UK birth cohort to investigate how successfully value‐added measures control for genetic differences between pupils. We use raw value‐added, contextual value‐added  and teacher‐reported value‐added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value‐added measures but explain up to 20% of the variation in contextual value‐added measures (95% CI = 6.06% to 35.71%). Value‐added measures built from teacher‐rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross‐sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value‐added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.Value‐added measures of educational progress have been used by education researchers and policy‐makers to assess the performance of teachers and schools, contributing to performance‐related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a  The largest GWAS of educational attainment published to date identified only 74 genetic variants with a discovery sample size of 293,723, with the single strongest variant estimated to account for only 0.035% of the variation in educational attainment defined by years of education , similar to the heritability of raw educational attainment .In this study, we exploit molecular genetic data to first estimate the heritability of educational attainment at three time points throughout the compulsory educational lifecourse, and second investigate the ability of VA measures created from examination assessment data to control for time‐invariant between‐individual genetic differences (one of their desirable properties). If VA measures are estimated to have non‐zero heritability, this suggests that they are susceptible to the influence of genetics. Throughout this article, we use the term et al. . The ALSPAC cohort is largely representative of the UK population when compared with the 1991 Census data; however, there is under‐representation of some ethnic minorities, single‐parent families and those living in rented accommodation. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. From the core sample of 14,775 children, 14,115 have data on at least one measure of educational attainment. From these children, genetic data were available for 7,988 after quality control and removal of related individuals. We use the largest available samples in each of our analyses to increase the precision of estimates, regardless of whether a child contributed data to the other analyses.Participants were children from the Avon Longitudinal Study of Parents and Children (ALSPAC). Pregnant women were eligible to enrol if they had an expected date of delivery between April 1991 and December 1992 and were resident in the (former) Avon Health Authority area in South West England . The stP < 5 × 10−7) were removed. Cryptic relatedness  was measured as proportion of IBD (> 0.1). Related subjects who passed all other quality control thresholds were retained during subsequent phasing and imputation. A total of 9,115 subjects and 500,527 SNPs passed these quality control filters.In short, DNA of the ALSPAC children was extracted from blood, cell line and mouthwash samples, then genotyped using reference panels and subjected to standard quality control approaches. In full, the children were genotyped using the Illumina HumanHap550 quad chip genotyping platforms by 23andme subcontracting the Wellcome Trust Sanger Institute, Cambridge, UK and the Laboratory Corporation of America, Burlington, NC, USA. The resulting raw genome‐wide data were subjected to standard quality control methods. Individuals were excluded on the basis of gender mismatches; minimal or excessive heterozygosity ; disproportionate levels of individual missingness (> 3%) and insufficient sample replication (identity by descent (IBD) < 0.8). Population stratification was assessed by multidimensional scaling analysis and compared with Hapmap II (release 22) European descent (CEU), Han Chinese, Japanese and Yoruba reference populations; all individuals with non‐European ancestry were removed. SNPs with a minor allele frequency of < 1%, a call rate of < 95% or evidence for violations of Hardy–Weinberg equilibrium (HWE) (Children's genotypes were jointly phased and imputed with the genotypes of the ALSPAC mothers (Illumina human660W quad (mothers)), combining 477,482 SNP genotypes which were in common between the samples. SNPs with genotype missingness above 1% were removed due to poor quality  and a further 321 subjects due to potential ID mismatches. This resulted in a dataset of 17,842 subjects containing 6,305 duos and 465,740 SNPs (112 were removed during liftover and 234 were out of HWE after combination). Haplotypes  were estimated using ShapeIT (v2.r644), which uses relatedness during phasing. We obtained a phased version of the 1,000 genomes reference panel  from the Impute2 reference data repository . Imputation of the target data was performed using Impute V2.2.2 against the 1,000 genomes reference panel  , using all 2,186 reference haplotypes (including non‐Europeans). This gave 8,237 eligible children with available genotype data after exclusion of related subjects using cryptic relatedness measures.Our measures of educational attainment are average fine‐graded point scores at each of the major key stages (KS) of education in the UK: KS2 assessed at age 11; KS3 assessed at age 14; and KS4 assessed at age 16. Point scores were used to obtain a richer measure of a child's attainment than level bandings, with the distributions of the raw scores presented in the supporting information Data We use two sets of VA measures in our analyses. First, we calculated a raw VA score as the difference between standardised point scores for each student at different key stages. This VA measure can be considered the child's cohort‐specific VA score as it is based upon the rank ordering of the child in the cohort at each occasion. Second, CVA measures were extracted from the NPD linked to ALSPAC participants. The CVA measures—using the example of a CVA score between ages 11 and 14—are calculated as the difference between a child's given exam score (age 14) and the score that would be predicted from that child's previous key stage exam score (age 11). The models used to calculate CVA measures are estimated within a multilevel framework whereby students are nested within schools and the intercept is permitted to vary across schools. The CVA models also account for gender; special educational needs; eligibility for free school meals (a proxy for low income); first language; school mobility; ethnicity; month of birth; an indicator of whether a child has been in care; and residential area level deprivation. We present results for both the raw VA and CVA measures throughout our analyses. Raw and CVA measures are strongly related, with correlations of 0.95 at age 11–14, 0.80 at age 11–16 and 0.78 at age 14–16.We also use a teacher‐assessed value‐added (TAVA) measure of the ability of children. Teachers are required to grade their students at multiple time points in English, Mathematics and Science on a scale of 1 to 8. These grades reflect the level at which a teacher deems a student to be working, with higher levels reflecting students working at a more advanced stage. Because the levels run throughout a child's educational career, they can be compared at different time points to assess progress. The TAVA measure we use was calculated as the difference between the mean level of English, Mathematics and Science at ages 11 and 14, thus representing progress during these years. Teacher‐reported ability is not available in the NPD at age 16, meaning that our TAVA measure only covers the one educational period. Correlations between the TAVA measure and the VA and CVA measures are 0.333 and 0.405, respectively, far lower than the correlation between the VA and CVA measures of 0.919. We return to this in the discussion.p = 5 × 10−8) in the largest GWAS of education to date  that can be attributed to common genetic variation:To estimate the proportion of variation in educational attainment and VA measures that can be attributed to common genetic variation (SNP heritability), we run a series of univariate analyses using generalised restricted maximum likelihood (GREML) in the software package GCTA. GCTA uses measured SNP level variation to estimate the genetic similarity between every pair of unrelated individuals in the sample and compares this to their phenotypic similarity. Unrelated participants (less related than second cousins) are determined using the ALSPAC Genetic Relatedness Matrices (GRMs). Our univariate analyses are specified as follows:https://github.com/timtmorris/VA-heritability.If, across the sample, genetically similar pairs are more phenotypically similar than genetically dissimilar pairs, then heritability estimates of the phenotype will be higher. Population stratification can bias heritability estimates, and can occur if different subpopulations in the sample have systematic differences in allele frequencies due to ancestral differences. To control for these population‐specific variations in allele distributions, the first 20 principal components of inferred population structure are included as covariates in analyses. All continuous variables were inverse normally rank transformed to have a normal distribution, a requirement of GCTA. Power calculations are presented in supporting information Data Table et al., The SNP heritabilities of educational attainment over time are presented in Figure hSNP2 = 20.09%; 95% CI = 6.06% to 35.71%) and the period of age 11–16 . Heritability estimates for the period of age 14–16 are lower, with little strong evidence for a heritable component . These results consistently suggest that relatively little of the variation in raw VA measures—the unadjusted progress that students make from one key stage to another—can be attributed to common genetic variation. However, they also suggest that the variation in some CVA measures—those that adjust for background factors—can in part be attributed to common genetic variation.Figure hSNP2 = 9.99%; CVA hSNP2 = 16.66% given measurement error of 0.25).To explore this, we ran a series of simulations to determine scenarios in which CVA measures may be more genetically biased than raw VA measures  or differences caused by alternative methods of assessment (a measurement issue). A TAVA measure similar to that used in the previous study was created for the period of education from age 11 to age 14 (teacher‐rated ability was unavailable at age 16). The ages that the teacher assessments were made were similar to TEDS (ages 10 and 12). Figure The final stage of the analysis was to investigate the amount of variance in educational attainment and VA measures that an educational attainment polygenic score (EA PGS) predicts. The results Figure  show thaOur genome‐wide heritability estimates using all common genetic variants provide molecular genetic evidence for a substantial heritable component of educational attainment in ALSPAC, conforming to previous findings from other samples. We find that the heritability of educational attainment increases from 47% at age 11 to 61% at age 16, suggesting that genetics