Gallus gallus domesticus, we demonstrate the use of random regression animal model and covariance functions to estimate genetic parameters for ascites resistance and tolerance and illustrate the way individual variation in resistance and tolerance induce both genotype re-ranking and changes in variation of host performance along increasing ascites severity. Tolerance to ascites displayed significant genetic variance, with the estimated breeding values of tolerance slope ranging from strongly negative (very sensitive genotype) to weakly negative (less sensitive). Resistance to ascites had heritability of 0.34. Both traits are hence expected to respond to selection. The two complementary defense strategies, tolerance and resistance, were genetically independent. Ascites induced changes to the correlations between ascites resistance and body weight, with the genetic correlations being weak when birds were ascites-free but moderately negative when both healthy and affected birds were present. This likely results because ascites reduces growth, and thus high ascites incidence is genetically related to low adult body weight. Although ascites induced elevated phenotypic and genetic variances in body weight of affected birds, heritability displayed negligible changes across healthy and affected birds. Ascites induced moderate genotype re-ranking in body weight, with the genetic correlation of healthy birds with mildly affected birds being unity but with severely affected birds 0.45. This study demonstrates a novel approach for exploring genetics of defense traits and their impact on genotype-by-environment interactions.Resistance and tolerance are two complementary mechanisms to reduce the detrimental effects of parasites, pathogens, and production diseases on host performance. Using body weight and ascites data on domesticated chicken  Resistance and tolerance are two complementary defense mechanisms against pathogens and parasites. Resistance is the host trait that prevents infection in the first place or reduces the performance of a pathogen on a host; both factors reduce the pathogen burden within a host individual. Tolerance to infections, in turn, is defined as the ability of the host to limit the impact of a given pathogen burden on host health, performance, and ultimately on fitness . In farmy-axis) is regressed against an increasing pathogen burden or abiotic factor (on x-axis)  can potentially change across increasing pathogen burden  and abiotic factors in general  . Geneticn burden . For ins general , there hGallus gallus domesticus.2 levels.Ascites is a metabolic disorder in individuals that fail to fully supply the demand for oxygen in their bodies because of a mismatch between cardiopulmonary system output and the demands of the body . AscitesIn this study, we defined ascites tolerance in chicken as a reaction norm of body weight along an increasing ascites severity, measured as the heart ratio. This approach follows the definition by The experiment was conducted at the facilities of Hendrix Genetics/Cobb Europe BV, located in Boxmeer, The Netherlands. The experiment was performed by licensed and authorized personnel under approval of Cobb Europe BV.The experimental offspring population consisted of 7722 purebred White Plymouth Rock broilers, of which 3745 were males and 3977 females. They descended from 83 sires and 788 dams. Each sire was mated to an average of 15.7 dams , and each dam was mated to an average of 1.65 sires . Sire−family sizes ranged between 22 and 209, with an average of 93 offspring per sire. There were 2677 ancestors in 25 generations, and they did not have any trait records. Eight sires with fewer than 20 offspring were removed from the data because large family sizes are needed to avoid biased genetic correlation between tolerance slope and intercept and biased genetic variance estimate in tolerance slope .2. During the whole experiment, birds had free ad libitum access to water and commercial feed with 12.970 KJ/kg. Birds were exposed to 23 hr of light per day.Eggs laid by all the dams were collected until a batch with a total of approximately 1600 chicks were obtained, and a total of five batches were produced. The eggs were individually numbered along with their dam code and transferred to a common brooding machine. At the day of hatching, the chicks were sexed, individually wing tagged, and group housed in two stables with 14 birds/m2 levels. At the time of hatching, temperature was held at 30°. During the successive 11 days temperature was gradually reduced to 12°. Thereafter, the temperature was kept at 12° until week 7 when the experiment was terminated. To increase CO2 level in the stables to approximately 1500 ppm, the ventilation was reduced from day 11 onwards. Except for the CO2 level and temperature, rearing conditions closely resembled the commercial practice. Survival from the first body weight recording at week 2 to the end of the experiment was 91.0%.To challenge birds to ascites, they were kept under cold conditions and increased COThe experiment with the two stables was repeated in five successive batches of offspring. Each sire and dam had offspring in an average of 3.54  and 2.58  batches, respectively.2. A postmortem examination was performed to measure heart ratio: the percentage of right ventricle weight from total heart weight (RATIO). RATIO was recorded by 11 different people trained for cutting. Using random regressions, we defined two additional traits: 7-week body weight (BW7) of ascites-free birds (trait: INTERCEPT) and tolerance slope for BW7 (SLOPE).Birds were individually weighed at 2 (trait: BW2) and 7 weeks of age (BW7). After BW7 recording, birds were euthanized using COx-axis in the tolerance analysis. The plateau-linear tolerance regression model with RATIOPlat  fitted the data better than the linear model in which RATIO was used as the x-axis . The genetic model 3 provided in the next section was used in this comparison for both models. Moreover, AIC and BIC were used to find the best-fitting threshold of 29% within the potential heart ratios of 27% to 30%. It should be noted that RATIO is used here as the continuous-scale measure of ascites resistance, whereas RATIOPlat is used only as the x-axis in the tolerance analysis . Sample size was 7710, 7039, and 6991 birds for BW2, BW7, and RATIO, respectively. The trait means are given in File S1.Heart ratio is generally agreed to be an indicator trait for ascites, and birds with heart ratio greater than 27% to 30% are ascitic ,b, heartijy is an observation of a trait i (i = 1-3) for the jth individual ; µi is the mean for trait i; animj is the random animal genetic effect with a pedigree; damk is the random dam effect without a pedigree (k = 1-788); GENDERl is the fixed effect of gender (l = 1-2); BATCHm×STABLEn is the fixed interaction of rearing batch (m = 1-5) and stable (n = 1-2); AGEp is the fixed effect of animal age when a trait i was recorded ; and PERSONq is the fixed effect of a cutter of a heart (q = 1-11), and error is the random error.All analyses were performed with multitrait animal models using ASReml  quantifying the genetic level of individuals for a trait.i.e., a regression term). This accounts for a possibility that ascites might have been more common among initially fast or slow growing individuals. In fact, BW2 had very weak genetic and phenotypic correlations with RATIO . Correlations were considered significant when 1.96 times their standard error did not include zero (two-tailed test).n = 83 sires), and 90% of the sires had 14.8% to 60.3% of their offspring affected. It is also good to note that sires and dams have brothers, sisters, and cousins in the data, and the animal model accounts for all these relationships in the genetic analysis, contributing further to a solid analysis.For tolerance analysis to be effective, each sire family should have both health and affected individuals. The sires had an average of 36% of their offspring affected  for BW7 combined with genetic (VG) and maternal variances (VM) estimated along RATIOPlat trajectory allowed the calculation of BW7 phenotypic variance (VP = VE + VG + VM), heritability (h2 = VG / VP), and maternal effect ratio (m2 = VM / VP) as a function of heart ratio trajectory. Finally, following G is the genetic (co)variance matrix of slope and intercept, x0 is a vector of [1 0]’ for healthy animals with RATIOPlat of zero, and RatioPlatx is as described earlier. To ease the interpretation of figures, RATIOPlat values were back-transformed to the original heart ratio values.Genetic and maternal (dam) variance of BW7 as a function of RATIOPlat trajectory was calculated: as wo terms . The terh2 estimates were greater than their standard errors. Maternal effect ratios for BW2, BW7, and RATIO were small but existent with estimates below or equal to 0.06  were weak and nonsignificant  were higher than the respective