Finding a genetic marker associated with a trait is a classic problem in human genetics. Recently, two-stage approaches have gained popularity in marker-trait association studies, in part because researchers hope to reduce the multiple testing problem by testing fewer markers in the final stage. We compared one two-stage family-based approach to an analogous single-stage method, calculating the empirical type I error rates and power for both methods using fully simulated data sets modeled on nuclear families with rheumatoid arthritis, and data sets of real single-nucleotide polymorphism genotypes from Centre d'Etude du Polymorphisme Humain pedigrees with simulated traits. In these analyses performed in the absence of population stratification, the single-stage method was consistently more powerful than the two-stage method for a given type I error rate. To explore the sources of this difference, we performed a case study comparing the individual steps of two-stage designs, the two-stage design itself, and the analogous one-stage design. Linkage methods test for co-inheritance of a genetic marker and trait through pedigrees, while association studies test for correlations between a marker and trait at the population level. Fulker et al.  decomposIt has recently been shown that for every two-stage multiple testing procedure there is a corresponding single-stage procedure that will identify a greater number of expected true positives for a fixed level of expected false positives . Leek anThe GAW15 Problem 3 data consisted of 100 simulated data sets of affected families with genotype and quantitative phenotype information patterned after data from rheumatoid arthritis studies . Due to The GAW15 Problem 1 data set consisted of a set of 14 CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees in which individuals have been genotyped for 2819 autosomal SNPs [yij = μ + axij + εij, where yij is the trait value of individual j from family i, μ is the average trait value, a is the genetic effect, xij is the number of copies of allele A at the causal SNP in individual j from family i, and εij is a N residual. For polygenic traits, we used the model yij = μ + axij + zij + εij, where in the parents, zij are normal random variables with mean 0 and variance zim and zif, the offspring polygenic values follow a N((zim + zif)/2, In order to examine the behavior of the methods under various trait models on realistic data with known causal SNPs, we simulated traits based on the Problem 1 genotypes. We used two models for our simulations: a simple additive model and an additive model with polygenic effects (hereafter referred to the polygenic model). Simple additive traits follow the model For a quantitative trait, the conditional means model test (CMMT) described by Lange et al.  examinesβ0 is the genetic effect, E is the child's expected genotype given the parental genotypes, zi is a random family effect, and ε is residual error. The CMMT is the Wald statistic to test the null hypothesis of no association that specifies β0 = 0 vs. the alternative β0 ≠ 0 [p-values for each SNP.where e β0 ≠ 0 . GeneralThe family-based association test (FBAT)  is a scoβ'0 = 0 and the alternative is β'0 ≠ 0. This test statistic follows a where the null hypothesis is p-values are considered to be the final genome-wide p-values for those ten SNPs.The CMMT and FBAT provide statistically independent tests corresponding to between- and within-family association analyses of a SNP and quantitative trait ,4,5. TheA single-stage analysis can be performed by adding the CMMT and FBAT tests statistics to obtain a chi-square sum test (CSST). Because both CMMT and FBAT are independent test statistics with an asymptotic ribution , the resLeek, Rohlfs, and Storey developed the conditional likelihood ratio test (CLRT) , another single-stage method analogous to the two-stage method. As in Lange's two-stage method, the CLRT one-stage likelihood can be divided into association and linkage portions . Note thθ and θ' are vectors of model parameters. The screening step from the two-stage design is mirrored in L and the testing stage corresponds to L.where yij = μ + β0E + zi + ε for the L part of the total likelihood. For the L part of the likelihood, we used the model logit) = μ' + β'0yij + ε'ij [cij value for each of its heterozygous parents with cij = 1 if allele A was transmitted from that parent and cij = 0 otherwise. This is not precisely analogous to the original two-step method testing stage where a simple linear model was used.The CLRT uses the linear mixed model j + ε'ij . Here, eβ0 and β'0 measure two different quantities. The null hypothesis in this method is that both β0 and β'0 are zero. A likelihood-ratio test statistic of the joint likelihood follows a As in the two-stage method, the two parts of our likelihood are calculated under different models, so p-values were corrected for multiple-testing with a family-wise error rate (FWER) of p < 5.0 × 10-8 and false discovery rate (FDR) of p < 0.05.As a case study, we analyzed Replicate 77 from the GAW15 Problem 3 data using CMMT, FBAT, CSST, CLRT, and Lange's two-stage method ,5. CMMT To compute empirical type I error rates and power, we ran a similar analyses using CSST, CLRT, and the two-stage method on 100 partially-simulated and 82 simulated data sets from Problems 1 and 3.Discrepancies were observed between the evidence of association from FBAT and CMMT analyses at the loci flanking the true susceptibility loci Table . The oneThe ranking of SNPs neighboring a causal locus varied considerably depending upon whether CMMT or FBAT scores are used. Performing a screening stage using only one of these measures is likely to miss causally linked SNPs. Specifically, in the two-stage design, SNPs 185, 191, and 192 are not considered for the second stage, so their high-ranking FBAT scores were not computed. In the CSST and CLRT one-stage methods, these SNPs did score relatively well, though they did not satisfy Bonferroni-corrected significance criteria.-6, although in testing whether our observed rate was different from the nominal rate, we did not take into account the dependence between tests that comes from linkage disequilibrium between chromosome 2 loci or from the fact that only 82 different sets of trait values were used. Table p-values, they are not low enough to pass the Bonferroni criterion.The one-stage methods are empirically more powerful than the two-stage method based on comparisons in 82 Problem 3 simulated data sets . The type I error rates for both methods are close to the rate expected given the α cutoff for significance and Bonferroni correction (data not shown).Empirical power in this data set is consistently higher using the CLRT one-stage method Tables  and 4 thp-value = 0.0024) after a Bonferroni correction of factor 10, however if 20 SNPs had been retained in the screening step, then SNP 186 would no longer be significant after a Bonferroni correction of factor 20. It is also important to note that causal loci may be missed if too few SNPs are selected in the screening stage. The two-stage method loses more power by not testing the vast majority of the SNPs than it gains in a smaller final multiple test correction. Our analysis demonstrates that these problems are addressed by using a single-stage test where both linkage and association contribute to final p-values with standard conservative Bonferroni correction.Combining transmission and association information, as the two-stage method does, is more appealing than using either alone. However, the screening step in the current two-stage design selects SNPs based on top ranks without consideration of multiple testing and statistical significance. In these analyses we retained the top ten SNPs based on the recommendation of Van Steen et al. . RetainiThe difference in power between the two methods is explained in part by the difference in the information each method uses. In the two-stage method, final conclusions are made using only the children's genotypes from families that are informative at the ten screenedSNPs. In contrast, the single-stage methods use all the children's genotypes. The single-stage methods use more information, allowing them more power. We note that applying the two-stage approach here to a data set with all children's genotypes available, provides no genotype cost savings. For population-level genome-wide association studies, two-stage approaches have been proposed as a means for reducing genotyping cost . HoweverThe Problem 3 simulations allowed us to compare power while conditioning only on family structure. However, because the data were entirely simulated, some subtle aspects of LD may not be represented. Our comparisons based on Problem 1 data used real genotypes, so the LD structure is realistic; however the power we compute is conditional on these specific genotypes. Neither of these studies is ideal, but the fact that they have different strengths and weaknesses, yet produce very similar results supports our conclusions.Both single-stage methods, CLRT and CSST, were found to havenominal type I error and comparable power. There remain several obvious improvements to our implementation of the CLRT single-stage method. In order to remain analogous to the original two-stage method, we used different genetic models in each part of the likelihood. A single genetic model including familial, polygenic, and environmental effects should be used for the entire likelihood.p-values from the two-step method are determined by the FBAT alone, the two-stage method is also robust to false positives caused by population structure. The CLRT and CSST one-stage methods, which combine transmission and association tests, are prone to false positives in the presence of population structure. In this case, both the CLRT and CSST approaches can be adjusted using a genomic control [It is important to note that all of these analyses were performed in the absence of population stratification. Transmission-based tests, like the FBAT, are robust to population structure. Since the final  control  or popul control  approachWe found that one-stage methods are empirically more powerful than the analogous two-stage method in two simulated data sets. Furthermore, when we investigated a particular simulation in detail, we found that causally linked SNPs often could be identified by either association or transmission, but not both. We conclude that rather than using only one of these methods to screen SNPs, the two should be used together, taking advantage of the strengths of each. In addition, it is sometimes believed that a large multiple testing problem will be reduced by using a two-stage screen and test method. Our results agree with previous results ,13 that The author(s) declare that they have no competing interests.


