CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs.Due to the complexity of sperm cell reproduction and maturation, the genetic structure of semen traits remains largely unknown. In our study, we used weighted single-step GWAS to detect genetic regions and further candidate genes related to semen traits in Duroc boars. This study provides in-depth understanding of the genetic structure of semen traits and the biological information provided by gene networks, and can be applied to speed up the genetic process of semen traits in boars. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars.Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility—sperm progressive motility, sperm abnormality rate, and total sperm count—explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes  Semen traits play a key role in the wide application of artificial insemination in the pig industry. The economic profit of artificial pig breeding stations is highly dependent on the quantity and quality of semen , and theIn recent years, with the rapid development of high-throughput genotyping and molecular technology, researchers can accurately identify quantitative trait loci by looking for the association between genetic markers and phenotypic records, which is called genome-wide association study (GWAS) . GWAS haWeighted single-step GWAS (WssGWAS), proposed by Wang et al. , is a meIn this study, we used WssGWAS to detect genetic regions and further candidate genes related to semen traits in Duroc boars. This study will have an in-depth understanding of the genetic structure of semen traits and the biological information provided by gene networks and can be applied to speed up the genetic progress of semen traits in boars.6/mL, measured by the CASA system).Animals used in this study were from the same artificial insemination station . A total of 24,983 semen records were collected from 583 Duroc pigs during 2020–2022. All boars had complete pedigree records for four generations. Four semen traits were measured. Sperm motility (SPMOT), sperm progressive motility (SPPMOT) and sperm abnormality rate (SPABR) were measured using Magapor Gesipor3.0 CASA system . Total sperm count (SPCOUNT) was calculated by multiplying semen volume (mL) by semen concentration (10The phenotypes for four semen traits of Duroc pigs are shown in Sus scrofa 11.1), and located in sexual chromosomes and with missing position information were removed, after which 50,897 SNPs were retained. After that, individuals with call rate <0.9, SNPs with call rate <0.9, minor allele frequency <0.01, and Hardy–Weinberg equilibrium <106 were removed using plink v1.90 [Genomics DNA of pig semen samples were extracted and purified from 571 Duroc pigs. These boars were genotyped by using the KPS 50k SNP array  containing 57,566 SNPs. SNPs that unmapped the reference genome   of the AThe following multiple traits repeatability model was used to estimate variance components:ormation ,24, and p matrix , where ZThe weighted single-step GWAS was conducted using the BLUPF90 software family in an iterative way adapted for genomic analyses . Brieflyt = 1, Step 1: Procedure initialization, let Step 2: Calculated GEBV of the entire dataset, via ssGBLUP method with Step 3: Calculated marker effects, via Step 4: Calculated SNP weights for the next iteration, via Step 5: Normalized SNP weights, readjust the SNP weights to stabilize the total genetic variance via Step6: Calculated G for the next iteration, via t = t + 1 and iterate from step 2.Step7: Let This procedure was run for three iterations based on the predicted accuracies of GEBV according to Legarra et al.  and ZhanQTL regions were selected according to the genetic variance of chromosome windows. Windows explaining more than 1% genetic variance were selected as candidate QTL regions, within which candidate genes were searched. The threshold of 1% was chosen based on the literature ,31,32 anSus scrofa 11.1 . For all the candidate genes, we manually searched they National Center for Biotechnology Information  to see if they had a previously identified relationship with the traits under study. Gene Ontology (GO) [Genome annotations were based on the gene database ogy (GO)  and Kyotogy (GO)  were useDescriptive statistics of phenotypes for all semen traits are given in In order to better understand the genetic structure of semen traits, we used two methods (BLUP and ssGBLUP) to estimate variance components, the latter to calculate genetic and phenotypic correlation. The variance components of semen traits are shown in In this study, we show the proportion of variances explained by each 0.4 Mb window for semen traits of Duroc pigs . The thrA total of 112 candidate genes were detected in the QTL regions of SPMOT, SPPMOT, SPABR and SPCOUNT traits , of whicThe GO terms used to enrich identified genes, with a total of four GO terms related to semen traits, are shown in The association network diagram of the GO terms of genes’ biological processes can be seen in In this study, we chose the WssGWAS method because of the following advantages. (1) It can integrate all phenotypes, genotypes and pedigree data at the same time, thus avoiding the calculation of the pseudo-phenotypes of genotyped animals to integrate all phenotypic information. It uses information from those without genotypes to improve the statistical power of QTL detection. (2) It allows different weights to be used according to the importance of SNPs, which deviates from the unrealistic assumptions of the GBLUP infinitesimal model and improves the accuracy of SNP effect estimation . (3) It The results of variance components showed that the repeatability of ssGBLUP was similar to that of BLUP and the additive variance of ssGBLUP lower than BLUP. Previous studies have shown that the additive variances and heritabilities estimated by the pedigree-based BLUP method may be too high. Compared with BLUP, ssGBLUP has a lower standard deviation . In thisSTRA8 gene is a candidate gene for these two traits. The same QTL region (chr12:58.83–59.21) was found in SPMOT and SPABR, and the candidate genes of ZSWIM7, TEKT3 and UBB were identified, which may be related to the high genetic correlation between SPMOT and SPABR  in SPMOT and SPPMOT, and determined that the nd SPABR . These rSTRA8, UBB and CATSPER1 genes are enriched into a common biological process and participate in male meiotic spermatogenesis. In order to further confirm the identified candidate genes related to semen traits, further molecular experiments are needed in future research.The STRA8, ZSWIM7, TEKT3, UBB, and CATSPER1 are significant candidate genes. STRA8 (stimulated by retinoic acid 8) controls the initiation of meiosis in male germ cells by activating the expression of meiotic genes [STRA8 knockout mice can eventually form sperm cells that cannot complete meiosis [ZSWIM7 (zinc finger SWIM-type containing 7) mutations lead to human male infertility [ZSWIM7 leads to azoospermia in men and primary ovarian insufficiency in women [TEKT3 (tektin 3) is a filamentous protein associated with microtubules in cilia, flagella, basal bodies and centrioles. TEKT3 is necessary for progressive sperm motility in mice [TEKT3 in bull spermatozoa may be related to capacitation or overactivation [UBB plays an important role in maintaining RNA binding regulatory factors and piRNA-metabolic proteins in testis to complete mouse spermatogenesis [UBB leads to male and female infertility in mice, and germ cells are blocked during meiotic prophase I [CATSPER1  protein is localized in sperm tail and expressed in human testicular tissue in the form of meiosis and postmeiosis [CATSPER1 in human azoospermic semen [CATSPER1 channel in epididymal sperm contributes to the pathogenesis of asthenospermia in rats [For sperm motility, ic genes . The spe meiosis , which w meiosis . It has  meiosis ,40,41 an meiosis . Recurreertility . The homin women . TEKT3  is very important for the communication between germ cells and Sertoli cells  [PTBP2 plays an important role in the development of male germ cells and spermatogenesis in mice [For sperm progressive motility, genesis) . The RNA in mice ,56.EIF2B2, MLH3, ZSWIM7, TEKT3, and UBB are significant candidate genes. In human male asthenospermia, EIF2B2 (eukaryotic translation initiation factor 2B subunit beta) is important for sperm motility [MLH3 can lead to male infertility with azoospermia or severe oligozoospermia.For sperm abnormality rate, motility ,58,59. TCCDC70 is a significant candidate gene. CCDC70 (coiled-coil domain containing 70) gene is highly expressed in mouse testis, mainly expressed in sperm cells, round sperm group and epididymal epithelial cells, and participates in the regulation of spermatogenesis and epididymal sperm maturation.For total sperm count, We used weighted single-step GWAS to identify candidate genes related to sperm motility, sperm progressive motility, sperm abnormality rate, and total sperm count in Duroc boars. These results can be used as genetic markers to improve semen production and quality. This study provides information for further understanding the genetic structure of semen traits in Duroc boars.


