC which were used as training set, while, randomly masked 100 individuals with missing phenotype records were used as validation animals. The breeding values were computed using genomic vs. pedigree relation matrices  millions, while the offspring had low average mean raw reads with a value of 4.22 (±2.464) millions. The trimming and quality filtering step of raw reads slightly reduced the number with a loss of 74.5 million (0.17%) reads which resulted in an average number of 9.864 (±2.458) and 4.215 (±2.301) million quality reads available for parents and offspring, respectively.The catalog which was developed from the quality reads of parents comprised of 269,660 unique loci, that was used as a reference sequence. The process of SNP calling discovered 33,684 2b-RAD tags which revealed at-least one SNP identified. The SNP data was further pruned based on MAF (≥0.02) and the locus specific genotyping rate (≥30%) with the aim of increasing overall informativeness and decreasing the amount of missing or erroneous information. This pruning step retained 21,773 quality SNPs for further analyses. Individuals were also filtered out if the individual specific rate of genotyping was < 30%, which resulted to a drop of 94 individuals (91 offspring + 3 parents), and ultimately SNPs based genotype data comprised of 21,773 loci typed on 841 individuals (724 offspring 117 parents).The analysis of parentage assignments revealed 154 full-sib families with 1–22 sibs per family . Out of SA01-SA24). The remaining 6,589 SNP markers did not get assignments to any group. The obtained total sex average map length was 1406.02 cM  correlation of 0.412. The sex specific maps  showed that the female genetic map length is larger than the males with total map length of 1,598.19 and 1,236.35 cM, respectively.The process of linkage mapping using 21,773 SNP markers in total, generated a linkage map comprised of 15,184 SNPs which were grouped into 24 linkage groups  and information sources (genomic vs. pedigree). The analysis using pedigree information resulted in heritability estimates of 0.147 ± 0.069, 0.155 ± 0.068, and 0.169 ± 0.072, for LPC, SGR, and BW2, respectively. Heritability estimates obtained with genomic information were 0.137 ± 0.061, 0.229 ± 0.077, 0.226 ± 0.074 for LPC, SGR, and BW2, respectively. The LPC trait showed a strong favorable negative genetic correlation of −0.807 ± 0.242 and −0.769 ± 0.224  with the SGR while −0.549 ± 0.269 and −0.701 ± 0.208  with BW2 .P-value of 4.02 × 10–5 and allele substitution effect α of −0.374 for LPC  and the stocks may require multiple treatments in a production cycle. The cost of treatment for amoebic gill disease, which is caused by amoebic parasite, Paramoeba perurans accounts for 10–20% of the production cost in Australian farmed A. salmon (S. chrysophrii (defined as low parasite count) using advanced selection methods may prove to be very useful, effective and preferred strategy especially when the trait is lowly heritable. Upgrading the overall robustness or resistance level against pathogens and/or parasites would avoid treatments using hazardous chemicals which should ultimately reduce environmental risks and promote sustainable production.h as 30% . The mor. salmon . Hence, S. chrysophrii leads to a poor welfare of fish with effects on growth. Limited information is available for genetic variation of host resistance against S. chrysophrii parasite. However, there are several studies on genetic variation for resistance against different ectoparasites in Atlantic salmon and livestock species. In Atlantic salmon, resistance against amoebic gill disease where amoebic parasite colonizes the gill tissues and causes distress for the host is also reported to have a moderate heritability  but significant, suggesting that the selection for improved resistance could be affective.In the current study, we found significant difference in average weight (BW2) of challenged vs. control fish which was 43.93 g (SD = 11) and 58.01g (SD = 12), respectively, which suggests that the tability . Similartability , and 0.1tability . Likewistability . HeritabS. chrysophrii. A similar favorable genetic correlation (–0.88) has been described for the severity gill disease due to Paramoeba perurans (gill parasite) and the body weight in Atlantic salmon , depicting the decreasing trend of parasite counts toward heavier and fast growing families. Favorable relations among the traits should allow selection for growth and resistance to be performed simultaneously. Moreover, selection for growth should help to get correlated response for increasing resistance against c salmon . The repc salmon .The current study produced a medium-density linkage map comprising 15,184 SNP markers distributed over 24 linkage groups. The generated numbers of linkage groups are consistent with the number of chromosomes/karyotype of gilthead seabream . The linS. aurata individuals in their first reproductive cycle mature as males (at the age of 2 years), though ovaries start differentiating in the larval stage but are replaced by the testes  that crossed the chromosome-wide Bonferroni threshold (SA17 (SA17 (SA19 and the two from unknown (Un) linkage group also showed strong significance P-value but did not cross the threshold line  that reached the genome-wide significance threshold for LPC . Howeverhreshold . This chhreshold , which mld  when an additional fixed effect of chromosome-wide significant SNP (Locus ID: 116287_33) was used in model 1. Hence, the proportion of genetic variance explained by the SNP was 22.68% which seems quite high compared to the QTL signal in the Manhattan plot . The tagS. chrysophrii infection remain largely unknown  encompassed in the candidate genomic region that have been reported to be involved in immune response in other species. RFN122 suppresses antiviral type I interferon (Exportin-1 (XPO1 or CRM1) is a nuclear export receptor involved in the transport of specific mRNAs. Inhibition of XPO1 was reported to down-regulated dendritic cell (DC) maturation  is involved in many aspects of the immune system. Particularly relevant is SRF’s role in hematopoiesis and neutrophil migration during inflammation  was observed  in Atlantic salmon have also shown a similar trend with advantages of genomic over pedigree based predictions . The advThe GS becomes even more beneficial compared to PBLUP when the trait is complex, difficult to record, lowly heritable and not recorded on the selection candidates. Additionally, GS can improve genetic gains through improving the accuracy of breeding values, and better use of variation within family . Hence, S. chrysophrii is significantly heritable (0.147 ± 0.069) and therefore genetic improvement is a valuable tool to reduce the prevalence of S. chrysophrii in farmed gilthead sea bream. The genetic architecture for host resistance to S. chrysophrii appears to be polygenic with one suggestive QTL detected at linkage group 17 which explains a large proportion (22.68%) of genetic variance. Since this SNP did not pass the genome-wide threshold, a validation of this QTL in an independent population is needed. The accuracy of predicting breeding values with genomic information was substantially higher (∼8%) than predictions using pedigree information. S. chrysophrii is a significant and increasing threat for Mediterranean sea bream production, and implementation of genomic selection in breeding programs may prove an efficient methodology to genetically improve host resistance to this parasite.The current study shows that the 2b-RAD approach for the detection of SNPs and for the genotyping of a sea bream population is an effective method which resulted quality genotype data for ∼21K SNPs. The analysis reveals that the resistance against Most of the data supporting the results are included within the manuscript as additional files. However, due to competitive nature and the privacy policy of commercial breeding companies, it is not permitted to share phenotypic, sequence, and genotypic data. The data can be made available for specific queries (on agreement) through the corresponding author.The animal study was reviewed and approved by local ethical committee for the use of animals in the study. The reference code for the acquired approval is EL 25 BIO 37. Written informed consent was obtained from the owners for the participation of their animals in this study.MA analyzed the data and drafted the manuscript. RC performed the 2b-RAD library preparations and the other molecular laboratory-based analyses. AS, TM, CT, GR, LB, and KT were involved in the design of study, sampling, phenotype recording, and actively contributed in discussions. KT also assisted in arranging population material. KT, LB, CT, and GR played role in collection of data and coordination at the testing facility. AS and TM assisted in statistical data analysis. All authors read the manuscript, gave suggestions and comments for the improvement, 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.