correlations of RATIO . This is expected when there is a nonlinear relationship between heart ratio and BW7  to strongly negative (more sensitive), but no completely tolerant genotypes were observed. Resistance, measured as the heart ratio, had moderate heritability of 0.34. These results show that both resistance and tolerance are expected to respond to selection. Improved resistance and tolerance can be both used to reduce the harmful effects of ascites on birds.e.g., growth curves) and the impact of parasites on growth (h2 = 0.26-0.34) than two tolerance indicators, live weight gain depression (h2 = 0.09) and wool growth depression (h2 = 0.08), during an experimental challenge with nematode larvae  for both resistance and tolerance to Cucumber mosaic virus, tolerance measured as the difference between a pair of control and infected individuals  between resistance  and tolerance (weight gain and wool growth depressions) against nematodes, but three of four correlations were nonsignificant with large SEs. r = −0.18 to 0.21). These results together imply no genetic trade-off between resistance and tolerance.In line with our study, the meta-analysis of 31 studies on 17 plant species demonstrates that in general tolerance and resistance to herbivores are weakly genetically correlated . Albers The analysis of five inbred mice strains showed a strong genetic trade-off between resistance and tolerance to malaria , whereas the correlation in the whole population with both affected and nonaffected individuals included is negative (r = −0.26)  under blowfly larvae attack. Infections can change heritabilities of performance traits. For instance, rG between environments = 0.28). The two forms of genotype-by-environment interaction, scaling effect and re-ranking, facilitate environment-dependent genetic changes. Moreover, when genetic correlations between host performance and resistance or tolerance are changed in response to infection, as shown here, the genetic outcome of selection in environments with differential pathogen burden becomes even more multifaceted.Ascites did not just induce a scaling effect (a change in variance), but also genotype re-ranking across healthy and affected birds. The most extreme genetic correlation between healthy and affected birds was 0.45. Ascites is a clear example in which selection in disease-free and diseased population would result in different genetic responses in host performance and ascites resistance/tolerance. The modification of genetic architecture of growth and life-history traits by pathogens, parasites, and production diseases, mediated by tolerance genetics, may play a more fundamental role in microevolution than was previously though.


An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction , with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant. Many common diseases result from a complex interplay of genetic and environmental risk factors. It is important to study the potential genetic and environmental risk factors jointly in order to achieve a better understanding of the mechanisms underlying disease development. The standard single-marker approach that studies the environmental risk factor and one genetic marker at a time could misrepresent the gene–environment interaction, as the single genetic marker might not be an appropriate surrogate for the underlying genetic functioning polymorphisms. We propose a method to look at gene–environment interaction at the gene/region level by integrating information observed on multiple genetic markers within the selected gene/region with measures of environmental exposure. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the proposed model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region and find evidence for gene–environment interaction , with the smoking effect varying according to a subject's genetic profile. Genome-wide association studies that focus on detecting the main effect from individual single nucleotide polymorphisms (SNPs) have successfully identified more than 4,000 SNPs associated with different diseases The standard approach to the study of gene–environment joint effect inspects one marker at a time, assuming that a single marker is the functional unit in the gene and environment interplay. This single-marker approach could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region, and interact with the environment risk factor in a complex way. A more global approach that simultaneously considers all genetic markers might capture more of the genetic variation within the entire targeted region, and provides a better opportunity to reveal complicated gene–environment interactions G at a set of SNPs within a given region and a measurement for the environment exposure E available for each subject. We seek to identify a latent genetic profile variable L that classifies the multilocus genotype G into different categories (clusters) such that subjects with their genotype assigned to the same genetic profile category share the same disease risk model, which is a standard logistic regression model with its own intercept term and slope. The intercept term represents the baseline log odds, common for subjects sharing the same genetic profile. The slope represents the effect  of the environment risk factor for subjects with the given genetic profile. The model that we try to build and make inferences from is essentially the logistic regression model consisting of L and E as main effects and their product as an interaction term; the unusual aspect is that the definition of the latent genetic profile L is a priori unknown. To account for the uncertainty in the cluster assignment underlying the definition of L, we adopt an idea from the hidden Markov model originally developed for modeling the spatial heterogeneity of the disease event rate, observed on a predefined set of areas G as an “area”. We would like to use the Potts model to guide the cluster assignment through a certain level of “spatial” dependence, i.e., similar genotypes (nearby areas) tend to be assigned to the same cluster, as in other applications in genetics studies, including the study of haplotype association We provide a flexible Bayesian modeling framework for the study of gene–environment joint effects. We consider a case-control study with genotypes et alWe use the Markov chain Monte Carlo (MCMC) sampling method e.g.,  to fit tCHRNA5-CHRNA3-CHRNB4 cluster of cholinergic nicotinic receptor subunit genes, has been shown to be associated with both lung cancer and smoking behaviors, such as cigarette smoking intensity We apply the proposed method to study the joint effect of cigarette smoking intensity and genetic variants in chromosome region 15q25.1 using data from EAGLE, a population-based case-control study conducted in Italy We first introduce the Bayesian model and describe the MCMC algorithm for fitting this model. Next we provide procedures for posterior inference using samples generated by the MCMC sampler, including a method for deciding the number of clusters and a method for identifying the optimal cluster assignment once the number of clusters is determined. We validate the proposed method using simulated data. We then apply the method to study the gene–environment joint effect using data generated from the EAGLE lung cancer case-control study.ith subject, we denote its observation by G-E joint effect that permits G-E interaction. More specifically, we assume the true underlying risk model has the following form:K represent a partition of the genotype space; k; E (in term of log odds ratio) in the disease model for cluster k, X and is constant regardless of a subject's genotype. Notice that if the partition of the joint genotype space is known a priori, we can derive the corresponding K-category genetic profile variable L based on the cluster assignment. The above model (1) is then essentially the standard logistic regression model consisting of L and E as main effects and their product as the interaction term, with adjustment for X, and has the following form:G-E interaction if Assume we have data collected from a case-control study, with G consists of just one SNP, the goal can be achieved easily by using a saturated logistic regression including both E and G  as the main effects and their product as the interaction term. For situations where G consists of multiple SNPs , as in the case of the EAGLE lung cancer study, we propose the following Bayesian model that simultaneously searches for the optimal partition of the genotype space and estimates the unknown parameters in the corresponding risk model (1).In real applications, we do not know a priori the partition of the genotype space. If K, the total number of clusters, is known. We will describe how to choose K later. Suppose there are H types of genotype configurations observed in the sample, labeled as genotype 1, 2, …, H. We define the latent genotype allocation