Different psychiatric disorders and symptoms are highly correlated in the general population. A general psychopathology factor (or “P-factor”) has been proposed to efficiently describe this covariance of psychopathology. Recently, genetic and neuroimaging studies also derived general dimensions that reflect densely correlated genomic and neural effects on behaviour and psychopathology. While these three types of general dimensions show striking parallels, it is unknown how they are conceptually related. Here, we provide an overview of these three general dimensions, and suggest a unified interpretation of their nature and underlying mechanisms. We propose that the general dimensions reflect, in part, a combination of heritable ‘environmental’ factors, driven by a dense web of gene-environment correlations. This perspective calls for an update of the traditional endophenotype framework, and encourages methodological innovations to improve models of gene-brain-environment relationships in all their complexity. We propose concrete approaches, which by taking advantage of the richness of current large databases will help to better disentangle the complex nature of causal factors underlying psychopathology. A trend has emerged that shows striking parallels between clinical psychology, psychiatric genetics and neuroimaging. The dense covariation between a range of psychopathology-related traits is described in terms of new constructs. These take the form of three general dimensions or factors: a general psychopathology factor or “P-factor” , 2, a geThis Perspective Article has three main aims: (1) to integrate knowledge from the three types of P-factors into a new unified theory; (2) to evaluate implications of this theory for widely held assumptions behind endophenotypes; (3) to translate the theory into concrete future directions for psychiatric genetics and imaging genetics. In the first section, we introduce each of the general dimensions individually. This section is on purpose brief and not meant as an exhaustive literature review. The second section is the core of this paper, and where its novelty lies. There, we advance previous interpretations of genetic and phenotypic P-factors , 8 by inPsychiatric disorders have fuzzy boundaries in their clinical definitions and phenomenological presentations. Few symptoms are unique to any diagnosis. A symptom can be a contributing criterion to multiple disorders, and a common comorbidity across disorders. Moreover, disorders may develop into other disorders through maturation and ageing  –10. UsinThe phenotypic P-factor is heritable –22, and To identify a genomic dimension that captures this shared genetic liability, SNP-based genetic correlations have been further analysed using genomic SEM  and prinThe phenotypic P-factor is associated with brain structure and function –36. A meAkin to the phenotypic and the genetic domain, these cross-diagnostic similarities have led to the construction of general dimensions describing inter-individual variation in neuroimaging traits. Such “neural P-factors” have been identified from functional connectivity , diffusiIn summary, recent neuroimaging and genetic research across psychiatric traits mirrors observations at the phenotypic level. Psychiatric symptoms and their biological correlates co-vary highly in the population. Within each discipline, this covariation can be captured by single phenotypic, genomic and neural P-factors.Together, the scientific developments described above mark a perhaps unsurprising, yet new development in our understanding of biological underpinnings of psychiatric disorders. Decades of work on pairwise associations between genome, brain, and single diagnoses generally revealed only modest effects. In comparison, the diagnosis-general phenotypic, genomic and neural P-factors explain undeniably large amounts of inter-individual variation. Whilst this quality is immediately appealing from a statistical perspective, it does not directly lead to mechanistic insights or clinical impact . As the Tables Similar to the phenotypic P-factor, neural P-factors tend to be derived from continuous traits in population-based cohorts , 6, 42. We ought to keep in mind these methodological differences until this has been tested directly (see Section 3). Nevertheless, we hypothesise that the three types of P-factors reflect to some degree the same inter-individual variation, on the basis of their similarities.They are based on the high degree of covariance between many variables.They describe substantial amounts of inter-individual variation.They are diffuse, comprised of subtle, widespread effects throughout the phenotypic, genomic, and neural domains, as opposed to restricted to a few brain regions, genomic loci, or behavioural domains.They do not infer directions of causality .They are associated with heritable variables that are rather “environmental” in nature. The phenotypic P-factor and the neural P-factors are strongly associated with household income, years of education, and welfare benefit use , 5, 6. WThe three P-factors share several key properties:The shared properties 1-3 and 5 suggest that the three types of P-factors, despite their differences, explain at least some similar aspects of inter-individual variation. The 4th shared property—the lack of causal information—is a critical limitation that impedes translation from correlations to mechanisms, and ultimately to clinical impact. We propose that the 5th property—the association with the heritable environment—may be particularly important in disentangling these potential causal mechanisms.In the context of quantitative genetics, the “heritable environment” may seem paradoxical, because in genetics “environment” tends to equal everything that is not explained by genetic variation. Here, we mean by “environment” the common sense conceptual notion, i.e., variables mainly located outside the body. Environmental variables like recreation, educational attainment and socio-economic status have heritability estimates around 40–50% in twin studies and 5–27% in SNP-based estimation , 54–58.mediated pleiotropy”  are not a problem, but a profoundly important source of information about the nature of psychopathology.” . The sam


