Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors are the number of alleles shared identically by descent (IBD) and the age categories at which the dependent variable is measured, allowing for dependence due to age. To identify a linked marker a rank statistic tests the influence of IBD group on phenotypic quadratic differences. No assumptions are made on normality or variances of the dependent variable. We apply our method to 71 sib pairs from the Framingham Heart Study data provided at the Genetic Analysis Workshop 13. For all 15 available markers on chromosome 17 we analyzed the influence on systolic blood pressure. In addition, different selection strategies to sample from the whole data are discussed. Long-term cohorts like the Framingham Heart Study (FHS) with regular follow-up examinations yield high-quality longitudinal data. Using phenotypic information at only one examination or an aggregate measure like the mean over time would lead to a substantial loss of information. However, for the analysis of quantitative genetic traits basically no methods for longitudinal data are available. For such data we propose a nonparametric factorial design, originally developed for clinical studies . We utilThe Genetic Analysis Workshop 13 (GAW13) data are based on the Framingham Heart Study. FHS selection criteria and study design have been previously described. Starting in 1948, 5209 subjects between the ages 28 and 62 were enrolled in the original cohort study [We considered all 15 markers from 0.63 cM to 138.03 cM on chromosome 17, where previous linkages to the region covering the angiotensin converting enzyme (ACE) gene located at 84.2 cM to 90.2 cM [n independent sib pairs are grouped in three IBD groups i , each consisting of ni pairs. For each marker and each sib pair the IBD probability distribution was determined by multipoint analysis in the complete pedigree of the original FHS data. Some extended pedigrees had to be truncated without loss of information. IBD probabilities were calculated using MERLIN [The nonparametric longitudinal allele-sharing model introduced here considers the relation between the quantitative trait and the number of marker alleles shared IBD in an independent sib-pair sample, considering the trait values at different ages of the sib pairs. In particular, g MERLIN  and grouphenotypic quadratic differences [k of IBD group i  are denoted by ϖikt = 2, where Yikt,1 and Yikt,2 are SBP-measurements at times t . There are several problems when defining time: 1) measurements are in general at 4-year interval, but the first interval is 8 years; 2) there are individuals with missing measurements; 3) the probands' ages at the first examination vary drastically, ranging from 13 to 48 years; 4) some individuals received treatment for high blood pressure. SBP under hypertensive treatment is generally lower than without treatment. Since treatment was rare in the sibships, we neglected it. In the 200 individuals of the 71 sibships, considering the measurements at the oldest ages, only 12 individuals received treatment and even fewer were treated at younger ages.The ferences  for sib t = 1: [26,30), t = 2: [30,34), t = 3: [34,38), t = 4: [38,42), t = 5: [42,46), and t = 6: [46,50). For a sib pair k with IBD group i the phenotypic quadratic difference ϖikt is accepted for a particular age group t if the pairs' mean age at the time of measurement is in the corresponding age interval.For comparability of SBP measurements within a time point, we considered age at examination rather than examination number as time and chose the age classes for sib pairs as follows: SLONGITUDINAL pairs are primarily chosen to minimize the amount of missing SBP measurements in the age groups and secondarily for small age differences within pairs. Random selection followed if necessary. This longitudinally driven strategy results in a sample independent of the considered marker. The other two genotype-driven strategies select sib pairs using IBD probabilities, thus yielding a different sample for each marker. SMAXPROB selects those sib pairs in a family who have the maximum probability for an IBD value of all pairs yielding surest classification in an IBD group. Should more than one pair be selected, the subsequent ordered selection criteria are the three criteria used for the first strategy. SEQUAL tries to optimize the factorial design by equalizing the number of pairs in the IBD groups. The expected IBD distribution is P(IBD = 1) = 0.5 and P(IBD = 0) = P(IBD = 2) = 0.25. Within a family, pairs with IBD Group 1 are deleted whenever pairs with another IBD group are available. The subsequent selection steps are as above.Since for some families several sib pairs are available, we consider three selection strategies to choose one pair per family, yielding an independent sib-pair sample. For strategy ikt, for different sib pairs. The longitudinal observations for pair k in IBD group i, denoted by ik ϖ= T, can be arbitrarily dependent.Originally the design for the described model was an experimental design for clinical studies . It assuikt by Fit, where Fit = P(X <x) + 0.5 P(X = x). No assumptions on Fit are made except exclusion of one-point distributions. There are a