vector h, i, we denote its genotype id by i having the disease outcome isThe Bayesian model is built up in a hierarchic framework. We first describe our model by assuming E varies with G. Thus we call it the Bayesian risk model allowing for G-E interaction. As a comparison in the analysis, we also consider a model assuming a homogeneous effect from E, which is defined asG-E interaction. In what follows, we will describe methods for fitting model (2), the one allowing for G-E interaction. Similar procedures can be applied to model (3).In the above model specification, we use the prospective likelihood function (2) for observed case-control data, which were collected under a retrospective sampling scheme given the disease outcome. The use of the prospective likelihood function can be partially justified by the general results from Staicu z, we first choose a similarity metric to define the spatial contiguity between any two genotypes. Let J denote the total number of considered SNPs within the region, with the genotype at a given SNP being coded as 0, 1, or 2 according to the number of copies of the minor allele. Let genotypes h and jth SNP for the multilocus genotype h. We first define the distance between them asj observed in the sample, with j for subject i, and h, and h) is among the 4 genotypes closest to genotype h  on z can be based on the following MCMC algorithm.Putting all the foregoing models together, we can express the joint distribution of all variables asThe full conditional function for coefficients z using a Gibbs kernel; that is, for the genotype h, its cluster assignment is updated by drawing from the following full conditional distribution,h and other genotypes currently assigned to cluster k.Following Green and Richardson z is discrete, the standard Gibbs sampler can be improved by the Metropolized Gibbs sampler Since the sampling space for z is large. Green and Richardson K. Those estimates were then used in the MCMC sampler. The estimate of The regulating parameter m auxiliary samples, in the MCMC iterations, thus allowing us to consider m to be between 50 and 100. A summary of the algorithm for updating Here we propose to use the recently developed Monte Carlo Metropolis-Hastings algorithm (MCMH) In our simulation studies and the real data application, we find the MCMC algorithm generally converges after 100.000 iterations. Below we describe a procedure for determining the number of clusters, and an ensemble averaging method for the identification of the cluster assignment based on the MCMC samples.et alK, define the deviance i; K clusters is then calculated asWe choose to use the deviance information criterion (DIC) proposed by Spiegelhalter K  rule originally proposed for the tree model selection K, and then pick the optimal number of clusters K over 20 runs, To determine the number of clusters, we run the algorithm with different values of K, we recommend picking Based on our numerical experiments, we found that the Monte Carlo standard error usually is less than 1 if the MCMC chain converges. So, if there is only one run for each et alh and After the determination of the number of clusters G-E interaction, under various numbers of clusters. Then we use the +1 rule to identify L. We require L. This model includes the main effects of L and E, as well as their interactions. We can conduct a likelihood ratio test comparing model (9) with the similar model without the interaction terms and obtain the corresponding “P-value”, denoted by It is usually desirable to have a formal statistical test or decision rule for inference regarding the presence of an interaction. Here we propose a resampling-based test for this purpose. First we fit model (2), the Bayesian risk model allowing for G-E interaction, on the observed data and identify Apply the MCMC procedure to fit model (3), the Bayesian risk model without Use MLE to fit the following logistic regression model based on the observed data,B sets of bootstrap null datasets. Each null dataset is a copy of the observed dataset, except the outcome for every subject is regenerated according to the probability model given by (10).Use the model given by (10) to generate bth null dataset, For the The estimated P-value for G-E interaction and the corresponding logistic regression model. We use the fitted logistic regression model (10) to generate multiple null datasets in Step 3 based on the parametric bootstrap procedure bth generated null dataset, we first apply the MCMC procedure to establish the Bayesian model given by (2) and next identify the optimal number of clusters with the +1 rule, as well as the corresponding consensual cluster assignment. Then we fit the corresponding logistic regression model with G-E interaction and obtain the test statistic In Steps 1 and 2 we establish the Bayesian risk model under the null hypothesis that there is no We used data generated by the lung cancer GWAS in the EAGLE study G-E interaction. In the simulation study we were interested in studying the interaction between a binary environment risk factor (G) within a candidate region. We used genotypes at 15 tagging SNPs . Their definitions are given in E (0 or 1) and G were correlated in the general population. The distribution of E depended on G in the following way: for a subject with genotype in region I, the probabilities of being unexposed  to identify For each simulated dataset, we applied our method with We evaluated the performance of the algorithm for cluster assignment by comparing the cluster assignment estimated at given by  that acci with genotype i based on MCMC samples generated under i, by a constant value, which was chosen as We also evaluated the accuracy of the estimated coefficients  and E  and the environment variable. PCs are derived from the principal component analysis of genotypes on all considered SNPs. Similar to the minP-SNP test, each PC-environment interaction test is derived from the likelihood ratio test comparing two logistic regression models with and without the interaction term. The PC effect is model as a continuous variable. Both minP-SNP and minP-PC were based on 15 univariate tests in the simulation study as there were a total of 15 SNPs in the considered chromosome region.th SNP in E and G was defined similarly as before. For a subject with genotype in cluster 1, the probabilities of being unexposed  has a dominant effect in its interaction with E, the minP-SNP test compares less favorably with the proposed test since each of single SNP-environment interaction test considered in the minP-SNP global test spends one more df than necessary  and genetic variants in chromosome region 15q25.1 on lung cancer risk, using data generated by the EAGLE study. We focused on former and current smokers who had been genotyped on the 15 tagging SNPs. We also removed, as outliers, 8 subjects who had smoked more than 3 packs of cigarette per day. The final dataset for our analysis consisted of 1326 controls and 1720 cases. In the analysis we treated smoking intensity as a continuous variable and adjusted for the effects of gender and of age at diagnosis . We used a Bayesian model that allowed for K, K over 20 replications. Based on the 1 SE rule given by (7), the optimal number of clusters was found to be 2, with its averaged DIC value being 3810.5. The partitioning of subjects into 2 clusters based on our proposed clustering algorithm is very consistent among 20 replications. The discrepancy rate between assignments from any two runs, defined as the proportion of subjects being assigned to two different clusters, is less than 1.4% under To determine the number of clusters, we ran the MCMC algorithm 20 times with different random seeds for each Below we present the posterior summary based on a single run of our algorithm. To present the summary result, we first conducted a PC analysis on the case-control samples using genotypes at the 15 tagging SNPs as coordinates. In h, h. We can interpret h. To summarize the genetic effect of genotype h, we used h and h and the reference genotype To summarize the effect of smoking on a subject with genotype In nd PC is helpful for interpreting the patterns observed in nd PC is driven mainly by the 8 SNPs with absolute loading values larger than 0.18, with the remaining having loading values less than 0.07. These 8 SNPs also turn out to be the ones that are most significantly associated with lung cancer risk . This suggests that the model allowing for G-E interaction fits the data better than the model without the G-E interaction.As a comparison, we also fit model (3), the Bayesian model without  20 runs . The DICG-E interaction further, we applied the resampling-based test described in the Methods section. The observed test statistic was G-E interaction. On the other hand, for each of the 32 relatively common SNPs (MAF>0.04) in this considered 15q25.1 region, we conducted the standard