Bayes-A, a generalization of Bayes-A where scale and degrees of freedom are estimated from the data (Student-t) and a Bayesian implementation of the Lasso method. Twelve scenarios were evaluated for predicting GEBV using low-density marker subsets, including selection of SNP based on genome spacing or size of additive effect and the inclusion of unknown genotype information in the form of genotype probabilities from pedigree and genotyped ancestors.Bayesian approaches for predicting genomic breeding values (GEBV) have been proposed that allow for different variances for individual markers resulting in a shrinkage procedure that uses prior information to coerce negligible effects towards zero. These approaches have generally assumed application to high-density genotype data on all individuals, which may not be the case in practice. In this study, three approaches were compared for their predictive power in computing GEBV when training at high SNP marker density and predicting at high or low densities: the well- known  Lasso, followed by Student-t and then Bayes-A. When comparing GEBV to true breeding values, Student-t was most accurate, though differences were small. In general the shrinkage applied by the Lasso approach was less conservative than Bayes-A or Student-t, indicating that Lasso may be more sensitive to QTL with small effects. In the reduced-density marker subsets the ranking of the methods was generally consistent. Overall, low-density, evenly-spaced SNPs did a poor job of predicting GEBV, but SNPs selected based on additive effect size yielded accuracies similar to those at high density, even when coverage was low. The inclusion of genotype probabilities to the evenly-spaced subsets showed promising increases in accuracy and may be more useful in cases where many QTL of small effect are expected.The GEBV accuracy  was highest for Student-t approach slightly outperformed the other methods when predicting GEBV at both high and low density, but the Lasso method may have particular advantages in situations where many small QTL are expected. When markers were selected at low density based on genome spacing, the inclusion of genotype probabilities increased GEBV accuracy which would allow a single low- density marker panel to be used across traits.In this dataset the BLUP and Bayesian approaches. In a BLUP framework SNP effects are sampled from a normal distribution and the variance is assumed constant across SNPs [ Bayes-A[2 prior is assigned to SNP variances. Scale and degrees of freedom of the distribution are in this case set as hyperparameters and samples of the posterior distribution are obtained through MCMC methods. A generalization in which the hyperparameters regulating the shrinkage are treated as unknown parameters and estimated from the data leads to the well known Student-t model [Lasso)				[ Lasso estimates can be seen as posterior mode estimates when the regression parameters have independent and identical Laplace priors. Yi and Xu [ Lasso and Student-t models for QTL mapping. Prediction of genomic breeding values can be seen as a generalization of the same problem. It has been reported [ BLUP methods. There are few published results, though, on the performance of different shrinkage methods for genomic breeding value prediction. These approaches were initially developed assuming dense genome- wide SNP coverage. This may not be the case in practice as it is often cost prohibitive to genotype all animals at high density and it may be desired to predict genomic breeding values using low density panels.A number of approaches have recently been proposed for the prediction of genomic breeding values for high-density single nucleotide polymorphism (SNP) panels. Methods commonly used fall into two categories,oss SNPs . In a Baoss SNPs , allowin[ Bayes-A. In this-t model  where thsso)				. In its i and Xu  recentlyreported ,7 that B Bayes-A, Student-t and Lasso, when training and predicting genomic breeding values at high density and when predicting at lower densities.This study investigated the predictive performance of different Bayesian hierarchical approaches,th QTL-MAS Workshop, see [The dataset used for analysis was simulated as part of the 13hop, see  for detaThe simulated dataset included phenotypes for five traits representing measures of yield at five different time points . A sixth phenotype was predicted to represent yield at a time point beyond the simulated data, time point 600 (t600). A number of non-linear models were tested to predict t600 , and theThe data were analyzed using three different approaches, considering additive genetic effects only. The general structure of the models in matrix form was:y is the vector of phenotypic effects, µ is the overall mean, β is the vector of additive effects for each marker,X is a matrix of genotypes expressed as number of copies of an arbitrary allele  for each SNP and e is a vector of residuals assumed N. Values of a and b were set at 0.05 and 1.0, respectively, so that prior of λ was essentially uniform over a wide range of values. The Lasso approach differs slightly from that of Park and Casella [for thees as in . The Stue number . The λ p Casella  and de l Casella , which i1) Sample µ from N Sample β Samplefrom Inv − χ2 4) i. Lasso method:				Samplefrom InvGaussSample λ from Gamma For Student-t only:2 from GammaSample s Sample v with a Metropolis step  return to Step 1The Gibbs sampling algorithm for all three methods was implemented in R . For eacGenomic breeding values were calculated for all individuals in the prediction set, for t530 and t600 by:mX is a matrix of genotypes expressed as  and mβ is a vector of posterior mean effects for a particular method, for m SNPs. A cross validation procedure was also used where phenotyped individuals were randomly split into training and prediction sets  10 times to assess the stability of the genomic predictions for t530 and t600.where m=19, 38 or 76) or a small number of markers with the largest absolute effects in each of the respective methods and traits . These SNP numbers were chosen to approximate the low-density panels that could potentially be used in livestock species . These GEBV calculations used effects only for these (m) markers from the training set. For each of the subsets above another subset was tested including genotype probabilities for all of the remaining markers in calculation of the GEBV. Each of these subsets contained all markers, and thus these GEBV calculations used all high-density SNP effects from training, but only a small subset of markers had actual genotypes . The genotype probabilities were calculated through marker and pedigree information from the full dataset, for all individuals in the prediction set, using segregation analysis for single markers following Kerr and Kinghorn [Subsets of the prediction dataset were created to simulate the situation where training can be done at high density, but prediction of GEBV occurs with a lower density panel. In this case the full training set, including 1000 individuals and 453 SNPs, was used to estimate the SNP effects, but GEBV were calculated using either a smaller subset of SNPs or a combination of genotypes for a small subset and genotype probabilities for the remaining markers  of m X is calculated as:In this case, the individual element ( P(1) and P(2) are the probabilities of individual (i) having the heterozygous and homozygous (coded as 2) genotypes, respectively, for each marker (j). When the actual genotype is known the matrix element is simply coded as before . This approach is related to the genetic predictor approach of Boer et al.[whereer et al..Bayes-A, Student-t and Lasso) as the correlation between the GEBV and estimated breeding values (EBV) calculated using the traditional animal model, for all animals in the prediction set , the GEBV calculated using Bayes-A were essentially the same as GEBV calculated using all markers. There was little loss in accuracy by reducing the marker set from 453 to 19 (with the largest effects) for all methods. The reductions in accuracy resulting from Lasso marker effects were generally similar to the other methods for all low-density subsets and the accuracy was still superior to Bayes-A and Student-t in all cases. In a number of the scenarios the accuracy actually increased from high-density to low-density. Many of the increases were small and thus the accuracies were practically unchanged, but large increases in accuracy were observed for subsets with evenly-spaced SNPs and genotype probabilities, particularly EVEN_GP_19.Correlations of GEBV for each low-density SNP scenario Table  and EBV  Lasso was the highest, Student-t was next and then Bayes-A, though differences were small. The accuracy in this case was the correlation between GEBV and EBV, which is the limit of information currently available, and thus the reported accuracies will likely change when true breeding values are available. In general, methods based on2 inverse -χ priors (Bayes-A and Student-t) appear to be more conservative in the shrinkage than Lasso, even when the scale and degrees of freedom parameters are estimated from the data  across the cross-validation replicates, indicating that this dataset included sufficient information for estimation. The marker effects shown in Figure  Lasso may be more sensitive to QTL with small effects than Student-t, which in turn is more sensitive than Bayes-A.The three methods applied to the simulated data performed similarly Table , where ta Figure . These p et al.