Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits  in the scions for 3 years (2015–2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a “genetic prediction” model to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits , whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock–scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in “Hass” avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock–scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado ( How different genomes interact to shape a unique phenotype has been one of the most pervasive questions in quantitative genetics and molecular evolution . HorizonGrafting is typically utilized to merge resilient rootstocks to clonal scions that produce the harvested product, either fruits or wood. Grafting side steps the bottlenecks of breeding woody perennials , primarih2), or the proportion of phenotypic variance among individuals in a population due to genetic effects, is regarded as a baseline of any breeding program  cv. Hass. Avocado originated in Central America from where it expanded southward to the northwest Andes, leading to three horticultural races, mid-altitude highland Mexican (P. americana var. drymifolia Schlecht. et Cham. Blake) and Guatemalan  races, and lowland West Indian (P. americana var. americana Mill.) race (Phytophtora cinnamomi (An important fruit tree crop that is nowadays seeing an unprecedented expansion  in tropil.) race . Previouinnamomi , in theiinnamomi  and in tinnamomi . For insinnamomi  and can innamomi . Rootstoinnamomi  and the innamomi , which cinnamomi , and plainnamomi .Despite that several studies have provided evidence of avocado rootstock effects on “Hass” crop performance, the genetic identity and the adaptive potential of the rootstocks that are already planted or are being offered by the nurseries remain a major knowledge gap due to their admixed origin. Additionally, because many “Hass” avocado orchards are yet to be established worldwide in upcoming years, demand for selected rootstocks is reaching its peak, but explicit rootstock effect estimates are still lacking. Hence, our objective was to quantify the inheritance of rootstock effects on “Hass” avocado traits by expanding a “genetic prediction” model to open-pollinated (OP) non-Hass seedling rootstocks from various provenances. This will enlighten the consequences of grafting while enhancing avocado rootstock breeding.leaflet and fmsb packages.Avocado cv. Hass production areas in Colombia are widely variable in terms of environmental factors such as altitude, solar radiation, relative humidity, temperature, and precipitation. This variability affects avocado production in terms of agronomic behavior, productivity, yield, and fruit quality. To discern rootstock-mediated heritability from environmental drivers, we chose eight commercial orchards of avocado cv. Hass at the Antioquia province that have been in production for the exportation market for 5 years since 2016. Orchards had comparable nutrient management , allowinAll 240 trees grafted on seedling rootstocks were measured in 2016 for eight morphological traits. Tree and trunk height were recorded, as well as the height of the rootstock and the scion, using the grafting scar as reference. Rootstock and the scion perimeter were measured below and above the grafting scar, too. Trunk perimeter at the grafting scar and a quantitative score following Astaena pygidialis (Frankliniella gardeniae), or Monalonion spp. Furthermore, fruits may not be suitable for exportation due to other imperfections such as low weight, early ripening, or stalk-cut below pedicel, which were annotated, too. Complete harvest categories were possible for 161 trees across all 3 years. Trait differences among trees at distinct agroecological regions and orchards were determined via the Wilcoxon rank-sum test for each trait. Additionally, Pearson correlations among phenotypic traits and between them and altitude were calculated using the PerformanceAnalytics package. All analyzes were carried out in R v.3.4.4 (R Core Team).Meanwhile, the annual harvest from 2015 to 2017 was cataloged in nine categories according to fruit quality. The number of fruits with exportation quality was recorded as a combined trait for yield and quality. If a fruit did not reach quality for exportation, the reason why it was discarded was also annotated. In this sense, the number of fruits that exhibited mechanical or sun damage was recorded as well as fruits with signs of damage by pests such as scarab beetles—gidialis , thrips ® Flexi DNA Polymerase kit . Reaction volumes and thermocycling profiles were set according to the manufacturer’s instructions. Resulting PCR products were evaluated for thermocycling reaction efficiency on 1.5% agarose gels and then analyzed using capillary electrophoresis in a CEQ 8,000 capillary DNA analysis system  at Corporación para Investigaciones Biológicas . Allele sizes were estimated in base pairs with Peak Scanner , allowing for a maximum of two alleles per sample. High quality genotype data were possible for 188 trees  environment.We further explored within-population relatedness using We used a mixed linear model to predict phenotypic scores for each trait from the rootstock genotypic information following yi is the score predicted for each trait for the ith individual, μ is the mean of each trait in the entire population, xij is the relatedness between the ith and the jth individuals, following m is the total number of samples, βj is the estimated effect for the relatedness to the jth individual on the trait, and e is the estimated error associated with the trait. By using xij was the genotype of the ith individual for the jth marker and βj was the estimated maker effect. To fit these models to our data, we used semi-parametric genomic regression based on reproducing kernel Hilbert spaces regressions methods (BGLR (where  methods  implemends (BGLR . We estih2) for all traits were computed following h2 was calculated as the proportion of phenotypic variance explained by additive effects Narrow sense rootstock-mediated heritability scores (r) between each phenotype and the trait’s genetic estimated (xi), based on the rootstocks’ relatedness, as shown in Eq. (3).In addition, we estimated the model fit for each trait as the Pearson correlation  the matrix of molecular markers genotyped in the rootstocks—xij or the genotype of the ith individual for the jth marker as in Eq. 1, and (3) the matrix of genetic relatedness among rootstocks—xij or the relatedness between the ith and the jth individuals as in Eq. 1. In all cases, we used 50 random permutations without replacement so that the resampling would approximate a random sample (“null” distribution) from the original population. All labels were exchangeable under the null hypothesis. We obtained one-sided p-values (type I error) for each permutation type, expressed as the proportion of sampled permutations where resultant heritability was larger than the observed heritability estimate. We used this strategy to highlight traits significantly linked with the rootstocks’ genotypes, that is, those for which significant p-values (p < 0.05) were obtained simultaneously for all three types of permutations.We used three different permutation strategies to obtain significance scores to validate whether scion traits were affected by rootstocks’ genotypes. We permuted the three separate inputs: (1) the observed phenotypic vector—xi) of traits significantly linked with the rootstocks’ genotypes against the admixture index recorded for each sample at the optimum K-value from STRUCTURE computation. Regressions controlled for the agroecological region as random effect via mixed linear models (MLMs) in R’s package nlme  and trunk  heights and the number of fruits with low weight .Regarding altitude, there were significant trait differences for 14 out of 20 traits  tree height and scion length  split, whereas the other orchard from the Northern plateau (ANSPCS) and two from the Eastern plateau (ANEREC and ANEREG) revealed high levels of admixture. At K = 3, two orchards from the Eastern plateau (ANEREC and ANPELA) differentiated from the others by high levels of admixture. At K = 4, all subpopulations were admixed for the fourth subpopulation except ANSPEB, which differentiated homogeneously since K = 2. Higher K-values did not contribute further divergence but increased overall admixture levels.Evaluation of population structure using an unsupervised Bayesian clustering approach implemented in STRUCTURE with ulations  based ontatistic  and crosK = 3 in the orchard of the Northern plateau (ANSPCS) and the two orchards of the Eastern plateau  that exhibited high heterogeneity from K = 2 were significantly higher than in the rest  were significant for 5 of the 20 measured traits  ranging from 0.58 to 0.73 . Only one morphological trait had significant results according to the permutation tests—trunk height with average h2 and r values of 0.37 and 0.64. The number of fruits was the only biomass/reproductive trait with significant results with average h2 and r values of 0.45 and 0.73.Estimates of rootstock-mediated heritabilities  than annual harvest traits . Meanwhile, trait predictability was high, especially for the significant biomass/reproductive trait total number of fruits (r = 0.73) and was lowest for the number of fruits with exportation quality (r = 0.58). Per-agroecological region h2 scores were marginally inflated due to decreased environmental variance  