SNP-smoking interaction test (2 df) based on the logistic regression model by treating the genotype as a three-level categorical variable. The smallest nominal P-value we observed was 0.021. The global minP-SNP test had a P-value of 0.29, which was well above the 0.05 level. We also conducted the PC-smoking interaction test by modeling each PC as a continuous variable. The smallest nominal P-value was 0.058. The P-value from the global minP-PC test was 0.62.Finally, to demonstrate the existence of Our new method can evaluate gene–environment interaction at the gene/region level by integrating information observed on multiple SNPs in the considered gene/region with measures of environmental exposure. This method reduces the impact of loss of efficiency and bias from the misclassification error inherent in the single-marker approach that studies the environmental risk factor and one SNP at a time. The method provides a coherent inference framework that allows us to evaluate the environmental effect on different strata defined by the multi-locus genotype. A heterogeneous environmental effect across different strata would signal the presence of gene–environment interaction. We also propose a resampling-based test to formally test for the existence of gene–environment interaction.Genetic variations within the 15q25.1 region have been shown to be associated with both lung cancer risk and smoking behaviors, such as the smoking intensity. Our analysis based on the EAGLE case-control study indicates that the smoking effect varies according to the subject's genetic makeup in the 15q25.1 region. The proposed resampling-based test also supports the existence of gene–environment interaction . On the other hand, two conventional tests of gene–environment interaction based on the single-marker and single-PC approaches are far from significant. This highlights the advantage of our proposed method over standard approaches.z statistic = 15.58) in the EAGLE study than in a lung cancer case-control study nested within the PLCO cohort , which includes 1390 lung cancer cases and 1924 controls. We also could not find evidence for smoking-15q25.1 interaction in this PLCO nested case-control study by using our proposed method. The difference in the smoking OR estimates and the absence of gene–environment interaction evidence using our method in the PLCO study may be a consequence of greater misclassification error in the smoking information assessment in the cohort study (PLCO) than in the case-control study (the EAGLE study).Accurate assessment of the environment risk exposure in the evaluation of gene–environment interaction is as important as identification of functional genetic variants or their proper surrogates In our method, we adopted the Potts model for the latent allocation vector for cluster assignment, as did Green and Richardson We proposed to use the +1 SE rule (or the +1 rule) based on DIC to identify the optimal number of clusters. We found through simulation studies that this approach works quite well. An alternative approach would be to treat the number of clusters as a random variable and integrate it into a Bayesian model The proposed procedure relies upon a user-specified similarity metric to define the neighborhood among different genotypes in the Potts model. This neighborhood structure is used to induce the spatial dependency in the cluster assignment. In this paper, for a given genotype, we chose its 4 nearest genotypes as its neighbors. We found that the analysis result was not very sensitive to how the neighborhood is defined as long as the chosen Markov structure can generate an appropriate spatial dependence. For example, we reanalyzed the EAGLE study with two other types of Markov structures: one using the 3 nearest genotypes as neighbors, and the other one using the 5 nearest genotypes as neighbors. We show in We used the prospective likelihood model in the Bayesian framework for case-control studies, even though the data were collected retrospectively according to a subject's disease status. According to http://dceg.cancer.gov/bb/tools/badge. Currently, we consider only binary or continuous environmental exposure variable. It is straightforward to expand the algorithm to deal with a categorical (with more than 2 levels) environmental variable. To use the program, the user needs to specify priors  for parameters in the risk model and a prior (a uniform distribution) for the regulating parameter in the Potts model. The program will be expanded further to incorporate other prior functions. The running time for 200,000 iterations using 50 auxiliary samples in the MCMH algorithm on a dataset of 1000 cases and 1000 controls, with approximate 450 unique genotypes, is about 14 minutes on a Linux machine with the 2.8 GHz AMD Opteron processor. For a dataset with a large number of genotypes , we can reduce the computing time by first dividing the whole genotype space into a few hundreds of subgroups through the PAM clustering algorithm We have created an R package called BaDGE (Bayesian model for Detecting Gene Environment interaction) implementing the proposed Bayesian model and the associated post-processing procedures. The package is freely available from the website Comparing to the standard single-marker or principal component based approaches, our proposed method is more computationally intensive, but it has several important advantages. First, it offers a more flexible way to model gene–environment interaction, especially complicated ones that cannot be depicted properly by the single-marker or PC based approaches, such as in situations where genetic variants (might or might not be directly genotyped) in multiple loci within the considered region interplay with the environment risk factor. Second, it provides an estimate of the environmental effect on subjects with a given joint genotype profile. This could be potentially useful to generate new hypotheses on how the gene and environment risk factor interacts. Third, as shown in the simulation studies and real application, the proposed resampling-based test derived from the Bayesian model has a more robust performance than the standard single-marker, or PC based testing procedures. For example, in situation where the single marker test is most appropriate, i.e., there is only one functional locus in the considered region, the proposed test is only slightly less powerful than the single-marker test. But it has a considerable power advantage over the standard tests when the underlying disease risk pattern cannot be properly approximated by a single SNP or PC.Although our method is described in the context of gene–environment interaction detection, it is in fact quite general. It provides a general strategy for studying the interaction between an observed risk factor and a latent categorical variable not directly observed or clearly defined, but one that can be derived from a set of observed relevant covariates. For example, our method can be used with some minor modifications to evaluate the interaction between smoking behavior  and a latent dietary pattern that can be derived from food frequency questionnaires. Also, it is possible to extend our method to study gene-gene interaction by introducing two latent factors to capture the effect of both genes, as was done in Figure S1Boxplots of the posterior medians of the intercept ((TIF)Click here for additional data file.Figure S2Boxplots of the posterior medians of the log odds ratio ((TIF)Click here for additional data file.Figure S3i−j is the posterior distribution summary for the coefficient ith, Posterior distribution comparison among 5 independent runs under the model (TIF)Click here for additional data file.Figure S4i−j is the posterior distribution summary for the coefficient ith, Posterior distribution comparison among 5 independent runs under the model (TIF)Click here for additional data file.Figure S5nd principal components. Each point represents a unique multilocus genotype with its x-coordinate being the total number of risk alleles among those SNPs with high loading values Click here for additional data file.Figure S6DIC plots for the Bayesian risk model without gene–environment interaction. For a given number of clusters, 20 DIC values were obtained by applying the model to the EAGLE study 20 times with different random seeds.(TIF)Click here for additional data file.Figure S7Pairwise correlations of estimates by the algorithm with different neighborhood structures. The MCMC procedure was applied to the EAGLE study using three different Markov structures, M1: using the 3 nearest genotypes as neighbors; M2: using the 4 nearest genotypes as neighbors; and M3: using the 5 nearest genotypes as neighbors. (a) Comparison of the estimated genetic effect (in term of the posterior median of (TIF)Click here for additional data file.Text S1MCMC algorithm details.(DOC)Click here for additional data file.