total number of 6n possible observations where n = Σni. The method allows for missing values [Denote the distribution function of ϖg values .In this model no distributional parameters, such as the mean, are specified. A nonparametric effect is defined by a contrast of the distribution functionspit = ∫ GdFit,G is the average of all marginal distributions over IBD groups i and age groups t. This relative effect quantifies the relation of the marginal distribution Fit with respect to G. If Fit tends to the left of G  then pit < 0.5, and likewise for pit > 0.5. pit = 0.5 indicates no such tendency. The relationship pit <pi't' indicates that Fit tends to smaller values than Fi't' with respect to G. The consistent estimator of the relative effect is based on ranks.where Fi. = Σ Fit. The null hypothesis is H0: F0. = F1. = F2. Under H0 there are no differences between IBD groups and thus no influence of the marker's IBD on the phenotypic quadratic SBP differences for sib pairs taking all longitudinal measurements into account. Rejection of H0 implies differences between IBD groups, and thus supports an influence of the marker on SBP assuming that phenotypic similarity increases with higher IBD group.Let Q is asymptotically F-distributed with appropriate degrees of freedom f1 and f2 (Rikt denotes the rank of ϖikt):To test the null hypothesis given above of no differences between IBD groups an ANOVA-like test statistic based on standardized squared differences of rank-means can be employed. This test statistic , where  with  mean over all ranks Rikt at the six time points,  the mean of the ranks  and  the mean over all ranks . The estimated degrees of freedom are  and .SMAXPROB and SEQUAL. For 71 sib pairs and six age groups there are 426 possible observations. The percentage missing values is approximately 40% and thus very high for all strategies. It is smallest for SLONGITUDINAL. This strategy also has the highest number of randomly selected pairs, using fewer selection criteria. The IBD probability, i.e., the certainty for a pair assigned to a particular IBD group for the corresponding IBD values 0, 1, or 2, is approximately 90% and thus sufficiently high for all strategies. SMAXPROB, using this, yields probabilities above 90% for all IBD values. For SLONGITUDINAL and SMAXPROB the numbers of pairs within IBD groups are approximately those expected. For SEQUALmore equal group sizes are reached with an overrepresentation of IBD group 2.The samples resulting from the three selection strategies differ by the amount of missing observations and random selected sib pairs differ with respect to IBD information and the size of each IBD group. Table p-value curves are similar across strategies, SLONGITUDINAL tends to smaller and SEQUAL to higher p-values.For each strategy and for all markers on chromosome 17 we tested for phenotypic differences between IBD groups. In order to reduce the number of missing values, pairs with less than three measurements in time were neglected. At 34.56 cM and 108.27 cM significance at 5% was reached for two strategies, with the highest peak at 34.56 cM  reported linkage on chromosome 17, but not to the ACE gene region (for a summary, see [On chromosome 17 markers with significant influence on SBP could not be identified. Previously linkage to the ACE gene  and to aThe sample selection strategies, driven by phenotype or genotype, lead to approximately 40% missing observations. This is very high and could effect the results in general. In contrast to other GAW13 groups we do not impute missing values. The described approach can handle missing data.p-value. The size of IBD groups varies drastically. SLONGITUDINAL and SMAXPROB place approximately half of the pairs in IBD group 1; SEQUAL emphasizes IBD groups 0 and 2. This results in less significant p-values for SEQUAL than for the other strategies.Also, IBD probabilities do not differ much between strategies. Thus this should not be the main cause for differences in As seen in Figure 2, the quadratic SBP differences are not normally distributed and variances between IBD groups are not equal. Therefore a nonparametric approach was necessary. Our approach can also be applied to a diallelic marker with three genotypes in an association study instead of IBD groups in a linkage study. In this context the dependent variable can also be ordinal, such as a score. We can also test for an interaction effect between age group and IBD group, appropriate for a marker influence on SBP with age of onset. In the future, we will further investigate these approaches and their properties.Our main aim was to introduce a new approach for longitudinal data, which explains the underlying example, the data of the Framingham study. We required independence of sib pairs, full genotyping in nuclear families including parents, and longitudinality SBP observations on each sib pair for at least two age categories. Thus, we could only use a small subset of the data, resulting in a loss of power. If planning a new study the design can explicitly be incorporated to make optimal use of the data. Also the effects of relaxing the requirements above can be examined, such as full genotyping also in parents for multipoint IBD determination.