To analyze multiple single-nucleotide polymorphisms simultaneously when the number of markers is much larger than the number of studied individuals, as is the situation we have in genome-wide association studies (GWAS), we developed the iterative Bayesian variable selection method and successfully applied it to the simulated rheumatoid arthritis data provided by the Genetic Analysis Workshop 15 (GAW15). One drawback for applying our iterative Bayesian variable selection method is the relatively long running time required for evaluation of GWAS data. To improve computing speed, we recently developed a Bayesian classification with singular value decomposition (BCSVD) method. We have applied the BCSVD method here to the rheumatoid arthritis data distributed by GAW16 Problem 1 and demonstrated that the BCSVD method works well for analyzing GWAS data. The fact that m>>n makes it unrealistic to analyze all SNPs simultaneously using traditional statistical methods, such as multiple linear regression analysis. It is therefore common for the analyses to be conducted one SNP at a time in GWAS. This means that 300,000 to 1,000,000 tests will be carried out for each GWAS study against each phenotype of interest. Such a large number of tests lead inevitably to a considerable problem with false positive results. To address this multiple testing issue in GWAS, two potential solutions have been investigated in recent years: evaluate false positive rates, e.g., Benjamini and Hochberg's false discovery rate and the q-value  appeared smaller than those from PLINK, we have to keep in mind that we used only datasets with 200 and 1,000 samples, respectively, in the BCSVD analysis, compared to the 2,062 samples in PLINK. More importantly, we have successfully avoided multiple testing issues because we performed only one test by evaluating all SNPs simultaneously. Similar results were observed in the datasets with 200 samples and 1,000 samples. We therefore conclude that the BCSVD method is a practical method for identifying genetic determinants in GWAS when sample size is much smaller than number of markers (m>>n). The BCSVD method has been implemented in our BAMGAS  program. While we are still working on a web-based user-friendly version, an executive version of the software is available from the authors.The BCSVD method was applied to RA case-control data from Problem 1 of GAW16 for 8 selected regions. When we evaluated the association between RA affection status and all SNPs in selected regions simultaneously using BCSVD, significant associations were detected for all the 8 chromosomal regions, and the highest peak was observed on chromosome 6, which were consistent with the PLINK results. Even though the magnitude of significance [-logBCSVD: Bayesian classification with singular value decomposition; GAW: Genetic Analysis Workshop; GWAS: Genome-wide association studies; HWE: Hardy-Weinberg equilibrium; IBVS: Iterative Bayesian variable selection; NARAC: North American Rheumatoid Arthritis Consortium; RA: Rheumatoid arthritis; SNP: Single-nucleotide polymorphism; SVD: singular value decompositionThe authors declare that they have no competing interests.SK participated in the design and analysis and drafted the manuscript. JC participated in data cleaning and GWAS analysis. SR helped with data analysis and manuscript writing. DT helped to manage the data and analysis. JIR helped to draft the manuscript. XG participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-positive association results. In this study, we have explored model-free alternatives to ANOVA using correlations between allele frequencies in the different quantile intervals of the quantitative trait and the quantile values. We performed genome-wide association scans on anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M, two quantitative traits correlated with rheumatoid arthritis, using the data provided in Genetic Analysis Workshop 16. Both the quantitative traits exhibited significant evidence of association on Chromosome 6, although not in the human leukocyte antigen region which is known to harbor a major gene predisposing to rheumatoid arthritis. We found that while a majority of the significant findings using the asymptotic thresholds of ANOVA was not validated using permutations, a relatively higher proportion of the significant findings using the asymptotic cut-offs of the correlation statistic were validated using permutations. Complex genetic traits are usually characterized by correlated quantitative precursors. Because quantitative traits carry more information on within-genotype variation compared to binary (affected/unaffected) traits, it has been argued that analyzing quantitative endophenotypes may be a more powerful strategy than analyzing clinical end-points for identifying genes controlling a complex trait. While some family-based methods of association have been developed for quantitative traits -3, populFor our analyses, we used data on anti-CCP and RFUW levels along with genotypes at 531,689 single-nucleotide polymorphisms (SNPs) distributed over the 22 autosomal chromosomes. Data on the two quantitative traits were available only on individuals affected with RA. After removing the individuals with missing phenotype data, our methods used data on 867 individuals  for anti-CCP and 746 individuals  for RFUW. We found that 51,524 SNPs had a minor allele frequency less than 0.05. Because these markers are unlikely to be involved in disease pathogenesis under a common-variant, common-disease (CVCD) model, they were also removed from the association analyses.Q1, Q2,..., Qk denote the k sample quantiles of the quantitative trait in study with Q0 defined as the minimum value of the trait in the data. Suppose pi denotes the proportion of the minor allele at a particular SNP among individuals with quantitative trait values in the interval , i = 1,2,..., k. It can be shown that the frequency of a marker allele conditioned on the value of the quantitative trait lying in an interval  is independent of a and b if and only if the coefficient of linkage disequilibrium between the QTL and the marker locus is zero. Moreover, in the presence of linkage disequilibrium between the two loci, the frequency of the marker allele in positive linkage disequilibrium with the QTL allele predisposing to high values of the quantitative trait will increase with the quantile intervals. Thus, one can test for allelic association based on the correlation between Qi and pi values. A test based on the direct correlation coefficient (r) between the two variables would be equivalent to testing whether the slope coefficient of a linear regression of pi values on Qi values is zero or not  of pi values with increasing values of Qi). The test statistic is √(n-2)|r|/√(1-r2) and is distributed as t with (n-2) degrees of freedom under the assumption of bivariate normality of the underlying variables, although the assumption can be relaxed for large samples. We note that this statistic is identical to that used for testing the slope coefficient in the linear regression mentioned above. A more robust statistic is based on the rank correlation (R) between the variables given by √(n-1)|R| and is asymptotically distributed as a standard normal variate [Suppose  variate . However variate  with an p(1-p)/n] cannot exceed 0.01. The minimum number of quantiles should be about 30 for appropriate use of large sample properties of the correlation coefficient.One of the statistical issues is the choice of an appropriate number of quantiles. The method involves the estimation of the minor allele frequency in each quantile interval as well as computing the correlation between these estimated frequencies and the quantile values. If the number of quantiles is too large, each quantile interval will comprise very few observations, leading to inefficient estimation of the allele frequencies. On the other hand, if the number of quantiles is too small, the total number of observations for the computation of the correlation coefficient will not be sufficiently large to make reliable inferences on association. We suggest that each quantile interval comprise approximately 25 observations so that the variance of the estimated allele frequency [p-value less than 10-7 as evidence of departure from HWE. Those SNPs that failed the HWE test were removed from the association analyses. We performed three tests of association: the first using ANOVA, the second using the direct correlation coefficient, and the third using the rank correlation coefficient for each of the two quantitative traits: anti-CCP and RFUW levels. To determine an effective number of independent tests for an appropriate FDR correction, we used the software HAPLOVIEW, which identified 367,392 tag SNPs, based on a threshold of pairwise r2 greater than 0.8.We first tested for possible deviation from Hardy-Weinberg equilibrium (HWE) for each of the 480,165 SNPs. To attain an overall significance level of 0.05, we used a Bonferroni corrected p-value < 0.01 using the Kolmogorov-Smirnov statistic and validated by Q-Q plots). There was also no sex effect in either anti-CCP levels  or RFUW levels  and hence, no sex adjustment was necessary for the association analyses. For the quantile-based methods, we divided the data into 31 quantile intervals for both the quantitative traits .Neither of the two quantitative traits follows a normal distribution , which contains the NKAIN2 (sodium/potassium transporting ATPase interacting 2) gene and the other in the 11q12 region  containing a psuedogene LOC100129670. We note that another contribution from our GAW group also found significant evidence of association in the 6p22 region with anti-CCP [We performed our association analyses on all the 22 chromosomes using ANOVA and the two quantile-based statistics. The most significant association findings are presented in Table p-value based on the rank correlation statistic was 0.006 on chromosome 2. However, unlike anti-CCP, the RFUW levels did not exhibit any genomic region with a cluster of significant SNPs.When we analyzed RFUW levels using ANOVA, we found 1399 significantly associated SNPs, while the direct correlation coefficient and the rank correlation statistics provided 178 and 15 significant SNPs, respectively. The most extreme As pointed out in the section entitled "Statistical Methodology," it was of interest to explore whether the above association findings, obtained using asymptotic tests, can be validated by permutation tests. We found that for anti-CCP, only 182 of the 935 SNPs that showed significant evidence of association using ANOVA were validated by permutations, while for RFUW, only 297 of the 1399 SNPs showed significant association using permutations. However, the corresponding numbers using the direct correlation statistic were 47 out of 180 SNPs for anti-CCP and 99 out of 178 for RFUW. Thus, among the SNPs exhibiting significant association using asymptotic thresholds, the permutation tests validated a higher proportion of SNPs for the correlation statistic compared to ANOVA. This can be intuitively explained by the fact that the quantile-based correlation statistic is model-free in nature and hence relatively more robust to violations in distributional assumptions. The rank correlation statistic did not provide any significant evidence of association using the permutation test procedure.PTPN22 gene (on 1p13), showed any significant evidence of association with either of the two quantitative phenotypes at the overall FDR of 0.05 using any of the methods.None of the SNPs in the two regions that have been implicated in RA pathogenesis, the human leukocyte antigen (HLA) region (on 6p21) and the n-1), where n is the number of observations, and hence, has limits to the p-values when the asymptotic distribution is used. The direct correlation coefficient statistic, which is equivalent to the test of the slope coefficient in a linear regression, is a more suitable alternative. However, a permutation strategy is likely to provide more accurate p-values compared to asymptotic distributions, especially if the number of quantiles is not very large. We also note that it is not possible to assess the relative performances of the different methods using real data efficiently. We are carrying out extensive simulations independently to compare the powers of the different tests. Our preliminary simulations based on a sample size of 500 suggest that the powers of the correlation-based statistic and ANOVA are comparable under a homoskedastic QTL model, but the correlation-based method is more powerful under a heteroskedastic model.The proposed quantile-based statistics are model-free alternatives to the ANOVA approach for association mapping of quantitative traits and circumvent the problem of departures from normality and homoskedasticity of the quantitative traits conditioned on the QTL genotypes. While these methods are more robust than ANOVA, they are expected to be less powerful when the underlying assumptions of ANOVA are valid. In particular, the rank correlation statistic is bounded by √(PTPN22 gene on chromosome 1 with either of the two quantitative phenotypes. A similar phenomenon was also observed in the analyses of these two quantitative traits in the data provided in Genetic Analysis Workshop 15 [An intriguing result of the present study has been the lack of evidence of significant association in the HLA region on chromosome 6 and the kshop 15 . This raBecause the data on the two quantitative traits were available only on individuals affected with RA, the association findings should be interpreted as polymorphisms involved in differential elevations of anti-CCP and RFUW levels in RA-affected individuals from the mean normal levels of these phenotypes. There is reduced variation in the quantitative phenotypes when restricted only to RA cases and hence, an appropriate design to identify polymorphisms associated with the two quantitative phenotypes would include data on controls in addition to the cases.ANOVA: Analysis of variance; anti-CCP: Anti-cyclic citrullinated peptide; CVCD: Common-variant, common-disease; FDR: False discovery rate; HLA: Human leukocyte antigen; HWE: Hardy-Weinberg equilibrium; GAW16: Genetic Analysis Workshop 16; NARAC: North American Rheumatoid Arthritis Consortium; QTL: Quantitative trait locus; RA: rheumatoid arthritis; RFUW: Rheumatoid factor immunoglobulin M; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.SG conceived the statistical method and drafted the manuscript. KRS managed the data and wrote the computer codes for the statistical methods. AG and SC developed the permutation testing procedure and carried out the statistical analyses. All authors read and approved the final manuscript.