The discovery of the genome has been one of the greatest advances in the development of tools for the genetic improvement of cattle in the last decades. The use of information derived from genomics has been demonstrated to be an efficient alternative for the validation of kinship, adjusted mating, and conservation strategies to generate dairy cattle with desirable traits. However, the impact of the genetic improvement strategies in breeding programs has closed bloodlines in several milk breeds, consequently generating inbreeding depression. Thus, to mitigate the negative effects of inbreeding, several tools associated with genomic analysis are currently available that may be useful to reverse undesirable genetic trends. The present review will provide an understanding of the importance of genomic analysis and how it can be used to mitigate the negative effects of inbreeding depression on different traits related to production, reproduction, health, animal welfare, linear type traits, and adaptability in dairy cattle. A historical overview of genomics and future perspectives will also be covered.Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future. The discovery of the genome has been one of the greatest advances in the development of tools used for the genetic improvement of cattle in the last decades. The use of high-performance genomics in the selection of dairy cattle represents a remarkable advance to increase the biological and genetic progress of different features and indices in several animal production systems .Recently, several techniques have been developed for the genomic analysis of dairy cows genetic traits related to production, reproduction, health, animal welfare, linear type traits, and adaptability. Several authors highlight the perspectives of using the genomic approach as a crucial genomic selection method for several dairy breeds and stress the great relevance and applicability of this knowledge in breeding programs to incorporate additional alternative features into the traditional pool of traits . In addiAlthough the use of techniques based on genomic analysis has produced important changes in selection and genetic improvement programs in common dairy cattle breeds , for theThe objective of the present review is to provide an understanding of the importance of genomic analysis and how it can be used to mitigate the negative effects of inbreeding depression on different traits related to production, reproduction, health, animal welfare, linear type traits, and adaptability. From here, we will address several topics covering the use and possible influence of genomic analysis on genotypic traits in dairy cattle as well as its evolution from origins to the present and future perspectives. This document is designed and organized as follows: In the first part, it will briefly describe the chronology and evolution of genomics. In the second part the use of genomics as a predictive tool will be described, as well as the different methodologies used for its application. In the third part, the incidence/impact/effect of genomics as a predictive tool for different parameters associated with production, reproduction, health, animal welfare, evaluation of linear type traits, and adaptability to different production systems and environments will be covered, including causal and highly predictive genetic variants that are key to the prediction of other complex traits.This review will describe how genomic selection based on genomic analysis represents a promising tool that will improve the predictive accuracy and genetic gain of genomic traits. However, challenges lie ahead in the integration of genomic analysis models covering all the genetic traits and expressing an ideal genomic selection method to adequately regulate inbreeding rates. Therefore, the perspectives to increase genetic gains based on the production and reproductive efficiency, health, animal welfare, and environmental adaptability of dairy cattle will increase the sustainability/sustainability of the production yields in the immediate future as a means for increasing profit in animal production systems.The discovery of genomics and related applications in the dairy cattle industry has led to a significant improvement in the biological prediction of different genotypic traits. Major dairy-producing countries, like United States, Canada, Great Britain, Ireland, New Zealand, Australia, France, the Netherlands, Germany, and the Scandinavian countries have implemented genomic evaluations in their breeding programs, leading to significant changes in the global dairy industry .Bos taurus) were carried out in Hereford beef cattle [Bos indicus the genome was sequenced as well in Gyr, Girolando and Guzerat dairy breeds [A brief history of genomics is depicted in f cattle . Later, f cattle . Around f cattle ,15. In By breeds . The firy breeds .Later, the release of complete genome sequences granted the use of high-performance genetic markers , thus improving trait predictions. This interesting technology requires high throughput genotyping platforms (DNA arrays or SNP chips) . In addiThere is growing concern about pedigree-based genetic evaluation analyses, as being less efficient and accurate than genomic methods. However, linking pedigree data to information obtained through genomics appears to be advisable, since it may significantly improve the accuracy of predicting traits and indices in dairy breeds . HoweverToday and in the immediate future, genomic evaluation as a functional and everyday tool must also consider the effect of new mutations that can generate a genetic variance for quantitative traits . In thisFinally, to decipher and associate the functionality of new dairy cattle traits, other ”omic” technologies have been integrated into genomics such as epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and meta-transcriptomics . TherefoThe incidence of the use of genomic selection in recent years has resulted in a significant increase in production yields for most dairy-related traits in Holstein cattle in countries such as the USA . HoweverAnalyses of cattle selection pressure to increase milk production yields suggest a positive association with a higher incidence of mastitis . Whole-gFertility rates in dairy cattle play a critical role in breeding programs . FertiliGenomic analyses of most reproductive traits in females show that they tend to be less heritable than in males . HoweverIn the last decade, genomics has received great attention from the perspective of the study of traits related to animal health and welfare. Hence, several traits such as resistance against infectious and non-infectious diseases as well as adaptability to the environment have been introduced as new selection indices in dairy cattle . Recent Several companies managing commercial herds of the American Holstein breed have recently developed and evaluated genomic predictions methods for the estimation of predicted transmission capabilities (heritability) for welfare traits, respiratory disease, diarrhea, and adaptability ,65. GiveFinally, the epigenetic effect of assisted reproductive technologies (ARTs), such as in vitro embryo production (IVP), has not yet been quantified or evaluated in depth. Much more research is needed to better evaluate and understand genomics and its correlation with IVP potentially affecting bovine health and possible effects during adulthood . FurtherThe environment, directly and indirectly, plays an important role in animal genomics . DetermiBos taurus, genomic introgression was found for the progenitors [Concerning the above and the current climate change scenario, the bovine species has permanently required substantial modifications in terms of adaptability; however, not all breeds have achieved these levels of adaptation . In the genitors . This adgenitors . Consequgenitors . This hagenitors . Considegenitors .One of the major environmental-related advances in genomics over the last decade has been the discovery of traits linked with methane emissions, hypoxia, high altitude adaptation, and heat stress. Several trait