[ Bayes-A, only a 7% reduction in accuracy.The use of low-density SNP subsets is based on the concept of Habier et al. where SNThe scenarios using genotype probabilities performed well and in most cases showed a small or no reduction in accuracy, compared to using the full marker set. Due to the population structure  and completeness of parental genotypes it is expected that the genotype probabilities are a good representation of the true genotypes in this case. In a situation where there are fewer ties between individuals the advantage of using genotype probabilities  is likely to be lower than what was found in this study. A number of the scenarios even showed large increases in accuracy to unrealistic levels . The differences between methods were small and more work is needed to determine if they are meaningful in practice. The Student-t method also had the highest accuracy for nearly all of the low- density SNP scenarios, though again the differences in these cases were small. Scenarios where markers were evenly-spaced had lower accuracies than when markers were selected based on effect size (EVEN versus SIG) due to the presence of few moderate or large QTL, but increasing from 19 to just 38 evenly-spaced SNPs was enough to yield accuracies greater than 0.70. There was a large increase in accuracy when including genotype probabilities in place of known genotypes for evenly-spaced SNPs, particularly when only 19 SNPs with actual genotypes were used. The comparisons with individual TBV also showed the expected decreases in accuracy with decreasing number of actual genotypes in the EVEN_GP scenarios that were not seen when comparing to EBV. Comparing the GEBV to the family mean TBV resulted in the paradoxical increase in accuracy as with the original GEBV/EBV comparison when using fewer markers in the EVEN_GP scenarios (results not shown). This highlights the need for care when using EBV for GEBV evaluation, in combination with genotype probabilities, in data with full-sib families. For this trait, though, including a small number of SNPs with large effects would be enough to obtain high accuracy GEBV while greatly reducing genotyping requirements. The results from using genotype probabilities are promising but are likely best applied in situations where many small QTL are expected.The availability of true breeding values (TBV) allowed for an improved evaluation of the effectiveness of the three analysis methods on alternative marker sets Table . As expe Lasso method slightly outperformed Bayes-A and Student-t when considering accuracy as the correlation between GEBV and EBV, but Student-t performed the best when comparing GEBV to TBV. Bayes-A and Student-t appeared to be more conservative in shrinkage of SNP effects indicating that Lasso may be more sensitive to small QTL and thus may perform better than other methods for traits where large or moderate QTL are not expected. In the analysis of reduced marker density few SNPs were needed to maintain levels of accuracy similar to the high-density SNP set when SNPs with large effect were selected. When markers were selected based on spacing, the use of genotype probabilities in place of known genotypes increased the accuracy of the GEBV, which would allow a single low-density panel to be used across traits.For this simulated dataset theThe authors declare that they have no competing interests.MAC performed analyses, participated in study design and drafted the manuscript. SF performed analyses and participated in study design. ND participated in study design and helped to interpret results. CM developed the methods for effect estimation, performed analyses, participated in study design and helped draft the manuscript. All authors read and approved the final manuscript.


N-acetylation capacity have proved to be important determinants of both the effectiveness of therapeutic response and the development of adverse drug reactions and toxicity during drug treatment. NAT2PRED is a web-server that allows a fast determination of NAT2 acetylation phenotype from genotype data without taking the extra step of reconstructing haplotypes for each individual . However, the classification accuracy of NAT2PRED needs to be assessed before its application can be advocated at a large scale.Genetically determined differences in The ability of NAT2PRED to classify individuals according to their acetylation status  was evaluated in a worldwide dataset composed of 56 population samples  from four continental regions.NAT2PRED correctly identified slow acetylators with a sensitivity above 99% for all populations outside sub-Saharan Africa. Nevertheless, NAT2PRED showed a poor ability to distinguish between intermediate and rapid acetylators, with a classification error rate reaching up to 10% in the non-African samples.NAT2 genotype data when the main interest is to distinguish slow acetylators from the others. This should facilitate the determination of the individual acetylation status in routine clinical practice and lead to better monitoring of risks associated with cancer and adverse drug reactions.NAT2PRED is an excellent tool to infer the individual acetylation status from  N-acetylation capacity have proved to be important determinants of both the effectiveness of therapeutic response and the development of adverse drug reactions and toxicity during drug treatment [The human acetylation polymorphism is one of the most intensively studied pharmacogenetic traits that underlie interindividual and interethnic differences in response to xenobiotics. Genetically determined differences in reatment ,2. Some reatment -6.NAT2 gene determine the acetylation phenotype. Thanks to the well-established genotype-phenotype correlation at this locus, the individual acetylation status can be reliably predicted from the haplotype combination at NAT2, according to the acknowledged classification of NAT2 haplotypes into either low-activity or fully functional alleles (see the consensus NAT2 gene nomenclature website: http://www.louisville.edu/medschool/pharmacology/NAT.html). Slow, intermediate and rapid acetylators are defined as carriers of zero, one or two functional haplotypes, respectively. Many studies did not distinguish between rapid and intermediate acetylators, categorizing both types of subjects as rapid acetylators.Single nucleotide polymorphisms (SNPs) in the coding region of the NAT2 is crucial to unequivocally assign an individual's multi-site NAT2 genotype to a particular combination of two multilocus haplotypes and to correctly infer his acetylation status. Ambiguous NAT2 genotyping data that may lead to patient misclassification appear indeed to be common in human populations [NAT2 gene by comparing the computationally inferred haplotypes to the real ones resolved through the use of molecular haplotyping techniques [in silico approaches may not be convenient to clinicians not familiar with computational haplotype analysis. Moreover, algorithmic techniques are statistical and require the analysis of a population rather than a single or a few individuals.Determining the linkage phase of SNP alleles along the coding sequence of ulations . Specialchniques -9. But set al. [NAT2. These six SNPs are the most commonly reported ones in surveys of NAT2 sequence variation in human populations [NAT2 genotyping data of 1,377 subjects of known acetylation status. This tool is publicly available http://nat2pred.rit.albany.edu and has a simple intuitive user interface. It showed a nearly perfect classification accuracy of 99.9% in a sample mostly composed of Caucasians [To circumvent these limitations, Kuznetsov et al.  have devulations . NAT2PREucasians . HoweverNAT2. In total the collected data consisted of 8,489 individuals from 56 human populations representing major geographic regions: sub-Saharan Africa (12 samples), Europe and North Africa (23), Central/South Asia (5), East Asia (13), and America (3). In each sample, NAT2 haplotypes were reconstructed using either molecular or computational methods. Therefore, these data provided an opportunity to compare, for each subject, the acetylation status inferred by NAT2PRED from the unphased NAT2 genotype data with the "true" one predicted from the pair of haplotypes reconstructed through molecular or computational haplotyping. NAT2PRED correctly identified slow acetylators with a sensitivity above 99% for all populations outside sub-Saharan Africa.The objective of the present study was to empirically evaluate the performance of NAT2PRED in a wide collection of population samples worldwide. To this end, we performed an extensive survey of the literature to identify those samples that were adequately genotyped for all the