Human height is a highly heritable and complex trait but finding important genes has proven more difficult than expected. One reason might be the composite measure of height which may add heterogeneity and noise. The aim of this study was to conduct a genome-wide linkage scan to identify quantitative trait loci (QTL) for lengths of spine, femur, tibia, humerus and radius. These were investigated as alternative measures for height in a large, population–based twin sample with the potential to find genes underlying bone size and bone diseases. 3,782 normal Caucasian females, 18–80 years old, with whole body dual energy X-ray absorptiometry (DXA) images were used. A novel and reproducible method, linear pixel count (LPC) was used to measure skeletal sizes on DXA images. Intraclass correlations and heritability estimates were calculated for lengths of spine, femur, tibia, humerus and radius on monozygotic  and dizygotic  twins. A genome-wide linkage scan was performed on 2000 DZ twin subjects. All skeletal sites excluding spine were highly correlated. Intraclass correlations showed results for MZ twins to be significantly higher than DZ twins for all traits. Heritability results were as follows: spine, 66%; femur, 73%; tibia, 65%; humerus, 57%; radius, 68%. Results showed reliable evidence of highly suggestive linkage on chromosome 5 for spine (LOD score  =  3.0) and suggestive linkage for femur (LOD score  =  2.19) in the regions of 105cM and 155cM respectively. We have shown strong heritability of all skeletal sizes measured in this study and provide preliminary evidence that spine length is linked to the chromosomal region 5q15-5q23.1. Bone size phenotype appears to be more useful than traditional height measures to uncover novel genes. Replication and further fine mapping of this region is ongoing to determine potential genes influencing bone size and diseases affecting bone. Human height is a highly heritable and complex trait. Heritability estimates measured in previous studies range from 0.68–0.98 (with men having higher heritability than women) Dividing height into sub-components may improve the search for height genes. Previous studies have shown demi-span and lower limb length as useful surrogates for height The aim of this study was to conduct a genome-wide linkage scan to identify quantitative trait loci (QTL) for lengths of spine, femur, tibia, humerus and radius as alternatives for height in a large, population–based twin sample and to identify the genes for height which may also be underlying diseases affecting bone size.2. The mean age of MZ individuals was approximately 2 years older than that of DZ individuals.Our results describe a normal population of 3,751 Caucasian females. The summary statistics comparing identical and non-identical Caucasian females are shown in Length of femur, tibia, humerus and radius were highly and significantly correlated with a value of 0.63 or above with all remaining variables with the exception of spine, between 0.20–0.25 and the correlation between lengths of radius and humerus at 0.49 .Heritability is shown in Multipoint linkage analysis was performed on all bone lengths but only two sites showed suggestive results. As a confirmation of the results, empirical p-values were obtained using a permutation approach. The results for each phenotype are shown in Our main findings showed that all bone sizes measured were highly heritable. Moreover, the length of spine had a highly suggestive LOD-score after linkage analysis at the region 5q15-5q23.1.There are few studies which have previously reported linkage results on chromosome 5 using traditional height measures. Deng et al, 2002 A more recent and larger study by Perola et al, 2007 An earlier smaller study by Perola et al, 2001 In our study, age was responsible for 11% of the variation in spine length which is why it may be useful to stratify the sample population into age groups. Age-stratification was applied to a subset of the Framingham Heart Study Bioinformatic investigation showed our peak marker to be WIAF-1594 at a LOD-score of 3.00 for the spine in the region 5q15-5q23.1. This marker codes for a key enzyme controlling cellular oxidative stress-glutaredoxin (thioltransferase). It works in catalyzing the reduction of glutathionyl protein disulfide bonds This chromosomal region has also been linked to kyphoscoliosis on chromosomal region 5q13 in a small study of seven families Finally, this same region has been previously implicated in certain rare, monogenic diseases yet may overlap in those which are more common and widespread. There is a well-defined syndrome called 5p-, whose characteristics include short stature, where there is an interstitial deletion in the 5q13.1-q15 region. Krishna et al, 1997 A major limitation of this study was the sole use of women. It has been widely established that there are significant gender differences in bone geometry where the genes for men may be different to those of women Another potential limitation in this study is that the sample population is twin-based. However, with regards to zygosity, it has been shown by Andrew et al, 2001 Finally, a potential drawback of linkage pronged analysis is one of power. Many recent publications using genome-wide association scans have highlighted that the genetic variants which underpin height variation as well many other complex traits are likely to have very small effect sizes Height has a higher variation amongst the general population than other bone measures such as femur or tibia length. This is mainly due to spine variation which can be due to a number of reasons, eg. spinal degeneration, osteoporosis or scoliosis. Height undoubtedly changes more dramatically than other bone sites after attainment of peak bone mass. For this reason, it is important to investigate alternative skeletal sites which are not affected by age or disease-related. We believe that alternative and more specific measures of skeletal size may indeed offer considerable advantages over classical height studies for gene discovery.In conclusion, we have shown that variations in bone lengths are highly heritable and we report suggestive, preliminary evidence that spine length is linked to the region 5q15-5q23.1. Replication and further fine mapping is ongoing to identify genes that may influence bone length which may be associated with the same genetic regions coding for diseases affecting bone size.The Twins UK Adult Twin Registry provided the data for this study The study used 3,782 normal Caucasian females, 18–80 years old, with whole body dual energy x-ray absorptiometry (DXA) data although the age was restricted to 70 years of age to exclude degenerative changes in analysis of the spine length measurement. Spine was also age-adjusted and residuals were used since age accounted for 11% of the variation in spine length affecting the distribution in our sample. This variation was reduced to 8% after subjects over 70 years of age were removed. A sub-sample of 90 subjects was first used to confirm the reproducibility and validity of a novel measurement technique in DXA total body analysis–linear pixel count (LPC). This method was reproducible with a mean coefficient of variation (CV%) of 1.6% among all bone size phenotypes measured. It was also validated against real clinical measures (X-rays and anthropometry) showing positive correlation & a relatively low population CV% LPC uses the regions of interest (ROI) sub-regional analysis mode on a Hologic QDR-4500W DXA scanner  to determine lengths of bones. The phenotypic measurements were of length of spine (C4 to L4), femur , tibia  and radius (olecranon process of ulna to ulna styloid process).A multipoint genome-wide linkage scan using 400 microsatellite markers spaced approximately every 10cM (with a surplus of 337 markers) was performed using statistical package, STATA v.9.0 (StataCorp) Estimates for heritability were then calculated on the same bone sites using the structural equation modelling package, Mx Standard fluorescence-based genotyping methodologies were used and these have been described in detail in previous papers


While high-throughput genotyping technologies are becoming readily available, the merit of using these technologies to perform genome-wide association studies has not been established. One major concern is that for studies of complex diseases and traits, the whole-genome approach requires such large sample sizes that both recruitment and genotyping pose considerable challenge. Here we propose a novel statistical method that boosts the effective sample size by combining data obtained from several studies. Specifically, we consider a situation in which various studies have genotyped non-overlapping subjects at largely non-overlapping sets of markers. Our approach, which exploits the local linkage disequilibrium structure without assuming an explicit population model, opens up the possibility of improving statistical power by incorporating existing data into future association studies. Yet the merit of using these technologies to perform genome-wide association studies has not been established . Insuffip < 10-6 .Several approaches are being developed to improve the efficiency of genome-wide association studies. One attractive solution is a multi-stage design, in which only a subset of individuals are genotyped at the full set of markers in the initial phase. In subsequent stages, additional individuals are genotyped at increasingly restricted marker sets . In the As an alternative, we note that association studies of some diseases, such as rheumatoid arthritis (RA), are being performed by more than one group of investigators, giving rise to multiple data sets ,6. One mIn this paper, we propose a novel approach that allows us to increase the effective sample size by combining data obtained from several studies. Specifically, we consider a situation in which both the subjects and marker panels are non-overlapping among the studies. In this situation, it is not possible to evaluate LD between markers from different panels. Our approach, which exploits the local LD structure without assuming an explicit population model, opens up the possibility of improving statistical power by incorporating existing data into future association studies. We illustrate our method by analyzing the simulated RA data. We had no knowledge of the "answers" at the time of analysis.We have previously described a multipoint transmission-disequilibrium test (TDT) method that is based on local smoothing . Our stuA or TDTB, can be computed at the genotyped markers [In this section, we outline our statistical methodology in a simple setting: two studies have genotyped non-overlapping sets of markers on independent sets of individuals in a common genomic region, and both studies have used a case-parents trio design. In each study, the TDT statistics, TDT markers .b1 denote the number of informative transmissions, in which A alleles are transmitted but a alleles are not transmitted, and let c1 denote the converse . Likewise, let b2 and c2 denote the corresponding numbers of informative transmissions in Study B. With complete genotype data, we would compute the TDT by pooling the data:To motivate our test statistic, we first consider a marker that has been genotyped in both studies. In Study A, let R1 = B1 + C1 > 0 , and R2 = B2 + C2 > 0. Under the null hypothesis, L(B1 | R1) ~ Binom, L(B2 | R2) ~ Binom, and B1 and B2 are independent. We then have:We next show that, under the null hypothesis, the last term in Eq. (1) has an expectation of 0. Let ni = bi + ci). Assuming that the two studies sampled comparable populations , we approximate these weights by the number of trios.Therefore, under the null hypothesis, the pooled TDT statistic is nearly a weighted average of the corresponding TDT statistics in the respective studies. The weights are proportional to the number of informative parents  be the results of applying a local linear