association studies primarily emphasize the reduction of negative impacts on the environment . With thBos grunniens) and domestic bovine (Bos taurus) show existing genetic differences in protein domains related to sensory perception, energy metabolism, hypoxic stress, and nutritional metabolism [Comparative analyses regarding the high-altitude adaptation of the genome of domestic yak  can be affected by several factors such as the method of prediction used, the proportion of the categories (sires and cows), or the genetic relationship between traits . SimilarAmong the potential uses of genomic analysis in dairy cattle breeding, direct selection of heritable measures of gene expression is available and specifically called expression-assisted selection and genomic genetic selection of QTL and eQTL (expression quantitative trait loci) . In thisAn improved and more accurate methodology for genomic prediction has recently been proposed for new individuals with a genotype, with and without phenotypes . This meGenomic evaluation is currently present in the leader dairy-producing countries worldwide. The discovery of the genomic analysis in the last years has notably improved production features (genetic merit) in most of the dairy cattle breeds . HoweverAlthough genomic analysis and derived applications have a very important role in the selection of economically relevant traits in dairy cows, the available reports referring to new traits have not yet been clarified in an extensive and detailed manner for specialized meat cattle breeds . In thisThe success of genetic evaluation applying genomic analysis information derived from breeding and selection programs have shown to be an efficient methodology to obtain higher genetic gain rates while reducing selection times and controlling the inbreeding coefficients in dairy cattle populations . LikewisRecently, many applications of genomics have emerged in the animal science field. Several countries leading in genotyping, including the United States through its Department of Agriculture (USDA), have already begun to conduct genomic evaluations of production and health traits in Holstein, Jersey, and Brown Swiss breeds . FurtherIn recent years, several studies dealing with the genotyping of continuous homozygous segments  have been generated, providing a new way to assess the degree of genomic inbreeding . In addiSeveral studies have used genomic selection in high-yielding Holstein and Jersey cows to determine the degree of association of the components of additive variation and dominance for fertility and milk production traits ,128,129,The genomics of nutritional and metabolic traits related to the environment opens possibilities to develop novel concepts for production functionality in dairy cattle populations. Understanding the mechanisms through which genes are expressed and interact with the environment is of remarkable importance because it allows correcting undesirable features and including other traits of zootechnical interest.Genomic analysis has recently proven to be an efficient methodology to estimate dairy cattle traits not traditionally considered important. Consequently, the interest in production traits in the dairy industry has led several researchers to study specific related genes. For example, several genes have been described that are differentially expressed during lactation and participate in biological processes such as the development of the mammary gland, protein and lipid metabolism, signal transduction or cell growth, and differentiation . Other sRegarding metabolic activity, genotyping work has been done in Holstein dairy cattle based on the number of births  highlighting the existence of high genetic correlations between non-protein nitrogen (MUN) and several production  and reprIn countries at the forefront of genomic evaluation, such as Australia, new traits such as feed efficiency and heat tolerance have been included in association with health traits . Thus, tFinally, some authors have established genomic reliabilities for different traits evaluated in dairy cattle breeds between 60 and 75%, and in new traits, they could be under 50% ,142,143.Genome editing comprises a set of genetic engineering tools that allow the direct manipulation of a sequence in the genome of an organism by removing, inserting, or replacing nucleotides for scientific or commercial purposes. This technology employs nucleases (enzymes coined as “molecular scissors”), which hydrolyze or catalyze the double-stranded DNA in a very precise way at specific sites in the genome . Genome The Cas9 protein is derived from the CRISPR Type II bacterial immune systems (CRISPR/Cas9) that can be easily programmed to target new sites by altering the RNA guide sequence . RecentlAnother cutting-edge technology in the field of genome editing besides CRISPR involves the transcription-activating nuclease (TALEN). This system can be artificially engineered to bind to a chosen DNA sequence, allowing it to be cut at desired locations for use in genomic editing or in situ genome editing, a capability shared with other nucleases like zinc finger nucleases (ZFN) that make use of the target proteins that bind to the DNA at specific locations .Homologous recombination has been used to edit genomes in different animal species . One exaRegulatory perspectives may slow the widespread implementation of genome editing in animals. Several international organizations consider that genome editing, used to replicate a “natural” mutation, should not be of regulatory concern because it would be equivalent to natural zootechnical management of existing alleles from the natural gene pool. On the contrary, the US FDA considers that there is a need for regulatory oversight of intentional genomics in animals because the editing of one target allele or nucleotide may indirectly or accidentally result in editing another with potentially detrimental effects. For example, there could be modifications affecting food safety due to changes in protein expression, interruption of protein functions, protein over-expression , or the creation of new expression products . TherefoThe application of genomic analysis in dairy cattle selection provides very interesting predictions for the genetic improvement in the short/medium term while controlling the inbreeding coefficient, reducing the generational interval, and improving the selection of progenitors, thus generating more productive genetic lines. The estimation of the different traits in dairy cattle is more efficient through the use of data derived from genomic analysis than through data derived from the pedigree. However, the association of both methodologies significantly improves the estimates in terms of prediction and accuracy. Genomic trait estimation accuracy must be supported by multi-generation databases to make predictions more consistent. This will be most noteworthy with the incorporation of new traits into the evaluations, considerably improving selection programs based on such estimates at both the individual and population levels. Selection rates based on genomic analysis provide a better understanding of the biology of the animals, new sources of data, novel genome editing methodologies, and pertinence for changing economic and environmental conditions. This becomes highly relevant as estimations derived from dairy cattle genomic data regarding adaptability traits in different environments or settings are more needed than ever. Genomic methodologies are a perfect fit during these challenging times of global climate change.Dairy cattle genomic-based selection will be used in the very near future for genetic selection in the beef cattle industry and other animal species. The frequency of rare alleles, recessive genes, and new mutations could generate genetic variability of desirable economic traits. Thus, the information derived from genomic analysis represents an important alternative for the production of databases of great interest in animal sciences and novel genome editing applications, accelerating the process of genetic evaluations in dairy cattle.