Surprisingly, only 17 probes exhibit significant levels of common environmental effects, suggesting that environmental and lifestyle factors common to a family do not affect expression variation for most transcripts, at least those measured in blood. Consistent with the genetic architecture of common diseases, gene expression is predominantly additive, but a minority of transcripts display non-additive effects.There is increasing evidence that heritable variation in gene expression underlies genetic variation in susceptibility to disease. Therefore, a comprehensive understanding of the similarity between relatives for transcript variation is warranted—in particular, dissection of phenotypic variation into additive and non-additive genetic factors and shared environmental effects. We conducted a gene expression study in blood samples of 862 individuals from 312 nuclear families containing MZ or DZ twin pairs using both pedigree and genotype information. From a pedigree analysis we show that the vast majority of genetic variation across 17,994 probes is additive, although non-additive genetic variation is identified for 960 transcripts. For 180 of the 960 transcripts with non-additive genetic variation, we identify expression quantitative trait loci (eQTL) with dominance effects in a sample of 339 unrelated individuals and replicate 31% of these associations in an independent sample of 139 unrelated individuals. Over-dominance was detected and replicated for a  Gene expression levels are known to influence common disease susceptibility in humans, with GWAS significant SNPs frequently found in regulatory regions. The expression levels of most genes are influenced by genetic variants, often located close to the gene itself. Expression Quantitative Trait Loci (eQTL) mapping studies have been very successful in identifying SNPs associated with expression levels; however, little is currently known about the extent of additive and non-additive genetic variance and the role of common environment on gene expression. Here we report a comprehensive study of the sources of genetic and non-genetic variation for gene expression levels using both pedigree and genotype information. We show that the majority of transcripts exhibit only additive genetic variance with congruence from independent methods using pedigree and genotype approaches. However, there are a small number of probes whose expression levels are influenced by non-additive genetic variance. For some of these probes we identify SNPs acting in a dominant and over-dominant manner that replicate in an independent sample. Surprisingly, only 17 probes exhibit significant levels of common environmental effects, suggesting that environmental and lifestyle factors common to a family do not affect expression variation for most transcripts, at least those measured in blood. Understanding the nature of genetic variation for complex traits, including disease, is important in human medicine, evolutionary biology and plant and animal breeding. Both the nature of complex trait variation, including the importance of non-additive genetic variation, and its dissection into contributions from individual genetic loci, have been debated for a century Gene expression is an important complex trait because of increasing evidence of its correlation with disease susceptibility In this study, we combine the power of pedigree and SNP-based designs to quantify and dissect the contribution of additive and non-additive variation using gene expression on 17,994 probes measured on 862 individuals from 312 nuclear families  We investigated the quantitative genetic architecture of gene expression using two orthogonal approaches. The first involved the decomposition of phenotypic variance using a family based analysis. In the second, additive and non-additive SNP effects were estimated from unrelated individuals within BSGS. We followed up associations with replication in an independent sample  and non-additive (dV) genetic variance components were estimated for RNA expression levels measured at 17,994 probes on 862 individuals across 312 families in BSGS, using restricted maximum likelihood. Of 17,994 probes that passed QC (methods), 14,753 (82%) had narrow-sense heritability (2h) estimates >0  = 0.64, in concordance with other studies dV  (Additive (mates >0 . Under tudies Vd . The obsmates Va . The resctively) . A smallctively) . Hence, ctively) .fV). The proportion of phenotypic variance attributed to within families was partitioned by inclusion of a family term alongside the additive genetic relationship term  only 17 probes (in 17 genes)   show a sl probes . We invep<4.8e−4 (cis) and p<6.2e−10 (trans)] with an additive effect across 3,364 probes (cis-regions and on average the top eSNP (SNP with the strongest association) explained 11.3% of the phenotypic variance of the probe with which it is associated. Our analysis of dominance eQTL fits a model that includes a dominance effect (d) as well as the main additive effect. From this analysis we identify 208 eQTL (179 cis-acting and 29 trans-acting) that have a study-wide significant  dominance effect, including 7 with over-dominance. Despite the power to detect dominance-acting eQTL being considerably lower than that to detect additive effects in SNP-trait association studies p<2.4e−4 = 0.05/208, the Bonferoni threshold  indicates that these loci are enriched for dominance effects.We then performed a global eQTL analysis of additive and dominance effects, associating the 17,994 expression traits with SNP genotypes in a dataset of 339 unrelated individuals drawn from BSGS. In total, we identify a total of 5,033 eQTL  of −1V represents the partial correlation between probes i and j, conditional on remaining probes. The matrix is sparse with non-zero elements representing conditional correlations between probes −1. For each of the conditionally correlated probes we extracted their association with the eSNP for the primary probe. On average, eSNPs were significantly associated with 64% of the conditionally correlated probes . Within the population-based eQTL analysis, these eSNPs were significantly [P<4.8e-4 (cis) and P<6.2e-10 (trans)] associated with their respective conditionally correlated probes in only 3% of cases, implying that in almost all cases they were not identified as contributing to the additive genetic variance of the conditionally correlated probe . This is in strong contrast to a mean proportion of variance for trans-acting eSNP of 3.2%. Such observations imply that proximal transcription-factor binding sites involved in RNA polymerase II recruitment and subsequent transcription are key components of the regulatory architecture and suggest that distal-acting elements exert a weaker influence.There is a strong relationship between narrow-sense heritability estimated from the pedigree and the proportion of variance that is explained by additive eSNPs that can be identified from an eQTL association analysis . For manan eQTL) . All SNPThe two approaches, the first a decomposition of the variance among related individuals and the second an association analysis of SNP genotypes in unrelated individuals, provide independent estimation of the genetic effects influencing transcript levels. We identified additive eQTL for 30%  of the probes that had only a significant additive component in the pedigree analysis, which contrasts sharply with only 2% (7/282) of additive eQTL for probes with only significant dominance variance in the pedigree analysis . Converscis and trans acting loci. The extent of non-additive genetic variance for gene expression has been investigated in theory We have used a complementary analysis of pedigree and SNP data to partition variation for gene expression in whole blood into components of additive, dominance and environmental variation, and have attributed a proportion of additive and dominance variation to specific cis-regions, provides strong support that the underlying molecular mechanisms that influence expression at these transcript positions can be identified through targeted sequencing.Due to low power to estimate three variance components jointly, we have estimated non-additive and common environmental components in separate models, and show little confounding with estimates of additive variance . Our ped = 0.38 . For manSignificant non-additive effects are identified for a few probes; with one particularly interesting finding of the SNP associations showing over-dominance with probe transcript levels. As a component of maintaining genetic variance, over-dominance has been discussed in livestock and model species cis-acting eQTL and multiple conditionally correlated probes , KIAA (n = 158) and LOC , 17,994 well-characterised probes remained for analysis, which corresponds to 13,486 RefSeq genes. Gene expression quantification and normalisation are described in Powell et al.The Brisbane Systems Genetics Study (BSGS) comprises 862 Individuals of European descent from 312 independent families et al.The CHDWB study comprises of 139 Caucasian (CHDWB_EA) healthy individuals, between the ages of 26 and 79. Gene expression profiles were generated using RNA extracted from whole blood collected with Tempus tubes  and hybridized to Illumina HT12 v3.0 bead arrays. Genotypes were measured using Illumina OmniQuad arrays. Full details of individuals, generation of RNA transcript abundance and genotype calling are given in Qin y is an n×1 vector of gene expression levels. Random additive genetic effects a and random dominance effects d are related to y by incidence matrices 1Z and 2Z respectably. The n×1 vector e contains the error terms. The joint distribution of all