regression fitting to  data. At each marker, we compute:For a marker that is not genotyped in one study (but is genotyped in the other), we try to impute the TDT statistic using neighboring markers. We then add the observed and imputed TDT scores from the two studies. Suppose vA = 1), we simply take the TDT statistic; if a marker ti is not gentoyped in Study A, we impute its expected TDT statistic using the predicted value. Similarly, we compute TB(t) using data from Study B. The combined test statistic at each marker is simply:In other words, if a marker is genotyped in Study A (comb on the simulated data. The observed maxiTDTcomb(ti) is compared with the null distribution of the corresponding maxima in the simulated data.We implement the imputation step using the loess function in R. The choice of the smoothing parameter depends on many factors such as the age of the disease mutation, the population under study, and the marker density. While an optimal window size is difficult to define, an examination of inter-marker LD guides our choice: we seek a region within which the genotyped markers are in high LD. Roughly speaking, we are faced with a trade-off between bias and variance: smoothing over a wide region tends to reduce variance of the imputed statistics at the cost of an increased bias. Therefore, an alternative to loess with pre-specified bandwidth is a smoothing spline with the degree of freedom chosen by cross-validation. To properly account for the imputation, and to correct for multiple comparison, we perform a simulation-based test: conditioning on the parents' genotype, we generate the transmitted and the non-transmitted haplotypes under the null hypothesis, re-impute the TDT statistics, and compute TDTTo illustrate our proposed method, we analyze Replicate 1 of the simulated RA data. This data set consists of 1500 nuclear families, each of which has both parents and two affected children genotyped. It is known that there is a strong effect of DR type at the HLA locus on chromosome 6. A simple TDT analysis using all 1500 families unambiguously demonstrates preferential transmission of DR-2 or DR-3 alleles to the affected individuals. However, is the DR allele the sole variant affecting the disease in the region? To address this question, we examine the transmission from parents who are homozygous 1/1 at the DR locus. If the DR locus explains the entire association in the region, conditioning on parents being 1/1, there should not be preferential transmission at any markers nearby. Among 1500 mothers, 70 have genotype 1/1. Our analyses highlight a practical difficulty: performing stratified analysis on a subset of samples further reduces the sample size; thus, stratified analyses are particularly likely to suffer from small sample size even when the main study has good power.6 bp around the DR locus. We consider a situation in which each third of the families are genotyped on a different platform. The 293 SNPs are randomly divided into three sets, and there is no overlap in the three sets of markers or individuals. Because the risk of RA is much higher among women, we hypothesize that there may be gene × sex interaction. Furthermore, there has been ambiguous evidence regarding maternally transmitted risk elements [comb, with a bandwidth approximately 15 markers.On chromosome 6, we restrict ourselves to the 293 SNP markers falling within 1.5 × 10p-values of TDT on the three subsets are 0.0011, 0.02, and 0.61, respectively. In Figure all, open square) versus TDTcomb (filled points). While the maximum value achieved by TDTcomb appears substantially lower than the corresponding value by TDTall, the same is true under the null hypothesis, because the imputed TDT statistics tend to be smoother than observed ones. As a result, at a specific significance level , the critical value for TDTall is 17.38, while the corresponding critical value for TDTcomb is 6.87. We perform a quantile transformation based on the null distribution, and Figure all with transformed TDTcomb. It indicates that, upon suitable transformation, TDTcomb can achieve similar significance level as if TDTall. However, the location of the peak shifts slightly: while the marker with highest TDTall lies to the right of the DR locus, that with the highest TDTcomb lies to the left of DR locus. Another consequence of smoothing and imputing TDT scores is that the "peak" of TDTcomb appears somewhat narrower than TDTall. In a similar fashion, we analyzed the other three types of transmission. The results, summarized in Table The results of various TDT test for the mother-daughter transmission in the 3-cM region are shown in Figure pool) > E(TDTcomb).We have performed additional simulations to assess the gain in power of our combined test (not shown). In a wide range of disease models and parameters, the combined test achieves a greater power than the probability that at least one of the two studies achieved statistical significance, even though the latter procedure has an inflated type I error. Furthermore, the gain in power of our approach depends on marker density. In the current setting, the entire SNP set on chromosome 6 has a density comparable to a genome-wide set of 300 K, and hence each of the three subsets we analyzed has a density comparable to the 100 K SNP array. When each subset of individuals has been genotyped on a denser marker set, the imputed TDT scores tend to be more accurate. Thus, had subsets of individuals been genotyped on different platforms of 500 K SNP arrays, we would expect the imputed TDT scores to be more accurate, and therefore combining across studies will achieve even greater power. On the other hand, compared to the pooled TDT with complete marker data, the combined test incurs a loss of power. This loss of power is to be expected for two reasons. First, the smoothing process introduces uncertainties both under the null and alternative hypotheses. Second, if a locus increases the disease risk in both studies and the high risk alleles are the same allele, the last term in Eq. (1) has an expectation greater than 0; hence, E declare that they have no competing interests.


Thymoma is a heterogeneous group of tumors in biology and clinical behavior. Even though thymoma is divided into five subgroups following the World Health Organization classification, the nature of the disease is mixed within the subgroups.We investigated the molecular characteristics of genetic changes variation of thymoma using cDNA microarray based-comparative genomic hybridization (CGH) with a 17 K cDNA microarray in an indirect, sex-matched design. Genomic DNA from the paraffin embedded 39 thymoma tissues  labeled with Cy-3 was co-hybridized with the reference placenta gDNA labeled with Cy-5. Using the CAMVS software, we investigated the deletions on chromosomes 1, 2, 3, 4, 5, 6, 8, 12, 13 and 18 throughout the thymoma. Then, we evaluated the genetic variations of thymoma based on the subgroups and the clinical behavior. First, the 36 significant genes differentiating five subgroups were selected by Significance Analysis of Microarray. Based on these genes, type AB was suggested to be heterogeneous at the molecular level as well as histologically. Next, we observed that the thymoma was divided into A, B  and B3 subgroups with 33 significant genes. In addition, we selected 70 genes differentiating types A and B3, which differ largely in clinical behaviors. Finally, the 11 heterogeneous AB subtypes were able to correctly assign into A and B  types based on their genetic characteristics.In our study, we observed the genome-wide chromosomal aberrations of thymoma and identified significant gene sets with genetic variations related to thymoma subgroups, which might provide useful information for thymoma pathobiology. Thymoma is a thymic epithelial cell tumor having organotypic features and no overt cytologic atypia. Although controversy still remains, the classical histological classifications of thymoma based on the proportion of reactive lymphocytes and tumor epithelial cells have been replaced by a histogenetic classification that basically subdivides thymoma into medullary, cortical, and mixed types, according to cytological features of epithelial cells -5. SeverA recent report on genetic alterations of thymoma using comparative genomic hybridization (CGH) and fluorescent in situ hybridization methods demonstrated that type B3 thymoma frequently occurred with losses of chromosomes 6, 13 q, 16 q and gains of chromosome 1 q, while type A thymoma rarely showed cytogenetic abnormalities . SubsequThe application of DNA microarray technology has provided us a high-throughput evaluation of the whole genome as well as significant genetic information at a single gene level and has enabled us to classify different neoplasms based on the characteristic genetic patterns. So far there has been no report focusing on differences of genetic alterations between subtypes of thymoma using a cDNA microarray based-CGH method (microarray-CGH). In our study, genetic alterations of all WHO-defined subtypes of thymoma were investigated using high resolution microarray-CGH followed by hierarchical cluster analyses of the data to identify specific patterns of genetic aberrations and genes associated with each subtype.2 ratio values of each gene from 39 thymoma patients, plotted on the location of the chromosomes using the Chromosome Analyzer and Map Viewer using S-plus (CAMVS)  Figure . When weSubtype specific analysis demonstrated losses of chromosomes 2, 4, 6q, and 13, identified in type A Figure , and losTo investigate the genetic differences of five subtypes of thymoma, we selected 36 distinctive genes at a false discovery rate (FDR) of 2.5% by multi-class SAM (23) , putative DNA/chromatin binding motif (PLU-1), protein phophatase1 regulator subunit 12B (PPP1R12B), and SEC22 vesicle trafficking protein-like 1 (SEC22L1) were amplified only in type B3.Based on the previous results, thymoma could be divided into three genetically distinct subgroups  and genetically heterogeneous type AB. Prediction Analysis of Microarray (PAM) (24) as in the method was carried out to predict to which subgroup type AB belongs. To equilibrate each group, the training set evenly included six cases from types A, B1+2, and B3. Eleven cases of type AB were used as a test set in the prediction analysis. The cross-validation of the selected 44 subgroup classifier genes showed 100% accuracy with types A and B1+B2 and 50% accuracy with type B3 in the training set Figure . The preA number of study results on systemic genetic analyses of thymoma have been reported in the past few years after a few case reports on the chromosomal abnormalities of thymoma -22, 25--25-27. ZWe used cDNA microarray based-CGH to investigate the genome-wide genetic aberrations of thymoma. In contrast to CGH and LOH analyses, cDNA microarray based-CGH could provide us a high-throughput evaluation of the whole genome and also with significant genetic information at a single gene level. The other advantages of using microarray-CGH are the utility of paraffin embedded tissue DNA, the requirement of a small amount of genomic DNA and the possible direct comparison with RNA expression. With this technique there was no need to amplify the DNA in order to obtain the genetic information of more than 10,000 genes, which sometimes may blur the results. Compared to RNA expression analysis, for which we need the fresh, well-stored tissue samples (the major limitation of tissue procurement), using the paraffin embedded tissues allowed us to do the retrospective studies using a large number of samples.The main purpose of our study was to identify differential genetic patterns to explain distinct morphologic findings and different biologic behaviors, according to subtypes of thymoma. For this, we included all WHO-defined subtypes of thymoma for genetic analysis, as in the previous reports -29. The 2 ratio after microarray-CGH of a total of 39 