Infectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health. Selection and breeding of host individuals with desirable traits regarding infectious diseases can help to fight disease transmission, which is affected by two types of (genetic) traits: host susceptibility and host infectivity. Quantitative genetic studies on infectious diseases generally connect an individual’s disease status to its own genotype, and therefore capture genetic effects on susceptibility only. However, they usually ignore variation in exposure to infectious herd mates, which may limit the accuracy of estimates of genetic effects on susceptibility. Moreover, genetic effects on infectivity will exist as well. Thus, to design optimal breeding strategies, it is essential that genetic effects on infectivity are quantified. Given the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effect of single nucleotide polymorphisms (SNPs) on both host susceptibility and host infectivity. To evaluate the quality of the resulting SNP effect estimates, we simulated an endemic disease in 10 groups of 100 individuals, and recorded time-series data on individual disease status. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimum recording interval when the number of records is limited.We present a generalized linear mixed model to estimate the effect of SNPs on both host susceptibility and host infectivity. SNP effects were on average slightly underestimated, i.e. estimates were conservative. Estimates were less precise for infectivity than for susceptibility. Given our sample size, the power to estimate SNP effects for susceptibility was 100% for differences between genotypes of a factor 1.56 or more, and was higher than 60% for infectivity for differences between genotypes of a factor 4 or more. When disease status was recorded 11 times on each animal, the optimal recording interval was 25 to 50% of the average infectious period.Our model was able to estimate genetic effects on susceptibility and infectivity. In future genome-wide association studies, it may serve as a starting point to identify genes that affect disease transmission and disease prevalence. Infectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health . InfectiTwo main sets of host traits affect transmission of infectious diseases: host susceptibility and host infectivity. Susceptibility is the relative risk of an individual to become infected when exposed to a typical (average) infectious individual or (for infectious diseases transmitted via the environment) the infectious material excreted by a typical infectious individual. Infectivity is the relative propensity of an infected individual to infect a typical (average) susceptible individual.Studies that investigate host genetic effects related to infectious diseases generally focus on host disease status, and link this to the genotype of the host , 8. By lA host genetic effect on infectivity is an example of an indirect genetic effect (IGE) , 11, whiGenome-wide association studies (GWAS), in which effects of single nucleotide polymorphisms (SNPs) on a phenotype are estimated, are a common way to quantify genetic effects. To estimate effects of SNPs on susceptibility and infectivity, a generalized linear model with a complementary log–log link function can be used. This model has been applied to data on the final disease status of individuals after an epidemic disease , 18, butGiven the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effects of SNPs on both host susceptibility and host infectivity for an endemic disease. The model accounts for variation among susceptible individuals in exposure to infectious herd mates and for the genotypes of those herd mates. To evaluate the quality of the SNP effects estimated by the model, we simulated an endemic disease and recorded data on individual disease status multiple times during the endemic. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimal recording interval.γ, and one locus for the infectivity effect G with value γG and allele g with value γg. The infectivity locus also had two alleles, allele F with value f with value γGG = γGγG = γG2 for genotype GG, γGg = γgG = γGγg for genotype Gg/gG, and γgg = γg2 for genotype gg. Likewise, infectivity values were FF,Ff/fF, and ff. Note that multiplicative allele effects on the original scale introduce dominance on the original scale. Because the value for the heterozygote is lower than the average value of both homozygotes, i.e. γGg < 0.5(γGG + γgg), the dominance is negative . In a SIS-model, two events can occur: infection of a susceptible individual and recovery of an infected individual. Infected individuals were immediately infectious and recovered individuals were immediately susceptible again. Thus, no lasting immunity to disease was assumed. Events (infection and recovery) occurred randomly with a probability per unit of time, depending on model parameters and disease status of individuals in the population.I is the number of infectious individuals, S the number of susceptible individuals, and S + I = N, i.e. the size of the closed population in which the endemic takes place [β is a population specific constant that contains information on the contact rate and transmission probability between hosts [In a genetically homogeneous population, the expected rate with which susceptible individuals become infected equals es place . The traen hosts .β varies between pairs of individuals, depending on the susceptibility genotype of the susceptible individual and the infectivity genotype of the infectious individual. We assumed that, between individuals, the susceptibility genotype and the infectivity genotype have independent effects, which is known as separable mixing in epidemiology [βij from an infectious individual with infectivity genotype j  to a recipient susceptible individual with susceptibility genotype i  was defined as:γi is the susceptibility value for genotype i and j. Without loss of generality, we chose γg = γgg = βggff = c. Thus, c represents the transmission rate parameter from an infectious individual with infectivity genotype ff to a susceptible individual with susceptibility genotype gg. Since, γg = γG represents the ratio of the value of allele G over the value of allele g, and F over the value of allele f. For example, γGG/γGg = γG, and γGg/γgg = γG.In a genetically heterogeneous population, emiology , i.e., ttotal infectivity to which susceptible individuals are exposed at time t, depends on the total number of infectious individuals of each genotype at that time Ij(t) and is measured by t for susceptible individuals with genotype ii and on the total infectivity of infectious group mates:Si(t) is the number of susceptible individuals with genotype i at time t.The α and was assumed to be the same for all genotypes. Note that a single α does not imply the same infectious period for all individuals; because α is a stochastic rate, the length of the infectious period follows an exponential distribution and thus shows random phenotypic, albeit not genetic, variation among individuals.The probability per unit of time for an individual to recover and become susceptible again was given by the recovery rate parameter To estimate the effect of single SNPs on both host susceptibility and host infectivity, we developed a generalized linear model (GLM). The GLM was based on the infection rate given by Eq. . We assut is dropped in the following and, hence, S, Si, I, and Ij refer to the number of individuals at the beginning of the interval. Then, the probability Pi for a single susceptible individual with genotype i to get infected when exposed to all infectious individuals during an interval Si = 1,For the sake of readability, the index mentclass2pt{minimi that become infected during Ci, follows a binomial distribution with binomial total Si, i.e., depends on the number of susceptible individuals of genotype i at the start of the interval and the probability to become infected given by Eq. (Ij). In other words, the effect on the Pi of individuals that became infected or recovered during the interval is ignored in Eq.  but not in log(log(γi) and log = 0, log(γGg) = log(γG), and log(γGG) = 2log(γG). Thus, under this assumption, the model can be expressed in terms of allele counts [c0 = log(c) is the intercept. To achieve that γg = G and F within individuals, rather than alleles g and f, such that the regression coefficients represent the value of a single copy of allele G or F. For example, the ratio of γG versus γg is CountG represents the number of G-alleles at the susceptibility locus of the susceptible individual, takes values 0, 1 or 2, and has coefficient CountF represents the average number of F-alleles at the infectivity locus in the infected individuals, takes real values between 0 and 2, and has coefficient CountF is calculated as IFF is the number of infected individuals with genotype FF at the beginning of the interval and IFf is the corresponding number of infected individuals with genotype Ff. The denominator of CountF is I rather than 2I because CountF is the average number of F alleles rather than its proportion. Table Δt determines the interpretation of the transmission rate parameter. For example, rates are per day when the time period Δt is expressed in days.By assuming multiplicative allele effects on the original scale, allele effects were additive on the log-scale. For susceptibility, for example, e counts . Furthermentclass2pt{minimTo evaluate the quality of the estimates from the above model, we simulated an endemic disease and quantified bias and precision of SNP effects estimated based on Model 3. Bias was defined as the difference between the estimated and true effects of each SNP and relative bias was defined as the bias relative to the true size of the effect. Absolute bias and relative bias were calculated on the original scale. Precision was measured by the root mean squared error (RMSE) of the estimated SNP effects on the original scale. Simulations were conducted in R version 3.2.3. and data were analysed with the R-package lme4 , 25, usipG and pg = 1 − pG), and infectivity (pF and pf = 1 − pF) for each group from a beta distribution with a mean of 0.5 and standard deviation of 0.05. We chose a beta distribution for p to ensure that allele frequencies are between 0 and 1. For the mean allele frequency, we used 0.5, which is simply the centre of the 0–1 interval. We assumed that the susceptibility effect of an individual and that same individual’s infectivity effect were not correlated. Within groups, genotypes were sampled assuming Hardy–Weinberg