High-throughput genotyping generates vast amounts of data for analysis; results can be difficult to summarize succinctly. A single project may involve genotyping many genes with multiple variants per gene and analyzing each variant in relation to numerous phenotypes, using several genetic models and population subgroups. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association.To facilitate visual display and summary of large numbers of association tests of genetic loci with multiple phenotypes, we developed a Phenotype-Genotype Association (PGA) grid display. A database-backed web server was used to create PGA grids from phenotypic and genotypic data . HTML pages were generated using Tcl scripts on an AOLserver platform, using an Oracle database, and the ArsDigita Community System web toolkit. The grids are interactive and permit display of summary data for individual cells by a mouse click . PGA grids can be used to visually summarize results of individual SNP associations, gene-environment associations, or haplotype associations.The PGA grid, which permits interactive exploration of large numbers of association test results, can serve as an easily adapted common and useful display format for large-scale genetic studies. Doing so would reduce the problem of publication bias, and would simplify the task of summarizing large-scale association studies. The advent of high-throughput technology is generating unprecedented amounts of genotypic data that are being used in association analyses for multiple phenotypes. A single project may involve genotyping many genes with several variants (such as single nucleotide polymorphisms [SNPs]) per gene and analyzing each variant in relation to numerous phenotypes. In turn, each phenotype-SNP pair may be subjected to multiple genetic models and subgroup analyses. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association within a vast sea of data. Ultra-dense genome scans using 300,000 to 1,000,000 SNPs [We are currently studying common SNPs in 200 candidate genes to test associations with alterations in echocardiographic phenotypes in participants from NHLBI's Framingham Heart Study. For each SNP, 144 statistical tests are performed: genotypes are analyzed with regard to six phenotypes  through four genetic models , with two levels of covariate adjustment  in three samples . Planned analyses of 1500 SNPs will generate nearly one quarter of a million statistical tests. Further details can be found on the CardioGenomics website [As analyses commenced, it became obvious that we needed summary methods of data distillation and presentation to highlight findings of potential importance and to identify patterns of association, such as associations limited to one of multiple phenotypes, or associations limited to one sex. Therefore, we developed an approach that displays strengths of statistical associations at a glance, and that makes supporting data available easily via graphs accessed by a mouse click.Figure The PGA grid is interactive. Clicking on a specific cell generates a plot of adjusted least squares means for the trait of interest by genotype for the corresponding genetic model. Figure The software to create PGA grids from user-supplied data  utilizes a database-backed web server. We generate HTML pages using Tcl scripts on an AOLserver platform  using anThe PGA grid was developed to summarize large numbers of phenotype-genotype association tests in a visually useful manner to facilitate interactive exploration of results. This approach could serve as a common format for large-scale association studies. Due to the large number of association tests performed, there will be many nominally significant results. One approach to multiple testing is to indicate P-values deemed statistically significant based on consideration of false discovery rates ,10. MostThe PGA grid provides a simple visual method for displaying a large number of results, potentially reduces the problem of publication bias, and simplifies the task of summarizing large-scale association studies.LVM = left ventricular mass; LVID = left ventricular internal diameter at end diastole; LVWT = sum of septal and left ventricular posterior wall thickness; FS = left ventricular fractional shortening; AoR = aortic root diameter; LA = left atrial anteroposterior dimension. M = men only; F = women only; MF = men and women.Daniel Levy: Conception of the display grid, drafting of paper, revisions to manuscriptSteven R. DePalma: Development of source code for display grid, revisions to manuscriptEmelia J. Benjamin: Conception of the display grid, revisions to manuscriptChristopher J. O'Donnell: Conception of the display grid, revisions to manuscriptHelen Parise: Statistical analyses for incorporation into display gridJoel N. Hirschhorn: Conception of the display grid, revisions to manuscriptRamachandran S. Vasan: Conception of the display grid, revisions to manuscriptSeigo Izumo: Principal investigator of CardioGenomics, funding of the projectMartin G. Larson: Conception of the display grid, development of statistical methods, revisions to manuscript."pga-grid-v1.01-src.zip" is the source code for PGA Grid, version 1.01, as a .zip archive containing 31 text files  for use with a Linux/AOLserver/Oracle/ACS web server platform. File descriptions are available in Additional File 2, pga-grid-v1.01-readme.htm. The most recent version of this software is available from Click here for file"pga-grid-v1.01-readme.htm" is an HTML-format file that lists and describes each of the files contained in Additional File 1, pga-grid-v1.01-src.zip.Click here for file


Population heterogeneity may be a significant confounding factor hampering detection and verification of late onset Alzheimer's disease (LOAD) susceptibility genes. The Amish communities located in Indiana and Ohio are relatively isolated populations that may have increased power to detect disease susceptibility genes.We recently performed a genome scan of dementia in this population that detected several potential loci. However, analyses of these data are complicated by the highly consanguineous nature of these Amish pedigrees. Therefore we applied the Combinatorial Mismatch Scanning (CMS) method that compares identity by state (IBS) (under the presumption of identity by descent (IBD)) sharing in distantly related individuals from such populations where standard linkage and association analyses are difficult to implement. CMS compares allele sharing between individuals in affected and unaffected groups from founder populations. Comparisons between cases and controls were done using two Fisher's exact tests, one testing for