explains a greater proportion of the variation in outcomes at age 16 than earlier ages. This is important, because the age 16 exams play a large role in setting a child's future chances in further education and the labour market. The differences across ages are small and could be due to various factors. For example, parents, teachers and others may work harder at later ages to make the environment more homogenous than at earlier ages. Attitudinal behaviours may also be partly responsible because older children have more freedom to choose their own educational effort with less parental influence. It is also important to note that in some subjects such as Mathematics, children are compelled to enter different tiers based upon ability as assessed after the age 14 exams, which will have placed ceiling and floor caps on the scores that they could attain in the age 16 examinations.et al., et al., Our estimated heritability is higher than the 40.0% (95% CI = 35.3% to 44.7%) estimated using meta‐analyses .minor allele frequency).Allele frequency is the prevalence of a given allele at a genetic locus in the sample, expressed as a decimal, fraction or percentage. The allele frequency is reported in terms of the proportion of alleles that are the effect allele  or in terms of the number of minor, or less frequent, alleles  of the gene encode protein and are interspersed with non‐coding regions (introns). This is distinct from a genetic variant, which can occur in a gene (intragenic) or outside a gene (intergenic).Identical twins share all their germline genome and have a genetic relatedness of one. Siblings share half their genetic code and have a genetic relatedness of 0.5. If two unrelated individuals drawn at random from the population are more genetically similar than you would expect by chance, they will have a genetic relatedness of greater than zero. If they are less alike than you would expect by chance, they will have a genetic relatedness of less than zero. The genetic relatedness matrix stores the genetic relatedness between every pair of individuals in a sample. It can be used to estimate heritability using GCTA (see below).P‐value thresholds  are used to account for multiple testing. The combination of these strict P‐value thresholds and small SNP effect sizes means that GWASs require very large samples. GWASs are typically separated into two parts: an analysis is first performed in a discovery cohort to identify nominally genome‐wide significant SNPs; and second performed in an independent validation cohort to validate these SNPs.A genome‐wide association study (GWAS) tests the associations of hundreds of thousands of genetic variants and an outcome (the phenotype). Due to the number of associations being tested simultaneously, and therefore issues of multiple hypothesis testing, strict Genome‐wide complex trait analysis (GCTA) is a statistical programme that uses a genomic‐relatedness‐based generalised restricted maximum likelihood (GREML) approach to estimate the proportion of variance in a phenotype that can be statistically accounted for by all common SNPs. GCTA compares the genetic similarity between unrelated individuals and compares it to their similarity on phenotypic traits; where pairs of unrelated individuals are genetically and phenotypically similar, this provides evidence that phenotypic variation can be explained by genotypic variation. GCTA studies typically require sample sizes in the many thousands.Haplotypes are a specific sequence of alleles that are inherited together from a parent, leading to conserved sequences across generations.Hardy–Weinberg equilibrium refers to the principle that genetic variation across a population will remain constant (in equilibrium) over generations in the absence of external disruptive or evolutionary factors. External factors that may disrupt Hardy–Weinberg equilibrium include non‐random mating, mutations, genetic drift and natural selection.H2) is defined as the total proportion of variance in a trait that is explained by all genetic variation, inclusive of additive genetic variance, dominance and epistasis (gene–gene interactions). Narrow‐sense heritability (h2) is the proportion of total variance in a trait that is explained by additive genetic variance. SNP heritability hSNP2 is a measure of narrow‐sense heritability calculated from a given set of genetic variants (SNPs). Heritability is a population rather than an individual parameter, and is specific to both the population and the environment under analysis.Heritability is the proportion of total phenotypic variance in a population that can be explained by genetic variance, and therefore ranges from zero (no phenotypic variance explained) to one . Broad‐sense heritability  is a summed score of the number of alleles associated with a phenotypic trait. These scores are often weighted by the genetic variant's effect size on the phenotype as estimated from a published GWAS. Polygenic scores can use genetic variants that were associated with the phenotype at different P‐value thresholds ranging from genome‐wide significance (p < 5 × 10−8) to liberal thresholds such as p < 0.5. Polygenic scores therefore indicate the summed influence that all genetic variants identified at a given level of GWAS significance have on a phenotypic trait. Because current GWASs are limited in the number of SNPs they can identify, polygenic scores based on these GWAS findings often omit many variants and therefore do not provide an estimate of the total genetic impact on a trait.A polygenic score  applied to the genome‐wide SNP data to infer population structure, then include the resultant principal components as covariates in analysis to account for population‐specific variations in allele distributions.A single nucleotide polymorphism (SNP) is a genetic variant of a single base pair at a specific position in the genome.Data S1. Additional informationClick here for additional data file.


Mnn1 gene as one of the most strongly latitudinally differentiated SNPs in the genome of Drosophila melanogaster in the United States and Australia. Here we report the results of our transgenic analysis of this amino acid polymorphism. Genotypes carrying alternative Mnn1 alleles differed in multiple phenotypes in a direction generally consistent with phenotypic differences previously observed along latitudinal clines. These results support inferences from earlier population genomic work that this variant influences fitness, and support the idea that the alleles exhibiting clines may be likely to have pleiotropic effects that are correlated along the axes favored by natural selection.While significant effort has been devoted to investigating the potential influence of spatially varying selection on genomic variation, relatively little effort has been devoted to experimental analysis of putative variants or genes experiencing such selection. Previous population genetic work identified an amino acid polymorphism in the  Drosophila populations  are typically interpreted in one of two ways  polymorphism at position 2L:7061538  codes for a predicted Histidine(H)/Glutamine(Q) polymorphism at amino-acid residue 703 (H703Q). Mnn1 has three transcripts RA, RB and RC; RB is unaffected by the focal SNP. All remaining melanogaster subgroup species reference sequences carry a Q at this residue, strongly suggesting that H is the derived allele. Indeed, all publicly available Drosophila reference sequences that include this residue are Q, with the exception of D. bipectinata, and D. ananassae, which are predicted to carry the H allele. The neighboring serine at amino-acid position 704 in D. melanogaster is predicted to be a phosphorylation site according to the NetPhos 2.0 software  between higher and lower latitude populations on both continents  , with the derived H allele occurring at higher frequency in higher latitude populations on both continents, consistent with strong, parallel selection. Other attributes of the SNP, such as the high degree of conservation of the corresponding residue and its location far from the breakpoints of any polymorphic cosmopolitan D. melanogaster inversions , is more than 4 Mb away, Our previous population genomic analyses of North America and Australia investigated allele frequency differentiation  and codes for a protein that is predicted to have histone-lysine N-methyltransferase activity. Phenotypic analysis of laboratory mutations has suggested a contribution of the gene to multiple traits, including DNA repair, generalized stress resistance, and development . A stock carrying the Mnn1 null allele, Mnn1-e30  was used for the Pacman injections. All stocks were sib-mated for four generations prior to being used in crosses to reduce the amount of genetic variation segregating within balancer chromosome classes and on the X chromosome.The double balancer stock with genotype Mnn1 gene region . This BAC also contains complete copies of genes CG31907 and Sem1, as well as the 3′ end of milt upstream of Mnn1, and the 5′ end of Nuf2 downstream of Mnn1. We confirmed by Sanger sequencing that the BAC contained the H allele, thus, we used recombineering in E. coli to engineer the Q allele. The region of interest was first replaced by a galK positive cassette, allowing successful recombinants to grow on selective media. A galK positive colony was then electroporated with a repair template to exchange the galK cassette for the Mnn1 Q allele. Positive events (successful exchanges) were isolated by screening for galK negative colonies on minimal media and confirmed by Sanger sequencing. As the BAC backbone already contains mini-white and an attB site, using PhiC31 recombinase, both alleles (H and Q) of the BAC chromosome could be directly inserted at the same attP site on chromosome 3 at cytological band 89E11 in D. melanogaster line #9744. Embryos from line #9744 were injected following standard procedures  and surviving adults were screened for the presence of red eye color reflecting successful insertion of a BAC carrying the mini-white transgene. Homozygous, true-breeding stocks of each transgene (MQ or MH) were established and then both were introgressed into the same Mnn1 homozygous null background (Supplementary figure 1). The resulting lines, which were designated MQ and MH, were validated at the focal SNP by Sanger sequencing. Lines MQ and MH are expected to be isogenic except for the Mnn1 H703Q variant. While we cannot rule out the possibility that low levels of balancer chromosome gene conversion resulted in other SNP differences between strains, such contamination should be random and unlikely to exhibit the large, directional effects on the phenotypes investigated here (below). Because experimental genotypes carried a non-functional endogenous copy of Mnn1, the transgenes were the only source of MNN1 protein for experimental animals.We used the Pacman recombineering resources  requiring a read quality score of 30 for inclusion. The mean coverage of US samples from Georgia, Pennsylvania, and Connecticut were 26.8, 25.9, and 29.7, respectively. We required a minimum of 20× coverage at a site in all populations and at least