Pisum sativum L.) is an important cool season food legume for sustainable food production and human nutrition due to its nitrogen fixation capabilities and nutrient-dense seed. However, minimal breeding research has been conducted to improve the nutritional quality of the seed for biofortification, and most genomic-assisted breeding studies utilize small populations with few single nucleotide polymorphisms (SNPs). Genomic resources for pea have lagged behind those of other grain crops, but the recent release of the Pea Single Plant Plus Collection (PSPPC) and the pea reference genome provide new tools to study nutritional traits for biofortification. Calcium, phosphorus, potassium, iron, zinc, and phytic acid concentrations were measured in a study population of 299 different accessions grown under greenhouse conditions. Broad phenotypic variation was detected for all parameters except phytic acid. Calcium exhibited moderate broad-sense heritability (H2) estimates, at 50%, while all other minerals exhibited low heritability. Of the accessions used, 267 were previously genotyped in the PSPPC release by the USDA, and we mapped the genotyping data to the pea reference genome for the first time. This study generated 54,344 high-quality SNPs used to investigate the population structure of the PSPPC and perform a genome-wide association study to identify genomic loci associated with mineral concentrations in mature pea seed. Overall, we were able to identify multiple significant SNPs and candidate genes for iron, phosphorus, and zinc. These results can be used for genetic improvement in pea for nutritional traits and biofortification, and the candidate genes provide insight into mineral metabolism.Pea ( Oryza sativa L.), wheat (Triticum aestivum L.), and maize (Zea mays L.), which now account for most calories consumed worldwide (Phaseolus vulgaris L.), cassava (Manihot esculenta L.), cowpea (Vigna unguiculata L.), lentil (Lens culinaris L.), maize, pearl millet (Pennisetum glaucum L.), sorghum (Sorghum bicolor L.), sweet potato (Ipomoea batatas L.), and wheat, with the most notable release being pro-vitamin A Golden rice (Pisum sativum L.) despite its potential to alleviate hidden hunger.In the past few decades, the plant breeding field has primarily focused on increasing yield in the staple cereal crops rice (orldwide . While corldwide . BiofortCicer arietinum L.) and cowpea (lpa) mutants have been created in pea  is 4.45 Gb in size and largely repetitive, both of which contribute to the lack of genomic resources for pea , and Pisum fulvum, a relative of P. sativum, that can be used in diversity analysis (n = 456). The collection constitutes multiple subpopulations , as well as accessions from Central Asia, representing a novel source of alleles  and HapMap files allows researchers to perform GWAS and identify associated genomic loci using the publicly available annotation. Here, we identified significant SNPs and candidate genes associated with mineral concentration. These results can be used to understand mineral metabolism and accumulation in the seed. Overall, this study aims to evaluate the genetic diversity and biofortification potential of pea and identify genomic loci related to nutrient concentrations in mature pea seeds that could be used to breed biofortified pea varieties.P. sativum accessions comprised 267 genotyped PSPPC accessions, 29 nongenotyped accessions, commercial cultivars Cameor, Hampton, and CDC Bronco were planted in a complete randomized design with three replicates at the Clemson University Greenhouse Complex, Clemson, SC, USA. Two plants per accession were planted in 6″ pots and grown in potting soil  under conditions of 16 hours day and temperatures of 20–22/18°C day/night. All pots were watered to 70–80% pot capacity. Plants were not inoculated with rhizobia, as they were supplied with adequate nitrogen, and nitrogen fixation was not necessary. A week after planting, all plants were given 2.84 g of osmocote (14-14-14); an additional starter of Peter’s Professional 20-20-20 fertilizer was given 5 days later. Plants were continuously fertilized with Peter’s Professional fertilizer every 2 weeks until they were physiologically mature, approximately 90 days after planting, with the mature seed then harvested.A total of 299 3). The seed samples were then heated to 150°C for 2 hours, with 4 mL of hydrochloric acid (70% HCl), then added to the solution and heated for an additional 1 hour. The digested solution was then filtered through Whatman paper (20–25 µm) and diluted to 10 mL with deionized H2O. Mineral concentrations of Ca, P, K, Fe, and Zn were determined by inductively coupled plasma emission spectrometry . Standards made from a 1000 mg L−1 stock solution were serially diluted to produce calibration curves from 0.5 to 5.0 mg L−1. Measurements using this method were validated using lentil and peach as standard references. Moisture content was analyzed from a random subsample of 28 samples for each replicate, with data averaged to obtain the moisture content for the specific replicate.The harvested mature seeds were ground into a fine powder using a KitchenAid coffee bean grinder and stored at 4°C until analyzed. To prepare samples for analysis, 200 mg of seed were digested overnight in 4 mL of concentrated nitric acid .Seed samples were prepared using the modified IP6 extraction from 2) heritability estimates for each mineral were obtained in JMP by determining the ratio of variance due to accessions divided by the total variance for the phenotype (VG/VP). For correlation analysis, accessions with missing data in one or more minerals were excluded, and Pearson’s r estimates were calculated in Python and assigned to correlation coefficients (ρ). IP6 was excluded from correlation analysis as it forced the exclusion of numerous accessions that negatively affected the analysis.The distributions of mineral concentration for each accession were visualized using JMP Pro 14 software , and accessions containing outliers in one or more replicates were identified as 1.5 times the interquartile range above the upper quartile and below the lower quartile and excluded from further analysis. In addition, outliers were defined as accessions with mean values greater than three standard deviations away from the population mean and excluded from the final dataset . After oP. fulvum (n = 456) accessions were previously genotyped by http://www.ncbi.nlm.nih.gov/bioproject/379298). The raw reads were aligned to the current pea reference genome (https://urgi.versailles.inra.fr/Species/Pisum) using the Burrow-Wheeler aligner (https://gatk.broadinstitute.org/) and hard