Petunia plant species are, in part, reproductively isolated by their different pollinators. To investigate the genetic causes and consequences of this ecological isolation, we deciphered the genetic architecture of three floral pollination syndrome traits in naturally occurring hybrids between the widespread Petunia axillaris and the highly endemic and endangered P. exserta.Theory suggests that the genetic architecture of traits under divergent natural selection influences how easily reproductive barriers evolve and are maintained between species. Divergently selected traits with a simple genetic architecture (few loci with major phenotypic effects) should facilitate the establishment and maintenance of reproductive isolation between species that are still connected by some gene flow. While empirical support for this idea appears to be mixed, most studies test the influence of trait architectures on reproductive isolation only indirectly. Using population genetics, Bayesian linear mixed modelling and genome-wide association studies, we found that the three pollination syndrome traits vary in genetic architecture. Few genome regions explain a majority of the variation in flavonol content  and strongly predict the trait value in hybrids irrespective of interspecific admixture in the rest of their genomes. In contrast, variation in pistil exsertion and anthocyanin content  is controlled by many genome-wide loci. Opposite to flavonol content, the genome-wide proportion of admixture between the two species predicts trait values in their hybrids. Finally, the genome regions strongly associated with the traits do not show extreme divergence between individuals representing the two species, suggesting that divergent selection on these genome regions is relatively weak within their contact zones.Among the traits analysed, those with a more complex genetic architecture are best maintained in association with the species upon their secondary contact. We propose that this maintained genotype–phenotype association is a coincidental consequence of the complex genetic architectures of these traits: some of their many underlying small-effect loci are likely to be coincidentally linked with the actual barrier loci keeping these species partially isolated upon secondary contact. Hence, the genetic architecture of a trait seems to matter for the outcome of hybridization not only then when the trait itself is under selection.The online version contains supplementary material available at 10.1186/s12915-023-01561-x. Adaptation to different ecological conditions often drives and maintains reproductive isolation between species despite the opportunity for ongoing gene flow –3. TheorAn often-used approach to investigate the genetic basis of ecology-driven reproductive isolation is screening the genomes of  reproductively isolated species for regions of exceptional differentiation. While such divergence scans are insightful in describing general patterns of differentiation across the genome, genomic regions of exceptional differentiation can also result from non-adaptive processes or from ecology-unrelated selection and thus only indirectly address the genetic architecture of the actual traits under divergent selection –15. An aAntirrhinum sp. and petal colour in Petunia axillaris and Petunia inflata [Aquilegia sp. and P. exserta [In plants, pollinator specificity is thought to be a major cause for reproductive isolation . Fl inflata , 23–26.  exserta , 28. Sim exserta  or a com exserta , 31. OveNotably, the aforementioned insights on the genetic architecture of pollination syndrome traits in plants come predominantly from QTL mapping — a type of forward-genetic approach that has several shortcomings when trying to understand the genetic architecture of natural trait variation. Most importantly, the genetic and phenotypic diversity investigated by most QTL mapping studies is limited to that of the two parents used to produce the artificial (F2) mapping population . This caPetunia. P. axillaris is hawkmoth-pollinated and has white, UV-absorbent flowers with partially fused petals forming a tube and a limb, and reproductive organs that do not extend outside of the tube  and quality filtering, the average genome-wide coverage was 5 ×  and P. exserta is UV reflective  Anthocyanin content was markedly lower in the white P. axillaris compared to the red P. exserta, in line with its role in defining visible petal colour  Fla FlaP. axr MYB-FL . In line species .To shed light on the genetic architecture of the pollination syndrome shift, we first used a Bayesian sparse linear mixed model (BSLMM) to estimate the genetic architecture for each pollination syndrome trait. In this model, the effect size of every variant is composed of two terms: one of very small (i.e. minor) effect that is common to all variants, and one of larger (i.e. major) effect that is only present for some variants (when reporting the results from this model we will hereafter use the terms of “major” and “minor” effect according to this definition) . The modP value inflation  Anthocyanin content did not reveal clear association peaks in the genome  that accounted for population structure and effectively reduced 11.7 Mb.  Anthocya11.7 Mb.  Anthocya11.7 Mb.  AnthocyaPetunia species [Petunia species compared to other regions of the genome. To identify genome regions of exceptional interspecific divergence, FST was calculated between individuals representing the pure P. exserta and P. axillaris species within the hybrid zones. Genome-wide divergence was heterogeneous and high (average FST = 0.27), suggesting strong differentiation between these species despite the possibility for ongoing hybridization. Compared to other chromosomes, chromosomes 4 and 5 showed substantially higher FST  were compared with genome regions containing significant trait-associated variants as found in the GWAS. The results indicated high-FST genome regions to overlap only with flavonol content associated variants, but a permutation test suggested that these overlaps are likely to have occurred by chance  from the GWAS. While this revealed some overlap between high-FST genome regions and variants for pistil exsertion (besides anthocyanin content), these overlaps again did not prove statistically significant in permutation tests  non-admixed representatives of either one of the two species, suggesting partial reproductive isolation of the species upon secondary contact [In the two contact zones of  contact , 48, 53.Petunia — were clearly distinct between individuals representing P. exserta and P. axillaris and presented the values typical of the corresponding species [P. exserta individuals: while most individuals had very low flavonol content as expected for this hummingbird-pollinated plant species, some P. exserta individuals showed exceptionally high flavonol content. Upon further examination, all of those high-flavonol P. exserta individuals were found to have at least one functional allele of the MYB-FL transcription factor which has previously been associated with flavonol content variation using QTL mapping [MYB-FL. Notably, this genome region extended across a large part of the chromosome. This physically extended signal may be explained by only MYB-FL or by MYB-FL and further linked variants, as the gene is located within a large genome region of low recombination [MYB-FL alleles — previously thought to be typical of P. axillaris — in the P. exserta-type individuals remains unclear, but possible answers include interspecific introgression, incomplete lineage sorting, or de novo mutation.Anthocyanin content and pistil exsertion — two important pollination syndrome traits in  species . Flavono mapping . In linebination . The oriP. exserta individuals also influences how we view the importance of this trait in species divergence. As previously argued, flavonol content in the petals defines UV absorption and should therefore affect hawkmoth visitation, whereas UV absorption should be of limited relevance for animals feeding in daylight, such as hummingbirds [Petunia species in allopatry. In the secondary contact zones though, the relatively high frequency of UV-absorbent P. exserta individuals suggests instead only weak divergent selection on this trait between the species. We speculate that the relatively simple genetic architecture of flavonol content — with one locus of overarching phenotypic effect — facilitated the relatively rapid shift from mostly low-flavonol P. exserta in allopatry to high-flavonol P. exserta in sympatry with P. axillaris.The finding of high-flavonol ingbirds , 57. It Petunia, the visible colour of petals depends on anthocyanin content [P. axillaris × P. exserta recombinant inbred lines identified loci on chromosomes 1, 2, 3 and 7 as being responsible for anthocyanin content variation [Not only UV colour, but also the visible colour of flowers is characteristic for different pollination syndromes, and in  content . Our modariation . The funP. axillaris and P. exserta [P. axillaris and P. exserta laboratory accessions used in the study of style length were maintained and inbred for decades. Such inbreeding could cause a loss of the naturally occurring variation, especially in a species such as P. axillaris which is known to be relatively variable in the wild [The model-based prediction for pistil exsertion suggested a genetic architecture that was intermediate in complexity compared to the architectures of flavonol and anthocyanin content. Accordingly, the GWAS identified two regions with strong  association with pistil exsertion variation. These regions may thus include some of the causative loci, but surprisingly they do not overlap with the transcription factor EOBII (situated on chromosome 2 at 249.3 Mb) previously shown to affect style length in  exserta . This dithe wild , 60. We Overall, our results indicate that different pollination syndrome traits have different underlying genetic architectures, varying in complexity from few variants of major effect explaining a majority of the phenotypic variation, to several dozens of variants with measurable effects controlling only a third of the phenotypic variation. Especially the complex architecture of anthocyanin content does not clearly follow our classical understanding of visual colour traits being controlled by only few loci of major effect , 61. InsPetunia species within the hybrid zones. Accordingly, the interspecific admixture proportion strongly predicted variation in these two traits: an individual with a more P. axillaris-like genome had a trait value more typical for P. axillaris, and vice versa for P. exserta. In contrast, flavonol content was not predicted by an individual’s overall genomic admixture proportion but rather by an individual’s