two observations of an alternate base call to consider a SNP in the analysis as described in Allele frequencies from African and European samples were obtained from the Drosophila Genome NEXUS  were collected with a clean needle and delicately placed on a new spoon with fresh food. All eggs were placed on their side on the food surface, not touching any other egg. Potentially damaged eggs were discarded. Egg collection lasted 1 hr, after which spoons with eggs were distributed into two treatment groups: a control group which was left 60s on the bench and an experimental group which was immediately exposed to UV in an irradiator for 60s as described by 2 and the temperature was maintained similar to the ambient temperature  from each transgenic line  were subjected to cold stress for four time-durations . Vials containing flies were placed on ice for the specified time and then placed back on a laboratory bench at room temperature (25°). The time required for recovery for each fly  was recorded, as was survival at 24 and 48 hr after treatment. These assays are a proxy for cold tolerance.Six-day-old flies  from each transgenic line  were subjected to desiccation stress . Vials were sealed with gauze and placed into a desiccation chamber containing silica beads. The chamber was sealed with petroleum jelly. The time until knockdown/death was recorded at hourly intervals until all flies had died.Five-day-old virgin flies  from each transgenic line  were subjected to heat stress by immersing flies in individual vials into a water bath set to 39° (without temperature ramping). The vial positions in the water bath were randomized. The time until knockdown was recorded.Four-day-old virgin flies  from each transgenic line were subjected to starvation stress . The flies were placed at 25° in a vial without media, sealed with gauze, and affixed end-to-end to a second vial containing wet cotton to provide moisture. The survival/death status was recorded at 4-hour intervals.U-tests, or proportional hazards survival analyses were used for other phenotypes.Statistical analyses were performed using R. As the data were unbalanced and departed from normality, type III ANOVAs were performed on both the untransformed values and the rank-transformed values of UV sensitivity index and chill coma recovery time. The untransformed and transformed data gave the same results (Supplementary Table 1); here we only report the results of the ANOVAs on rank-transformed values. Wilcoxon tests, Mann-Whitney D. melanogaster populations can be found in the NCBI Short Read Archive under BioProject PRJNA495885. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7302002.New sequencing data from US Mnn1 gene region. STF outlier in the US and Australia  and R2 = 0.67 (P = 0.025) for the New World and Old World, respectively. Fisher’s combined probability for the two regions rejects the null hypothesis of no correlation between allele frequency and latitude (P = 0.016). Linear regression on the combined data were also highly significant . Overall, the data in D. melanogaster experience spatially varying selection on embryonic DNA repair resulting from latitudinal variation in UV incidence. First, we demonstrated that lower latitude embryos tend to be more resistant to UV exposure than higher latitude embryos. We further demonstrated that embryo UV resistance is correlated with oocyte repair of damaged sperm DNA. Finally, we observed latitudinal differentiation for early embryo transcript abundance and SNPs associated with DNA repair genes. In spite of the strong correlational support for our hypothesis, our previous work presented no direct evidence of a strongly differentiated variant affecting embryo DNA repair. In D. melanogaster had reduced egg hatch rates compared to higher latitude populations. P < 0.001). The H allele increases embryo viability by about 9%.F = 39.48; P < 0.001). The transgenic lines showed different response to cold treatment, as the Genotype factor was statistically significant  with MH, the high latitude allele, recovering more quickly than MQ  and Treatment  terms were significant, with females recovering faster than males, and longer cold exposures correlated with longer recovery times. We observed no significant interactions between Sex and Treatment factors.The full model ANOVA with genotype, sex and treatment as factors was significant  than males but we did not detect a significant effect of genotype (P = 0.76). Similarly, we found no significant differences between genotypes for heat tolerance , but no effect of Sex or Genotype-by-Sex. This result was consistent with a re-analysis using Wilcoxon tests on the same data sorted by sex (P < 0.001 for both sexes).We used a proportional hazards fit analysis to compare the starvation resistance of the MQ an MH genotypes. We found a significant effect of Genotype  is that while fitness effects can be indirectly estimated from population genetic data and effects of individual variants on particular phenotypes can, in principle, be estimated in the laboratory or in semi-natural populations e.g., , the mea


Final N incl. siblings = 6,297–10,098). A mega‐genome‐wide association study (mega‐GWAS) for each psychotic‐like experience domain was performed. Single nucleotide polymorphism (SNP)‐heritability of each psychotic‐like experience domain was estimated using genomic‐relatedness‐based restricted maximum‐likelihood (GREML) and linkage disequilibrium‐ (LD‐) score regression. Genetic overlap between specific psychotic‐like experience domains and schizophrenia, bipolar disorder, and major depression was assessed using polygenic risk score (PRS) and LD‐score regression. GREML returned SNP‐heritability estimates of 3–9% for psychotic‐like experience trait domains, with higher estimates for less skewed traits  than for more skewed traits . Mega‐GWAS analysis identified one genome‐wide significant association for Anhedonia within IDO2 but which did not replicate in an independent sample. PRS analysis revealed that the schizophrenia PRS significantly predicted all adolescent psychotic‐like experience trait domains . The major depression PRS significantly predicted Anhedonia and Parent‐rated Negative Symptoms in adolescence. Psychotic‐like experiences during adolescence in the community show additive genetic effects and partly share genetic influences with clinically‐recognized psychiatric disorders, specifically schizophrenia and major depression.This study aimed to test for overlap in genetic influences between psychotic‐like experience traits shown by adolescents in the community, and clinically‐recognized psychiatric disorders in adulthood, specifically schizophrenia, bipolar disorder, and major depression. The full spectra of psychotic‐like experience domains, both in terms of their severity and type , were assessed using self‐ and parent‐ratings in three European community samples aged 15–19 years ( Based on principal component analyses, psychotic‐like experiences can be separated into replicable and specific domains, such as positive, cognitive, and negative domains  of adolescent psychotic‐like experiences assigned 3,483 individuals to high‐ or low‐scoring groups based on a measure of positive psychotic‐like experiences that combined paranoia, hallucinations, and delusions , a negative symptoms scale, a depressive disorder scale, and an anxiety disorder scale  from three European studies. As such our study also aimed to identify novel common genetic variants associated with specific psychotic‐like experience domains and to estimate their SNP‐heritability.22.1Three European general population samples of comparable ages were included: Twins Early Development Study (TEDS)  matched items across samples that were capturing the same underlying construct  based on their clinical knowledge and experience with psychotic‐like experience measures. Second, the matched items within each sample were analyzed via principal components analysis, a psychometric approach used to determine individual components underlying the items. This process was used to identify the presence of distinct psychotic‐like experience domains.2, sex*age, sex*age2, study, and the top 8 principal components of ancestry. Principal components of ancestry were jointly calculated across all three samples using PLINK1.9 (https://www.cog-genomics.org/plink2)  and merging the three data sets for combined analysis. For further information see Supporting Information Note S3. Information on DNA collection and genotyping is given in Supporting Information Note S4.2.5SNP‐heritability estimates were calculated within each sample and then meta‐analyzed to provide meta‐SNP‐heritability estimates. The meta‐analysis of SNP‐heritability estimates approach was taken to avoid a downward bias due to possible genetic heterogeneity between samples. Inverse variance weighted meta‐analysis was used. Secondary analysis of mega‐SNP‐heritability, estimated across samples simultaneously, was also performed to estimate the proportion of phenotypic variance that could be explained by the mean genetic effects across samples.Two methods were used to estimate SNP‐heritability: genomic‐relatedness‐matrix restricted maximum‐likelihood (GREML) in genome‐wide complex trait analysis (GCTA), and linkage disequilibrium (LD)‐score regression. Related individuals were included in both meta‐ and mega‐GREML analyses   and unrelated individuals was performed in PLINK (2.7r2 = 0.93) using the haplotype reference consortium data via the Sanger imputation server  at nominal significance.After the discovery sample had been prepared and analyzed for this project, an independent subsample of TEDS participants was genotyped. Of the newly genotyped individuals, 2,359 (incl. 635 MZ pairs) had reported on self‐rated Anhedonia. This independent TEDS sample was used as a replication sample for the validation of the genome‐wide significant association between rs149957215 and Anhedonia identified by the mega‐GWAS in this study. rs149957215 genotypes were imputed : 0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5. Linear regressions were performed in R and GEE was used to account for related individuals. Quantile plots were created to further examine the relationships. If there was evidence of a non‐linear relationship from the quantile plots, the non‐linear relationship was formalized by performing linear regression in a subset of individuals.Using the software PRSice  were used  within a gene called indoleamine 2,3‐dioxygenase 2 (IDO2) . This SNP was imputed in both samples included in the Anhedonia GWAS (TEDS and ALSPAC) with an average imputation quality (INFO) of 0.87 and minor allele frequency of 0.015. The minor allele frequency was similar in both TEDS (0.017) and ALSPAC (0.013). Due to limited LD with rs149957215, neighboring genetic variation showed no significant evidence of association. This association between rs149957215 and Anhedonia did not replicate in the independent TEDS replication sample, showing a non‐significant association (p = 0.81) in the opposite direction. Several loci achieved suggestive significance (p < 1 × 10−5) across the four psychotic‐like