equilibrium. The loci for susceptibility and infectivity were simulated in linkage equilibrium.A group (defined as closed and random mixing) consisted of 100 individuals, which resembles for example, a dairy herd in the Netherlands. In dairy herds, a limited number of sires is used, so that cows in the same herd are (slightly) more related to each other than to cows in other herds. We simulated such genetic heterogeneity by sampling allele frequencies for susceptibility . For all recording intervals in Scenario 2, γG = γG = In scenario 2, we varied the recording interval while keeping the total number of recordings constant, in order to find the optimal recording interval. The recording interval ranged from 4.8 to 133.3% of the average infectious period , was 100% for all values of γG, except for γg − γG = 0.1, for which power was 78%. For infectivity, power increased from 5% for In scenario 1, we varied 7 Tables . Since γγG = In Scenario 2, we varied the recording interval while keeping the total number of recordings constant. Figure Given the potential importance of genetic effects on infectivity, we developed a model to estimate effects of host SNPs on both susceptibility and infectivity. The model accounts for variation among susceptible individuals in the exposure to infectious herd mates, and for the genotypes of those herd mates. To test our model, we simulated an endemic disease in 10 groups of 100 individuals and recorded time-series data on individual disease status. For different SNP effects and recording intervals, we quantified bias and precision of model estimates. SNP effects were on average underestimated, thus estimates were conservative. Underestimation of SNP effects on infectivity increased with length of the recording interval. In spite of the limited sample size simulated, power to detect SNP effects for susceptibility was high. Power to detect effects for infectivity was lower but became higher than 60% when the allele effect size was greater than a factor of 0.5. The optimal recording interval was similar for susceptibility and infectivity, around 25 to 50% of the length of the average infectious period.In the development of our model, we followed Anche et al. , who conIj(t), as explanatory variable in our model. However, there is loss and gain of infectious individuals during the interval because on the one hand, some of the initially infectious individuals may recover during the interval and thus no longer contribute to transmission, while on the other hand, some of the initially susceptible individuals may become infected during the interval and contribute to transmission from that time onwards. This loss and gain of infectious individuals is not accounted for by the model, which is more pronounced for longer intervals. In a (dynamic) equilibrium, the number of infectious individuals will, on average, tend to move towards its median value. Hence, the number of infectious individuals of a certain genotype at the beginning of the interval is systematically more extreme than the actual number of infectious individuals of that genotype averaged over the interval. Thus, in the model, the variance of the CountF-term is systematically too large, especially for longer intervals. This explains the underestimation of the infectivity effect  and the increase of this underestimation when the recording interval is longer. However, when the recording interval is short, there are no or only a few infections within an interval and, thus, the number of cases is too limited for precise estimations. Thus, given a fixed total number of recordings, short recording intervals lead to reduced precision of estimates, whereas long intervals lead to bias  determines the optimum recording interval, rather than prevalence.An assumption of our model is that cases within an interval are caused by the infected individuals at the beginning of that interval. Thus, there is a gain and loss of infectious individuals that is not accounted for by the model. The impact of this error depends on the number of cases and the number of recoveries relative to the number of infected individuals at the beginning of the interval. In an endemic equilibrium, the number of cases within an interval equals, on average, the number of recoveries within an interval, Estimates of genetic effects on infectivity were less accurate than those on susceptibility. This is partly because infectivity is expressed only by the infected individuals. Furthermore, there is a trade-off between the quality of the susceptibility and infectivity estimates in relation to group size . In largd/a following Falconer and Mackay [γG < γg. So, for example, for a twofold effect with γg = 1.0, the dominance deviation is one-third of the additive effect. For a tenfold effect, d/a = −0.81. Hence, in our model, alleles that cause a large increase in susceptibility or infectivity are almost completely recessive. Recessive alleles for susceptibility may be plausible because selection against recessive alleles with detrimental effects on fitness is inefficient, particularly when the frequency of the recessive allele is low. Hence, alleles that cause a large increase in susceptibility but are still segregating are probably recessive. Whether completely recessive alleles for infectivity are also plausible, is unknown at present.We assumed that allele effects on susceptibility and infectivity were additive on the log-scale, such that the model could be formulated in terms of allele counts within individuals and the model could be tested without introducing estimation errors that might be present with additive allele effects on the original scale. Allele effects were, therefore, multiplicative on the original scale. With multiplicative allele effects, negative dominance is introduced on the original scale. The magnitude of the dominance relative to the additive effect, denoted as d Mackay  is:\docuα) of an allele, including the relevant dominance component (α = a + (q − p)d; [An alternative perspective is that our model estimates the average effects of alleles on the log-scale, regardless of presence or absence of dominance on the log-scale. This is analogous to using ordinary additive models for estimating SNP effects, where the model captures the full average effect (q − p)d; ).γGG = γGg = γgg = p = q = 0.5, the average effect α = 0.42 [γGG = γGg = γgg = p = q = 0.5, the average effect α = 0.42. Estimates were We determined α = 0.42 . Estimatpartial regression coefficients and, therefore, account for LD. As in any single-SNP GWAS, there may be genes elsewhere in the genome that affect the same trait and show LD with the SNP of interest. Such genes would bias estimates of the SNP of interest. Hence, after an initial single-SNP GWAS, the significant SNPs should ideally be fitted simultaneously, in order to account for LD. Moreover, in GWAS studies, significance thresholds need to account for multiple testing to avoid many false positives, and GWAS studies need to take population stratification into account. For traits affected by direct effects only, stratification can be accounted for by including a random polygenic effect in the model, with a covariance-structure given by a genomic relationship matrix. For infectious disease data, that model would need to be extended with polygenic effects for infectivity of infected contact individuals [We estimated the effect of two SNPs, one for infectivity and one for susceptibility, without fitting the effect of other genes that may affect these traits. This approach is similar to genome-wide association studies (GWAS) or candidate gene studies, where SNP effects are often fitted one at a time. Hence, the model presented here can be used as a starting point to explore and identify which loci affect the trait of interest. One approach could be to estimate both the susceptibility effect and the infectivity effect of the same SNP, one SNP at a time. This would imply full linkage disequilibrium (LD) between the susceptibility SNP and the infectivity SNP, because they are one and the same SNP. However, in contrast to GWAS for ordinary (“direct”) traits, this would not imply full confounding of the two effects, because they are expressed in phenotypes of distinct individuals. Nevertheless, both effects may be partially confounded because herd mates are usually related. Hence, for GWAS, further research is required to investigate the effect of LD between SNPs for susceptibility and infectivity. Note that, while we considered absence of LD between loci in the simulated data, the statistical model that we developed Eq.  does notividuals . The latAnche et al. showed that relatedness within groups resulted in better estimates of susceptibility and infectivity . When reKnowledge of the amount of genetic variation in infectivity is very limited at present. In general, natural selection has a tendency to exhaust heritable variation in traits related to individual fitness. Infectivity, however, is an indirect genetic effect, that affects disease status of other individuals rather than that of the individual itself. Natural selection targets such indirect genetic effects only in the presence of feed-back mechanisms, such as with kin and group selection . Even inWhen our model is extended with the relevant polygenic effects (as discussed previously), it can be used to estimate SNP effects on susceptibility and infectivity, in particular when more data on disease status and genotype become available. Opportunities to measure disease status on a regular basis lie in the increasing number of sensor systems that are used and will be used in the future . CurrentWe developed a generalized linear mixed model to estimate SNP effects on both host susceptibility and host infectivity from time-series data on individual disease status for an endemic disease. In contrast to common models used in animal breeding, our model accounts for variation among susceptible individuals in their exposure to infectious herd mates and for the genotypes of those herd mates. With the use of simulated data, we quantified bias and precision of SNP effects estimated by the model and showed that the optimal recording interval is between 25 and 50% of the average infectious period when disease status is observed 11 times. When the recording interval was close to optimal, SNP effects were on average slightly underestimated. Infectivity estimates were less precise than susceptibility estimates. In future genome-wide association studies, the model presented here may be useful to estimate SNP effects that affect disease transmission and disease prevalence.