The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed. During the last two decades, various genotyping techniques have been used to discover genetic factors responsible for variations in human appearance. Face character prediction has been a challenge for anthropologists, medical human geneticists and criminalists. In this review this type of prediction will be referred to as forensic DNA phenotyping (FDP). FDP aims to infer the unknown, externally visible characteristics (EVCs) of a person from DNA. After the anthropologists have established the bases of the phenotypes for human identification purposes, geneticists carry out research into genetic variations involving morphological features commonly used in human identification, such as age, ancestry, eye color, hair color, skin color, and facial features ,2,3,4,5.This review is organized as follows; in The genetic influence on facial features has been investigated through studying various factors such as the impact of Sonic-Hedgehog, bone morphogenetic proteins, and homeobox genes on facial feature development ,15,16,17ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59, were also correlated with a mean absolute deviation (MAD) of 3.844 years from chronological age ..156].p-value 1 × 10−79–9 × 10−2) [The results shown in the identification stage of  were obt × 10−2) ,171,172. × 10−2) . A total × 10−2) ,168,169. × 10−2) ,171,172. × 10−2) ,167,172.Analysis of population backgrounds in these papers revealed that most of the studied populations were of European ancestry (80%), followed by East Asians (10%), Hispanic or Latin Americans (7%), Africans (2%), and those of Admixed ancestry (1%) . Such rep-value threshold of (5 × 10−8) or higher were reported. This threshold was selected based on the Bonferroni correction method. This method accounts for the p-value based on the assumption that every SNP is tested independently of an array [p < 5 × 10−8) [p-value threshold [p-value for the correlated trait, which may be the p-value of discovery, replication, or meta-analysis. Therefore, the database was used as a filtering tool for all SNPs that reached the genome-wide significance threshold. A total of 614 associations with 98 traits  met our inclusion criteria  . This ishreshold ,175. In criteria . Figure When assessing genes of two or more associations with facial features, inconsistent patterns were observed within and between the population groups . Some geTMEM74) gene was associated with the height of the vermillion lower lip in the European population [TMEM74 gene in tumor cell survival through the induction of autophagy in multiple tumor cell lines [In contrast, some genes showed associations with different facial features within the same population group. For example, the transmembrane 74  is an enzyme engaged in regulating gene expression. Although HDAC9 is not expressed in the craniofacial tissues of developing mice, it is proposed that it regulates the expression of TWIST1, a neighboring gene affecting limb and craniofacial development in mice [Other findings suggest the association of genes in different face areas among different population groups. The mericans , nose momericans , and chimericans ,169. Thepatterns . It is apatterns . Anothermericans , while imericans . HDAC9 , vocal pitches, and other parameters in the prediction algorithms used for identification. The prediction accuracy with respect to facial structure was enhanced when BMI and age were considered. Facial features were predicted using multiple types of algorithms, such as PC, linear discriminant, neural networks, sparse representation, and the local presentation of facial features. Their approach was mainly to deform the face and map it against a template and calculate the displacements between them to overcome the challenge regarding data privacy in personalized medicine. Using 1000 ancestry PC data, the algorithm predicts the face’s PC value based on ridge regression and multiple covariates such as sex, age, and BMI. This algorithm was developed based on maximum entropy in order to combine phenotypic features and GWAS. Future studies shall include populations of different ethnic groups in order to validate the current research outcomes and explore facial traits that are less common among individuals of European ancestry .Considering the differences between the two approaches, the authors of  tested t2 were used to calculate the general performance or the accuracy of prediction models [Considering the complexity of the process of facial prediction, applying the power of machine learning techniques, algorithms, and other statistical models to the big data available can alter the approaches to the challenge at hand. Although simple, linear models are considered great classifiers when aiming to avoid overfitting. There are regularized linear models, such as maximum likelihood, and those with less regularization using variable selection simultaneously, such as lasso, which is a preferred model for increasing accuracy in high-dimensional data ,189. UsiIn addition, researchers have used partial-least-squares regression (PLSR) for predicting face-related features based on genomic ancestry, sex, and 24 SNPs . Others Multiple challenges need to be addressed before applying FDP in forensic cases. First, the accuracy of FDP needs to be assessed, mainly when the result of this evidence is used for exclusion or conviction. Second, FDP raises ethical concerns by revealing medical information that the suspects/victims may not wish to know/have released. Third, FDP has been legislated in only a few countries, and the legal terms need to be updated in others. Fourth, some individuals make cosmetic or surgical alterations to their facial features, making FDP more challenging. Lastly, the generated FDP data and their analytical procedures need to be further assessed by forensic and research laboratories worldwide to obtain a valid scientific basis before using FDP as legal evidence in forensic cases.The success of FDP can be measured by validating the accuracy of predicted facial features in real forensic cases. However, the current research on FDP focuses on associating genes with face-related phenotypes, which predicts a class of phenotypes but does not yet individualize one face from another . MoreoveAs the research on FDP progresses, more information can be predicted using DNA. Initially, DNA was used to generate profiles for identification in forensic cases. These STR profiles were thought to be uninformative and were mainly used for identification purposes. However, research has shown that these genetic markers have a regulatory role in gene expression. For instance, Bañuelos, M., et al. demonstrated correlations between genotypes in the Combined DNA Index System (CODIS) loci and expression variations of the neighboring gene and, possibly, medical information . TherefoNot all characteristics revealed by FDP have the same level of sensitivity. For instance, some characteristics are trivial and are not private, such as the external features of a subject, which includes voice type or right-handedness. In addition, police and law enforcement agencies have access to portrait photographs on drivers’ licenses and ID cards. On the other hand, medical history is characterized as a sensitive trait. If this trait is revealed to the public, it can be used as a filter criterion in the employment process. Moreover, it is argued that the advantages of the limited use of these features in criminal investigations do not override the privacy risks faced by the individual . It is sFDP can potentially reveal information regarding genetic diseases that individuals may not wish to be informed about, thus violating the “right not to know” principle. Regarding the human genome, United Nations Educational, Scientific and Cultural Organization (UNESCO), in Article 5c, declared that “The right of every individual to decide whether or not to be informed of the results of the genetic examination and the resulting consequences should be respected” . SimilarFrom another perspective, some argue that the weight of the right to maintain ignorance is dependent on the value of information revealed by FDP. For example, some traits do not reveal medical information and do not necessarily violate privacy rights, such as left-handedness, the tendency to smoke, voice type, or geographic origin. These traits are mostly previously known by the individual and will not raise an issue if confirmed by FDP ,50,51,52To address the data protection and privacy issues raised by individuals, it is suggested that FDP data are destroyed following the criminal investigation or when a match is found. This option ensures that the collected data are used for their original purpose, which is identification, and potentially eliminates the use of such information for other purposes. However, destroying FDP information may violate the Universal Declaration on the Human Genome, which gives the individual the right to know/be unaware of the result of a genetic test ,198. FurThere is evidence of bias in the research into FDP. This bias mainly stems from the researchers or the machines used. Researchers may use easier-to-access populations to conduct their research, and they may try to obtain specific grants based on the population of interest. Accordingly, some populations, such as Europeans, are more prone to be included in human genetics research. In addition, since most researchers are from universities and research centers in well-developed countries, it will be easier to target populations around them rather than collect samples from other areas, which invokes logistical bias.Moreover, it is easier for researchers to compare and use pre-established databases such as the GWAS Catalog. However, this will create persistent bias throughout the years because machine learning and the verification of the results are easier when using more data from the same subjects’ ancestry. Hence, there are many variables to control when introducing a new population and more to correct for before learning from novel populations. In addition, some machines that have already been developed may find it harder to compare and provide accurate results since the ancestry information available (genotypes) and phenotypic information—such as lifestyle, facial characteristics, and diet—vary between populations.Hence, some technologies may not accurately present complete perspectives of phenotypic information if this bias is not considered. For example, light, shades of darker colors, and reflection are not well-considered by scanning machines that have already been developed using lighter skin-color populations. It is necessary to optimize these machines to gather the full potential of the data in order to reach higher levels of accuracy.Some researchers have shown that GWAS is a great tool for discovering the genetic factors involved in complex diseases. Hundreds of thousands of significantly associated biological characteristics and genetic loci have been found. These associations have been of high value as they helped understand the biological mechanisms of diseases and other phenotypes. However, admixed populations or those of non-European ancestry are under-represented in the GWAS Catalog. Hispanic and Latin American ancestry, Pacific Islanders, Native Peoples, and Arab and Middle Eastern subjects comprised less than 1% of the catalog in the year of 2016. Arab and Middle Eastern populations have contributed to only about 0.08% of the whole GWAS dataset as of 2016. This continuation of bias will create many implications for research, such as (1) impairing the accuracy of findings if direct associations are used across populations, (2) hindering the discovery of novel genetic associations, and (3) limiting the understanding of the face-related genomics in the forensic, anthropological, and medical research on unexplored populations .PARK2 gene (SNP rs9456748) was found to be significantly associated with the height of the midface [COL23A1 (collagen type 23 α 1) (SNP rs118078182) is associated with variations in the nasal shape of individuals across Eurasia [FDP was recently introduced in forensic investigations. Therefore, some countries are still updating their laws regarding the use of FDP in forensic cases. Netherlands, Slovakia, and Germany are the only countries that legalized FDP for forensic purposes, while Belgium, Greece, France, Luxembourg, and Ireland prohibited the use of FDP ,200. Whi midface . Additio Eurasia .As FDP technology advances, another approach to legislating the use of FDP in forensic investigations is to specify the forensic purpose, i.e., identification or 3D facial prediction, rather than restricting the use of certain markers . OverallThe number of plastic