filtered for quality, missing data, and minor allele frequency (>0.05) using GATK and BCFtools, prior to genomic analysis. Beagle version 5.1 was used to impute missing genotype data in the VCF file assembled from GATK. The final VCF file was converted into the HapMap format using Tassel version 5.2.52.The PSPPC and  aligner , with a P. fulvum population was estimated using ADMIXTURE . The Q matrix was then sorted by the ancestry coefficients for each subpopulation, assigning individuals with coefficients >50% to the corresponding subpopulation  testing was conducted to determine if there were any batch effects associated with the date of sample digestion or the date of analysis . If signy is a vector of observed phenotypes; β is an unknown vector containing fixed effects that account for the genetic marker, population structure (Q), and intercept; u is an unknown vector of random additive effects from background QTLs and individuals; X and Z are the known design matrices, and e is the unobserved vector of residuals according to the GAPIT user manual.GAPIT was used to perform a GWAS on mineral and IP6 concentrations of mature pea seed using the HapMap file. The default GAPIT parameters were used, as well as a model selection with Bayesian Information Criterion (BIC), which determines the degree of population structure that should be accounted for in a model to avoid overfitting. The BIC analysis determined that the inclusion of PCs was not necessary for any of the models. A mixed linear model with a kinship matrix was selected for analysis to account for population stratification. The mixed linear models were fit using the following form:y is a vector of observed phenotypes; is is a testing marker; S is a pseudo quantitative trait nucleotide (QTN), and e is the unobserved vector of residuals according to the GAPIT user manual. Analysis was also conducted using multiple loci mixed models (MLMMs) and compressed mixed linear models (CMLMs) (10(0.05/n), where n equals the number of SNPs used in the GWAS for each mineral.The Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) method was also utilized for GWAS of the nutritional traits, as it has high statistical power and does not assume that causal genes are distributed normally across the genome, which can lead to false positives and exclusion of causal genes ( (CMLMs) . A BonfeP. sativum v1a genome (https://www.pulsedb.org/jbrowses) or through the National Center of Biotechnology Information (NCBI) BLAST tool with Medicago truncatula and Glycine max as the reference organisms.LD decay was estimated for each chromosome using PopLDdecay , and thehttps://github.com/selizpowers/GWAS. All raw GBS data for the PSPPC are accessible at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA379298.All scripts, data, and the VCF and HapMap used for this project are available at G3 online.2) heritability estimates are low for most minerals, except for Ca (50%)  . In addide novo assembly of the PSPPC, admixture analysis, and PCA were performed on the reference-based alignment of the PSPPC population as well as the study population , while the accessions assigned to P. sativum–Central Asia and P. sativum subsp. elatius appear to exhibit some population differentiation based on their separation from the main breeding germplasm  were genotyped, so the GWAS population for each mineral was as follows: P (n = 247), Ca (n = 229), K (n = 257), Zn (n = 248), Fe (n = 225), and IP6 (n = 175). Phenotypes considered outliers were removed from the GWAS, as were any accessions that did not have available GBS data, resulting in the different population sizes between minerals. All GBS data for the PSPPC+P.fulvum (n = 456) were used to make both the final VCF and HapMap files to call the maximum number of SNPs. In total, 319,141 SNPs were generated, of which 54,344 high-quality biallelic SNPs were used for the GWAS. A mixed linear model, as well as a BLINK model, was used to map BLUPs of each trait to the P. sativum v1a genome. The LD of each chromosome decayed rapidly, which is supported by previous studies in pea  has the second-highest heritability estimate among minerals after Ca (H2 = 50%)  , so sele P and K , and theIn terms of pea biofortification, these results suggest that breeding for increased mineral concentration cannot be accomplished by phenotyping the seed concentration alone, especially as narrow-sense heritability has been reported to be lower than broad-sense heritability estimates  for Fe, P, and Zn (Psat5g034720 was identified as an ATP-binding cassette 1 (ABC1) family mRNA, which is a gene family that’s involvement has been observed in the assembly of Fe-S clusters and iron homeostasis in Arabidopsis   . Another2 estimates. These findings suggest alternative phenotypes besides seed concentration should be considered for biofortification research in pea, such as those that consider mineral uptake, mobilization, and accumulation of final mineral concentration. In addition, the PSPPC is genetically diverse and a valuable resource for genomic research in pea, as it has been previously genotyped, is publicly available, and incorporates novel alleles, which is useful to investigate the genetic basis of many agronomic and nutritional traits. Finally, candidate genes for Fe, P, and Zn concentration have been identified through GWAS, and their roles could be further characterized through additional molecular studies via knockouts or gene overexpression experiments. Overall, this research contributes to our understanding of nutritional traits and the future of biofortification and genomic research in pea.In summary, this study shows significant variation in pea in mineral concentrations but minimal variation for IP6. Mineral concentrations appear to be predominately influenced by environmental factors, except Ca, which shows moderate HS.P. is a doctoral student with D.T. who developed the experiment design, conducted the research, data analysis, and manuscript preparation. J.L.B. advises S.P. on genomic analysis and manuscript preparation. D.T. is the project P.I., mentoring S.P. on the study and manuscript preparation.Funding support for this project was provided by the Organic Agriculture Research and Extension Initiative (OREI)  of the United States Department of Agriculture, National Institute of Food and Agriculture, and the USDA-ARS Pulse Health Initiative.None declaredjkab227_Supplementary_DataClick here for additional data file.