P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680–760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive  However, most of traditional phenotypic tools are destructive, measure only a limited number of phenotypes, and hinder the development of the study of functional genomics and crop breeding2. To address the challenge of high-throughput screening of increasing genetic resources, plant phenomics is rapidly developing, and phenotyping platforms have been developed4. In recent years, phenomics has been frequently used to bridge the gap between genotypes and phenotypes and environmental factors5, and provides a new approach to expedite proceedings in understanding the environmental responses and identifying gene function of plant6.With the growing demand to support and accelerate functional genomics and plant breeding for agronomic traits, large numbers of plant parameters need to be accurately quantified7. Rice is an important food crop worldwide and has been adopted as a model plant in plant science research8. Biomass, leaf area, and chlorophyll contribute to rice yield; therefore, methods for accurately and non-destructively quantifying these phenotypic traits are urgently needed11. In addition, these traditional agronomic traits are complex and poorly inherited12 and should be dissected into simpler and more heritable traits13.To meet the food demands of the world’s population, it is critical to improve crop productivity14. Currently, hyperspectral imaging has been widely used as a new technology to non-destructively capture plant phenotypes, including analyzing small-scale sugar beet disease symptoms15, identifying the leaf pigment status of Arabidopsis mutants16 and predicting the above-ground biomass of individual rice plants17. With the availability of low-cost genotyping technologies and easy access to the germplasm resources, genome-wide association study (GWAS) are widely used to link genotype and phenotype and to explore allelic variation7. In plants, GWAS have been conducted for agronomic traits and metabolites to understand genetic architecture20. To our knowledge, there are no GWAS of hyperspectral traits at the whole-plant level to reveal natural genetic variation in plants.A hyperspectral imaging system that integrates spectroscopic and imaging techniques produces a three-dimensional dataset containing two spatial dimensions and one spectral dimensionIn our study, we developed a high-throughput hyperspectral imaging system (HHIS) that is able to obtain thousands of indices to quantify traditional agronomic traits, including biomass-related traits, green leaf area (GLA), and chlorophyll content (Chl), at the whole-plant level. We used this system to explore the genetic variation in rice. With hyperspectral variation, we identified many significant loci associated with indices of potential importance in genetic research and rice breeding. The combination of HHIS and GWAS provides a powerful strategy for understanding natural genetic variation and accelerating crop improvement.indica and japonica)20. A total of 2,700 GB of data  were collected for three growth stages , T106 (653 nm) , dA162 (788 nm), and dT136 (725 nm) , ddT126 (701 nm), and ddT124 (696 nm)  ≥0.5, rg (genetic correlation coefficient with the agronomic traits) ≥0.5, and h2  ≥0.5, we identified 502 indices of potential significance (designated as ‘hyper-traits’ hereafter), which has potential in breeding selection at earlier stages in a high-throughput, nondestructive, and dynamic manner ; h2 = 0.75) could be used as a reliable indicator to select rice material with high yield potential at the heading stage. Further, these 502 hyper-traits were enriched at wavelengths from 680 to 720 nm, which is exclusively the red edge in spectroscopy, indicating the significance of the red edge in rice research  in rice, we defined a chromosomal region in which the distance of adjacent pairs of SNPs was less than 300 kb as a locus25. As a result, 989 loci and 23,264 associations were scanned in our study  with 3,498 SNPs with P-values < 10−4. For the index dA233 at the heading stage, the P-value of the lead SNP sf0719864659 was 1.17 × 10−11 in the whole population; this characteristic was in LD with 633 SNPs with P-values < 10−4. For the index ddA137 at the ripening stage, the P-value of the lead SNP sf0820477603 was 2.39 × 10−10 in the whole population; this SNP was in LD with 603 SNPs with P-values < 10−4. A total of 55 loci were associated with traditional agronomic traits, of which 54 were also identified by indices (Supplementary Table PGWAS = 5.03 × 10−8) and the hyper trait ddT132  at the heading stage  and dA142 at the tillering stage , respectively  and dT134 at the heading stage , respectively  and sf0920791791 (in locus 766) failed to be detected with Chl at the tillering stage but were associated with ely Fig. . The leaely Fig. . These rdA142 at the tillering stage mentioned above, was detected with 28 indices at the tillering stage, of which 24 were related to the red edge, such as CPT14 (the red edge area of average reflectance) ) in Arabidopsis, which is also predicted to function in chloroplasts. Forty-two chloroplast-related genes are positively co-expressed with the candidate gene (Pearson correlation coefficients > 0.7)  is mainly expressed in green tissues and is involved in chlorophyll degradation26. These results indicate that the candidate gene maybe the causal gene underling the locus. This gene and its prompter region (2 kb upstream of TSS (transcription start site)) contain 13 significant SNPs, of which 11 SNPs are in nearly complete LD (r2 > 0.93) with the lead SNP , is located 630 bp (base pair) upstream of the TSS. These results suggest that the natural variation in the promoter region of this gene may result in the expression polymorphism, ultimately contributing to phenotypic diversity. Furthermore, we performed haplotype analysis of this gene based on these significant SNPs . These results further indicate that LOC_Os09g36130 could be the causal gene affecting Chl. Furthermore, 97.2% from H1 accessions and 75.5% from H2 (favorable haplotype) accessions belonged to indica and japonica subspecies, respectively. The Chl of japonica subspecies was significantly higher than indica at the tillering stage . These results indicated that the gene may contribute to the divergence of chlorophyll content between the two main rice subspecies.Locus 766, associated with ce) Fig. . Based oSNP Fig. . These SNPs Fig. . The accS (projected area) was not among the dependent variables at the tillering and heading stages  with high resolution. Based on the phenotypic modeling and genetic analyses, we discovered that the red edge is quite valuable in rice research, as it can evaluate biomass, GLA, Chl, and yield component from phenotypic and genetic insights, and the indices related to red edge perform well in genetic analyses. The red edge is beyond the visible range; therefore, traditional phenotyping technologies cannot obtain this valuable information. In addition, comparison of the eight different index types revealed that the derivative index types  could detect more genetic loci than the raw index types (T and A) in the GWAS, which implies that derivative data are necessary when the raw data consist of time or wavelength series.Hyperspectral imaging in our study obtained the indices of the wavelengths ranging from 400 to 1,000 27. The correlation coefficients between the Chl and Field-Chl-2012 at the tillering and heading stage were 0.571 and 0.619, respectively. And the correlation coefficients between the Chl and Field-Chl-2013 at the tillering and heading stage were 0.569 and 0.550, respectively. Moreover, five loci were repeatedly detected with Chl in both our present study and the previous field work in GWAS while only seven common loci associated with Chl were detected in 2012 and 2013 in the previous field work O. sativa accessions was used in our study24, of which 100 accessions were chosen for conventional measurement and modeling. In 2013, seeds were sown in a field, and three healthy 20-day-old seedlings of each accession were transplanted to three pots at Huazhong Agricultural University. Finally, 91, 71, and 69 accessions were used for conventional measurement and modeling at the tillering, heading, and ripening stages, respectively. Moreover, we determined the levels of hyperspectral indices and agronomic traits of 371, 370, and 328 accessions at the three respective stages for genetic analyses  before image acquisition. To further improve the signal-to-noise ratio, every reflectance was an average of three repeated scans. The actual reflectance was calculated using the following equation:rj indicates the reflectance of the i-th wavelength, Ij indicates the raw data of the i-th wavelength, Idark indicates the dark current data, and Iref indicates the whiteboard data.At each harvest time, pot-grown rice samples from the corresponding stages were imaged twice with two angles (0° and 90°) in a darkroom. The tillering, heading, and ripening stages were imaged from July 8 to July 13, August 3 to September 15, and August 23 to September 29, 2013, respectively. The differences in the time of growth stages were based on the diversity of varieties. The reflectance values of the pot-grown rice were obtained using HHISion Fig. . The spe17. Here, we did not use ENVI , which is typically used to process spectral data to extract an image, because using ENVI to process large-scale data is labor-intensive. Instead, we used LabVIEW  software to write a program to implement automatic extraction of images from the binary data stream that would be fully automated without any human intervention. The detailed steps are presented in Supplementary Fig. The hyperspectral data that were obtained from the previous step consisted of a binary data stream. Thus, the data were reorganized to extract images24. Subsequently, all the plant parts were used to measure the FW and the DW using an electronic balance. The FW was the weight of all the organs of the rice plant except the root and included the FLW, the FSW, and the FPW. The DW included the DLW, the DSW, and the DPW.After the pot-grown rice was imaged with HHIS, a sample was taken to measure Chl with a SPAD-502 . For each tiller, the measurement point was at the center of the second leaf from top to bottom. Each leaf was measured twice, and four healthy tillers were measured from each plant. The final Chl per plant