experience domains . There was no evidence of confounding with lambdas of 0.99–1.01 and LD‐score regression intercept of 1.00 in all analyses .The mega‐GWAS identified no genome‐wide significant variation for the Paranoia and Hallucinations, Cognitive Disorganization, or Parent‐rated Negative Symptoms domains. The mega‐GWAS of Anhedonia identified one SNP (rs149957215) associated at genome‐wide significance . The top ten genes showing differential expression for each psychotic‐like experience domain are listed in Supporting Information Table S14.Analysis of predicted frontal cortex gene expression associated with psychotic‐like experience domains showed p = .030 at pT = 0.10), Cognitive Disorganization (p = .035 at pT = 0.01) and Parent‐rated Negative Symptoms (p = 5.41 × 10−3 at pT = 0.05) (Table p = 2.47 × 10−3 at pT = 0.010) (Table p = .010 at pT 0.5) and Parent‐rated Negative Symptoms (p = 8.29 × 10−3 at pT = 0.001) (Table The schizophrenia PRS significantly and positively predicted Anhedonia (p = 7.90 × 10−3 at pT = 0.001) . The majority of individuals (81%) below the median had a raw score of zero. A single post‐hoc analysis was performed to formalize the presence of this non‐linear relationship. Post‐hoc removal of individuals with a raw Paranoia and Hallucinations score of zero led to the schizophrenia PRS positively predicting Paranoia and Hallucinations , and the Cognitive Disorganization psychotic‐like experience domain and major depression .Table Table 4A genetic relationship was identified between clinical schizophrenia and positive, cognitive, and negative psychotic‐like experience trait domains in adolescence. A higher genetic risk for schizophrenia significantly predicted adolescents having more cognitive disorganization, anhedonia, and parent‐rated negative symptoms, as well as more paranoia and hallucinations (this last finding was in the non‐zero scorers). Thus our findings suggest that psychotic‐like experience trait domains in adolescence comprise partly of the genetically‐influenced phenotypic manifestation of schizophrenia. Furthermore, higher genetic risk for major depression significantly predicted having more self‐rated anhedonia and parent‐rated negative symptoms as a teenager. Our results discredit the hypothesis that psychotic‐like experience trait domains in the general population are epiphenomena that do not share biological pathways with clinically‐recognized psychiatric disorders.Genetic risk for schizophrenia and major depression in adulthood predicted 0.08–0.11% variance in psychotic‐like experience domains in adolescence. The effect sizes are likelydownward biased because of the conservative approaches taken here to align different samples and to handle skewed variables. Nevertheless, comparable effect sizes have been reported in equivalent studies on other phenotypes, for example, linking social communication traits to genetic liability for autism  phenotypically predict major depressive episodes in adulthood , however the p‐values for each selection threshold result have not been corrected for multiple testing. Due to the high correlation between PRS selection thresholds, even when considering all possible selection thresholds, it has been estimated that a significance threshold of p = .001 is appropriate  did not replicate.Some methodological details deserve consideration. A key strength of the study is the derivation of psychometrically‐sound quantitative individual psychotic‐like experience domains: These were derived using principal component analysis and have content validity. It is noted that our terminology of psychotic‐like experiences, like others (Alemany et al., Collectively, these findings reveal novel evidence for some shared common genetic etiology between psychotic‐like experience domains in mid to late adolescence and clinically‐recognized psychiatric disorders in adulthood. Evidence is accruing that psychotic‐like experiences manifested prior to adulthood form part of a wider phenotype or prodrome related to psychiatric disorders such as schizophrenia and depression. This study joins the existing evidence that individuals with a family history of schizophrenia score higher on psychotic‐like experience scales (Jeppesen et al., The authors declared that they have no conflict of interest.Additional Supporting Information may be found online in the supporting information tab for this article.Supporting InformationClick here for additional data file.


Cervus elaphus). Female red deer exhibited a higher mean and phenotypic variance in autosomal crossover counts (ACC). Animal models fitting genomic relatedness matrices showed that ACC was heritable in females (REC8 and RNF212B, with an additional region on linkage group 32 containing TOP2B approaching genome-wide significance. The REC8/RNF212B region and its paralogue RNF212 have been associated with recombination in cattle, mice, humans and sheep. Our findings suggest that mammalian recombination rates have a relatively conserved genetic architecture in both domesticated and wild systems, and provide a foundation for understanding the association between recombination loci and individual fitness within this population.Recombination is a fundamental feature of sexual reproduction, ensuring proper disjunction, preventing mutation accumulation and generating new allelic combinations upon which selection can act. However it is also mutagenic, and breaks up favorable allelic combinations previously built up by selection. Identifying the genetic drivers of recombination rate variation is a key step in understanding the causes and consequences of this variation, how loci associated with recombination are evolving and how they affect the potential of a population to respond to selection. However, to date, few studies have examined the genetic architecture of recombination rate variation in natural populations. Here, we use pedigree data from ∼ 2,600 individuals genotyped at ∼ 38,000 SNPs to investigate the genetic architecture of individual autosomal recombination rate in a wild population of red deer ( It has RNF212, REC8 and HEI10, among others, have been identified as candidates driving variation in rate, with PRDM9 driving recombination hotspot positioning in mammals  on the island of Rùm, Scotland  and has been subject to individual monitoring since 1971 . Individuals with high numbers of crossovers per gamete (>60) were assumed to have widespread phasing errors and were removed from the analysis; the maximum number of crossovers for an individual was 45 in the remaining dataset.A standardized sub-pedigree approach was used to identify the positions of meiotic crossovers  as there is substantial relatedness within the population, and initial models included parental effects and common environment which controls for effects of shared environments between relatives. ACC was modeled first using a univariate model:y is a vector of ACC; X is an incidence matrix relating individual measures to a vector of fixed effects, a and e is a vector of residual effects. The narrow-sense heritability i.e., permanent environment) to account for repeated measures in the same FID, maternal and paternal identity, and common environment effects of FID birth year and offspring birth year. A dominance GRM constructed in GCTA was also fitted but was not significant and explained <1% of the phenotypic variance, and was therefore not included in the final models. The significance of fixed effects was tested with a Wald test, and the significance of random effects was calculated using likelihood-ratio tests  was modeled as a trait of the FID. A restricted maximum-likelihood (REML) “animal model” approach  was usedBivariate models of male and female ACC were run to determine whether additive genetic variation was associated with sex-specific variation and the degree to which this was correlated between the sexes. The additive genetic correlation rGLS in the R library RepeatABEL v1.1 , rather than SNPs associated with recombination rate globally across the genome . Therefore, we repeated the GWAS modeling trans variation only, by examining associations between each SNP and ACC, minus the crossovers that occurred on the same chromosome as the SNP. For example, if the focal SNP occurred on linkage group 1, association was tested with ACC summed over linkage groups 2-33. Marker positions are known relative to the cattle genome vBTA_vUMD_3.1; in cases of significant associations with recombination rate, gene annotations and positions were obtained from Ensembl (Cattle gene build ID BTA_vUMD_3.1.89). LD was calculated between loci in significantly associated regions using the allelic correlation Genome-wide association studies (GWAS) of ACC were conducted using the function ffective  specifyii.e., a regional heritability approach) incorporates haplotype effects and determines the proportion of phenotypic variance explained by defined regions. The additive genetic variance was partitioned across all autosomes in sliding windows of 20 SNPs (with an overlap of 10 SNPs) as follows . Females had significantly higher phenotypic variance in ACC than males (P = 0.002) and within females only (P = 0.033), but was not heritable in males alone . Heritability estimates from the GRM were similar to those estimated using pedigree relatedness, which were 0.14 (SE = 0.052), 0.13 (SE = 0.064) and 0.10 (SE = 0.110) for both sexes, females and males, respectively. Bivariate models of ACC between the sexes indicated that the genetic correlation . Raw and quality controlled ACC counts and positions for each FID and offspring combination per chromosome are provided in Table S2.Autosomal crossover count (ACC) was significantly higher in females than in males, where females had 4.32 cela1_red_10_26005249 on deer linkage group 12 (CEL12), corresponding to position 26,005,249 on cattle chromosome 10 (BTA10). This marker was also the most highly associated SNP when considering recombination in trans, indicating that this region affects ACC across the genome (Table S3). The observed association was primarily driven by female ACC . Based on its position relative to the cattle genome, cela1_red_10_26005249 was OR5AU1 and TOX4, CHD8, SUPT16H and CCNB1IP1 (trans across the genome (Table S3).No SNPs were significantly associated with ACC at the genome-wide level . Detailed examination of this region in sliding windows of 6, 10 and 20 SNPs found the highest association at a 10 SNP window of REC8 . The total significant region after detailed examination was cela1_red_10_18871213 and cela1_red_10_21878407 , a homolog of RNF212, which has been directly implicated in synapsis and crossing-over during meiosis in mice ; inhibitors of this gene lead to defects in chromosome segregation and heterochromatin condensation during meiosis I in mice, Drosophila melanogaster and Caenorhabditis elegans  and 1.8 - 2.8 fewer crossovers per gamete in males  but not in males ; homozygotes for the haplotype were rare  and so the large effect size estimate was again likely to be subject to large sampling effects . Of these, two haplotypes, AGGAGAGAAG and AGAGAAGAGA, had a significant effect on ACC