genotype at the major-effect locus MYB-FL that appeared to segregate largely independently from the rest of the genome.In theory, fewer loci with relatively large phenotypic effects should build stronger barriers to homogenising gene flow between species, and such simple genetic architectures should also be favoured under divergent selection with gene flow , 5. WhilP. axillaris and P. exserta within their contact zones and thus be (partly) responsible for why these species have not fully collapsed into a hybrid swarm despite hundreds of generations of secondary contact [With only these phenotypic patterns at hand, it seems obvious to conclude that flavonol content is under weak divergent pollinator selection within the species' contact zones, while the clear shifts in the two other traits could imply strong divergent selection between the species. If so, interspecific differences in the visible colour of petals (anthocyanin content) and in pistil morphology could help isolate  contact . InteresPetunia vary in complexity from relatively simple to very complex. Theoretically, simple trait architectures with few major-effect loci should facilitate the establishment and maintenance of reproductive isolation between species in the presence of gene flow, including within secondary contact zones. In contrast to this idea, phenotypic differentiation was stronger for traits with complex architectures (many minor-effect loci) between the species in secondary contact zones. However, the genome regions containing the largest-effect loci for any of these traits did not show exceptional divergence between the species. We conclude that while different pollinators might have been important drivers of primary divergence between these plant species in allopatry, pollinator-mediated prezygotic isolation may not be strong and thus does not keep these species apart within contact zones. Genetic incompatibilities or other traits under divergent selection appear to build stronger barriers between these species upon secondary contact. If so, the observed interspecific differences at two of the three pollination syndrome traits might simply be a by-product of the traits' complex genetic architectures. That is, these phenotypic differences may simply be maintained between the species because many of the genome-wide loci underlying these traits are in coincidental linkage with actual barrier loci.We found that the genetic architecture of three typical pollination syndrome traits in P. axillaris subsp. axillaris and P. exserta in the Guaritas region of Brazil: contact zone 1  and contact zone 2 . These contact zones have been described in much detail before [This study considers two contact zones of l before , 64. We t-test for pistil exsertion. Correlation between principal genetic components and the phenotypic values was performed with Pearson’s correlation and the results were Bonferroni-corrected. All statistical analyses were performed in R v. 4.1.2, in RStudio 2021.09.1.From each plant, between 3 and 8 flowers were used for phenotype recording. Flowers were collected 2 days post anthesis and the corolla was cut open longitudinally. The flower was then pinned to a flat surface and photographed. Photos were processed with the software Fiji (ImageJ) to obtain pistil and tube length . Pistil MYB-FL was performed as described in Sheehan et al. 2016.Leaf tissue was collected from each plant and DNA was extracted with a modified CTAB protocol . The DNAP. axillaris (version 4.03) available on NCBI GenBank under the accession number JANRMM000000000 [Raw reads were quality controlled with FastQC version 0.11.7 . Trimmin00000000 . Alignme00000000 , 72 at b00000000  were useFST) were performed on genotype likelihoods rather than on called genotypes, to account for the uncertainty introduced by the low coverage on the genotype calls [https://github.com/kuhlemeier-lab/petunia_hybrid_pollinator_genetics. The genotype likelihoods were then used to perform an admixture analysis with NgsAdmix available in ANGSD version 0.933 [K from 1 to 8 were tested, and the analysis was repeated 10 times. The run with the best likelihood was chosen for each K. The most likely K was calculated using Evanno’s method [Population genomic analyses . Its application allows for the correct modelling of traits controlled by few variants of major effect and additional small effect variants, as well as traits controlled only by numerous small effect variants [P values . To test overlap in traits that had no or few sites passing the Bonferroni-corrected threshold, we selected the ten sites with the lowest P value in each trait, and used the 20 kb surrounding region. In addition, we tested all the regions just described for overlap with chromosome-wise high FST regions. For this, we considered one chromosome at a time, selected the regions with a FST above the 95th quantile of the chromosome, and calculated overlap with the GWAS variant regions of that chromosome . The overlap test was performed with the R library regioneR on a significance threshold for overlap of 0.05 [The GWAS and the predictions of the genomic architecture for each trait were performed with GEMMA version 0.98.4 . To do svariants . We usedvariants . The meavariants . We thus of 0.05 .http://www.id.unibe.ch/hpc), the HPC cluster at the University of Bern. Statistical data analyses and plotting were performed in R [Resource-demanding computations were performed on UBELIX (med in R , 81.https://github.com/kuhlemeier-lab/petunia_hybrid_pollinator_genetics.Complete scripts used in the analyses are available at Additional file 1: Table S1. Average read coverage per individual on genome and gene regions. Table S2. Normality of the trait distribution and statistical difference between phenotype of the pure species individuals. Table S3. Correlation between phenotype and genetic principal components. Pearson’s correlation, with Bonferroni correction. Table S4. Overlap between loci with statistically significant association to phenotypic traits and genome regions of high interspecific differentiation, as calculated by FST. Table S5. Overlap between the 10 loci with the strongest association to each phenotypic trait with genome regions of high interspecific differentiation, as calculated by FST. Figure S1. Evanno’s choice of K and individual admixture proportions for K = 2 and K = 3. Figure S2. Major axes of a PCA of genome-wide variation of P. axillaris and P. exserta individuals and their hybrids from natural contact zones. Figure S3. Broad variation in visible floral colour in wild individuals growing in the hybrid zones. Figure S4. Phenotypic trait correlations. Figure S5. Association between interspecific admixture proportion and trait values. Figure S6. Q-Q plots of the GWAS P values.Additional file 2. Phenotype measurement data. The table includes the phenotypic values of each plant. The details on the headers of the measurement files are found in the github repository, at https://github.com/Kuhlemeier-lab/Petunia_hybrid_pollinator_genetics/blob/main/data/phenotype_header_info.csv.Additional file 3. Read and alignment metrics. Table showing the read pair numbers and genome coverage of each DNAseq library.


The embryos from Japanese Black cows are used for embryo transfer in Japan, which could contribute to obtaining a greater number of fattened progenies from dams with superior genetic abilities for the sustainable supply of high-quality Wagyu beef. We previously reported the possibility of genetically improving the in vivo embryo production performance of Japanese Black donor cows. We performed single-step genomic prediction of breeding values for superovulatory response traits in Japanese Black donor cows using genome-wide single-nucleotide polymorphism markers. The results indicate that introducing the single-step genomic prediction scheme could allow more effective selection for superovulatory response traits at a younger age of donor cows, and it might achieve more efficient genetic improvements than breeding value prediction based on only pedigree information. To the best of our knowledge, this is the first study performing single-step genomic prediction of female reproductive traits in Japanese Black cattle. In future work, an approach suitable for managing genetic diversity while continuing sound selection should be developed.A matrix) and the other considering both pedigree and SNP marker genotype information (H matrix). Estimated heritabilities of TNE and NGE were 0.18 and 0.11, respectively, when using the H matrix, which were both slightly lower than when using the A matrix (0.26 for TNE and 0.16 for NGE). Estimated genetic correlations between the traits were 0.61 and 0.66 when using H and A matrices, respectively. When the variance components were the same in breeding value prediction, the mean reliability was greater when using the H matrix than when using the A matrix. This advantage seems more prominent for cows with low reliability when using the A matrix. The results imply that introducing single-step genomic prediction could boost the rate of genetic improvement of superovulatory response traits, but efforts should be made to maintain genetic diversity when performing selection.We assessed the performance of single-step genomic prediction of breeding values for superovulatory response traits in Japanese Black donor cows. A total of 25,332 records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) per flush for 1874 Japanese Black donor cows were collected during 2008 and 2022. Genotype information on 36,426 autosomal single-nucleotide polymorphisms (SNPs) for 575 out of the 1,874 cows was used. Breeding values were predicted exploiting a two-trait repeatability animal model. Two genetic relationship matrices were used, one based on pedigree information ( Japanese Wagyu cattle comprise four modern Japanese native cattle breeds: Japanese Black, Japanese Brown, Japanese Shorthorn, and Japanese Polled. Japanese Black cattle are the primary breed of Japanese Wagyu in Japan and are well known for their excellent meat quality, including a high degree of marbling, around the world, e.g., ,2,3. ForA matrix), were higher than those at the ages of first calving and calving interval, e.g., [Improving the reproductive efficiency of Japanese Black cattle has become increasingly important to strengthen the foundation for the domestic production of high-quality Wagyu beef in Japan . The vall, e.g., ,19,20, il, e.g., ,22. Furtl, e.g., .Our previous studies on genetically improving superovulatory response traits in Japanese Black cows did not use genomic information ,22,23. TAnimal Care and Use Committee approval was not needed because the data were obtained from existing databases.Bos taurus autosomes after updating the annotation to ARS-UCD1.2 