Nearest-neighbor Projected-Distance Regression (NPDR) is a feature selection technique that uses nearest-neighbors in high dimensional data to detect complex multivariate effects including epistasis. NPDR uses a regression formalism that allows statistical significance testing and efficient control for multiple testing. In addition, the regression formalism provides a mechanism for NPDR to adjust for population structure, which we apply to a GWAS of systemic lupus erythematosus (SLE). We also test NPDR on benchmark simulated genetic variant data with epistatic effects, main effects, imbalanced data for case-control design and continuous outcomes. NPDR identifies potential interactions in an epistasis network that influences the SLE disorder. An important challenge for machine learning in GWAS is to perform computationally efficient screening for variants involved in complex genetic models, including epistatic effects. The identification of interactions in GWAS may lead to an increased understanding of pathogenic mechanisms and potential therapeutic targets, but low minor allele frequencies and the curse of dimensionality make interaction detection difficult. Machine learning methods also face the challenge of identifying statistical thresholds that limit false discoveries and handling the intricacies of biomedical studies such as covariates and population structure.Recently we developed a flexible nearest-neighbor-based machine learning feature selection method called Nearest-neighbor Projected Distance Regression (NPDR) to address these challenges . NPDR inIn the current study, we demonstrate the capabilities of the NPDR framework to detect variants involved in complex genetic models and to adjust for population structure. We compare the performance of NPDR with random forest and univariate analysis on a panel of benchmark simulated genetic variant data described by In addition to adjustment for multiple testing, NPDR enables the adjustment for covariates such as sex, age, or population structure – due to population stratification or cryptic relatedness. Population structure leads to linkage disequilibrium (LD) and this deviation from independence may increase false associations . The conSystemic lupus erythematosus (SLE) is an autoimmune inflammatory disease characterized by antinuclear autoanti bodies, complement and interferon activation, and tissue destruction. It predominantly affects women. Numerous immune-related genes and genes with other functions have been shown to predispose to SLE , but theRelief-based methods are known for their ability to identify interactions with computational efficiency but generally do not account for statistical significance of the attributes that may lead to high misclassification rate. In order to control false discoveries and adjust for covariates, we developed NPDR to use the GLM to perform regression between nearest-neighbor pair distances projected onto each predictor dimension . We defii is a point in p attribute  dimensions, and the topological neighborhood of i is labeled by Ni. This neighborhood is a set of other instances trained on the dataset Xm×p of m instances and p attributes and depends on the type of Relief neighborhood method  and the type of metric . If instance j is in the neighborhood of i(j ∈ Ni), then the ordered pair is in the overall neighborhood  ∈ 𝒩) for the projected-distance regression analysis. The ordered pairs constituting the overall neighborhood can then be represented as nested sets:In NPDR, instance  j ≠ i : j ∈ Ni} is ki, the number of nearest neighbors for subject i. In the analyses in the current study, we use an adaptive k for hits and misses, k = 0.154 (m-1), that has shown good balance between detecting main effects and interaction effects  in this study. The projected difference or diff function [dij(a)] between two instances i and j onto a SNP is of critical importance to the NDPR algorithm and can be computed by various difference functions. The standard difference used by Relief-based algorithms for categorical variables is a binary mismatch. For SNPs, this genotype mismatch (GM) is a 0 or 1 difference between two individuals  for a SNP, a, based on the individuals’ genotypes for this SNP. Specifically, the diff function iswhere |a, Ri) is the genotype for individual Ri for SNP a. In other words, two individuals have zero diff if they have identical genotypes and they have unit diff if they have different genotypes.where genotype  diff accounts for the difference in the number of alleles for a SNP when computing the distance between two individuals . The AM a, Ri) is the genotype encoding for number of copies of the reference allele forSNP a for individual i. In other words, the value of genotype is the number of minor alleles in the genotype: 0, 1, or 2. Then the return value of where genotype, NPDR uses linear regression of the numerical difference y between neighbors:The quantity a.and feature importance and significance are again determined from the coefficient βFalse-positive associations can arise in GWAS due to population stratification or cryptic relatedness. A standard approach to correct for population structure is to include PCs in the regression model to account for the genetic background. Many machine learning feature selection algorithms have limited ability to adjust for population structure or other potentially confounding covariates. However, the NPDR formalism can adjust for multiple covariates by including projected differences where the covariate coefficient vector for 10 PCs is,and again https://github.com/EpistasisLab/rebate-benchmark/. For simplicity, many of the benchmark simulations use 20 total variants, but we also compare performance for multiple replicate simulations with 10,000 total variants to demonstrate computational feasibility and the effect of higher dimensionality  pairwise interactions (We apply NPDR to the SLEGEN data, which is a real GWAS composed of females with SLE and healthy controls. Although the study is composed of European ancestry individuals, we include 10 PCs as covariates in NPDR to account for possible cryptic relatedness. Following filtering we create a network from significant (adjusted ractions . This edractions  in the eMachine learning feature selection methods are needed to enrich for attributes involved in complex interaction network effects in high dimensional data, such as GWAS and gene expression, for case-control and quantitative trait studies. In addition to interactions, machine learning methods need to handle complicated modeling scenarios, such as controlling for potential confounders from demographic data or population structure, which is a perennial challenge for GWAS data.In the current study of GWAS data, we applied a new feature selection technique called NPDR that uses the GLM to perform regression between nearest-neighbor pair distances projected onto predictor dimensions. NPDR detects interaction structure using local nearest-neighbor information in the full space of predictors, which may be SNPs or expression levels. Using simulated GWAS, we showed that NPDR has good power to detect functional variants in a variety of simulation scenarios including case-control data with and without imbalance, quantitative trait outcomes, main effects, and multiple pairwise epistatic effects. Similar to our previous findings , NPDR isWe demonstrated NPDR’s ability to handle covariates by including the first 10 PCs in the NPDR models for a GWAS of SLE. Previously we showed that using the covariate term in NPDR can remove genes from nearest-neighbor feature selection that are confounded by sex . In the https://github.com/insilico/npdr.Software available at: MA and BM conceived of the project and wrote the initial draft. MA performed the analyses. CM and PG provided the statistical and biological expertise. 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.