was the average of eight measurements. The sample was then harvested to measure the GLA with a high-throughput leaf scorer29. LSR analysis was used to select some effective hyperspectral indices for these phenotypic traits when there were numerous independent variables, and some of them were not completely independent of each other.In total, 1,541 independent variables were obtained for each stage; therefore, the data needed to be reduced dimensionally. Regression analysis improved the grass biomass estimation compared with the hyperspectral index, such as the normalized difference vegetation index and the red edgeAPE) of the prediction set were calculated to evaluate the performance of the model as follows:n indicates the number of samples used in the model, Yi indicates the manual measurement, and yi indicates the predicted value of the model. This step was programmed and achieved using LabVIEW.After the LSR, 5-fold cross validation was used to evaluate the accuracy of the predicted model. The average R² of the modeling set, the MAPE, and the SD of absolute percentage error  in the whole, indica, and japonica populations, respectively. A linear mixed model was adopted for the GWAS using the software FaST-LMM30. The effective number of independent SNPs (N) was estimated with the GEC tool31. Genome-wide suggestive (1/N) and significant (0.05/N) P-value thresholds were set to control the false-positive rates of the GWAS. The suggestive/significant P-value thresholds were 1.21 × 10−6/6.03 × 10−8, 1.66 × 10−6/8.30 × 10−8, and 3.81 × 10−6/1.91 × 10−7 for the whole, indica, and japonica populations, respectively. To determine the independent lead SNPs from multiple significant SNPs associated with a given trait, the function–clump from the tool ‘Plink’ was used to remove the dependent SNPs caused by LD (r2 > 0.25)32. The LD statistic r2 was estimated by the function–ld from Plink. The lead SNPs, which surpassed the suggestive threshold and were in LD (r2 > 0.25) with ≥5 SNPs (PGWAS < 1 × 10−4), remained in our study.Bivariate REML analysis was performed using the software GCTA to estimate genetic correlation and heritability kb upstream and downstream). The databases ‘Rice DB’ (http://ricedb.plantenergy.uwa.edu.au/)33 and ‘RICECHIP.ORG’ (http://www.ricechip.org/) provided the predicted subcellular localization. The website ‘Rice Oligonucleotide Array Database’ (http://www.ricearray.org/)34 supplied the co-expression information. The putative homologies in rice and Arabidopsis were available in the database ‘CREP’ (http://crep.ncpgr.cn/crep-cgi/home.pl)35. Arabidopsis information was collected from the website ‘tair’ (http://www.arabidopsis.org/index.jsp). The gene expression profiles were available in the database ‘RiceXPro’ (http://ricexpro.dna.affrc.go.jp/)36.Based on GWAS results, candidate genes were chosen from the genes around the lead SNPs of associated loci (100 Supplementary Table 9Supplementary Table 10Supplementary Table 11Supplementary Table 12Supplementary Table 13Supplementary Table 1-8,14 and Figures


Fusarium culmorum is one of the species causing Fusarium head blight (FHB) in cereals in Europe. We aimed to investigate the association between the nucleotide diversity of ten F. culmorum candidate genes and field ratings of aggressiveness in winter rye. A total of 100 F. culmorum isolates collected from natural infections were phenotyped for FHB at two locations and two years. Variance components for aggressiveness showed significant isolate and isolate-by-environment variance, as expected for quantitative host-pathogen interactions. Further analysis of the isolate-by-environment interaction revealed the dominant role of the isolate-by-year over isolate-by-location interaction. One single-nucleotide polymorphism (SNP) in the cutinase (CUT) gene was found to be significantly (p < 0.001) associated with aggressiveness and explained 16.05% of the genotypic variance of this trait in rye. The SNP was located 60 base pairs before the start codon, which suggests a role in transcriptional regulation. Compared to a previous study in winter wheat with the same nucleotide sequences, a larger variation of pathogen aggressiveness on rye was found and a different candidate gene was associated with pathogen aggressiveness. This is the first report on the association of field aggressiveness and a host-specific candidate gene codifying for a protein that belongs to the secretome in F. culmorum. Fusarium is a worldwide threat to many agricultural crops and commodities reducing not only the yield, but contaminating the grain with mycotoxins [Fusarium graminearum, F. culmorum, and F. avenaceum are the most common in Europe [Fungi are the most important pathogens that attack cereal crops in Central Europe. Among them, the genus cotoxins . They incotoxins . From inn Europe . Outbrean Europe . An estin Europe .F. culmorum (W.G. Smith) Sacc., firstly described in 1892, is a soil-borne pathogen and the principal origins of inoculum are crop residues containing fungal mycelium and long-living chlamydospores in the soil [the soil . Main rithe soil . When ththe soil , or actithe soil .F. culmorum and F. graminearum belong to the category of hemibiotrophic pathogens. Hemibiotrophs present a short biotrophic phase throughout the primary phase of infection and then switch to necrotrophy with secretion of mycotoxins and enzymes for degradation of host cell walls [TRI genes located at the trichothecene gene cluster [ll walls ,9. Trich cluster ,10. AmonFrom the host perspective, the genetic basis of FHB resistance in cereals has been explored in a large number of studies that observed a quantitative inheritance ,7,8,9. Tr = 0.7, p < 0.01) between aggressiveness and DON production using 50 isolates of F. graminearum. A similar outcome was reported for 100 F. culmorum isolates in wheat .F. culmorum and F. graminearum, there is a high probability that many genes are associated with aggressiveness but the precise number and interaction between them are still to be established.In contrast to a large number of studies on host resistance, studies on the genetic basis of fungal aggressiveness are very limited. Therefore, it is necessary to close this knowledge gap about genetic and environmental determinants of aggressiveness to make assumptions on the possible adaptation of the pathogens to host resistance. In the case of F. graminearum with frequent sexual recombination the development of mapping populations is possible. In a study [TRI5 locus were identified. Unfortunately, this approach is not an option for F. culmorum because no teleomorph has been identified yet [In the related species  a study  using thfied yet .F. graminearum with four chromosomes comprising 36.6 Mbp [MetAP1, Erf2) were significantly associated with aggressiveness in F. graminearum in wheat [F. graminearum [With the advantage of having the complete genome sequence of 36.6 Mbp ,18, it i36.6 Mbp . This re36.6 Mbp . The sec36.6 Mbp . Finallyin wheat . An alteminearum  and resuF. culmorum has a broad host spectrum including all small-grain cereals [Triticum aestivum L.) was grown on about 62.5 million hectares in 2016, rye  across 3.6 million hectares [ cereals . In Eurohectares . Both ceF. culmorum aggressiveness quantified with two different Data sets  untangle the relative importance of the components explaining the variance of aggressiveness measured in field experiments across two replications, two locations and two years, with an experimental, genetically homogeneous winter rye genotype as a host; (ii) compare the phenotypic information from rye and wheat; (iii) evaluate the association of SNPs in the candidate genes with ata sets  using  medium and transferred in 2.5 mL Eppenmeyer tubes in distilled water at 6 °C for storage. One agar plug out of the stored isolates was placed in Erlenmeyer flasks with 400 mL of the SNA medium and incubated under constant shaking at 110 rpm and UV light for stimulation of sporulation during 1 week at 22–25 °C . With a The spores were inoculated on the rye heads at full flowering with a manual atomizer and 100 mL suspension per square meter. A tractor was used to generate a stable air pressure of 3 bars to guarantee the even application of the spores on rye heads across the plot.Secale cereale L., “L2177-P×L2184-N”, HYBRO Saatzucht GMBH & Co., KG, Schenkenberg, Germany). The trial was made in two locations: Oberer Lindenhof  and Hohenheim  in two years (2015 and 2016). For comparison, previously reported phenotypic data from wheat were used [F. culmorum isolates tested on a moderately susceptible winter wheat cultivar  with the same experimental conditions at the same locations and experimental design in 2014 and 2015. Comparison between crops was restricted to 2015, because only in this year the experiments were placed on the same field as split-plot design with crops as main plots and isolates as subplots. Means of annual temperature at OLI and HOH in 2015 were 8.88 °C and 10.86 °C and in 2016 were 8.5 °C and 10.12 °C, respectively. The mean precipitation at OLI and HOH were 709.8 mm and 492.1 mm in 2015 and 779.3 mm and 595.4 mm in 2016.A susceptible, cytoplasmic-male sterile single cross of winter rye was used as host across the whole experiment  and “Conduct” (KWS LOCHOW GMBH) to secure pollination. Plots were sown with 220 kernels mThe experiment was arranged according to an alpha-lattice design with two replications per environment and an incomplete block size of ten plots. The randomization of genotypes was done by PLABPLAN , 70599 Stuttgart, Germany) within the program package PLABSTAT .The ratings started with the initiation of symptoms about two weeks after inoculation and continued in 2 to 5 days intervals until the start of yellow ripening. Typical symptoms are the prematurely bleaching of infected cereal spikelets while the non-infected part of the head is still green ,16. In iF. culmorum in the same locations in one year (2015). Therefore, in the analysis of this Data set, we added a crop effect to the model.The phenotypic data from each environment were separately screened for outlier detection with the Bonferroni-Holm method with re-scaled MAD standardized residuals as suggested by Bernal-Vasquez . AdditioData set 1: Rye 2015 + 2016 across 2 locations per yearData set 2: Rye & wheat 2015 across 2 locationswhere yijn is the aggressiveness of the ith isolate in the jth year at the kth location, mth block and lth crop. Iso, Loc, Rep and eilknm denote