Family members show behavioural strategies predicted to maximize individual fitness. These behaviours depend directly on genes expressed in focal individuals but also indirectly on genes expressed in other family members. However, how sibling and parental behavioural strategies are modified by genes expressed in family members, and to what degree, remains unclear. To answer this question, we have used a split litter design in an experimental population of genetically variable mouse families, and identified loci that indirectly affected sibling and maternal behaviour simultaneously. These loci map to genomic regions that also show a direct effect on offspring behaviour. Directly and indirectly affected traits were significantly correlated at the phenotypic level, illustrating how indirect effects are caused. Genetic variants in offspring that influence solicitation also impacted their siblings' and maternal behaviour. However, in contrast to predictions from sibling competition, unrelated litter mates benefited from increased solicitation. Overall, such indirect genetic effects explained a large proportion of variation seen in behaviours, with candidate genes involved in metabolism to neuronal development. These results reveal that we need to view behavioural strategies as the result of conjoint selection on genetic variation in all interacting family members. Understanding the evolution of social behaviour is a major challenge in biology because trait variation is not just influenced by genes expressed in focal individuals but also by genes expressed in social partners . While tFor mammals, the most important social interactions occur during early development between parent and offspring, and among siblings ,6. HowevWhat remains unclear is the degree to which a particular behavioural strategy (such as the level of offspring solicitation) is influenced by genes expressed in a focal individual (a direct effect) or genes expressed in other family members (an indirect effect). Further, we do not know how genes expressed in one family member affect the behaviour of other members.A key problem in answering these fundamental questions is to separate direct from indirect effects on behaviours during social interactions because in a genetically variable population all genotypes are likely to exert both direct and indirect effects. Our previous work  has show2.SocInt1 would denote a locus on chromosome 1.We investigated behavioural interactions among siblings of different genotype and their adoptive mothers on 3 days during lactation, and used single nucleotide polymorphism (SNP) interval mapping to map trait variation in genetically uniform individuals (B6 mothers and B6 offspring) as a function of genetic variation in their (BXD) siblings to investigate indirect genetic effects (IGE). By contrast, direct genetic effects were investigated by mapping behavioural variation among BXD individuals to their own genotype. The QTL analysis was followed by a systems genetics analysis to identify functional candidate genes and biological pathways. We then analysed the phenotypic correlation between the behavioural traits affected by direct and indirect genetic variation. The genetic variants are identified as social interaction loci together with the chromosome number, e.g. (a)http://www.genenetwork.org). BXD is derived from two divergent mouse strains , in which different recombination patterns have been inbred, each with a fixed recombination pattern of exactly two possible alleles. BXD incorporates 4–5 million segregating single nucleotide polymorphisms, 500 k insertions and deletions, and 55 k copy number variants [Our project used mice of the BXD recombinant inbred population, which consists of experimentally tractable and genetically defined mouse lines capturing a large amount of naturally occurring genetic variation, which underlies variation at the phenotypic level (e.g. ). BXD isvariants ,13.In this experiment, we used 32 lines , which were selected to exclude very poor breeding lines. For each line, three within-line replicates were set up, although breeding success reduced this in some lines. C57BL/6 J (B6) inbred mice were used as the genetically uniform strain such that all mothers and half of each litter had the same genotype in all cases . BXD mic(b)ad libitum. Cages were cleaned once a week but never within the first 6 days after birth to minimize disturbance. The parental mice were all sexually mature and females were nulliparous. Groups of up to five sibs were housed together in single-sex cages until mating, which occurred between six and 10 weeks of age, when females were greater than or equal to 18 g. Prior to mating bedding from the prospective mate's cage was added to the female cages to encourage synchronized oestrus [Mice were maintained under standard laboratory conditions in the same room, exclusively used for the experiment, in individually ventilated cages (IVC Tecniplast Green line), maintained at 20(±2)°C with a relative humidity of 55% (±10%). Because we investigated behavioural patterns in a nocturnal species, we used a reverse dark : light cycle with red light between 10.00 and 22.00 h. Food and water was provided  oestrus , and ind(c)Females in individual cages were checked daily for new-born litters. Litters were weighed and cross-fostered, such that each B6 mother had a litter composed of half B6 and half BXD offspring . On a feFor 6 days following cross-fostering, the litter was left undisturbed, apart from visual checks from outside the cage twice daily, to minimize disturbance.On postpartum days 6, 10, and 14, observations were conducted following . Litters(d)p > 0.05) terms were removed sequentially and in a stepwise manner until only significant covariates remained in the model following [To account for differences between litters not due to genotype differences, residuals were calculated from a general linear model (GLM) with the following covariates: maternal bodyweight, average bodyweight of the B6 offspring (weight of the B6 litter divided by the B6 litter size), B6 litter size, average bodyweight of BXD offspring (weight of the BXD litter divided by the BXD litter size), BXD litter size, and batch. Non-significant . Interval mapping relies on 3 795 informative SNP markers across all chromosomes, except Y. The BXD strains were genotyped using the MUGA array in 2011, along with genotypes generated earlier using Affymetrix and Illumina platforms , and mm9http://www.informatics.jax.org/allele) on Mouse Genome Informatics [To identify candidates, we firstly used the ‘Phenotypes, Alleles & Disease Models Search’ . It is important to note that there is no genetic variation among either mothers or litter mates so we do not necessarily expect that the same traits in both half litters are affected by direct and indirect effects. Further, given we expect competition between siblings over resource share, it is surprising to see that unrelated litter mates actually benefited from increased sucking behaviour of their litter mates. Given limited access to maternal teats, we expected that genes increasing sucking behaviours would increase competition and thus reduce sucking in their litter mates. However, our results suggest that the indirect effect on increasing maternal provisioning benefited unrelated littermates who then also increased their sucking behaviour.Specifically, three loci on chromosomes 4, 10, and 15 directly affected offspring sucking behaviour either during early lactation (d6) or during the weaning period (d14)Finally, while we have standardized environmental conditions among both BXD and B6 mothers pre- and postnatally as much as possible, it is important to note that differences among B6 half litters may arise for reasons other than genetic differences among BXD genotypes. Firstly, pre-natal maternal  effe(a)We next sought to establish how important IGE are in explaining variation in behavioural strategies during family interactions. Here, we calculated the proportion of variation explained, either for direct (i.e. variation in BXD phenotype) or for indirect effects (i.e. variation in B6 phenotype). Indeed, a large proportion of variation in behavioural traits could be explained by IGE . For mat(b)Our hypothesis was that variation in behavioural traits observed during family interactions are caused by genetic differences between the lines (i.e. genotypes). We used a systems genetics analysis for directly affected traits to identify a list of candidate genes within the loci identified in the mapping analysis. However, we note that as with any mapping study, our loci contain many genes . While we focused on those candidates that have functional polymorphisms among the BXD genotypes, it may be possible that causal genes are located outside the interval, or affect expression of genes outside the locus.SocInt2 . This locus directly affected offspring solicitation on d14, as well as maternal suckling behaviour indirectly. Genes within this locus are linked to early growth and body weight. Twenty of the 49 genes within this locus have nsSNPs or indels among the BXD genotypes and, among those 20, several alter behaviour and weight, such as Chn1 and Atf2 [Nfe2l2 [We begin with and Atf2 ,29 and N [Nfe2l2 , which aSocInt4  affected offspring sucking directly, and maternal suckling, sibling activity, and sucking behaviour indirectly during early lactation (day 6). Only 13 genes within the locus have nsSNPs or indels, producing a limited list of potential candidates. A potential candidate is Dpy19l4, which contains 18 nsSNPS and three indels, and has been linked to early brain development [Rbm35a (now known as Esrp1) is another candidate, as ablation causes cleft lip and palate [elopment . Rbm35a d palate , and theSocInt10  influenced pup sucking directly, and sibling sucking and maternal suckling indirectly. This region has been linked to several weight and obesity QTL. Only seven of the 27 genes within the confidence interval have nsSNPs or indels. Dusp6 is a possible candidate, as it contains three nsSNPs, and is involved in several metabolic pathways, and is important for normal development [Cep290 is another potential candidate, as mutations in the gene have been linked to slow postnatal weight gain (https://www.jax.org/strain/013702), which may be due to reduced feeding. Mutations in the gene have also been linked to problems with vision, olfaction, and taste [Next, elopment . Cep290 nd taste –37, possSocInt15  affected the level of offspring sucking behaviour directly, and, indirectly, maternal suckling and activity, and sibling sucking during the weaning period on d14. Within the locus are 11 genes with nsSNPs or indels, producing a small number of candidate genes. We find two out of eight possible candidates alter behaviour, Ghr and Sepp1. Ghr influences feeding behaviour and growth [Sepp1 influences parental behaviour [Sepp1 has nsSNPs and Ghr has both nsSNPs and indels.Finally, d growth , while Sehaviour  and grooehaviour . Sepp1 h4.The key result of our study is that IGE significantly contribute to explaining differences between family members in behavioural strategies, and are as important as direct genetic effects, with fundamental consequences for trait evolution . We haveBy identifying the direct and indirect sources of genetic variation in key traits during family interactions our study demonstrates that trait variation is not just caused by genes expressed in a focal individual but that such direct effects influence variation in multiple traits in different family members indirectly. Thus, direct effect loci may cause indirect effects either pleiotropically or by being closely linked to variants causing indirect effects. Our results suggest that we need to view behavioural strategies really as the result of selection on genetic variation in all interacting partners.


Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits . We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models. Phenotypic prediction of complex traits from genomic data could transform clinical practice by enabling tailored treatment and targeted disease screening programs based on the genetic make-up of the individual, and by facilitating more efficient allocation of resources within the health systems –3. UltimRecent studies have shown that SNP arrays containing common genetic variants capture a substantial amount of the genetic variation for each trait and that the contributing SNPs have effects generally too small to be detected with current GWAS sample sizes due to the stringent genome-wide significance levels applied –8. FurthWe show that modelling individual-level data of circa 110,000 individuals can lead to accurate predictions across multiple traits by jointly fitting the SNPs of a single array of common SNPs. We first focused on human height, a highly heritable quantitative trait commonly used as a model in the study of the genetic architecture of complex traits ,7,12 andFor our analyses, we used the 152,736 genotyped individuals available from the UK Biobank cohort . After ahttps://www.dissect.ed.ac.uk), a software specifically designed to perform genomic analysis in large supercomputers. Each analysis required ~1h of computing time on the ARCHER supercomputer, harnessing the joint power of 1,152 processors. Using the jointly estimated SNP effects (SNP-BLUPs) to predict the genetic value of individuals in an independent validation dataset (Online Methods), we computed the prediction accuracy as the correlation, r, between these predicted genetic values and the phenotypes corrected for gender and age.In order to jointly estimate the additive effects of all SNPs we fitted them as random effects in a Mixed Linear Model (MLM) on the training population, with gender and age as fixed effects (Online Methods). As the computational requirements of MLM fitting rapidly increases with incrementing sample sizes, we used DISSECT  . Predictions obtained in the White cohort  were almAlthough samples sizes for training the models will increase in the future, it is unlikely that they will increase indefinitely. Therefore, we argued that it would be useful to know what sample size would be required to exploit all the genetic variation captured by the SNP array. To gauge this, we computed prediction accuracies for samples of decreasing size, by randomly subsampling the unrelated White-British individuals (Online Methods). Our data fitted very well to a well-known theoretical model . Our res−8 to 1  (Online Methods). The maximum accuracies obtained by the SNP-BLUP approach are up to 25% better (i.e. explain up to 55% more variance) and are consistently larger than the maximum obtained using the selected SNPs from the GWAS approach with a p-value over the optimal threshold  than that obtained by GWAS meta-analysis hits based predictors developed through large collaborations, which used similar number of training samples . If insthttp://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=155580). From the overlapping markers, we excluded those which were multi-allelic, their overall missingness rate exceeded 2% or they exhibited a strong platform specific missingness bias . We also excluded individuals if they exhibited excess heterozygosity, as identified by UK Biobank internal QC procedures , if their missingness rate exceeded 5% or if their self-reported sex did not match genetic sex estimated from X chromosome inbreeding coefficients. These criteria resulted in a reduced dataset of 151,532 individuals. Finally, we only kept the common variants (i.e. with a MAF > 0.05) and those that did not exhibit departure from Hardy-Weinberg equilibrium (P < 10−50) in the unrelated  White-British cohort (see below).For our analysis, we used the data for the genotyped individuals in phase 1 of the UK Biobank genotyping program. 49,979 individuals were genotyped by using the Affymetrix UK BiLEVE Axiom array and 102,750 individuals by using the Affymetrix UK Biobank Axiom array. Details regarding genotyping procedure and genotype calling protocols are provided elsewhere  that passed our SNP QC protocol. The related and unrelated White-British individuals were defined as those for whom the projections onto the leading twenty genomic principal components fell within three standard deviations of the mean and who identified themselves as White-British. We defined the other removed White-British as self-reported White-British. The other ethnicities were defined using the self-identified ethnic background . As we did with White-British individuals, we only retained those individuals whose projections onto the leading twenty genomic principal components fell within three standard deviations of the ethnicity group mean  of all individuals passing genotypic QC using a linkage disequilibrium (LD) pruned set of 99,101 autosomal markers . The PCA, MLMs fittings for estimating SNP effects and phenotype predictions were performed using DISSECT (https://www.dissect.ed.ac.uk) on the UK National Supercomputer (ARCHER). DISSECT software is designed to perform genomic analyses on very large sample sizes without the need to perform mathematical approximations by using the power of large supercomputers.The genotype quality control and data filtering was performed using plink  .The MLM of the previous section assumes that all SNP effects follow a Gaussian distribution with one variance. However this is may not be true. To improve the model we first fitted all SNPs independently using a standard GWAS model,, is distributed as , is distributed as Here, the parameters are the same as in the previous MLM, and the SNP effect size mafk and the distribution of the vector of SNP effects , is We performed predictions using a model similar to that introduced in the previous subsection, but now grouping SNPs based on their MAF. We created three groups: SNPs with MAF > 0.05, SNPs with MAF<0.05 and MAF>0.01, and SNPs with MAF<0.01 and MAF>0.001. Then, we fitted the model:t. In this model, the vector of SNP effects for the trait t, at, has the covariance structure,t, and t1 and t2.We estimated the SNP effects by fitting different traits together in a multivariate model. These models can be expressed as:GWAS for performing the predictions detailed in We computed accuracies for samples of decreasing size, by randomly subsampling 5 of the 10-fold cross-validation subsets used in the within unrelated White-British population predictions .S1 Fig(PNG)Click here for additional data file.S2 FigThe individuals from different ethnic backgrounds are plotted using different colors.(PNG)Click here for additional data file.S3 FigA) Body fat percentage, (B) BMI, (C) WHR, and (D) BMR. Blue dots indicate prediction accuracies achieved on several trials. The dashed straight line shows the linear regression fit to the blue dots. The regression intercept indicates the maximum accuracy achievable using common variants. The red dot is the expected prediction accuracy with a training sample size of 500,000 individuals.Inverse of the square of the prediction accuracy as a function of the inverse of the training sample size for ((PNG)Click here for additional data file.S4 FigGenetic and environmental correlations displayed over and below the diagonal, respectively.(PNG)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment  and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels,  Image-based plant phenotyping can be used to collect data with high temporal and spatial resolution. Here, the authors develop a computationally efficient method using smoothing splines and a new marker-by-trait association model to identify loci in a diverse rice population associated with early response to salinity. Oryza sativa L.), the most salt-sensitive cereal12http://www.fao.org/water/en/). For example, the Indo-Gangetic Basin in India and the Indus Basin in Pakistan suffer losses in rice yield as high as 45% and 36–69%, respectively, from soil salinity1For more than half of the world's population, rice (sensu Munns and Tester)+ and Cl− accumulate in the cells of the shoot, known as the ‘ionic component' of salt stress, which causes premature leaf senescence28Exposure of plants to soil salinity rapidly reduces their growth and transpiration rates (TRs) due to the ‘osmotic component' of salt stress  was undertaken with the aim of identifying new loci that contribute to the early responses of rice to soil salinity. Two rice diversity panels, indica and 257 aus varieties) from 24-day-old plants to 150 mM NaCl. Over 13 days, several physiological responses of plants exposed to high salinity were monitored and compared with those of plants maintained in low salt concentrations. This was done using high-throughput, non-destructive imaging. From three RGB images (one top and two side views), we made daily measurements of the total number of pixels for each plant, as a proxy for shoot biomass. There are numerous mathematical models to describe growth curves1112To assess the early responses of two rice diversity panels to saline conditions, we exposed a total of 553 rice accessions  to smooth the trend in the projected shoot area (PSA) for each plant. Examination of the plots of the smoothed values for PSA, absolute growth rate (AGR) and RGR  indicater2=0.945 for indica and r2=0.91 for aus in the northeast (NE) Smarthouse, using Pearson's correlation; We found that PSA strongly positively correlates with shoot biomass when using the squared Pearson correlation coefficient , have a higher EGRI than accessions previously reported as salt sensitive, such as ‘IR28' (refs The early growth response index (EGRI) provides an estimate for the early responses of plants to salinity. For all earlier intervals, than aus . Althoug28' refs , 18 .Our study also examines the previously unstudied traits of TR and TUE in response to moderate salinity stress under waterlogged growth conditions. To accurately describe TR, we fitted splines to daily measures of transpired water for each plant. As expected, we observed a clear acceleration in TR over time in control plants, and only a small increase over time in salt-treated plants . These rh indica . The subindica panel had a lower average decrease in TUE compared with the aus panel  On average, TUE decreases marginally over time for control plants and more rapidly for salt-treated plants . Salinitindica panel—‘GBS 44k SNP'α=0.05. In contrast, use of the recently generated data from the ‘HDRA SNP' data set provided a high resolution of SNP detection, allowing the identification of multiple highly significant SNPs for both rice panels. Hence, using the high-resolution ‘HDRA SNP' array provided a power gain for detecting genetic association loci.We used GWAS to identify genetic loci associated with the early responses of rice to salinity stress. We compared two sets of genotypic information for association analyses in the indica and aus panels independently and also as a combined set named INDAUS. When using the conventional MLM, we found that no significant P values were obtained, whether using the Benjamini–Hochberg procedure, the false discovery rate or the Bonferroni thresholds. Even though previous studies have used a significance threshold of P=10−5 for the conventional MLM24α=0.05), reflecting the change in response due to the addition of salt. Significant loci found to be associated with one of the traits, in the marker term, suggests that the detected loci contribute to the traits regardless of the treatment, whereas significant loci in the marker-by-treatment term represent loci that are responsive to salinity treatment. The most significant phenotypic traits from our GWAS study are summarized in In the association analyses, we used the traits RGR, TR and TUE at each interval. We initially performed GWAS using the conventional mixed linear model (MLM) with a kinship matrix between accessions to correct for genetic relatednessindica (P=3.03 × 10−7), but not in the last interval (9–13 days after treatment).Unique to this study is our assessment of a large number of genotypes for changes in TR and TUE immediately after salt imposition. Because TUE is a derived complex trait that has often been phenotyped using surrogate traits, genetic association with TUE has been challenging132627indica , where tP values using our interaction model , that disappeared from later time intervals  and considerably smaller on model ; Table 1ntervals . We obseindica at SNP-11.3637597 on 36.3–36.4 Mb on chromosome 11 or in regions with high linkage disequilibrium . The LD region surrounding significant association peaks for TUE is presented in We found association signals that are located close to specific genes of TUE for indica panel, one of the most promising regions is located at 3.62–3.76 Mb on chromosome 11. This region was detected by both the conventional MLM and our marker-by-treatment method. Although this region harbours several candidate genes, Os11g07230 (encoding a receptor kinase) and Os11g07240 (encoding a serine/threonine-protein kinase BRI1-like 2 precursor) are the most prominent candidates for this region. Another QTL is also found on chromosome 11 (at 2.78–2.79 Mb), between two candidate genes, Os11g05930 (encoding a response regulator receiver domain-containing protein) and Os11g05935 . Note that Os11g05930 is the orthologue to HvPRR59 in barley, which has been associated with its early flowering time29Os11g05935 has an orthologue in Caragana jubataIn the aus panel under salt stress harbour genes related to signalling and signal transduction, such as Os03g16130 , Os05g39870 , Os05g39900 (encoding a CBL-interacting serine/threonine-protein kinase 15), Os05g46320  and Os05g47670 . Hence, our results show that candidate genes found in the early intervals, 2–6 and 2–9 days after treatment, are mainly encoding for signalling proteins, further supporting the hypothesis that the early responses to salt stress are related to signalling mechanisms.Using our marker-by-treatment term, we found that several regions significant for TUE traits in the To date, few genetic studies have used high time- and spatial-resolution non-destructive image-based phenotyping to address the complex response of plants to abiotic stresses13a priori assumptions about the shape of the curve and allow us to faithfully follow the growth and transpiration trends in an unbiased way, while removing transient influences on the growth (such as a shady day). Furthermore, as we describe next, it is possible to characterize growth in terms of a few parameters calculated from splines. To our knowledge, this is the first time that splines have been used to analyse high-throughput phenotypic data.First, a method for calculating the growth and transpiration must be chosen. For young seedlings and short growth intervals, exponential growth models have been used1213Second, using a longitudinal model in a joint analysis of all plants consumes high-computational resources, encouraging the development of more efficient methods for analysing high-throughput phenotyping data. In this study, we used splines to establish the trends in growth independently for each individual plant; this was a purely descriptive curve-fitting procedure that requires minimal computing resources. Growth rates over time were then calculated from the fitted splines and time intervals were identified and selected based on knowledge of the underlying biology and/or from characteristic growth patterns. Measures of plant growth and transpiration were obtained for these intervals and each formed a single trait for which there was one value for each plant. This calculation of measurements for a set of time intervals is a unique aspect of the approach that we describe here, although our method harks back to the approach of Rowell and Watersaus panel, specific to salinity response in our marker-by-treatment interaction model, appears likely to correspond with the early responses to salinity. Consistent with these findings, 2–6 days after treatment, we found that a major involvement of signalling genes in response to salt, as previously hypothesized by others27OsHKT1;5, on chromosome 1.We also present a new association model that takes into account the interaction between treatment  and the genetic marker. The use of this interaction model enables identification of significant loci specifically associated with salinity stress. The interval 2–6 days after treatment represents the early response phase to salinity, while the later intervals, 6–9 and 9–13 days after treatment, appear to be affected by different processes, which may represent a shift in the relative importance of ‘osmotic' and ‘ionic' processes between these intervals. This is suggested by the presence of QTLs that have a stronger effect during the first interval, 2–6 days after treatment, which disappear after 6 days after treatment; for example, the QTL associated with TUE on chromosome 5 in the Rice sensitivity to salinity varies according to growth stage, being particularly sensitive at both seedling and reproductive periods373837indica and aus, which contain 297 and 257 accessions, respectively. These panels were selected by breeders at the International Rice Research Institute and collaborating partners in the context of the ‘Phenomics of Rice Adaptation and Yield Potential' (PRAY) project, funded by the Global Rice Science Partnership (http://ricephenonetwork.irri.org/). The two Phenomics of Rice Adaptation and Yield Potential panels represent sub-species level genetic diversity, including landraces of different geographic origin and agro-ecological adaptation and mega-varieties that are cultivated in vast areas. Information about the accessions, including genotype name and Genebank accession code, can be found in In our study, we used two diversity panels composed of two major rice subpopulations, We phenotyped both rice diversity panels at The Plant Accelerator  between January and March 2015. This experiment used two quarantine-approved greenhouses fitted with conveyor systems, described as the NE and northwest (NW) Smarthouses. Throughout the two experiments, the temperature in the greenhouses was set to 28 °C during the day and 26 °C at night. Relative humidity was increased using three humidifiers per room  and reached an average of 66% over the growing period in the NE Smarthouse and 62% in the NW Smarthouse. Rice seeds were treated according to Australian quarantine guidelines by immersion in water at 57 °C for 15 min followed by surface sterilization with Thiram fungicide and germinated on moist paper towels in plastic boxes for 4 days. Three uniformly germinated seeds of each genotype were transplanted into soil. A blue pot  with drainage holes containing 1.35 kg of U.C. mixSalt treatment was applied at 24 DAT (29 days after germination) by adding 100 ml of 1.425 M NaCl to the bottom of the outer white pot to a final concentration of 150 mM NaCl in the soil solution after drying down to 950 ml. Control plants received 100 ml of water on the same day. The concentration of salt in the soil was maintained at constant levels by watering each pot to a target volume of 950 ml. Daily imaging and watering were continued for 13 days after treatment until 37 DAT. A time-lapse video of a randomly selected genotype shows plant growth in control and saline conditions .indica panel was a partially