excess in IBS allele frequency and the other testing for excess in IBS genotype frequency for 407 microsatellite markers.In all, 13 dementia cases and 14 normal controls were identified who were not related at least through the grandparental generation. The examination of allele frequencies identified 24 markers (6%) nominally (p ≤ 0.05) associated with dementia; the most interesting (empiric p ≤ 0.005) markers were D3S1262, D5S211, and D19S1165. The examination of genotype frequencies identified 21 markers (5%) nominally (p ≤ 0.05) associated with dementia; the most significant markers were both located on chromosome 5 (D5S1480 and D5S211). Notably, one of these markers (D5S211) demonstrated differences (empiric p ≤ 0.005) under both tests.Our results provide the initial groundwork for identifying genes involved in late-onset Alzheimer's disease within the Amish community. Genes identified within this isolated population will likely play a role in a subset of late-onset AD cases across more general populations. Regions highlighted by markers demonstrating suggestive allelic and/or genotypic differences will be the focus of more detailed examination to characterize their involvement in dementia. PSEN1 on chromosome 14) [PSEN2 on chromosome 1) [APOE) [APOE ε4 allele (frequency approximately 16%) [APOE accounts for less than half of late-onset AD susceptibility [With over 4 million individuals affected with Alzheimer's disease (AD) in the U.S., dementia of the Alzheimer's Type (DAT) is the leading cause of dementia in the elderly. These current estimates are projected to triple over the next 50 years as the population ages -3. AD hasome 14) -9, and posome 1) ,11 are a) [APOE) . The APOely 16%) ,14 acts ely 16%) ,16. Althtibility . Given ttibility -35. WhilThough numerous promising LOAD candidate genes have been examined, the lack of replication across studies has made a definitive declaration of their involvement difficult . GenOne challenge in performing linkage analysis in Amish pedigrees is to utilize the extensive pedigree information available while maintaining tractability of the computations. Due to their strong religious and cultural beliefs, the Amish very rarely marry outside of their communities, thereby promoting a genetically isolated population -41. ThisCombinatorial mismatch scanning (CMS) is an alternative technique to search for IBS sharing in distantly related individuals from isolated founder populations where standard linkage and association analyses are difficult to implement. While several other methods could be implemented, this approach was used because of its simplicity in examining existing data. This method was also chosen because at the onset of analysis, we lacked the more detailed knowledge of the inter-relatedness of our sample often required to perform similar, but more sophisticated approaches within large inbred pedigrees -48. ThisThe Amish often have large sibships and extensive pedigree records that permit the accurate estimation of IBS gene sharing to be accurately evaluated. The estimated coefficient of inbreeding for the entire population is 0.0151, which is approximately equivalent to having second cousins as parents ,52. We cThe subjects included in this study are a subset of individuals described in extensive detail elsewhere . BrieflyFive Amish pedigrees were included in this study. Three families were from Elkhart and LaGrange counties in Indiana, one extended family from Adams county Indiana, and one extended family from Holmes county Ohio. The extended pedigree from Adams county has been the subject of other previous and ongoing studies of dementia in the Amish ,57. AmonFollowing informed consent, blood samples were collected from each individual and genomic DNA was extracted from blood using standard procedures. Cell lines have been initiated on most sampled individuals. All DNA samples were coded and stored at 4°C until used.®from National Diagnostics, Atlanta, GA) for ~1 hr. at 75 W. Gels were stained with a SybrGold®rinse  and scanned with the Hitachi Biosystems FMBIOII laser scanner . Marker genotyping at the Duke laboratory was performed using fluorescence imaging (Molecular Dynamics SI Fluorimager) and a semi-automated allele calling system [Markers were genotyped at both the Vanderbilt and Duke laboratories for all DNA samples. Laboratory personnel were blinded to pedigree structure, affection status, and location of quality control samples. Duplicate quality control samples were placed both within and across plates and equivalent genotypes were required for all quality control samples to ensure accurate genotyping. At the Vanderbilt laboratory, marker primer sequences were obtained from the Genome Database  or desigg system .Hardy-Weinberg equilibrium calculations were performed for each marker and Mendelian inconsistencies were identified using PedCheck in the overall dataset . Suspectp that the pattern of alleles observed in the sample would be obtained if there were truly no difference between the allele frequencies among affected and unaffected individuals. While our current sample size is adequate to detect moderate to major effects (odds ratio of >6 with 80% power), it does not preclude our ability to detect smaller effects given that these power calculations are based on the assumptions of complete independence of samples and random sampling of the population, neither of which is true.Comparisons between case and control genotype data for the 407 microsatellite markers were conducted using the R software package to perform Fisher's exact tests in r × c Contingency Tables -64. EachTo empirically evaluate the statistical significance of the p-values computed in the CMS study, we permuted our dataset. We randomly re-assigned affection status for each of the 27 individuals maintaining the original total of 13 cases and 14 controls. We then executed the Fisher's exact test using the same allele and genotype data in the original dataset for each of the 407 markers. The distribution of p-values obtained from Fisher's exact testing on 1000 randomized sets of data was then created for both the allele and genotype comparisons to assess the empiric thresholds. We would expect the Fisher's exact p-value to match the p-value within the large distribution. These permutations were needed to correct for any residual bias from unrecognized kinship correlation present.We tested 407 microsatellite markers for differences in both allele frequency and genotype frequency between Amish dementia cases and controls. We considered all pointwise p-values and have chosen to report only markers demonstrating Fisher's exact p-values < 0.05 for