using the LiftOver tool (https://genome.ucsc.edu/cgi-bin/hgLiftOver) (accessed on 19 October 2022) for the 52,524 SNPs. For each SNP, the criteria of QC were set as minor allele frequency > 0.01, call rate > 0.95, and p-value > 0.001 for the Hardy–Weinberg equilibrium (HWE) test. For each cow, the criterion of QC was set as call rate > 0.95. After quality control, missing genotypes were filled in using Beagle v5.4 [The TNE and NGE per flush were recorded from 25,332 superovulation treatments of 1874 Japanese Black donor cows, meaning the number of flushes was 13.5 per cow on average, between 2008 and 2022 at the Zen-noh Embryo Transfer Center, Hokkaido, Japan. gle v5.4 . Finallygle v5.4 .A for the 575 cows with their own SNP genotypes based on pedigree data containing 4684 individuals with the saveA22 option of BLUPF90+. The genomic relationship matrix (G matrix) based on genotype information on 36,426 SNPs, which were calculated using VanRaden’s method l [For a detailed explanation of how different relationship matrices are calculated using pedigree and/or SNP marker information via BLUPF90+ software , see, fomethod l , was obtA minus 1. The genome-based inbreeding coefficients were calculated as the diagonal elements of G minus 1. The kinship coefficient between cows i and j (ijR) was calculated as follows:ija is the  element of A for calculating pedigree-based kinship coefficient and G for calculating genomic kinship coefficient. Note that the A matrix is twice the matrix of identity-by-descent probabilities, while the G matrix is twice the matrix of identity-by-state probabilities under the allele frequencies used to obtain the G matrix [The pedigree-based inbreeding coefficients for the 575 cows were calculated as the diagonal elements of the block of G matrix .i is the vector of phenotypic records for trait i; bi, ai, pei, and ei are the vectors of fixed effects , breeding values, permanent environmental effects, and errors; and Xi, Zi, and Wi are the design matrices relating yi to bi, ai, and pei, respectively. When performing pedigree-based breeding value prediction, the (co)variance structure was as follows:σ2ai is the additive genetic variance of trait i; σa12 is the additive genetic covariance between traits; σ2pei is the permanent environmental variance of trait i; σpe12 is the permanent environmental covariance between traits; σ2ei is the error variance for trait i; σe12 is the error covariance between traits; and A is the additive genetic relationship matrix for 4684 animals in the whole pedigree data. When performing the analyses using genomic information, the A matrix was replaced by a matrix H calculated as follows [G* = 0.95 G + 0.05 A22, and A22 is the block of A for the 575 cows with their own genotypes. Then, the inverse of the H matrix (H−1) can be obtained as follows [The following two-trait repeatability animal model was used :yTNEyNGE follows ,11,12:H= follows ,36:H−1=AVariance components were estimated via the average information-restricted maximum likelihood (AI-REML) algorithm with the method VCE option of BLUPF90+.A or H) and the variance components (two patterns: those estimated using either A or H). Therefore, four patterns were considered to predict breeding values in this study. The reliability of predicted breeding values for cow i (irel) was calculated as follows:iia is the ith diagonal element of the relationship matrix used for breeding value prediction (A or H); σ2a is the additive genetic variance estimated using either A or H; and iPEV is the prediction error variance of breeding value for cow i under one of the four settings.Using the BLUPF90+ software, breeding values were predicted by changing the relationship matrix  used in predicting breeding values and different variance components (estimated using A or H). As already reported in [A matrix were used than when using those estimated with the H matrix. This would be because the estimated heritability was higher when using the A matrix than when using the H matrix , the smallest p-value was obtained when the matrices for breeding value prediction and variance component estimation were the H and A matrices, respectively. Meanwhile, the p-value was largest when the matrices for breeding value prediction and variance component estimation were the A and H matrices, respectively. Interestingly, the p-value for the pedigree-based kinship coefficient was smaller when the matrices for breeding value prediction and variance component estimation were both the H matrix, while that for the genomic kinship coefficient was smaller when the matrices for breeding value prediction and variance component estimation were both the A matrix. These findings might have been due to the pedigree-based and genomic kinship coefficients being able to reflect expected and realized relationships, respectively.For Japanese Black cattle, studies on GP of carcass traits, e.g., ,14,15, aG matrix calculation and STRUCTURE analysis using SNP markers genotyped using the Illumina 50K chip reflected the population structure of Japanese Black cattle. These findings indicate the possibility of assessing the genetic similarity among Japanese Black cattle using commercial SNP markers, although there are concerns about marker density and SNP ascertainment bias. Recently, Fujii et al. [The advantages of the ssGBLUP approach over BLUP include that the relationship matrix for covariance structure of breeding values is denser and that information on Mendelian sampling is accounted for. Komiya et al.  illustrai et al.  performeIn dairy cattle populations, the introduction of a genomic selection scheme accelerated the rate of decline in genetic diversity, e.g., ,49,50. CThe pedigree information used in this study could be shallow because of limited information on the ancestry of the externally introduced cows . The perIn Holstein cattle populations, genome-wide association studies (GWASs) have been conducted for in vivo embryo production traits and plasma anti-Müllerian hormone levels using commercial SNP markers ,64,65. IH matrix than when using the A matrix. This was more prominent for the cows with low reliability when using the A matrix. Therefore, introducing the ssGBLUP approach could boost the rate of genetic gain of superovulatory response traits in this population. Meanwhile, there is a need to develop an approach to manage genetic diversity while continuing sound selection.The embryos from Japanese Black cows are used for embryo transfer, which could contribute to producing fattened progenies for the stable supply of high-quality Wagyu beef. Here, we performed GP of superovulatory response traits, namely, TNE and NGE, in Japanese Black donor cows using the ssGBLUP approach and approximately 36,000 commercially available autosomal SNP markers. The main finding was that, when the variance components were the same in breeding value prediction, the mean reliability was greater when using the


Schizophrenia is characterized by symptoms of psychosis and sociocognitive deficits. Considering oxytocin’s antipsychotic and prosocial properties, numerous clinical, and preclinical studies have explored the neuropeptide’s therapeutic efficacy. Sex differences in the clinical course of schizophrenia, as well as in oxytocin-mediated behaviors, indicate the involvement of gonadal steroid hormones. The current narrative review aimed to explore empirical evidence on the interplay between schizophrenia psychopathology and oxytocin’s therapeutic potential in consideration of female gonadal steroid interactions, with a focus on estrogens. The review was conducted using the PubMed and PsychINFO databases and conforms to the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines. The results suggest a potential synergistic effect of the combined antipsychotic effect of oxytocin and neuroprotective effect of estrogen on schizophrenia. Consideration of typical menstrual cycle-related hormonal changes is warranted and further research is needed to confirm this assumption. Schizophrenia is a neurodevelopmental mental disorder associated with positive and negative symptoms, as well as sociocognitive deficits . UnravelIn the early 70′s, a neuropeptide hormone principally known for its role in parturition and lactation, oxytocin (OXT), gained attraction as a potential treatment agent for schizophrenia and general psychopathology , based oOxytocin’s central and peripheral role has been extensively examined in numerous physiological and psychological studies. Other than its contribution to maternal care and infant–mother bond formation , sexual Sex modulates the clinical course of schizophrenia  . OxytociThe aim of the present narrative review is to explore an interplay between the psychopathology of schizophrenia and the therapeutic potential of OXT, taking into consideration interactions with gonadal steroids, such as estrogens. To date, no clinical trial has investigated the prospect of exogenous OXT administration  while controlling for typical menstrual cycle-related hormonal changes. To the best of our knowledge, this is the first review to discuss the possibility of an interaction between OXT’s antipsychotic and estrogens’ neuroprotective effects in schizophrenia. A synergistic action of combined estrogens and OXT on the clinical course of schizophrenia is hypothesized.This narrative review conforms to the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines . The datSeveral clinical and preclinical studies provide salient evidence in support of OXT’s natural antipsychotic properties e.g., ,33). In . In 33])Studies indicate that the endogenous OXT system is perturbed in individuals diagnosed with schizophrenia compared to healthy individuals. These abnormalities correspond to either increased or decreased levels of OXT in relation to normal values. For instance, Walss-Bass and colleagues found increased OXT in the plasma of schizophrenic patients with delusional symptoms, suggesting that the presence of delusions may drive secretion of OXT . On the According to recent systematic reviews and meta-analyses, endogenous OXT concentrations are typically lower in the plasma but higher in cerebrospinal fluid (CSF) of schizophrenic populations ,39. Low After OXT’s natural antipsychotic properties were introduced, the relationship between the neuropeptide and positive symptoms of schizophrenia attracted significant attention. According to Rubin and colleagues, higher levels of OXT in plasma correlate with a reduction in positive symptoms in female patients with chronic schizophrenia . In addiConsiderable research has been devoted to the association between OXT levels and negative symptoms, including cognitive and sociocognitive deficits. Generally, in schizophrenic patients, higher plasma OXT is associated with improved