Despite the importance and prevalence of intellectual disability (ID), its origins have not been well understood until now. Lichtenstein et al. report in this issue findings from a population‐based sample four times larger than all previous family studies of ID put together . From more than four million people, 37,787 individuals were identified with ID. Relative risks (RRs) are reported for relatives of ID probands  as compared with matched relatives of individuals without ID. These relatives plus 400 pairs of twins in which at least one twin was diagnosed with ID yield an astonishing estimate of 95% heritability with no evidence for shared environmental influence in their model. Another important finding is that maternal half‐siblings of ID individuals were at greater risk than paternal half‐siblings, a maternal effect that could indicate X‐chromosome linkage. Finally, profound and severe ID is etiologically distinct from the normal distribution, due in part to noninherited (de novo) genetic mutations and chromosomal abnormalities. However, 90% of individuals with ID are moderate or mild, and these represent the low end of the normal distribution of genetic influence on cognitive ability, which has important implications for DNA research on ID. The paper by Lichtenstein et al. in this issue is certain to become a classic in research on intellectual disability (ID) , second‐degree relatives , and third‐degree relatives . And a population comparison sample of four million relatives of individuals without ID!The study provides a rich harvest of results about family risk, genetic influence, environmental effects, sex differences, and whether ID is best viewed as a disorder or the lower end of the normal distribution. One unavoidable limitation is that these results describe a particular population (Sweden) at a particular time (1973‐2013 births).The RR estimates are useful clinically to predict family risk. For example, full siblings of ID probands are eight times more likely to receive an ID diagnosis than siblings of individuals without an ID diagnosis (RR = 8.4). For children of ID probands, the RR is 14.8. The RR drops to 3.5 for second‐degree relatives. The RR falls further to 2.0 for third‐degree relatives.The overall pattern of resemblance for first‐, second‐, and third‐degree relatives is consistent with genetic influence, but it could also be explained by shared family environment that decreases with decreasing relatedness. The twin design can disentangle these competing hypotheses of nature and nurture. The population sweep picked up the expected number of twins – 116 pairs of MZ twins and 284 pairs of DZ twins in which at least one member of the twin pair had received an ID diagnosis. For MZ twins, the probandwise twin concordance was 73%. In contrast, the DZ twin concordance was only 9%, similar to the concordance for full siblings. These results strongly support the genetic hypothesis. MZ and DZ tetrachoric correlations derived from the liability‐threshold model were 0.97 and 0.43, respectively, suggesting a heritability of liability of almost 100%.A liability‐threshold model‐fitting analysis of these twin data together with all the other relatives yielded a heritability estimate of 95%, which makes this the highest estimate of heritability for a behavioral disorder. However, three points should be noted about this astonishingly high heritability estimate. First, this estimate depends on the high MZ concordance of 73%, much more than twice as high as the DZ concordance of 9%. This pattern of twin concordance is also seen for autism. For narrowly defined autism, MZ and DZ concordances are 76% and 0%, respectively; for broader autistic spectrum disorder, concordances are 88% and 31%  based on 79 pairs of MZ twins and 123 pairs of DZ twins  or MZ concordance (73%), the data provide strong evidence for the importance of inherited DNA differences in the etiology of ID.The second‐degree relatives include two groups of half‐siblings: those related through their mother or their father. The relative risks for ID for the two groups are 4.6 and 2.9, respectively, a statistically significant difference. This maternal effect could be a shared environmental effect, although this would seem unlikely if the twin study estimate of zero shared environmental influence is to be believed. This remains an open question because the other Swedish twin study of mild ID mentioned earlier yielded a shared environmental estimate of 30%  is half the relative risk of full siblings (RR = 8.4), even though both types of siblings began life in the same womb. Another genetic contributor to the low relative risk for paternal half‐siblings (RR = 2.9) is mistaken paternity. A specific genetic hypothesis is that recessive DNA variants on the X chromosome are involved, as explained in the following section.A puzzle in child psychiatry is why the prevalence of several childhood disorders, including ID, is much greater in males than females. In the Lichtenstein et al. study, 59% of the ID probands were male and 41% female. They also showed that for ID probands, risk was greater for male relatives than for female relatives.One genetic hypothesis is that recessive alleles on the X chromosome are responsible for such sex differences. Females have two X chromosomes, one from the mother and one from the father; males receive one X chromosome from their mother and a small Y chromosome from their father. If recessive alleles on the X chromosome are associated with ID, males will manifest the disorder more often because with only one X chromosome, they express recessive alleles on the X chromosome. Females only manifest the disorder if they inherit the recessive allele on both of their X chromosomes. Thus, the ID risk for half‐siblings related through their mother would be expected to be greater than for half‐siblings related through their father, which is what was found in the Lichtenstein et al. study (RR = 4.6 vs 2.9). This effect would be expected to be greater for male half‐siblings than for female half‐siblings, but the authors did not report this comparison.There is another more direct test of the X‐chromosome linkage hypothesis which the authors did not report: comparing father‐son ID risk to risk for