either allele or genotype frequency differences. This arbitrary threshold was chosen to limit the results to be displayed and to provide a reference point for discussion of markers demonstrating nominally significant  evidence of association to dementia within our population.As an example, Table We have detected a few microsatellite markers of particular interest, which demonstrate significant differences between dementia cases and controls within our Amish founder population using the combinatorial mismatch scanning approach. The CMS concept is based on excess IBS allele/genotype sharing between individuals sharing a distant set of common founders . The mosSST, the gene encoding somatostatin, which functions as a neurotransmitter in the central nervous system. Somatostatin inhibits the release of glucagon, growth hormone, gastrin, insulin, and secretin. Additional evidence for this region stems from our genome-wide linkage study within the Amish population, for which we observed a suggestive two-point lod score of 2.42 at the nearby marker D3S2398 (209 cM) [We also found evidence of allele frequency differences on chromosome 3q27.3 at D3S1262 (201 cM). One study tested for association with AD in a geographically distinct Finnish population descended from a small group of original founders . This gr(209 cM) .An additional marker demonstrating suggestive allele frequency differences is located on chromosome 19p13.2 D19S1165 at 36 cM). Hiltunen et al. had detected evidence for association at two nearby markers (D19S1034 and D19S433) spanning the region containing our significant results  at 36 cM. This asAnother region of relative interest is on chromosome 4q31.2 at marker D4S1625. This marker located on chromosome 4q at approximately 146 cM lies between two markers (D4S2394 at 130 cM and D4S1548 at 154 cM) demonstrating highly suggestive evidence for linkage within our Amish population . FurtherGiven that our data may violate assumptions  of the Fisher's exact test, we determined the empiric p-value for our results through permutation. We performed the Fisher's exact test on 1000 replicates containing the same original genotype data, but with randomized affection status. The resulting distribution of p-values was then used as an empiric measure of significance for our results Table . On the We have previously performed a genome-wide linkage screen for dementia within this population; however the complex nature of the Amish pedigrees provides a challenge for linkage analysis, given the size and number of consanguineous loops within these extended families. Accordingly the linkage analysis by itself does not allow taking full advantage of the data available to us. To examine our data more thoroughly, we performed the combinatorial mismatch scan. Both this approach and the linkage analysis utilize the high level of inter-relatedness, within the Amish population, to their advantage. The nature of the CMS analysis, allowed us to examine these data without being computationally burdened by the size or family structure of our population. These two methods complement each other by allowing the examination of the same data using both a family-based approach and a "pseudo" case-control approach to identify regions across the genome which are potentially involved in AD susceptibility. We are fully aware of the limited power of our current sample; however, these analyses should be viewed as an adjunct to our recent genomic screen.We have reported several markers across the genome  to have significant allelic and/or genotypic frequency differences between dementia cases and controls within the combined Amish communities of Ohio and Indiana. While the evidence presented here is not overwhelming for any specific region, these results must be viewed in conjunction with not only our genomic screen but with findings across other studies within additional populations. In conclusion, our results provide the groundwork for future detailed study of these regions within our growing sample of Amish individuals.The author(s) declare that they have no competing interests.JLM directed and performed some of the analyses, collated the results, and was responsible for preparing and editing the manuscript and tables therein. DWH was involved in the drafting of the manuscript and providing input on the analyses. LJ was responsible for data management and analysis. WKS provided input for the analysis, helped in editing the manuscript, and provided financial support through grant funding. KAW was key in all clinical evaluations and provided input for the manuscript. CEJ has been a longtime consultant for ascertainment and recruitment from the Amish community due to his extensive interaction within this isolated population. JMV coordinated the genotyping of the microsatellite markers used within this study. JLH and MPV are Principal Investigator (PI) and co-PI, respectively. Both PIs were instrumental in providing the infrastructure, aiding in the study design, providing input in the manuscript, and supporting this project as well as additional projects surrounding this manuscript through their grant funding.The pre-publication history for this paper can be accessed here:


Using the simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14), we investigated the ability of three bootstrap-based resampling estimators  to reduce the selection bias for genetic effect estimation in genome-wide linkage scans. For the given marker density in the preliminary genome scans (7 cM for microsatellite and 3 cM for SNP), we found that the two sets of markers produce comparable results in terms of power to detect linkage, localization accuracy, and magnitude of test statistic at the peak location. At the locations detected in the scan, application of the three bootstrap-based estimators substantially reduced the upward selection bias in genetic effect estimation for both true and false positives. The relative effectiveness of the estimators depended on the true genetic effect size and the inherent power to detect it. The shrinkage estimator is recommended when the power to detect the disease locus is low. Otherwise, the weighted estimator is recommended. After a genetic marker or candidate gene has been identified from a genome-wide scan as a putative disease susceptibility locus, it is of interest to estimate the associated genetic effect on the related phenotype. However, locus-specific effect estimates are subject to upward selection bias because of stringent test criteria adopted in genome-wide scans. Göring et al.  formallyThe simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14) provided a microsatellite marker