social cognition, processing speed, working memory, affective theory of mind (ToM), and facial emotion recognition ,47,48,49Clinical studies typically utilize intranasal OXT treatment in patients diagnosed with schizophrenia who are stable on antipsychotic medication . In geneExogenous OXT treatment has been also associated with improvements in the sociocognitive deficits of schizophrenia. For instance, participants showed improvement in the ability to recognize and identify emotions, as well as indirectly expressed emotions, thoughts, and intentions, following 20–40 IU intranasally administered OXT ,66,67. SAdditionally, longitudinal OXT intranasal administration has led to a significant improvement in the perception of trustworthiness and ToM, interpersonal empathy, as well as verbal memory and learning, in male and female participants with schizophrenia ,72,73. AHowever, several randomized controlled clinical studies have failed to replicate a therapeutic effect for positive and negative symptomatology. For instance, 6 weeks of intranasal OXT treatment in patients with schizophrenia or schizoaffective disorder and individuals with early psychosis showed no evidence of OXT’s therapeutic efficacy on negative symptoms, cognitive deficits, and social behavior, functioning, and cognition ,77,78,79Numerous researchers have introduced a therapeutic-like effect of exogenous OXT in various preclinical studies . For exaSociability may also be enhanced by acute OXT treatment. For example, OXT-treated rats exhibit greater levels of social interaction  compared to vehicle-injected rats ,89. SimiMeanwhile, social recognition, social and spatial memory and learning, and generally cognitive performance seem to improve after both acute and long-term OXT treatment in animal models. Social perception was enhanced after intranasal OXT administration in rhesus macaques by reducing attention to negative stimuli . In addiStudies exploring OXT’s neural and behavioral activity suggest differences between male and female participants. Although OXT promotes social behavior in females, OXT seems to facilitate negative social approach in males . AdditiIt is evident that the major sex hormones, namely testosterone, estrogens, and progesterone, interact with the endogenous OXT system. However, cyclic hormonal changes are typical during the menstrual cycle  106]. T. T106]. Oxytocin is able to modulate the menstrual cycle and ovulatory phase by affecting follicular luteinization and ovarian steroidogenesis . Plasma estrogen hypothesis, after observations of a possible relationship between the menstrual cycle and psychotic symptoms in schizophrenic female patients [Early in the 19th century, clinicians formed the so-called patients . Accordipatients ,111. Forpatients ,112.Estrogen concentration rises during the midfollicular phase and decreases post-ovulation, followed by another slight increase in the midluteal phase . OverallEstrogen augmentation has been proposed as an effective treatment strategy in improving total symptom severity and reducing positive and negative symptoms in female patients diagnosed with schizophrenia ,119,120.2) receptor sensitivity in the ventral tegmental area; therefore, higher D2 receptor occupancy of antipsychotic drugs is observed in female patients [Differences in the efficacy of antipsychotic medication among male and female patients diagnosed with schizophrenia spectrum disorders have been reported . The prepatients ,125. In patients . Therefo2 receptor blockage in the anterior pituitary gland and enhanced prolactin secretion [In addition, chronic use of antipsychotics  increases the risk for hyperprolactinemia, which results from Decretion ,127,128.ecretion . Therefoecretion . Therefoecretion .The purpose of this narrative review was to accentuate an interplay between schizophrenia psychopathology, OXT’s therapeutic potential, and estrogen’s neuroprotective effect. Indeed, the endogenous OXT system appears to be dysregulated in the clinical course of schizophrenia, while exogenous OXT is proposed as an antipsychotic and prosocial agent ,39. Sex-Considering these independent interactions, Rubin and colleagues suggested that symptoms of psychosis vary across the menstrual cycle in female patients diagnosed with chronic schizophrenia . SymptomA possible explanation for the dysregulation of the OXT system may be attributed to antipsychotic- and stress-induced hyperprolactinemia, resulting in estrogen deficiency . Indeed,Current empirical evidence suggests an interaction between the OXT system and estrogen, translating into similar concentration patterns across the female menstrual cycle . ImprovAlthough the present review was able to provide a comprehensive overview of the independent interactions between schizophrenia, OXT, and sex hormones, there are certain limitations. First, the interplay was proposed under the assumption that OXT is a neuropeptide with prosocial and antipsychotic properties . HoweverThe validity of measuring plasma OXT levels in the peripheral system as an indicator of OXT levels in the central nervous system is also debated, most likely due to the restricted permeability of neuropeptides across the blood–brain barrier (BBB) . The endAnother limitation is the inclusion of empirical data, which are not sex-specific. Although our focus was on estrogen concentration, we explored the interaction between the OXT system and schizophrenia in studies that included and analyzed male and female participants altogether. However, the relationship between the neuropeptide and schizophrenia may be susceptible to sex-specific effects of OXT’s neural and behavioral activity . AccordIndependent correlations between schizophrenia, OXT, and estrogens are evident, i.e., schizophrenia–OXT, estrogen–OXT, schizophrenia–estrogen. Therefore, the interaction between estrogen’s neuroprotective features and OXT’s antipsychotic and prosocial properties may have a synergistic therapeutic effect in schizophrenia’s psychopathology. To support this hypothesis, further empirical evidence in clinical and preclinical studies is warranted. Furthermore, menstrual cycle-related hormonal fluctuations are an essential consideration in psychopharmacological treatment and the clinical course of psychopathology, in general.


We propose rb_dplr, a novel and computationally efficient algorithm for generating high-dimensional binary random variates that effectively recapitulates AM-induced genetic architectures using the Bahadur order-2 approximation of the multivariate Bernoulli distribution. The rBahadur R library is available through the Comprehensive R Archive Network at The online version contains supplementary material available at 10.1186/s12859-023-05442-6. The simulation of realistic genotype/phenotype data is a fundamental tool in statistical genetics and is essential for the development of robust statistical methods for the analysis of genome-wide data. As such, much prior effort has focused on generating synthetic data that recapitulate salient characteristics of genetic marker data, including local linkage disequilibrium (LD) structure induced by variable recombination rates –3, relatDespite these advances, existing methods are ill-suited for generating synthetic genotype/phenotype data reflecting the consequences of recent assortative mating (AM); in contrast to the effects of recombination, which yields banded covariance structures, AM induces dense covariance structures reflecting sign-consistent dependence among all causal variants across the genome , 8. On tm-dimensional multivariate Bernoulli (MVB) distribution [m haplotypes [SNP haplotypes subject to AM-induced long-range dependencies can be represented mathematically by the ribution , which imentclass2pt{minimplotypes , 15, 16 plotypes , 11. HowO(m) operations. We then present numerical experiments demonstrating that the proposed methods outperform existing direct sampling methods and verify that they faithfully represent the effects of AM by comparing results to forward-time simulations. We provide these methods, together with a collection of utilities for characterizing the equilibrium distribution of haplotypes under AM, in rBahadur, an open-source library for the R programming language.In the current manuscript, we introduce a novel collection of efficient methods for directly sampling high-dimensional MVB random variables satisfying particular moment conditions . In parThe rBahadur library consists of three component collections: First, we provide two general-purpose MVB samplers, rb_unstr and rb_dplr, the implementation of which we discuss in the following section. Second, we provide utilities for modeling equilibrium AM, including a set of convenience functions for computing equilibrium parameters given initial conditions and for parametrizing the corresponding MVB distribution. Third, we provide a routine for end-to-end simulation that combines the first two components to efficiently construct equilibrium genotypes and phenotypes given population parameters.f in  is developed specifically for the case when the correlation matrix is diagonal-plus-low-rank  operations. Both methods sample the entries of the random vector sequentially: First, a realization of conditionally on the realization of the previously drawn variables We consider Bahadur order-2 approximations to the distribution in ; i.e., wmentclass2pt{minimr. Following Nagylaki [Here we demonstrate how the DPLR order-2 MVB distribution is used to model the consequences of assortment. Consider the equilibrium distribution of haploid causal variants Nagylaki , the equAssuming casual variants are unlinked at panmixis, the correlation matrix of causal haploid variants will be of the form r et al. , \documer. Finally, am_covariance_structure parametrizes the corresponding DPLR MVB distribution for a specified set of allele frequencies, causal effects, and initial conditions, by using . am_simulate returns genotypes, phenotypes, as well as additional architectural components, including the allele frequencies, allele-substitution effects, and the heritable component of the generated phenotype. We provide a vignette illustrating usage of am_simulate in further detail in Additional file m binary haplotypes for Figure haplosim , three mmentclass2pt{minimhaplosim . Resultshaplosim , which hFigure https://CRAN.R-project.org/package=rBahadur.Given that AM is both widespread , 11 and Our approach is limited by the requirement that the target distribution admits a second-order Bahadur approximation . For far apart causal variants, this is of little consequence as the true values of moments up to order four are expected to be smaller than their sampling variances unless practice . Howevermentclass2pt{minimAdditional file 1: Supplementary note.