isolate, location, replication or their interactions and the residual error, respectively.We estimated variance components using the linear mixed model:2) was estimated on an entry-mean basis as the ratio of genotypic to phenotypic variance according to Piepho and Möhring [The variance components were estimated by applying the restricted maximum likelihood (REML) approach and their significance was verified by model comparison with likelihood ratio tests . Heritab Möhring . Further Möhring .F. culmorum isolates [Ten candidate genes previously found as polymorphic in our set of isolates  were useisolates . Finallyp values were corrected for potential inflation [p value of <0.05 as the cutoff. The proportion of genotypic variance (pG) explained by each SNP was derived from the sums of squares of the SNP in a linear model divided by h2. All calculations were done with statistical software R version 2.14.2  [The association analysis was calculated using principal coordinate (PCo) and pairwise kinship coefficients  for corrnflation . The sigAustria)  includinAustria) ,46 and AAustria) ,48.F. culmorum, and large differences among the tested isolates were found as shown by the ranges  calculated from the mean FHB rating followed a normal distribution  as expecThe correlation of mean FHB aggressiveness on rye and wheat was significant .CUT (FGRRES_02342_M) was associated with field aggressiveness in both analyzed Data sets. p < 0.001.The SNP located at position −60 in the gene p < 0.01) for mean FHB aggressiveness. The isolate-by-year and the three-way interaction variances were also significant (p < 0.001), isolate-by-location interaction variance was not important. The entry-mean heritability for Data set 1 was 0.80.The variance components were estimated for Data set 1, which corresponds to two years and two locations in rye a. The isp <0.05. The genotype-by-location interaction variance was not significant in this analysis and the entry-mean heritability was 0.83.When aggressiveness measured during 2015 on rye and wheat was combined  has increased in recent years, candidate gene association studies allow a direct identification of genes, which play a role in the performance of the pathogen population, even when the genome information is still scarce . This mein wheat ,21. In tad wheat . From angenicity  with fieF. culmorum populations displayed a high genotypic variance of field aggressiveness within individual field populations, similar to the variance displayed by the international collection  isolate effects were obtained. Heritability is used in plant breeding as an indicator of the precision of the trials or a series of trials and for partitioning the total variance into the genetic and non-genetic components [F. culmorum isolates in winter rye, where the heritability value was 0.85 [F. graminearum [F. culmorum populations [In the analysis of the two datasets analyzed for FHB aggressiveness of 100 isolates, high heritabilities and significant (mponents . The meawas 0.85 . Signifiminearum  and F. culations . These rp < 0.001) different from zero , all interactions with isolate and year were significantly , isolate-by-crop interaction. Accordingly, the correlation between the aggressiveness of isolates for wheat and rye was significant  i.e., the isolates ranked similarly on both crops . The second group recently presented a draft assembly for a British strain (UK99) from an infected wheat ear [F. graminearum genome sequence and the high homology between these two Fusarium species were exploited [The sequence of the heat ear . For theCUT gene displayed significant association to FHB aggressiveness and was still significant after correction for population structure with a kinship matrix coupled by a principal coordinate analysis (PCoA). Using Data set 1, which involves the aggressiveness measured on rye alone, the SNP CUT-60 explained 16.05% of the genotypic variance with a p-value of 0.001. In Data set 2 which analyzed data from wheat and rye, the genotypic variance explained by the SNP was 5.96% only with p <0.01. Clearly, rye alone had a larger effect on this SNP than rye and wheat together.The SNP-60 in the F. culmorum population was larger in rye than in wheat in 2015 (1 single cross between two inbred lines (A × B) was designed, which was genetically homogeneous and more susceptible than the commercial rye cultivars. The wheat genotype used for comparison was the moderately susceptible line cultivar “Inspiration”. Consequently, the aggressiveness variation in rye was larger than in wheat in 2015.Usually, susceptible plant genotypes allow the expression of larger aggressiveness differences when exposed to different pathogen isolates. In this study, the variability of aggressiveness expressed by the  in 2015 , althougCUT was the gene having most SNPs with a minor allele frequency (MAF) >5%  >5% . The isoMAF) >5% c,d. The  alleles . However alleles  present CUT was significantly associated with FHB aggressiveness and showed high nucleotide diversity. Comparative genomic studies have shown that genes involved in niche adaptation, such as the colonization of living plant tissue, appear to have a high diversity among isolates of the same Fusarium species [ species . Cutinas species , but the species . The cutF. culmorum and F. graminearum, an active route for colonization is the invasion of the cuticle and cell wall with short hyphae [F. culmorum inoculation on wheat [F. graminearum genome preserves diverse cutinase genes [F. graminearum during infection on barley heads [Verticillium dahliae extracellular cutinase (VdCUT11) is an important secreted enzyme affecting aggressiveness in Nicotiana benthamiana [In the field of host-pathogen interactions, different functions have been attributed to the cuticle: spore attachment  and hostt hyphae ,85. Disron wheat . The dirse genes  and 32 uey heads . Accordithamiana .CUT regulation may influence the capacity of F. culmorum to penetrate the host in its initial biotrophic phase or may help in its saprophytic phase. Some authors suggested an essential role of the protein during saprophytic development [Based on this information and the results in this study, we hypothesize that variations in elopment .Fusarium sp. cutinases stimulate host defense responses by creating a complex with plant nonspecific lipid transfer proteins (nsLTPs), and thus facilitating cutin repair [CUT-60 polymorphism associated with aggressiveness produce a decrease or increase in the cutinase expression. Under this defense model of the host and an evolutionary scenario, e.g., host evasion from Fusarium sp., low expression of the enzyme could delay the recognition by the host defense system and thus increase aggressiveness.A model was proposed in which cutin monomers that result from the action of n repair . It is nF. graminearum and F. culmorum, however, can enter passively by innate openings, such as stomata, or actively by direct penetration, where the plant cuticle still may play a role in resistance. Yoshida et al. [The cuticle could have a role in the plant defense by influencing the deposition of inoculum in the initial stages of infection. Waxes on the plant surface can repel water and therefore prevent the formation of a water film that the pathogen needs to germinate. The role of the cuticle as a mechanical barrier is still not clear. In pathogens that enter the host plant only by direct penetration, a thick cuticle could increase resistance to infection. a et al.  evaluateHOG1 previously reported as associated with F. culmorum aggressiveness in wheat as host [HOG1 was significantly associated with aggressiveness, although there was nucleotide diversity also in rye, might be that the effect of the SNP is not stable across environments. QTL-by-environment interactions are typical for quantitative traits [F. culmorum in wheat [CUT gene (CUT position 536 + 1) significant at p <0.1, did not display any significance with CUT-60. Given the nature of the cutinase protein, codified by the CUT gene of F. culmorum, a possible explanation for the differences could be due to differences in the cuticles of wheat and rye. Cuticles vary significantly in their architecture, for example changes in thickness according to the species and ontogeny [F. graminearum.Interestingly, the gene  as host  was not e traits . Some QTe traits . On the in wheat , althougontogeny . There aontogeny ,94. Therontogeny  observedCUT gene (CUT promoter that was not sequenced. Another explanation might be that the polymorphism is in fact located within the promoter region, given that those regions are normally directly adjacent to the gene. Therefore, a change in this region could influence the gene transcription levels. Regulation elements of the cutinase gene promoter have been identified in the upstream non-transcribed region of the cutinase gene in F. solani f.sp. pisi [The associated polymorphism was located −60 bp upstream from the start codon in the CUT gene b and is sp. pisi . AccordiF. culmorum possessed a high genetic diversity in aggressiveness that enables the infection process and increases FHB damage. The identified cutinase gene should be further analyzed by gene expression studies to validate its importance in F. culmorum aggressiveness using different cereal hosts including rye. Whole-genome sequencing of the fungus in future will enable a verification of our association mapping study, allow detection of more genes relating to aggressiveness and improve our understanding of the genetics that contributes to this important, quantitatively inherited trait. This study demonstrates the potential of candidate gene association mapping to reveal genes that affect fitness traits for populations of plant-pathogenic fungi under field conditions. This approach is an alternative to traditional QTL studies, especially when recombinant mapping populations are not available. Natural field populations of