common SNPs in NAT2 (191G>A (rs1801279), 282C>T , 341T>C , 481C>T , 590G>A , 803A>G , and 857G>A ) and for which genotype frequency data were available in the paper. We also included those non-African samples that were genotyped for all SNPs except SNP 191G>A as this is rare in non-African populations. The final dataset used for the present study describe 56 population samples from throughout the world, representing 8,489 individuals. A summary description of the data set is provided in Additional File We selected from published reports up to November 2009 all the population samples that were genotyped for the seven most common SNPs in NAT2 haplotypes were either directly resolved using molecular-haplotyping techniques  or were computationally inferred from the unphased multilocus genotypes using statistical algorithms . For some samples, a combination of the two approaches was used. The acetylation phenotype of each individual was then predicted by assuming that the homozygous or compound heterozygous genotype for two haplotypes of the series NAT2*4, NAT2*11, NAT2*12 or NAT2*13 results in the rapid acetylator status, the occurrence of one of these haplotypes in combination with a haplotype of the series NAT2*5, NAT2*6, NAT2*7 or NAT2*14 results in the intermediate acetylator status and the occurrence of two haplotypes of the series NAT2*5, NAT2*6, NAT2*7 or NAT2*14 results in the slow acetylator phenotype.In each sample, NAT2. The web-server was used in the standard way, by specifying the genotype at each polymorphic position using radio buttons and clicking the 'Submit' button. We retained for each subject the final prediction . The performance of NAT2PRED was measured by computing in each sample the classification error rate, which is the proportion of individuals whose acetylation status was incorrectly inferred by the web-server. The classification error rate of NAT2PRED was evaluated by considering either two (slow vs. others) or three (slow vs. intermediate vs. rapid) phenotypic classes for the acetylation status.Besides, NAT2PRED was applied to the unphased genotype data of each sample  to provide an inferred acetylation status that was compared to the "true" one predicted from the diplotype configuration at The classification error rates of NAT2PRED in each sample for both classification issues (two or three acetylation phenotypes) are shown in Figure NAT2 haplotypes were not actually determined by a molecular resolution of mutation linkage patterns but were rather inferred through computational methods , NAT2PRED showed a poor classification accuracy with a classification error rate of 10% and 16% when considering two (slow vs. others) or three (slow vs. intermediate vs. rapid) phenotypic classes for the acetylation status, respectively. In the five remaining non-African samples, the ability of NAT2PRED to distinguish slow acetylators from the others was very high with an average classification error rate of only 0.5%, ranging from 0% in UK Caucasians and Nicaraguans to 1.2% in Dravidian Indians. This means that in 99.5% of cases, NAT2PRED correctly identified slow acetylators in the same way the usual procedure did . On the other hand, a poorer performance was observed when intermediate acetylators were to be distinguished from rapid acetylators in these five samples .It is important to note that for most of the samples investigated, nference , it is oNAT2 genotype data when the main interest is to distinguish slow acetylators from the others. Its classification error rate is indeed too high to ensure a reliable discrimination between intermediate and rapid acetylators. However, since the slow acetylator phenotype is the one which exhibits today the strongest association with susceptibility to bladder cancer and adverse drug reactions [Our results show that NAT2PRED is an excellent tool to infer the individual acetylation status from eactions , the webeactions . In parteactions , in Coyaeactions , in Brazeactions , and in eactions .The authors declare that they have no competing interests.AS: planned the analysis, collected and analyzed the data, drafted the manuscript. PD and MV: participated in the interpretation of data and critically revised the manuscript. All authors have read and approved the content of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/10/148/prepubTable S1. Classification error rates of NAT2PRED in the 56 worldwide samples collected from the literature.Click here for file


P = 0.0089). We found that the 5-HTTLPR S allele interacted with CRHR1 haplotypes and child abuse to predict current depressive symptoms . These data suggest that G × E interactions predictive of depressive symptoms may be differentially sensitive to levels of childhood trauma, and the effects of child abuse are moderated by genetic variation at both the CRHR1 and 5-HTTLPR loci and by their G × G interaction. © 2009 Wiley-Liss, Inc.Gene × environment (G × E) interactions mediating depressive symptoms have been separately identified in the stress-sensitive serotonergic (5-HTTLPR) and corticotropin-releasing hormone (CRHR1) systems. Our objective was to examine whether the effects of child abuse are moderated by gene × gene (G × G) interactions between CRHR1 and 5-HTTLPR polymorphisms. We used an association study examining G × G × E interactions of CRHR1 and 5-HTTLPR polymorphisms and measures of child abuse on adult depressive symptomatology. The participant population  was African-American, of low socioeconomic status , and with high rates of childhood and lifetime trauma. Depressive symptoms were measured with Beck Depression Inventory (BDI) and history of Major Depression by Structure Clinical Interview based on DSM-IV (SCID). We first replicated an interaction of child abuse and 5-HTTLPR on lifetime SCID diagnosis of major depression in a subsample (N = 236) of the study population—the largest African-American 5-HTTLPR cohort reported to date. We then extended our previously reported interaction with both a CRHR1 SNP (rs110402) and TCA haplotype interacting with child abuse to predict current symptoms (N = 1,059;  Epidemiological and molecular genetic data have established that both environmental and genetic factors contribute to the risk of major depression, with heritability estimates ranging between 30% and 40%, with the remaining variance influenced by mostly individual specific environmental factors Kendler, . Twin anThe most established environmental risk factors for major depression are stressful life events, with early exposure to trauma being a particularly strong risk factor in conferring risk for the development of depression. Specifically, a history of adverse childhood experience  increases the risk for developing major depression as an adult by two- to threefold, and there is a highly significant dose–response relationship between the degree of early adverse experience and depressive disorders as adults [Chapman et al., In fact, both rodents and non-human primates exposed to early adverse experiences exhibit evidence of hyperactivity of the CRF system as adults, as measured by cerebrospinal fluid (CSF) CRF concentrations, CRF and CRF receptor type 1 (CRF1) mRNA expression, and CRF receptor binding [Plotsky and Meaney, In rodents, maternal separation in the early postnatal period also leads to persistent alterations in serotonergic systems, including changes in cell firing of serotonergic raphe neurons, frontal cortical 5-HT concentration, and expression levels of certain serotonin receptors in cortex and hippocampus [Arborelius et al., The fact that early adverse experience impacts both CRF and 5-HT systems is likely due, in part, to the strong interconnection between the two neural circuits. CRF has been reported to inhibit serotonergic neurons in the dorsal raphe nuclei [Price et al., Maternal deprivation in rats is associated with an increase in CRF binding sites in the raphe nuclei [Ladd et al., CRHR1, to predict adult depression in two independent cohorts [Bradley et al., In light of these data, it is thus not surprising that genetic variants impacting the function of genes within these systems have been shown to moderate the effects of early trauma on depression. The serotonin transporter-linked polymorphic region [Lesch et al., BDNF Val66Met polymorphism as well as a functional interleukin-6 (IL6) polymorphism with the 5-HTTLPR, for example [Kaufman et al., CRHR1 polymorphisms with childhood abuse in predicting current symptoms of depression in a sample of over 1,000 African-American men and women.Because of the above-described interaction between these two systems on a biological level, it is also possible that G × G interactions across these systems moderate the impact of early adverse experience. In fact, such G × G × E interactions have been reported for the CRHR1 polymorphisms × child abuse interactions with depression [Bradley et al., The data from this study were collected as part of a larger study investigating the roles of genetic and environmental factors in predicting response to stressful life events in a predominantly African-American, urban population of