surgeries and cosmetic procedures has exponentially increased worldwide. This causes a limitation that needs to be addressed, especially for technologies that are based on face recognition algorithms . CosmetiConsequently, caution must be taken in FDP-guided investigations to avoid falsified appearances ,205. TheThe evaluated research studies demonstrated that the prediction of ancestry and pigmentation traits such as skin, eye, and hair color is more developed than the prediction of facial features, indicating the need for additional research before accepting the use of this technology in forensic fields ,193,200.Face base also serves as a hub for collaborators/researchers interested in craniofacial research. FaceBase has criteria for accepting and sharing facial data from different models  . In addiAlthough the promising results of FDP indicate that it could replace the current STR-profiling techniques, this replacement may not be feasible in the near future due to the large amount of money spent on the existing STR profiles and infrastructure in place in developed national databases worldwide. As a result, future genetic markers used for FDP will likely be added to the core STR markers without replacing the existing STR technology .The current state of the art regarding DNA-phenotyping techniques has been highlighted in this paper. Multiple technologies  are available for acquiring facial feature measurements, which vary with respect to their ease of use and data accuracy. In the meantime, new technologies are evolving fast, reflecting the importance of revisiting the literature to remain informed of the latest technologies and algorithms developed in such research areas.Most scientists believe that it is too early for DNA technologies to be fully employed in face prediction. Nevertheless, genetic studies have cleared the picture of the influence of genetics on facial morphology. With the use of GWAS on limited population groups, researchers have been able to identify some associations between genetic markers and facial features. However, facial analysis based on DNA differs from Mendelian diseases due to the multiple and complex factors affecting facial morphology. Thus, other approaches or combinations of them need to be considered.Some challenges FDP encountered by include those related to assessing its accuracy before implementing it in forensic cases. In addition, applying practices to maintain FDP data privacy is fundamental, especially if such data reveals medical information. The regulation of access to FDP data and storage methods are essential in order to avoid information exploitation by third parties/less scientifically sophisticated police officers. Limited efforts have been made to address these concerns, which is understandable considering that it is an emerging technology, and its extent and validity are not fully established . Moreove


Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites—avenanthramides, avenacins, and avenacosides—we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats ( Arabidopsis have contributed to our understanding of specialized metabolism (Solanum lycopersicum L.) (Zea mays L.) , there is not a consistent relationship between cultivation status and specialized metabolites across multiple species (Avena sativa L.) and how the metabolomic profile has been shaped by plant breeding. Oats were domesticated from weedy progenitors  leading to changes in patterns of variation. Characterizing the genetic bases of variation will provide a starting point for plant breeding.We explored existing variation of specialized metabolites in oats   , and dem avenae) . Core bie.g., Pun1 mutation prevents capsaicin production in pepper  were calculated for each metabolite for the diversity panel, and separately for each environment of the elite panel. To account for skew, data were log2 transformed. Then, relative concentration of each metabolite was modeled with a linear mixed model in R  with lmeS ≈ 4π *((lw)1.6+(lh)1.6+(wh)1.6))/3)(1/1.6). Separately, 100 hand dehulled seeds and their respective hulls were weighed  and the percent groat (kernel) was calculated as the percent of total (kernel plus hull) weight. A summary of the raw measured seed size and mass values  is presented in After dehulling, 50 seeds were used for evaluating seed length, width, and height. The seeds were scanned with a 2D scanner, where seed length and width were extracted with the software WinSeedle . Seed height was measured separately using an electronic caliper manually with accuracy of 0.01 mm. Seed length and width measurements are not available from the elite panel that was evaluated in South Dakota. Seed volume was estimated as an ellipsoid (https://npgsweb.ars-grin.gov), some in Triticeae Toolbox (https://triticeaetoolbox.org/POOL), others in the United States (https://apps.ams.usda.gov/), Canada (https://www.inspection.gc.ca/english/plaveg/pbrpov/cropreport/oat), or Europe  plant registrations, and finally as published variety releases. In sum, we identified the year of variety release for 155 varieties (We conducted an extensive literature search to determine the year of variety release for as many varieties in the diversity panel as possible. Most varieties were identified from information on USDA GRIN (arieties .https://oat.triticeaetoolbox.org/), filtered to remove markers with more than 60% missingness and markers with a minor allele frequency of <0.02, and then imputed using the glmnet function (https://wheat.pw.usda.gov/GG3/graingenes_downloads/oat-ot3098-pepsico). All 54,284 SNPs were used for the diversity panel, and 54,219 SNPs were used for the elite panel after these imputed SNPs were again filtered by minor allele frequency. Kinship matrices were calculated for the diversity and elite panels with their SNPs using the “A.mat” function, and genomic heritability  was sufficient  enrichment analysis for each of the phenotypes for biological processes GO terms. Enrichment analysis was implemented in the R package topGO, where significance was determined based on the default “weight01” algorithm followed by a Fisher test  and the preceding shikimate pathway using Ensemble Enzyme Prediction Pipeline (E2P2) annotations .PFDR < 0.05 per transcript. As only half of the transcripts are mapped, we did not differentiate between cis and trans eQTL, although future genome and transcriptome assemblies will facilitate this analysis.We implemented eQTL analysis in Matrix eQTL  in R usie.g., within AVNs) across populations and environments, with the exception of avenacoside B (AOS_B)  were measured in seeds of a diverse germplasm panel evaluated in one environment and an elite set of oat germplasm evaluated in three environments. Genomic heritability was low to moderate for most metabolites, and some metabolites had heritability <0.05 . In gene (AOS_B) . In the  (AOS_B) . This tr (AOS_B) . While t (AOS_B) .h2 = 0.72; elite panel Minnesota h2 = 0.50; elite panel Wisconsin h2 = 0.33). There were significant relationships between some metabolites and seed size , as well as kernel and hull weight and percent groat (kernel). In general, heritability of the seed traits was greater than those of the specialized metabolites  and seedeed size  and seedeed size . In botheed size .Using year of variety release as a proxy for plant breeding intensity (where later years indicate more intensive breeding efforts), we tested if breeding intensity affected seed size or metabolites in the individuals in the diversity panel for which these data are available , there were no shared SNPs between any two or more traits , indicating that the metabolite and seed traits do not have common large effect loci. Within AVNs, only results from the diversity panel met this threshold  TWAS results (72 for each AVN_A and AVN_B), with 51 shared and expression of most of these shared transcripts (50) positively correlated with increased AVNs (PFDR < 0.25).A TWAS was conducted for each of the specialized metabolites in the diversity panel to assess the relationship between gene expression and metabolite relative concentration. Of these, both AVNs had significant , GO terms related to the shikimate biosynthesis  and L-phenylalanine catabolic processes (GO: 0006559) were top GO terms for both AVNs . In contrast, there was only weak evidence for enrichment of any transcript class in known avenanthramide biosynthetic enzymes  or Shikimate pathway enzymes  at any time point  as measured by coefficient of determination . For insPFDR <0.05 threshold, TRINITY_DN1008_c0_g2_i2 a serine hydroxymethyltransferase 4, and TRINITY_DN13684_c0_g1_i1 a mitochondrial aconitate hydratase 3. These two genes neither colocalized with the AVN GWAS result nor were definitively annotated to a single position in the oat genome. Relaxing the significance threshold to PFDR < 0.2 revealed eQTL of four additional genes .Because we predicted that expression variation is important for oat specialized metabolites, especially AVNs, we conducted eQTL analysis on genes detected in TWAS and on known pathway genes and examined if those eQTL colocalized with our GWAS results. Two AVN TWAS results had eQTL at a al genes , but thePFDR = 0.002; chromosome 3A, position 15737366, avgbs_cluster_12707.1.49). We also examined eQTL from pathway genes at a PFDR < 0.2 threshold and identified eQTL of five additional genes .Of the pathway genes , only TRal genes . We founAvena sativa L.) is a cereal crop with known health benefits from consuming the grain or through topical skincare application. These benefits are derived from a diverse suite of metabolites, including unsaturated fatty acids and β-glucans as well as the specialized avenanthramides, avenacins and avenacosides. We characterized the genomic and transcriptomic bases of specialized metabolite variation in diverse and elite oat germplasm in the context of seed size and selection over a century of oat breeding. We found that heritable genetic variation is diminished in elite germplasm, but selection for larger seeds only accounts for part of that reduction. For avenanthramides in particular, we found in addition to increased abundance in larger seeds, there was also variation in biosynthetic enzymes upstream of the committed pathway enzymes that contributed to phenotypic variation. Broadly, this work contributes to our understanding of how crop breeding has shaped specialized metabolome profiles, and prospects for continued plant breeding.Oat  we report here is lower than that of FAMEs (h2 > 0.61) , plant breeders have changed crop metabolomic profiles. However, working with specialized metabolites compared to major nutritional metabolites presents different challenges and thus may require different plant breeding approaches. As an example, fatty acid methyl esters (FAMEs) are healthful fats in oat seed that comprise 3–11% of oat seed composition, compared with 0.2% for avenanthramides. Also, while fatty acid biosynthetic enzymes have some degree of cross-species conservation, this is not true for avenanthramides that are only present in a few (nonmodel) plant species  and a caAn understanding of patterns of variation in the plant specialized metabolome is relevant for developing health-promoting functional food crops that may also withstand biotic stress. Due to the low concentrations and lineage specificity of specialized metabolites, they are infrequent direct targets of plant breeding, but may have been inadvertently shaped through processes like selection on other traits or genetic drift. In a diverse panel of cultivated oats, we measured seed size and specialized metabolites and conducted genomic and transcriptomic analyses to characterize existing variation and the processes that contributed to it. Overall, we show that the increased seed size associated with modern plant breeding has uneven effects on the oat seed metabolome, and variation also exists independently of seed size. Broadly, despite the multitudes of phenotypic changes in crops from plant breeding, variation for some specialized metabolites persists in cultivated plants and could be targeted by future plant breeding efforts.https://datacommons.cyverse.org/browse/iplant/home/shared/GoreLab/dataFromPubs/Brzozowski_OatMetabolome_2021). Associated raw data have been previously published to public repositories [Metabolomics: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/HaixiaoHu_OatMultOmicsPred_Jun2021 (doi:10.25739/8p1e-0931)]. Scripts used in this work are publicly available in github .Data files used in these analyses, including all metabolite and seed trait phenotypes for all panels and locations, gene expression data, gene annotations, and genotypes are publicly available in CyVerse (G3 online.jkab419_Supplementary_DataClick here for additional data file.