Association mapping conducted in 189 Spanish bread wheat landraces revealed six key genomic regions that constitute stable QTLs for yield and include 15 candidate genes.Genetically diverse landraces provide an ideal population to conduct association analysis. In this study, association mapping was conducted in a collection of 189 Spanish bread wheat landraces whose genomic diversity had been previously assessed. These genomic data were combined with characterization for yield-related traits, including grain size and shape, and phenological traits screened across five seasons. The association analysis revealed a total of 881 significant marker trait associations, involving 434 markers across the genome, that could be grouped in 366 QTLs based on linkage disequilibrium. After accounting for days to heading, we defined 33 high density QTL genomic regions associated to at least four traits. Considering the importance of detecting stable QTLs, 6 regions associated to several grain traits and thousand kernel weight in at least three environments were selected as the most promising ones to harbour targets for breeding. To dissect the genetic cause of the observed associations, we studied the function and in silico expression of the 413 genes located inside these six regions. This identified 15 candidate genes that provide a starting point for future analysis aimed at the identification and validation of wheat yield related genes.The online version contains supplementary material available at 10.1007/s00122-023-04492-x. Triticum aestivum L.) is one of the major staple crops, providing about 20% of dietary calories and proteins  ), already described in Pascual et al. (Triticum aestivum subsp. vulgare (Vill.)  Gadea . This se.) Gadea .To obtain the phenotypic data, all landraces were sown during five consecutive seasons in an augmented design in plots of four rows per genotype (1 m long). In the 2016–2017 season, the accessions were sown in Alcalá de Henares . In the following seasons , the accessions were sowed in the same conditions in the experimental fields of the ETSIAAB, Universidad Politécnica de Madrid . Daily meteorological data were recorded over the period of study (autumn 2016 to summer 2021) at nearby weather stations.Phenotyping was conducted for a total of eleven traits, including: (i) grain traits: grain area (Ar), grain perimeter (Perim), grain major ellipse (Majell) and grain minor ellipse (Minell); (ii) yield-related traits: thousand kernel weight (TKW), grain number per spike (GrnSpk), number of spikelets per spike (SplN), spike length (SpkLng) and plant height (PH); and (iii) phenological traits: days to heading (DH) and days to maturity (DM). Some data were available from previous studies , and significant traits were log transformed to achieve normality if possible . Mean, standard deviation, maximum and minimum values, and coefficient of variation were calculated for each trait by season. Correlations between years inside each trait and correlations among traits were calculated with Spearman coefficient . Homocedasticity was checked using the Levene test. The effect of season, the genetic structure of the collection, and their interaction were evaluated with the Kruskal–Wallis  and Wilcox tests .Statistical analysis was conducted using R v.4.0.3  among markers was calculated using TASSEL 5.0  test were corrected by Bonferroni. For this purpose, the threshold was calculated dividing the standard p-value = 0.05 by the number of independent tests obtained with Tagger function of Haploview v4.2 software with r2 = 1 threshold  were considered to belong to the same QTL and grouped in Marker Trait Association Quantitative Trait Loci (MTA-QTLs). High-density MTA-QTLs regions were defined as the regions with single or overlapping MTA-QTLs, including more than 4 associated traits.Associations between phenotypic and genotypic data were detected using TASSEL 5.0  during five seasons  ranged from 28.56% for chromosome 1A to 9.41% for chromosome 4D. LD differed between homoeologous genomes with an average of 24.58% significant locus pairs (r2 mean = 0.07) corresponding to the B genome, 23.86% (r2 mean = 0.06) to the A genome and 11.82% (r2 mean = 0.04) to the D genome  based on the LD between flanking markers Fig. . RegardiMTA-QTLs linked to the same trait when characterized in different environments are especially interesting and can be considered as stable QTLs. Stable QTLs could be target for the traits TKW, GrnSpk and PH analysed by season due to the lack of correlation between seasons. From the total of 89 MTA-QTLs identified for TKW  none of them was stable among all seasons. However, 10 were coincident in three seasons and 15 in two. For GrnSpk a total of 42 MTA-QTLs were identified  being only 4 stable on two seasons. Finally, for PH, 24 MTA-QTLs were detected , and also 4 were stable across 2 seasons. Besides stable QTLs, MTA-QTLs linked to several correlated traits also constitute a target that pinpoints genes with a possible pleiotropic effect. First, all co-localizing MTA-QTLs harbouring associations with grain size related traits were grouped. A total of 30 common MTA-QTLs for Ar, Perim and Majell, that could be considered key QTLs controlling grain size, were identified. For the two phenological traits DH and DM, 52 and 34 MTA-QTLs were detected, 17 common in both traits.VRN-B2, MTA-QTL_5A.215 included VRN-A1 and MTA-QTL_2D.115 was located close to PPD-D1, being all of them associated with DH and DM in cereal species  were defined as a genomic interval including associations to four or more traits in only one MTA-QTL or in two or more overlapping MTA-QTLs. In total 46 regions were identified, most of them harbouring associations with grain traits and TKW (Table S4). Fourteen of those key regions were associated with DH. As it has been reported that DH might affect grain and yield related traits, DH effect on the associations identified in each region was tested. After this analysis, 33 regions remained associated to at least four traits  represent a cornerstone for breeding, out of the 33 regions described previously, the six that were associate with these traits (TKW in three seasons) were selected as the most promising ones Fig. , Table 4To dissect the genetic cause of the observed associations, the function fo the 413 genes located inside these 6 regions was studied. First, these genes were classified based on their GO terms . According to Biological Process, 162 and 143 genes were included in “cellular process” and “metabolic process”, followed by biological regulation (52 genes). Regarding Molecular Function, the main categories with 167 and 146 genes were “catalytic activity” and “binding”, followed by “transferase activity” (80 genes). Second, to select putatives candidate genes, the genes were filtered by relevant tissue-specific expression see "", obtainThe aim of the present study was to identify in a panel of wheat landraces, new genomic regions associated with key breeding traits, including grain traits, yield-related traits and phenological traits.The phenotypic diversity of any collection of accessions is the limiting factor that will determine the chance to identify novel MTAs when conducting a GWAS. Thus, a successful study requires a collection as diverse as possible, but that at the same time is adapted to the target environment. For this analysis, a total of 189 Spanish bread wheat landraces, selected from 522 accessions to capture the available diversity regarding to collection site data  and morphological spike traits  from the highly diverse collection of landraces (189 accessions), and the set of high-quality SNP markers, considering the genetic structure  (for MTA-QTL 2D.127 21–32Mb), TaELF3-1DL homolog to Early Flowering from Arabidopsis . After taking into account the effect of DH, the region was reduced by 2 Mb, to the interval from 584.83 to 588.52 Mb at chromosome 5A, and remained associated to Ar, Perim, Majell and TKW  Fig. . The totTraesCS1B03G0803000 and TraesCS1B03G0817400) were identified. This family of transcription factors is involved in different physiological and biochemical processes, including control of cell development and cell cycle, hormone synthesis, and signal transduction , as well as, the transcription factor TraesCS2B03G1382600 with a high homology to rice ILI1 gene. This rice gene, according to Zhang et al.  receptor kinases SERK2, SERK4 and BAK1 from rice, involved in the regulation of plant growth through the brassinosteroid signalling pathway  whose role in developmental process in widely known  that codifies for a TOPLESS-related protein. The TOPLESS proteins play multiple roles throughout plant development , in which four candidates were selected, TraesCS5A03G0945200 and TraesCS5A03G0956000) were found within the R5A.4 region. The first one is highly similar to BRI1 a Brassinosteroid LRR receptor kinase from rice, which increases the biomass and grain production in this species , two kinase proteins  harboured In summary, the present study of a collection of Spanish bread wheat landraces highlighted the high phenotypic diversity of this collection and identified more than 350 MTA-QTLs, including at least 150 novel ones. Those MTA-QTLs allowed the targeting of 33 high dense QTL regions in the genome, that remained associated to at least four traits after considering the effect of days to heading. Finally, taking into account the importance of detecting stable QTLs, six regions associated to several grain traits and TKW in at least three environments were selected as the most promising ones to harbour targets for breeding. Moreover, the preliminary screening for candidate genes reported in this study provide a starting point for future analysis aimed at the identification and validation of wheat yield related genes.Supplementary file1 (XLSX 57 KB)Supplementary file2 (DOCX 13 KB)Supplementary file3 (XLSX 100 KB)Supplementary file4 (XLSX 13 KB)Supplementary file5 (PDF 256 KB)Supplementary file6 (PDF 360 KB)Supplementary file7 (PDF 241 KB)Supplementary file8 (PDF 283 KB)Below is the link to the electronic supplementary material.


Inbreeding depression is expected to be more pronounced in fitness-related traits, such as pig litter size. Recent studies have suggested that the genetic determinism of inbreeding depression may be heterogeneous across the genome. Therefore, the objective of this study was to conduct a genomic scan of the whole pig autosomal genome to detect the genomic regions that control inbreeding depression for litter size in two varieties of Iberian pigs (Entrepelado and Retinto). The datasets consisted of 2069 (338 sows) and 2028 (327 sows) records of litter size  for the Entrepelado and Retinto varieties. All sows were genotyped using the Geneseek GGP PorcineHD 70 K chip. We employed the Unfavorable Haplotype Finder software to extract runs of homozygosity (ROHs) and conducted a mixed-model analysis to identify highly significant differences between homozygous and heterozygous sows for each specific ROH. A total of eight genomic regions located on SSC2, SSC5, SSC7, SSC8, and SSC13 were significantly associated with inbreeding depression, housing some relevant genes such as FSHR, LHCGR, CORIN, AQP6, and CEP120. The most significant consequence of inbreeding in the phenotypic performance of livestock populations is the occurrence of inbreeding depression . TheoretOne method widely used to detect identical-by-descent (IDB) genomic segments is to conduct runs of homozygosity (ROH) . ROH areIn the context of Iberian pig populations, non-uniform effects of inbreeding depression across the genome were observed in a closed experimental flock of the Guadyerbas variety . HoweverThe dataset utilized comprises 2069 records (pertaining to 338 sows) for the Entrepleado variety and 2028 records (related to 327 sows) for the Retinto for both TNB  and NBA (Number Born Alive). In conjunction with this, a pedigree that contains