variables in A (n×n) is the additive relationship matrix and D (n×n) is the dominance relationship matrix and I is an identity matrix. A and D are calculated from the pedigree relationship information between individuals. Lynch and Walsh A and D given pedigree information. For each probe genetic variance components -7. The estimated variance components are expressed as ratios of the total phenotypic variance (a) and family (f) effects to the expression levels of each probe we can estimate the proportions of variance attributable to common environment . Estimates of 2h and 2f  is a vector containing family identifiers. For each probe the significance of a, d and f estimates were determined by comparing the full model to a reduced model where the relevant term was dropped from the model  and 6.2e-10 (trans). For probes that were included for series of conditional analyses, the false discovery was again controlled at 0.05 by correcting for the number of running 100 permutations where the top associations were included as conditional main effects. In addition to testing additive effects, we tested for associations that included a dominance component for each SNP by probe. Associations were tested using the –genotypic command in PLINK P–value thresholds for the dominance term (deviation) were 4.1e-4 (cis) and 4.7e-10 (trans).We tested for association between the 528,509 genotyped SNPs and the normalised expression levels of 17,994 probes using the linear regression functions in PLINK Figure S12h) (a), non-additive genetic (2d) (b) and common family (2f) (c) effects. The distributions for all probes  are shown. Estimates of 2h and 2d were obtained by fitting an ADE model, whist 2f estimates were obtained from a ACE model.Distributions showing the proportion of phenotypic variance attributable to additive genetic ((DOCX)Click here for additional data file.Figure S2Relationship between the estimated variance components under full and reduced models.(DOCX)Click here for additional data file.Figure S3The cumulative components of phenotypic variance for the 17,994 probes as obtained by fitting an ADE model using family relationship information. Probes are ordered according to the proportion of (DOCX)Click here for additional data file.Figure S4n = 17,994 variance components estimated under a null model where the variance component is equal 0  addi(DOCX)Click here for additional data file.Figure S5Distribution of the phenotypic correlations between the parent pairs (n = 71) for the 17,994 probes. The red arrow denotes the mean correlation of the 17 probes showing a significant common environmental effect .(DOCX)Click here for additional data file.Figure S6d) for 176 SNPs estimated in BSGS and CHDWB_EA samples. SNPS were identified as having a significance dominance effect in BSGS. Red denotes SNPs that replicated at a significance threshold of p<2e-4 in CHDWB_EA.Estimates of the dominance effect ((DOCX)Click here for additional data file.Figure S7Analysis of the dominance association on chromosome 12 for ILMN_1789596. A 3-marker sliding haplotype window parameterized for just (a) additive or (b) additive and dominance terms. Manhattan plots additive (c) and non-additive (d) association tests using imputed genotype data +/−5MB of rs12313805. Across this region there are no two SNPs with additive effects large enough that should they be in opposite directions, could combine to cause a spurious over-dominance association of the magnitude observe here.(DOCX)Click here for additional data file.Figure S8p-value) eSNP(s) identified from the additive model eQTL analysis on unrelated individuals. This relationship for all probes  is shown. (a) gives the proportion of variance explained by one eQTL and (b) shows the combined proportion of variance explained from up to two eQTL (c) up to three eQTL and (d) up to four eQTL. 3,364 probes had 1 or more eQTL, 1,376 had 2 or more eQTL, 217 had 3 or more eQTL and 76 had 4 eQTL Click here for additional data file.Figure S92h are determined from an Additive and non-additive genetic variance model Click here for additional data file.Figure S10Off-diagonal elements from a genomic relationship matrix calculated using 501,279 genome-wide SNPs on 339 individuals.(DOCX)Click here for additional data file.Table S1p<1e-4) common family effect. Variance components 2h and 2d were estimated using equation 2f using model Information on the 17 probes that have a significant ((DOCX)Click here for additional data file.Table S2Primary and their conditionally correlated probes. is model . Heritab(DOCX)Click here for additional data file.Table S3Summary statistics for the 832 individuals in BSGS. Phenotypic correlations were calculated between pairs of individuals for normalised expression levels in each of the 17,994 probes.(DOCX)Click here for additional data file.Table S4Full and reduced models for variance components.(DOCX)Click here for additional data file.Text S1Confounding between variance component estimates.(DOCX)Click here for additional data file.Text S2Sampling variance of variance components.(DOCX)Click here for additional data file.


SCARB2 , providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related individuals, we estimated the heritability of PD to be at least 0.27. Finally, using sparse regression techniques, we constructed predictive models that account for 6%–7% of the total variance in liability and that suggest the presence of true associations just beyond genome-wide significance, as confirmed through both internal and external cross-validation. These results indicate a substantial, but by no means total, contribution of genetics underlying susceptibility to both early-onset and late-onset PD, suggesting that, despite the novel associations discovered here and elsewhere, the majority of the genetic component for Parkinson's disease remains to be discovered.Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and online survey instruments, we conducted the largest case-control genome-wide association study (GWAS) of PD based on a single collection of individuals to date . We discovered two novel, genome-wide significant associations with PD–rs6812193 near  We conducted a large genome-wide association study (GWAS) of Parkinson's disease (PD) with over 3,400 cases and 29,000 controls (the largest single PD GWAS cohort to date). We report two novel genetic associations and replicate a total of twenty previously described associations, showing that there are now many solid genetic factors underlying PD. We also estimate that genetic factors explain at least one-fourth of the variation in PD liability, of which currently discovered factors only explain a small fraction (6%–7%). Together, these results expand the set of genetic factors discovered to date and imply that many more associations remain to be found. Unlike traditional studies, participation in this study took place completely online, using a collection of cases recruited primarily via PD mailing lists and controls derived from the customer base of the personal genetics company 23andMe. Our study thus illustrates the ability of web-based methods for enrollment and data collection to yield new scientific insights into the etiology of disease, and it demonstrates the power and reliability of self-reported data for studying the genetics of Parkinson's disease. SNCA (LRRK2 (leucine-rich repeat kinase 2) PARK2 (parkin) PINK1 (PTEN induced putative kinase 1) PARK7 (DJ1) ATP13A2 (ATPase type 13A2) genes To date, a number of different genetic susceptibility factors have been identified for Parkinson's disease. Autosomal dominant factors involved in PD include mutations in the SNCA and an inversion of the region containing the MAPT (microtubule-associated protein tau) gene are associated with PD. In addition, NUCKS1 (nuclear casein kinase and cyclin-dependent kinase substrate 1) HLA (human leukocyte antigen) region GAK (cyclin G associated kinase) BST1  Parkinson's disease is sometimes thought of as consisting of an early-onset form, characterized by familial clustering and high penetrance mutations, and a late-onset form, which occurs more sporadically. In contrast with the evidence for Mendelian inheritance of early-onset PD, studies comparing rates of concordance in monozygotic and dizygotic twins Here, we present a genome-wide association study of PD with a number of distinguishing features. The recruitment of PD cases took place primarily through targeted emails to PD foundations and support groups over the course of 18 months. The large set of controls was recruited through the 23andMe customer database. Determination of case status was conducted through a set of online questionnaires. We present three main scientific results. First, we identify two novel loci associated with PD and replicate many more. Second, we present lower-bound estimates of PD heritability based on observed genome-wide sharing that imply a large fraction of the genetic component underlying the etiology of PD has not yet been discovered. Third, we use sparse regression techniques to construct risk estimation algorithms that account for roughly 6–7% of the total variance in liability to PD and that suggest the existence of true associations lying just beyond genome-wide significance.Participants were drawn from the customer database of the personal genetics company, 23andMe, Inc. The majority of cases were recruited into that database through PD support groups and tertiary clinics. All cases reported via web-based surveys having been diagnosed with Parkinson's disease by a physician. Most cases (approximately 84%) also provided detailed information about their disease progression, other diagnoses, symptoms, response