low socioeconomic status (SES). This is an expanded study from our original publication demonstrating Participants verbally completed a battery of self-report measures, including the Beck Depression Inventory (BDI) and the brief Child Trauma Questionnaire, which took 45–75 min to complete (dependent in large part on the extent of the participant's trauma history and symptoms). We read the instruments to participants to guard against relatively high rates of impaired literacy. Each person was paid $15.00 for participation in this phase of the study. The measures used in the current study are described below.Depressed mood was assessed with the 21-item BDI [Beck et al., P's > 0.1). This demonstrates that the moderation effects of child abuse and CRHR1 genotype were unlikely to be affecting medical condition as a possible contributor to depressive symptoms.Additionally, because these analyses were performed in subjects identified in a general medical clinic, it is possible that the BDI is a proxy for medical-related depressive symptomatology separate from Major Depressive Disorder (MDD). To examine this, we have previously used history of prescribed medicine as a proxy variable for medical illness category [Bradley et al., Two hundred ninety-seven individuals underwent more extensive psychopathological evaluations including the Structured Clinical Interview for DSM-IV  or into Oragene saliva kits  for the rest of the sample using the Qiagen M48 system and the Purelink 96 Genomic DNA Kit from Invitrogen  (Cat # K1821-04). High-quality DNA was available for 1,234 individuals. The 158 individuals with no genotype information consist of individuals for which DNA was not collected or for which we attempted to collect DNA using Oragene-saliva samples but DNA extraction failed completely in two separate extraction trials . When comparing the individuals with genotypes to those without, we found no significant difference on any parameters relevant for the study as described before in Binder et al. CRHR1 SNPs  and the resulting haplotypes based on our previous examination of this gene and depression [Bradley et al., http://www.appliedbiosystems.com/). PCRs were performed in 5 µl reaction volumes in 384-well plates and contained 5 ng of DNA. The standard protocol provided with the kit was followed. Thermal cycler conditions were 95°C for 10 min, 40 cycles of 95°C for 15 sec and 60°C for 1 min. The SDS 2.3 software was used for allelic discrimination. For quality control, 45% of the samples were genotyped as duplicates across and within a 384-well plate. Ten discordances were recorded for 4,685 non-duplicate genotypes (0.20%) and excluded from the analysis. Call rates for the three SNPs ranged from 90.7% to 99.2%. All assay IDs, SNP position, and Hardy–Weinberg equilibrium are available on request from the authors.The genetic methodology for SNP genotyping is described more extensively in our previous publications from this sample [Binder et al., MspI (New England Biolabs) in a total volume of 10 µl for 90 min at 37°C to detect the A/G SNP rs25531 shown to influence the functional effects of the long and short alleles [Hu et al., Genotyping of the 5-HTTLPR utilized the following primers . PCR was carried out in 384-well plates in a 10 µl volume with 10 ng DNA. Each PCR reaction contained 0.5 µM of each primer, 0.08 µM of dATP, dCTP, and dTTP, and 0.04 µM of dGTP, 0.2 µM of 7-deaza GTP , 5% DMSO, and 1.25 U of AmpliTaq Gold (Applied Biosystems). The cycling parameters were as follows: 95°C for 5 min, then 94°C for 30 sec, 63°C for 30 sec, and 72°C for 1 min for 1 cycle, then the annealing temperature reduced to 62°C for one more cycles and then to 59.5°C for 38 cycles. Five microliters of the resulting PCR products was then digested with 5 U We genotyped 506 samples in duplicates and out of these only 3 discordant genotypes were observed and the call rate was 94.6%.CRHR1 SNP genotyping and 1,133 entered 5-HTTLPR genotyping. Of the 1,392 total samples, 1,213 had a valid entry for both the BDI as well as the CTQ. Of these, all 1,213 had attempted CRHR1 genotyping, 990 had attempted 5-HTTLPR genotyping, and 964 had overlapping CRHR1 and 5-HTTLPR genotyping. Due to less than 100% call rates the final N in the G × E analyses was smaller than the attempted genotypes and is specified for each analysis.Of the 1,234 samples with sufficient DNA, all entered CRHR1 variants, we investigated the effects of the best SNP from Bradley et al. CRHR1 risk = 1, and the presence of at least one copy of this haplotype as CRHR1 risk = 0 . Haplotypes were estimated using SNPHAP [Adkins, CRHR1 rs11402 A-carrier status and the two-level child abuse variable and their interaction terms as predictors, adjusting for age and sex. Due to the much smaller N with available SCID data, we did not analyze G × G × E interaction analyses for this outcome.We used linear regression to assess whether the genotypes itself or in interaction with child abuse predicted current depressive symptoms. Predictors: 5-HTTLPR was coded as SS, SL, LL, extra-long alleles (VL and XL), or as S-allele carriers or using functional classification as previously described [Hu et al.,  = 0 see . Child a Adkins, . All hapAs shown in P < 0.0001), there was no significant main effect of the 5-HTTLPR nor interaction between the 5-HTTLPR and two-level child abuse on BDI total score, for all types of 5-HTTLPRs coding. The same outcome was observed when using a three-level categorization of child abuse  would predict current depressive symptoms, using the continuous total score from the BDI as outcome, and co-varying for sex and age . We found that although there was a highly significant effect of level of child abuse history (two-level) on BDI  as well as its interaction term with the 5-HTTLPR genotype  were included in the model. When using the S-allele carrier status, only its interaction term with child abuse remained a significant predictor in the model . As previously reported [Caspi et al., We then tested the interaction of the 5-HTTLPR genotype, S-allele carrier status, and 5-HTTLPR functional classification with child abuse (none to mild vs. moderate to severe) to predict lifetime diagnosis of depression as measured with SCID-DSMIV, with age and sex as co-variates (N = 236). In these analyses, childhood abuse  and the presence of the protective CRHR1-TCA haplotype  but no genetic main effects. Similar results were observed for the three-level child abuse variable with a significant interaction of child abuse and rs110402 A carrier status  and a main effect CRHR1 A carrier status  as well as a significant interaction of TCA risk haplotype carriers with child abuse  (see P < 0.0001), as were sex and age .In this analysis, we first re-examined our previously reported finding of 033) see B,C. In aP < 0.05) in addition to age, sex, and child abuse. No analyses indicated a significant presence in analysis × rs110402 × child abuse interaction, indicating that the interaction effect was not different in the two sets of samples , lower CTQ total scores  and were older (41.2 ± 13.9 years vs. 39.2 ± 14.2 years). There was no difference in gender distribution  and CRHR1 genotype distribution .Because data from a fraction of this sample (N = 435) had been previously reported, we investigated whether similar G × E interactions were observed in the first and the second data sets that comprise the present sample. We added “presence in the first report” coded as 0 or 1 as a predictor in the analysis in addition to its interaction term with child abuse and with child abuse × rs110402 A carrier status. We re-reran the analyses with both the two-level and the three-level abuse categorizations. In both analyses, we found a significant main effect of subsample on BDI total score and interaction between child abuse and presence in first article (ples see . The twoP = 0.00022) was included in the model. While rs110402 carrier status and its interaction term were not significant, we found a nominal over-representation of protective A-allele carriers in the abuse but not major depression group (see We then tested the interaction of the rs110402 A carrier status with child abuse (none to mild vs. moderate to severe) to predict lifetime diagnosis of depression as measured with SCID-DSMIV, with age and sex as co-variates (N = 275). In these analyses only childhood abuse (roup see .CRHR1 haplotype (protective haplotype carrier) and level of child abuse history. Given the number of predictors in the model with the 856 subjects for whom we have all genotype and phenotype data, we found that we would have >95% power to detect a P-value at 0.01 that accounts for more than 5% of the variance in a G × G × E model.We next examined the data for a G × G × E interaction between the 5HTLLPR  and P-values < 0.05), as well as a significant G × G × E interaction . The protective effect of the CRHR1 TCA haplotype is only seen in absence of the 5-HTTLPR S allele , lower levels of child abuse severity are sufficient to elicit clinically relevant symptoms of depression in individuals with both genetic risk factors, as