were significant for 5 of the 20 measured traits and ranged from 0.33 to 0.45 h2 with model fits (r) between 0.58 and 0.73 across orchards. The only morphological trait that we found having a significant genetic-estimated heritability value was trunk height, likely an emergent property of the rootstock–scion interaction in orchards of the same age only subjected to comparable annual light correctional pruning. Additionally, there were significant rootstock effects for various harvest and quality traits such as total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight and damage by thrips. These findings suggest the inheritance of rootstock effects on a wide spectrum of “Hass” avocado traits relevant for yield, which will be critical to meet the demands of the growing worldwide market.We quantified the genetic effects of avocado seedling rootstocks on 20 “Hass” scion traits using a “genetic prediction” model that related traits’ variation with the SSR identity of rootstocks from eight different orchards. Trees exhibited high levels of admixture across orchards, consistent with rampant gene flow among putative races. Genetic estimates of rootstock-mediated heritability  and Mexican (P. americana var. drymifolia Schlecht. et Cham. Blake) races, and lowland West Indian (P. americana var. americana Mill.) race. Previous genetic analyses and screenings of key botanical descriptors have marginally reinforced this race structure , or the entire tree , but not the root phenotype itself . This speaks for a predominant role of the rootstock–scion interaction rather than independent additive effects of each genotype, which is expected when combined effects are mainly due to transport of water and nutrients and large-scale movement of hormones, proteins, messenger RNAs, and small RNAs .P. cinnamomi  can be found in the article/CD-D, OD-P, and AN-A conceived the original sampling, led phenotypic data collection, and root sampling. VV-Z and LP performed DNA extraction and SSR genotyping and alleles size estimation. PR-H, LM-B, OD-P, and AC filtered and prepared input datasets. AC, LM-B, and PR-H carried out data analyses. AC, OD-P, LM-B, AN-A, and PR-H interpreted results. AC and PR-H drafted a first version of this manuscript, edited by the other co-authors. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


N = 6297–10,098), schizotypy (N = 3967–4057) and positive psychotic experiences in adulthood , schizophrenia , bipolar disorder , and depression . Linkage disequilibrium score regression was used to estimate genetic correlations. Implicated genes from functional and gene-based analyses were compared. Mendelian randomization was performed on trait pairs with significant genetic correlations. Results indicated that subclinical auditory and visual hallucinations and delusions of persecution during adulthood were significantly genetically correlated with schizophrenia (rg = 0.27–0.67) and major depression (rg = 0.41–96) after correction for multiple testing. Auditory and visual subclinical hallucinations were highly genetically correlated (rg = 0.95). Cross-age genetic correlations for psychotic experiences were not significant. Gene mapping and association analyses revealed 14 possible genes associated with psychotic experiences that overlapped across age for psychotic experiences or between psychotic experiences and psychiatric disorders. Mendelian randomization indicated bidirectional associations between auditory and visual hallucinations in adults but did not support causal relationships between psychotic experiences and psychiatric disorders. These findings indicate that psychotic experiences in adulthood may be more linked genetically to schizophrenia and major depression than psychotic experiences in adolescence. Our study implicated specific genes that are associated with psychotic experiences across development, as well as genes shared between psychotic experiences and psychiatric disorders.This study explores the degree to which genetic influences on psychotic experiences are stable across adolescence and adulthood, and their overlap with psychiatric disorders. Genome-wide association results were obtained for adolescent psychotic experiences and negative symptom traits ( Negative symptom traits in the community resemble negative symptoms of psychotic disorders such as apathy, anhedonia, and social withdrawal. Schizotypy3 is a related, older personality-based construct compared with psychotic experiences and negative symptoms (PENS). Positive psychotic experiences during adolescence or adulthood, especially when persistent, are associated with an increased risk of developing psychotic disorders9 and, to a lesser extent, with other psychiatric disorders12. Likewise, schizotypy is associated with subsequently developing psychotic disorders13.Psychotic experiences  resemble positive symptoms of psychotic disorders, such as paranoia, hallucinations, and cognitive symptoms, and have a median prevalence rate of ~7%–8% in the general population19. Genome-wide association studies (GWAS) indicate modest single-nucleotide polymorphism heritability (SNP-h2) for some PENS in mid-adolescence (3%–9%) and for schizotypy in adults (20%–27%)22. Psychotic experiences share genome-wide genetic influences with schizophrenia and major depression26, although not all studies found this, particularly those that used comparatively smaller samples or polygenic scores (PGS) from less well-powered GWAS31. Schizophrenia PGS has been associated with schizotypy in adults assessed using semi-structured interviews, but not with self-rated PENS32. Studies have mainly focussed on adolescents and young adults rather than older adults and have not reported on the genetic overlap of psychotic experiences across age, despite this being an important topic for understanding the etiology and development of mental illness. It is also not known whether psychotic experiences and schizotypy share genetic influences.Twin studies suggest heritability accounts for a third to a half of variation in PENS during adolescenceThis study investigates the genetic overlap between psychotic experiences across age using the largest current GWAS summary statistics available for adolescent PENS and for schizotypy and positive psychotic experiences measured in adulthood. We evaluate genome-wide genetic correlations and overlapping associated genes between these trait measures and with schizophrenia, depression, and bipolar disorder. For traits and disorders that share common additive genetic influences, we further explore the nature of these associations using Mendelian randomization (MR).N = 6297–10,098)20: Twins Early Developmental Study (TEDS)33, a community sample born between 1994–1996 in England and Wales (mean age 16.32 years); Avon Longitudinal Study of Parents and Children (ALSPAC)35, a birth cohort from the United Kingdom (mean age 16.76 years) born in 1991–1992; and Child and Adolescent Twin Study in Sweden (CATSS)36 that recruited twins born in Sweden since 1992 (mean age 18.31 years).Summary statistics for adolescent PENS came from a mega-GWAS of three European community samples : perceptual aberrations were assessed with the Perceptual Aberration Scale39 and included experiences that resemble clinical features of schizophrenia. Hypomania was from the Hypomanic Personality Scale40, devised to assess hypomania, gregariousness, grandiosity, and euphoria. Two scales from Chapman’s Schizotypia Scales were employed: the Revised Social Anhedonia Scale and the Revised Physical Anhedonia Scale41, devised to assess the inability to take pleasure from physical and social stimuli, respectively.Schizotypy was assessed in the Northern Finland Birth Cohort 1996 (NFBC)http://www.nealelab.is/uk-biobank) for individuals of European ancestry . Items assessed psychotic experiences in adults aged 40–69 years: whether participants ever experienced auditory hallucinations, visual hallucinations, delusions of persecution, and delusions of reference.GWAS summary statistics of four dichotomous items from the UK Biobank were obtained from Neale Lab . Summary statistics for depression excluded 23andMe participants.GWAS summary statistics for schizophreniaQuality-control procedures were applied to summary statistics prior to analyses. Genetic variants were removed if they had incomplete association statistics, were non-biallelic, or strand ambiguous. Variants with alleles that did not match those in the 1000 Genomes reference panel (phase 3), with info scores < 0.9 and minor allele frequency < 0.01 were excluded.45 was used to estimate SNP-h2 for traits and the genetic correlation (rg) between traits. Variants were merged with the HapMap3 reference panel to ensure good imputation quality45. Heritability and genetic correlations were converted to liability scales using lifetime prevalence of 1% for schizophrenia, 15% for depression, and 2% for bipolar disorder48. Effective sample sizes were used for adolescent PENS, because TEDS and CATSS included siblings20. To account for sample overlap, the LD score regression intercept was left unconstrained to estimate associations between positive psychotic experiences and depression, because both included UK Biobank participants, and between psychiatric disorders that include PGC participants. Benjamini–Hochberg correction for multiple testing was performed at a false discovery rate of 0.05 to account for 105 correlations estimated.Linkage disequilibrium (LD) score regression49 and results were compared across psychotic experiences