cases, suggesting the characteristic genetic aberrations of thymoma. The chromosome 6 loci, known to possess many tumor suppressor genes, well-established deletion sites in thymoma such as 6q21, 6q23, and 6q25-27, and the genes selected in our study, were also frequently located on 6q, confirming the previous data [The CAMVS, which was developed in our institute for the effective analysis and visualization of microarray-CGH results, was used to identify the significant genetic aberrations of thymoma. Chromosomal deletions in chromosomes 1, 2, 3, 4, 5, 6, 8, 12, 13 and 18 were observed by the comparison of the mean logous data .Subtype specific analyses demonstrated that losses of chromosomes 2, 4, 6, and 13 were identified in all subtypes of thymoma. Type A thymoma had the least number of chromosomal abnormalities while type B had many more chromosomal abnormalities in accordance with the previously reported data -22. ThymFurthermore, type B demonstrated heterogeneous and overlapping chromosomal aberrations in our study. Losses of chromosomes 1p, 2q, 3q, 4, 5, 6q, 8, 13, and 18 and a gain of chromosome 9q were identified in type B1. Type B2 demonstrated losses of chromosomes 1p, 2q, 3q, 4, 5, 6q, 8, 13, and 18 and type B3 revealed chromosome 2q, 4, 5, 6, 8, 12q, 13, 18 losses and chromosome 1q gain. We could confirm the type B3 specific 1q gain and losses of 6, 13, and 16 in all three cortical subtypes, as in previous reports -22. HoweAfter the systematic insight into genetic aberrations of thymoma was achieved according to molecular characteristics, the types were dissected into genetically related subgroups for further detailed analyses. First, we identified that type AB is genetically heterogeneous, so we excluded type AB for further analyses. Then, we identified that type A was distinct from type B, based on the molecular characteristics.The cortical subtypes did not demonstrate clear separation by hierarchical clustering analysis. Among cortical subtypes, type B1 morphologically maintains distinct cortico-medullary differentiation with indistinct tumor epithelial cells and usually shows indolent biologic behavior, as compared with types B2 and B3. Type B2, which definitely shows more aggressive biologic behavior than type B1, has more prominent tumor epithelial cells than type B1 and gray zones are sometimes present between type B2 and B3 histologically. Furthermore, tumors having both type B2 and B3 areas are commonly present. So we expected genetic similarities between type B2 and type B3. However the pattern of small branches showed more similarities between types B1 and B2 than between types B2 and B3.Based on the hierarchical clustering analysis, we could assume that thymoma could be divided into four genetically different subgroup of types A, AB, B1+2, and B3. We tried to assign 11 genetically heterogeneous cases of type AB into types A and B using the prediction analysis. The prediction analysis results were coherent with the clustering analysis results, showing that three clustered in type A were predicted to be type A, and eight clustered in type B were designated as type B. Type AB was reported to be genetically more heterogeneous than type A and some chromosomal aberrations characteristic of type B were reported to be present in type AB ,22. TypeThe WHO classification of thymoma is reported to be associated with the invasiveness and recurrence of thymoma. Among the various gene sets, the 70 genes distinguishing types A and B3 might be related to the malignancy of thymoma, because B3 is the typical malignant tumor with a metastatic property among 5 subtypes. Among the 70 genes, the significant number of genes were related to cell structure and adhesion. NEDD9  and CTNNB , which are known to be the adhesion-related genes, were deleted in type B3, suggesting increased cell motility for metastasis. As Penzel et al. reported the genetic amplification in chromosome 1 in malignant type B2 and B3, frequently amplified genes chromosome 1 were observed in B3 .There have been no standard methods to detect the significant genes among the distinct groups in microarray-CGH. Several reports mentioned the correlation of the CGH and expression profiling suggesting the possibility of using similar approach for both in selecting the significant genes ,34. AmonOnly one concern of this study is the limited numbers of the samples in each type. Especially with the microarray-CGH, which could evaluate thousands of genes simultaneously, the sample size is the significant matter. To overcome the over-fitting problem, one effort is to divide the samples into the training and the independent test set. However, the sample size is not large enough to select the genes based on this approach, requiring us to apply the cross-validation method in the same training set. As all these efforts to define the subgroups based on the clinicopathologic or molecular level result from the lack of good prognostic markers and treatment strategies of thymoma, the capacity of prediction of current proposed genes sets needs to be validated in more samples prospectively in accordance with the clinical parameters.In this study, we evaluated the genetic characteristics of the WHO-defined five subtypes of thymoma using microarray-CGH. We observed that thymoma could be divided into four genetically distinct groups of A, AB, B1+2, and B3. Type AB was determined to be genetically heterogeneous in morphology. We identified sets of genes which characterize the molecular subgroups for the basis of understanding thymoma biology and the candidate biomarkers of each group. In conclusion, this study provides significant information on the genetic background of thymoma for classification purposes.Thirty-nine cases of thymoma tissue samples  were obtained as paraffin embedded tissue blocks from the files of the Department of Pathology at the Severance Hospital, Yonsei University College of Medicine. The median age of patients at diagnosis was 50 years (range 26–71 years), and the male versus female ratio was 23:16.Ten serial 10 μm thick tissue sections were cut from representative paraffin blocks for genomic DNA extraction. The pathologist (Professor Yang WI) confirmed the diagnoses and subtypes according to the WHO classification (9), and localized tumor areas in the corresponding hematoxylin-eosin (H&E) stained tissue section. Fresh normal placental tissues from healthy newborns were snap-frozen for reference samples. Genomic DNA extraction from the slide was performed according to the conventional protocol. BrieflyIn this study, we used 17K cDNA microarrays  that included 15,720 unique genes. Of these genes, 11,552 were mapped by SOURCE, a web based database provided by the Genetics Department of Stanford University . Microar2 (R/G) intensity ratio from the log2 transformed data.Fluorescent spot signals were obtained by subtracting background intensity from the total spot intensity. The genes with missing values for more than one of the experiments were removed for further analysis. The variation from the different labeling efficiencies was corrected using within-slide global normalization, which subtracted the median of log2(R/G) < 0.3 (mean ± 1SD), while the changes in the sex chromosomes were -0.64 < log2(R/G)< 0.64. Hence, cut-off values of log2(R/G) > ± 0.64 (mean ± 1SD) as amplification or deletion, log2(R/G)> ± 0.3 (mean ± 1SD) as gain or loss were determined.The obtained data were analyzed using the Chromosome Analyzer and Map Viewer using S-plus (CAMVS) developed by the Cancer Metastasis Research Center (CMRC), Yonsei University College of Medicine, Seoul, Korea. To evaluate the general genetic pattern of whole chromosome, 0.025 span was introduced to give the weighted mean through the neighboring 250 probes, resulting in the advantage of evaluation of ratios of 250 proves simultaneously. Smoothing line is based on the more weight on the nearer probes. In this study, we used the predetermined cut-off value for the significant copy changes by comparing the level of genetic changes using normal tissue DNAs including placenta, lymphocyte and gastric tissues in the previous study . Based oThe Significant Analysis of Microarray (SAM) was used to identify the specific genes that showed differences between the groups  with morGYL performed the experiments, data analysis and drafted the manuscript. WIY was participated in case selection and pathologic review. HCJ participated in obtaining the clinical information and data analysis. SCK performed the set-up of analysis program and preliminary analysis MYS performed the set-up of microarray-CGH using gDNA from paraffin-embedded slides. CHP is participated in data analysis. HCC participated in its design and coordination, and data interpretation. SYR conceived of the study, participated in data analysis, interpretation and finalized manuscript. All authors have been read and approved the manuscript.Distinctive 36 genes between thymoma 5 subgroups. The ID indicate GeneBank ID and the symbol and the cytoband information are from their SOURCE . The incClick here for fileList of distinctive 50 genes between types A and B . The ID indicate GeneBank ID and the symbol and the cytoband information are from their SOURCE . The incClick here for fileList of distinctive 48 genes between types B  and B3. The ID indicate GeneBank ID and the symbol and the cytoband information are from their SOURCE . The incClick here for file


A common SNP upstream of the INSIG2 gene, rs7566605 , was reported to be associated with obesity  in a genome-wide association scan using the Framingham Heart Study but has not been reproduced in other cohorts. As BMI is a relatively insensitive measure of adiposity that is subject to many confounding variables, we sought to determine the relationship between the INSIG2 SNP and subcutaneous fat volumes measured by MRI in a young adult population.Functional Polymorphism Associated with Human Muscle Size and Strength, FAMuSS cohort, n = 752 volunteers 18–40 yrs). In this longitudinal study, we examined the effect of the INSIG2 polymorphism on subcutaneous fat and muscle volumes of the upper arm measured by magnetic resonance imaging (MRI) before and after 12 wks of resistance training. Gene/phenotype associations were tested using an analysis of covariance model with age and weight as covariates. Further, the % variation in each phenotype attributable to genotype was determined using hierarchical models and tested with a likelihood ratio test.We genotyped the INSIG2 SNP rs7566605 in college-aged population enrolled in a controlled resistance-training program, ; but men did not show any such association. Men homozygous for the G ancestral allele showed a loss of subcutaneous fat, while those with one or two copies of the C allele gained a greater percentage of subcutaneous fat with resistance training .Women with a copy of the C allele had higher levels of baseline subcutaneous fat (GG: n = 139; 243473 ± 5713 mmINSIG2 rs7566605 polymorphism underlies variation in subcutaneous adiposity in young adult women and suppresses the positive effects of resistance training on men. This supports and extends the original finding that there is an association between measures of obesity and INSIG2 rs7566605 and further implicates this polymorphism in fat regulation.Our results show that the  Obesity is increasing in prevalence and is an important public health problem. In the United States, 17.1% of children 2 to 19 years are overweight and 32.2% of adults aged > 20 years are obese. The prevalence of being overweight among children and adolescents and obesity among men increased significantly between 1999 and 2004 [INSIG2 promoter SNP rs7566605  was associated with increased BMI in a cohort of the 694 participants in the Framingham Heart Study [INSIG2 is a ~21.5 Kb gene located on chromosome 2q14 that has been functionally linked to lipid metabolism, most notably due to its role in endogenous cholesterol and fatty acid synthesis