to medication, and family history. All participants included in this study were unrelated individuals of primarily European ancestry, based on the criteria described in We used two different sources of data for validating SNPs discovered and models constructed using the 23andMe cohort. To replicate our association results, we exchanged SCARB2 and SREBF1/RAI1–and replicated six previously reported regions–LRRK2, SNCA, GBA, MAPT, MCCC1/LAMP3, and GAK. A seventh replication (SLC41A1/PARK16) and two other potentially novel regions (RIT2/SYT4 and USP25) appear nearly genome-wide significant as well.We performed a GWAS using the 23andMe dataset consisting of 3,426 cases and 29,624 controls, controlling for age, sex, genotyping platform, and five principal components. All 23andMe samples were genotyped using a custom Illumina HumanHap 550+ panel, with 522,782 markers passing quality control see . ManhattFAM47E  with rs11868035  of M47E see , upstreaEBF1 see . This asLRRK2 gene, well known to be associated with PD GBA N370S is one of the mutations causing Gaucher's disease and has recently been associated with PD SNCA, MAPT, GAK, and SLC41A1 have been reported multiple times. Here we provide the first independent confirmation of the association of rs10513789 in the MCCC1/LAMP3 region with PD, as first reported in Among replications of previously reported associations, rs34637584 is the non-synonymous G2019S mutation in the SLC41A1/PARK16 has been previously reported RIT2, with an OR of 1.16 and USP25, a ubiquitin-specific protease. This association, however, fails to replicate in the IPDGC cohort, with a RIT2/SYT4 and USP25 regions.Of the three suggestive associations that do not reach genome-wide significance, rs823156 near http://www.pdgene.org/LRRK2 G2019S, 19 other previously reported associations replicated with the correct directionality in the 23andMe cohort using a significance threshold of 0.05  to replicate all 42 of these associations, assuming the reported odds ratio . The AUC for a model can be interpreted as the probability that a randomly selected case will have a higher estimated risk of developing PD than a randomly selected control. An AUC of 1 implies that a model discriminates perfectly between cases and controls, whereas an AUC of 0.5 corresponds to a model based on random guessing. To measure predictive accuracy, we used a covariate-adjusted measure of AUC that removed the effect of potential confounding by sex, age, population structure, and, where appropriate, cross-validation fold or genotyping platform see .To avoid making manual decisions in the choice of SNPs to include, we used a sparse logistic regression algorithm for building risk prediction models, and varied the strength of the sparsity-inducing prior see . For a gment see , the difTaken at face value, these results seem to suggest that the larger models, which include many more genomic regions than those deemed genome-wide significant, may harbor associations that account for their increased predictive accuracy. An alternative possibility, however, is that the differences in performance between models are actually just a consequence of differing levels of bias arising from the use of sparsity-inducing regularization. In Regardless, our heritability estimates imply an upper bound on AUC for a genetic risk prediction model of roughly 0.83 to 0.88 based on the method of lity see .SCARB2 (rs6812193) and SREBF1/RAI1 (rs11868035), both of which were replicated in data from RIT2 and USP25) just under the level of significance, one of which (RIT2) was also replicated. While it is difficult to pinpoint any causal genes from a GWAS, there are a few biologically plausible candidates worthy of discussion.We found two novel associations at a genome-wide level of significance near FAM47E gene, which gives rise to multiple alternatively spliced transcripts, many of which are protein-coding; the functions of these hypothetical proteins are unknown. A more attractive candidate, located SCARB2 , which encodes the lysosomal integral membrane protein type 2 (LIMP-2). LIMP-2 deficiency causes the autosomal-recessive disorder Action Myoclonus-Renal Failure syndrome (AMRF), which combines renal glomerulosclerosis with progressive myoclonus epilepsy associated with storage material in the brain GBA) causes the most common lysosomal storage disorder, Gaucher's disease. Recently, mutations in GBA have also been identified in PD SCARB2 and PD.The PD-associated SNP rs6812193 lies in an intron of the SREBF1 (sterol regulatory element-binding transcription factor 1), within the Smith-Magenis syndrome (SMS) deletion region on 17p11.2. SREBF1 encodes SREBP-1 (sterol regulatory element-binding protein 1), a transcriptional activator required for lipid homeostasis, which regulates cholesterol synthesis and its cellular uptake from plasma LDL RAI1 (retinoic acid-induced protein 1), which regulates transcription by remodeling chromatin and interacting with the basic transcriptional machinery. Heterozygous mutations in RAI1 reproduce the major symptoms of SMS, such as developmental and growth delay, self-injurious behaviors, sleep disturbance, and distinct craniofacial and skeletal anomalies rs11868035 appears in an intron of the alternatively spliced gene, RIT2 (Ras-like without CAAX 2) gene that encodes Rit2, a member of the Ras superfamily of small GTPases. Though we do not claim this SNP as a confirmed replication, there are a number of reasons to suspect that this association may also be real. Rit2 binds calmodulin in a calcium-dependent manner, and is thought to regulate signaling pathways and cellular processes distinct from those controlled by Ras RIT2 was previously proposed as a candidate gene for PD, based on the possibility that dopaminergic neurons may be especially vulnerable to high intracellular calcium levels, perhaps through an interaction with SYT4 (synaptotagmin IV), which encodes synaptotagmin-4, an integral membrane protein of synaptic vesicles thought to serve as SYT4 is particularly interesting as a SNP near SYT11 (synaptotagmin XI) has been associated with PD in The SNP rs4130047, slightly below the genome-wide significance threshold, lies in an intron of the USP25, encoding ubiquitin specific peptidase 25, which regulates intracellular protein breakdown by disassembly of the polyubiquitin chains. Other ubiquitin-specific proteases  have been proposed as candidate genes for PD USP24 fails to replicate here, see The suggestively associated SNP rs28233572 lies in a gene-poor region with only one candidate gene downstream, Our heritability estimates, which suggest that genetic factors account for at least one-fourth of the total variation in liability to PD, represent the tightest confidence bounds determined for the heritability of PD to date. These estimates, which rely on observed genetic sharing rather than predicted relationship coefficients, avoid confounding from shared environmental covariance by restricting attention to very distantly related individuals. Furthermore, they complement estimates of heritability from twin studies by considering large numbers of individuals with low amounts of genetic sharing, rather than small numbers of twin pairs with large amounts of genetic sharing.SNCA, PRKN, PINK1, DJ1, LRRK2, and GBA) have been reported to account for 16% of cases These estimates should only be interpreted as lower bounds on the actual heritability of liability of PD for two reasons. First, they only reflect phenotypic variation due to causal variants in LD with SNPs on the genotyping platform. Second, they only capture the contribution to additive variance that arises from a polygenic model of many SNPs of small effect, but do not include the variance arising from known specific associations. This limitation is most apparent in our estimate of heritability based on only early-onset cases . Patients were invited to fill out a screening questionnaire asking if they had been diagnosed with PD and their physician's name, phone number, and institution. Patients who stated they had been diagnosed with PD and who gave complete, non-suspicious answers to the other questions were offered the 23andMe Personal Genome Service for a nominal fee of $25.The 23andMe cohort consisted of customers of 23andMe, Inc., a personal genetics company. Patients with PD were recruited to join this cohort through a targeted email campaign in conjunction with the Michael J. Fox Foundation, The Parkinson's Institute and Clinical Center, and many other PD patient groups and clinics. Emails or hard copy mailings were sent to all individuals who had registered with these groups as PD patients. A limited number of patients were also recruited in person at PD workshops and conferences. Family members of individuals with the Individuals included in the 23andMe cohort were selected for being of primarily European ancestry, as determined through an analysis of local ancestry via comparison to the three HapMap 2 populations, using an unpublished method substantially similar to http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000089.v3.p2, supplemented with individual-level data for 200 subjects from phs000089.v2.p2 who were left out of the later version as of December 12, 2010. Cases in the NINDS cohort consisted of North American Caucasians with Parkinson's disease, as assessed by a neurologist. Controls consisted of neurologically normal, unrelated, white individuals with no family history for a number of neurological conditions, including Parkinson's disease. A complete description of the inclusion and exclusion criteria can be found directly at the NINDS Database website as referenced above, and in related