A common genetic factor influenced both indices, respectively, at the two time points, with no ‘new’ genetic variance at Wave 2; the non-shared environment was time-specific. Thus, both genetic and non-shared environmental influences are important for behavioral disinhibition (Q-Score) and planning (Test Age).The Porteus Maze Test (PMT) provides measures of planning and behavioral disinhibition. The PMT was administered to 941 twins during Wave 1 (9–10 years) and 320 twins during Wave 2 (11–13 years). Participants were drawn from the University of Southern California Risk Factors for Antisocial Behavior Study (RFAB). Heritability of behavioral disinhibition, determined by PMT Q-Score, were 33% at Wave 1 and 52% at Wave 2. For planning, determined by Test Age, heritability was 53% at Wave 1; at Wave 2, the non-shared environment was important in boys, whereas genetic influences were important in girls. Both indices were modestly stable (The online version of this article (doi:10.1007/s10519-016-9827-x) contains supplementary material, which is available to authorized users. The Porteus Maze Test (PMT) was developed in the 1960′s in order to evaluate motor intelligence as a supplement to the Stanford-Binet intelligence test. It was devised by Stanley Porteus as an assessment of planning capacity in a restricted situation, based on the idea that planning is a key element of intelligent behavior Porteus . PorteusThere are three versions of the PMT: the Vineland revision, the Extension, and the Supplement.The PMT has been utilized frequently in empirical research; however, there is substantial variation in PMT administration and scoring procedures across studies. Despite numerous revisions, the procedures outlined by Porteus are often confusing and easily misinterpreted, and therefore insufficient for the replication of findings. Articles often cite the Vineland revision of the PMT , and the variance in planning was explained primarily by shared (30%) and non-shared environmental factors (63%), with additive genetic factors being of much more limited importance (7%) inhibition across socioeconomic status  Twin study ; during Wave 2 (2003–2006) the twins were 11–13 years old ; during Wave 3 (2006–2010), the twins were 14–16 years old ; during Wave 4 (2008–2011), the twins were 17–18 years old ; and during Wave 5 (2011–2015), the twins were 19–20 years old . The twins and their families were recruited through Los Angeles schools and demonstrate diverse demographic characteristics representative of the greater Los Angeles area. Complete details on study protocol, including zygosity determination, can be found elsewhere  Twin Study of Risk Factors for Antisocial Behavior (RFAB). RFAB is a longitudinal study of the interplay of genetic, environmental, social, and biological factors on the development of antisocial and aggressive behavior from childhood to young adulthood. To date, five waves of data have been collected: During Wave 1 (which took place between 2001 and 2004), the twins were 9–10 years old (n = 320) were administered the Extension series of the PMT during Wave 2 (see below).The PMT was administered during Waves 1 and 2. The present study considers data from 941 participants with complete data on PMT performance from Wave 1. These include 218 monozygotic (MZ) male twins, 218 MZ female twins, 132 dizygotic (DZ) male twins, 148 DZ female twins, and 110 males and 115 females of opposite-sex twin pairs. A subset of these participants  have been met, he allows for relaxing this rule “when a complete qualitative record is desired” (p. 250). Administration of the same set of mazes to all subjects is required in order for the Q-Score to be comparable across subjects. Otherwise, low Q-Scores could result either from the fact that fewer mazes were administered (for less planful subjects) or from lower error rates (for more planful subjects).Due to several ambiguities in the maze test instructions detailed by Porteus , we expar = 0.97 (p < 0.05). In the interest of parsimony, further analyses proceeded with the non-weighted Q-Score.In his original instructions, Porteus assigned a weight ranging from 1 to 3 to each qualitative error . He alsoIn the classic twin design, covariances between MZ and DZ twins are utilized to separate the variance of a measured trait into genetic and environmental components  into A, C, and E factors. The decomposition has the same number of factors in each of the A, C, and E components as the number of observed variables. That is, the first genetic factor loads on the PMT score at both waves, whereas the second genetic factor only loads on the PMT score at Wave 2; this same procedure repeats for the C and E components.Univariate genetic models were fit with the structural equation program Mx  and environmental correlations . A genetic correlation (rg) indicates the extent to which genetic effects on one measure overlap with genetic effects on another measure, in our case, Q-Score and TA within Wave 1 and Wave 2. While a shared environmental correlation (rc) and non-shared environmental correlation (re) indicate overlap among shared and non-shared environmental factors for the different symptoms. These statistics vary from −1.0 and +1.0, and are independent of the magnitudes of genetic and environmental influence for each set of measures  tests were utilized to compare goodness of fit between each model and a baseline saturated model, which perfectly captures observed variances, covariances, and means for each twin and zygosity group. The parsimony of the models—based on the balance between model fit and number of parameters—was evaluated with the Akaike Information Criterion (AIC) (Akaike Chi squared (χ) Akaike  and the ) Akaike , where lt(463) = 1.64, p = 0.10; female MZ vs. DZ t(474) = 1.76, p = 0.08) or sex (t(946) = 1.53, p = 0.13), and there was no significant difference in mean or variance of TA across zygosity  = 1.87, p = 0.06; female MZ vs. DZ t(479) = 0.88, p = 0.38) or sex (t(957) = 1.77, p = 0.08).Means and standard deviations of the untransformed PMT scores as well as twin correlations for the log-transformed scores are shown in Table t(149) = − 0.19, p = 0.85; female MZ vs. DZ t(156) = − 0.17, p = 0.87) or sex (t(308) = 0.42, p = 0.67), and there was no significant difference in mean or variance of TA across zygosity  = 0.75, p = 0.06; female MZ vs. DZ t(158) = 0.59, p = 0.56) or sex (t(311) = 1.90, p = 0.058).In Wave 2, there was no significant difference in mean or variance of Q-Score across zygosity .Univariate genetic model fitting results within each measurement time-point are summarized in Table p < 0.05) of the variance, shared environment 22%, and the non-shared environment accounted for the remaining 46% (p < 0.05) of the variance. For TA at Wave 1, the full ACE model with estimates equated across males and females was further reduced by dropping the shared environment  . Genetic influences accounted for 53% (p < 0.05) of the variance, with non-shared environmental effects accounting for the remaining 47% (p < 0.05) of the variance.For Q-Score at Wave 1, based on AIC and BIC it was not clear whether the full ACE male equal to female model could be further reduced, as such the results of the full ACE male equal to female model are presented here. Genetic influences explained 33% . The full ACE males equal to females model could be further reduced by dropping the shared environment . Genetic influences accounted for 52% (p < 0.05) of the variance, with non-shared environmental effects accounting for the remaining 48% (p < 0.05) of variance. For TA at Wave 2, as the DZ male correlation was significant and higher than the MZ male correlation (Table p < 0.05). For girls, genetic influences accounted for 55% (p < 0.05) of the variance, with non-shared environmental effects accounting for the remaining 45% (p < 0.05) of variance.For Q-Score within Wave 2, a full ACE model fit the data well in comparison to the saturated model. This model could be reduced without significant loss of fit by constraining parameters between males and females to be equal (Q-Score Wave 2: Δr = − 0.64 (p < 0.0001), and within Wave 2 the phenotypic correlation was r = − 0.62 (p < 0.0001). The results of the bivariate genetic modeling within Waves are presented in Table χ2 = 8.86, df = 9, p = 0.45) and by dropping the shared environment . The genetic correlation between TA and Q-score in Wave 1 was rg = 0.81 (95% CI 0.71–0.90) and the non-shared environmental correlation was re = 0.41 (95% CI 0.30–0.51). It should be noted that the non-shared environmental correlation (re) could also reflect correlated measurement error between the two measures. Within Wave 2, a full ACE model provided a good fit to the data relative to the saturated model . The model was further reduced by equating parameters across males and females  and by dropping the shared environment . However, based on AIC the ACE males ≠ females model fit the data better, whereas based on BIC and AE males = females model better described the data. This may partly be explained by the small sample size at Wave 2. As such, the genetic and environmental correlations should be interpreted with caution. The genetic correlation between TA and Q-score in Wave 2 was rg = 0.68 (95% CI 0.48–0.82) and the non-shared environmental correlation was re = 0.59 (95% CI 0.45–0.71).For both Q-Score and TA within Wave 1, the phenotypic correlation was r = 0.52 (p < 0.0001) for Q-Score and r = 0.37 (p < 0.0001) for TA. The results of the bivariate longitudinal genetic modeling are also presented in Table χ2 = 27.55; df = 34; p = 0.78) . In both cases, the model was further reduced by equating parameters across males and females   and by dropping the shared environment  . These models could be further reduced by dropping non-significant estimates A22, E21  .Further, Q-Score and TA showed significant longitudinal stability between Waves: 11)2, i.e., 0.722 = 0.51, and at Wave 2 (a21)2 + (a22)2, i.e., .632 + 0.02 = 0.40. Importantly, a single genetic factor influenced TA at both Waves, with no significant ‘new’ genetic variance appearing for TA at Wave 2. The influence of the non-shared environment on TA was specific to each time of assessment: 0.49 at Wave 1 and 0.61 at Wave 2. The estimated heritability in Q-Score at Wave 1 was (a11)2, i.e., 0.742 = 0.54, and at Wave 2 (a21)2 + (a22)2, i.e., .682 + 0.02 = 0.45, again with a single genetic factor influencing both assessments. The influence of the non-shared environment was again time-specific, 0.46 at Wave 1 and 0.46 at Wave 2. In general, the total genetic and environmental effects estimated for each measure in a bivariate Cholesky decomposition are consistent with those derived in a univariate genetic model. Slight variation in the parameter estimates are a result of additional information available in cross-twin cross-age covariance.Figures The PMT assesses the executive functions of planning and behavioral (dis)inhibition across socioeconomic status Below is the link to the electronic supplementary material.