This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH The diagnosis is carried out based on a comprehensive examination including visual acuity measurements, testing for refractive error and focusing movement in addition to medical inquiries about symptoms the patient has experienced. Although CVS is associated with a variety of symptoms, Sheedy et al.2 stated that the underlying problem can be identified by the location of the symptoms and categorized into the two groups of internal and external symptoms. According to the general review by Blehm et al.3, there are four types of symptoms  and they can be subdivided into three main potential pathophysiological causes . Numerous studies have been conducted so far to address health and safety issues for VDT users, but the efficacy of proposed treatments for minimizing CVS-related symptoms have not been adequately demonstrated yet4. A better understanding of the underlying physiology behind CVS is important to enable more accurate diagnosis and treatment.With the growth of digital devices, there are many complaints nowadays about a variety of eye-related symptoms such as eye strain, blurred vision, and double vision, and it is collectively referred to as digital eye strain or computer vision syndrome (CVS). In addition, it is also known to be associated with non-ocular symptoms such as headaches, and shoulder and neck pain. The American Optometric Association has defined CVS as a combination of eye and vision problems linked to the use of visual display terminals (VDTs)5 has become a common genetic approach to identify potential susceptibility genes for diseases. To date, a number of GWAS have been successful in identifying the genetic pathways associated with ocular diseases such as myopia, age-related macular degeneration and glaucoma7. Although GWAS has brought forth an adequate amount of knowledge on the genetic etiology of ophthalmic traits, through relatively easy to recruit severe cases, more common diseases or syndromes will require an increase in sample size and well-designed analytical methods. No GWAS therefore has been reported for CVS, and we need an elaborate method to integrate multiple symptoms related to CVS, which cannot be directory measured, into a model in order to precisely evaluate genetic effects using GWAS.The genome-wide association study (GWAS)8. Many methods have been proposed and applied so far, including dimensionality reduction using principal component analysis (PCA), multivariate mixed models as an extension of regression analysis, Bayesian polynomial regression and so on9. In recent years, methodologies using structural equation modeling (SEM) have been applied for multi-trait GWAS11. SEM is a multivariate statistical method in which the interactions between variables are described as simultaneous equations and their parameters are inferred12. Unlike other methods, the SEM-based method can incorporate latent variables, which are hypothetical quantities that have not been measured and can be inferred from other measured variables, into the model. A SEM-based multi-trait GWAS is attained by assuming a model structure in which a genomic variant is an exogenous predictor that influences observed variables via the latent factor, and then repeating the statistical test genome-wide for each variant. SEM has enabled investigations of complex traits such as psychology and psychiatry, in which their unmeasurable phenotypes are indexed by multiple symptoms or behaviors. Since this feature is similar to CVS, we applied a SEM-based multi-trait GWAS to CVS, which we expected increased power to identify genetic associations.With the establishment of cohorts with more detailed phenotypic information, multi-trait GWAS, which is a multivariate analysis on the same subjects, has also become possible. Multivariate methods are generally considered more powerful than univariate methods unless only one trait is associated with a genetic marker or all traits are very highly correlatedIn this study, a web-based survey about CVS was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, we performed the SEM-based multi-trait GWAS for the CVS-related multiple symptoms for the first time to the best of our knowledge under the assumption of the existence of a potential phenotype behind the CVS-related symptoms.3 were designed (Table Among customers of a direct-to-consumer (DTC) genetic testing service (MYCODE), 1998 Japanese volunteers  were recruited in August 2019  and then projected study samples onto the 2D subspace. Samples falling outside the Japanese cluster were excluded , both of which additionally contained approximately 30,000 custom probes, was used. The stringent quality control (QC) procedures of genotyped results were applied with call rate ≥ 95%, minor allele frequency (MAF) ≥ 0.01, identity by descent < 0.1875, and Hardy–Weinberg equilibrium . The gender on the declaration was confirmed to match the genotyped one. All the above procedures were performed using PLINK (ver. 1.9)14, was introduced. Age, sex, and the top 2 eigenvectors from PCA were added into the model as external covariates. The number of principal components to be incorporated was determined based on examination of the scree plot of their eigenvalues. After running the SEM-based multi-trait association analysis on all variants, p values were subsequently calculated, adjusting each test statistic by a genomic inflation factor. Genotype imputation was conducted on variants which were located within ± 500 kb of GWAS hits, and SEM-based GWAS was rerun on estimated genotypes. Eagle2 (ver. 2.4.1)15 was used for the phasing step referring to the East Asian population  of 1KGP, and then imputation was performed using Minimac3 (ver. 2.0.1)16. The accuracy index of imputation performance, correlation between reference and predicted genotypes (R squared), was 0.942 ± 0.116 for common variants  in R, which was developed by Verhulst et al.17 We utilized GSE115828 data (N = 523)18, the only publicly available retinal tissue eQTL data from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information as of May 2020. The whole blood eQTL data in the Genotype-Tissue Expression 19 was analyzed as well. In addition, two data sources for eQTL data (GSE5335120 and hum0099.v121) were added, available from GEO and the National Bioscience Database Center and consisting of more than 100 healthy Japanese subjects. The former is whole blood data (N = 301) and the latter is eQTL data from whole blood cells and five immune cell populations  of 105 subjects. Note that previous studies22 have demonstrated that a sample size of about 100 individuals is sufficient to detect major eQTL effects in various cell types collected in a single population. Posterior probability of H4 (PPH4) of colocalization was calculated for each ± 100 kb of the identified loci containing lead variants using the coloc package in R.Colocalization between GWAS summary statistics and expressed quantitative