genetically interconnected individuals was incorporated, with a total of 581 individuals for Entrepelado and 541 individuals for Retinto. The mean phenotypic performance values for TNB and NBA are summarized in Each sow was genotyped using the Geneseek GGP PorcineHD 70 K  chip. Subsequent to genotyping, the genotypic data underwent filtration using PLINK 1.9 . FiltersFirstly, we formulated a mixed linear model to assess the variance components and calculate the inbreeding depression through the gradient of a covariate associated with the percentage of individual heterozygosity, measured as the number of heterozygous SNPs per individual × 100 divided by the total number of SNPs.y represents the vector comprising phenotypic records , f is a vector encompassing individual heterozygosity, and b is the vector of systematic effects, which incorporates order of parity at 5 levels . Additionally, h is a vector of random herd–year–season, with 96 levels for Entrepelado and 113 for Retinto, u denotes the vector of additive genetic random effects, p is the permanent environmental sow effect and e stands for the vector of residuals. Moreover, d serves as a covariate of the relationship between individual heterozygosity and phenotypic performance. The matrices X, T, Z and W are the corresponding incidence matrices. The genomic relationships (G) among the additive genetic effects (u) were calculated using the single-step approach [http://nce.ads.uga.edu/wiki/doku.php?id=application_programs, accessed on 8 October 2023) [The model we postulated for both varieties is as follows:approach ,17. For approach  was adoper 2023) .https://github.com/jeremyhoward/Unfavorable-Haplotype-Finder, accessed on 8 October 2023) [Secondly, the Unfavorable Haplotype Finder software (er 2023)  was empler 2023) .t-test.Concluding this step, the blupf90+ software  was utilThe results of the variance component estimation are presented in The estimates of the (co)variance components were similar to the ones provided by Srihi et al. , and thep = 0.017) and 0.057 ± 0.028 (p = 0.021) for NBA and TNB in the case of Entrepelado and 0.077 ± 0.051 (p = 0.065) and 0.067 ± 0.050 (p = 0.090) for NBA and TNB in the case of Retinto. In all traits and populations, there was an increase in the litter size as the percentage of heterozygosity increased, leading to significant results for the Entrepelado population.Given the estimates of the variance components, the estimates of the covariate with the percentage of heterozygosity were 0.055 ± 0.026  consisting of more than 15 SNPs within the Entrepelado and the Retinto varieties, respectively. The ROH with the minimum length had 145,783 base pairs, and the larger ROH comprised 15,162,018 base pairs. These distributions highlight the prevalence of short ROHs in the Entrepelado population, in contrast to the right-skewed distribution observed in the Retinto population. This observation may suggest more recent inbreeding within the Retinto population. The average size of the ROHs in each population was 25.79 SNPs (±18.00) for Entrepelado and 35.96 SNPs (±24.30) for Retinto. Furthermore, the average percentage of an individual’s genome covered by ROHs, considering overlapping regions between ROHs, was 26.87% (±3.78%) for Entrepelado and 40.74% (±3.20%) for Retinto.Among all the detected ROHs, we were able to identify 20,143 (Entrepelado) and 26,771 (Retinto) ROHs shared by at least 5% and at most 95% of individuals, composed of more than 15 SNPs, in which we expected to find that most of the variance was due to the ROH effect. Therefore, we solved 20,143 and 26,771 mixed-model equations for Entrepelado and Retinto, respectively. The objective was to obtain estimates of the effects related to the presence or absence of each specific ROH. The distributions of these effect estimates, pertaining to TNB and NBA, are illustrated in p < 0.05) encompassed 1123 and 1533 runs of homozygosity (ROH) for NBA in Entrepelado and Retinto, respectively, while for TNB, they numbered 1197 and 1453 regions. These findings represent a proportion of significant ROHs that ranged from 5.4% (for RR and TNB) to 5.9% (for EE and TNB), slightly higher than what would be expected at random.The average estimate of the effects was consistently close to zero across all scenarios, indicating that most of the ROHs were not associated with inbreeding depression. The genomic regions associated with inbreeding depression  gene, whose encoded protein plays an essential role in embryo development and implantation [BAG Cochaperone 2) and RAB23 . The former is potentially linked to infertility, as the mediated inhibition of CHIP expression contributes to endometriosis [The regions identified in the NBA are also observed in the TNB results and are located on SSC7, having the lowest p-values. These regions span between 20,074,761 and 20,586,603 base pairs (bp), wherein proximity to QTLs associated with pig litter size ,28 is noantation . Additioetriosis , and theetriosis , and witetriosis . Lastly,Centrosomal Protein 120) gene, which has been associated with maternally derived aneuploidy [Activating Transcription Factor 1) gene, known to be involved in the estrogenic signaling pathway [Aquaporin 5 and Aquaporin 6), which have been suggested as markers for male infertility in livestock [Rac GTPase-Activating Protein 1), the inhibition of which is required in vitro for human embryonic trophoblast invasion into endometrial stromal cells [Calsyntenin 2), which has been proposed as a potential candidate gene in Erhualian pigs [The remaining regions with p-values lower than 0.001 in the TNB are distributed across SSC2 (7 ROHs), SSC5 (2 ROHs), and SSC13 (2 ROHs) in a contiguous manner. Within SSC2, spanning from 126,506,354 to 127,890,446 bp, we identified the CEP120 , which plays a role in inhibiting GnRH neurons. This inhibition is essential for the production of the GnRH hormone, which, in turn, is crucial for the synthesis of LH (luteinizing hormone) and FSH (follicle-stimulating hormone), both of which are crucial for reproduction [Corin, Serine Peptidase) is up-regulated in the decidua of the pregnant uterus, which suggests a potential role during pregnancy [follicle-stimulating hormone receptor) and LHCGR (luteinizing hormone receptor), both critical in regulating female reproductive processes. Additionally, GTF2A1L  may play an important role in testis biology and male infertility [Ubiquitin-Specific Peptidase 16) gene, responsible for regulating embryonic stem cell gene expression [Cilia- and Flagella-Associated Protein 298) has been described, with a mutation known to cause infertility in human patients [In the case of the RR population, we identified 17 ROHs with p-values lower than 0.001 in the NBA, 10 of which were shared with the TNB, as detailed in oduction ,42,43. Cregnancy , and it regnancy . Furtherertility . On SSC1pression . In thispatients . Lastly,The results of this study indicate the presence of inbreeding depression in the litter size traits of two strains of Iberian pigs. Furthermore, the distribution of the inbreeding depression effects is heterogeneous along the genome, and the architecture of inbreeding depression differs between populations. Additionally, we were able to identify eight genomic regions significantly associated with inbreeding depression that contain several relevant genes, such as FSHR, LHCGR, CORIN, AQP6 and CEP120.


We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size  and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of In this paper we characterize the performance of linear models trained via widely-used  These large datasets provide the statistical power necessary to overcome false signals (fluctuations) resulting from examination of millions of genetic variants at a time. With the advent of very large biobanksThe analysis of large genotype and phenotype datasets has led to the development of polygenic scores (PGS). A PGS is simply a score built from numerically weighting the state of a persons genome. In most work to date, and in this paper, we are interested in linear PGS built from single nucleotide polymorphisms (SNPs), i.e., 3 (UKB) is more than 4 (TPMI). Other novel projects have focused on gathering samples with ancestral equity in mind, e.g., All of Us5 (AoU). However, until more diverse data become available, it is necessary to adapt the European results for other ancestral populations in order for PGS to have utility for the largest number of individuals possible—i.e., in applications such as disease risk estimates, potential clinical interventions, etc.The vast majority of available GWAS and biobank data is from individuals of European ancestry. For example, the UK Biobank17. In order to “transport” PGS to other ancestry groups, many techniques have been proposed, using features that are most important in different groups and adjusting their specific weights22 . The complexity of this analysis clearly scales with the number of relevant features. Third, future benefits of PGS43 rely on genotyping future participants. If this genotyping can be restricted to a small number of SNPs (e.g. as opposed to more costly WGS) it can be more cost effective to implement. Fourth, most PGS development approaches and methods use linear models. Further challenges include non-linear SNP effects , the relationship between tagged vs causal SNPs, and incorporating genome-environment interactions. Addressing these challenges again scales with the number of relevant features.As genotype databases become larger and as sequencing technology incorporates more SNPs , novel difficulties arise in PGS construction. First, larger samples and features require greater computational power . As we discuss, these improvements, while important, are unlikely to close the gap completely. To directly close this gap, large data cohorts from or including diverse ancestry groups are needed to perform de novo, ancestry specific training. This is difficult and expensive. Hence it is valuable to understand in advance what the resulting benefits will be for polygenic prediction. In this work we present a novel method for projecting and predicting the results from sparse algorithms in a novel biobank.As mentioned, there are many efforts to modify PGS trained in primarily European ancestries to improve performance in non-European ancestries. This is a complex endeavor that involves trait specific characteristics, allele frequency and effect size dependencies, LD structures, and more. , with uncertainty bands, for biobank-sized datasets. This new projection method can be used to guide biobank recruitment.We explore details of “phase change” behavior of compressed sensing/LASSO with increasing data size, and the corresponding the SNP content of resulting predictors.The main results of this paper can be summarized as follows 1 (1kg). Projections are given for de novo training in TPMI and AoU. We refer to ancestry groupings European (EUR), South Asian (SAS), East Asian (EAS), African (AFR), and American (AMR). These labeling conventions come from63.Here we present the main results of this project. Additional details concerning specific methods are found in Section “purely genetic PGS. In brief, phenotypes are first regressed on covariates  – this allows them to explain as much of the variance as possible so that we can conservatively estimate purely genetic effects. Only then are SNP predictors are trained (using genotype-phenotype data or summary statistics in the case of PRScs) on residual phenotypes. Further details about the training and evaluation of PGS can be found in Section “We compare the performance of several sparse methods: LASSO, Elastic Net, L1-penalized Logistic regression (for case-control conditions), and PRScs with LD matrix information from either UKB or 1KG. It is important to note that the results presented here for AUC and correlation are for 7. Sib refers to a set of white siblings  where the ancestry