the three other parent‐offspring sex comparisons. Because sons only inherit the Y chromosome from their fathers, if DNA differences on the X‐chromosome impact ID, father‐son ID risk would be expected to be lower than ID risk for the other parent‐offspring combinations.Common disorders are generally the quantitative extreme of the same genetic and environmental factors that affect variability throughout the normal distribution  analyses comparing ID probands to controls. The implication of finding that 90% of individuals with ID represent the lower end of the normal distribution is that GWA studies of normal variability in cognitive ability will yield polygenic scores that predict ID just as well as they predict high cognitive ability and everyone in between. Polygenic scores can already predict more than 10% of the variance of cognitive ability (von Stumm & Plomin, Polygenic scores are unique predictors because they can predict from early in life and do not change, which means that they can be used to predict ID before intelligence can be assessed. This makes them especially valuable, for example, in helping to identify infants at risk for later learning difficulties so that they receive the support they need early on. Polygenic scores are also unique in that they can predict differences within a family. Even if ID was 100% heritable, siblings would still differ substantially because they correlate only 0.5 genetically.In summary, the implications of the Lichtenstein et al. study will reverberate for a long time to come in research, in clinics, and, eventually, in society.


Barley is the most common source for malt to be used in brewing beer and other alcoholic beverages. This involves converting the starch of barley into fermentable sugars a process that involves malting, that is germinating of the grains, and mashing, which is an enzymatic process. Numerous metabolic processes are involved in germination, where distinct and time-dependent alterations at the metabolite levels happen. In this study, 2,628 plots of 565 spring malting barley lines from Nordic Seed A/S were investigated. Phenotypic records were available for six malting quality (MQ) traits: filtering speed (FS), wort clearness (WCL), extract yield (EY), wort color (WCO), beta glucan (BG), and wort viscosity (WV). Each line had a set of dense genomic markers. In addition, 24,018 metabolomic features (MFs) were obtained for each sample from nuclear magnetic resonance (NMR) spectra for wort samples produced from each experimental plot. The genetic variation in the MFs was investigated using a univariate model, and the relationship between MFs and the MQ traits was studied using a bivariate model. Results showed that a total of 8,604 MFs had heritability estimates significantly larger than 0 and for all MQ traits, there were genetic correlations with up to 86.77% and phenotypic correlations with up to 90.07% of the significant heritable MFs. In conclusion, around one third of all MFs were significantly heritable, among which a considerable proportion had significant additive genetic and/or phenotypic correlations with the MQ traits  in spring barley. The results from this study indicate that many of the MFs are heritable and MFs have great potential to be used in breeding barley for high MQ. Although different types of starchy plants have been used for brewing, such as rice, wheat, maize, millet, and sorghum, barley is the most common source for malt to be used in brewing beer and other alcoholic beverages . In the Brewing is a process to convert the starch of a cereal into alcohol and other fermentation products. The first stage of the brewing process is hydrolysis of the starch into fermentable sugars and the second stage is the conversion of these sugars into alcohol and carbon dioxide . The firMetabolites are typically intermediates of biochemical reactions, which connect complex interactions, cellular pathways, DNA, RNA, protein, and environmental stimuli . TherefoDifferent from animals, the growing ability of green plants primarily depends on their own photosynthetic and metabolic capacity because plants mostly produce their own organic compounds . TherefoTherefore, the objectives of this study were to (1) investigate genetic variation in MFs in malted spring barley; (2) study the genetic and phenotypic relationships between MFs and MQ traits.There were 565 spring barley malting lines from Nordic Seed A/S included in the current study. These lines were part of the standard breeding program of the commercial breeding company and were grown in two locations in Denmark and in each location harvested separately in 3 years . The fields were divided in trials consisting of smaller plots, and each trial was a randomized block-design with three replicates of each line . Therefo1H NMR spectra, the intensities were integrated over small chemical shift (δ) intervals, expressed in parts per million (ppm) in the frequency range from 0.70 to 9.00 ppm.Genotypic data were based on the Illumina iSelect9K barley chip and a total of 3,889 single-nucleotide polymorphism (SNP) markers were used after editing according to minor allele frequency more than 5% and missing markers less than 20%. These genotypic data were used to define additive genetic relationships between lines and used as a basis for the estimation of additive genetic variances and covariances. MFs were NMR data expressed as 24,018 intensities obtained from one-dimensional (1D) In total, there were 2,628 samples originating from individual plots with records of six MQ traits and 24,018 MF intensities, the plots were from 565 lines, and each line had genomic information on 3,889 SNPs evenly spread over the genome.2O), which included 0.5 mg ⋅ ml–1 sodium trimethylsilyl propanesulfonate . The DSS methyl signal will serve a dual role as NMR chemical shift reference (0.00 ppm) as well as internal concentration standard for NMR spectroscopy later. Samples were collected in 96-well containers, with one reference position that only contained DSS solution as a control. Each 96-well plate was then heated at 70°C for 60 min in a thermocycler with a shaking frequency of 800 rpm. After heating, samples were cooled in a water bath at room temperature for 60 min. To precipitate any remaining insoluble debris, plates were centrifuged for 20 min at 4,000 rpm. Subsequently, 700 μl of supernatant from each sample was then carefully pipetted to new 96-well plates, which were flash frozen, covered with parafilm, and transferred into a freezer drier under vacuum to sublimate all ice. The containers with dry frozen powder were then stored at −20°C.For each sample to be analyzed, exactly 0.050 g freeze dried malt was dissolved in 1 ml distilled water (H2O). Subsequently, each solution was transferred to a 5 mm NMR-tube and placed in 96-tube trays suitable for a Bruker SampleJet automatic sample changer. NMR metabolite spectra were then measured at 25°C on a Bruker 500 MHz spectrometer, using the zgpr pulse sequence  with 16 scans, using an inter-scan delay of 2 s and 4.1 s acquisition. The NMR spectrum