The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet.To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation–maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results.https://github.com/YangLabHKUST/PALM.The source code is available at Bioinformatics online. Over the past 15 years, genome-wide association studies (GWASs) have greatly deepened our understanding of genetic basis of human phenotypes  prioritize more risk SNPs and (ii) detect trait-relevant cell types or tissues to better understand the biological mechanism of common traits/diseases. In statistics, the two-groups model (TGM)  is widelDespite the above progress, the existing methods still have their own limitations. First, most existing methods assume a linear relationship between functional annotations and the association status. Ignoring the potential non-linearity may undermine the valuable information embedded in functional annotations and thereby degrade the performance of prioritizing risk SNPs. Although GPA-Tree adopts the decision tree algorithm to characterize the potential non-linearity, a single decision tree often cannot fully capture the relationship between functional annotations and association status. In addition, a single decision tree is known to be not very stable . This maIn this article, we propose a powerful and adaptive latent model (PALM), to integrate GWAS summary statistics with functional annotations. Unlike existing methods, PALM uses a tree ensemble as the non-linear model to characterize the relationship between functional annotations and the association status. To make PALM scalable to hundreds of annotations and millions of genetic variants, we develop a functional gradient-based expectation–maximization (EM) algorithm, where the posterior of SNP association status is evaluated at the E-step, and a new tree is added into the model in the M-step by a boosting strategy . In suchP-values of genome-wide SNPs M is the number of SNPs. We introduce a latent variable j-th SNP. We consider a TGM, where the P-value of each SNP is either from a null group  to increase the power of prioritizing risk SNPs and offer more biologically interpretable GWAS results.The above TGM assumes that ,j=1,…,M . Thus, tL is the number of functional annotations, entry j-th SNP given by the l-th functional annotation. In the simplest case, j can be active or inactive according to the l-th functional annotation, respectively. In our formulation, we allow j is more likely to have a functional role. To model the relationship between functional annotations and SNP association status, we assume that the prior of SNP j’s association status can be modulated by its functional role as j-th row of the annotation matrix corresponding to the j-th SNP, F can be a linear or non-linear function. For example, LSMM  serves as a negative control, where all annotations are irrelevant; Case (B) is a linear relationship with two relevant annotations; Case (C) is a simple quadratic function with interaction among two annotations; Case (D) is a more complicated function with a quadratic term and a sinusoidal term involving five relevant annotations; and Case (E) is a case function involving interaction between a continuous annotation P-values , two-groups model of z-scores  and the Benjamini–Hochberg (BH) procedure, and three methods integrating functional annotations with GWAS results: LSMM, GPA-Tree and PALM. Here, we considered fitting PALM with Tree depths 1 and 2, denoted as PALM-D1 and PALM-D2, respectively. PALM-D1 can characterize the non-linear relationship with additive models, and PALM-D2 is a more flexible non-linear model by allowing interaction among annotations. For each method, we use the default parameter setting. We controlled global FDR at the nominal level 0.1, and evaluated the empirical FDR as the fraction of falsely identified SNPs among all the identified SNPs and statistical power as the fraction of correctly identified SNPs in the non-null group of each method.We set the number of SNPs M, as the model can be better fitted with a larger number of SNPs. In this scenario, there was a notable gap between the power of GPA-Tree and PALM-D2, indicating that a single decision tree could not accurately capture some complicated relationship between association status and annotations. For Case (E), the power of PALM and GPA-Tree was roughly matched, dominating other methods but GPA-Tree tended to produce more false positives. In summary, PALM remarkably increased statistical power for various relationship between annotations and association status. We also conducted additional simulations with alternative z-score distribution shapes, i.e. ‘big-normal’, ‘near-normal’, ‘skew’ and ‘spiky’. The patterns of FDR control and statistical power for all the compared methods are similar to z-scores of SNPs are typically calculated from a linear model with individual data. We further investigate the performance of these methods under the setting where z-scores are obtained from linear regression with simulated genotype and a realistic heritability. The patterns of FDR and power are similar to z-scores generated from a linear model.gle tree . When alPALM can automatically rank relevant annotations. tatistic . Detailsz-score distributions , only a small number of trees in the final model are allowed; for Cases (B), (C) and (E) with relatively simple non-linear relationships, PALM-D2 has fewer trees than PALM-D1 as PALM-D2 is more prone to overfitting; for Case (D), PALM-D2 is assigned with more trees than PALM-D1 to better learn the relatively complicated non-linear relationship. This adaptive regularization mechanism helps PALM well control FDR and improves statistical power.The computational time of PALM mainly depends on the CV folds PALM shows great robustness under different hyper-parameter settings. First, by applying PALM with 2-fold CV and 5-fold CV to the same simulated data, we find that the FDR and power are almost the same under different scenarios for both PALM-D1 and PALM-D2 . Second,lidation . In partnflation .In the real data analysis, we integrated summary statistics from 30 GWASs given in  with 9 gWe compared the power of risk variants prioritization using TGM-Pval, LSMM, GPA-Tree, PALM-D1 and PALM-D2. P-values. For CAD risk SNP rs7947761, it is an intronic SNP annotated by the important annotations, such as Lung and Fetal Heart. Although it neither has the smallest P-value nor prior probability, the combination of the two results in the highest posterior probability amongst the nearby SNPs.Some of the SNPs prioritized under er GWASs . For auter GWASs . Bipolarn = 17 115 SCZ2: n=21 856 SCZ3: n=32 143 and SCZ4: n = 150 064). In any of the four GWASs, PALM-D2 prioritized more risk SNPs compared with TGM and LSMM while the majorities of SNPs prioritized by TGM, LSMM or PALM-D2 are in common  and years of education. The sample sizes of the four SCZ GWASs increase successively (SCZ1: n common . This sun common . The abon common .We proposed a novel statistical method, PALM, to integrate the cell-type/tissue-specific functional annotations with GWAS summary statistics. Comparing with existing methods, PALM can adaptively model the flexible relationship among functional covariates and accommodate a great number of functional annotations. Both simulation studies and real data analysis demonstrate its great power in risk variants prioritization with FDR controlled at the nominal level. Moreover, PALM provides a statistically feasible way to evaluate the relative importance of each covariate, which makes the model more interpretable. From the perspective of computing, the developed EM algorithm is efficient and can scale up to millions of genetic variants and a large number of annotations. We believe that PALM can serve as a useful tool for risk SNP prioritization.btad068_Supplementary_DataClick here for additional data file.