and psychiatric disorders. To identify genes at genome-wide significance, MAGMA v1.0650 was used to aggregate the p-values of SNPs within gene coding regions (0 kb annotation window). Default parameters and an SNP-wide mean model were employed. Bonferroni-corrected p-value thresholds were set for the number of genes tested within each phenotype. Positional mapping of variants within 10 kb of gene regions and likely to have functional consequences (Combined Annotation-Dependent Depletion score (CADD score) ≥ 12.37), gene mapping using expression quantitative trait loci associations and chromatin interactions were performed for independent lead SNPs  and 1000 Genomes reference panel variants in LD with independent SNPs at r2 ≥ 0.6 using recommended parameters49. Additional details are provided in the Gene-wide associations and gene mapping were performed using the FUMA pipeline51 was conducted to test for causal relationships between phenotypes that had significant genetic correlations. SNPs were selected as instrumental variables based on the clumping algorithm in PLINK52 using an r2 threshold of 0.05 within a 500 kb window at genome-wide significant levels (p < 5 × 10−8) for schizophrenia. Summary statistics for depression did not include 23andMe participants and had an insufficient number of genome-wide significant variants for MR analyses. Instead, genome-wide significant variants were obtained from the publication44. Insufficient genome-wide significant variants associated with psychotic experiences meant that p-value thresholds for psychotic experiences were set at p < 5 × 10−5 to allow for at least 20 instrumental variables. We report MR analyses using equivalent p-value thresholds for all phenotypes (p < 5 × 10−5) in the MRr2 > 0.1) and for sampling variation in the exposure and outcome GWAS53. MR-Egger regression54, weighted median55, and weighted mode56 methods were conducted as sensitivity analyses for possible violation of MR assumptions. SNPs identified as potentially pleiotropic or with residual LD structure in GSMR Heidi-outlier analyses were also excluded from sensitivity analyses.Generalized summary-data-based MR (GSMR) was used due to its advantages of accounting for residual LD structure between instrumental variables , 30%–37% in schizotypy during adulthood, and 7%–10% in positive psychotic experiences during adulthood , which indicates direction of correlation  and depression , but not with bipolar disorder . Likewise, visual hallucinations was significantly correlated with schizophrenia  and depression  but not with bipolar disorder . We observed high and significant genetic correlations between delusions of persecution with schizophrenia  and depression , whereas the genetic correlation with bipolar disorder was not significant after correction for multiple testing . Genetic correlations between delusions of reference with schizophrenia , depression  and bipolar disorder  were at nominal significance (p < 0.05) but did not survive correction for multiple testing.For positive psychotic experiences in UK Biobank, auditory hallucinations were significantly genetically correlated with schizophrenia  and depression  and between anhedonia and depression . Negative symptoms covaried positively with schizophrenia  and depression . Positive genetic covariation was observed between paranoia and hallucinations, and depression . No significant genetic correlations were found with bipolar disorder.Between adolescent PENS and psychiatric disorders, genetic correlations were not significant after correction for multiple testing. Nominally significant genetic correlations were observed between cognitive disorganization with schizophrenia . We did not find evidence that the schizotypy scales correlated with schizophrenia or bipolar disorder.Between psychiatric disorders and schizotypy in adults, hypomania was genetically correlated with depression at nominal significance . Physical anhedonia showed a negative genetic correlation with auditory hallucinations , but not after correction for multiple testing.PENS during adolescence were not significantly genetically correlated with positive psychotic experiences and schizotypy during adulthood. Between the adult samples, hypomania was genetically correlated with visual hallucinations . Genetic correlations between delusions of reference with auditory  and visual hallucinations  were not significant after correction for multiple testing. Delusions of persecution were not genetically correlated with auditory or visual hallucinations. A genetic correlation between delusions of persecution and delusions of reference could not be computed due to low SNP-h2 in both phenotypes. but these two items showed positive genetic covariance .Within positive psychotic experiences in UK Biobank, auditory and visual hallucinations were highly and significantly genetically correlated . No other genetic correlations between schizotypy scales were significant.There was a nominally significant association between physical and social anhedonia . No significant genetic overlap between other adolescent PENS was found.Within adolescent PENS scales, positive genetic covariance was identified between anhedonia and negative symptoms , and NADK2 to adolescent negative symptoms and delusions of reference in UK Biobank; none overlapped with psychiatric disorders. Seventy-three genes were found for adult schizotypy including the aforementioned PAN3 and six genes that were also indicated for schizophrenia. ANK3 overlapped with both positive psychotic experiences  and schizophrenia. For positive psychotic experiences in adults, 104 genes were identified, seven of which overlapped with schizophrenia and, as mentioned above, one with schizotypy and one with adolescent PENS  and replicated in all MR sensitivity analyses.GSMR analyses provided evidence for a bidirectional association between auditory and visual hallucinations (PGSMR = 9.33 × 10−5); however, the effect was small and replicated in weighted median but not in MR-Egger, nor in weighted mode analyses. No evidence of a significant effect in the other direction was found.We observed evidence of a directional effect of schizophrenia liability on delusions of persecution  shared a moderate to substantial amount of common genetic variation with depression and schizophrenia, consistent with an independent report44, which allowed more reliable estimations compared with previous studies20. Our results add to the growing body of evidence that there are genetic associations between adolescent PENS and psychiatric disorders24. There were more significant genetic correlations between psychotic experiences and depression than with schizophrenia. This could be explored further in terms of the underlying reasons.This study reported genetic overlap between adolescent PENS and psychiatric disorders. We used more recent summary statistics from larger GWAS of depression and bipolar disorder13, but longitudinal evidence of an association between schizotypy and subsequently being diagnosed with schizophrenia is limited58 and findings inconsistent59.Schizotypy domains did not appear to share common additive genetic influences with schizophrenia nor with bipolar disorder. There was suggestive evidence of genetic overlap between hypomania and depression. Schizotypy has been associated with an increased risk of schizophrenia-spectrum disordersBipolar disorder does not appear to share genetic overlap with adolescent PENS or adult schizotypy. It is possible that common genetic influences on bipolar disorder, in contrast to those on depression and schizophrenia, are not involved in these earlier adolescent forms of psychopathology. Future studies with better-powered GWAS will be able to test this hypothesis further.ANK3, a gene in the 10q21.2 region, has previously been associated with bipolar disorder and schizophrenia62. TSNARE1 (8q24.3) has previously been implicated in schizophrenia and cognitive function63. The gene SNX7 (1p21.3) has known associations with mathematical ability64. Three neighboring genes located on band 12q24.11, FAM216A, PPP1CC, and GPN3, and a nearby gene TMEM116 (12q24.13) have been associated with white blood cell and platelet count, heart rate, and body mass index67.We found evidence that some genes may be involved in both psychotic experiences and psychiatric disorders. Seven genes were implicated in both positive psychotic experiences in UK Biobank and schizophrenia: ANK3 discussed above. As noted elsewhere22, we found evidence that CACNA1C (12p13.33), with well-replicated associations with psychiatric and developmental disorders61, may be involved in hypomania, schizophrenia, and bipolar disorder. Located within the same 12p13.33 region, the genes DCP1B, TSPAN9, and FKBP4 also overlapped between hypomania and schizophrenia, and have previously been associated with a range of physical health phenotypes including obesity and waist–hip ratio68. Findings indicated the possible involvement of CLK1 (2q33.1) in perceptual aberrations and schizophrenia, a gene previously associated with breast cancer69.Six mapped genes overlapped between adult schizotypy and psychiatric disorders, including 70. It is also possible that some of those in the adolescent samples may be genetically liable to psychotic experiences but will have not yet developed them. A previous study found that genetic liability to schizophrenia was positively associated with adolescent paranoia and hallucinations only after excluding non-zero scores20, suggesting that genetic