relative to all other haplotypes  is 1.2Using complementary trait mapping approaches, we identified a WAS hits ; associaAt present, it is not clear why the results of the two analyses occur in close vicinity, yet do not overlap. Assuming homology with humans, cattle, sheep and mice : inhibitors of this gene lead to defects in chromosome segregation and heterochromatin condensation during meiosis I in mice, Drosophila melanogaster and Caenorhabditis elegans , Chromodomain Helicase DNA Binding Protein 8 (CHD8) and SPT16 Homolog Facilitates Chromatin Remodelling Subunit (SUPT16H). These genes are involved in with chromatin binding and structure , histone binding , nucleosome organization (SP16H) and cell cycle transition (TOX4). One of these genes, SUPT16H, interacts with NIMA related kinase 9 (NEK9), which is involved with meiotic spindle organization, chromosome alignment and cell cycle progression in mice (cela1_red_10_26005249 was CCNB1IP1), also known as Human Enhancer Of Invasion 10 (HEI10), which interacts with RNF212 to allow recombination to progress into crossing-over in mice ; indeed, most phenotypic variance in recombination was attributed to residual effects. This suggests that despite some underlying genetic variation, recombination rate is mostly driven by stochastic effects, or otherwise unmeasured effects.We have shown that recombination rate is heritable in female red deer, and that it has a sexually dimorphic genetic architecture. Genomic regions associated with recombination rate in red deer are associated with this trait in other mammal species, supporting the idea that recombination rate variation has a conserved genetic architecture across distantly related taxa. A key motivation for this study is to compare how recombination rate and its genetic architecture is similar or different to that of model species that have experienced strong selection in their recent history, such as humans, cattle, mice and sheep. The heritability of recombination rate in deer was lower than that observed in other mammal systems (This represents one of the smallest datasets in which recombination rate has been investigated, and so it may be that the observed effects are underestimated due to the small sample size, sampling effects, or perhaps that other genetic variants present in this species do not segregate in the Rùm deer population. Nevertheless, identification of clear candidate genes and their effects on phenotype represents a valuable contribution to understanding the genetic architecture of recombination more broadly. Ultimately, our findings allow future investigation of the fitness consequences of variation in recombination rate and the relationship between identified variants and individual life-history variation, to address questions on the maintenance of genetic variation for recombination rates, and the relative roles of selection, sexually antagonistic effects and stochastic processes in contemporary natural populations.


Gryllus firmus). Similar to results from previous work, animals reared at high densities were generally smaller and took longer to mature, and body mass and development time were moderately heritable. In contrast, neither boldness nor activity responded to density treatments, and they were not heritable. The only trait that showed significant genotype‐by‐environment differences was development time. It is possible that adaptive behavioral plasticity is not evident in this species because of the highly variable social environments it naturally experiences. Our results illustrate the importance of validating the assumption that behavioral phenotype reflects genetic patterns and suggest questions about the role of environmental instability in trait variation and heritability.A fundamental goal of evolutionary ecology is to identify the sources underlying trait variation on which selection can act. Phenotypic variation will be determined by both genetic and environmental factors, and adaptive phenotypic plasticity is expected when organisms can adjust their phenotypes to match environmental cues. Much recent research interest has focused on the relative importance of environmental and genetic factors on the expression of behavioral traits, in particular, and how they compare with morphological and life‐history traits. Little research to date examines the effect of development on the expression of heritable variation in behavioral traits, such as boldness and activity. We tested for genotype, environment, and genotype‐by‐environment differences in body mass, development time, boldness, and activity, using developmental density treatments combined with a quantitative genetic design in the sand field cricket ( Significant areas of study include identifying the relative importance of genetic and environmental influences on the expression of phenotypes . Gryllus firmus naturally inhabits ephemeral habitats, and individuals can experience high variation in physical and social environments. Individual females will lay both fast‐developing and diapause eggs, likely as an adaptation for this variable environment , as well as morphological and life‐history traits. Boldness has been shown to be repeatable in Hedrick, , and bolHedrick, . We testHedrick, . MoreoveHedrick, , geneticHedrick, . Finally22.1G. firmus eggs from a laboratory stock at MacEwan University , which was originally established from 35 wild‐caught adults  collected near Gainesville, FL, in the fall of 2010 and maintained in large numbers since. The eggs used to start the current experimental colony were drawn from a large colony bin  and shipped to UQÀM in the fall of 2015. We maintained the current UQÀM experimental colony at a constant temperature (28°C) and humidity (60%) with 12‐hr day/night light schedule. We maintained colony animals in 70‐L mixed‐sex bins of about 50 adult individuals, provided with cotton‐plugged water vials and ad libitum Iams™ Proactive Health™ adult original cat food. Each bin had stacked cardboard egg cartons to provide refuge and wire mesh lids to provide ventilation. Minimum generation times from egg to final molt (eclosion) of offspring were less than 60 days, and the long‐winged morph was present at >90%, suggesting “optimal” growing conditions  as the parents in a nested maternal half‐sib, paternal full‐sib experimental design. We randomly removed a subset of crickets from colony bins just before eclosion to insure that they had no previous mating experience and placed them individually in 250‐ml transparent square plastic containers with ad libitum food and water and a piece of cardboard egg carton for refuge. At sexual maturity (at least 7 days after eclosion), we formed mating groups with one male and three females in a 750‐ml plastic container with ad libitum food and water and several pieces of egg carton as refuge. For logistical reasons, we could not create and manipulate all the families at the same time, so we created families in seven temporal blocks, each of them composed of a median of three sires . We kept mating groups together for 5 days before all individuals were separated and weighed. We subsequently placed females individually into containers of the same size and set‐up, provided with a small cup of damp vermiculite for oviposition. We removed females from the oviposition containers after 5 days. We kept the vermiculite in the same container and moistened until hatchlings began appearing about 12 days after laying. We removed the vermiculite after at least about 50 hatchlings had emerged, usually about 3 days. While this restricts experimental animals to earlier hatchlings, we preferred to reduce the amount of variation from this source. Hatchlings remained in the original container for 7 days, provided with powdered cat food, water, and egg cartons.We initially created mating groups with 30 sires and 90 dams. After excluding unsuccessful females that produced no offspring and mating groups with questionable conditions , we placed hatchlings from 24 sires and 68 dams into experimental density treatments.2.2After 7 days, we randomly assigned hatchlings of each maternal family to one of two density treatments. High‐density treatments consisted of groups that were started with 10 siblings in a 750‐ml transparent plastic container, while low‐density treatments consisted of one individual in a 250‐ml transparent rectangular plastic container. We note that our treatments thus necessarily represent a combination of differences in density and container size/space, so differences trait expression might be attributable to both. However, our goal was to examine the effect of social interactions on development, specifically behavioral and physiological anticipation of potential future competition, rather than to affect resource limitation or environmental stress, and small containers would have been very crowded for 10 individuals with food, water, and refuge. All animals had ad libitum food and water and ample refuge, and conditions in the high‐density treatment were relatively uncrowded until animals neared adult size. We cleaned the containers and changed food and water once per week or more often as needed throughout development.For each dam, we created 10 low‐density and two high‐density groups , with the initial goal of testing six offspring  from each treatment for each dam . Some mortality during rearing and variation in sex ratio of the selected nymphs resulted in some variation in the exact numbers and sexes of experimental animals from each family. Although some high‐density groups ended with fewer than 10 individuals, we expected that these still effectively represented increased social experience, so all high‐density groups were considered valid unless they dropped to five individuals or fewer. If the combined number of offspring in high‐density treatments for a maternal family  fell to fewer than 10 individuals, we combined the two high‐density groups to create one group to maintain the difference in social experience between low‐ and high‐density treatments. Animals in both treatments experienced the same overall level of disturbance during regular feeding and cleaning. We moved rearing containers for all offspring haphazardly and periodically around the rearing chamber to try and minimize microhabitat variation.Upon eclosion , we weighed crickets to the nearest 0.0001 g using a Sartorius  analytical balance and transferred them to a new 250‐ml container with food, water, and a cardboard refuge. We kept experimental animals individually for at least 7 days  before behavioral testing. We weighed experimental animals a second time on the day of behavior testing (described below). While adult mass is not a fixed measure of size, we used it as a simple morphological measure known to be heritable . Behavioral testing took place between 9:00 and 16:00 hr, during the animals' nocturnal period, in a dimmed laboratory with ambient white noise. Each animal first received an open‐field test to assess its level of activity in a novel environment. We transferred the animal from its home container to a 250‐ml plastic container and placed it in the center of an arena, with the container