is self-reported, but the sibling status is determined by a genetic analysis as detailed in the 65. It also allows for performing sibling selection experiments as described in Section “In Fig. Uncertainty (error bars) depends on cross-validation (i.e. multiple training sets), finite size effects from computing AUC/correlation, and from sample sizes. Details about uncertainty calculations are given in the 52). The amount of fall-off is phenotype specific and ranges from complete fall-off, e.g. diabetes fall-off from sib to AFR, to negligible fall-off, e.g. breast cancer AUC from sibs to EAS. The relative order of fall-off is also phenotype specific. For most traits, the (EUR-ancestry) sibling set shows the least reduction—then either SAS, EAS, and AMR,—and finally AFR. However, there are clear exceptions like the direct bilirubin correlation which goes from largest metric to smallest: Sib, SAS, AFR, EAS, AMR. There have been recent arguments that PGS fall-off is roughly linear as a function of local genetic distance52. We should note that this claim is not necessarily in conflict with the results we present here. First, there are many exceptions to this general linear behavior, e.g., Figure 5 in52. Second, the claimed linear fall-off is a function of Euclidean distance in PC space of training population. For genetically distant groups, the axes of variation will be different: any measure of PC distance is therefore a local measure. In other words, only ancestry groups that are near-enough to the original population where PCs were computed can be considered well-ordered in terms of genetic difference. These effects can also be exacerbated by the fact that these are all sparse predictors and, after projecting onto PC space, the order of Euclidean genetic-distance may change.Comparing performance across ancestry groups, we see a well known fall-off behavior which is observed when predictors trained in one population are applied to another  where one person is a case and one is a control. Then we can ask what fraction of the time does the higher PGS correspond to the case vs control. We can also condition this question on the PGS difference being larger than some cut-off . For continuous phenotypes we can simply compare the fraction of the time where the person with the higher PGS also has the higher phenotype value. We can again condition this question for siblings whose phenotypes are separated by a cut-off . These selection rates for asthma and BMI are shown in Fig. The performance of PGS can always be confounded due to environmental factors, interactions between genes and the environment, and non-linear genetic effects . To attempt to guard against some of these effects we can perform sibling tests similar to those described ininclusive odds ratio (OR) for asthma. It is inclusive in the sense that the OR corresponds to the ratio of the cases to controls  at a specific PGS value or above. As before uncertainties are conservative and include contributions from multiple cross-validation folds and finite size effects. Similar plots for the other case control conditions can be found in the PGS for case-control conditions can be converted to more clinically interpretable metrics. In Fig. This section details how training at various sizes in current biobanks can be used to model the growth dependence on training size. For most clinically relevant metrics, e.g., correlation and AUC below, this growth can be modeled with uncertainty. Using relatively simple parametric functions, we find that we can model the growth of these metrics. The results can be used to guide future studies and genotype-phenotype database construction by identifying where large gains can be made.11. Nonetheless, after sorting on self-reported ancestry, we perform an additional regression on the top 20 PCs to adjust for any remaining population stratification.For sparse methods like LASSO, it has been previously shown that using self-reported ancestry performs similarly to principal component based clustering when based on AUC and correlation metrics67 and is consistent with the projection function predictions.To be conservative in the projections for PRS performance in other biobanks, several modeling assumptions are made. First, to be conservative we assume that these biobanks will have population prevalence rates for diseases even though some biobanks over-recruit cases to enrich their datasets. Additionally, the actual incidence rate for disease conditions fluctuates over time. For the conditions considered in this paper we try to consider the most recent surveys of all ages. Finally, when there are various estimates for disease prevalence within ancestry sub-groups, we choose a conservative (i.e. low) prevalence as the representative for the overall ancestry prevalence. Specific details about within ancestry prevalences are given in the In Fig. explained by the predictor by simply squaring the correlation. In Table lower bound on the REML heritability in that there may not be enough data to saturate the REML estimates. In Fig. uncorrelated random component . In this case, the heritability is simply the square of the correlation between P and G. This allows a direct comparison of the new correlation results presented here and traditional methods like GCTA and LDSR. For traits like BMI and height it appears that, eventually, sparse predictors will capture all the linear SNP heritability. For much sparser conditions, e.g. Lipoprotein A presented in the The fraction of phenotypic variance captured by SNPs  of traits is traditionally estimated via means such as Restricted Maximum Likelihood estimates (REML) and using Linkage Disequilibrium Score Regression (LDSR). For continuous traits, the correlation of the residual phenotype with the PGS can be related to the linear SNP heritability An advantage of sparsity is that, because there are fewer features, it is relatively easier to categorize features compared to non-sparse methods. Here we identify important features for predictors for each trait. Because a simple LASSO routinely performed as one of the best predictors in terms of AUC and correlation in Section “71 comes from the field of compressed sensing and generally refers to sharp behavior in the recovery of a sparse signal in under-determined systems. In simple language, signal recovery or predictor construction changes sharply as relevant parameters change. In genomics, key parameters are trait heritability, sparsity , and the sample size. In the case of genomics, crossing the phase transition boundary does not guarantee that true causal SNPs are found. For modern applications to this phase boundary for the LASSO see, e.g.,73 and for its application to genetics see74.In Fig. At the largest training sizes—i.e., after the phase transition—we also look at how the SNP content varies across cross-validation (CV) folds. As detailed in Section “75. All predictors, except those for Lipoprotein A, include SNPs that account for at least To interpret these plots we have collected and identified every SNP that accounts for at least 80Asthma: SNPs around FLG, IL1RL1, TSLP, IL33, SMAD3, HLA-DQ, RORA, CLEC16A, and SERPIN7 which have been previously been identified by GWAS84, and while none of these exact SNPs appear in the 58 SNPs identified using LASSO, many of the LASSO SNPs are located around the genes KCNN3, PMVK, LMNA, KIFAP3, PRRX1, SCN5A, SCN10A, PITX2, FAM13B, WNT8A, CAV1, SH3PXD2A, HCN4, ZFHX3, RPL, and FBXO32 which are associated with some of the gwas SNPsAtrial fibrillation: at least 164 SNPs have been identified in GWAS studies85Breast cancer: none of the SNPs identified here are near the genes identified in86Type 2 Diabetes: we find relevant SNPs in the associated genes GCKR, TCF7L2, and SLC12A187Type 1 Diabetes: we find SNPs near HLA-A, TRIM26, MICA, HLA-DRB1, and LAT89Coronary Artery Disease: we find SNPs near the associated genes PCSK9, PLPP3, IL6R, MIA3, VAMP5, ZEB2, SLC22A4/A5, SLC22A3, LPAL2, LPA, PLG, and CETP90Hypertension: we find important contributions near the ULK4, NR3C2, PRRC2A, and NOS3 genes92Direct Bilirubin: we find results near the UGT1A1, SLCO1B3, and SLCO1B1 associated genes93Body Mass Index: we find SNPs in genes TMEM18 and the well studied FTO – both appear with more than 94 categorized over 400 genes associated with height that were later reanalyzed by Yengo et al.95. Of these, SNPs near ORC1, COL11A2, FANCE, BRAF, ACAN, ANKRD11, CDK10, CDT1, FANCA, GALNS, and RPL13 all appear in our analysisHeight: Liu et al.97 and additionally contribute to coronary artery diseases98.Lipoprotein A: all SNPs appear in or near the genes LPA, LPAL2, and SLC22A3 which are all known to be associated with Lipoprotein A levelsThere are several interesting aspects of examining SNPs in this manner. First we note that we are interested in common variants and exclude SNPs with a minor allele frequency below 0.001 to avoid any spurious associations. Because of this, rare variants can’t appear in our analysis, even if they are known to be associated with a phenotype. An example of this can be seen in the case of breast cancer where the BRCA mutations aren’t included in our analysis. Interestingly there are SNPs that have previously been identified via GWAS, that are available on our array, but are not selected by LASSO. An example would be rs116716490 which is part of the ZBTB10 gene, previously associated with asthma via GWAS78, but not selected by LASSO.SNPs which we identify that are located near known associated genes for several phenotypes are listed here:and controls), and training with the largest possible equal number of cases and controls. We highlight some of the notable results. For atrial fibrillation, max training and equal case control training both find a large fraction of SSV on chromosome 4, but the signal is much larger for equal cases and controls. On chromosome 15 there is a large signal for max training, but not for equal cases and controls. For type 2 diabetes a large fraction of SSV is on chromosome 10, but the signal is largest for equal cases and controls. For type 1 diabetes. equal cases and controls find a strong signal on chromosome 6 while max training also finds signals on chromosomes 1, 11, 15, and 17. For CAD, the equal case control training finds the largest signal in chromosome 6 while max training finds similarly large signals in chromosomes 1, 2, 3, 6, and 12. Hypertension shows varying signals all throughout the chromosome with the most precise signal coming from chromosome 1. Breast cancer has the most diverse differences between the two types of training with large signals for equal case control training on chromosomes 10 and 16, and large signals on 7, 11, 16, and 19 for max training. For continuous phenotypes we only have one measure of SSV per chromosome. For direct bilirubin the largest contributions are on chromosomes 1 and 2. For BMI the largest signals are on chromosomes 1, 2, and 3. For height there is strong signal throughout most of the genome. Finally for Lipoportein A, the signal seems to be concentrated on chromosome 6.A more coarse grained interpretation of the impactful regions can be found in the 99. Using all metrics together, the scatter of data can be regressed linearly in 74 it was shown, using simulated data, that for We can see examples of all the definitions of sparsity in Fig. below the critical training size may suggest a deceptively high estimate. For most phenotypes–regardless of training size, metric, or method–the estimated heritability is below what can be estimated from GCTA. However, there are a few exceptions: for asthma and type 1 diabetes, at the largest training size PRScs using the except PRScs results in a higher value than GCTA.Finally we can see estimates of heritability using a variety of metrics in Fig. step (1) initial populations are separated into groups; step (2) quality control (QC) for both phenotypes and genotypes; step (3) phenotypes are regressed on covariates and adjusted phenotypes are built; step (4) SNP set is filtered down to a computationally manageable size; step (5) machine learning on adjusted phenotype and genotypes using cross-validation; step (6) predictors are tested on withheld groups.We start with a very brief description of the general training pipeline for generating predictors: 7 and described in the As an initial grouping of UKB data, we separate participants using self-reported ancestry . We use participants reporting ancestry as White (EUR), South Asian (SAS), East Asian (EAS), and Black (AFR). Additionally we construct an American ancestry (AMR) set done using the approach fromany relevant code, then they are counted as a case. The UKB array contains 805,426 SNPs. We run QC using PLINK to filter out (remove) variants (SNPs) with more than 3% missing values, samples (participants) with more than 3% missing values, and variants with minor allele frequency less than 0.001 (i.e. 0.1%). After QC this leaves 663,533 SNPs and 487,048 participants.  