liability to schizophrenia may vary in adolescents who did not (yet) report psychotic experiences. This may explain why we found that genetic overlap between psychotic experiences and psychiatric disorders increased with age.Our results suggest that psychotic experiences may share more genome-wide genetic overlap with schizophrenia and major depression in adulthood than adolescence. Participants had narrow age ranges in the adolescent PENS (15–19 years old) and adult schizotypy  samples, and were assessed on current psychotic experiences, whereas UK Biobank participants had a wider and older age range (40–69 years old), and were asked to report on lifetime psychotic experiences. It is therefore possible that psychotic experiences reported in UK Biobank may have been persistent rather than transitory. According to the persistent-impairment model, those with persistent psychotic experiences are at a higher risk of developing psychotic disorders and may therefore share more genetic influences with psychiatric disorders26.We did not find significant genetic overlap across PENS (adolescence), schizotypy (adulthood), and positive psychotic experiences (adulthood). Different genetic influences may be involved in psychotic experiences across the lifespan. For example, some psychotic experiences may be adolescent-specific, tied in with development and lifestyle factors such as sleep problems, risk-taking, and experimenting with drugs. Psychotic experiences in older adults may have different etiological pathways, in part associated with conditions such as medication side effects, dementia, eye conditions, and Charles Bonnet syndrome. However, it was not possible to rule out that the different measurements used across ages led to the lack of genetic overlap. Another reason why no cross-age genetic overlap was found may be the smaller GWAS sample sizes for adolescent PENS and adult schizotypy. In agreement with our findings, an independent report found that genetic risk for psychotic experiences in adults did not predict psychotic experiences during mid-adolescenceANK3 was implicated for adult schizotypy and adult psychotic experiences. The gene PAN3 (13q12.2) was implicated in adolescent cognitive disorganization and schizotypy. NADK2  on 5p13.2 was mapped to both negative symptoms in adolescence and to adult psychotic experiences.We found that some of the same genes may be involved in psychotic experiences across age. The gene We also investigated within-sample genetic associations and found that auditory and visual hallucinations during adulthood shared the same common genetic influences and were bidirectionally associated in MR analyses. This bidirectional association may indicate a pervasive shared genetic basis between auditory and visual hallucinatory experiences rather than true causal associations, because pleiotropic effects of the genetic instruments through a latent common precursor (which would violate MR assumptions) seem likely. As such, experiences resembling auditory and visual hallucinations may share biological pathways.71. We may not have detected true causal effects due to weak instrument bias in analyses that used psychotic experiences as the exposure, because genetic variants below conventional genome-wide significance levels were used. Future studies could re-evaluate these causal associations once larger GWAS for psychotic experiences become available. Furthermore, future studies with greater power might explore the associations of psychotic experiences with a wider array of phenotypes.We note caution in interpreting MR results that compared within-sample psychotic experiences, because sample overlap could lead to weak instrument bias away from the null72, therefore providing strong hypotheses for variant–gene associations. For psychotic experiences and depression, functional consequences on genes were annotated to variants clumped below conventional genome-wide significance levels. As such, these results will benefit from replication once better-powered GWAS become available.Many of the overlapping genes we report were identified based mainly on chromatin interactions. The chromatin interaction data had a high resolution (10 kb) resulting in more mapped genes and included enhancer–promoter and promoter–promoter interactions only74. Loss to follow-up in the cohort studies used in GWAS may have an impact on the generalizability of the genetic findings75. However, we note that attrition based on phenotypic or genetic risk for psychosis would lead, if anything, to underestimates of the genetic overlap between psychotic experience traits and psychiatric disorders.There are known limitations when using summary statistics from large biobanks including ascertainment bias and the possibility that collider bias may result in overinflated genetic correlationsPsychotic experiences during adulthood and adolescence share genetic influences with psychiatric disorders. This study implicated specific genes that may be involved in both psychotic experiences in the community and psychiatric disorders, and genes associated with psychotic experiences across development. Subclinical experiences of auditory and visual hallucinations in adults may have similar biological etiologies. Our findings and other independent reports indicate that psychotic experiences assessed using different measures at different developmental stages may not reflect genetically similar phenomena.Supplementary Figures and Supplementary NoteSupplementary Tables S2-7


Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations.A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center’s (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank.Heritability estimates of lipid values  were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB.Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.The online version contains supplementary material available at 10.1186/s13073-020-00820-8. Genetic studies of EHR-based labs could reveal novel biomarker-disease or biomarker-gene associations, which in turn could lead to better understanding of biological processes in disease, improved diagnostic algorithms, and new therapeutic targets.The overarching goal of this study was to determine whether laboratory (lab) test results collected in a hospital and outpatient setting could be mined against polygenic scores (PGS) to identify known and novel biomarker associations for complex disease. Lab test results are essential to routine clinical care. These targeted biochemical measurements facilitate disease diagnosis and influence health care delivery. Clinical lab values are also monitored as mediators of disease risk and are targeted by interventions to reduce disease incidence . Lab test results in the electronic health record (EHR) are a vast and growing resource for novel biomarker discovery, especially as EHRs are increasingly linked to patient DNA samples  pipeline to scan for associations between any variable of interest (genetic or otherwise) and the cleaned EHR labs. The LabWAS method is similar to the Phenome-Wide Association Scan (PheWAS) which scans for association between an exposure variable  and many phenotypes . The Phe. We focused on genetic analysis of blood values of high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides (TG) and on coronary artery disease (CAD) as proof-of-principle examples to test the association between PGS for CAD and known biomarkers of disease  using the QualityLab and LabWAS methods across populations. We show that EHR-derived lipids values are genetically similar to those in population-based studies and that PGS for lipids robustly associate with their respective lab in a LabWAS. Additionally, our LabWAS revealed that PGS for CAD associated with known lipid biomarkers, even in individuals without a history of CAD, and with potentially novel immune biomarkers.We hypothesized that EHR-based lab values could be used to identify known and novel relationships between genetics, biomarkers, and disease. We deployed our framework in the Vanderbilt University Medical Center (VUMC) EHR and linked biobank, BioVU, and replicated it in an independent biobank, Massachusetts General Brigham BiobankOur primary analysis was performed at VUMC which is a tertiary care center providing inpatient and outpatient care in Nashville, TN. The VUMC EHR was established in 1994 and includes data on billing codes from the International Classification of Diseases, 9th and 10th editions (ICD-9 and ICD-10), Current Procedural Terminology (CPT) codes, laboratory values, reports, and clinical documentation. The de-identified mirror of the EHR, known as the Synthetic Derivative, includes patient records on more than 2.8 million individuals. In 2007, VUMC launched a biobank, BioVU, and the BioVU Consent form is provided to patients in the outpatient clinic environments at VUMC. The form states policies on data sharing and privacy and, upon consent, makes any blood leftover from clinical care eligible for BioVU banking . The VUMEX array. Using PLINK v1.9 [R2 > 0.3) and converted to hard calls. We restricted to autosomal SNPs, filtered SNPs with minor allele frequency > 0.01, or with allele frequencies that differed by more than 10% from the 1000 Genomes Project phase 3 CEU or ASW set respectively [p > 1 × 10−10). The resulting dataset contained 6,303,629 SNPs on 72,824 individuals of European genetic ancestry and 12,798,111 SNPs on 15,283 individuals of African genetic ancestry.We obtained genotype information on 94,474 BioVU individuals