trait locus (eQTL) signals was analyzed based on a Bayesian statistical methodology proposed by Giambartolomei et al.23, a graphical modeling technique, was applied to derive a partial correlation network consisting of only well-fitting edges between items by stepwise removing edges with low partial correlation coefficients  was transmitted via the latent variable Fig. . ComprehWe performed single-trait GWAS on each of the four categories using gwsem, and several peaks detected by the multi-trait GWAS were identified as well  when GWAS and eQTL are associated and share a single causal variant, combinations of locus, genes and expression tissue with > 0.5 of PPH4 were comprehensively explored. As a result, four combinations were detected  between GWAS signals at the locus  ± 100 kb on chromosome 2 and eQTL signals on the myelin transcription factor 1 like (MYT1L) gene in retinal tissue was detected. As seen in the regional association plots generated by the locuscomparer package (ver. 1.0.0)27 in R, the p values distribution in GWAS and eQTL signals showed a similar shape and the high degree of correlation was visually confirmed as well  in retina, is a paralog of MYT1 and encodes a member of the zinc finger superfamily of transcription factors. It is mainly expressed in neural tissue and is known to play an important role in neuronal differentiation by specifically suppressing the expression of non-neuronal genes28. The eQTL data showed rs9677043, detected as a possible causal variant, has a positive effect on MYT1L expression levels in retinal tissue. The path coefficient β estimated by SEM was 0.132, showing that the mutation of rs9677043 worsens the observed symptoms. This suggests that the differentiation of neurons in retinal tissue has a potential to be associated with CVS.The MYT1L gene, the expression level of which showed a strong colocalization , and they were associated with the TraB domain containing 2B (TRABD2B) and SDK1 genes according to the position on the genome, respectively. TRABD2B is a metalloproteinase having the function of a negative regulator of the Wnt signaling pathway, which has been reported to regulate the transdifferentiation of retinal nerves into the ciliary bodyWe will describe the limitations of this study from the following four of viewpoints: (1) self-reported questionnaires via a web browser, (2) sufficiency of the level of statistical significance, (3) impact of racial differences in eQTL analysis, (4) need for validation study using different sample groups.33. On the other hand, one of the DTC genetic testing services, 23andMe in the US, has uncovered numerous biological findings utilizing self-reported medical history and web-based questionnaires from a large group of customers34. This is a new research approach that is driven by mass data and this study is a continuation of that trend.There is no doubt that web-based questionnaires are less clinically reliable than diagnosis by a doctor. Furthermore, it has been pointed out that there are some problems, such as the bias caused by the web-mediated process, compared to traditional laboratory questionnaire interviews35. This requires sufficiently narrowing the window for the possibility of false positives, and this study used colocalization with eQTL to give additional validity to biological certainty.The ordinary GWAS has established a very strict criteria—a genome-wide significance level. This study instead used the suggestive significance level to give a biological interpretation. The main reasons for the low statistical power may be the subjective way in which the trait was assessed, the wide spectrum of traits, and the small number of samples. Considering the original purpose of GWAS, we should find its meaning and value in the discovery of unknown important molecular mechanisms or new markers for diagnosis. In fact, other studies have led to an extended list of associated variants, which can provide a resource for functional studies, by using more exploratory criteria20. We used HaploReg (ver. 4.1)36 to check rs9677043 detected from the European retinal eQTL data, and the MAF in the European and East Asian populations were 0.53 and 0.47, respectively. Although there is no large difference compared to this study (0.464), it is desirable to obtain retinal tissue eQTL data from the Japanese population, considering the fact that transcriptional regulation at the gene level cannot be explained by a single variant alone.It has been noted that the expression levels of many genes are variable between human populations, and it is mainly explained by differences in genotype frequencies37, and it allows “reverse genetic” experiments to be virtually conducted on human subjects39. The proof-of-principle for the hypotheses generated in this study is expected by making use of cohorts in the future. Besides, the Mendelian randomization approach is an extension of PheWAS to test for causal relationships between phenotypes, and the SEM methodology has been used in many cases41.GWAS can be positioned as a “forward genetic” experiment to identify the genetic basis of a trait and provide an opportunity for exploratory hypothesis generation. In order to strengthen the hypothesis, it is ideal to conduct external validation on different sample groups, and this study is no exception. On the other hand, the common study design of examining the functional role of genetic variation is to knock out a specific gene in a model organism. Such an experiment based on “reverse genetics” is necessary for hypothesis verification. In recent years, national biobanks and DTC genetic testing services provide a large-scale cohort linked to genetic and rich phenotypic information, mainly obtained from electronic medical records. An approach of extending GWAS to the entire set of phenotypic entities is called the phenome-wide association study (PheWAS)Last of all, we will discuss the benefits of the use of SEM for multi-trait GWAS. The path coefficients estimated by SEM reflect the strength of each causal relationship and quantify the influence of a variant on the target phenotype via intermediate variables (latent factors) and predefined causal paths. For instance, the positive path coefficient from an intermediate variable to an observed variable suggests that an increase in the intermediate variable directly leads to an increase in the observed variable. In the case of this study, the coefficients (γ) from the four categories  to the intermediate variable were 0.842 ± 0.024, 0.657 ± 0.018, 0.787 ± 0.027, and 0.823 ± 0.022, respectively, and “asthenopic” and “extra-ocular” were found to be relatively strong symptoms for CVS. Thus, we believe that SEM has the potential to enhance our understanding of complex phenotypes, such as syndromes, by genetically unraveling the relationship between observed symptoms. SEM is expected to be a core technique for performing a series of association analyses from GWAS to PheWAS.Supplementary Figures.