replicated, blocked, row-and-column design; the design of the main plot for the aus panel was an unequally replicated, blocked, row-and-column design. In both cases, each block contained the zones of four consecutive lanes. The design of the main plot was generated using DiGGerExperiments were performed concurrently in NW and NE Smarthouses . A splitindica and aus panels is available online through The Plant Accelerator data portal.Plant imaging started at 23 DAT and continued until 37 DAT (which was 13 days after treatment). Shoot images were taken using the LemnaTec 3D Scanalyzer system  at The Plant Accelerator. Plants were imaged daily in an imaging chamber using two 5-megapixel visible/RGB cameras (Basler Pilot piA2400-17gm). Three images were taken per plant, two images from the side at a 90° rotation and one from the top. All captured images were analysed using the LemnaTec Grid software package . In brief, digital image processing consisted of a nearest-neighbour colour classification for foreground-background separation, noise reduction and object composition . Non-pla16aus panel, which prevented weighing on this day. All subsequent analyses were performed on plants between 26 and 37 DAT, corresponding to 2–13 days after treatment. Note that we excluded day 1 after salt treatment (DAT 25) from the analyses due to a marked fall in the RGR from 1 to 2 days after treatment, caused by the reduced confidence in their values after spline fitting.Data corresponding to the PSA, the weight before imaging, the weight after imaging and the weight of the evaporation cart were recorded daily between 23 and 37 DAT. One exception was caused by an equipment malfunction on DAT 35 for the imageDatahttp://cran.r-project.org/web/packages/imageData/index.html), the data were input into the R statistical computing environment16Using the R package T is transpiration, WA is weight after imaging, WB is weight before imaging and E is the weight of evaporation cart. In this equation, t is time, k refers to the beginning of a time interval of interest and k−1 refers to the previous day. The weight of the evaporation cart was calculated in the same way as transpiration, but from the weight after imaging and weight before imaging for the nearest evaporation cart  allowed the identification of plants with anomalous growth patterns. The following criteria were used to exclude plants from subsequent analyses: (i) dead or dying plants, (ii) plants that were not loaded; (iii) plants that missed the correct salt treatment; and (iv) salt-treated plants that markedly outperformed their associated control plant. After removing these plants, PSA and TR values were smoothed by fitting a cubic smoothing spline to the data for each remaining plantFive derived indices were calculated for each of the three main traits  at each chosen interval, as described in The EGRI was calculated for each interval as the RGR in salt divided by the RGR in control for the pair of plants forming a main plot.To produce phenotypic means adjusted for the spatial variation in the Smarthouses, a MLM analysis was performed for the AGRs, RGRs, TRs and TUEs from each interval. The maximal MLM for this analysis was calculated from the formula:y is the response vector of values for the trait being analysed, β is the vector of fixed effects, u is the vector of random effects and e is the vector of residual effects. X and Z are the design matrices corresponding to β and u, respectively.where For the maximal MLM, the fixed effect vector, μ is the overall mean and βS are the vectors of: Smarthouse effects, linear coefficients for trend within each Smarthouse over the zones, linear coefficients for trend within each Smarthouse over the east-west positions of main plots, line fixed effects, treatment fixed effects and fixed effects for line-treatment combinations, respectively.where Also, the random effects vector, uS are the vectors of the following: coefficients of the spline basis functions for fitting smooth trends within each Smarthouse over zones, coefficients of the spline basis functions for fitting smooth trends within each Smarthouse over the east-west positions of main plots, deviations within each Smarthouse from the smooth trend over zones, deviations within each Smarthouse from the smooth trend over the main-plot positions and main-plot random effects within each zone within each Smarthouse, respectively. The design matrices X and Z are partitioned to conform to the partitioning of β and u, respectively. It is assumed that each subvector of random effects, ui, is distributed mi-vector of zeroes, ith set of random effects, mi, and mi is the order of ui. Further, with y being ordered so that all observations for the control treatment are followed by those for the salt treatment, the distribution of the residual effects e is assumed to be:where the ASReml-RASRemlPluswhere F-tests were conducted for linear trends and terms involving the terms lines and conditions from the equations. From these analyses, the best linear unbiased estimates were obtained. RGRs, TRs and TUEs were obtained for the line-treatment  combinations and the EGRI indices were obtained for each genotype.The MLM analysis for EGRI was based on an MLM derived from the model above, by removing the terms involving Treatments. In the case of RGRs, TRs and TUEs, REML ratio tests were conducted to test for unequal condition variances. For all traits, REML ratio tests were used to test for zone and main-plot deviations and curved trends. The fitted model reflected the results of these tests. Wald chart.Correlation from the package PerformanceAnalytics a package for the R statistical computing environment44At each time interval, correlation analysis was performed for the main traits  with the Pearson option using the function indica and aus) independently and also with a combined single set (INDAUS). Genotypic data were composed of the HDRA data set, a 700k SNP array designed by McCouch's laboratory at Cornell Universityindica subpopulation, 394,785 SNPs in the aus subpopulation and 304,877 SNPs in the combined INDAUS subpopulations for GWAS. This project also compared two sets of genotypic information in the indica panel, -‘GBS 44k SNP'13Association analyses were performed with the two diversity panels (K) matrix as a random effect to control for population structure50INDAUS, we also included principal components to control for population structure, because of the deep population structure in rice21http://www.maizegenetics.net/tassel)ASReml-RWe used two GWAS approaches: one considers the response for each salt treatment in a separate model, and the other, referred to as the interaction model, amalgamates each response for both treatments. Both approaches use an MLM that is fitted to include the kinship , γ is the vector of fixed interaction effects between the treatment and marker, u is the random vector of line effects and e is the vector of residual effects. X, S, T and Z are the design matrices corresponding to β, α, γ and u, respectively. The variance of the line random effects are K is the genomic relationship matrix calculated using TASSEL software and I is the identity matrix and P values for marker–trait associations were determined using the Bonferroni-adjusted threshold of α=0.05, which corresponded to P=8.99 × 10−6, 2.57 × 10−6 and 3.02 × 10−6 for the INDAUS, indica and aus subpopulations, respectively.Here P values. Although other previous rice GWAS studies have used a significant threshold of P=10−5 for the conventional MLM using TASSEL24Because the use of the conventional MLM with TASSEL is less statistically powerful than our interaction model, we found that neither the Bonferroni nor false discovery rate5310 of the genome-wide observed P values were displayed in Manhattan plots The LD statistic ASReml-R, the data were processed using a laptop computer.No computational approaches were used to reduce computational time because the use of splines and further interval selection significantly reduced the number of calculation time points for our dynamic analyses. For TASSEL, the data were processed in parallel in a cluster environment, while for the interaction model in http://datadryad.org/, doi:10.5061/dryad.3118j). The authors declare that all other data supporting the findings of this study are available within the article and its The data from which the traits were calculated, the trait values, the codes used in producing the trait values and the code for the interaction model used for GWAS analyses are all available in Dryad .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figure 1-10, Supplementary Tables 1-6 and Supplementary References.Time lapse movie of two rice plants of the same accession growing over 13 days, one in control conditions and one under saline conditions.2) was based on genotype allele counts and was estimated for pairs of SNP loci.Significant SNP and Local LD surrounding the significant SNPs for all the traits. Significant SNP list for the interaction model used for the traits: RGR, TR & TUE. Each excel sheet presents the significant SNPs and Local LD for indica, aus, INDAUS, respectively. The LD statistic (r


ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation.Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene  Let ei be an environmental risk factor for the ith subject. Based on the above notation, we define the MixGE model as::We now introduce a unified set-based mixed effect model for gene-environment interaction (MixGE). We assume that there are g(·) is a link function. When yi only takes the values of 0 or 1, g(·) can be a logit function. When yi is continuous, g(·) can be an identity function. T represents the vector transpose operation. βx and βe are the regression coefficients for Xi and ei respectively, where βx is a m × 1 vector and βe is a scalar. W is a p × q weight matrix based on q variant characteristics  of p SNPs, if effects of the various characteristics are expected to be different  and π(2) are q × 1 vectors and account for fixed effects of the variant set Gi, and its interaction with an environmental risk factor ei respectively. On the other hand, p × 1 vector and accounts for random effects of individual SNPs in Gi, and their interaction with the environmental risk factor ei, that cannot be explained by the fixed effects. We assume that δ follows a distribution with mean 0 and variance τ2.2 = 0. The MixGE model is hence simplified to an interaction model of burden test. In particular, when W is set as a p × 1 vector of 1/p, the MixGE model becomes an interaction model of Cohort Allelic Sum Test  and π(2) are set to zero, this model is equivalent to an interaction model of SKAT  is a generalized form of commonly used models such as burden test and SKAT  = 0 and τ2 = 0. We use score statistics to test H0 so that – unlike the likelihood ratio test – there is no need to estimate τ2 under the alternative. Estimating τ2 can be computationally expensive as it requires p-dimensional multiple integration. As the score statistic for π(2) asymptotically follows a normal distribution and the score statistic for τ2 asymptotically follows a mixture of chi-square distributions, it is challenging to derive the joint distribution of π(2) and τ2, especially when they are correlated. We propose a sequential way to calculate the score statistics for τ2 and π(2), which will make them independent of one another and hence make the testing of H0 feasible. For this, we first derive the score statistic for the variance component under the null hypothesis of τ2 = 0 without assuming that π(2) = 0. We then derive the score statistic for π(2) under H0. This sequential derivation ensures the independence of these two score statistics, which has been proven in Sun et al. (From the model in Equation (1), our null hypothesis n et al. .E as the diagonal elements of an N × N matrix. We now show how to compute the score statistics for τ2 and π(2).To simplify notations, we rewrite Equation (1) in the matrix form. By denoting 2 can be calculated as:First, the score statistic for τx, βe, π(1), and π(2) respectively under the null hypothesis of τ2 = 0. These coefficients can be obtained via solving the least-squares linear regression of 2 = 0, 1≥ … ≥ λs are the non-zero eigenvalues of M = . This mixture of chi-square distributions can be approximated using the method in Liu et al. . For instance, ωj can be set to be negatively correlated with MAF (MAF) since by evolutionary theory rarer genetic variants tend to have larger effect sizes.To be more general, when the variances of δ(2) as:Second, we compute the score statistic for πx, βe, and π(1) respectively under the null hypothesis of τ2 = 0 and π(2) = 0. These coefficients can be obtained via solving the least-squares linear regression of q-dimensional normal distribution with mean of zero and covariance of identity matrix. V = . yi is continuous and yi is binary. To avoid computing the square root of Σ, we compute 2 distribution with q degrees of freedom. We denote the p-value of Based on the central limit theory and the law of large number, we can show that 2 = 0 and π(2) = 0. Two possible ways to combine H0 is rejected if H0 is rejected if Based on the above construction, the score statistics of h et al. , becausei refers to the ith subject, j refers to the jth variant of the genetic set, Ei ~ N is an environmental factor, and Gij is a genetic set generated under Hardy-Weinberg equilibrium with MAF ~ U. How aj and bj were set differs depending on whether the simulation is for type 1 error or for power, and will be explained accordingly in the respective subsections 2.3.1 and 2.3.2. The constant coefficients are set to be the same as Lin et al. (Yi can be generated by taking the logistic of the right hand side of Equation (8), and using that as the probability of success for a Bernoulli trial. Success would represent a disease case subject, whereas failure would represent a control subject. Simulated subjects were generated until there were sufficient cases and controls.MixGE was run on simulated data to ensure that it has a reasonable type 1 error rate, and to compare its power against other existing methods. Two separate simulations were carried out for binary and continuous phenotypes. For binary phenotype, simulated data were generated according to the following model adapted from Lin et al. :(8) logn et al. . Yi can 2). The intercept term log(0.01/0.99) found in Equation (8) was removed for Equation (9) as it models disease prevalence and is thus meaningless for the continuous phenotype, although the simulation results would not have been affected even if it were included.For continuous phenotype, simulated data were generated according to the following model modified from Equation (8):Simulated data were generated separately for small samples with 200 subjects (100 disease cases and 100 controls) as well as large samples with 2000 subjects (1000 disease cases and 1000 controls). The reason for simulating small samples is to examine the behavior of MixGE when the number of subjects available is small, which is typical for imaging genetics studies. For every simulated scenario, 10,000 simulations were run.a was fixed as T while b was fixed as zero meaning that there is no G×E effect. Gij and Ei were regenerated for each simulation. To determine type 1 error rate, p-values for the G×E effect for 10,000 simulations were calculated. The p-values were then thresholded at a significance level of 0.05. Type 1 error rate is the proportion of simulations that pass the significance threshold, even though there is no G×E effect, out of the total 10,000 simulations run.For type 1 error simulation, aj ~ Bernoulli(0.5) × [1−2 × Bernoulli(0.5)]. Three different scenarios were tested in the power simulation, each with a different b. b was fixed as C × T for the burden scenario, as C × T which has zero mean for the variance component scenario, and as C × T for the mixture of burden and variance component scenario. For binary phenotype, C is given an arbitrary range of positive values such that the simulation is within reasonable power range. For continuous phenotype, C is fixed as 1 while varying the variance of the Gaussian noise. aj, Gij, and Ei were regenerated for each simulation. Power in this simulation is defined as the rate at which the various methods are able to significantly detect G×E effect, i.e. the proportion of simulations that pass the significance threshold of 0.05 out of the total 10,0000 simulations run for each C for each scenario.For power simulation, MixGE was compared against two existing methods: set-based gene-environment interaction test (SBERIA) , the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public private partnership. The primary goal of ADNI has been to test whether biological markers such as serial magnetic resonance imaging (MRI) and positron emission topography (PET), and clinical and neuropsychological assessments can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials. ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. The initial goal of ADNI was to recruit 800 adults, ages 55 to 90, to participate in the research approximately 200 cognitively normal older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years, and 200 people with early AD to be followed for 2 years (see http://www.adni-info.org/ for up-to-date information). The data were analyzed anonymously, using publicly available secondary data from the ADNI study, therefore no ethics statement is required for this work.The data used in this study were obtained from the ADNI database This study only included 697 subjects (age: 55–91 years) with MRI scans and genotype, environmental and demographic data. Among them, there were 295 females and 402 males. The amount of education received by these subjects ranged from 4 to 20 years.The environmental factors used in this and previous studies : All ADNI 1.5T structural brain MRI scans were processed using FreeSurfer  adjustment on the obtained p-values, according to the procedure outlined in Storey  matrix with the first column of constant, the second column of intra-cranial volume, and the third column of education. W is a p × 1 vector with elements of 1/p.For real ADNI data, we run MixGE on hippocampal volume to compare with the results obtained by Ge et al.  using KMAccording to the simulated data for binary phenotype, MixGE had type 1 error rates of 0.0479 and 0.0507 for small samples of 200 subjects and large samples of 2000 subjects respectively. The type 1 error rates for SBERIA  is a transmembrane transporter that mediates the biogenesis of high-density lipoprotein . The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.This work was supported by the Singapore National Medical Research Council , and the Canadian Institutes of Health Research [MOP-115110]. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI)  and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimers Association; Alzheimers Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Analyses of sequence variants of two distinct and highly inbred chicken lines allowed characterization of genomic variation that may be associated with phenotypic differences between breeds. These lines were the Leghorn, the major contributing breed to commercial white-egg production lines, and the Fayoumi, representative of an outbred indigenous and robust breed. Unique within- and between-line genetic diversity was used to define the genetic differences of the two breeds through the use of variant discovery and functional annotation.FST) analysis and subsequent gene ontology (GO) enrichment analysis elucidated major differences between the two lines. The genes with high FST values for both breeds were used to identify enriched gene ontology terms. Over-enriched GO annotations were uncovered for functions indicative of breed-related traits of pathogen resistance and reproductive ability for Fayoumi and Leghorn, respectively.Downstream fixation test  contains supplementary material, which is available to authorized users. With the availability of new and more powerful next generation sequencing technologies, massive amounts of molecular data can be generated from individual or pooled genomic DNA samples. Discovery and characterization of variants within and among individuals allows definition of the genetic dissimilarities that may underlie phenotypic variation. Genome resequencing data from within-population pooled samples can be effectively used to characterize genetic variation within and between populations, and to accurately estimate allele frequencies . There aEimeria tenella, a parasite that causes coccidosis [Since domestication, the selection for specific traits and directed evolution in livestock has led to the creation of different breeds or a “Domestication Phenotype” within a species . The chiccidosis . For theccidosis . These tThe Fayoumi and Leghorn pooled sequence data were each compared against the Red Jungle Fowl (RJF) reference genome  to identify single nucleotide variants (SNV) and insertion/deletions (indels) present in these populations Table . AnalysiP < 0.01) more variants annotated as downstream, upstream, start_gained, and frame_shifts in Leghorns than Fayoumis.Table P < 0.01) difference in the number of variants that had an effect either upstream or downstream of their location . Of the 61 usable assays for validation of the presence of an SNV, over half (38) showed evidence of duplication based on analysis of the KBiosciences Kompetitive Allele Specific PCR genotyping system (KASP) KlusterKaller software output . DuplicaFST analysis.The lines used in this study have not undergone genetic selection since their establishment in 1954, with the exception of selection for adequate reproduction in both lines. The fixed or segregating variation should represent a combination of natural selection, genetic drift, and mutation in the Fayoumi and Leghorn lines. To categorize the potential impact of variations, the annotation program groups the functional impacts of variants as high, moderate, low and modifier. These impact categories were analyzed for fixed, unique (line-specific) variants from either Fayoumi or Leghorn using gene ontology enrichment analysis. Fixed unique variants for each line are assumed to represent either the alleles that were selected during domestication, breed selection, or those fixed during the over 60-year process of inbreeding. There were no statistically over-represented GO terms in the analysis of the high and low impact variants. However, variants that were classified as moderate impact were statistically associated with GO terms (Table FN3), and tyrosine-protein phosphatase 3 (PTPc) were statistically significant , a member of the cytokine receptor superfamily, adipocytokine, and JAK-STAT signaling pathways that promote inflammatory responses to pathogens, as well as angiogenesis and cellular repair processes. Leptin receptor expression can also affect follicle formation in breeder hens [FDR ≤ 0.05). However PTPc disappeared and was replaced with statistically significant GO terms for cytokine-cytokine receptor interactions and natural killer cell  was represented in the Fayoumi gene list under the GO term FN3. The prolactin receptor has a role in egg production in chickens [LEPR and PRLR both were present in the statistically significant GO term lists based on the exploratory and strict variant filter lists. Both genes showed a decrease in the number of variants associated with all functional impacts  based on filtering, but still remain associated to the over-enriched FN3 term.The Prolactin receptor  for the GO terms of cell division, calcium ion binding, phospholipid activity and extracellular matrix annotations , an inhibitor of metalloproteineases that degrades the extracellular matrix and suppresses endothelial cell proliferation. Highly conserved in multiple species, TIMP2 plays a role in chicken eggshell production and embryogenesis [EGF) which affects multiple pathways including extracellular growth and differentiation, focal adhesion, and MAPK signaling pathways [Within-population analysis of the fixed and unique Leghorn variants different from the RJF reference, revealed GO terms related to reproductive phenotypes. Gene set enrichment analysis of the moderate impact variants gave significant results  based on filtering.Strict filtering of the variants for the Leghorn  have been previously deposited in dbSNP at the time of analysis, generating 3,223,583 (72.2 %) previously uncharacterized variants called within the Fayoumi experimental line. Similar results were obtained in the analysis of the Leghorn resequencing data vs. RJF reference assembly. A total of 4,605,732 variants were discovered of which 3,287,720 (71.3 %) were previously uncharacterized. We also examined the number of variants in each population using SnpEff categories of “effects by type” and “effects by region”. A subset of 2,052,537 variants was unique to Fayoumi and 2,196,553 unique to Leghorn after removal of variants common to both groups.FST) analysis was conducted using the program PoPoolation2 [FST method [FST value between the populations was calculated for each gene represented in the variant call file output based on the allele frequencies at every base for each gene. The FST analysis generated a list of genes with FST values of 1 in which the structure of the two lines showed the most differentiation. Further analysis, using DAVID, of the genes from this analysis indicated that the two lines mainly differed in nucleoside and nucleotide binding, catalytic activity, and ATP usage (adjusted p < 0.05). The DAVID  were over-represented terms that emerged from the GO analysis of the FSToutput. The biological processes represented by ontology terms such as immune system process are consistent with the historical breed phenotype for the Fayoumi and may be related to the reported phenotypic differences in pathogen resistance between the breeds [FST and GO analyses included: biological adhesion, developmental process, and cellular protein modification process . It is possible that these terms may be associated with the Leghorn breed’s historical breed phenotype of selection for egg production [To help characterize the differences and similarities between the inbred populations of Fayoumi and Leghorn chickens, a fixation index  were located in micro-RNAs and small nuclear RNAs e.g. gga-mir-6676 and gga-mir-6616, which are related to chicken gastrulation and embryogenesis [To facilitate between-line analysis of variants and reduce bias related to choice of the reference sequence, the nucleotides in the RJF reference genome were replaced with the discovered Leghorn variants to create an alternate genome assembly. The Fayoumi was then realigned to this new reference for variant analysis. When compared for SNVs against the Leghorn, the Fayoumi displayed a total of 3,792,327 differences. Of these differences, 2,697 had high and 25,095 had moderate impacts, numbers higher than the 1,130 high and 15,468 moderate impact variants called in Fayoumi against the RJF sequence. In this comparison to the Leghorn, the Fayoumi had less 5’UTR, and downstream annotated variants than were called against the RJF sequence . The low levels of within-line variation were consistent with the lines’ extreme levels of inbreeding and the high between-line variation was concordant with the lines’ diverse backgrounds. The Leghorn had more fixed variants and the Fayoumi more heterozygous variants, compared to the RJF reference. Greater fixation in the Leghorn line may be a result of stringent historical selection for a limited number of traits in this breed, where as greater genomic heterozygosity may be an advantage for disease-resistance traits in the Fayoumi. For both lines, most variants were in intergenic and intronic regions, limiting their impact on the survivability of the populations. The major genetic differences between breeds by The chickens were produced and maintained in the Iowa State University poultry genetics program. The Fayoumi and Leghorn breeds broadly represent a divergent history of either natural selection for disease resistance or artificial selection for reproduction, respectively , 36. TheThe DNA from 16 birds per line were pooled by line, in equal quantities, and used for resequencing. DNA was isolated from blood using an in house DNA isolation procedure. Quality and concentration were determined through NanoDrop testing. DNA was sequenced using DNA Landmarks via the Hiseq 2000 using TruSeq V3 chemistry.The Burrows-Wheeler Aligner was used to align sequence reads to the Galgal4 reference genome using the default settings for gap extensions, gap and mismatch penalties . The SamSNVs and indels were called using the Genome Analysis Tool Kit Unified Genotyper (GATK-UG) tool. The GATK-UG has the ability to call variant sites within pooled samples and thereby provide an estimate of allele counts and frequency within a population , 41. Thein silico reference genome based on one of the experimental populations was created with the GATK FastaAlternateReferenceMaker option. The Leghorn vcf file was used as a variant file to replace the RJF reference alleles with Leghorn variants. Leghorn vcf was chosen to create the alternate reference because it is the breed most used for commercial white-egg layers and could be useful for other comparisons of lines of commercial interest. This tool can only lift over consensus Leghorn SNV positions (not indels) to the reference assembly. Because of this limitation, all downstream analyses of the Fayoumi vs. Leghorn data were based only on SNVs.To facilitate a direct comparison of the variants contained within the Fayoumi and Leghorn populations, an Gene annotation and prediction of the functional consequences of variants was done with SnpEff . Each vaFST method [FST value of 1.0 to represent genes showing possible differences in function between the populations.The fixation analysis was performed using PoPoolation2  to examiFST data was carried out in REViGO, which reduces the list of terms based on uniqueness and dispensability. Uniqueness is a measure of whether the term is an outlier when compared semantically to the list of generated GO terms. In addition, the program also reduces the functional redundancies [FST analysis, a list of 9,573 genes was used for the FST GSEA analysis.For the within population data analysis, gene set enrichment analysis (GSEA) and GO analysis was performed using DAVID  and (GO)ndancies  by filtendancies  was re-andancies  and (GO)ndancies  based on


Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort  and schizophrenia in the Psychiatric Genomics Consortium cohort , we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology. This is not only the case for personality disorders but also other mental disorders such as schizophrenia4. However, the mechanisms underlying this relationship remain elusive. Although personality and schizophrenia may relate to each other in several ways, such as influencing the expression of one another, or predisposing for the development of one another, an intriguing question remains whether variation in personality and schizophrenia in fact share causative factors3. Both personality traits and schizophrenia are influenced by genetic factors with moderate to high heritability estimates . Genome-wide association studies (GWAS) have revealed that the genetic architecture of personality traits and schizophrenia are composed by a number of common genetic variants with small effects11. Recently, we10 and others12 reported significant genetic correlations between several personality traits and schizophrenia12. These findings indicate that personality traits and schizophrenia exist on a continuum in genomic space, and that some genetic variants associated with personality traits also affect susceptibility to schizophrenia12. In order to increase the understanding of the specific molecular genetic mechanisms jointly influencing personality traits and schizophrenia, and inform the underlying biology linking these normal and pathological mental phenotypes, we here aimed to identify genetic loci shared between schizophrenia and personality traits.Research on the construct and variation of human personality has revealed that personality and psychopathology are related14. Through questionnaires, individuals are placed on continua of all five traits, which intend to account for basic behavioral tendencies that are universally present and stable throughout life15. Studies on personality in schizophrenia consistently find increased neuroticism, decreased extraversion and decreased conscientiousness compared to normative levels or healthy controls24, and these differences appear to persist through active and residual phases of the illness24. Moreover, longitudinal studies report that apparently healthy individuals with high neuroticism and low extraversion were more likely to be diagnosed with schizophrenia later on26, indicating that differences in personality might precede the onset of schizophrenia.The most influential personality model to emerge is the five-factor model, in which personality is defined by the five broad traits (the Big Five) neuroticism, extraversion, openness, agreeableness and conscientiousness11, while fewer SNPs are identified for personality traits10. Recently, we conducted a GWAS on the Big Five personality traits in the 23andMe cohort and identified six replicable variants associated with personality traits10. We also quantified genetic correlations between schizophrenia and personality traits using linkage disequilibrium (LD) score regression and compared personality trait GWAS data from 23andMe  with GWAS data on schizophrenia from the Psychiatric Genomics Consortium . We found that schizophrenia was significantly correlated with openness and conscientiousness, but not with the other personality traits10. However, another study reported a significant genetic correlation between neuroticism and schizophrenia using GWAS data on neuroticism in the UK Biobank sample  and GWAS data on schizophrenia in the larger PGC cohort 12. It is important to note that while correlations estimated by LD Score regression indicate the degree of shared genetic influences between two traits at the genome-wide level, they are agnostic about the individual variants jointly influencing these phenotypes.Despite the assembly of very large GWAS cohorts (over tens of thousands of participants) much of the genetic architecture underlying susceptibility to schizophrenia and personality traits remains to be defined, and their biological underpinnings are still largely unknown. To date, GWAS have identified hundreds of single nucleotide polymorphisms (SNPs) for schizophrenia11. However, in aggregate, common SNPs explain a substantial portion of the variance of schizophrenia27 and personality traits10, indicating the presence of many variants with associations too weak to be detected using standard GWAS statistical analysis. To extract more of the “hidden heritability” within existing GWAS, we have developed Bayesian statistical tools that leverage the polygenetic overlap between complex traits30. Specifically, we condition the false discovery rate (FDR) for discovery of SNPs in a primary trait on overlapping associations in a secondary trait30. Using this methodology, we have successfully increased discovery of genetic risk loci and identified shared loci between schizophrenia and associated phenotypes including bipolar disorder31, cardiovascular disease risk factors32, and multiple sclerosis33. Here, we applied the same statistical approach and analyzed GWAS on the Big Five personality traits and schizophrenia to identify shared genetic loci.The genome-wide significant SNPs identified by GWAS only explain a minor fraction of the estimated heritability11. Further, the Regional Committees for Medical Research Ethics - South East Norway have evaluated the current protocol and found that no additional institutional review board approval was needed, because no individual data was used. All methods were performed in accordance with the relevant guidelines and regulations.All GWASs investigated in the current study were approved by the local ethic committees and informed consent was obtained from all participants10 and data on schizophrenia from the Psychiatric Genomics Consortium (PGC) 2 study 11. Details of the inclusion criteria, genotyping and phenotype characteristics are described in the in the original publications11. We corrected all p-values for inflation using a recently developed genomic inflation control procedure35.We obtained GWAS results in the form of summary statistic . Data on personality traits were acquired from the 23andMe cohort 34. The enrichment seen can be directly interpreted in terms of true discovery rate (TDR = 1 − FDR)36 . We also constructed fold-enrichment plots, which are equivalent to conditional Q-Q plots, but provide a more direct visualization of polygenic enrichment. To assess for polygenic effects below the standard GWAS significance threshold, we focused the conditional Q-Q plots and fold-enrichment plots on SNPs with nominal log10(p) < 7.3 (corresponding to p > 5 × 10−8) and after random pruning, where one random SNP per LD block (defined by an r2 > 0.1) was used37. As with other methods evaluating genetic overlap using GWAS data, including LD score regression38 and stratified FDR39, complex correlations among the test-statistics may bias the estimate of the conditional FDR. The extended major histocompatibility complex (MHC)11 (location 25652429–33368333) and chromosomal region 8p23.1 (location 7242715–12483982) are two regions with well-known complicated LD structures40. Therefore we also constructed conditional Q-Q plots after excluding SNPs within these regions. Furthermore, to be conservative, we removed SNPs outside the MHC or 8p23.1 in LD (r2 > 0.1) with any SNP in these regions.To visualize overlap in SNP associations, we constructed conditional Q-Q plots where we display the distribution of p-values for the primary phenotype conditional on significance levels in a secondary phenotype. Associations in the primary phenotype  were conditioned on a p-value threshold in the secondary phenotype , i.e. p < 0.1, p < 0.01 and p < 0.001. If statistical enrichment of the primary phenotype exists, there should be successive leftward deflections as levels of association with the secondary phenotype increase28. The standard FDR framework derives from a model that assumes that the distribution of test statistics in a GWAS can be formulated as a mixture of null and non-null effects, with true associations (non-null effects) having more extreme test statistics than false associations (null effects). The FDR can be interpreted as the probability that a SNP is null given that its p-value is as small as or smaller than its observed p-value. Conjunction FDR, denoted by