map of 416 markers with a resolution of 7 cM and a denser single-nucleotide polymorphism (SNP) marker map of 917 markers with 3-cM density. The disease expression was under the influence of multiple genes in a complex manner. We compared performance of the two maps in multipoint linkage analysis in terms of power and localization accuracy. The main objective of this study was to further investigate the effectiveness of bootstrap resampling methods in reducing the bias of genetic effect estimates in genome-wide linkage scans. The new methods, were applied to both the microsatellite and SNP data for selected replicates. With the knowledge of the answers to the simulated data, we were able to investigate the performance of the new methods under stratification of true and false positives.To evaluate the power to detect linkage, we conducted multipoint analyses in all the 100 replicates using ALLEGRO . There wSpair as the scoring function for affected relatives. The genetic effect was measured by δ the excess identity-by-descent (IBD) allele-sharing parameter in this model. The genome-wide significance criterion was set to 2.2 × 10-5 [Zlr value of 4.09, where Zlr is the test statistic for linkage in the exponential model. In each replicate of the three populations, we identified all loci that met the significance criterion.We adopted the exponential allele-sharing model of Kong and Cox  and usedn families; we repeatedly drew random samples of size n with replacement from it. In each bootstrap replication b , the selected families constitute the detection sample, and the remaining families (out-of-sample families) comprise the estimation sample, thus providing independence within each of the B resampling replications. To reduce the upward selection bias in the genetic effect estimates of δ we implemented three bootstrap-based estimators [, an out-of-sample estimator , and a weighted estimator , where ω = 0.632 was analogous to Efron's 0.632 estimator [We implemented a simple bootstrapping method in this study. Suppose that the original dataset has timators : a shrin, at location mD, where the maximum test statistic exceeded the significance criterion in the original data. Note that the location mD was the overall gene localization and the three bootstrap-based estimators were then applied only to genetic effect estimation at this location. The shrinkage estimator was constructed by reducing the naïve estimate  by a shrinkage factor of , which was constructed by taking the average of the difference between  and  over B* bootstrap replications, with B* ≤ B, where B* is the number of replications with significant results. In bootstrap replication b,  is the genetic effect estimate at location  with the maximum significant genome-wide test statistic in the detection sample;  is the genetic effect estimate at the same location  in the estimation sample. Note that  could be different from mD. The out-of-sample estimator was the average of  at location  in the estimation sample over B* bootstrap replications. It resembles the estimate that would have been obtained in an independent sample. The weighted estimator combined  and  with the weight of ω. The weight was chosen to be 0.632, which was derived from a distance argument based on the fact that bootstrap samples are supported by about 0.632n of the original families [. Therefore, it can be considered as a variant of shrinkage estimator, with the amount of shrinkage depending on ω and . Although an adaptive choice of the weight is attractive, as in the 0.632+ method [We first obtained the naïve estimate, families . Note th+ method , time coBias reduction of the three estimators was compared according to whether the localization was a true or false positive. We classified significant findings in the 100 replicates into true or false positives, according to the answers . A true positive was defined if the detection was within 10 cM of the true disease gene location. The true genetic effects were estimated by averaging corresponding estimates from all 100 replicates.Averaging over all 100 replicates, the genome scans based on microsatellite markers at 7-cM density yielded similar performance in power and in accuracy of location estimates to those based on SNP markers at 3-cM density (Table For the microsatellite marker analysis, we used replicates 1 and 35 to illustrate the application of the three bootstrap-based estimators Table . ReplicaThe bias reductions for the SNP marker analysis are presented in Table The bootstrap-based estimators reduced the upward selection bias in genetic effect estimation for both microsatellite and SNP based linkage analysis. The performance of the three estimators differed according to true or false positive status. The shrinkage estimator had the smallest bias for false positives but over-corrected for the true positives. On the other hand, the weighted estimator had the smallest bias for true positives but under-corrected for the false positives. It has been shown that the bias depends on the power to detect linkage . In thesIn this study, our bootstrap estimators focused on genetic effect estimation for the most significant locus in a genome scan, without considering other loci that also exceeded genome-wide significance criteria. However, the underlying genetic model has multiple loci. Further research is warranted to construct a joint estimator that would simultaneously handle multiple significant loci and thereby extend bias-reduction methods to more general settings.The reliability of gene detection, the accuracy of locus-specific effect estimates, and the failure to replicate initial claims of linkage or association have emerged as major concerns in genome-wide studies. Estimation of the genetic effect for a specific locus in a genome-wide scan is subject to upward bias because of selection by strict significance criteria. This bias is most severe for locations with small genetic effect and low power. Our results indicate that, in a complex disease setting, the three bootstrap-based estimators appear to be effective in reducing the selection bias of the naïve estimator. The shrinkage estimator is recommended when the power to detect the disease loci is low. Otherwise, the weighted estimator is recommended.GAW: Genetic Analysis WorkshopIBD: Identity by descentSNP: Single-nucleotide polymorphismLYW implemented the bootstrap methods and drafted the manuscript. SSFL assisted in preparing the manuscript for publication. HSS conducted the genome-wide MS and SNP scans. LS and SBB developed the bootstrap estimators and assisted in revising the manuscript. All authors read and approved the final manuscript.