Our results confirmed the role of LALBA and AQP genes, on OAR3, as candidate genes for milk production traits in sheep.The aim of this study was to identify genomic regions underlying milk production traits in the Valle del Belice dairy sheep using regional heritability mapping (RHM). Repeated measurements for milk yield (MY), fat percentage and yield (F% and FY) and protein percentage and yield (P% and PY), collected over a period of 6 years (2006–2012) on 481 Valle del Belice ewes, were used for the analysis. Animals were genotyped with the Illumina 50k SNP chip. Variance components, heritabilities and repeatabilities within and across lactations were estimated, fitting parity, litter size, season of lambing and fortnights in milk, as fixed; and additive genetic, permanent environment within and across lactations, flock by test‐day interaction and residual as random effects. For the RHM analysis, the model included the same fixed and random effects as before, plus an additional regional genomic additive effect (specific for the region being tested) as random. While the whole genomic additive effect was estimated using the genomic relationship matrix (GRM) constructed from all SNPs, the regional genomic additive effect was estimated from a GRM matrix constructed from the SNPs within each region. Heritability estimates ranged between 0.06 and 0.15, with repeatabilities being between 0.14 and 0.24 across lactations and between 0.23 and 0.39 within lactation for all milk production traits. A substantial effect of flock‐test‐day on milk production traits was also estimated. Significant genomic regions at either genome‐wide ( The ewes were distributed in nine half‐sib families with an average size of 50 daughters per ram . Some individuals across families were related through known pedigree relationships. The pedigree over three generations included 1,304 animals, of which 101 are rams and 823 dams.Phenotypic data from 481 ewes were collected between 2006 and 2012 in four flocks . QC was performed using PLINK v 1.07 . After QC, 37,228 SNPs and 481 individuals were retained for further analyses.Blood samples from 481 Valle del Belice ewes were collected in order to extract DNA. Genotyping was performed with Illumina OvineSNP50 (50k) BeadChip. Using GenomeStudio v2.0 software (Illumina), genotypes of 54,241 SNPs were available for each individual. The genomic positions of SNPs on chromosomes were updated to the ovine Oar_v4.0 genome assembly , litter size , season of lambing  of the random animal's additive genetic effects u∼; pu is a vector (481) of the random permanent environmental effect of the individuals across lactations pu∼; pw is a vector  of the random permanent environmental effect of the individuals within parity class pw∼; h is a vector (180) of the random flock by test‐day (FTD) interaction ; e is a vector of the random residual errors e∼; and X, 1Z, 2Z and 3Z are the corresponding incidence matrices. The σu2, σpu2, σpw2, σh2 and σe2 terms are the additive genetic, permanent environment (across and within lactation), FTD and residual error variances, respectively; and G and I are the additive genetic relationship and identity matrices, respectively. The G matrix is the GRM calculated using VanRaden's method 2  was divided into regions—or equivalently, windows—(throughout the paper both terms will be used and they will be interchangeable) of a predefined number of consecutive SNPs, and the variance attributable to each window estimated . In this study, a window size of 100 adjacent SNPs was used and the window was shifted every 50 SNPs so adjacent windows overlap by half. Because the number of SNPs varies across chromosomes, the last region at the end of each chromosome was excluded if it contained less than 25 SNPs.v) assumed to be distributed as v∼, where σv2is the additive genetic variance associated with the region and Gvis the regional GRM estimated in the same way as G but using only the SNPs located within the genomic region in question  and hv2=(σv2/σp2), where σp2is the phenotypic variance equal to the sum of all variances .Each genomic region or window was tested by fitting a linear mixed model accounting for all the fixed and random effects included when estimating the variance components, plus an additional random additive effect associated with the genomic region being tested. The regional genomic additive effect (σv2 (H1) against that of the null model without σv2 (H0). The LRT for a specific region is equal to 2*(logL(H1)‐logL(H0)), and it was assumed to follow a mixture of 12χ12 and 12χ02 distributions  of 3.92) and p < 2.41 × 10−3 (−log10(p) of 2.62), respectively. The RHM analysis was performed using an in‐house REML software, which does not require the inverse of the GRMs as suggested by Lee and van der Werf , which compares the log‐likelihood (logL) of the full model including der Werf  and implder Werf  and MTG2der Werf .2.6https://www.ncbi.nlm.nih.gov/genome/gdv/browser/?context=genome&acc=GCF_000298735.2) and in the SheepQTLdb  for milk production QTL, respectively. To investigate the biological function and the phenotypes that are known to be affected by each annotated gene, we also conducted a comprehensive literature search, including information from other species.To investigate if the significant SNPs detected in this study mapped within genes or previously reported QTL for relevant traits, we searched them in NCBI Genome Data Viewer for Ovis aries v4.0 genome assembly  for P% was found by Gutiérrez‐Gil et al.  on OAR3 LALBA  on OAR3 is considered a strong functional candidate gene affecting the traits analysed. Alpha‐lactalbumin is a major whey protein that forms a subunit of the lactose synthase binary complex. Because lactose synthase is necessary for the production of lactose and the subsequent movement of water into the mammary secretory vesicles, this enzyme is critical in the lactation control and secretion of milk (García‐Gámez, Gutiérrez‐Gil, Sahana, et al., LALBA gene were studied in the 1990s as possible markers related to milk production in dairy species. LALBA gene was identified as candidate gene that influenced milk protein percentage in Spanish Churra sheep (Gutiérrez‐Gil et al., AQP6, AQP5 and AQP2 (in bold in Table Several genes, pseudogenes and tRNA genes have been found in the identified regions Table , with thIn this study, we used RHM approach instead of association analysis from a standard GWAS to detect genomic regions underlying milk production traits in Valle del Belice dairy sheep. Literature suggests that our method of choice performs better than a standard GWAS, especially when associated SNPs do not have large enough effect to be declared significant at the genome‐wide level. Moreover, the RHM method may be able to reduce the false discovery rate, a common problem with GWAS method in populations that are characterized by a high inbreeding coefficient (Manenti et al., 4LALBA and AQP genes, on OAR3, as candidate genes for milk production traits in sheep. Moreover, heritability estimates for milk production traits ranged between 0.06 and 0.15, with repeatabilities being between 0.14 and 0.24 across lactations and between 0.23 and 0.39 within lactation. A substantial effect of flock‐test‐day on milk production traits was also estimated. Further studies to refine heritability estimates might be needed in the future using bigger sample size.We have successfully detected genomic regions associated with fat and protein percentage in the Valle del Belice sheep breed, using RHM approach. Our results confirmed the role of The authors declare that there is no conflict of interest regarding the publication of this article.Fig S1Click here for additional data file.Table S1Click here for additional data file.