compared to the no risk or single genetic risk groups  when using the three-level child abuse variable. In this model, we observe significant effects of age, sex, and child abuse level (all oups see B.This article presents three primary findings. First, we did not find a main effect of 5-HTTPLR genotype or an interaction effect of 5-HTTPLR and child abuse on current level of depression symptoms in a sample of over 900 African-American men and women but did replicate an interaction of child abuse and 5-HTTLPR on lifetime diagnosis of major depression in a subsample of 234 individuals. Second, using a data set of 624 more individuals, we extended our previously reported interaction of CRHR1 variants with child abuse to predict adult depressive symptoms in a sample of now over 1,000 individuals. Third, we found that the 5-HTTLPR interacts with CRHR1 variants and child abuse to predict current, adult depressive symptoms: individuals carrying the risk alleles in both genes exhibit clinically relevant depressive symptoms at less severe levels of child abuse than individuals with no or only one of the risk alleles.To the best of our knowledge this the first article to present data on 5-HTTPLR and depressive symptoms in an African-American sample—though a previous study in a similar population investigated this polymorphism as predictor of suicide attempts in a sample with a history of drug abuse [Roy et al., Consistent with the majority of the extant literature, we did not find a main effect of 5-HTTLPR genotype on current symptoms of depression in this sample. We replicate the previously described increased prevalence of incidence of lifetime major depression in individuals carrying the 5-HTTLPR S allele and exposed to child abuse [Caspi et al., CRHR1 variants and child abuse to predict adult depressive symptoms [Bradley et al., CRHR1 variants are more pronounced with the presence of at least one type of severe abuse. However, it is important to note, as in CRHR1 variants and child abuse in the same direction, but less pronounced than in the first sample. This might be due to differences of the initial sample versus the second set of recruited individuals. The more recently recruited individuals had lower BDI and CTQ scores and were on average 2 years older, while gender and CRHR1 haplotype and 5-HTTLPR genotype distribution was the same. These differences in environmental exposure and depressive symptoms might, in part, explain the differences in effect size between the first and second set of samples (see CRHR1 variants and child abuse on lifetime diagnosis of major depression; however, the distribution of the protective alleles in the different groups was consistent with an over-representation of the protective CRHR1 alleles in individuals with child abuse but no lifetime major depression. This analysis was further limited by a small sample size, prohibiting analysis with a more graded child abuse variable due to very small Ns/cell (see In a sample expanded to 1,059 individuals, we were able to extend our previously reported interaction of ples see . We did cell see .CRHR1 variants and child abuse to predict adult depressive symptoms. In fact, the protective effects of the CRHR1 TCA haplotype were only observed in individuals carrying the 5-HTTLPR LL genotype. On the other hand, the 5-HTTLPR S allele appeared to enhance the effects of lower levels of child abuse only in the additional presence of the CRHR1 risk haplotypes. Overall, individuals carrying the risk alleles in both genes exhibit more severe depressive symptoms already at lower “doses” of child abuse than individuals with no or only one of the risk alleles (see The main finding of this manuscript is that the 5-HTTLPR interacts with eles see B. The fiCRHR1 variants alone. In fact, different frequencies in the combination of 5-HTTLPR and CRHR1 risk or protective variants could alter the effect size of the individual G × E interaction, if the second variant is not considered. Likewise, our finding that this interaction is present when child abuse is divided across three levels but not across two-level points to the importance of paying attention to the ways in which broad constructs such as “stressful life events” or even more specific constructs such as “child abuse” are defined in evaluating the meaning of G × E research. Within these broad categories are varying stressful experiences for which the underlying neurobiological processes contributing to risk/resilience are likely to be different.Although this G × G × E interaction needs further independent replication to be confirmed, it is well in agreement with the previously reported interactions between the serotonergic and CRF system on a cellular and systems level as described in the introduction [De Bellis et al., Our study has several limitations such as the use of a non-diagnostic, self-report measure of current depression symptoms for the main analysis. A subset of our data now has lifetime and current psychiatric diagnoses according to the SCID-DSMIV [First et al., By design, G × G × E interactions require large sample sizes, so that independent replication and extension in a larger sample will be needed to confirm our finding. Nonetheless, our sample represents one of the larger samples available for G × E interaction analyses and suggests that more complex analyses than case–control associations are necessary to identify the genetic variants relevant in the genetics of depression.


The inability of aspirin (ASA) to adequately suppress platelet aggregation is associated with future risk of coronary artery disease (CAD). Heritability studies of agonist-induced platelet function phenotypes suggest that genetic variation may be responsible for ASA responsiveness. In this study, we leverage independent information from genome-wide linkage and association data to determine loci controlling platelet phenotypes before and after treatment with ASA.Clinical data on 37 agonist-induced platelet function phenotypes were evaluated before and after a 2-week trial of ASA (81 mg/day) in 1231 European American and 846 African American healthy subjects with a family history of premature CAD. Principal component analysis was performed to minimize the number of independent factors underlying the covariance of these various phenotypes. Multi-point sib-pair based linkage analysis was performed using a microsatellite marker set, and single-SNP association tests were performed using markers from the Illumina 1 M genotyping chip from deCODE Genetics, Inc. All analyses were performed separately within each ethnic group.-4) and significant  linkage to post aspirin platelet response to ADP, and ten additional factors had suggestive evidence for linkage  to thirteen genomic regions. All but one of these factors were aspirin response variables. While the strength of genome-wide SNP association signals for factors showing evidence for linkage is limited, especially at the strict thresholds of genome-wide criteria (N = 9 SNPs for 11 factors), more signals were considered significant when the association signal was weighted by evidence for linkage (N = 30 SNPs).Several genomic regions appear to be linked to ASA response factors: a 10 cM region in African Americans on chromosome 5q11.2 had several STRs with suggestive (p-value < 7 × 10Our study supports the hypothesis that platelet phenotypes in response to ASA likely have genetic control and the combined approach of linkage and association offers an alternative approach to prioritizing regions of interest for subsequent follow-up. Platelet activation plays a critical role in atherothrombotic diseases such as acute myocardial infarction (MI) and stroke. Aspirin  is a mainstay of both primary and secondary prevention of MI and stroke . ASA inhPreviously, we have found evidence of moderate to high heritability of platelet function phenotypes, in both the presence  and abseGenetic Study of Aspirin Responsiveness (GeneSTAR). GeneSTAR was designed to examine genetic and environmental determinants of platelet function in response to low-dose aspirin therapy. Eligible subjects were ≥ 21 years of age and had no history of any coronary heart disease, thrombotic event, peripheral vascular disease, stroke, transient ischemic attacks, known derangement in hematologic profiles , renal or hepatic failure, autoimmune diseases, glucocorticosteroid use, hemorrhagic event, measured blood pressure ≥ 180/105 mmHg or current pregnancy. Subjects were also excluded if they had an allergy or intolerance to ASA, baseline platelet count ≤ 100,000 or ≥ 500,000 cells/μL, hematocrit ≤ 30%, or white blood cell count ≥ 20,000 cells/μL. Anticoagulants, ASA, nonsteroidal anti-inflammatory agents, illicit drugs, were proscribed for 14 days before the baseline visit and during the study interval. Consumption of tea and coffee and flavenol-rich foods  and other dietary items known to affect platelet function (fish rich in omega-3 fatty acids and garlic) were prohibited for 24 hours and smoking for 12 hours prior to assessments. Subjects with zero aggregation of platelets to arachidonic acid in both whole