In the first part of this study we proposed a new classification approach for spinal deformities (3-DEMO). To be valid, a classification needs to overcome the repeatability issue which is inherent both in the used classificatory system and in the measured object.The aim of this study is to present procedures and results obtained within the repeatability of 3-DEMO classification for scoliosis analysis.We acquired the data of 100 pathological and 20 normal spines with an optoelectronic system (AUSCAN) and of two dummies with simulated spine deformity. On the obtained 3D reconstruction of the spine, we considered the coronal view with a spinal reference system (Top View) and its three related parameters, defined in part I, constituting the 3-DEMO classification. We calculated the repeatability coefficient for the subjects , whereas we evaluated the system measurement error calculating the standard deviation of 50 consecutive acquisitions for each dummy.Comparing the results of the two types of acquisition, it emerged that the main part of parameters variability was due to postural adjustments The proportion of agreement for the 3-DEMO parameters gives a k value above 0.8; almost 10% of patients changed classification because of postural adjustments, but none had a "mirror-like" variation nor a change in more of one parameter at a time Repeatability coefficient is lower than the previously calculated normative limits.The 3-DEMO classification has a high repeatability when evaluated with an optoelectronic system such as the AUSCAN System, whose systematic error is very low. This means that the implied physiological phenomenon is consistent and overcomes the postural variability inherent in the measured object . The third dimension today is a clinical problem to be solved every time surgery -3, braciFor a classification to be valid, it is necessary to evaluate its stability by examining the parameters variation on which this classification is based. The adoption of an optoelectronic device like the AUSCAN system guarantees a very high precision : system We included in our study 20 normal subjects : 16 femaData have been acquired with AUSCAN system, while Top View and related parameters have been calculated as described in the first part . In ordeThe measurement system variability has been evaluated by means of the standard deviation of the parameters, calculated basing on the 50 acquisitions on dummies. Subjects variability has been evaluated through the repeatability coefficient , that isThe incidence of measurement error due to the AUSCAN System on the 3-DEMO parameters evaluated on dummies Table  is very When considering a new classification of pathological processes, it is determinant to be sure that it registers pathological parameters that are stable at a short-term. Another future problem will be to monitor changes due to disease treatment and/or progression. Conceptually, there is a distinction between the method used to obtain the data, and the data themselves. The "rumour" of the measuring system must be lower than the one of the pathological process itself. In scoliosis field, where radiographic measurements are considered the gold standard, the measuring system error is classically evaluated through intra- and inter-observer variations in Cobb measurements ,17. EvenThe variations due to the measurement device are much lower than those due to the subjects: this implies that the error for the subjects is mainly due to postural adjustments. Posture is a phenomenon that should always be carefully considered when looking at patients with scoliosis: it has been studied in the past as the variation between standing and supine radiographs ,20, but The repeatability coefficient should be used as a limit for the parameters of the 3-DEMO, supposing that they overcome normative parameters , but we The new 3-DEMO morphological classification has a high repeatability when evaluated with an optoelectronic system such as the AUSCAN System, whose systematic error is very low. This means that the implied physiological phenomenon is consistent and overcomes the postural variability inherent in the measured object . If in the future alternative methods will be developed to be applied in everyday clinical usage , the repeatability of each single method needs to be assessed.


Leishmania donovani is under genetic control. A recent genome scan provided evidence for a major susceptibility gene on Chromosome 22q12 in the Aringa ethnic group in Sudan. We now report a genome-wide scan using 69 families with 173 affected relatives from two villages occupied by the related Masalit ethnic group. A primary ten-centimorgan scan followed by refined mapping provided evidence for major loci at 1p22  and 6q27  that were Y chromosome–lineage and village-specific. Neither village supported a visceral leishmaniasis susceptibility gene on 22q12. The results suggest strong lineage-specific genes due to founder effect and consanguinity in these recently immigrant populations. These chance events in ethnically uniform African populations provide a powerful resource in the search for genes and mechanisms that regulate this complex disease.Familial clustering and ethnic differences suggest that visceral leishmaniasis caused by  The parasitic disease kala-azar, or visceral leishmaniasis, is associated with liver, spleen, and lymph gland enlargement, as well as fever, weight loss, and anaemia. It is fatal unless treated. Three major foci of disease occur in India, Sudan, and Brazil. Importantly, 80%–90% of infections are asymptomatic. Understanding why two people with the same exposure to infection differ in susceptibility could provide important leads for improved therapies. We studied families with multiple cases of clinical disease from two villages in Sudan. After typing 300–400 genetic markers across the human genome, we determined which chromosomes carry susceptibility genes. We were surprised that our results differed from those published earlier for a village 100 kilometers from our site. All of these villages are occupied by people of the same ethnic group who migrated from western Sudan late last century following a major drought. We stratified our analysis by village, and used male Y