FDRtrait1&trait2 is defined as the posterior probability that a SNP is null for either phenotype or both simultaneously, given that its p-values for both traits are as small as or smaller than the observed p-values34. We obtained a conservative estimate of conjunction FDR via the conditional FDR. The conditional FDR, denoted by FDRtrait1|trait2, is defined as the posterior probability that a given SNP is null for the first trait given that the p-values for both traits are as small or smaller than the observed p-values34. A conservative estimate of FDRtrait1&trait2 is given by the maximum between FDRtrait1|trait2 and FDRtrait2|trait141. Hence, the conjunction FDR is the maximum of the conditional FDR for schizophrenia given a personality trait and vice versa, and SNPs that exceed a stringent conjunction FDR threshold are highly probable to be non-null in both schizophrenia and the personality trait simultaneously. While the conditional FDR can be used to reorder SNPs based on the additional information provided by the associated secondary traits, the conjunction FDR pinpoints shared loci, since a low conjunction FDR is only possible if there is an association with the two traits of interest jointly. We used an overall FDR threshold of 0.05. Given that the conjunction FDR is a genome-wide approach, it is possible that inclusion of larger LD blocks can impact the model fit and confound the results. Therefore we computed the model after random pruning and excluding SNPs within the MHC and 8p23.1, two genomic regions that show intricate LD and are associated with schizophrenia11 and neuroticism12, respectively.To detect shared genetic loci we used a genetic epidemiology framework based on the conjunction FDR2 > 0.1 with any higher ranked SNP. On the basis of 1KGP LD structure, significant loci identified by conjunction FDR < 0.05 were clustered into LD blocks at the LD – r2 > 0.1 level. These blocks are numbered (locus #) in Table 2 > 0.1 with any higher ranked SNP.To visualize the location of the shared genetic variants associated with personality traits and schizophrenia, we constructed a ‘Conjunction FDR Manhattan plot’, showing all SNPs with a significant conjunction FDR within an LD block in relation to their chromosomal location. The strongest signal was identified after ranking all SNPs based on the conjunction FDR and removing SNPs in LD r11. Sub-studies were randomly partitioned 500 times. For each random partition, half of the sub-studies were randomly assigned to the “discovery” sample and the complement to the “replication” sample. The combined discovery z-score and combined replication z-score of each SNP were calculated, and the average rate of replication (p < 0.05) was assessed across 1,000 equally spaced bins spanning the range of –log10 observed in the discovery samples. Cumulative replication rates were calculated independently for each of the three pleiotropic enrichment categories, as well as for all SNPs. For details, see Supplementary methods.We assessed whether pleiotropic enriched schizophrenia-SNPs replicate at a higher rate using the 52 schizophrenia GWAS sub-studies10(p)], we found approximately 10-fold enrichment using neuroticism, 20-fold enrichment using extraversion, 20-fold enrichment using openness, and 10-fold enrichment using agreeableness. In the reverse fold-enrichment plots displaying personality traits conditional on different levels of significance in schizophrenia, we found approximately 175-fold enrichment for neuroticism, 100-fold enrichment for extraversion, 10-fold enrichment for openness, and 8-fold enrichment for agreeableness, but no detectable enrichment for conscientiousness  with such SNPs had a minor effect on enrichment. Given the strong association of chromosomal region 8p23.1 with neuroticism12, and intricate LD in this region, we constructed conditional Q-Q plots excluding SNPs located within 8p23.1 and other SNPs in LD (r2 > 0.1) with such SNPs. The figures demonstrate that the genetic overlap between schizophrenia and neuroticism largely depends on associations in this region , SDCCAG8 , LRRC16A , PSORS1C1 , DGKI (3′-UTR variant), and AK093940  , TNKS  and EP300  , whereas the small points represent the non-significant SNPs. We also constructed conditional FDR Manhattan plots for neuroticism given schizophrenia, openness given schizophrenia, and schizophrenia given neuroticism and openness, to visualize conditional loci , the effect directions were concordant. Due to a T-A polymorphism, z-scores were not computable for the locus at LRRC16A (rs940404). The 8p23.1 loci show inverse associations in neuroticism and schizophrenia, while the EP300 locus shows concordant associations.To visualize the shared loci, we constructed a conjunction FDR Manhattan plot Fig. . All SNP11. Supplementary Figures 10(pneuroticism) ≥ 3). Consistent with the pattern observed for replication rates in schizophrenia substudies, we found that the effect sizes of SNPs in enriched categories  ≥ 3) replicated better than effect sizes of SNPs in less-enriched categories  ≥ 1; Supplementary Figures To address the possibility that the observed pattern of differential enrichment results from spurious  associations, we also calculated the empirical replication rate across the independent substudies contributing to the schizophrenia GWAS meta-analysis43.In the present study, we analyzed GWAS data on schizophrenia and the Big Five personality traits using the conditional FDR approach to evaluate overlap in common genetic variants. By conditioning on overlapping SNP associations, we were able to identify six loci shared between openness and schizophrenia, and two loci shared between neuroticism and schizophrenia. To validate our approach, we show that schizophrenia SNPs replicate at a higher rate across independent schizophrenia sub-studies as a function of association with personality traits. Altogether, our study provides new insights into the genetic architecture of schizophrenia and personality traits by identifying genetic loci that link personality trait variation and susceptibility to schizophrenia. The findings comply with accumulating evidence from genetics and neuroscience suggesting that mental disorders are not discontinuous with normal variation in neurobiological and behavior dimensions10. In line with this result, five of the loci here found to be shared between schizophrenia and openness showed the same direction of allelic effects in the phenotypes, while one locus showed ambiguous effect directions   shows the same direction of allelic effects in schizophrenia and neuroticism, while the effect directions were opposite at chromosomal region 8p23.1  has been implicated as a potential hub for neuropsychiatric disorders56. The region is subject to structural variants associated with schizophrenia and autism, and shows high linkage to schizophrenia, bipolar disorder, and neuroticism among other phenotypes 57 were analyzed10, while the present study analyzed GWAS data on schizophrenia in the recent and larger PGC2 cohort 11. Another possibility is that the observed negative genetic correlation between conscientiousness and schizophrenia using LD score regression is driven by a number of loci with too small effects to be detected by the conditional FDR approach. Further, although schizophrenia is associated with decreased levels of extraversion24, we did not detect any significant loci shared between schizophrenia and extraversion in the present study. However, the pleiotropic enrichment demonstrated by the conditional Q-Q plots (Figure 10.Despite the clinical association between schizophrenia and low levels of conscientiousness33, cardiovascular disease traits32 and immune-related diseases34. The current findings in the Big Five personality traits demonstrate that it may also be applied to genetic research on personality. By combining the 23andMe personality sample with the independent PGC2 schizophrenia GWAS11, we identified significant overlapping signals in a total of eight independent loci. Apart from the loci within chromosomal region 8p23.1, all conjunctional loci were identified by the schizophrenia GWAS11. However, in the 23andMe cohort, only loci associated with neuroticism were identified10. This illustrates the increased power of our combined analytical approach. Although it is likely that the shared loci discovered here would have been identified if the sample sizes in original GWAS had been adequately large, we show how combining summary statistics from independent samples provides an asset for gene discovery without the extra cost and resources needed to obtain new samples. Another strength of our conditional FDR approach is that it enables detection of overlapping variants even when the direction of effect is inconsistent across loci.The conditional FDR methodology has been useful to elucidate genetic overlap in several complex phenotypes including neuropsychiatric disorders43. Further investigation is required to determine the biological implications of the identified genetic variants to elucidate how neurobiological processes are altered to influence personality and risk of schizophrenia.In conclusion, we provide new insights into the genetic etiology underlying schizophrenia and personality traits by increasing discovery of genetic loci and identifying common genetic variants shared between schizophrenia and the Big Five personality traits openness and neuroticism. By highlighting genetic loci that transcend boundaries between schizophrenia and personality dimensions, our study may align with novel conceptual approaches to psychiatric nosologies, in which mental disorders are considered continuous with normal variation in psychological and neural phenotypesSupplementary Information