P = 0.02). cg10917602 is 87 kb away from the closest reported GWAS hit. The employed imputation methodology implies that variation of DNAm levels at cg10917602 is predictive for Parkinson’s disease risk, suggesting a possible causal role for methylation at this locus. More generally this study demonstrates the feasibility of identifying predictive epigenetic markers of disease risk from readily available data sets.Understanding how genetic variation affects intermediate phenotypes, like DNA methylation or gene expression, and how these in turn vary with complex human disease provides valuable insight into disease etiology. However, intermediate phenotypes are typically tissue and developmental stage specific, making relevant phenotypes difficult to assay. Assembling large case–control cohorts, necessary to achieve sufficient statistical power to assess associations between complex traits and relevant intermediate phenotypes, has therefore remained challenging. Imputation of such intermediate phenotypes represents a practical alternative in this context. We used a mixed linear model to impute DNA methylation (DNAm) levels of four brain tissues at up to 1826 methylome-wide sites in 6259 patients with Parkinson’s disease and 9452 controls from across five genome-wide association studies (GWAS). Six sites, in two regions, were found to associate with Parkinson’s disease for at least one tissue. While a majority of identified sites were within an established risk region for Parkinson’s disease, suggesting a role of DNAm in mediating previously observed genetic effects at this locus, we also identify an association with four CpG sites in chromosome 16p11.2. Direct measures of DNAm in the substantia nigra of 39 cases and 13 control samples were used to independently replicate these four associations. Only the association at cg10917602 replicated with a concordant direction of effect ( GENOME-WIDE association studies (GWAS) have been successful in identifying associations between common genetic variations, typically SNPs, and a common complex disease or trait. However, despite this success GWAS have provided only limited information about the mechanistic role of genetic variation in the etiology of disease. In part, this result follows from the challenge to map the exact location of the causative variant and identify its functional consequence as well as the highly polygenic nature of many phenotypes targeted by GWAS. Understanding how heritable tissue-specific cellular phenotypes  vary with respect to both the genotype and complex disease assists us in the construction of medically relevant biological networks. To this end there has been an increasing interest in both GWAS of intermediate phenotypes  with a concordant effect direction in this data set.Imputing tissue-specific DNAm for four brain regions into 15,711 individuals from five case–control GWAS, we identified six candidate DNAm sites across two regions in chromosomes 16 and 17 showing association with Parkinson’s disease case–control status. We were able to test four of these six candidate sites that passed quality control (QC) in a separate data set of 39 cases and 13 controls. One of these associations, cg10917602 in 16p11.2, replicated , that is, an imputation-based association study, and apply it using brain region-specific DNA methylation levels and Parkinson’s disease as the intermediate and ultimate phenotypes, respectively. Specifically we utilized case–control data from five GWAS of Parkinson’s disease  in conjug is a vector of standardized genotypes for a set of m SNP markers, such that g and p, where, as previously, indicated z denotes DNAm levels at a CpG site and p denotes the ultimate phenotype of interest, i.e., in our case Parkinson’s disease status. We base imputation of z on the standard linear model for quantitative traits. Specifically we assume z arises from a linear combination of a subset of local marker effects and a further term capturing environmental and unmodeled genetic effects, so that for individual i we havez and j inp can then proceed in the usual fashion based on a suitable statistical test.Our imputation model, briefly summarized in the following, has been utilized in previous work ( (eBLUP) . The eBLP-value as p with a random intermediate phenotype and P-value by generating 1000 alternative hypothetical intermediate phenotypes and comparing their association statistics with the statistic observed on the imputed intermediate phenotype. These generated intermediates were matched to the imputed intermediate phenotypes. Specifically, for the computation of a specificity P for imputed intermediate z we sampled marker effect vectors z. Generated intermediate phenotype i for individual j was then computed as z for generation of alternative intermediates, which yielded qualitatively similar results. As test statistics we used the P-values obtained from the test for association.We define a specificity z and p, i.e., i, z on p. Note that for the standardized intermediate phenotype The use of imputed intermediate phenotypes in lieu of their observations may adversely affect the statistical power to detect associations with the ultimate phenotype. The exact loss of statistical power will depend on many factors, including the relation of the intermediate and the ultimate phenotype and the estimator used. In general, we may analyze the statistical properties of the imputation methodology by adapting the framework of analyses , rather x and p isα is given byIn general, the asymptotic noncentrality parameter of a p we have Considering the use of imputations, we assume that the estimate follows sumption , we may We make use of a total of six publicly available data sets from the database of Genotypes and Phenotypes (dbGaP) that have been previously reported in the context of a GWAS for DNA methylation in brain tissue or Parkinson’s disease.methylation data, was reported by cerebellum (CRBL), frontal cortex (FCTX), pons (PONS), and temporal cortex (TCTX). The data comprise 150 individuals genotyped for 561,466 SNPs with the Illumina HumanHap550v3.0 platform, levels of DNAm from the four brain tissues for 27,578 DNAm sites quantified using the Illumina HumanMethylation27 Beadchip, and a list of potentially relevant covariates: sex, age, postmortem interval, assay plate, and study of enrollment.The first data set, which we refer to as the Parkinson’s data, comprise SNP genotypes, obtained with varying genotyping platforms, for individuals with Parkinson’s disease and unaffected control subjects. Three of the data sets were obtained through the NCBI Data Repository . In addition, we compiled a data set using Wellcome Trust Case Control Consortium (WTCCC) data sets obtained from the European Genome–phenome Archive (EGA). Specifically, the WTCCC data set comprises cases with Parkinson’s disease (EGA data set ID EGAD00000000057) and WTCCC2 controls from the 1958 British Birth Cohort and UK Blood Service Control Group (EGA data set IDs EGAD00000000022 and EGAD00000000024) genotyped using the Illumina platform. Finally, we used data for individuals with Parkinson’s disease and unaffected control subjects from the NeuroGenetics Research Consortium (NGRC), for which details regarding recruitment and quality control protocols have been reported previously . In both the methylation data and the Parkinson’s data the inbreeding coefficient F was estimated from the observed genotype frequencies of SNPs on the X chromosomes and it revealed each sample to be of recorded sex. Additionally, in the Parkinson’s data we excluded all individuals who shared a proportion >0.0625 of their genome identical by descent with any other individual in the same data set. In the methylation data, multidimensional scaling of the genotypic data and comparison with individuals from Yoruba in Ibadan (Nigeria), Japanese in Tokyo, Han Chinese in Beijing (China), and Utah residents with ancestry from northern and western Europe from the Centre d'Etude du Polymorphisme Humain collection (CEU) participating in the International HapMap Project .In all data sets genotyped samples were considered for downstream analysis if they were successfully typed for 95% of the SNPs assayed. SNP markers were included if they were typed in at least 95% of individuals within the data set, were in Hardy–Weinberg equilibrium at  Project  showed t Project . SimilarP ≤ 0.01, leaving sample sizes of 102, 109, 120, and 125 for the CRBL, FCTX, PONS and TCTX, respectively. Similarly, individual DNAm sites were considered for downstream analysis if they were detected above background noise levels at P ≤ 0.01 for ≥95% of the samples assayed. Additionally, DNAm probes listed in the Illumina HumanMethylation450 v.1.2 Manifest File (available at http://support.illumina.com) as containing a SNP within their probe sequence were removed. Recorded sex of each sample was checked against the average level of DNA methylation calculated from CpG sites located on the X chromosome. The methylation levels of each CpG site were rank transformed and adjusted for all the available covariates, which were sex, age, time of the sample extraction from death, study of the sample, and processing plate of the sample.Tissue samples from each of the four brain regions were included for analysis if ≥95% of the 27,578 DNAm sites assayed were detected above background noise levels at cis-heritability (P < 0.05) based on SNPs located within 1 Mb of a specific DNAm site. Accuracy was measured as the correlation between the residual and the predicted phenotype.To assess the quality of imputation we used fivefold cross-validation for 125 samples and 1826 DNAm sites measured in the temporal cortex, which were found to have nonzero cis genetic effects . We restricted imputation to DNAm sites for which the local variation explained a significant proportion of the phenotypic variation (P < 0.05).Imputation in each of the Parkinson’s data sets was based on postquality control SNPs shared by the data set with the methylation data. Previous work indicates that a significant portion of variation in methylation is due to Within each data set, association of imputed DNAm and case/control status was tested by means of a logistic regression, for each DNAm site in each of the four regions. Within each data set we adjusted the analysis for available covariates, which for all data sets included gender, age, and the first two principal components obtained from the genotypes. Additionally, we adjusted for the source of the sample in the Parkinson’s data set