and opaque lid over the animal. After 2 min of acclimation, we removed the container and lid, replaced the arena cover, and started behavioral tracking (Ethovision® XT). We tracked the movement of each animal for 5 min.Gryllus cricket species  in a manner similar to tests in related We used up to four Phenotyper® units at a time; thus, most animals were tested in groups of four, where test animals were sequentially placed into arenas and sequentially manipulated with a preset amount of time between each. While there could have been slight variations among arenas, preliminary analyses suggested that there were no apparent arena or order effects on behavioral measures, so we do not consider these variables in further analyses. We distributed testing of siblings over multiple days and over different groups of four when same‐day testing was unavoidable so that all days contained multiple families and all families were tested over multiple days.2.42). We did not include the effect of wing morphology in statistical models because the great majority (91%) of test animals were long‐winged morphs.We fit univariate linear mixed models (LMMs) to examine the effects of family, density treatment, and sex on multiple traits: body mass at eclosion (to nearest 0.0001 g), body mass at sexual maturity , development time , total activity in the entire 5 min. of the open field , activity‐per‐minute of the open field , and boldness. We confirmed that all estimated effects (both fixed and random) were similar for development time and latency to emerge whether these variables were analyzed with survival models, but we report results from LMMs to allow calculations on variance components. All estimated effects for boldness were also similar if we excluded individuals that did not emerge from the analysis. We included density treatment (high or low), sex, and their two‐way interaction as fixed effects in all models. The models of behavior (boldness and activity) also included a fixed effect of days after eclosion (to account for slight differences in maturity on day of testing). The model for activity‐per‐minute additionally included fixed effects of time  and a quadratic term for time (minuteWe ran a first set of models that included random intercepts for dam nested within sire and sires nested in mating block (to identify mating groups that were formed at the same time). Models of behavioral variables  further included a random intercept for day of testing. We tested for genotype‐by‐environment (G × E) interactions using a reaction norm approach, where we included random slopes for density treatment within sire with correlated intercept and slope, with high density as the intercept. We interpreted significant effects of random slopes as evidence for differences in genotype response to density. When random slopes were significant, we provide variance components for the two treatments separately . Otherwise, we present variance components from the full dataset in the main text and provide treatment‐specific variance components in Supporting Information Tables p < 0.05) of random effects using log‐likelihood ratio tests (LRTs) of nested models that differed only in the random effect of interest . Note that LRTs represent conservative null hypothesis tests in this case, where variances are bounded at 0, and p‐values can be up to twice as large as they should be  interactions by including random slopes for density within mother. However, these were not significant for any trait , except for maturation rate, where M × E was confounded with sire × environment (S × E), as a model with both terms did not converge. When S × E and M × E were considered in separate models of maturation rate, the model with S × E had higher likelihood and lower information criteria values, so we considered this the better model. Thus, in effect, we do not consider M × E interactions further, and GxE interactions in this study represent S × E interactions.We tested for significance ; therefore, we present results from this model.Additionally, for the model of activity‐per‐minute, for which we had five 1‐min observations per individual, we fit three alternate models of random effects: (a) random intercepts for individual, sire, dam, block, and test day; (b) random slopes for time within individual, with correlated intercept and slope, plus random intercepts for sire, dam, block, and test day; and (c) random slopes for time within sire, with correlated intercept and slope, plus random intercepts for individual, dam, block, and test day. Model 1 assumes no G × E or individual‐by‐time (I × T) effects on change in activity. Model 2 assumes that individuals differ in their change in activity, but that there is no G × E effect . Model 3 assumes that there is a G × E effect that explains the I × T effect found in model 2. We tested models 2 and 3 against model 1 using LRTs and took the one with the greatest likelihood (and lower AIC) with h2) as VA/(VA + VM + Vblock + VR), where VA is the additive genetic variance, VM is the variance attributable to the mother above and beyond the variance among sires (VS), Vblock is the variance due to difference in mating periods, and VR is residual variance. In the nested half‐sib, full‐sib design, VS is approximately VA/4, thus, we used VA = 4 × VS in our calculation of h2. We also estimated maternal effects (m2) as the ratio VM/(VA + VM + Vblock + VR).For the primary set of models, we estimated narrow‐sense heritability  as VA/(VA + VM + VR). We assumed that if block really has an effect on the variable, independent of sires and dams, we should observe an increase in both VA and VM in models without temporal block. On the other hand, changes in only VA or VM as a result of removing blocks from the model would suggest that variance among blocks actually represents part of that variance component. We present results for variance components calculated from this second set of models in Supporting Information Tables The above set of models included temporal block as a random effect. However, we could not randomize families among blocks with our experimental design, as blocks necessarily represented separate periods during which we selected some individuals to create families and each family is nested in a block. In this situation, variance among blocks may take part of the variance among sires or dams, as blocks may be composed of different genotypes due to uncontrolled factors . Therefore, including block in the model may lead to underestimation of both We excluded any families that experienced irregular rearing conditions from these statistical analyses, resulting in 679 offspring  from 23 sires and 59 dams . We conducted all statistical analyses in the R statistical environmental  and with 2h of 0.24 or 0.51, respectively. There was no significant VM in either set of models, explaining <0.1% VP and with 2m <0.01 in both cases. Mass at sexual maturity only had a significant VS when block was not included in the model, explaining 16.9% of VP and with 2h = 0.45, compared with 3.0% of VP and 2h = 0.11 when block was included in the model. There was significant VM in both sets of models, explaining 3.8% or 3.9% of VP and with 2m = 0.04 or 0.03 in models with and without block, respectively.Variance components and heritability estimates from models with block effect are presented in Table χ2 = 33.72, p < 0.001, Figure VS was not significant when the block effect was included , but was significant when the block effect was not included . Similarly, at high density, VS was not significant when the block effect was included , but was significant when the block effect was not included . At low density, VM was significant whether or not block effect was included . Similarly, at high density, VM was significant whether or not block effect was included .Development time was the only trait that had a significant density × sire interaction (VS in either set of models (<0.1% of VP and 2h <0.1 in both cases) nor did it show significant VM . Total activity did not show significant VS in either set of models (<0.1% of VP and 2h <0.1 in both cases) but did show significant VM in both sets of models . Similarly, activity‐per‐minute did not show significant VS in either set of models (<0.1% of VP and 2h <0.1 in both cases) but did show significant VM in both sets of models . Additionally, individuals differed consistently in how much distance they covered during each minute of the open field and in the slope of their activity over time. A positive correlation between intercept and slope (0.38) indicates that overall activity during the test was linked to a stronger increase in activity with time.Boldness did not show significant 3.2SD = 0.144, range: 0.313–1.239, N = 678) was significantly affected by rearing density and differed by sex. Low‐density crickets were heavier than high‐density crickets, and female crickets were heavier than males. Body mass at sexual maturity  showed a similar pattern, with even larger sex differences due to both an average mass gain in females (mean = 0.079 g) and an average mass loss in males (mean = −0.025 g) from 0 to day of behavioral testing. Development time  showed a significant density × sex interaction, such that there was no sex difference at high densities, but male crickets took longer to mature than females at low densities  was not predicted by rearing density or sex, but there was a nonsignificant tendency for more sexually mature animals to be bolder (emerge more quickly). Total activity in the 5‐min open‐field test  was not affected by density treatment or sexual maturity but did differ by sex. Male crickets covered less distance than females did. Similarly, activity‐per‐minute  was not affected by density treatment or sexual maturity, but did differ by sex, with male crickets covering less distance than females did. The sex difference increased with time, with male crickets becoming less active as time went on. Activity also showed a significant convex quadratic pattern, such that activity increased more steeply as time went on.Cricket boldness  did not respond to density treatments, nor was it heritable. This contrasted with results for morphology and life history, which responded to treatment density and showed significant genetic components. Animals reared at high densities were generally lighter and took longer to mature. Body mass was moderately heritable, and response of development time to rearing density differed across genotypes. Overall, our results suggest that, in this system, genetic factors play little role in determining variation in the behavioral traits measured and that these behaviors might have relatively limited evolvability and response to selection.We quantified variance components and response to rearing density of behavioral traits in G. integer also found only weak to marginal heritability for boldness measures  in oviposition  and mother (on activity). Other studies have commonly documented some degree of heritability in a wide variety of behaviors, often of a similar magnitude to life‐history traits  in combination with the individual differences in activity‐per‐minute suggests that