feedback inhibition [INSIG2 in regulating triglyceride levels in rats [Herbert et al.  conducteINSIG2 gene region and with obesity related traits such as fat depots  and serum cholesterol levels [Data mapping of quantitative trait loci in mice (QTL) for obesity related traits and their response to high fat diet data have shown linkage to the l levels .INSIG2 rs7566605 with markers of obesity in humans, with ten publications reporting inconsistent findings [et al. [et al. [A series of follow-up studies sought to validate the association of findings -18. Consfindings  used thefindings  French D [et al. . The ana [et al.  did not  [et al. . Many ofINSIG2 rs766605 have been performed on patient cohorts or relatives of probands recruited because of end stage disease phenotypes such as hypertension, heart disease, cancer, type 2 diabetes mellitus and have used the relatively non-sensitive and non-specific phenotype of BMI as a measure for obesity [INSIG2 genotype and subcutaneous fat. We also opted to stratify for gender, and limit to Caucasians, given variability seen in previous associations of this SNP. Finally, we examined if INSIG2 genotype affected change in subcutaneous fat with resistance training.Most of the previous studies on  obesity -18. We s obesity -22, to tThe FAMuSS cohort is described elsewhere . BrieflyResistant training (RT) was performed with the non-dominant arm (trained arm). The protocol was described elsewhere . Brieflyth slice going from the top of the arm toward the elbow. Scans for both arms were taken by Fast Spoiled Gradient Recalled (FSPPGR) and Fast Spin Echo (FSE) with TE 1.8/TR 200 msec. All 8 centers submitted the MRI data to the Research Center for Genetic Medicine at Children's National Medical Center (CNMC) in Washington, D.C, via e-mail or DAT disc, and all scans were integrated into the study SQL database.In the FAMuSS study, we were only able to obtain useable MRI images from 517 of the individuals enrolled due to the quality of some of the scans , a PC based software that allows the semi-automatic quantification of muscle, bone and subcutaneous fat ,23. VoluBlood samples were obtained from all individuals in EDTA anticoagulant, sent to the coordinating site in Washington, DC, without subject identification. The DNA was isolated using Puregene kits . Genotyping was done using a TaqMan allele discrimination assay that employs the 5' nuclease activity of Taq polymerase to detect a fluorescent reporter signal . Both alleles were detected simultaneously using allele-specific oligonucleotides labeled with different fluorophores, and genotypes determined by the ratio of the two fluorophores used. The PCR for the each SNP contained 20 ng of DNA, 900 nM primers, 200 nM probes, and TaqMan Universal PCR Master Mix, No AmpErase UNG Applied Biosystems, Foster City, CA) in a final volume of 15 μL. PCR was performed on an MJ Research Tetrad thermal cycler . The PCR profile was 10 minutes at 95°C (denaturation), 44 cycles of 15 seconds at 92°C, and 1 minute at an annealing temperature of 60°C. Reactions were set up using a MWG robot, and fluorescence ratios and allele calling done using an ABI 7900. To test for reproducibility of our genotyping assay, 96 DNA samples were tested three times independently, and genotypes compared. There was 100% concordance of genotypes in all three replicates, with no dropouts, no calls, or ambiguous calls.All analyses were stratified by gender as this is a well known factor in genotype-phenotype correlations in anthropomorphic measures, such as adiposity. Hardy-Weinberg equilibrium was tested using a Chi-square analysis with one degree of freedom. Statistical tests of normality  showed minor deviations from normality, but visual inspection of phenotype histograms showed normal distributions. This and the relatively high n's for our sample led us to use parametric tests. Associations between the SNP and each phenotype were testing using ANCOVA. Bivarate correlations between phenotype and body weight and age showed strong relationships; therefore each model included baseline body weight and age as covariates. The % variability attributable to genotype was determined by comparing the full model (containing genotype and covariates) with the constrained model (containing covariates only). Likelihood-ratio tests were done to determine if the variability attributable to genotype was statistically significant. Analyses used a nominal p-value of 0.05 as significant. To address to some extent the problem of multiple testing, we compared our unadjusted p-values to a significance level of 0.007 (Bonferroni-adjusted for 7 phenotypes tested). All analyses used Stata V10 .In our population of individuals that are of European descent (Whites), the G allele frequency was 0.69 and C allele was 0.31 . This matches well with the allele frequencies from the HAPMAP data set that reports an allele frequency for the G allele of 0.725 and a frequency of 0.275 for the C allele in the thirty U.S. trios provided samples, which were collected in 1980 from U.S. residents with northern and western European ancestry by the Centre d'Etude du Polymorphisme Humain. The rs7566605 variant was in Hardy Weinberg Equilibrium in our population (p = 0.3).3 and gained an average of 5900 ± 31141 mm3 (2.2%) of subcutaneous fat after the 12 weeks exercise intervention. Similar values were obtained for both trained and untrained arms, indicating a systemic effect of exercise on subcutaneous adiposity. The untrained arm showed a mean baseline volume of 2601186 ± 120065 mm3 with a gain of 3668 ± 29388 mm3 (1.4%) that was not significantly different from the trained arm. As expected, men showed less baseline subcutaneous fat than women (172577 ± 90778 mm3). In addition, men gained 2.5% subcutaneous fat volume (4368 ± 24211 mm3) with single arm resistance training after the 12 wk intervention. The untrained arm showed a mean baseline volume of 176227 ± 97396 mm3 with a gain of 312 ± 29076 mm3 (0.2%) that was not significantly different from the trained arm.For subcutaneous fat in the upper trained arm Table , women hINSIG2 SNP was associated with baseline subcutaneous fat volume . Women that were homozygous for the G allele had 9.3% less subcutaneous fat than women in the trained and untrained arm with a copy of the C allele   have varied from cohort to cohort  association with BMI in 5 cohorts out 8. The authors suggested that the effect of SNP rs7566605 on BMI may be heterogeneous across population samples. However, in the same paper, a deCode cohort  showed a strong negative association between BMI and rs7566605. Our findings are in general agreement with Lyons et al. where the C allele in young adult females is associated with increased adiposity as measured by subcutaneous fat volumes of the arm. In our young adult cohort, we did not show a significant association between BMI and rs7566605 for either sex . Subcutaneous fat volumes are thought to be a better measure of adiposity in young populations, while BMI becomes a better phenotype in older adults (see text below). Previous studies of associations of rs7566605 used BMI, studied older cohorts, and generally did not stratify by gender.We found the rs7566605 C allele to be associated with increased baseline levels of subcutaneous fat in women but not men Figure . Our finThe recent articles have suggested there appear to be significant gender differences in the storage and utilization of fats . CompareINSIG2 C carriers, in that even limited localized exercise may trigger an increase in adiposity. This finding is similar to a study of PPARα L162V polymorphism in this same population, where male carriers of the rare allele also gained considerably greater adiposity following unilateral resistance training of one arm [INSIG2 polymorphism, the increased subcutaneous fat gain in men was exacerbated by an unexpected decrease in muscle strength by isometric measures. Our findings agree with a previous study that examined rs7566605 in 293 obese children  who participated in a 1-year exercise intervention [Particularly interesting is our finding of the associations of rs7566605 C allele with loss and gain of subcutaneous fat and muscle strength after a unilateral resistance training of the arm; however these associations were not significant if adjusted for multiple testing. Men generally gained subcutaneous fat volume in both trained and untrained arms 2–3% gain), but men with one or more copies of the C allele showed 6–7% gains  but measured the subcutaneous fat in the upper arm. Multiple studies have pointed out that VAT is more closely associated with metabolic syndrome markers , and thaINSIG2 genotype. We need further studies to pinpoint the effect that INSIG2 genotype has on the response of fat to exercise and our exercise study did not control for diet. However, this may be one of the first studies to examine the effect of a genotype on the response of fat to exercise and with this data we may begin to unravel genetics behind the differences in fat response to exercise.The findings reported here show that a part of inter-individual response to resistance training can be explained by The authors declare that they have no competing interests.FS designed the study, designed genotyping assay, genotyped families, analyzed data, and drafted the manuscript. HD did statistics analyzed and interpreted data and drafted the manuscript. PMC, PDT, TJA, PMG, NMM, LSP, PSV, RFZ, RLS collected the subjects, and the clinical data, supervised exercise trainings. BH assisted genotyping, analyzing data. JMD and EPH designed the study, collected samples, and drafted the manuscript. All authors contributed to the final critical revision of the manuscript.The pre-publication history for this paper can be accessed here:INSIG2 rs7566605 in the FAMUSS cohort. Associations of The data provided represents the statistical analysis of associations with rs7566605 in FAMUSS cohort.Click here for fileINSIG2 variant in the literature up to March 01 2008.Summary Table of Results for the  This worksheet summarized and compares previous study results on INSIG2 rs7566605.Click here for file


FST and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the "answers". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well.Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index  It is desirable to include related cases in case-control studies because pedigrees of multiple affected individuals have a higher expected frequency of susceptibility allele(s), leading to increased power . Severaln1 cases and n0 controls (n1 + n0 = n) with corresponding allele frequencies p1 and p0 and common frequency p under the null hypothesis of no association. There are m cases from sibships with at least two sibs and n1 - m independent cases. At the candidate locus, each individual has two alleles, Xi1 and Xi2  coded as 0/1. Usually only the genotype Xi. = Xi1 + Xi2 is known. For all individuals the affection status yi = 0/1 is given. The cases from families comprise k = 1,...,K sibships of size mk, and zi denotes the sibship of individual i. For the cases, the Xij values have a Bernoulli(p1) distribution. Cases from different sibships are assumed to be independent, cases from the same sibship are not independent. To describe the correlation structure between sibs we use a model from population genetics that considers a population consisting of different subpopulations based on the coefficient FST and the inbreeding coefficient FIT. Sibships are regarded as small subpopulations and FST denotes the correlation between two randomly chosen alleles of two individuals from the same sibship. Under the assumption of no population structure, correlations within sibships only arise from IBD sharing between sibs and FST equals the expected kinship coefficient between two siblings. FIT measures the correlation of the two alleles within an individual and equals 0 under assumption of random mating and no further population structure.The study sample contains FST and FIT, the true variance of the numerator of the allelic χ2-test statistic can be calculated. One component is the sum of all alleles from cases of sibships Based on the correlations γ, is the variance inflation in comparison to the variance of the sum of alleles from independent cases. If the data set only consists of affected sib pairs, the inflation factor simplifies to γ = 1 + FIT + 2FST. The total numerator can be expressed as the estimated allele frequency difference between cases and controlswhere the term in square brackets, in the following denoted by Under the null hypothesis of no association, its variance can be derived by dividing the sum of alleles within cases into two parts: one for affected sib pairs and one for independent cases, leading toγ for the allelic χ2-test Varγ=1(T) is defined as λ = VarγT/Varγ=1T.The inflation Var(T), different strategies for determining FST and FIT were investigated. In strategy I ("no linkage") FST is directly calculated under the assumptions of no linkage and FIT = 0. Here FST corresponds to the prior kinship coefficient of a sib pair. FST = 0.25, since 2FST is the probability that two alleles from the same parent of a sib pair are IBD. In the two other strategies FST is estimated to account for regions of linkage where the true FST is larger than 0.25.To estimate FST and FIT are estimated by analysis of variance based on the marker data of the affected sibships at the candidate locus [FST and FIT can be expressed as ratios of variance components, estimates for FST and FIT can be derived as functions of the sums of squares.In strategy II ("ANOVA") te locus . This stFST estimate assuming FIT = 0, requiring genotype information at adjacent markers, e.g., for cases previously analyzed for linkage with these markers. FST can be directly estimated from the estimated mean number Y of alleles IBD within the affected sib pairs. The expectation of Y can be expressed as E(Y) = 2N·2FST, where 2FST, is the probability that such an allele pair is IBD. The estimated number Y of alleles IBD has to be calculated from individual IBD estimates. If there are only affected sib pairs in the data (N = K), Y can be derived from the nonparametric linkage-score (NPL- or Z-score), which is then equivalent to the classical mean test statistic Strategy III ("MULTI") uses a multipoint FST = 0.25, for strategy II FST was estimated in the ANOVA framework implemented in R, and for strategy III we calculated NPL-scores with Merlin.To evaluate the strategies we implemented the test statistic in the computer program R. For strategy I χ2-test based on allele frequencies ignoring familial correlations. We compared our results to Plenge et al. [The proposed methods were applied to case-control data from 20 candidate genes for rheumatoid arthritis previously analyzed by Plenge et al. . The 839e et al.  who analAdditionally, the SNP genome data from Replicate 1 of the simulated Genetic Analysis Workshop 15 data were analyzed knowing the solutions. The data contain 1500 families of two parents and an affected sib pair and 2000 controls. We calculated our test statistics based on strategies I-III for all 9187 SNPs of the genome scan comparing 3000 cases to 2000 controls. Subsequently, in order to remove true associations, we excluded SNPs in a region around ±3 cM of simulated disease loci to analyze data simulated under the null hypothesis of no association but allowing for linkage. For the remaining SNPs we verified the type I error rate of the test statistics. We also analyzed chromosome 6 containing the major disease locus to concentrate the analysis on a region of known linkage.χ2-test. It shows whether these associations remain significant after accounting for the IBD sharing of the cases. In the ANOVA model FST is slightly underestimated, being below 0.25. Thus in this example the p-values for the "no linkage"-strategy are slightly more conservative than for ANOVA. The variance inflation λ of the allelic χ2-test is estimated around 1.20–1.25. The exact value depends on the strategy of estimating FST and the number of missing values. By using a significance level of 0.05, all test statistics remain significant with the correct variance estimate. If a Bonferroni corrected significance level of 0.0025 is used, PTPN22, CTLA, and SUMO4/rs237025 (unexpected direction) are significant for the two-sided allelic χ2-test. For the test statistics accounting for familial correlations, only PTPN22 clearly remains significant, CTLA is no longer significant and the p-value for SUMO4 is very close to the significance level.Table FST values for chromosome 6. As expected, the multipoint FST estimation is more stable than the single-point ANOVA method. However, even with the single-point method, the FST estimate is in most cases larger than 0.25, thus accounting for linkage correctly. For the simulated data an FST value of 0.25 leads to an inflation factor of 1.2, whereas an FST = 0.3 corresponds to λ = 1.24. Because of this small difference between the inflation factors, the method to determine FST is expected to have only a minor impact on the test statistic. After excluding regions of true associations, 9055 SNPs remained, including 627 out of 674 SNPs on chromosome 6. Figure χ2-test is far too liberal. In contrast, the observed type I error rates for the test statistics accounting for familial correlations are all very close to each other within the expected range for all significance levels up to 0.1. The separate analysis of chromosome 6 confirms that even in a region of known linkage there is only a minor difference between the three strategies, with the "no-linkage" being the most liberal.Figure χ2-test can lead to an increased rate of false positives, as indicated by the simulations and the real data analysis. All strategies to correct the variance perform quite well and lead to an almost correct type I error rate on the entire genome. In the presence of linkage, test statistics based on estimating the correlations from data are somewhat superior, but a single-point strategy based on the candidate gene data seems sufficient. Moreover, our conclusions are consistent with the simulation results of Bourgain [If related cases are included in a case-control study, the allelic Bourgain , who obsBourgain  based onThe author(s) declare that they have no competing interests.


Forage quality of maize is influenced by both the content and structure of lignins in the cell wall. Biosynthesis of monolignols, constituting the complex structure of lignins, is catalyzed by enzymes in the phenylpropanoid pathway.Zea mays L.) contrasting in forage quality traits. Six loci, encoding C4H, 4CL1, 4CL2, C3H, F5H, and CAD, displayed different levels of nucleotide diversity and linkage disequilibrium (LD) possibly reflecting different levels of selection. Associations with forage quality traits were identified for several individual polymorphisms within the 4CL1, C3H, and F5H genomic fragments when controlling for both overall population structure and relative kinship. A 1-bp indel in 4CL1 was associated with in vitro digestibility of organic matter (IVDOM), a non-synonymous SNP in C3H was associated with IVDOM, and an intron SNP in F5H was associated with neutral detergent fiber. However, the C3H and F5H associations did not remain significant when controlling for multiple testing.In the present study we have amplified partial genomic fragments of six putative phenylpropanoid pathway genes in a panel of elite European inbred lines of maize (While the number of lines included in this study limit the power of the association analysis, our results imply that genetic variation for forage quality traits can be mined in phenylpropanoid pathway genes of elite breeding lines of maize. Zea mays L.) is widely used as a silage crop in European dairy agriculture. While breeding efforts in recent decades have substantially increased whole plant yield, there has been a decrease in cell wall digestibility, and consequently feeding value, of elite silage maize hybrids .,48.28,48PAL gene was investigated in a set of 32 European elite inbred lines, overlapping with the lines used in this study [PAL, introducing a premature stop codon, was associated with high IVDOM. Two CCoAOMT genes have been investigated in a set of 34 diverse lines used in both European and US breeding programs [CCoAOMT1, an SSR-like insertion in the first exon of CCoAOMT2 was associated with an increase in cell wall digestibility. For the COMT gene, indel polymorphisms in the intron region have been associated with cell wall digestibility in two different sets of lines, one of which are overlapping with the set employed in the present study [ZmPox3 gene, encoding a peroxidase putatively involved in monolignol polymerization, was associated with high cell wall digestibility [With the results presented here, genes encoding most of the enzymes in the phenylpropanoid pathway in maize have been tested for association with forage quality traits. The is study . A 1-bp programs . While nnt study ,30. Specnt study . LikewisPAL [4CL1  based on maize mRNA sequences identified in GenBank  according to the manufacturers instructions, and sequenced directly using internal sequence specific primers and the Big Dye1.1 dye-terminator sequencing kit on an ABI 377 . Electropherograms of overlapping sequencing fragments were manually edited using the software package Sequence Navigator version 1.1 from PE Biosystems. Full alignments were built up using default settings of the Clustal program version 1.8  followedThe exon-intron structure of the amplified genomic sequences was estimated by alignment to the mRNA sequences used for primer development Table  and valiπ), the average number of nucleotide differences per site between two sequences was estimated by using DNASP Version 4.10 [π . Tajima's D statistic [r2) [PAL, COMT, CCoAOMT1, and CCoAOMT2   were choCoAOMT2 ,21, unpu, unpuπ, Structure 2.0 software [Structure applies a Bayesian clustering approach to group individual lines in subpopulations based on marker profiles. A Q matrix is produced that lists the estimated membership coefficients for each individual in each subpopulation. A burn-in length of 50.000 followed by 50.000 iterations was used. The Admixture model was applied with independent allele frequencies. Data were defined as haploid.Lines were genotyped with 101 simple sequence repeat markers (SSRs) providing an even coverage of the maize genome. The employed SSR markers are publicly available . Populatsoftware ,62. StruTASSEL to test for associations between individual polymorphisms and mean phenotypic values across five environments  were tested and the P-value for individual polymorphisms was estimated based on 10,000 permutations of the dataset. Associations were further tested by the unified mixed model method for association mapping (MLM) in TASSEL [Association analysis was carried out as a general linear model (GLM) analysis in ts Table . The Q mn TASSEL . The MLMn TASSEL  between n TASSEL  based onn TASSEL . The dian TASSEL  was applp-coumarate 3-hydroxylase, C4H: cinnamate 4-hydroxylase, CAD: cinnamyl alcohol dehydrogenase, CCoAOMT: caffeoyl-CoA O-methyltransferase, COMT: caffeic acid O-methyltransferase, DNDF: digestibility of neutral detergent fiber, F5H: ferulate 5-hydroxylase, indel: insertion-deletion polymorphism, IVDOM: in vitro digestibility of organic matter, LD: linkage disequilibrium, NDF: neutral detergent fiber, PAL: phenylalanine ammonia-lyase, SNP: single-nucleotide polymorphism4CL: 4-coumarate:CoA ligase, C3H: JRA performed the data analysis and prepared the manuscript. IZ carried out allele sequencing. GW contributed to experimental design. BD and JE provided phenotypic data. MO provided the SSR data and together with GW contributed to experimental design. TL coordinated the project and together with JRA prepared the manuscript. All authors read and approved the final manuscript.