of different ancestral and racial backgrounds genotyped on the Illumina MEGAINK v1.9 , genotypINK v1.9 , 17 and INK v1.9  using thectively , and HarIn parallel with the BioVU genotyping project, we extracted data on all lab tests collected in the routine clinical care of 1,521,125 VUMC patients, amounting to 275,991,157 observations across 11,061 lab tests , we first calculated pairwise relatedness in the BioVU genotyped sample and removed one related individual from pairs with pi-hat greater than 0.05. This stringent threshold was chosen based on prior experience and previously published best practices in the application of restricted maximum likelihood (REML) approaches to the calculation of h2SNP [n = 181) and performed GWAS of the median, INT-transformed lab values using fastGWA [2SNP and GWAS analyses included covariates for sex, cubic splines (knots = 4) of median age across the medical record (to control for non-linear effects of age), and the top 10 principal components of ancestry.Prior to calculating SNP-based heritability  [2SNP for HDL, LDL, and TG were calculated from GWAS summary statistics using LDSC [2SNP in BioVU using Linkage Disequilibrium Score regression (LDSC) applied to our fastGWA summary statistics for HDL, LDL, and TG  between the BioVU lipid GWASs and the GLGC and MVP lipid GWASs using LDSC and the pre-computed European LD scores from 1000 Genomes Phase 3 European data [We benchmarked our lipid hm (GLGC)  and the ing LDSC . We compile 2SNP , we alsoean data . We alsoean data , which f2SNP estimates to different lab value and patient filters. First, we excluded lipid measurements that occurred after the first mention of lipid-altering mediation in the EHR  for association with common population variation in lab values. We therefore only included the 335 labs with non-zero hPGS) was calculated using SNP weights from CARDIoGRAMplusC4D GWAS summary statistics [Prior to polygenic scoring, we randomly removed one related individual from pairs with pi-hat greater than 0.2, leaving 66,732 individuals of European genetic ancestry and 12,383 individuals of African genetic ancestry. , HDL (PGSHDL), and TG (PGSTG) were calculated in BioVU participants using PRS-CS and applying SNP weights from the MVP GWAS summary statistics. We then ran LabWAS of PGSLDL, PGSHDL, and PGSTG to test whether lipid labs were robustly associated with the genetic scores to which they corresponded. Next, a PGS for CAD (CADPGS) was calculated using SNP weights from CARDIoGRAMplusC4D GWAS summary statistics [CAD to test whether the score could identify lab traits associated with genetic risk for CAD, before and after controlling for a CAD diagnosis ).PGS for LDL , which provides a large-scale resource of linked longitudinal electronic health records (EHR) data, genomic data, and self-reported survey data [We next sought to replicate the associations between lipids PGS and referent lipids as well as the significant associations with CADvey data . All patLab values were extracted from EHRs and cleaned using QualityLab, resulting in 759 labs for analysis. The median value for each lab trait for each individual was selected and inverse normalized. Lab heritabilities were calculated using REML in GCTA. Of 759 labs that passed QualityLab, 241 demonstrated measurable heritability and included a sample size of at least 100 individuals.n = 25,698) using the same criteria as BioVU. Lipids and CAD polygenic scores were associated with each of 234 labs using LabWAS. Lastly, the associations between CADPGS and lab traits were controlled for CAD diagnosis, defined by phecode 411 . All associations were controlled for sex, top 10 principal components, and the first two splines of median age across the medical record.Polygenic scores for HDL, LDL, TG, and CAD were calculated on individuals of European descent in MGBB  are available in the GWAS Catalog  and TG . When comparing BioVU and MVP, the correlations for HDL  and TG  were nearly perfect. The LDL and LDL pre-medication genetic correlations between GLGC and BioVU were not calculable using LDSC due to low heritability. Using high-definition likelihood, GLGC LDL levels were significantly correlated when median LDL values across the entire EHR  and median pre-medication LDL values . The comparison between BioVU and MVP showed a stronger correlation for LDL . The genetic correlation with MVP increased when we restricted to pre-medication values of LDL in BioVU    , decreased TG , decreased total cholesterol to HDL ratio , increased total blood cholesterol , and decreased blood glucose , decreased blood urea nitrogen , decreased glycated hemoglobin , decreased bedside glucose , and decreased whole blood glucose . HDLPGS was also associated with four immune labs, white blood cell count , absolute neutrophil count , immature granulocytes , and monocyte to leukocyte ratio . Five blood biomarkers associated with HDLPGS, mean corpuscular volume , blood carbon dioxide , mean corpuscular hemoglobin , international normalized ratio , and red blood cell distribution width . Finally, three other labs associated with HDLPGS, urate , creatinine , and urine pH . In the African ancestry group, HDLPGS significantly associated with increased HDL , decreased triglycerides , and increased total cholesterol  , followed by increased total blood cholesterol , increased directly measured LDL , increased non-HDL cholesterol , increased total cholesterol to HDL ratio , and increased triglycerides . LDLPGS also associated with four blood biomarkers, mean corpuscular hemoglobin , total protein in blood , total protein in serum , and mean corpuscular hemoglobin concentration . LDLPGS in the African ancestry group associated with LDL cholesterol  and increased total cholesterol  , followed by decreased HDL , increased total cholesterol to HDL ratio , increased blood glucose , increased lipemic index , increased total blood cholesterol , increased glycated hemoglobin , increased bedside glucose , and increased non-HDL cholesterol . Additionally, TGPGS showed associations with seven immune labs, white blood cells , immature granulocytes , absolute lymphocytes , monocyte to leukocyte ratio , absolute neutrophils , complement C4 , and monocyte count . Several blood associations also emerged with TGPGS, including carbon dioxide , total protein in blood , mean corpuscular volume , mean corpuscular hemoglobin , anion gap , total protein in serum , and calcitriol . Lastly, TGPGS associated with albumin to creatinine ratio , urate , urinary pH , and urinary albumin concentration . In the African ancestry group, TGPGS showed significant associations with increased triglycerides , decreased HDL cholesterol , and increased glucose  , increased TG , increased blood glucose  and glycated hemoglobin , and bedside glucose . The CADPGS also associated with other known biomarkers of cardiovascular health such as increased troponin-I  and brain natriuretic peptide . CADPGS associated with six blood composition markers, red blood cell distribution width , mean corpuscular hemoglobin , mean corpuscular volume , carbon dioxide , red blood cell sedimentation rate , and international normalized rate . Finally, CADPGS associated with white blood cell count , creatinine , and blood urea nitrogen .We next sought to recapitulate the risk biomarker profile for CAD through a LabWAS of a CADPGS was not initially associated with LDL values . The lack of association, however, was attributable to lipid altering medication use and a significant association between the CADPGS and LDL levels was detected when we restricted to pre-medication values .Notably, the CADPGS for the coronary atherosclerosis phecode (411) , pre-medication LDL , HDL , LDL-C , blood glucose , total cholesterol , and glycated hemoglobin . The CADPGS also remained associated with one immune marker, white blood cell count , and two other blood biomarkers, mean corpuscular volume  and mean corpuscular hemoglobin .To determine which biomarkers were explained by the clinical presence of CAD as opposed to the genetic risk for CAD, we adjusted the LabWAS of CAD phecode 11 , increased glucose , and increased LDL cholesterol  ; however, this association did not pass multiple testing correction. After controlling the analysis for CAD diagnosis, the association between CADPGS and pre-medication LDL surpassed the Bonferroni correction for phenome-wide significance   and the average age was 56.1 years. The MGBB patients contained 1094 CAD cases and 20,405 CAD controls.PGS most strongly associated with HDL cholesterol , followed by decreased triglycerides , increased total cholesterol , and decreased very low-density lipoprotein . HDLPGS also associated with decreased values of glucose , hemoglobin A1c , and mean glucose value . Additional associations with HDLPGS included cardiac relative risk , alanine aminotransferase , white blood cell count , mean corpuscular volume , non-HDL cholesterol , red blood cell distribution width , neutrophils , urate , and alkaline phosphatase  , total cholesterol , calculated LDL cholesterol , and non-HDL cholesterol . The LDLPGS also associated with complement C4 , red blood cell sedimentation rate , and increased cardiac relative risk  , followed by increased very low-density lipoprotein , decreased HDL , increased non-HDL cholesterol , increased glucose , average glucose , total cholesterol , anion gap , total protein , globulin in serum , aspartate aminotransferase , and sodium . TGPGS also associated with seven immune labs, white blood cell