individuals differed relatively consistently on this short timescale. For future tests, activity in the first minute of the open field might be an adequate behavioral measure and has the advantages of being easier to collect and likely to be less influenced by factors occurring later in the test. In general, more analyses of temporal activity patterns in open field and other behavioral tests will facilitate more detailed understanding of individual and group variation, allow better comparison of results from different studies, and test for potential biases of different test lengths  and of mother (on activity) suggest other unmeasured environmental influences on behavior. This result differs from the common observation of behavioral responses to rearing density Dingle,  or sociad Kortet  found thhtermann  found thIn contrast with results for behavior, our results for body mass at eclosion, body mass at sexual maturity, and development time generally aligned with expectations based on life‐history theory and are consistent with previous findings in this and other systems Stearns, . Animalsh2 = 0.45–0.50; development time h2 = 0.52–0.60; in models not including block effect) similar to those previously reported for G. firmus (Roff, Gryllus species (Niemelä et al., Adult body mass and development time showed significant sire components and were heritable, with estimates of heritability (mass at eclosion us Roff, , other GGryllus with much smaller sample sizes and that substantial work has shown density effects on activity in many species, we believe our results are unlikely to be entirely due to this issue. However, we acknowledge this issue limits our interpretation of results. Additionally, because experimental families were created over the course of a few months within one generation, some parental components cannot be untangled from temporal components. For example, we could not remove the effect of earlier developing individuals being used as parents in earlier blocks, that is, sires and dams were assigned to blocks as they matured, rather than randomly. In our study, heritability decreased, and VS was not significant for development time when accounting for block. Including or removing block from the model mostly affected variance among sires for life‐history traits but did not have any effects on behavioral traits or on among‐dam variance. This result suggests that variance among blocks was partly caused by genetic variance rather than the opposite and that thus the model with block might be underestimating sire effects. Another important consequence of the study design is that, for practical reasons and to reduce variation from these sources, experimental animals were limited to relatively fast‐hatching and fast‐maturing individuals in the experiment. Hence, we have less insight on later‐maturing animals and cannot generalize to animals that would have emerged from diapause eggs. Consequently, this study likely reflects a much narrower range of trait variation than is present in the system.Several aspects of our study design warrant further discussion of their potential effects on interpretation of results. First, as we did not have repeated measures of behavioral traits, we cannot rule out the possibility that the lack of heritability and density effects were due to the behavioral measures failing to represent consistent interindividual variation. Given that previous work detected repeatability of boldness in Gryllus firmus inhabits highly variable sandy habitats, and, within the same clutch, females can lay a mixture of fast‐developing and diapause eggs (Walker, Developmental plasticity is just one way that organisms can respond to environmental uncertainty, and our results for behavioral traits might be more consistent with a bet‐hedging strategy (Donaldson‐Matasci, Lachmann, & Bergstrom,  Walker,  as well  Walker, . AdditioNone declared.All authors contributed to the conceptualization and design of the study. TWW conducted the experiment, collected and analyzed data, and wrote the initial manuscript with input from DR and CDK. All authors contributed to the critical evaluation of results and revisions of the manuscript. Click here for additional data file.


IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset were obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study.Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net (EN) regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis.Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN.SLDA and EN had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination. IgA nephropathy (IgAN) is the most common primary glomerulonephritis in the world. IgAN isTo address the extraction of biological information due to joint impacts of variables and dimension reduction, different feature selection and classification methods such as least absolute shrinkage and selection operator (LASSO), adaptivIn order to compare the performance of two methods (SLDA and elastic net) for diagnosis of IgAN, urine protein profile of patients with IgAN and of healthy subjects, which was obtained using nanoscale liquid chromatography-high resolution tandem mass spectrometry, was used. The dati and xj are ith row and jth column of the matrix X, respectively. The number of observations in kth group is shown with Ck, that is a subset of[. An estimate for the within-class covariance matrix  and an estimate for the covariance matrix between classes  can be defined as follows, respectively:LDA, which uses a linear combination of features as the criterion for classification, has often been shown to produce the best classification results for low dimensional data-26. Althsubset of . An est is the sample mean vector for kth class. Finally, the following command acquires kth penalized discriminant vector:That k is a convex penalty function on the kth discriminant vector, and  is a positive definite estimate for ΣW. SLDA analysis was performed using PenalizedLDA package.Where P2-norm , and the L1-norm  of the coefficients are considered as penalty in ridge and lasso regressions, respectively, while a hybrid of these two penalties is considered in elastic net regression.The elastic net-type regularized regression  is a vector of p variables, elastic net logistic regression is defined as follow:Where |β|m the fit. The valWhere ln (β) is a maximum likelihood estimator for logistic regression, α is a value between zero and one, and λ is a positive constant called tuning parameter that manages the shrinkage degree. Elastichttp://www.genome.jp/kegg/) were also identified. The statistical test used for the enrichment was based on a two-sided hypergeometric option with a Benjamini-Hochberg correction, a p value less than 0.05, and a kappa score threshold of 0.4. The minimum number of genes was considered 3.To further understand the biological relevance of the characterized biomarkers, we performed gene ontology analysis using Cytoscape v 3.4.0 software and a Cytoscape plug-in named ClueGO (version 2.2.5),31. Cluep < 0.05) between the case and control groups in 144 out of 493 variables (the results not shown). Because the sample size was small, we directly used fivefold cross-validation to determine the training data and the test data and selected the best parameters (e.g. λ and α) for the methods. For assessing simultaneous effects of aforementioned variables on IgAN disease, elastic net and SLDA models were fitted based on λ = 0.005 and λ = 0.06, respectively. The results of two models indicated that 133 out of 493 variables were effective in discrimination of IgAN in SLDA model, whereas 120 predictive variables were important in elastic net model. Summary of models are shown in In this study, we examined the effect of 493 variables in urinary protein profile of IgAN patients and healthy subjects. Univariate analysis using Mann-Whitney test revealed that there was a significant difference , fibrinolysis (p = 35.0 × 10-6), and platelet degranulation (p = 3.1 × 10-9), encompassing seven terms of biological process were remained significant. The significant terms and their nodes are displayed in p = 2.1 × 10-6), secretory granule lumen (p = 15 × 10-9), and blood microparticle (p = 250 × 10-12) were the important biomarkers enriched in three clusters composed of seven terms of cellular component. The GO levels were different for each term, and vary between 2 to 12. However, each term was reported under multiple levels from general nodes (higher parents) to more specific child nodes (lower nodes). In contrast, no GO term was enriched for the categories of molecular function. The results of pathway enrichment analysis revealed two significant pathways: complement and coagulation cascades (p = 1.9 × 10-5) and extracellular matrix (ECM)-receptor interaction (p = 1.9 ×10-5). The enriched pathways and their nodes are displayed in To better understand the biological functions of the most important discriminative proteins, we carried out functional enrichment analyses via ClueGO. The integrative list of the biomarkers identified by two models composed of 43 proteins Tables  and 3 waIgAN is the most common type of primary glomerulonephritis worldwide. This disease has a significant morbidity and leads to end-stage renal disease in about 40% of patients within 20 years of diagnosis. The hiset al.[et al.[The urinary excretion of the overexpressed biomarkers in patients with IgAN have been reported earlier,34-36, aet al. and Prikl.[et al. resultst2-dependent elastin-binding protein and presents in interstitial renal tissue, as well as distal renal tubules[There is less evidence on the biomarker role of proteins that suggested as down-regulated candidates for IgAN unless OSTP, and they are presented in this paper for the first time. FBLN5 that is named as FBLN5 is associated with elastic fiber formation as a Cal tubules,44. It hl tubules,44. DownSAP is a highly conserved lysosomal glycoprotein hydrolases involved in the hydrolysis of sphingo-lipids. AccordiSignificant reduction of urinary excretion of OSTP in our analysis corresponds with a previous study, which het al[Investigation of the gene ontology and pathways that enriched in the list of significant biomarkers paved the way to translate invisible information into interpretable biological information. Involvement of acute phase response and processes related to coagulation in IgAN that have been enriched in the dataset is similar to previous studies-51. The et al. The hypet al, may resDecreased excretion of several involved molecules in ECM-receptor interaction pathway in comparison with normal subjects indicates the instability of glycocalyx in the disease process. Glycocalyx injury will affect a broad spectrum of endothelial function including filtration barrier that leads to proteinuria. In addiIn conclusion, functional analysis of the important biomarkers from SLDA and elastic net models revealed that biological processes related to coagulation and acute phase response are involved in the pathogenesis of IgAN. Furthermore, complement and coagulation cascade and ECM-receptor interaction are the two important pathways that impaired in IgAN.