Genomic information was included for the first time in the prediction of breeding values for Atlantic salmon within the Australian Salmon Enterprises of Tasmania Pty Ltd selective breeding program in 2016. The process to realize genomic selection in the breeding program begun in 2014 with the scheme finalized and fully implemented for the first time in 2018. The high potential of within family selection to accelerate genetic gain, something not possible using the traditional pedigree‐based approach, provided the impetus for implementation. Efficient and effective genotyping platforms are essential for genomic selection. Genotype data from high density arrays revealed extensive persistence of linkage disequilibrium in the Tasmania Atlantic salmon population, resulting in high accuracies of both imputation and genomic breeding values when using imputed data. Consequently, a low‐density novel genotype‐by‐sequence assay was designed and incorporated into the scheme. Through the use of a static high‐ and dynamic low‐density genotyping platforms, an optimized genotyping scheme was devised and implemented such that all individuals in every year class are genotyped efficiently while maximizing the genetic gains and minimizing costs. The increase in the rates of genetic gain attributed to the implementation of genomic selection is significant across both the breeding programs primary and secondary traits. Substantial improvement in the ability to select parents prior to progeny testing is observed across multiple years. The resultant economic impacts for the industry are considerable based on the increases in genetic gain for traits achieved within the breeding program and the use of genomic selection for commercial production. Consequently, harvest weight, AGD disease resistance, and maturation incidence are all recognized as important traits for the Tasmanian salmon industry.Since inception, the industry has been quick to recognize that neither the environmental conditions nor the stock that is farmed are identical to those in other parts of the world. Consequently, systems and processes continue to be optimized for a Tasmanian context. Early research examined topics ranging from smoltification, lighting, maturation, ploidy levels, sex ratios, and smolt input. The development of the Tasmanian breeding program mirrors this progression, with the development and incorporation of innovations in response to both global and uniquely Tasmanian issues. The implementation of the breeding program and the desire to push gains using genomic selection represent a progression of that innovation.This paper details the transition of the Tasmanian Atlantic salmon selective breeding program from conventional family‐based breeding to genomic selection. The aim is to provide a comprehensive overview of the steps taken to achieve commercial implementation with sufficient detail to enable the information to be presented in a single reference. This is the first time all aspects of such a process have been specifically outlined and addressed for an aquaculture species in a commercial breeding program. First, the breeding program is overviewed, detailing the population structure, breeding objectives, and the program logistics. Second, the process of developing genotyping tools and a breeding strategy is described, culminating in the full commercial implementation of genomic selection. Third, the validation and rates of genetic gain are reported. Finally, the challenges, results, and learnings from the implementation and use of genomic selection in the Tasmanian salmon industry are discussed.2The Tasmanian Atlantic salmon breeding program commenced in 2004 and, since then, has been a family‐based program producing a cohort of pedigreed families annually. It is now an advanced generation breeding program with up to 6 generations of known pedigree in some lineages. It is owned and operated by Salmon Enterprises of Tasmania Pty Ltd (SALTAS). Founders were deliberately sourced for diversity from the Tasmanian Atlantic salmon landrace, which originates from wild stocks imported from River Philip, Nova Scotia, Canada. Stock was imported as ova to Gaden, New South Wales, in 1963 to 1965 and then moved to Tasmania in 1984–1986 when salmon farming commenced ; however, there are no significant genetic correlations between AGD resistance and freshwater traits. Consequently, AGD selections have been made via pedigree relationships alone.The broodstock population is maintained in a biosecure freshwater hatchery at Wayatinah in central Tasmania and, annually, 1.25‐year‐old smolt are transferred to two marine sites in southeastern Tasmania for grow‐out and assessment. Marine tests occur in purpose‐built pens that contain approximately 2500 individuals at stocking. Repeated measures of AGD resistance are taken  together with the higher order genetic relationships  that naturally occur through using overlapping generations. Repeat spawners are used as both live animals and as cryopreserved sperm. All broodstock, whether they be designated genetic links or individuals from older year classes, are selected on genetic merit. Genetic evaluations are done using multivariate individual animal models Section  in ASRemThis breeding program has used genotyping to determine pedigree relationships since inception. Families are fertilized separately and pooled at the eyed‐egg stage when 250 eyed‐eggs are removed from each family to form a pooled group of approximately 50,000 individuals, which are then communally reared. At 12 months, approximately 8000 presmolt are randomly selected from this group for use as the freshwater broodstock and the marine test individuals. All 8000 individuals are, tagged using passive integrated transponder (PIT) tags, biopsied by removing a piece of caudal fin, and then genotyped for pedigree determination. Broodstock are re‐biopsied at spawning and the genotypes from these parents, plus the genotypes from the progeny, are used for pedigree determination.The typical cycle of operations is illustrated in Figure 3Genomic selection was first proposed as an opportunity within the SALTAS selective breeding program in 2010 where the business case and value proposition for its use to improve the rate of gains for AGD resistance was examined. Acceleration of studies scoping the potential of genomic selection occurred in 2014 with the reception of the first set of high‐dimensional genomics data. From that point on, the evolution of the genomic selection strategy and genotyping scheme involved multiple stages and these are shown in Figure 3.1In order to assess the feasibility of genomic selection for the Tasmanian breeding population and the genotyping tools requirements for the successful implementation, high‐density genotype data were sourced using a custom 220K SNP Affymetrix array developed by AquaGen and CIGENE  were run using the cleaned 220K data (99K) to check for the presence of large effect QTL that could be used via marker assisted selection (MAS). None were found, indicating that MAS was not a viable approach for the selected traits within the breeding program. This complemented existing GWAS results completed previously using data from an Illumina iSelect SNP Chip (5568 markers) where a total of 1637 individuals from three year classes had been genotyped. After quality control filtering, a total of 2240 SNPs and 1574 individuals were used to undertake GWAS on the key traits with the breeding program . These markers were consolidated to form a list of 270 potentially useful SNP for inclusion on any new array. This list included 25 markers from the Illumina platform that were not on the 220K array.3.3r2 < 0.2) with any other sets of markers to retain all possible distinct chromosome segments and the selected trait‐associated markers. All remaining markers then were placed in 100 kbp bins, scaffolded using the previously selected markers. The location of these previously selected markers, MAF and LD, was all then used to optimize the final set of markers. Markers with higher MAF (minimum MAF >0.02) and lower LD values were selected to optimize the final MAF distribution while ensuring that all chromosome segments were tagged with a marker.The process to design and manufacture a custom 50K array was undertaken in 2015 through a partnership with Center for Aquaculture Technologies (CAT). The design process commenced with the aim to tag each segregating chromosome segment with at least one SNP to ensure even genome coverage. Consequently, the design process began by selecting all markers from the complete set (98K) not in high LD . A comparison of the final list with the alternative 50K array found only 28,302 markers in common, confirming that that array would have delivered suboptimal data and results. The final in silico design of the custom Affymetrix array was carried out by CAT and resulted in 50,687 unique markers on a custom Affymetrix array.3.4r2 = 0.2) extending to 500 kbp, indicating there was high potential for using of a low‐density assay as part of the genotype scheme. The use of low‐density assays as part of genomic selection strategies has been shown to contribute to a more cost‐effective approach . To assess the viability of a low‐density SNP panel, two sets of analyses were carried out. The first was an assessment of imputation accuracy by masking (setting to missing) sets of markers and then imputing these missing data using the marker data that remained. The imputed data were then compared to the original data and the accuracy of imputation assessed based on the number of correctly imputed genotypes. The second analysis looked at the impact of using the imputed data to calculate genomic breeding values (GEBV). The results from these analyses compared, via correlation, GEBV calculated using the original genotype data with GEBV calculated using imputed genotype data.The first set of 50K data, from 2889 individuals, was received in 2015. After quality control , 45,840 SNPs were retained. Analysis of the LD in this dataset, supported by results from 220K data, revealed long extending linkage disequilibrium (LD). The trend showed moderate LD . The imputation and GEBV accuracies were then assessed when predicting the 2013 year class  using the remaining data  from the 2001–2012 year classes. Two different sizes of low‐density assays were considered for implementation with SNP subsets of either 1000 (1K) or 3000 (3K) SNPs retained with all other markers masked. The choice of these densities reflected the genotyping options made available by CAT, using a targeted genotype‐by‐sequence (GBS) platform. An optimal set of markers for each density were selected to optimize genomic coverage, trait association, LD and MAF. Then, using the complete pedigree containing individuals from the 2001–2013 year classes and FImpute version 2.2 . Two models were fitted using ASReml. In the first, GEBV for harvest weight were estimated using a univariate model with cohort fitted as a fixed effect and, family and animal as random effects. In the second, GEBV for marine maturation were estimated using a multivariate model including maturation and weight measured in the marine and freshwater environment at 23 months (4 traits). This model included a mean trait effect and trait by cohort effects as fixed effects. The genetic effects were modeled as random by fitting trait by family (representing common environment plus nonadditive genetic effects) and trait by animal effects (representing additive genetic effects). Animal effects in both models were fitted using the H matrix, the relationship matrix based on both pedigree and genomic information  and marine maturation , while e is a vector of residuals. It is assumed that un∼N and e∼N and that they are uncorrelated where Gn and R are the matrices of nongenetic and residual covariances across traits.Standard breeding value production for the breeding program has always included four multitrait linear mixed models. These models remain the basis for the program and are now run using the Wombat software package Meyer, . All havZgug, are assumed to be ug∼N, where Gp is the matrix of genetic covariances across traits and A is the numerator relationship matrix. GEBV are produced when ug∼N, where H is