count , lymphocytes , complement C4 , automated lymphocyte count , neutrophils , automated neutrophil count , and monocytes . Ten additional labs significantly associated with TGPGS, including increased cardiac relative risk , mean corpuscular volume , glycated hemoglobin A1c , urinary pH , red blood cell sedimentation rate , alanine aminotransferase , alkaline phosphatase , blood carbon dioxide , mean corpuscular hemoglobin , and urate  , increased glucose , increased glycated hemoglobin A1c , mean glucose , and increased triglycerides . The CADPGS also associated with increased red blood cell distribution width , increased red blood cell sedimentation rate , increased alanine aminotransferase , decreased hemoglobin , increased alkaline phosphatase , increased white blood cell count , decreased albumin , increased globulin , decreased iron , and decreased hematocrit  , increased glucose , increased hemoglobin A1c , increased mean glucose , and increased triglycerides . Additionally, associations with red blood cell distribution width , alanine aminotransferase , red blood cell sedimentation rate , alkaline phosphatase , serum globulin , and white blood cell count  remained , and we were unable to investigate the effects of cholesterol lowering medications on the association.After adjusting for CAD diagnosis, CADPGS recapitulated associations with known biomarkers in individuals of European ancestry in two biobanks. Unlike CAD, many complex diseases do not yet have bona fide biomarkers, but do have well-powered GWAS that can be used to mine large biobanks and identify quantitative labs which may be correlated, even weakly, with genetic risk for disease. Importantly, the association between CADPGS and canonical risk factors was significant even among those who did not have a CAD diagnosis. In analyses in Mass General Brigham Biobank, several of the associations with CADPGS replicated, helping to validate our approach to cleaning and analyzing EHR laboratory data. Interestingly, CADPGS also associated with white blood cell count, an inflammatory marker that is not currently used to diagnose or monitor heart disease. This association remained after controlling for CAD diagnosis, indicating that CAD genetics could play a role in increasing inflammation. These results highlight the usefulness of our approach which takes advantage of the entire patient population regardless of disease status. This approach offers a potential path forward for the detection of novel biomarkers and for improved understanding of biomarker activity during the prodromal phase of disease. Furthermore, while disease PGS are not diagnostic, they may be useful in identifying pre-symptomatic individuals whose lab values should be monitored more closely.The results of our study add to a growing body of evidence indicating that lab values from EHRs with linked genetic data can be mined at scale to identify biomarkers for complex disease –5. Our pPGS was strongly associated with pre-medication median LDL values, but was not associated with combined pre- and post-medication median LDL values. This finding also has important and complex implications for the clinical use of PGS recently discussed in the literature [Furthermore, we show that treatments  can influence the detection of risk biomarkers at the genetic level. For example, we found that the genetic correlation between LDL measurements in BioVU and MVP increased considerably when we restricted to pre-medication LDL measurements and controlled for CAD or diabetes diagnosis. Additionally, the CADterature , 36. Theterature , 38.Though the results and approach presented provide an exciting path forward for genetic analysis of EHR-lab data, important limitations should be acknowledged. First, our analyses yielded more associations in patients of European ancestry compared to patients of African ancestry. This is likely to due to decreased power from both the discovery GWASs and the target sample. BioVU has considerably fewer patients of African ancestry than European ancestry, impacting our statistical power to find associations. The polygenic scores of lipids, which were trained on trans-ancestry GWAS summary statistics including individuals of African descent, strongly associated with the referent lipid in the African ancestry sample with effect estimates similar to those found in the European sample. However, the CAD polygenic score, which was trained on a trans-ancestry GWAS that did not include African ancestry samples, yielded far fewer significant associations. These results highlight the critical importance of diversity in GWAS as the downstream applications of such studies are dramatically impacted by representation. As the number of ancestrally diverse GWAS increase, so too will our ability to identify novel biomarkers in different ancestral groups, and the QualityLab pipeline is poised to deliver on these analyses. The QualityLab pipeline could also have more immediate clinical impact for diverse populations. Genetic ancestry, race, sex, age, and ethnicity strongly influence the distribution of lab tests results in healthy people , but manSecond, polygenic scores are based on GWAS summary statistics that are typically unadjusted for phenotypic comorbidities. While this approach is optimal in GWAS for many reasons, it introduces the possibility of “phenotypic hitchhiking” in which a comorbid trait is unintentionally selected during the ascertainment of the index trait. Thus, two heritable phenotypes that might share common environmental risk factors but no genetic risk factors can subsequently appear correlated in PGS analysis, even in independent samples. We therefore emphasize that this genetic approach is still fundamentally correlational.PGS. Multiple testing correction was another statistical challenge inherent to the high-throughput analysis of lab traits. We used the Bonferroni threshold for statistical significance, but this threshold is likely to be overly strict because it ignores the correlation between lab tests.Third, high-throughput analysis of 939 lab traits in our LabWAS required us to prioritize statistical model performance over coefficient interpretability. In our primary analysis, we transformed lab values to fit the normal distribution to improve the performance of the linear regression models . We applHere, we propose that PGS for complex disease can be used to discover genetically related biomarkers of disease by mining quantitative physiological measurements collected during routine clinical testing, but caution that mindful interpretation of correlational results is paramount to progress. We demonstrate the robustness of this discovery paradigm in a proof of principal analysis focused on CAD. As EHR resources grow in size, standardized quality control and analysis pipelines will be necessary to compare results across samples. QualityLab and LabWAS provide a starting point for consistent analysis of lab results stored in various EHR systems. Furthermore, we demonstrated that EHR-derived lipids are similar to measurements ascertained in traditional cohort studies, providing additional rationale for analyses of EHR labs . QualityAdditional file 1. Contains supplementary methods information on genotyping and quality control, definition of coronary artery disease and lipids lowering mediation, and polygenic scoring methods.Additional file 2: Contains all supplementary tables. Table S1. Descriptive statistics generated by the QualityLab pipeline. Table S2. Lipid-altering medications abstracted from free text in clinical notes. Table S3. Characteristics of patients with clean lab and genotyping data. Table S4. Estimates of SNP-based heritability and sample sizes for all labs passing QualityLab using REML. Table S5. Estimates of SNP-based heritability for blood lipid levels across study populations and estimation methods. Table S6. LabWAS of HDLPGS in the European sample in BioVU. Table S7. LabWAS of HDLPGS in the African sample in BioVU. Table S8. LabWAS of LDLPGS in the European sample in BioVU. Table S9. LabWAS of LDLPGS in the African sample in BioVU. Table S10. LabWAS of TGPGS in the European sample in BioVU. Table S11. LabWAS of TGPGS in the African sample in BioVU. Table S12. LabWAS of CADPGS in the European sample in BioVU. Table S13. LabWAS of CADPGS in the European sample in BioVU controlling for CAD diagnosis. Table S14. LabWAS of CADPGS in the African sample in BioVU. Table S15. LabWAS of CADPGS in the African sample in BioVU controlling for CAD diagnosis. Table S16. LabWAS of CAD diagnosis in the European sample in BioVU. Table S17. LabWAS of HDLPGS in the European sample in MGB. Table S18. LabWAS of LDLPGS in the European sample in MGB. Table S19. LabWAS of TGPGS in the European sample in MGB. Table S20. LabWAS of CADPGS in the European sample in MGB. Table S21. LabWAS of CADPGS in the European sample in MGB controlling for CAD diagnosis. Table S22. The GWAS Catalog study accession numbers for summary statistics from GWAS of each lab trait.Additional file 3: Contains all supplementary figures. Figure S1. QualityLab data visualizations. Figure S2. Manhattan and QQ plots from GWAS of BioVU lipids. Figure S3.. Predictive abilities of different polygenic scoring methods. Figure S4. Lipids levels by genetic ancestry. Figure S5. Histogram of BioVU lab heritability estimates. Figure S6. LDL genetic correlation sensitivity analyses. Figure S7. LabWAS Manhattan of CAD diagnosis. Figure S8. LabWAS of Lipids PGS in MGBB. Figure S9. LabWAS of CAD PGS in MGBB.