Barley is the world’s fourth most cultivated cereal and is an important crop model for genetic studies. One layer of genomic information that remains poorly explored in barley is presence/absence variation (PAV), which has been suggested to contribute to phenotypic variation of agronomic importance in various crops.An mRNA sequencing approach was used to study genomic PAV and transcriptomic variation in 23 spring barley inbreds. 1502 new genes identified here were physically absent from the Morex reference sequence, and 11,523 previously unannotated genes were not expressed in Morex. The procedure applied to detect expression PAV revealed that more than 50% of all genes of our data set are not expressed in all inbreds. Interestingly, expression PAV were not in strong linkage disequilibrium with neighboring sequence variants (SV), and therefore provided an additional layer of genetic information. Optimal combinations of expression PAV, SV, and gene abundance data could enhance the prediction accuracy of predicting three different agronomic traits.Our results highlight the advantage of mRNA sequencing for genomic prediction over other technologies, as it allows extracting multiple layers of genomic data from a single sequencing experiment. Finally, we propose low coverage mRNA sequencing based characterization of breeding material harvested as seedlings in petri dishes as a powerful and cost efficient approach to replace current single nucleotide polymorphism (SNP) based characterizations. A priority of modern agriculture is to increase the productivity of crops to meet the demands of a growing human population. The urge of achieving significant yield gains is amplified by the current context of climate change, competition for land, and limited natural resources . Plant gTo exploit the natural genetic variation present in the gene pool of barley, genomic tools such as single nucleotide polymorphism (SNP) arrays have been developed . The avaGenome wide quantification of gene expression has also been accessible in barley since many years through the development of gene expression arrays . This teIt is now accepted that a significant proportion of the genes of plant genomes are not expressed  or are even completely absent  in subsets of genotypes, and make up what is known as the dispensable transcriptome or genome –21. The Prediction of phenotypic variation in the context of genomic selection, which is nowadays an essential component of plant breeding programs, is performed based on SNP genotyping profiles. Previous studies on the use of metabolome and lipidome variation to predict phenotypic traits of maize revealed high but lower prediction accuracies compared to SNP information , 28. OnlIn this study, we explored the genomic and transcriptomic landscape of 23 spring barley landraces and cultivars which were selected based on their genetic and phenotypic diversity as parents of a joint linkage and association mapping population. The objectives of our study were to (i) characterize genomic and transcriptomic variation in the barley genome using multi-tissue mRNA sequencing, (ii) assess the proportion of ePAV that are due to gPAV, (iii) examine how accurately the different layers of genomic and transcriptomic variation predict phenotypic variation of various agronomic traits.To study genomic diversity in spring barley inbreds, we first selected 23 inbreds from a panel of 224 representing a broad range of origins   performed based on SV and ePAV. Both PCA revealed the existence of two clusters of inbreds defined by the row type of the inbreds , and a similar result was observed for principal components 2 . However, these analyses also reveal that the relationship of the inbreds within clusters differs between the two sources of molecular variation. A similar trend was observed when comparing the transcriptomic variation (T) with that of ePAV and SV. Mantel tests of distance matrices calculated from T, SV, and ePAV data indicated only significant correlations between the seedling transcriptome and SV .We were interested in knowing how independent the detected ePAV are from the local genomic pattern. First, the mRNA sequencing data was used to call sequence variants (SV) within exon sequences. A total of 133,566 SV were detected. We then determined the extent of linkage disequilibrium (LD) between each ePAV and neighboring SV located within 100 kb. Only 17.5% of all ePAV have at least one SV within 100 kb that has an 2 values between 0.69 and 0.76 were observed. In order to obtain unbiased estimates of the prediction accuracy, we randomly subdivided in 1000 cross-validation runs the 23 inbreds in training and validation set. Prediction accuracies of SV, T, and ePAV were compared to the prediction accuracy of the SNParray data set that we used as the baseline predictor. The median prediction accuracy across 1000 cross-validation runs observed for the SNParray data set ranged from -0.49 for heading date to 0.70 for leaf angle  resulted across the three traits in the highest median of prediction accuracy of all the examined single predictors.Therefore, we examined the prediction accuracy that can be obtained when predicting the traits leaf angle, heading date, and plant height, for which hs. Therefore, a grid search in which the relative weights of the relationship matrices of two or three predictors varied in increments of 0.1 prior to summing them up, was used to identify those combinations of SV, ePAV, and T s that resulted in the highest prediction accuracies. For all three traits, the highest median of the prediction accuracy was observed when using more than one predictor  for a scenario in which only the gene expression in a window of 10 bp around the SNP was considered. In this case of using a single tissue to detect gPAV, the proportion of gPAV that are detected by our ePAV procedure increases to about 46%  , C:  to detect gPAV by mRNA sequencing was calculated as the percentage of genic PAV-SNP that were located within the coding sequence of ePAV. Furthermore, the empirical type I error (α∗) of our ePAV procedure was estimated as the proportion of genes that comprised a genic SNP, no genic PAV-SNP, but were detected as ePAV out of the total number of detected ePAV. In addition, we calculated the proportion of correct allele assignments (o) as the proportion of common presence/absence ePAV calls and presence/absence calls at genic PAV-SNP across all 23 inbreds. We estimated 1- β∗ and α∗ firstly for ePAV determined based on T of the entire gene as well as based on T calculated for 10 bp large windows surrounding the genic SNP.The statistical power  T s. W is a matrix of feature measurements for the respective predictors. The dimension of W is determined by the number of barley inbreds and the number of features in the corresponding predictor . The columns in W were centered and standardized to unit variance. For each predictor, an additive relationship matrix G was calculated according to VanRaden [G of two or three predictors were weighted and summed up, resulting in one joined weighted relationship matrix [s, ePAV s, and T s and the combination of these predictors, was calculated for leaf angle, heading date, and plant height as average across the 30 replications.Each of the three phenotypic traits leaf angle, heading date, and plant height was analyzed across the four environments using mixed models. This allowed to estimate adjusted entry means as well as the heritability on an entry mean basis.The adjusted entry mean of each barley inbred for each trait was predicted using genomic best linear unbiased prediction (GBLUP) –57. GBLUVanRaden . The matp matrix . A grid Additional file 1Supplementary Table S1: Summary of barley inbreds. Supplementary Table S2: Number of expression presence/absence variation (ePAV) observed for our detection procedure. Supplementary Table S3: Gene ontology term enrichment analysis. Supplementary Fig. S1: Characterization of the not annotated contigs established by the transcript calling. Supplementary Fig. S2: Procedure to evaluate the detection of presence/absence variation. Supplementary Fig. S3: Population structure of the 23 barley inbreds. Supplementary Fig. S4: Prediction accuracy of single predictors.