for each sample was recorded in under 2 min. Including sample changes and stabilization, ten samples were analyzed per hour.Nuclear magnetic resonance samples were prepared by dissolving the dry material in each vile into 550 ml heavy water  and the region of the added standard (−0.2 to 0.2 ppm). The raw data was normalized using the probabilistic quotient method ; second, the relationship between MFs and the MQ traits was studied by bivariate analyses (Model 2). These models were designed to decompose the total phenotypic variances into different components. In order to achieve this goal, phenotypic variances were allocated into components of genetic effects, environmental effects and their interactions. The genetic components included both genomic and non-genomic effects of the lines, the interaction components included both genomic and non-genomic by environmental effects, and the environmental component included batch effects and residual error.In the first step, the following model was applied:ym referred to the vector of each MF, b was a vector of location × year × trial effects to correct for the differences might be caused by experimental location, year and trial, g was the vector of additive genomic effects for each line which could be explained by genomic markers, l was the vector of line effects for differences between lines not explained by additive genomic markers, ig was additive genotype by environment (six location × year environments) interaction effects which accounted for the differences in genotype caused by various location × year environments, il was line by environmental interaction effects which accounted for the differences in line caused by various location × year environments, t was the vector of effects for batch of samples malted and mashed simultaneously which accounted for the environmental effects induced by the different batches, X, Zg, Zl, Zig, Zil, and Zt were the corresponding design matrices allocating MF to b, g, l, ig, il, and t, respectively, and e was a vector of residual terms which were the variances could not be explained by the other effects in the model. In this model, b was a fixed parameter, and g, l, ig, il, t, and e were random parameters with diag denoted the diagonal matrix with G as block-diagonal elements in six location × year environments, and I denoted the identity matrix. By applying a G matrix, the effects of all markers were summarized as the genomic variance components and are equivalent of regressing each MF on all markers simultaneously  was used with Model 1 as sub-model for each trait (MF and MQ traits) involved in the analyses, and in which all dispersion parameters were expanded to 2 × 2 covariance matrices.The (co)variance components in all models described in the previous paragraph were estimated by restricted maximum likelihood (REML) using the DMU software package .In the first step, the phenotypic variances of MFs were calculated as the sum of variance components (VCs) in Model 1:where z > 2.326), z-scores were calculated as follows  of 0.05 were in the interval . The cut-off point corresponding to a significance level of 1% was 0.015. Therefore, there were 8,604 MFs (35.82%) having  larger than 0.015, and were treated as MFs with significant additive genetic variance.. In . For example, the maximum  (0.38) was at 1.423446 ppm, while the maximum MFm (8.55) was at 3.834322 ppm. To investigate the relation between the intensity and heritability, the entire range of NMR profiles was equally divided into 100 intervals and the proportion of MFs having  significantly different from 0 among all the MFs in each ppm interval were plotted versus the sum of MF intensities in corresponding interval as shown in In order to visualize the MF intensities and their heritabilities, the heritability estimates for each MF and unstandardized mean intensities of MFs were plotted. In the second step, bivariate analyses were carried out to investigate the relationship of MFs having significant heritability (MFh) with each of the MQ traits. The statistical significance of correlation indicates the relationship between MQ traits and MFs rather than the sign of the correlation.−1.000 to 0.349 and 86.77% of these correlations were significantly different from 0. The additive genetic correlations between BG and MFh tended to be significant for correlations lower than −0.4. The proportion of significant additive genetic correlations between WV with MFh were 75.05%, which was the second largest among all six MQ traits. Similar with BG, the additive genetic correlations between WV and MFh tend to be significant for correlations lower than −0.4. For WCO, 64.53% of the additive genetic correlations with MFh tended to be significant for correlations higher than 0.3. Whereas for three other traits, i.e., EY, FS, and WCL, the proportion of significant genetic correlation with MFh were much smaller (less than 12%) compared with the three traits mentioned above. For example, for WCL, the additive genetic correlation with MFh ranged from −0.694 to 0.842 and only 0.19% of these correlations were significantly different from 0.In −0.364 to 0.067, which was much narrower than the corresponding additive genetic correlations, and the significant phenotypic correlations tend to be significant for correlations with absolute value larger than 0.1. Similar to BG, the phenotypic correlations between WV and MFh ranged from −0.358 to 0.099, which was also much narrower than the corresponding additive genetic correlations, and the phenotypic correlations also tended to be significant for correlations lower than −0.1. The phenotypic correlations between WCO and MFh, which ranged from −0.100 to 0.416, tend to be significant for correlations higher than 0.1. Different from additive genetic correlations, the lowest significant phenotypic correlations were found for EY (19.72%), followed by FS (35.59%), and WCL (39.76%).Histograms of the estimated phenotypic correlations between MFh and six MQ traits are presented in The current study analyzed the genetic variance in 24,018 individual MFs using univariate models, and then investigated genetic and phenotypic correlations between significantly heritable MFs and MQ traits in spring barley. A previous study of In this study, the estimates of heritability for all MFs were low to medium. There were 35.82% MFs that were significantly heritable. The heritability of MFs measured on blood extracted from 843 male Holstein calves were investigated by From the comparison between patterns in estimates of heritability and the pattern in MFs intensity, no clear relationship was observed between magnitude of intensities and their magnitude of heritabilities. For example, for the MFs between 2.75 ppm to 2.85 ppm, there were multiple high peaks for the MF intensities, while the estimates of heritability in this interval were not high . For the MFs between 0.50 ppm to 1.00 ppm, the intensities were low but there were several MFs having medium heritability and even the highest estimated heritability was located in this area. Although an identification