Medicago truncatula reference genome revealed several putative candidate genes based on their roles in response to salt stress. Additionally, eight GS models were used to estimate breeding values of the training population under salt stress. Highest prediction accuracies and root mean square errors were used to determine the best prediction model. The machine learning methods (support vector machine and random forest) performance best with the prediction accuracy of 0.793 for yield. The marker loci and candidate genes identified, along with optimized GS prediction models, were shown to be useful in improvement of alfalfa with enhanced salt tolerance. DNA markers and the outcome of the GS will be made available to the alfalfa breeding community in efforts to accelerate genetic gains, in the development of biotic stress tolerant and more productive modern-day alfalfa cultivars.Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by genome-wide association studies (GWAS) and GS using different models. GWAS identified 27 single nucleotide polymorphism (SNP) markers associated with salt tolerance. Mapping SNPs markers against the  The impacts of soil salinization on world agriculture will become more pervasive and severe in the future. Quadir et al.  estimateSalt affects plant growth indirectly by its negative effect on soil water potential and by direct toxicity when it is taken up by the plant. In the former, the increased ion concentration in the soil decreases the soil water potential, which makes it more difficult for the plant to access water. Therefore, salt stress affects plants in a similar manner to drought stress with shared physiological mechanism. These mechanisms include stomatal closure, decreased rates of photosynthesis, formation of reactive oxygen species (ROS), decreased water content in plant cell and attendant problems with protein folding . Consequ+) accumulation within the cell and the homeostasis between Na+ and potassium (K+) [++) and potassium (K+) ultimately resulting in reduced growth, leaf chlorosis, and early leaf senescence. Cross talk between gene regulatory networks attributed to drought and salt stresses has been found. It has been suggested that a combined effect of dehydration and osmotic stress may cause greater regulation in plant response to salt stress  . Sodium e review ). FunctiCultivated alfalfa is an outcrossing autotetraploid 2n = 4x = 32) species with a genome size of 800−1000 Mb . Alfalfax = 32 spAlfalfa cultivar development efforts have largely focused on the phenotypic selection in field environments. Recurrent selection is used for improving traits of interest in a quantitative manner. The strategy is to gradually increase the frequency of favorable alleles and maintain genetic variability for future selection. Progress on the improvement of the traits under recurrent selection are made, but it takes long periods to successfully develop new varieties. Recurrent selection methods would be most effective when integrated with marker-assisted selection (MAS) . MAS is Genomic selection (GS) is a promising alternative to phenotype-based selection of crops in breeding programs. The objective of GS is to determine the genetic potential of an individual based on whole genome markers, instead of focusing of specific QTL. Therefore, GS does not depend on prior knowledge about QTL effects. This technique is based on the association of phenotypic traits with genome-wide markers to obtain the genomic estimated breeding values (GEBV) . GEVB arIn GS, several statistic models have been used for predicting breeding values. Support vector machines (SVM) and random forest (RF) are supervised machine learning methods used to predict the target phenotype mization . The objmization . These ss 50–100  and theyThe objectives of this work were to use GWAS and GS methods to identify loci associated with salt tolerance and to predict breeding values using single nucleotide poplymorphism (SNP) markers with allele dosage in breeding populations of autoteraploid alfalfa. Agronomic traits such as biomass yield and plant growth vigor under salt stress were evaluated in the field. Genome-wide DNA markers were developed using genotype-by-sequencing (GBS) and used for GWAS and GS. Six statistic models were used in GWASpoly to identify loci associated salt tolerance and eight genomic prediction models were tested on the prediction accuracy for GEBV in the breeding populations toward improving salt tolerance in alfalfa.M. truncatula genome v5.0. After filtering, 6862 high quality biallelic single nucleotide variants (SNVs) were obtained and annotated using the functional annotation of variants module of Next Generation Sequencing Experience Platform (NGSEP). The biallelic SNVs were annotated as follows: 5234 markers as protein-coding loci (76.8%) and 1628 markers as non-coding loci (23.7%)  and 100,635,037 reads multiple times (41.8%) to the  (23.7%) . The dis (23.7%) A. The di regions B. Finall regions .M. truncatula genome v5.0 as reference. Of 16 loci identified, 14 were targeted to the coding regions of protein loci  were identified in populations evaluated at the Castle Dale, Utah site A,B. A toein loci . The proThe GWAS identified six markers significantly associated with yield under salt stress. These markers were chr2_8865320, chr3_5484686 and 17906891, chr4_54035230, chr6_1909362, chr 8_32682521. Two of these markers were associated with the yield in 2018 field evaluations. Among them, marker chr3_5484686 was identified by 2-dominant reference model and chr6_1909362 was identified by general model A,B. One M. truncatula genome v5.0 as reference and all markers were targeted to protein-coding loci (The six markers identified were annotated to their genomic regions using the ing loci . The proing loci .Linkage disequilibrium (LD) analysis was performed with all markers associated with yield and vigor under salt stress and their adjacent markers in a 10 kb window by Haploview v4.2 . Among 2The growth vigor under salt stress collected in Othello and Castle Dale and yield collected in Othello were used for GS using eight different models: rrBLUP, BayesA, BayesB, BayesC, BRR, BL, SVM, and RF. GS used 10-fold cross validation between a training population of 90% and a testing population of 10% to predict breeding values. The accuracy of Pearson’s correlation between predicted GEVB and phenotypic values was used in all datasets. Mean accuracies for the eight models tested were 0.264 (SD ± 0.015) in Castle Dale and 0.337 (SD ± 0.011) in Othello, and mean RMSE values were 0.889 (SD ± 0.005) in Castle Dale and 0.6962 (SD ± 0.005) in Othello. The best fitting model was SVM in both datasets with accuracies of 0.287 in Castle Dale and 0.361 in Othello .The prediction accuracy, based on Pearson’s correlations, varied across harvest dates for the yield trait. The highest prediction accuracy was obtained for the September 2018 harvest data with mean accuracy of 0.457 (SD ± 0.021) for all models (data not shown). The lowest prediction accuracy was found in harvest data for All 2019 with a mean accuracy of 0.087 (SD ± 0.031) for all models. Harvests in August 2018, All 2018, July 2019 and September 2019 had similar mean accuracies of 0.262 (SD ± 0.011), 0.239 (SD ± 0.030), 0.254 (SD ± 0.021), respectively . The ranTo analyze the variation in the errors in a set of forecasts, the mean absolute error (MAE) and the root mean squared error (RMSE) were used to measure the average magnitude in the continuous variable errors . Comparisons between the models and datasets by MAE and RMSE identified high correlations  thereforFinally, in order to test machine learning models, 10% of the dataset was left-out during the model’s training by 10-fold cross-validation. Finally, the model trained was used to predict yield of the 10% dataset left-out comparing the goodness of fit of the models by accuracy and RMSE values of model trained and model with future data . This apEstimation of allele dosage is crucial for precise GWAS and GS analyses in polyploid species. Pipelines using diploidization of polyploid makers could affect GWAS  or GS 1 results.Arabidopsis thaliana (https://medicago.toulouse.inra.fr/MtrunA17r5.0-ANR/). Mutation of At2g32040.2 in A. thaliana increased the total chloroplast folate content and decreased the proportion of 5-methyl-tetrahydrofolate [Using GWASpoly, we identified 27 SNPs associated with salt stress tolerance. Among them, six were associated with yield and 21 were associated with vigor under salt stress. Of the 27 markers identified, three were found to be in non-coding regions and four were associated with hypothetical proteins. The 20 remaining markers were associated with 16 protein-coding loci annotated with known functions. Locus MtrunA17_Chr1g0205221 was associated to a putative folate-biopterin transporter. The folate-biopterin transporter (FBT) belongs to the major facilitator superfamily (MFS). Some members of this family are Zinc-Induced Facilitator-Like 1 (ZIFL1) proteins that have reported activity as polar auxin transport modulators and alternative splicing for drought tolerance . FBT hasM. truncatula genome browser [A. thaliana and 78.3% and 80.3% similarity to 3-beta-hydroxy-Delta(5)-steroid-dehydrogenase in green bean (Phaseolus vulgaris) and soybean (Glycine max) proteomes, respectively. SDR5 belongs to a NAD(P)-binding Rossmann-fold superfamily protein which have been shown to be induced with Methyl jasmonate and reduce the effects of abiotic stresses in plants [Locus MtrunA17_Chr2g0324021 was annotated as putative oxidoreductase in the short-chain dehydrogenase reductase (SDR) class. SDR proteins are involved in oxidative reduction affecting multiple metabolic processes. According to  browser  MtrunA17n plants .A. thaliana [A. thaliana. The process of acetylation and methylation of histones in response to salt stress control the ABA signaling process which play an essential role in organ to organ communication [A. thaliana [Oryza sativa) [GmPPR4 a DYW subgroup of pentatricopeptide-repeat (PPR) proteins was induced under salt and drought stresses [Locus MtrunA17_Chr3R0014140, associated with 2641319 and 2641320 SNPs, was annotated to an RLX_singleton_family134 and a domain search in interproscan  predictethaliana . Additionication . Similarnication . Furtherthaliana  rice (Or sativa)  and soybTriticum aestivum) [Enterobacter cloacae SBP-8 [Locus MtrunA17_Chr3g0083861 annotated to putative serpin family protein. Serpins act as protease inhibitors of serine proteases with other described roles in plant pathogen interactions , grain destivum) , transpoestivum)  and drouestivum) . The invae SBP-8 . The rolae SBP-8 .Locus MtrunA17_Chr5g0410771 with three SNPs 12453276, 12453319, and 12453328 was annotated to a HSP20-like chaperone, P-loop containing nucleoside triphosphate hydrolase. HSP20-like chaperone is a stress responsive protein which is considered an early indicator of oxidative stress and ER stress. Previous reports has found HSP20-like chaperone upregulated by high salinity in Arabidopsis ,39, riceArabidopsis thaliana [The SNP 1909362 associated with yield in three different harvest was identified in the protein-coding loci MtrunA17_Chr6g0451341 annotated as Putative transcription regulator IWS1 family. IWS1 is a transcriptional regulator involved in brassinosteroid induced gene expression after its recruitment by BES1 in thaliana . These rthaliana ,44,45. Tthaliana .Locus MtrunA17_Chr6R0226110 was annotated as Putative potassium channel, voltage-dependent ERG. In mammalian Erg family voltage-gated K+ channels are specialized in repolarization of plateau potentials such as cardiac action potentials .However, in plants, K+ channels have a fundamental role in homeostatic balance of the K+ . In barlLocus MtrunA17_Chr6g0486011 was annotated as Putative RanBP2-type zinc finger protein. Zinc finger proteins are involved with the interaction with DNA, RNA, or proteins regulating in different plant processes like development and programmed death cell. The RanBP2-type zinc finger proteins are ssRNA-binding proteins with high affinity to RNA sequences containing a GGU motif . AlthougLocus MtrunA17_Chr8g0369441 was annotated as Putative brevis radix (BRX) domain, transcription factor. BRX domain-containing protein has been identified as a modulator of root growth in a dosage-dependent dominant negative effect  and it hLocus MtrunA17_Chr7g0235641 was annotated as Putative RIN4, pathogenic type III effector for the avirulence factor Avr cleavage. RIN4 has been described as one of the most important and best studied hubs involved in the regulation of two branches of plant immunity: PAMP triggered immunity and effector trigger immunity (reviewed in ). AdditiA. thaliana and Nicotiana benthamiana regulating and stabilizing the function of light-dependent protochlorophyllide oxidoreductase (POR) [Finally, locus MtrunA17_Chr4g0062111 annotated as Putative protein chaperone-like protein of POR1 (CPP1) (previously known as Cell growth defect factor 1), shows localization in mitochondria . CPP1 hase (POR) . CPP1 hase (POR) .The different markers associated in this study highlights the complexity of salt stress response and the multiple mechanisms of response to salt stress, which include control of protein degradation, chromatin modification, chaperon and TF gene activations, plant hormones signaling or homeostasis Na+/K+. However, there were some loci without a clear role in response to salt stress according to literature search. For example, locus MtrunA17_Chr7g0259771 annotated as Putative small GTPase superfamily, EF-hand domain pair, locus MtrunA17_Chr5g0435221 annotated as Putative 23S rRNA (adenine(2503)-C(2))-methyltransferase, or locus MtrunA17_Chr5g0444321 annotated as Putative leucine-rich repeat domain, L domain-containing protein.Genomic selection has been significantly used in animal breeding over the past 15 years and has been applied to different crops as well . UsuallThe RMSE approach is useful in GS when continuous variations of phenotypic values were used. It was used in genomic selection to avoid misselection of an appropriate prediction model . In the The best performance of machine learning methods in GS in this work was likely due to the ability of these methods for identification of the top-ranking SNPs with major effects on the phenotypic variation and hence explained the large proportion of the additive genetic variance. Additionally, machine learning methods can capture complex SNP–SNP interactions and nonlinear relationships increasing the genetic variance and the heritability of the trait. Our results agree with the previous reports where supervised machine learning methods performed better when traits had dominant and epistatic effects .The strategy of leave-out 10% of the individuals of cross-validation used in the present work allowed to test goodness of fit of the model in predicting the phenotypic traits in new testing individuals. In our analysis, the mean accuracy for all harvests increased from 0.275 to 0.377 with RF model and from 0.264 to 0.411 with SVM. Additionally, the RMSE values decreased from 0.426 to 0.412 with RF and there were no changes in SVM (0.425) . The prep-value: 0.069. However, we found that RMSE values of GS were correlated with residual SD, p-value: 3.675e-07) and MAE values were correlated with residual SD, and coefficient of variation . In the multiple correlations of RMSE and MAE residual SD was the most significant predictor variable with the effects of 1.208 and 0.929, respectively.Other works had described different factors affecting the accuracy in GS such as SNPs density, prediction models or architecture and heritability of the traits ,71. In tc values . PearsonOur GS approach with prediction accuracy reached up to 0.793 in yield data for two years. It can be used to predict yields in the next cycles. Similarly Li et al.  found thThree hundred and four alfalfa individuals from 38 half-sib families were developed by polycross with the original parents of cultivars Malone, Salado, Saranac, Alfagraze, P53V08, Renovator, Spreader III, Wrangler 5, Archer II, Cimarron, Forager, Mesa Sirsa, and U2948 2, followed by four cycles of recurrent selection for salt tolerance. Two populations, the SII and ChkSltn populations were selected based on plant survival in a greenhouse following the method described by Peel et al. . In 2009−1. Salinity of the irrigation water was also recorded and varied but was typically in the range of 7–9 dS m−1. Historical average annual precipitation at the site has been 20.4 cm . In the establishment year and as part of field preparation, 70 kg ha−1 mono-ammonium phosphate (11N-52P-0K) was applied prior to establishing the trial providing 7.7 and 36.4 kg ha−1 each of N and P, respectively. Based on subsequent soil tests no other amendments were needed. A randomized complete block design with three replications was used in the field trial. One plant was grown per plot with plants on one meter spacings. Above ground fresh weight biomass (yield) was collected from the field during July, August, and September of 2018 and May, June, July, and September of 2019. Plant vigor under salt stress was scored for each plant a 1–5 scale, where 1 = weak and 5 = vigorous. Susceptible (‘AZ-90NDC-ST) and tolerant (‘AZ-88NDC’) standard checks from the Forage Production Under Salt Stress standard test were included as references [Three hundred and four individual plants from the 38 half-sib families (eight plants/family) were clonally propagated, maintaining six clones per plant in greenhouse under controlled environmental conditions. Clones from the same original plants were used for the field trails. Prior to field establishment, plot soil salinity was measured 24–48 h following a late June irrigation and averaged 7.4 dS mferences .Phenotypic data were spatially corrected using splines to obtain the best linear unbiased estimates (BLUEs) of fixed effects. BLUEs were estimated using a two-dimensional P-spline mixed model with Mr.Bean web application  using thhe model :y=Xβ+f following the manufacturer’s instructions. DNA concentration and quality were measured using a NanoDrop ND1000 spectrophotometer . The extracted DNA was sequenced at the University of Minnesota Genomic Center for GBS according to Elshire et al. . The seqMedicago truncatula genome v5.0 [The raw sequencing data (fastq files) were obtained and used for aligning to the ome v5.0  using Boome v5.0  with higome v5.0 ). Varianome v5.0  and filtThe variant call format (VCF) file with biallelic single nucleotide variants (SNVs) was transformed into GWASpoly format  using NGThe association studies were performed using the R package GWASpoly using a Q+K linear mixed model as follow :y=Xβ+ZSτs vector .M. truncatula genome v5.0 genome browser [The GWAS analyses were generated with six different models including general, additive, diploidized additive, diploidized general, duplex dominant ( browser .VCF file with allele dosage was numerically transformed using the python scripts VCF2SM and SuperMASSA software  and convjth SNP genotype of plant The Bayesian models for continuous variables are represented by the equation:SVM and RF are machine learning methods for classification and regression tasks ,89. SVM Marker–trait association identified a group of 27 SNP markers associated with salt tolerance. BLAST search in the reference genome revealed several functional genes associated with the significant marker loci and assigned as putative candidate genes based on their roles in response to salt stress. Additionally, genomic selection allowed to predict the breeding values on Logan alfalfa population for salt tolerance with good accuracy. Among the models tested, the machine learning methods were the best models according to high Pearson’s correlation and low RMSE values in yields of different harvests and vigor under salt stress for two years. The identification of the models and the accuracies obtained in this work are likely sufficient to predict breeding values in breeding programs for salt tolerance in alfalfa.