the augmented genomic relationship matrix containing information from both the genomic relationship matrix (G) and the numerator relationship matrix (A)  and genomic (G) information coming from the respective matrices  using the breeding values for the AGD scores (EBV or GEBV) from the AGD model. All breeding values are scaled to be in units of genetic standard deviation. The index is then calculated using the selection pressures defined in Table 992 using3.6With the finalization of the cost‐effective genotyping scheme, full commercial implementation of genomic selection was achieved in 2018. The final genomic selection breeding cycle is shown in Figure Also supporting the efficient use of genomic data was the customized genotyping scheme. Designed specifically to be both cost‐effective and deliver optimal gains, the final scheme specified that all individuals in each new year class, totaling more than 8000 in the final scheme, are genotyped on the 3K GBS assay, while all parents, approximately 200 individuals, are genotyped on the 50K SNP array. A feature of the final scheme is that every marine tested and measured animal in every year class forms part of the training set, thereby providing strong links between genotypes and phenotypes and avoiding the need to define a training set as a subset of measured animals.A key benefit of GBS technology is the ability to update the assay. The first version of the assay delivered useable data on 2372 markers. As a result, in consultation with CAT, a second version of the assay was developed with 820 new markers added to the informative markers from the first version. This is now delivering on average approximately 2500 consistently useable markers based on seven runs of the assay. The GBS data are imputed up to the standard set of 45,997 markers for consolidation with all other available SNP data for the purpose of GEBV production.4To assess the success of the commercial implementation of genomic selection, the improvement in prediction/selection accuracy and the rate of improvement in genetic gain were assessed. The potential improvement in the rate of genetic gain was evaluated using the data available in 2019 by comparing the rates of gain using the GEBV to what would have been obtained using EBV. The increase in selection/prediction accuracy was examined by comparing the breeding values for broodstock at the time of spawning and after progeny information was available using the same models. These comparisons across multiple spawnings allows for a direct robust assessment of whether the use of genomic information improved the breeding program's ability to more accurately rank broodstock individuals as parents. This method provided a means to utilize the data from higher order relationships in the validation (such as aunt/uncle to niece nephew), and such relationships are numerous in this population due to the large family sizes. It differs from the classic approach of parent–progeny relationships, which uses first order relationships only.4.1The length of the salmon breeding cycle means that validation information  are not available until 2 years after spawning. The EBV and GEBV for the parents of three year classes were compared to the updated breeding values once progeny information was available two years later . Three comparisons were made, one retrospectively using the parents of the 2015 year class individuals, and two more using parents of the 2016 and 2017 year class individuals. The updated breeding values (EBV or GEBV) from the run two years after spawning with the lowest standard errors were used as the closest estimate of the true breeding value. In most instances, this was the GEBV, but in all instances, the GEBV and EBV were more than 98% correlated. The ability of the index to accurately rank individuals was assessed in a similar manner to the individual breeding values. The index was calculated using the EBV or GEBV at the time of parent selection and then again using the updated breeding values. These were then compared to assess the accuracy of the index (GEBV or EBV based) to correctly rank the individuals for selection.The phenotypic data used in the validation analyses are summarized in Table 5The results for the analyses examining the increase in selection/prediction accuracy are found in Figure The rate of genetic gain improvement, based on the performance up to and including 2019, is shown in Table 66.1The impetus for exploring the potential of genomic selection for the Tasmanian Salmon selective breeding program was a desire to increase the rates of genetic gain through enabling within family selection. Initially, a business case was built around increasing the rate of genetic gain for AGD resistance and economic analyses were done for that trait alone. That was assumed to be the main and perhaps only driver of value because other traits already benefited from moderate genetic correlations between marine and freshwater populations Section . The incThe magnitude of the improvement in other traits and the extra value they provided was an unexpected benefit. Most notable is the improvement in total weight, where the annual rate of genetic gain increased from 3.6% to 4.3% per year Table . This 19The largest benefit from genomic selection is seen in the increase in the rate of gain in the index, which is the measure that combines all traits weighted by their assumed economic value . The first of these was the decision to develop a Tasmanian population specific 50K genotyping array rather than use an existing product. There were expectations that an existing array should be used given the relatively small size of the Tasmanian breeding program when compared on a global scale. While the alternative product did have informative markers for the Tasmanian population, it was not ideal, with only 56% of markers uniquely informative, and the coverage of markers was sparse in places Section . The desThe second important juncture was the decision to develop a low‐density array, to use that across all individuals in every year class, and to impute the 50K markers. Exploration of the use low‐density panels and imputation have been explored in multiple studies and shown to be useful across multiple aquaculture species . The increase in the commercial production could potentially have been 8.2% had all male candidates being sexually mature, a consequence of husbandry of broodstock. The value of the gain provided by applying genomic selection in the male multipliers has been very high. While not cumulative as it is in the breeding population, the value for the 2019 commercial production is estimated at 8 times more than the annual gain in the breeding population (AU$40 million).7Genomic selection has been implemented at a commercial scale in the Saltas selective breeding program. Operational implementation was first achieved in the breeding population with the production of the 2016 year class, and in 2018, the application was extended to the male multiplier population. Implementation was achieved through the development and use of a 50K population specific SNP array, together with a low‐density 3K array. Examination of imputation accuracies revealed the viability of a low‐density panel, and this has provided a cost‐effective genotyping scheme allowing all individuals in both the freshwater broodstock population and the marine sib‐test populations to be genotyped with the low‐density assay, with parents genotyped on the 50K SNP array.Validation results show improvement in prediction accuracies for all traits at the point of broodstock selection, which results in clear and substantial increases in the rate of genetic gain for all traits. Increases are highest for AGD resistance. The increase in gains for harvest weight was half that for AGD resistance; however, the value of these gains is also substantial. Overall, the rate of gain for the index, combining all traits, was doubled through genomic selection.The authors declare no conflicts of interest.


Genetic pleiotropy, the phenomenon of a single gene or a single genetic variant influencing multiple phenotypes, appears to be pervasive in human genome. Thousands of genome-wide association studies (GWASes) have identified hundreds of thousands of genetic variants associated with hundreds of complex traits or diseases. In recent years, with the increasing publicly availability of GWAS summary statistics, a number of statistical methods have been developed to evaluate the genetic overlap between multiple traits and diseases. Existing multi-trait methods can be broadly divided into multivariate methods and univariate methods . UnivariIn the research topic on “Genetic Pleiotropy in Complex Traits and Diseases,” eight original studies are collected. It mainly focuses on the field of novel methodology development, identification of novel pleiotropic loci and the pathogenic mechanism of gene polymorphism in complex diseases.Liu et al. presented powerful procedures to jointly analyze multiple traits based on GWAS summary statistics. They first adopted the generalized Berk-Jones (GBJ) test and the generalized higher criticism (GHC) test to analyze multiple traits and then combined the p-values of GBJ, GHC and the existing MinP  test by the aggregated Cauchy association test, namely omnibus (OMNI) test. The proposed tests can well control the type I error rate and the OMNI test is robust to the signal directions, the correlation structures and the degrees of sparsity. Replication plays a key role in evaluating the credibility of pleiotropic effects. Ning et al. evaluated four methods for replicating multi-trait associations and introduced a four-level replication strategy. The described methods are implemented in the open-source R package MultiABEL. Fu at al. developed a hierarchical clustering approach (HCDC) to jointly analyze multiple phenotypes. Simulation and real data analyses demonstrated that existing methods embedding HCDC could improve power and uncover novel associated variants.Specifically, three studies focus on the development of novel methodology for detecting multi-traits associations based on GWAS summary data. Lu et al. performed eQTL-weighted integrative analysis and produced a single p-value for the gene, and then utilized conjunction conditional FDR (ccFDR) approach to identify pleiotropic genes. They identified 14 genes commonly related to systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), four of which may be novel. Their study provided new insights into the shared genetic basis between RA and SLE. Similarly, using summary statistics from publicly available single-trait GWAS, R. Bellomo et al. conducted 15 separate multi-trait GWAS centered around coronary artery disease (CAD), peripheral artery disease (PAD) and seven atherosclerosis risk factors. They identified 25 novel pleiotropic loci which may elucidate novel genetic etiologies of atherosclerosis. In addition, Feng et al. showed that ICAM-1, a candidate gene associated with multiple cancers , was associated with the risk of cervical cancer in the northern Chinese Han population.In the field of identifying pleiotropic loci for complex disease, three contributed papers identified several novel pleiotropic loci and revealed shared genetic components between complex traits. Barowsky et al. performed a bi-directional MR analyses and a cross-disorder meta-analysis for osteoarthritis (OA) and major depression (MD), suggesting that OA and MD share common genetic risk mechanisms. Moreover, Yang et al. investigated the causal association of phospholipid fatty acids with breast cancer and prostate cancer using MR approach, but identified little evidence of causal association.The other two studies investigated common genetic risk mechanisms underlying multiple traits/diseases using Mendelian randomization (MR) analyses. In conclusion, these articles included in this research topic comprehensively expounded the significance and challenges of genetic pleiotropy from the aspects of new methodology development, the identification of novel pleiotropic genetic loci and the molecular mechanisms underlying pleiotropic genes. We expect that improvements in statistical methods and biotechnology will promote more developments on this important topic.