blood and platelet-rich plasma (PRP) at the baseline visit and questionable adherence to the list of proscribed substances, were omitted from the study. At the end of the first screening visit, eligible subjects were given a supply of 81-mg ASA tablets and instructed to take 1 pill each day for 14 days. Adherence to ASA use during the study was assessed at the post-ASA visit using pill counts and an adapted standardized medication adherence questionnaire [The study protocol was approved by the Johns Hopkins Institutional Review Board, and all subjects gave written informed consent. Subjects were identified from European American and African American families with premature coronary artery disease (CAD) through a proband with documented CAD prior to 60 years of age. Apparently healthy siblings of the probands, the adult offspring of both the probands and their siblings, and the coparents of the offspring were recruited for the Blood for the measurement of platelet function in whole blood (WB) and platelet rich plasma (PRP) was obtained by venipuncture at the same time of day at baseline and following 14 days of ASA therapy. Baseline and post-ASA measures included aggregation, slope, and lag time in response to selected doses of 4 known platelet agonists, and urinary excretion of the thromboxane B2 metabolite 11- dehydrothromboxane B2 (Tx-M). Optical aggregation was measured in PRP in a PAP-4 aggregometer  after samples were stimulated with arachidonic acid , ADP , and epinephrine . Whole blood impedance aggregometry was measured in a Chrono-Log dual-channel lumiaggregometer  after samples were stimulated with arachidonic acid (0.5 mmol/L), collagen , and ADP (10 μmol/L). Platelet function under shear stress was determined by the platelet function analyzer (PFA) test . Whole blood was loaded into standard proprietary cartridges (Dade-Behring) containing collagen and epinephrine, and aperture closure time was recorded in seconds (maximum of 300 seconds).Plasma fibrinogen was assayed in the Johns Hopkins Clinical Coagulation Laboratory and von Willebrand factor (vWF) was measured using enzyme-linked immunosorbent assays .Urine was collected at the same time of day for pre- and post-ASA measurements of Tx-M using an enzyme-linked immunosorbent assay  and values were normalized to urinary creatinine.2). Plasma glucose, total cholesterol, triglyceride, and HDL-cholesterol levels were measured after patients had fasted for 12 hours overnight. LDL cholesterol was estimated using the Friedewald equation. Diabetes was defined as self-reported diabetes with the use of diabetes medications, or measured glucose levels ≥ 126 mg/dl.Blood pressure was measured according to methods of the American Heart Association and hypertension was defined as the average of 4 resting blood pressures ≥ 140/90 mm Hg and/or the taking of antihypertensive medications. Current smoking was defined as any reported cigarette smoking within the past 30 days and nonsmoking status was validated by exhaled CO levels < 8 ppm on the average of 2 measurements. Height and weight were measured and body mass index was calculated as the weight (kg)/height squared . There were 574 successfully released STR markers with an overall duplicate error rate (3 CEPH sample duplicates on each of 18 plates) of 0.1% and an overall Mendelian error rate of 2% on 99.7% of samples. The STR genotyping data were checked for Mendelian inconsistencies using PEDCHECK , and all-6).SNP genotyping was performed at deCODE Genetics, Inc. using the Human 1Mv1_C array from Illumina, Inc. and 1,044,977 markers were released with an average call rate per sample of 99.65% and an overall missing data rate of 0.35%. Using 25 duplicate pairs of CEPH samples, the reproducibility rate was >99.95% for all duplicate pairs. Analyses at deCODE Genetics revealed Mendelian errors (> 5%) in 14 samples, which were eliminated from further analysis. Finally, 9 samples with gender discrepancies and an additional 3,427,500 inconsistency calls over all SNPs were eliminated from the final data prior to analyses. While all markers were analyzed, SNPs were flagged for closer examination where minor allele frequency was low (2%) and/or deviation from Hardy Weinberg Equilibrium was severe . In the formulation of the LME model, SNP genotypes were included as fixed effects setting the genotypes to be additive in effect, and family identification number was included in the random effects . We tested whether the additive effects of each SNP was different from zero. The LME in SAS (v. 9.1.3 for Linux OS) was applied with PROC MIXED using the option for EMPIRICAL variance.In an attempt to offset the diminished power to detect association that arises as a result of multiple testing issues inherent in genomic searches and to determine the best set of loci to pursue in further follow-up studies  in the absence of external replication data, we used the False Discovery Rate (FDR) approach proposed by Roeder et al . This neClinical data on agonist-induced platelet function, PFA, and Tx-M phenotypes were evaluated in healthy subjects with a family history of premature CAD before and after a 2-week trial of ASA 81 mg/day). There were 1231 European American subjects  from 398 families, and 846 African Americans  from 243 families. Linkage analysis was performed using 257 sib-pairs in European Americans and 158 in African Americans. The clinical characteristics of the population are presented on Table  mg/day. Figure Most of the principal components were heritable, as shown in Table -4). At thresholds of significant linkage  a ~10 cM region on chromosome 5q11.2 appears to be linked to a post-ASA principal component representing aggregation to ADP in whole blood (Factor 4B (POST): ADP) in African Americans; no significant evidence was noted in European Americans. In addition, a total of eleven factors, five in European Americans and six in African Americans, appear to have suggestive evidence for linkage across fourteen regions in the genome. The single most striking feature of these results in Tables response to aspirin intervention therapy.Tables -4 (see Tables -8), seven SNPs reach a region-specific Bonferroni threshold  as presented in Additional file Figure -4 in Tables -8). Specifically, 9 SNPs met these criteria as indicated in Table Full genome-wide tests for association in these six factors in African Americans and five in European Americans with suggestive evidence for linkage  on chromosome 3q21-27, associated with ASA response to collagen induced aggregation in PRP).While many significant association signals are intergenic, several genes are identified in Table Given that GeneSTAR is the most comprehensive assessment of platelet response to aspirin done to date, this also leads to two general weaknesses: our limited sample size for linkage and the lack of external replication. In light of these two limitations, we have implemented several novel approaches to best search for susceptibility loci, implementing the principal component approach to first find factors that represent the underlying biological correlation between the multitude of measured phenotypes and the combined approaches of linkage and association with two sets of marker data. In conclusion, this study is unique in its ability to identify loci controlling platelet response to aspirin intervention in both European American and African American families identified to be at risk for CAD, and our combined novel approaches have yielded several loci that we believe worthy of further follow up.ASA: Aspirin; CAD: coronary artery disease; STRs: microsatellite markers; GWAS: genome-wide association study; SNP: single nucleotide polymorphism; PRP: platelet-rich plasma; WB: whole blood; vWF: von Willebrand factor; PCA: principal components analysis; LME: linear mixed effects models; FDR: False Discovery Rate.The authors declare that they have no competing interests.Conceived and designed the linkage and genome-wide association study: NF, LCB, DMB. Conceived and designed the research questions proposed within this manuscript: RAM, YK, IR, AFW, NF, LCB, DMB. Conceived and designed the phenotyping and acquired the phenotype data: LRY, EHG, IR, NF, LCB, DMB. Analyzed the data: RAM, YK, HS, VM. Wrote the first draft of the paper: RAM, YK. Contributed to the manuscript editing, revising, and final approval of the version to be published: RAM, YK, HS, LRY, VM, EHG, IR, AFW, NF, LCB, DMB.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/3/22/prepubTable S1. Association signals under linkage peaks in Figure Click here for file


Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity.We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster.1 percent predicted, 2) percent bronchodilator responsiveness, and quantitative CT measurements of 3) apical emphysema and 4) airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1) emphysema predominant, 2) bronchodilator responsive, with higher FEV1; 3) discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4) airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant) was associated with TGFB1 SNP rs1800470.We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1) post-bronchodilator FEVCluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies. Chronic Obstructive Pulmonary Disease (COPD) is defined by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) as airflow limitation that is not fully reversible. This deSubstantial evidence indicates that genetic variation contributes to differences in COPD susceptibility; however, replication of genetic associations in COPD - and in many other complex diseases - has generally been poor. DiseaseStatistical learning techniques offer an opportunity to extract novel patterns and trends from phenotypic data, and thu1 ≤ 45% predicted, evidence of hyperinflation on pulmonary function testing, and bilateral emphysema on chest CT scan. Enrollment in the NETT Genetics Ancillary Study began after the initiation of the clinical trial, and thus only a subset of the original cohort had DNA available for genotyping. The characteristics of NETT subjects included and excluded from this analysis are shown in Additional File Details of subject recruitment and phenotyping in the National Emphysema Treatment Trial (NETT) have been reported previously. BrieflyEPHX1 and GSTP1) and surfactant homeostasis (SFTPB), as well as two genes identified in part through linkage studies: TGFB1, a cytokine growth factor, and SERPINE2, a thrombin and urokinase inhibitor. A limited number of SNPs were selected in order to limit multiple statistical testing in this relatively small study population[SERPINE2[2 cutoff of > 0.8 in Tagger [We selected a set of 31 CT, lung function, and other key phenotypic variables, based on clinical relevance, inclusion in previous genetic association studies, and complete data, to avoid subject drop-out Table , and incopulation. For the[SERPINE2,28, in wn Tagger  as implen Tagger .We used factor analysis as a guide to determine which COPD phenotypic variables to include in our clustering analysis. Factor The goal of cluster analysis is to assign subjects to groups, where subjects in the same cluster are more similar to each other than they are to subjects in other groups. Similar1 and FVC percent predicted); 2) airway wall thickness ; 3) FEV1/FVC ratio; 4) quantitative emphysema severity and distribution , using a cutoff of -950 Hounsfield units; 5) bronchodilator responsiveness; and 6) maximum work and gender. The following variables were chosen as representative based on relatively high factor loadings, accounting for a greater proportion of shared variability: post-bronchodilator FEV1 percent predicted (for factors 1 and 3), airway wall thickness (factor 2), apical emphysema (factor 4), and bronchodilator responsiveness (factor 5).A total of 308 subjects from the NETT Genetics Ancillary Study were included in the analysis. Phenotypic variables selected for inclusion in the factor analysis are shown in Table -3). Cluster 1 had the greatest degree of emphysema and the least airway wall thickness, as well as a lower FEV1. Conversely, cluster 4 had the highest airway wall thickness and the least emphysema, and also had lower bronchodilator responsiveness and FEV1. Cluster 2 was a milder subgroup, with the highest FEV1 and bronchodilator responsiveness, as well as less emphysema. Cluster 3 also had less emphysema and in addition, less airway wall thickness; however, in contrast to cluster 2, this cluster was more severely affected with the lowest FEV1 and bronchodilator responsiveness.Based on measures of silhouette width, the k-means clustering algorithm using four clusters was found to be optimal for this dataset. This optimal value was low (~0.2) consistent with modest separation of the clusters. A plot of the clusters with each pair of phenotypic variables is shown in Additional file 2 and lower six minute walk distance. Cluster 2, the milder severity, bronchodilator responsive subtype, had a higher BMI, greater FVC and DLCO, a lower PaCO2, higher six minute walk distance and maximum work, fewer symptoms of dyspnea, and fewer exacerbations, despite being of slightly older age. Cluster 3, with a lower FEV1 despite less severe radiographic emphysema and airway wall thickness than the other clusters, had more dyspnea and a higher PaCO2, with slightly younger age.Additional phenotypic variables, not included in the cluster analysis, were then examined to determine other characteristics of these clusters. Significant differences among the groups were found for several characteristics Table . ClusterTGFB1, rs1800470; no other P values were nominally (<0.05) significant. In pairwise testing using an additive model of each cluster versus all other clusters, this SNP was associated with membership in cluster 1 (P = 0.002).To determine whether specific SNPs were associated with cluster membership, we tested genotypes for each of the 12 candidate gene SNPs with cluster membership. A chi-squared P value of 0.034 was seen for a SNP in Further details on study methods and additional results are available in Additional File Despite the description of COPD subtypes more than 40 years ago and subsSeveral methods have attempted to address issues of disease heterogeneity in obstructive airway diseases. Statistical learning techniques such as factor analysis have been used to reveal novel insights into characteristics such as dyspnea or inflammation in COPD,35-37. CTo date, however, there has been limited use of disease subtypes in genetic association studies in COPD. Investigators have tested for specific associations with classic subtypes,40,41, o1, differ from previous reports[TGFB1 SNP rs1800470 with cluster 1 is consistent with a previously reported association of apical emphysema in this cohort [Our cluster analyses identified four subtypes of subjects in this cohort with severe emphysema: 1) emphysema predominant, 2) milder severity, bronchodilator-responsive, 3) discordant lung function/CT emphysema and airway severity, and 4) airway predominant. Some of the phenotypic associations in these groups, such as a lower BMI with more severe quantitative CT emphysema, have been previously seen,45, whils reports,47. The s cohort  and assos cohort . Notablys cohort . Severals cohort -52 and is cohort  in subjes cohort .Conversely, most of the previously reported SNP associations with COPD-related phenotypic characteristics did not demonstrate associations with our clusters. Nonsignificant findings could be due to loss of power from categorical cluster assignment and resulting small sample size, and the use of an omnibus test for genetic association. More importantly, our analysis attempts to determine whether genetic variants lead to a subtype of COPD subjects which share a set of phenotypic characteristics; as such, it does not attempt to determine the specific genotypic-phenotypic variables whose relationship leads to a significant association. Whether one of these approaches - association analysis with individual phenotypic characteristics, or with subtypes of subjects- is superior in identifying replicated genetic associations, or whether the approaches are separately informative, remains to be seen.Our study has several strengths. First, we used relatively unbiased methods, in both factor analysis and cluster analysis, to select uncorrelated variables and determine severe COPD subtypes using the rich set of phenotypic and quantitative measures available in NETT. Second, our analysis is the largest reported cluster analysis using CT phenotypic variables. Third, despite our homogeneous study population, we were able to discern emphysema subtypes, which differed on variables not used to perform clustering. While all four of these subtypes have not previously been identified, our emphysema and airway-predominant clusters are consistent with a priori defined subtypes used in previous studies. ImportaOur results should be regarded as exploratory for several reasons. First, our dataset was based on available NETT data. Specific relationships between variables - for example, the high correlation between apical and total emphysema - may be due to selection biases of the NETT population. NETT subjects were likely biased towards those without predominant airway disease, and CT scans were suboptimal for assessment of airway wall remodeling due to the thicker slices associated with pre-MDCT (multi-detector CT) imaging. Similarly, our genotypic data was limited to a pre-specified subset of previous positive associations in candidate genes, and our cohort was limited to those enrolled in the NETT Genetics Ancillary Study . EKS has received honoraria from GlaxoSmithKine, Wyeth, Bayer, and Astra-Zeneca, consulting fees from GlaxoSmithKline and Astra-Zeneca, and grant support from GlaxoSmithKline.MHC carried out the data analysis and drafted the manuscript. EKS conceived and designed the study, and assisted in data analysis and interpretation. GRW and EAH generated the CT data. TH and NL assisted in the statistical analysis. GJC, EAH, and FJM participated in generating the data and in data analysis. JJR helped design the study and assisted in data analysis. All authors read, helped revise, and approved the final manuscript.Supplementary Information. Supplemental Methods, Results, and Figures.Click here for file