Chronic obstructive pulmonary disease (COPD) is characterized by both airway remodeling and parenchymal destruction. The identification of unique biomarker patterns associated with airway dominant versus parenchymal dominant patterns would support the existence of unique phenotypes representing independent biologic processes. A cross-sectional study was performed to examine the association of serum biomarkers with radiographic airway and parenchymal phenotypes of COPD.Serum from 234 subjects enrolled in a CT screening cohort was analyzed for 33 cytokines and growth factors using a multiplex protein array. The association of serum markers with forced expiratory volume in one second percent predicted (FEV1%) and quantitative CT measurements of airway thickening and emphysema was assessed with and without stratification for current smoking status. Significant associations were found with several serum inflammatory proteins and measurements of FEV1%, airway thickening, and parenchymal emphysema independent of smoking status. The association of select analytes with airway thickening and emphysema was independent of FEV1%. Furthermore, the relationship between other inflammatory markers and measurements of physiologic obstruction or airway thickening was dependent on current smoking status.Airway and parenchymal phenotypes of COPD are associated with unique systemic serum biomarker profiles. Serum biomarker patterns may provide a more precise classification of the COPD syndrome, provide insights into disease pathogenesis and identify targets for novel patient-specific biological therapies. While airflow obstruction is the hallmark of chronic obstructive pulmonary disease (COPD), the distinct processes of parenchymal destruction and small airway fibrosis can induce similar impairments in expiratory flow. Varying contributions of airway remodeling and emphysema can be found in any individual, leading to significant heterogeneity in disease expression Although the presence of systemic inflammation in COPD is widely accepted The following is an abridged version of the The study protocol was approved by the University of Pittsburgh Institutional Review Board. Participating subjects provided written informed consent for research use of their CT scans and blood samples.Two hundred and thirty-four participants were selected from the Pittsburgh Lung Screening Study (PLuSS) cohort. Participants were current or former smokers ages 50–79 and were selected to represent the spectrum of visual radiographic emphysema and airflow obstruction . SubjectSpirometry was performed on all subjects upon entry into the PLuSS cohort. Testing was performed using standard methodology The subjects underwent low-dose CT examinations performed on either a LightSpeed Plus 4-detector (n = 110) or LightSpeed Ultra 8-detector (n = 124) . The CT examinations were acquired using a helical technique at 120 or 140 kVp with a mean tube current-time product of 28.9 (+7.9) mAs. Images were reconstructed contiguously at 2.5 mm section thickness with a 2.5 mm interval using a GE Healthcare high-spatial frequency kernel with a range of pixel dimensions from 0.54 to 0.98 mm.The apical bronchus of the right upper lobe was manually selected from the CT images and analyzed in cross-section. Wall area as a percentage of total airway area (WA%), which has been associated with lung function Stored serum samples were analyzed for thirty-three serum chemokines and growth factors using a bead-based cytometric immunoassay system . A detailed description of the methods of the multiplex assay performed at the Core facility has been described previously by others Continuous data were summarized as either mean ± standard deviation or median and quartiles and categorical data were expressed as percentages. The association between the forced expiratory volume in the first second percent predicted (FEV1%) and WA% and F-950 was analyzed using univariate linear regression analysis. The contribution of WA% and F-950 to FEV% was then determined using multiple regression analysis. Finally, the relationship between WA% and F-950 was assessed with the Pearson's correlation coefficient.Serum biomarkers with concentrations above or below the detection threshold of the assay were respectively assigned the highest or lowest extrapolated value for that given marker . BecauseThe selected cohort consisted of 149 current and 85 former smokers . The subBoth WA% and F-950 were correlated with FEV1% in univariate analysis  and each CT parameter independently contributed to FEV1 decline in multivariate analysis (r = 0.6). Although there was a strong association of airway thickening and parenchymal emphysema CT phenotypes with FEV1%, there was no association of the CT phenotypes to each other  suggesting that each parameter represents an independent, unique phenotype .Four of the 33 markers were modestly associated with FEV1% (p = <0.05) before and after adjustment for current smoking status . EotaxinSeven of the 33 biomarkers were associated with WA%. Interleukin (IL) -6, IL-13, IL-2 receptor, Interferon gamma (IFN-γ), and c-reactive protein (CRP) were directly associated with WA% while regulated on activation normal T cell expressed and secreted (RANTES) was inversely associated with this CT parameter . NotablyThree of the 33 inflammatory markers, IL-6, MMP-7 and tumor necrosis factor alpha (TNF-α), were associated with quantitative emphysema . While IStratified analysis by smoking status revealed a relationship between specific biomarkers and FEV1%  that difLikewise, the relationship between serum biomarkers and WA% in current versus former smokers demonstrated associations dependent on current smoking status . While IRecently, the paradigm has shifted away from viewing COPD as a single disease entity to viewing it as a heterogeneous syndrome with variable contributions of peripheral airway fibrosis and emphysema. Our study illustrates this heterogeneity in a cohort of high-risk smokers and demonstrates systemic inflammatory patterns distinctive to individual anatomic phenotypes. Although the presence of a generalized systemic inflammatory response in individuals with both stable and progressive COPD has been well established The severity of airway remodeling and parenchymal emphysema can be characterized with quantitative CT analysis. Histological-radiological correlate studies have demonstrated a relationship between the extent of low attenuation areas depicted on CT images and histologic emphysema Our findings of distinct inflammatory patterns associated with airway thickening and parenchymal emphysema supports the existence of unique biological processes contributing to the syndrome of COPD. Of the markers demonstrating a significant association with airway thickening, only EGFR demonstrated an association with FEV1 and only IL-6 was simultaneously associated with parenchymal emphysema. Because our study focused on the measurement of serum biomarkers, we can not know whether the patterns of peripheral inflammation reflect a “spill-over” of the local inflammatory milieu within the lungs or represent a synchronous, systemic molecular diatheses associated with either airway or parenchymal disease. The significant but modest associations imply that a complex interplay of chemokines and growth factors, rather than one or two inflammatory mediators, is associated with the development of individual phenotypes.In a complex disease where pathogenic processes progress after cessation of cigarette smoking Although the multiple simultaneous measurements enabled by the high-throughput nature of Luminex technology increases the probability of type I error, we did not correct for multiple comparisons in our analysis. A formal adjustment would minimize the number of false positive findings but would also increase the probability of missing clinically meaningful associations. In this exploratory study, we did not want to overlook possible relationships and instead focused on those markers that demonstrated both biological plausibility and consistency in their associations with CT parameters. For instance, we found that IL-13, a cytokine associated with lung In conclusion, our study illustrates the heterogeneity of the COPD syndrome exemplified by independent variability of airway remodeling and parenchymal destruction and demonstrates an association between distinct quantitative CT phenotypes and serum inflammatory biomarker patterns. Many of these associations varied according to current cigarette use, suggesting a complex interplay of inflammation and environment leading to the variable expression of airway disease and obstruction. The traditional definition of chronic airflow obstruction fails to recognize the diversity of biologic processes represented by varying patterns of disease expression, which most likely will vary in response to molecular therapeutics. The challenge is to develop tools that precisely classify individuals based on their unique pathophysiologic phenotypes. Whereas quantitative CT indices of emphysema and airway remodeling have been shown to correlate with physiology and histology, the identification of inflammatory markers that segregate with anatomic phenotypes further validates and facilitates more robust disease sub-classification and provides further insight into molecular-cellular mechanisms, potential therapeutic targets and easily measured surrogates of disease activity.Methods S1(0.04 MB DOC)Click here for additional data file.Table S1Distribution of eligible subjects by GOLD classification and semi-quantitative emphysema score  N = 3297(0.03 MB DOC)Click here for additional data file.Table S2Distribution of subjects randomly selected from each of the 9 strata formed by cross-classification according to GOLD and emphysema score N = 260(0.03 MB DOC)Click here for additional data file.Table S3Distribution of final subjects selected for analysis by GOLD and emphysema score N = 234(0.03 MB DOC)Click here for additional data file.Table S4Number of out-of-range (OOR) values and values assigned for 33 markers(0.05 MB DOC)Click here for additional data file.Table S5Regression co-efficients for associations between log-transformed serum analyte levels and WA and F-950(0.05 MB DOC)Click here for additional data file.