KAP22-1) gene in Barki (KAP22-1 variants and CR (KAP22-1 gene as a candidate gene for wool production traitsin Egyptian sheep, with new useful insights into the visually assessed wooltraits. The identified genetic markers may be incorporated into breedingstrategies and genetic improvement programs of wool traits in Egyptiansheep.The objective of this study was to investigate the genetic polymorphisms inthe keratin-associated protein ( In Egypt, Barki, Rahmani and Ossimiare considered the three major sheep breeds ,representing about 65 % of the Egyptian sheep population . They are distributed along the western Mediterranean coastalregion, the middle of Egypt and the northern Nile delta, playing animportant role in the livelihood of large portions of Egyptians . Egyptian sheep are considered a source of low-quality woolens withcoarse fleece and include some kemp , which may beappropriate only for handmade carpets. Under extensive conditions where high lamb production is not allowed, wool could be an important sourceof increasing the impact of raising these animals . Severalattempts were made to improve wool production of local breeds by crossingwith imported breeds, such as Merino, Romanov and Finn sheep .Globally, sheep contribute significantly to the animal production industrywith various products including, milk, meat and wool. The total sheeppopulation in Egypt is about 5.1 million heads that produce about 11 217 metric tons ofgreasy fleece annually . Wool traits arequantitative traits that are affected by both environment and the animalgenotype. Additionally, multiple genes contribute to traits withdifferent biological mechanisms. Understanding the genetic contribution andbiological functions of a particular trait is essential for subsequentselection and genetic improvement programs .KAP1.1, KAP1.3 and KAP6) were identified forwool traits in Egyptian sheep .Recently, the advent of genotyping technologies enabled breeders to pinpointthe genetic architecture of economically important traits . Over the last 2 decades, multiple candidate genes harboringsingle-nucleotide polymorphisms (SNPs) for various production traits werereported in livestock . Association analysis usingSNPs is the most effective approach to identify genetic markers related to atrait of interest . SNPs screen the candidate genes,which may be biologically related to the desired trait, for putativemutations . Subsequently, these mutations aregenotyped in a group of individuals and correlated with certain phenotypes. Therefore, SNPs became the most common genetic markerused in selection and evaluation programs in livestock . Conversely, scarce information is available about the geneticbackground of wool traits as few genes , which are the mainstructural proteins of wool and hair fibers . The physicomechanical properties of the wool fibers aredetermined by these proteins . The KAPs containhigh levels of cysteine, glycine and tyrosine, which are predominantlylocated in the wool fiber cortex. It was reported that different numbers ofKAPs are found in different wool types, which raises questions about theirfunction in identifying wool fiber characteristics .The gene is located at chromosome 1, spanning 391 base pairs and consisting ofthree exons . The gene was identified as a candidate genefor wool traits , harboring putative SNP markers for wooltraits . The objective of this study was to investigatethe genetic polymorphisms in the KAP22-1 gene and then correlate these variants withimportant wool traits in three major sheep breeds in Egypt.22.1All animal procedures included in the current study were approved by theanimal breeding committee at the Desert Research Center (DRC). A total of 262individuals from three different Egyptian sheep breeds were included in thestudy. Blood and wool samples for Barki sheep  were used to amplify the coding and flackingregions of the Ovis ariesKAP22-1 gene (GeneBank: KX377618.1). The PCR products (305 bp) were performedusing thermal cycler PCR apparatuses  in tubes containing 15 Blood samples were collected from the jugular vein of each animal in thestudy. Genomic DNA was extracted from the whole blood samples usingIntronbio  following the manufacturer protocol.Finally, DNA samples were stored in a laboratory freezer at KAP22-1 in sheep. Briefly, a 15 The single-strand conformation polymorphism (SSCP) technique was used togenotype the ovine 2.3https://www.macrogen.com/en/mainSeoul, last access: 2 August 2021, South Korea) for sequencing inboth directions according to the BigDye terminator protocol. Sequences forthe forward primers were aligned against the reference sequences from NCBIdb(NC_056054) for the corresponding amplified region. Likewise, the alignment wasconducted against the sequence of the reverse primer of the correspondingregion to ensure that the identified SNP is real. Identification of SNPs wasperformed using the 4Peaks software  for the pairwise alignment.To identify the polymorphic SNPs, PCR amplicons representing different SSCPbanding patterns were delivered to the Macrogen sequencing company(2.4logit and cumlogit link functions) in SAS (2004) was used totest the association of the binary and categorical subjective wool traits ofKS, HG, GCG, BS, LS and SST. The following model was used:Levels of the traits were adjusted according to the trait . Each trait was treated in a different way as they differ innature. For the continuous traits : a linearregression was fitted using a general linear model (GLM) to estimate theeffect of the genotypes on the continuous trait. For the binary traits,distinct levels (2–5) were assigned, and the logistic regression processusing the GENMOD procedure . Generally, frequencies of thecorresponding alleles in the whole studied population  were about 48 %, 51 % and 1 % (Table 2). However,the C allele was observed exclusively in the Ossimi breed. The direct sequencingof PCR amplicons representing the three SSCP patterns identified three SNPslocated at the 5KAP22-1 gene . Two SNPswere novel  and three alleles  were observed for 3.2The breed of the animal had a significant effect . This study reported significant differences in woolcharacteristics among the major sheep breeds in Egypt. Barki sheep producelustrous, finer wool with higher medullation and crimp percentages andslightly lower KS and SST. In comparison, Rahmani have more colored woolwith coarse FD, the lowest medullation and SST, and the highest PF and FD. Likewise,wool produced from Ossimi sheep was characterized by higher SL and KS andtended to be lustrous. However, FD was higher in Rahmani wool, but Barkiwool may be the best as it had low PF and KS and was whiter, which means higherdying flexibility during manufacturing. Therefore, it is expected that Barkifleece is more economically valuable compared to Rahmani and Ossimi fleece.The abovementioned characteristics indicate that a wide range of wool typesare produced from the local Egyptian sheep . The mixedstructure  fibers found in the same fleece was alsoreported in Egyptian wool. Therefore, it is recommended to assess the woolsubjectively and objectively .KAP22-1 gene tended to be polymorphic inthe studied Egyptian sheep population as three alleles were identified . This result is consistent with other findings on KAP family genes . Conversely, higher  and lower  numbers of alleles were identified in otherKAP genes and different sheep populations. KRTAP6-5 was the most polymorphic gene inthis family, which was identified in goats with 12 variants . However, the C allele was less common, with frequency less than 5 % inthe current Egyptian population, similar minor allele frequency wasidentified in KAP21-1 in Merion Generally, the amplified fragment of the Interestingly, allele A was identified as the common allele in the Ossimi breedthat is recognized as producing whiter wool compared to other breeds. The presence of this allele significantly increased GCGin the wool  were associated with GCG and SST in Barki sheep .Results of this study suggest that genetic markers may significantlyinfluence subjective wool traits as well as objective measurementsand should be considered. As expected, animals that carry the BB genotypeincreased the desirable wool yield , but it was accompanied by anincrease in the unfavorable KS (So far, no information is available about the effect of 5KAP22-1 gene, and theystrongly affect multiple wool characteristics. Significant associations wereidentified between the KAP22-1 variants and CR (KAP22-1 gene is a putative genetic marker for wool traits in theEgyptian sheep.It is notable that Egyptian sheep produce a wide diverse wool type even inthe same individual. This may be useful commercially in blending differentwool types to make an appropriate composite for the carpet industry. In thecurrent study, genetic polymorphisms were detected in the