studies using the data from this cohort Genotype and phenotype data for the National Institute of Neurological Disease and Stroke (NINDS) cohort were obtained from the NINDS Database found at This study was conducted according to the principles expressed in the Declaration of Helsinki. The 23andMe study protocol and consent were approved by the external AAHRPP-accredited IRB, Ethical and Independent Review Services (E&I Review). Our consent and privacy statement preclude the sharing of individual-level data without explicit consent. We have, however, shared summary statistics for all SNPs with s under  . We alsoPatients recruited through the PD outreach initiative as well as individuals from the general 23andMe customer base were asked to take online questionnaires, including a general medical questionnaire and a detailed questionnaire specifically on PD . Both of these questionnaires asked the subject if he or she had ever received a PD diagnosis from a physician and if so, the age of onset. The detailed questionnaire also asked for much more specific information regarding the symptoms, clinical history, and family history of the patient.We selected as cases all participants who provided an affirmative diagnosis of PD from a physician  and who did not provide any potentially contradictory information, defined here as:Providing an affirmative answer to the screening question but only negative answers to the two questions about PD.Answering “yes” to PD on the general medical questionnaire but “no” to PD on the detailed questionnaire.Stating their diagnosis changed because they no longer had symptoms or because the cause of their symptoms was unknown.Stating they had been diagnosed with any of 19 other neurological conditions see .Due to the low prevalence of PD, we used controls taken from general 23andMe customer base in the analysis. Some of the controls filled out no questionnaires; however, others answered questions about possible PD-like symptoms or filled out a general medical history. In order to maximize our power to detect genetic associations with PD, we excluded some putative controls who might be at higher probability for developing PD in the future. Thus, the controls consisted of all consented European 23andMe customers who met all of the following criteria:Were not part of a PD related recruitment drive Speech or voice become softerDragging one or both feet while walkingFeet shuffling while walkingWalking more slowlyTaking smaller steps than beforeSteps becoming faster and fasterFeet getting stuck as if glued to the floorSwinging arms less than beforeStooping or bending forward more than beforeFalling or balance troubleApproximately 1,430 individuals in total were excluded from the control set due to these filters. We note that as a consequence of both our exclusion criteria and other recruitment biases associated with the 23andMe customer base, our controls are unlikely to be exactly representative of the general population.For the 23andMe cohort, DNA extraction and genotyping were performed on saliva samples by National Genetics Institute (NGI), a CLIA-certified clinical laboratory and subsidiary of Laboratory Corporation of America. Samples were genotyped on the Illumina HumanHap550+ BeadChip platform, which included SNPs from the standard HumanHap550 panel augmented with a custom set of approximately 25,000 SNPs selected by 23andMe. Every sample that failed to reach 98.5% call rate was re-analyzed. Individuals whose analyses failed repeatedly were re-contacted by 23andMe customer service to provide additional samples, as is done for all 23andMe customers. Two slightly different versions of the genotyping platform were used in this study. See The NINDS dataset consisted of 519 samples genotyped on a combination of an Illumina HumanHap 250 K and Illumina HumanHap 300 K chip, and 1,183 samples genotyped on an Illumina HumanHap 550 K chip. As the proportion of cases genotyped on each platform in the NINDS dataset differed between the two platforms, any marker with differing frequencies across the two platforms (due to problems with clustering or other genotyping error) would show up as associated with PD status in the NINDS dataset. To account for potential stratification arising from this, we defined a binary covariate to indicate genotyping platform, which we used for covariate adjustment during analyses involving the NINDS dataset.In all analyses, SNPs with a call rate For the association analysis, all http://www.pdgene.org/http://www.genome.gov/gwastudies/Principal components were computed using multi-dimensional scaling over the allele-sharing distance matrix as in To estimate heritability of liability, we used the GCTA package (v0.90.3) For We note that in We performed risk prediction experiments using a sparse logistic regression solver based on the “elastic net” regularization penalty For all experiments, we used a default value of We performed two types of cross-validation experiments: an internal cross-validation analysis using only the 23andMe cohort, and an external cross-validation analysis testing the predictive accuracy of a model trained using the 23andMe cohort on the NINDS cohort. In both cases, all aspects of the analysis following QC were included as part of the cross-validation.For the internal cross-validation experiment, we generated a matched dataset using a portion of the 23andMe cohort. More specifically, we separated the 23andMe dataset into 20 partitions based on sex and age decile. Next, all partitions were then balanced to contain roughly the same ratio of cases to controls. Finally, five equally-sized cross-validation folds were formed, containing the same amount of representation from each of the partitions. In total 3,380 cases and 21,640 controls were used across the five cross-validation folds. For the external cross-validation experiment, the entire 23andMe cohort was used as a training set, and evaluation was performed on the NINDS dataset.matched control. More details on our procedure for covariate adjustment are provided in When estimating AUC using a test set, stratification biases can arise when the apparent discriminative accuracy of the model can be attributed to one or more covariates. This could occur, for example, if the prevalence of the disease were to vary by population, provided that the SNPs included in the risk prediction model were informative of ancestry. To control for confounding from covariate imbalance, we used a stratified variant of the AUC known as the “covariate-adjusted AUC,” defined as the probability that a randomly selected case will have a higher estimated risk of developing PD than a randomly selected The use of AUC-based statistics for risk prediction is not without controversy. In the setting of a case-control study, the AUC has the advantage of being neither dependent on an arbitrarily set threshold for risk (which would be needed when computing sensitivity or specificity) or the relative proportion of cases and controls in the study. Some authors have contended that the AUC is not a clinically relevant measurement of performance and may be insensitive to changes that would otherwise be considered important in a diagnostic setting Since the goal of our experiments was to measure the predictive capacity that could be attributed to SNPs in our model, rather than covariates such as sex, age, or ancestry, we intentionally excluded covariates when fitting our predictive models. We note that because of our use of covariate-adjusted AUCs, the decision to exclude covariates had little impact on our results since changes in predictive performance arising from the inclusion of covariates would have been “factored out” by the stratification procedure anyway. For example, the covariate-adjusted external validation accuracies of the smallest and largest models in Figure S1Manhattan plot (TIFF)Click here for additional data file.Figure S2Quantile-quantile plot Observed (TIFF)Click here for additional data file.Figure S3RIT2/SYT4. Colors depict the squared correlation (Plot of (TIFF)Click here for additional data file.Figure S4USP25. Colors depict the squared correlation (Plot of (TIFF)Click here for additional data file.Table S1Genotype by phenotype tables for SNPs in (PDF)Click here for additional data file.Table S2Details for all SNPs with (XLS)Click here for additional data file.Table S3Internal and external cross-validation AUC difference test for sparse logistic regression models. Rows and columns of each table correspond to models being compared, and are labeled using the theoretical upper bound on (PDF)Click here for additional data file.Table S4External cross-validation AUC difference test using bias-corrected models. Each row of the table represents a comparison of a “test” risk prediction model based on the significance threshold indicated in the first column against a “reference” model containing only SNPs found in genome-wide significant regions. In all cases, reported AUCs have been adjusted for covariates see , and all(PDF)Click here for additional data file.Table S5Bias-corrected (PDF)Click here for additional data file.Table S6Bias-corrected (PDF)Click here for additional data file.Table S7Test for heterogeneity. The low confidence group consisted of participants who did not answer a questionnaire, whereas the high confidence group consisted of participants who did. The second and third columns show the estimated log odds-ratio for each SNP from (PDF)Click here for additional data file.Table S8Exclusionary conditions. People reporting any of the above diagnoses were excluded from the analysis.(PDF)Click here for additional data file.Text S1(PDF)Click here for additional data file.