Human impacts on the natural world often generate environmental trends that can have detrimental effects on distributions of phenotypic traits. We do not have a good understanding of how deteriorating environments might impact evolutionary trajectories across multiple generations, even though effects of environmental trends are often significant in the statistical quantitative genetic analyses of phenotypic trait data that are used to estimate additive genetic (co)variances. These environmental trends capture reaction norms, where the same (average) genotype expresses different phenotypic trait values in different environments. Not incorporated into the predictive models typically parameterised from statistical analyses to predict evolution, such as the breeder's equation. We describe how these environmental effects can be incorporated into multi‐generational, evolutionarily explicit, structured population models before exploring how these effects can influence evolutionary dynamics. The paper is primarily a description of the modelling approach, but we also show how incorporation into models of the types of environmental trends that human activity has generated can have considerable impacts on the evolutionary dynamics that are predicted. This last function has been referred to as the inheritance function by some authors  are discrete‐time population models structured by one or more continuous traits  involved – for example each trait is decomposed into a bivariate distribution of the breeding values and the environmental components .Selection operates on phenotypic traits , is operated on by a kernel, ϕA′,E′|A,E,t, that describes all possible transition rates from  at time t to  at time t + 1, including those that occur at rate zero because they are biologically impossible. The primes here depict that the values of A and E can change between parents and their offspring. The model can be written as follows:The bivariate distribution of A and E but are not displayed to simplify notation .The integral limits are taken to be below and above all possible values of ϕA′,E′|A,E,t is constructed to include the biological processes that determine the dynamics of the bivariate distribution of A and E and their drivers. The ‘trick’ in formulating a model is to specify the functions and the rules they encode. In our simple model, we will consider a fitness function RA+E,θ,t, that determines the strength and direction of selection, followed by a parent–offspring phenotypic difference function D|,θ,t) (the transition function) that captures the genetic inheritance of breeding values and various nongenetic inheritance processes that can influence the dynamics of the environmental component of the phenotype. Consequently, ϕ|A,E,t)=D|,θ,tRA+E,θ,t andThe kernel D|,θ,t) and RA+E,θ,t do not commute, which means their order matters. We consequently treat selection operating first, following by the transmission of breeding values and environmental components of the phenotype between parental and offspring values. This means that D|,θ,t) is conditional on reproduction: if you do not reproduce you will not contribute to the components of the phenotypic traits in offspring.The functions RA+E,θ,t informs that lifetime reproductive success is determined by the phenotypic trait value z = A + E, and the potentially multidimensional environment, θ, experienced at time t. The form of this function will combine with the phenotypic variance to determine the strength of selection.The fitness function N  to produce a bivariate distribution of A and E postselection: NsA,E,t=RA+E,θ,tNA,E,t. The means of both the breeding value and the environmental component of the phenotype in this bivariate distribution will differ from the means of these quantities in N  if (i) selection is directional  is nonzero) and (ii) the additive genetic variance and the variance in the environmental component of the phenotype are both greater than zero.The fitness function can be thought of as operating on the distribution of iz = iA + iE), this means that directional selection must displace A¯ and E¯ in the same direction, even if their rates of change differ . The symbol ‘|’ means ‘given’. So, A′ and E′ take their values given A, E and θ at time t. This function is a probability density function, such that all possible transitions out of location  sum to unity  to capture specific rules. The first biological rule we need to respect is genetic inheritance for breeding values. This means that the mean of the breeding values in offspring, A′¯, must be equal to the mean of mid‐point breeding values of each offspring's parental pair, As¯, where the subscript s means postselection  . These assumptions mean we only need to track the dynamics of a single distribution of A.Convolve the distribution of selected parents from step 1 with itself to generate a distribution of mid‐point values of A′.Convolve the distribution in 3 with a Gaussian distribution with a mean of 0 and a variance that captures the segregation variance to produce a distribution of offspring We now need to specify this parent–offspring phenotypic difference function As¯, and a constant variance that does not change with time . In other words, we are incorporating the effect of a trending fixed effect that is found to influence the mean of a phenotypic trait value in a statistical quantitative genetic analysis. We continue to assume positive selection. In this case, we would generate the type of phenotypic trait dynamics displayed in Figure Next, we turn to the case where there is a trend in some component of the biotic or abiotic environment Finally, we turn to the case where there are maternal effects, or nongenetic inheritance, such that there is a correlation between environmental components of the parental and offspring phenotypes  by categorizing it into many small bins to generate the column vector N (t). In Table N (t) describes the number of individuals in each  discrete category at time t. The mid‐point values of each  category is described in columns 3 and 4 of Table A and E ranging from 1 to 10 with 100 categories for both. Note that these values could be centred on zero if desired, as depicted in Figures z which are determined by summing the values of A and E.We approximate Equation N (t = 1) as bivariate normal with two means (one each for A¯ and E¯) and a variance–covariance matrix Σ=σAAσEAσAEσEE that can be estimated from statistical analyses used in quantitative genetics  can be identified by the statistical analysis of phenotypic trait and reproductive success data. For example, if it is linear, it would take the formxβs are statistically estimated parameters, and θi is a variable used to characterize one aspect of the environment θ. The function does not need to be linear  (column 5 of Table R (t) describing the fitness of each z = A + E. The matrix R (t) is square and with the same length and width dimensions as the length of N (t) .R (t) for a value of A = 2 and E = 3 would be the same as the value of R (t) for A = 2.5 and E = 2.5 as both give the same phenotypic trait value of z = A + E = 5.We specify the vector N t =  as bivarD can also be approximated as a matrix D (t) with the same dimensions as R (t). This matrix describes the probability of transition from all parental phenotypic trait component values  to all offspring phenotypic trait components values . In our example, the top left element of the matrix would describe the transition probability from  to ; the cell in the top row