Finally we reduce our SNP set down to only the autosome as this allows us to roughly double our training base, i.e. use all sexes in training (except for sex specific phenotypes).Step 2 involves performing quality control on both the genotypes and phenotypes. Full phenotype definitions, including UKB codes, are given in the each sex individually and roughly double our training data. The ultimate aim is to identify genetic variants that we are most confident are related to a phenotype. To do this we assume that common covariates and population stratification have a maximal effect. That is, we regress covariates on the raw (or z-scored) phenotype and then adjust  phenotypes for the contribution explained by these covariates. Common covariates included are: age, sex (except for sex specific traits like breast cancer), and the top 20 principal components as computed by the UKB. Even though we do a sex specific z-scoring, at this stage we still assume sex can have an impact. That is, for phenotype, adjusted phenotype, Step 3 can be briefly described as sex-specific z-scoring and covariate adjustment. The z-scoring is only done on continuous phenotypes while covariate adjustment is done for all phenotypes. We z-score to improve the efficiency of the machine learning algorithms . If we assume that we are looking for common genetic factors that are independent of sex then we can z-score N, roughly as Unfortunately, before we employ a machine learning algorithm, we have to reduce down the 107 lasso path linear_model.lasso_path and enet path linear_model.enet_path algorithm. The Elastic Net algorithm minimizes the objective function,108 which is a Bayesian shrinkage prior which runs directly on summary statistics  and an LD matrix. We use three fold cross validation and run with the global shrinkage/sparseness parameter, Next, in step 5 we run machine learning algorithms. For LASSO and Elastic Net we use the Scikit-learnonly using the genetic weights to emphasize how well cases and controls can be identified using only genetic information. For case control phenotypes we can also compute an odds ratio, as a function of polygenic score, as seen in Fig. inclusive odds ratio: at each score, we compute the ratio of the number of cases to controls  of all individuals with that score or a higher score. I.e., the 80th percentile represents all individuals with a score purely genetic contribution.In the final step 6 we apply the predictors to all testing sets: the EUR genetic siblings set, an SAS set, an EAS set, an AFR set, and an AMR set. Case control phenotypes are evaluated by computing the Area Under the receiver operator Curve (AUC) which compares the true positive rate to the false positive rate, i.e. how well the predictor is calling cases and controls. Within this work we generally report the AUC In addition to standard metrics like AUC and correlation, we are often interested in the amount of variance described by a particular polygenic predictor. For a particular polygenic score, the variance is described bymentclass2pt{minim65. All siblings used in these analyses are genetic siblings identified using KING110 and filtered in a manner similar to110. More detail is provided in the For both types of phenotypes there are sibling specific tests that we perform. These were developed in(1) the asymptotic (i.e. best possible) metric obtained from infinite data, as in Table (2) which traits are nearing that asymptotic maximum or which are still improving substantially with more training data, and (3) what sample size other biobanks will need to obtain meaningful results. These projection bands can be compared to the application of European trained predictors applied to other ancestry groups. In Fig. We can model improvement in predictor performance metrics as a function of training data size. In Fig. entclass1pt{minimaMore details can be found in the several ways and it is not a priori clear which definition is most appropriate. The definitions used here are: 74. Additionally, for algorithms like LASSO there is debate as whether to use the maximal metric for hyperparameter selection or to step one standard deviation “back” (i.e. towards fewer features) in hyperparameter space to avoid over-fitting when applying to outside true testing sets. (see11 and references therein).The number of SNPs with non-zero weights using all possible cases and controls. This quantity can appear to continue to grow without reaching an asymptote even though traditional metrics (e.g. AUC or correlation) appear to asymptote. However, this definition is obviously impacted by training sample size and difficult to compare between datasets/biobanks. Assuming infinite data generated by a linear model plus noise, compressed sensing guarantees complete signal recovery and the sparsity will reach an asymptotic value corresponding to the underlying model that generated the dataWe can use the previous definition but for the training case with the maximum number of equal cases and controls. The same caveats from definition 1. apply.relative metric, i.e. Because we expect traits not to be 100% heritable, we expect metrics to asymptotically approach a limit value. We can look at relative increase in these metrics to try to identify the point of largest growth (or above some cut), i.e. the inflection point. We can do this by looking at a Instead of looking at the metrics, we can instead look at the relative increase in the number of features (SNPs) at each training size and look for the relative maximum increase (or increase above some cut) in features to define a sparsity value.Comparisons for these definitions can be seen in Fig. The sparsity associated with a phenotype can be defined in not the original raw phenotype. Therefore, we can consider the ratio of the variance of the PGS to the adjusted phenotype . Predictors (i.e. SNPs and weights) and some code used to produce results can be found at https://github.com/MSU-Hsu-Lab/biobank-scale-methods-paper-2023/.Raw data is not available for direct sharing, but can be obtained via application to the UK biobank  as possible during training. In contrast, non-sparse methods construct predictors in which potentially every SNP in the genome has non-zero  effect size. Because sparse predictors perform about as well as predictors constructed using non-sparse methods61) rely on understanding the SNP content of the original predictor. This issue is considerably simplified if the predictors are trained using sparse algorithms to narrow down the possible resulting SNP set.In this paper we analyzed many aspects of sparse predictors. Within the class of algorithms that produce spare predictors we find that LASSO, also known as compressed sensing, is competitive with other more complex techniques. In fact, methodological improvements in predictor training, although important, generally produce improvements which are relatively small , whereas increases in data size are likely to produce much larger gains. Additionally, we demonstrate the performance characteristics promised from the compressed sensing literature. Specifically, we demonstrate phase transition behavior in performance using actual genotype/phenotype pairs—above a certain data threshold we recover the SNPs which provide the strongest contribution to performance metrics. Additionally, techniques to improve predictor portability  whereas this work involves machine learning algorithms that go beyond single marker regression. Secondly, much of the previous work makes assumptions about how performance metrics, e.g. AUC or Area Under receiver operator Curve, depend on trait heritability, model parameter effect distributions, and their functional form. For example, in125, AUC is modeled with a cumulative distribution function for the Gaussian distribution and depends on the population variance of the PGS. Using these approaches, after fitting to the training of a single PGS, predictions can be made for other training sample sizes. In this work we make no such assumptions about the functional form of AUC or the scaling dependence of an underlying genetics effects model. Instead we simply assume that AUC is a finite and bounded quantity. We then use a series of different functional forms to model the growth of AUC. These functional forms are chosen because based on parsimony, i.e. that they have the fewest number of parameters for functions that satisfy the simple physical constraint. This is an advantageous approach when there are limited or small samples with which to train. In contrast, in this work for each phenotype we use 11 different training sizes to model the growth of performance metrics. In using such a large number of different training sizes, we developed a novel method for taking into account the uncertainty/error associated with the metrics when modeling the growth as a function of training size.Other researchers have investigated the question of how prediction and association results depend on sample size and have tried to predict the results for future large sample sizes. We highlight here some lines of research in this direction so that we can emphasize how our new analysis is different. First, most of the previous work involves GWAS based methods  based method. We stress that that our main results are concerned with training methods that directly use genome matrices themselves and might not generalize to the comparison to generic GWAS based methods. For example, although a simple LASSO regularly outperformed or performed comparably to PRScs, it is not necessarily true that this will be the case for all GWAS based methods with similar training sizes—especially if it includes additional information like fine-mapping, functional information, other -omics, etc. However, it has been found that for some phenotypes, e.g. height, methods like LASSO usually require much smaller sample sizes than GWAS based methods to find the same phenotype-PGS correlationOne of the main challenges of polygenic prediction is that most of the available data comes from studies in which most of the participants are individuals of European ancestry. Thus the current predictors perform poorly when applied to other ancestry groups . The hope of developing methods which “transport” a predictor trained on one ancestry to other ancestral groups remains, but despite ongoing research efforts the goal remains elusive. In the absence of such a breakthrough, much larger cohorts of non-Europeans are required to produce predictors of comparable quality. The new methods developed here allow us to predict, e.g., how well new predictors trained on biobank-scale data from the Taiwan Precision Medicine Initiative (which is planned to surpass 1 million genotypes) and the US All of Us project. In the former case, we predict that new predictors will exceed AUC / correlation metrics for current best-in-class predictors for European ancestry.Supplementary Information.