Results showed that the proposed ATS has good performance and could detect the regions containing a susceptibility locus.A new type of test is presented for genome-wide association studies using a case-control design. It is referred to as the adaptive two-stage (ATS) analysis, being based on both the Hardy-Weinberg disequilibrium trend test (HWDTT) and the Cochran-Armitage trend test (CATT). The procedure for the ATS is to screen single-nucleotide polymorphisms (SNPs) using the HWDTT in a first stage, and then test a reduced number of SNPs that pass the screening step in a second stage using the CATT. In the Genetic Analysis Workshop 15 simulated data set, this ATS analysis captured, after Bonferroni correction, the region from 32447.149 kb to 32859.819 kb and the region around 37363.880 kb that are close to the actual trait loci on chromosome 6. We compared the ATS with other ways of combining the  The advance of genotyping technologies and reduction of genotyping costs are resulting in genome-wide association studies using 100,000 to 500,000 (100 k–500 k) single-nucleotide polymorphisms (SNPs) across the whole genome in which tests for association are performed between each SNP and a disease in a case-control design. However, one of the big challenges of genome-wide association studies is the issue of multiple testing [p-value of an allele-based test is obtained for each SNP, but it is only necessary to correct for the number of SNPs included in the second-stage analysis.Zhang et al.  proposedp-values, referred to as Fisher's test, was considered by Zhang et al. [T = -2log(pHWDTT) - 2log(pCATT), which under the null has a chi-square distribution with 4 df. Recently, Hwang et al. [α ≤ 1) using a nonparametric distribution with a Gaussian Kernel density.As an alternative approach, Fisher's combination of g et al. . Becauseg et al.  is giveng et al.  extendedIn this study, we applied the ATS analysis to the Genetic Analysis Workshop (GAW15) simulated data set in order to find susceptibility disease genes. We compared the results of the ATS with those of the CATT, the HWDTT, the classical Fisher test (CFT), and the weighted Fisher test (WFT). The ATS was Bonferroni-corrected for multiple testing, so, for the sake of comparison, the CATT, HWDTT, and Fisher tests (CFT and WFT) are also adjusted using the same correction method.α1 chosen on the basis of the conditional power of the HWDTT. The smallest α1 is chosen such that the power is at least 1 - β, where β is the type II error.For the ATS analysis of association, Zhang et al.  applied i = 0, 1, 2, so that D1) and controls (D0), where D1 = p2 - (p2 + p1/2)2 and D0 = q2 - (q2 + q2/2)2. The HWDTT statistic can be written asDenote the estimators of the genotype frequencies in cases and controls d Elston  considerni = (ri + si) and where wheref = (1 - 2b - a)2b(1 - b) + 2ab(b + a/2)(1 - 2b - a) + (b + a/2)2a(1 - a),f/(r's'), r' ≈ r/n and s' ≈ s/n,nVar(D1 - D0),N, andΦ is the distribution function of the standard normal α1/2)th percentile of N.α2 = α'/(mα1), where α' is obtained by the parametric bootstrap to control the overall type I error rate of the ATS analysis. Then α2 controls the overall type I error rate to α (taken to be 0.05) for a total of m simultaneous hypothesis tests (SNPs). As in Van Steen et al. [p-value of the ATS is the p-value of the second analysis, which here is the CATT.The SNPs for which the null hypotheses are rejected in the first stage are tested in the second stage analysis by the CATT at the level n et al. , the oveWe used the GAW15 simulated Problem 3 data set for rheumatoid arthritis (RA), which includes 100 replicates. Each replicate contains 1500 families with an affected sibling pair and 2000 unaffected control subjects. To obtain a sample of cases and controls, we randomly chose one case from each affected sib pair. Thus, from each replicate we selected 1500 cases with RA and 2000 controls. In order to compare the performance of all methods in a small sample size, we randomly sampled 200 cases and 200 controls from each of the 100 replicate samples of 1500 cases and 2000 controls. To examine type I error rate, we concentrated on 100 markers that were at least 20,000 kb distant from the identified peaks on chromosome 6. Therefore, the total number of marker tests to examine type I error was 10,000 (100 markers × 100 replicates). To examine power, we concentrated on all 674 markers on chromosome 6 using only one replicate of 200 cases and 200 controls. Among these 674 markers, 5 markers are causative, in the region between 32447.149 kb and 37363.880 kb on chromosome 6.α = 0.05 and α = 0.001. Table The results for type I error are shown in Table p < 0.001) with a susceptibility disease gene in the region  at 37363.880 kb, which was close to a trait locus (37233.784 kb). The results also indicated that i) CATT and CFT have similar power near the peak, ii) CFT is more powerful than WFT near the peak, and iii) the HWDTT is not powerful near the peak.Without multiple testing corrections, the HWDTT, CATT, CFT, and WFT showed strong associations p < 0.00 with a sp-values for HWDTT, CATT, WFT, and CFT are compared at the significance level 0.05/674 = 7.42 × 10-5. To obtain the p-value of the ATS analysis, first we calculated the asymptotic power of the HWDTT for α1 = 0.01 to 1.00 with increments of 0.01 for each of the 674 SNPs. Then for each SNP we chose the α1 that had at least 80% conditional power. Results showed that 535 SNPs had p-values of HWDTT less than this α1 and these were analyzed in the second stage. Then we obtained the adjusted overall level for α' using the parametric bootstrap with increments of 0.001 and 10,000 replications. For example, for 80% conditional power, the SNP in location 32447.149 kb requires α1 = 0.77 in the first stage. Then, based on the bootstrap, we obtained α' = 0.03. Thus, the adjusted level required for the second stage is α2 = 0.03/(674 × 0.77) = 5.78 × 10-5. The p-values of the HWDTT is 0.53 <α1 = 0.77. Thus, the SNP in location 32447.149 kb is significant in the first stage. The p-value of the CATT in the second stage is 2.7 × 10-15 <α2 = 5.8 × 10-5. Hence, the SNP in location 32447.149 kb is significant when the adaptive two-stage analysis is applied.With Bonferroni multiple testing correction, Table α2, the three SNPs in the region between 32447.149 kb and 32772.203 kb were associated with RA. In particular, after Bonferroni correction the ATS showed that the SNP at 37363.880 kb was associated with RA. In the region between 32447.149 kb and 32859.819 kb, the CATT and CFT had Bonferroni-corrected average p-values < 1.34 × 10-12 and p < 5.80 × 10-7, respectively. In addition, the corrected p-value of CFT is 0.000802 for a marker at location 37363.880 kb. However, the Bonferroni-corrected p-value of WFT is not significant. This could indicate that WFT with Bonferroni correction may be too conservative, because it already minimizes the number of false positives and false negatives.Applying the ATS with the optimal p < 0.05 in regions that were distant from the true locations, so that the falsepositive rate of CFT seems to be higher than that of WFT in these data. This agrees with the original paper [We have presented the use of a new method for genome-wide association studies with optimal choice of significance level to maximize the power and at the same time asymptotically control the overall type I error. The ATS analysis uses two independent test statistics-here the HWDTT and the CATT. We compared the performance of the HWDTT, CATT, Fisher's tests combining the HWDTT and CATT, and the ATS in this data set with Bonferroni correction for multiple testing and found that the ATS had good performance. Compared to WFT or CFT, the ATS showed higher power in this study. CFT captured the region that is close to a trait locus and gave higher power than WFT, but had p-values were significant for the CATT, CFT, and ATS. Zhang et al. [In this study, we applied all test statistics to the GAW15 simulated data. However, the simulated effect near the peak is so strong that all test statistics were able to detect the susceptibility disease gene on chromosome 6 with a sample size as small as 200 cases and 200 controls; the Bonferroni corrected g et al. .α' using the parametric bootstrap.Finally, we should note the limitations of the ATS method we used. The ATS analysis will be more costly than other two-stage analysis studies, which uses separate portions of the sample for each stage, because the ATS analysis uses all subjects in both stages. In addition, the ATS analysis is more computationally intensive than the other tests because it is a necessary to obtain the adjusted overall level for Using the ATS analysis, a sample size as small as 200 cases and 200 controls showed good performance after Bonferroni correction for association with a susceptibility disease gene in the region between 32447.149 kb and 37363.880 kb on chromosome 6.The author(s) declare that they have no competing interests.