and second column describes the transition probability  to ; the first cell in the second row describes the transition probability  to  etc.The kernel D (t) and it can be significantly computationally faster to construct the vector N (t + 1) directly from sN (t) = R (t) N (t). We illustrate this here, because for readers who are unfamiliar with Lefkovitch matrices their construction can be opaque, and we do not have space to elaborate here.It is not always computationally necessary to construct the matrix N (t + 1), removing the need to populate the matrix D (t):nt+1=∑Nst where n (t + 1) is the population size at birth of the next generation.Calculate the sum As¯=∑ANst/∑Nst where A is a column vector of the values of A in the third column of Table Calculate the mean of AAσ.Choose your assumption about the effect of selection on the additive genetic variance. We will assume that it remains Gaussian with a constant variance As¯ and a variance AAσ discretized into the number of unique bins used to categorize the distribution of A. Call this NAshortt+1.Given (3), generate a Gaussian probability distribution with a mean of NAshortt+1 by the number of unique bins used to categorize E  to sum to unity.A in NAlongt+1 . Call this vector NEshortt+1.Within each block of values of +1 Table , we now EN (t + 1) by the number of unique bins used to categorize E to generate NElongt+1.Replicate the vector NElongt+1 to sum to unity.Standardize n (t + 1). This produces N (t + 1).Multiply the two vectors produced in (5) and (8) and standardize to sum to The following algorithm could be used to construct the vector The code to implement these algorithms is provided on Zenodo.2.3In this section, we describe the two models we use in this paper. The parameters we choose are simply for illustration, and the general results we report are not specific to the parameterization although the specific rates are. We do not identify parameter values from statistical analyses. Simmonds et al. , SimmondA and for E and upper and lower integration limits for each of 0 and 40. We define the initial bivariate distribution of N  as Gaussian with A¯=E¯=18 and Σ=2002. In both models, we specify a constant fitness function R  = −2.5 + 0.1z that is constant with time and not impacted by environmental variation. Although this function would generate negative values of fitness for z < 25, we have chosen parameter values to ensure that this does not happen. Alternatively, we could choose a nonlinear function that remains within bounds.We use 500 bins for both E¯=18 and EEσ = 2. This is equivalent to saying that the environmental component of the phenotype is determined solely by developmental noise and remains constant in the phenotypic trait distribution of offspring from one generation to the next. The choice of a constant value of E¯ is irrelevant for the dynamics. Model 1 is a dynamic version of the univariate breeder's equation.In model 1, we assume that E¯=18‐0.5t with EEσ = 2 in each new generation. This would capture the statistical effect of a human‐induced trending environment that influences the mean phenotypic trait value in a statistical analysis of phenotypic similarity between relatives.In model 2, we assume a reaction norm where the phenotypic trait value produced by all breeding values is a function of a temporally deteriorating environment. We define the mean environmental component as 3E¯ (model 2), evolution (defined as change in A¯) occurs at a faster rate than when E¯ remains constant with time  providing background that has not been explicitly described in previous papers using EE‐IPMs and (ii) introducing very simple models, one of which includes the effects of a deteriorating on the environmental component of the phenotype. Nonetheless, even these simplified models provide interesting insight.In both our models, we have a constant, linear, fitness function. The only difference between our two models is that one contains a deteriorating environment designed to mimic human‐induced environmental change that impacts the mean of the environmental component of the phenotype, while the other does not. Such effects of the mean environment on the mean value of phenotypic traits are well‐documented in statistical analyses used by statistical quantitative geneticists Kruuk,  but are Δz¯=ΔA¯. In contrast, in model 2, where there is a deteriorating environment, we observed dynamics like those depicted in Figure Δz¯≠ΔA¯ due to the trend in E¯. However, in addition, the trend in E¯ generates divergence in the dynamics of selection between the two models, and this leads to a difference in the rate of evolution.Why do these results arise? Model 1, where there is no environmental deterioration, generates dynamics like those depicted in Figure covz,ww¯ where w, in our model, is absolute lifetime reproductive success and w¯ is mean lifetime reproductive success. For an annual life history, w¯ is the population growth rate  remains constant with time – it is determined by the slope of our linear fitness function. However, w¯ changes with time at different rates between our two models. The reason for this is we have a constant fitness function: mean fitness consequently increases with the mean of the phenotype. When there is a trend in E¯ with time, there is consequently a trend in the mean phenotype with time, and hence, mean fitness will change at a different rate compared to when there is no trend in E¯. When the direction of selection is positive, a negative trend in E¯ will accelerate evolution, while a positive trend will slow it via its effect on mean fitness. For example, when nongenetic inheritance is adaptive it will accelerate the rate of change in mean fitness, and consequently decrease the selection differential, thus slowing the rate of evolution. In contrast, when nongenetic inheritance is maladaptive, it will decrease the rate of change in mean fitness and consequently accelerate the rate of evolution (Figure A selection differential on a phenotypic trait can be written as  Fisher, . In bothn Figure .The dynamics of selection are rarely decomposed into the dynamics of the covariance between phenotypic traits and absolute fitness and the dynamics of mean fitness. However, doing this does allow some useful insight. For example, because evolution simultaneously alters the mean value of phenotypic traits and mean fitness Fisher, , it shouOur models are deliberately very simplistic. In real settings, fitness functions are likely to include environmental variation and density dependence (Ellner et al., Despite the positives of IPMs, they are not a panacea. To date, no one has constructed EE‐IPMs for environment‐specific phenotypic traits. Traits that are only expressed at specific ages have been incorporated into models (Coulson et al., There are, of course, many other structured populations models that have been developed to examine evolutionary dynamics (Barfield et al., It is our belief that structured models and statistical quantitative genetics are both powerful tools to study evolution. There are ways these approaches can be combined, and once they are they offer potential to shed light on evolutionary dynamics. Investigating both the statistical quantitative genetic and structured modelling literature is time consuming given that both are large, specialized and use different vocabularies. Nonetheless, collaboration rather than distrust between researchers in each discipline could pay dividends.There are no conflicts of interest to declare.