of the molecules responsible for the MFs was not attempted, this region typically includes methyl groups from lipid ends and beta-branched amino acids. The medium heritabilities for these MFs indicated that this area represented biological compounds that were under considerable influence of additive genetic effects. The areas with highly heritable MFs represent typical amino acids such as tyrosine, phenylalanine and alanine. This pattern was also observed in the previous study especially for the range from 1 to 6 ppm, where most of the significant heritabilities appeared .Among all the 24,018 MFs analyzed in the current study, the heritabilities of 64.18% MFs were not significantly different from 0. It is not surprising that such a large proportion of MFs were not significantly heritable, because the entire NMR spectra were analyzed, which means that the areas with low or even no signal from metabolites were included and analyzed. The wide range of frequency (0.70 ppm to 9.00 ppm) of the NMR profile examined in this study could also be a factor. In the setting of the NMR spectral window, all relevant signals were expected to be detected. Some regions do not show (many) signals, for example in the right side of the spectra in The definition and the estimation of heritability in plant breeding are complicated and diverse across studies since the observational units varies from individual plants to means of a genotype tested across different environments . HeritabIn addition to plant species and livestock, there have been several studies on heritability in human metabolites . A very ig) and line by environment interaction effects (il). The magnitudes of ig and il are plotted as relative variance components (RVCs) in ig ranged from 0 to 0.18 and RVCs of il range from 0 to 0.35, which indicate that environment did have differential effects on MFs. Therefore, in our study, the model which considering location × year environmental effects, did decompose the total phenotypic variance of MFs properly and extract variance of additive genomic effects  as well as genotype by environmental effects.There were two locations and 3 years involved in this study, and MFs for same variety can vary across different environments (location × year). Therefore, the model used in current study included also additive genotype by environment interaction effects  bivariate analysis were carried out. Both genetic and phenotypic correlations were investigated in each of the bivariate analysis.For all the traits, the average magnitude of phenotypic correlation between traits and MFs was smaller than the genetic correlation, while the proportion phenotypic correlations that were significant was larger than the proportion the genetic correlations that were significant. In the study on Holstein calves  the magnAmong six MQ traits, WCO, WV, and BG displayed a high proportion of correlation (both genetic and phenotypic) with MFs, whereas EY, FS, and WCL exhibited a lower fraction. This indicates the larger impact of genetic effects on the relation between the metabolome and the traits of WCO, WV, and BG compared to EY, FS, and WCL. This might be useful to develop more efficient selection indices when formulating breeding plans for WCO, WV, and BG. The relationship between metabolites and MQ traits in barley was also investigated in a previous study, where non-targeted LC-MS metabolite profiling was applied . This prThe technology of NMR spectroscopy was used in this study to profile the metabolome in wort samples. Compared to the mass spectrometry (MS)-based metabolomic methods, LC-MS and GC-MS, NMR spectroscopy is very efficient for studies with large sample sizes. Though the price of the NMR instrument is not lower when compared to GC-MS and LC-MS, the NMR approach (i) needs virtually no preparation or derivatization, (ii) the process for measurement is faster and non-destructive, (iii) the procedure is highly reproducible, (iv) the output data is quantitative, (v) the output is comparable to data from other instruments, as all metabolites present in each sample are captured at the same time, (vi) there is no drift in the instrument. Collectively, these attributes strongly advocate the applicability of NMR metabolomics for further studies . The maiThe models used in the current study implied estimation of effects from each marker as such models were equivalent of regressing each MF on all markers simultaneously . TherefoIn this study, records of six MQ traits and MFs for 565 spring malting barley lines grown in two locations and harvested separately in 3 years were studied. On average, each line was replicated 4.65 times. Additive genetic relationships between lines were estimated from a set of dense markers covering the whole genome.Genetic variation in MFs were investigated using a univariate model for each MF. The estimates of heritability for all the MFs ranged from 0 to 0.38. Among 24,018 MFs, 8,604 were significantly heritable and were selected for further bivariate analysis to estimate their genetic and phenotypic correlation with MQ traits. The large proportion of MFs having heritabilities not significantly different from 0 likely resulted from the inclusion of areas the investigated NMR spectra that had no biological signals. WCO, WV, and BG exhibited a high proportion of significant genetic and phenotypic correlation with selected MFs, while EY, FS, and WCL showed a low proportion. This indicates the large impact of genetic effects on the relation between the metabolome and WCO, WV, and BG, and might be useful to develop more efficient selection indices when formulating breeding plans for these traits. The profile of the metabolome in wort samples were produced by NMR, which has many advantages, e.g., low labor intensity, high reproducibility, fast, and non-destructive. With these advantages, this approach is suitable for large metabolomic data profiling both in practical industrial applications and in theoretical researches.In conclusion, around 36% of MFs were significantly heritable, among which many were correlated with MQ traits of WCO, WV, and BG in spring barley. The results from this study indicate that many of the MFs are heritable and MFs have great potential to be used for more efficient selection for MQ in spring barley.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/NK carried out malting and extraction for NMR analysis. NK, PS, and FM developed the protocol for metabolite NMR analysis. NK, JDJ, and JO provided the genotypic and phenotypic data. XG implemented and carried out the statistical analysis, interpreted the results, and drafted the manuscript. NK, JDJ, AJ, JJ, and FM contributed to the experimental design. PS, JJ, and XG developed the statistical models. All authors participated in interpreting the results and read and approved the final manuscript.PS, JDJ, JO, and AJ are employed by the Nordic Seed A/S, and NK is employed by SEGES. The funders had no influence on the study design or choice of analysis methods. The remaining 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.