The marker ‘BobWhite_c2988_493’ on chromosome 2B was significantly correlated with grain yield under rainfed conditions. Under irrigated conditions, the marker ‘AX-94653560’ on chromosome 2D was significantly correlated with grain yield at Sidi Al-Aidi station. The maker ‘RAC875_c17918_321’ located on chromosome 4A, associated with grain yield was linked with the gene TraesCS4A02G322700, which encodes for F-box domain-containing protein. The markers and candidate genes discovered in this study should be further validated for their potential use in marker-assisted selection to generate high-yielding wheat genotypes with drought tolerance.Drought is one of the most important yield-limiting factors in Morocco. Identification and deployment of drought-tolerant wheat varieties are important to cope with the challenge of terminal moisture stress and increase wheat productivity. A panel composed of 200 elite spring bread wheat genotypes was phenotyped for yield and agronomic traits for 2 years (2020 and 2021) in Morocco under rainfed and irrigated environments. The panel was genotyped using 20K SNPs and, after filtration, a total of 15,735 SNP markers were used for a genome-wide association study (GWAS) using a mixed linear model (MLM) to identify marker-trait associations (MTA) and putative genes associated with grain yield and yield-related traits under rainfed and irrigated conditions. Significant differences were observed among the elite genotypes for grain yield and yield-related traits. Grain yield performance ranged from 0.97 to 6.16 t/ha under rainfed conditions at Sidi Al-Aidi station and from 3.31 to 9.38 t/h under irrigated conditions at Sidi Al-Aidi station, while Grain yield at Merchouch station ranged from 2.32 to 6.16 t/h under rainfed condition. A total of 159 MTAs ( Drought is the most significant abiotic factor impacting wheat yield and production, particularly in West and South Asia, North Africa, and Sub-Saharan Africa . ClimateUsing molecular markers such as single nucleotide polymorphism (SNP) and diversity array technologies (DArT), marker-assisted selection (MAS) has become an essential strategy in wheat breeding for the development of novel adapted lines to biotic and abiotic stresses. Based on linkage disequilibrium (LD), a genome-wide association study (GWAS) is a strong method for identifying marker-trait associations (MTA) and quantitative trait loci (QTL) associated with the trait of interest . GWAS emThe primary goal of plant genetics and breeding research is to improve agricultural productivity and stability in order to provide food security for the world’s rapidly rising population. Plant breeding methodologies, genetic designs, genomics, statistics, and bioinformatics must all be combined to adjust adaptive, agronomic, and economic qualities to meet this challenge. Gene identification and complex trait dissection are two linked components of this effort. Complex trait dissection emphasizes genetic contribution and modes of action from numerous loci, which contributes to phenotypic variance, whereas gene identification focuses on individual genes. The two common goals of any QTL mapping in plants are: (1) to increase our biological knowledge of the inheritance and genetic architecture of quantitative traits, both within a species and across related species, and (2) to detect markers that can be used as indirect selection tools in breeding programs .In recent years, significant efforts have been made to complement traditional plant breeding methods based on a phenotypic pedigree with molecular genetic techniques for the discovery and application of QTLs for marker-assisted breeding and genomic selection (GS). Linkage mapping  and association mapping (linkage disequilibrium (LD) mapping) are the most commonly used approaches in dissecting complex traits and identifying genes underlying trait variation in plants, animals, and humans .Recently, research on association mapping in several agricultural plants has been conducted to address the constraints identified in linkage-based QTL mapping. Fast improvement in genotyping methods, such as genome sequencing and high-density single nucleotide polymorphism, allowed for the creation of association mapping, the mapping of complex characteristics, and the discovery of contributing genes ,9. In th2 (NP/m2) with a value of 16.85 and 14.39%, respectively. While DMA recorded the lowest value of CV (2.97%). Traits in irrigated conditions recorded a higher heritability than in rainfed conditions. Under irrigated conditions at Sidi Al-Aidi station, NSS recorded the highest value of heritability with 0.80 followed by biomass and plant height (PLH) with 0.77 and 0.72, respectively. While GY traits showed the lowest value of heritability. Under rainfed conditions at Sidi Al-Aidi station, PLH recorded the highest value of heritability with 0.65 followed by NSS and days to heading (DHE) with 0.63 and 0.62, respectively. While the GY trait showed the lowest value of heritability. Under rainfed conditions at Merchouch station, Biomass recorded the highest value of heritability with 0.70 followed by PLH and DHE with 0.67 and 0.64, respectively. While the GY trait showed the lowest value of heritability. The top 10 elite genotypes for grain yield are indicated in Principal component bi-plot analyses show the association between grain yield and yield-related traits . Under t2 value of 0.18, followed by genomes A and D with r2 = 0.17 and r2 = 0.15, respectively. p < 0.001 were found  as well as in Sub-Saharan Africa (SSA). In the CWANA region, water shortage is becoming a severe challenge for wheat production. The creation of novel drought-tolerant bread wheat genotypes might be a long-term solution for increasing wheat yield in challenged regions. ICARDA’s bread wheat breeding program targets generating genotypes that are abiotic and biotic stress-resistant and have acceptable end-use quality. Grain yield is a complicated property governed by numerous genes, the expression of which is controlled by environmental conditions. Therefore, this study was carried out to evaluate the drought-tolerance capacity of 200 bread wheat genotypes and to discover markers that are strongly related to grain yield and yield-related traits. Phenotype observations showed that wheat in rainfed conditions had lower GY and fewer DHE than wheat in irrigated conditions. The days to heading and maturity exhibited greater heritability than grain yield at all conditions, as previously reported ,36. As ap < 0.001 were found associated with grain yield and agronomic traits under rainfed and irrigated conditions, respectively.GWAS has been used in several studies on bread wheat to uncover novel MTAs and QTLs linked with distinct traits in different conditions. We found a total of 104 significant markers under rainfed and irrigated conditions associated with agronomic traits across chromosomes in this study, where 44 MTAs found on the A genome, followed by the B and D genomes with 35 and 25, respectively, similar to another study that found the majority of MTAs for grain yield and yield-related traits on the A and B genomes, followed by the D genome . DroughtThe highly significant marker for grain yield in rainfed conditions was identified on chromosome 2B with −Log10(p) = 3.81 at position 338 Mbp. We identified two other significant markers on chromosomes 2D and 4A. Several studies detected many MTAs/QTLs for grain yield in the same chromosomes ,35. In t2 plot following an alpha lattice design with two replications. In Sidi EL-Aidi station the experiments were carried out in two different water regimes, rainfed and irrigated. For Marchouch station, the experiment was carried out under rainfed conditions. During the growth cycle in the irrigated experiment, the plots were watered by drip irrigation with three irrigations each week. The Sidi El-Aidi station is characterized by vertisol soil type with annual precipitation of about 300 mm over the two years. The Marchouch station is characterized by cambisol soil type with annual precipitation of about 400 mm over the two years. The stations are characterized by moderate humidity with an annual temperature ranging between 10 and 40 °C. The panel was seeded at a rate of 100 kg ha−1 during the first week of December.This study employed a population of 200 bread wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA) . The fie2 (NPM), number of seeds per spike (NSS), biomass, chlorophyll content (CC), canopy temperature (CT), and thousand kernel weight (TKW). After threshing each plot, GY was measured in kg/plot and then converted to t/ha. When 50% and 90% of the plants reached the heading and maturity phases, respectively, DHE and DMA were recorded for each plot. When the plants reached maturity, the PLH in centimeters was recorded in each plot by measuring the distance from the soil surface to the top of the spike, without measuring the length of the awns. The biomass was measured for all plots in kilograms. The leaf chlorophyll content was measured on the flag leaf using a SPAD-502Plus chlorophyll meter at the heading stage on a clear day between 9:00 and 12:00. Canopy temperature was measured at the booting stage using a thermometer. TKW was calculated in grams using a grain counter. The “RcmdrMisc” package in R was used to determine the means, maximum, minimum, and standard deviation, as well as the coefficient of variance.The GWAS panel was phenotyped for 11 traits: grain yield (GY), days to heading (DHE), days to maturity (DMA), plant height (PLH), number of tillers per plant (NTP), number of plants in mFresh leaf samples were used to obtain genomic DNA from 2-week-old seedlings. Before extraction, the samples were frozen in glace liquid and maintained at −80 °C according to the methodology outlined in . A totalLinkage disequilibrium (LD) was calculated using Tassel software, where LD values of the 200 bread wheat genotypes were investigated using the SNP markers distributed across wheat genomes. Remington and his collaborator’s approach was used to conduct the LD decay study . The pophttp://plants.ensembl.org/Triticum aestivum/Info/Index , accessed on 5 April 2022 and the National Center for Biotechnology Information (NCBI) database. Uniport  was used to identify protein function.Genome-wide association analysis for grain yield and yield-related traits was carried out using Genomic Association and Prediction Integrated Tool version 3 (GAPIT 3) in the R environment . A kinshSome genotypes demonstrated good grain yield and acceptable agronomic performance in this study, and they were included in the worldwide nurseries given by ICARDA to national partners in the CWANA and SSA areas for release and future usage as crossing block parents. Using 200 spring bread wheat genotypes, we discovered 104 MTAs and 46 genes linked with various agronomic parameters under rain-fed and irrigated conditions. The markers discovered in this work will be utilized for marker-assisted selection after validation using a new set of elite genotypes.