phs000394. Where available, age was taken as age of onset for Parkinson’s disease cases.An overall measure of association was obtained by combining results across the five data sets in a meta-analysis. As the data sets stem from disparate studies of varying design and varying implementation details, we opted to base the meta-analysis on a random-effects model. Specifically, we combined the regression coefficients obtained by logistic regression within each data set, using the Hedges–Vevea procedure  Edinburgh Brain and Tissue Bank. We applied the same quality control procedures as described previously for DNAm data. With a significant age difference between the case and control groups (mean age 78.0 years for cases and 53.8 years for controls) we restricted the analysis to individuals >60 years of age  to avoid confounding of age and disease status effects. After QC four of the sites identified through imputation were present in our data . We tested for differential methylation between cases and controls at these sites by performing a logistic regression of methylation level on case–control status, while adjusting for the covariates age, gender, and postmortem interval.The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article.cis-heritability. Overall the mean accuracy ranged from −0.0006 to 0.79 with an overall average of 0.27 . To assess the performance of imputation relative to this limit across all traits we normalized the mean accuracy across validation folds by dividing by the square root of the estimated trait cis-heritability. The normalized mean accuracies for the 1826 DNAm sites ranged between −0.0012 and 1.115 with a mean of 0.5409 and approximated a normal distribution . We verified that the resulting normalized mean accuracy below zero and greater than one, for one and three DNAm sites, respectively, could be attributed to sampling variation. Additionally, we found that the number of SNPs used to capture the regional effects did not explain a substantial proportion of the variance in the estimates of the normalized accuracy (r2 = 0.0580). Taken together these results suggest that for a majority of DNAm sites exhibiting significant cis-heritability, DNAm levels can be reliably imputed from SNP effects estimated in the same tissue.Cross-validation demonstrated that accuracy of DNAm imputation from local SNPs was high. As seen in m; the effect of the standardized intermediate phenotype on the standardized ultimate phenotype, cis-heritability of the intermediate phenotype, cis-heritability, we are referring to the heritability captured by the local genotype that may not necessarily correspond to the total genomic heritability of the intermediate phenotype. We examined the loss of statistical power resulting from using imputed intermediate phenotypes as opposed to their observations. Using the derived expressions for the statistical power, we evaluate the power at level enotypes . Specificis-heritability (P < 0.05), we obtained predictions for 1793, 1717, 1635, and 1826 sites in CRBL, FCTX, PONS, and TCTX, respectively. Meta-analysis results across the different data sets for association are illustrated in Table S1 for full results and Figure S4 for forest plots of the effects). We found six methylation sites across two regions that showed significant association for at least one tissue after adjusting for multiple testing within tissue (adjusted P < 0.05). The relation of the GWAS and PITAS in regions surrounding these loci is illustrated in P = 4 × 10−7 and P = 2 × 10−12, respectively) and that represent the only variants on chromosome 16 contained in the GWAS catalog . However, the association of predicted DNAm at cg10917602 in TCTX with Parkinson’s disease status remained significant (P = 0.002) if a polygenic effect for the local genotypes was included in the model . All other sites to reach significance, cg02301815 , cg07321605 (FCTX), cg08929103 , cg09038914 (FCTX), and cg14154330 , are located in a 2.5-Mb region on chromosome 17. This coincides with a region containing a number of SNPs that were found to be significantly associated with case–control status, although cg07321605, the most telomeric DNAm site, is located ∼1.7 Mb upstream of the closest SNP reaching significance in the GWAS meta-analysis of our data, i.e., rs11012. Furthermore, rs199533 (in MAPT), which is the strongest association (P = 6.7 × 10−16) within the region of significant SNPs in our GWAS meta-analysis, has been previously reported as being associated with Parkinson’s disease , while cg02301815 reproduced with P = 0.03 but with a discordant effect direction (see Table S2 for full results).We replicated the putative associations identified based on imputations using a data set of DNAm assayed in substantia nigra of 39 cases and 13 controls. Following quality control, we were able to test four of the six methylation sites identified previously, specifically cg10917602, cg09038914, cg08929103, and cg02301815. The cg10917602 site reproduced with cis-effects, which allows us to follow a regional approach that greatly reduces the number of model parameters, thus reducing the estimation error. These factors are not unique to DNA methylation but hold for other intermediate phenotypes as is demonstrated by results obtained in gene expression  based on genotype information. This is, however, not unexpected due to two factors. First, the expected proximity, in terms of molecular interactions, of DNAm and the genotype leads to a reduction in biological noise. Second, the genetic variance is concentrated in While we restrict imputation to the genetic component of the intermediate phenotype, it can be extended to include environmental components by utilizing estimated fixed effects during prediction, provided corresponding covariates have been observed in both estimation and the target data set. Including such effects may in general be expected to increase the power for detecting associations and may be of particular relevance for DNAm.Materials andMethods) that allow for comparison of various designs within the first two options, allowing investigators to make informed decisions.A natural question arising in the context of imputation, which previous applications failed to address, is the loss of statistical power. Statistical power to detect significant associations will in general be larger using direct observations of the phenotype compared to the same number of imputed observations. On the other hand, using imputation we may increase statistical power by augmenting any available data sets of relevant joint observations of intermediate and ultimate phenotype with additional data lacking observations of the former. In practice, a more relevant consideration is the allocation of resources in the design of a study. In particular, should investigators concentrate all resources on obtaining a joint data set, obtain a limited joint data set together with an extended GWAS data set, or concentrate purely on a GWAS data set for the ultimate phenotype? We have derived equations for the statistical power (see e.g., the regions around SNCA and HLA on chromosomes 4 and 6, respectively (P-values, this suggests that the observed associations at MAPT are not a consequence of an underlying enrichment of generic disease-associated markers in cis with the DNAm sites. Rather we may speculate about the role of DNAm in mediating the effects of genetic variation at the MAPT locus, noting that the distance between genetic marker and DNAm site is consistent with previous suggestions of DNAm as an intermediate of a genetic effect (We studied associations between DNA methylation in four brain tissues and Parkinson’s disease and identified associations in two regions, only one of which is located near previously robustly identified disease-associated genetic variants. We are able to replicate the association in one of these regions based on direct measurements of DNAm in substantia nigra of cases with Parkinson’s disease and controls. However, because the replication size was modest, it would be desirable to perform further confirmatory replications in other well-powered cohorts. Despite this, it is worth highlighting that a standard GWAS meta-analysis applied to our data did not yield any significant associations near the identified CpG site on chromosome 16. This illustrates the potential of imputation approaches to identify novel associations. Considering the five sites upstream of the MAPT locus, failure to replicate for the three tested candidates could be a consequence of the lower power of the replication study. In particular, we may compare MAPT with other robustly reported Parkinson’s disease-associated regions, like, c effect .i.e., differences in DNAm that predict disease risk. In particular, imputation may be used to establish whether associations identified in a standard case–control epigenome wide association study are predictive. To this end we may test for association of imputed rather than observed values at these sites. Provided these imputations are based on estimates obtained from a separate random sample of the population, they will be independent of disease status. Thus a significant association for imputed values provides evidence for the predictive nature of the changes found in the observed data. In our particular case we hypothesize that the association at cg10917602 is an antecedent of Parkinson’s disease, and a closer examination of methylation in this region as part of a prospective study could lead to the development of a marker for Parkinson’s disease risk and establish the causal nature of these changes.A challenge in the analysis of multiple phenotypes in observational data is the assignment of causality. While the chosen terminology of intermediate and ultimate phenotype might suggest a causal chain from genotype to intermediate to ultimate phenotype, it is important to note that we cannot distinguish between correlation and causation, and the direction thereof, in identified associations between intermediate and ultimate phenotypes. However, an important advantage of the imputation framework arises when applied to case–control data. Specifically, although we cannot discern causality, we may gain information concerning whether changes in cell phenotypes are an antecedent or a consequence of disease. This question, which cannot be addressed using a typical case–control design, is important for the construction of individualized disease risk predictors. Using an imputation methodology can help identify predictive associations,