chromosome markers to tag extended pedigrees. Our results suggest that recent immigration, in combination with consanguineal marriage in a strongly patriarchal society, has amplified founder effects resulting in different lineages within each village carrying different susceptibility loci. This demonstrates the importance of understanding population genetic substructure in studying genes that regulate complex disease. L. donovani species complex  occur in three foci in India/Bangladesh/Nepal, Sudan, and Brazil. Skin-test data and lymphocyte proliferation assays [2S = 34) of disease in further siblings of affected sibling pairs [SLC11A1, IL4, and IFNGR1 in controlling susceptibility to visceral leishmaniasis or post kala-azar dermal leishmaniasis in Sudan [p = 3 × 10−5) on Chromosome 22q12 controlling VL in the Aringa ethnic group. We now report on a second genome-wide scan undertaken in two villages occupied by the related Masalit ethnic group in eastern Sudan, in which we provide evidence for major loci at 1p22 and 6q27 that are Y chromosome–lineage and village-specific. Neither village provided evidence for a VL susceptibility gene on 22q12. The results suggest strong lineage-specific genes due to founder effect and consanguinity in these recently immigrant populations, and point to the potential power these chance events in ethnically uniform African populations provide in the search for genes and mechanisms that regulate this complex disease.Ninety percent of clinical visceral leishmaniasis (VL) cases caused by protozoa of the n assays –4 indican assays , providing pairs . In Sudang pairs ,8 supporng pairs , differein Sudan –12. A gein Sudan  also repp value of ≤0.01 in a second independent study. In the case of a sib-pair study, the first three categories correspond to point-wise significance levels of 7 × 10−4, 2 × 10−5, and 3 × 10−7 . To analyze our genome-wide scan data we used nonparametric linkage analysis comparing identity-by-descent allele sharing across all relative pairs in the extended pedigrees. The thresholds proposed by Lander and Kruglyak vary marginally for other relative pairs combinations [p values provided here. All p values reported here are nominal one-sided p values, except where we state that we have carried out simulations to calculate empirical p values. The nonparametric linkage analysis for the primary genome scan in Sudan provided evidence for linkage of VL to four regions on Chromosomes 1, 5, 6, and 13  across El-Rugab and Um-Salala  and 1q31.3 for El-Rugab . Refined mapping provided genome-wide [p = 1.45 × 10−5) for a major susceptibility gene at D1S2868 on 1p22 in Um-Salala, and improved the evidence  for linkage at D1S238 on 1q31.3 in El-Rugab (p = 8.74 × 10−4) affecting both villages, with genome-wide [p = 2.47 × 10−5) for a susceptibility gene at D6S281 in El-Rugab alone.The Masalit ethnic group is polygamous; males have up to four wives at any time, depending on socio-economic status. The founding population for each village comprised 10–15 related males with their families. By 1997 population sizes were ~2,000 in El-Rugab and ~1,200 in Um-Salala. Using the program Pedigree Relationship Statistical Test (PREST)  to estimome-wide  evidenceEl-Rugab A. SimilaEl-Rugab A provideome-wide  significS from the ratio of the expected (0.25) proportion of affected sibpairs sharing zero alleles identical-by-descent under no linkage to the observed proportion. This ratio indicates the risk to siblings of affected individuals compared to the general population risk. At 1p22 in Um-Salala the λS was 2.90, and 1.63 at 6q27 in El-Rugab. Thus, these loci make an appreciable contribution to the total genetic component of disease susceptibility in each village, equivalent to or greater than that observed for the major genes for VL [S = 1.83 for all families; 2.11 for families affected towards the beginning of an outbreak), or for leprosy reported from genome scans at 10p13 in India (λS = 1.66) [S = 2.21) [To determine the contribution that loci at 1p22 and 6q27 make to genetic susceptibility to VL in each village, we estimated  the locus for VL  at 22q12 = 1.66)  and 6q25 = 2.21) .p = 1.7 × 10−7), with locus-specific λS 5.07 at D1S2868. Consistent with the result obtained using stratification by village, neither E3b1 nor A3b2 lineages from El-Rugab village showed linkage to disease at 1p22 (p = 1.68 × 10−5), locus-specific λS 2.31 at D6S281. Again, neither E3b1 nor A3b2 lineages from Um-Salala village showed linkage to disease at the major peak at D6S281 (Genethon cM position 186.7) on 6q27  obtained when the real data were re-analysed with inbreeding loops added to the pedigrees. For the peak at 6q27 in El Rugab, the point-wise empirical p value was <1 × 10−4 , again broadly in line with the LOD score 2.84 (p = 1.5 × 10−4) obtained for real data re-analysed with inbreeding loops added to the pedigrees. Given the increased locus-specific λS values observed with the stratified analyses, the susceptibility loci at 1p22 in Um Salala and 6q27 in El Rugab make a substantial contribution to the genetic susceptibility to disease in E3b1-tagged lineages in these villages. In our analysis of population substructure we also sequenced the hypervariable HVS-I region [To understand more about population substructure within villages, we genotyped Y chromosome markers  for all  at 1p22 C. The pe at 1p22 B for El- on 6q27 C. Althou on 6q27 , and hen on 6q27 . For theificance . This isI region  of the mOne interpretation of the Y chromosome stratified data could be parent-of-origin effects. We looked at this in two ways: (i) by comparing identity-by-descent allele-sharing for maternally versus paternally derived alleles across all affected relative-pairs in the pedigrees ; and ii. NeitherHere, we identified major lineage-specific genes at 1p22 and 6q27 controlling VL in adjacent Masalit villages in eastern Sudan. We hypothesized that chance events in the founding males and their families carried specific susceptibility alleles into each village, and that consanguineal marriage within patriarchal communities has enriched these alleles, leading to strong lineage-specific effects. This explains failure to replicate the major linkage peak on Chromosome 22q12 , althougp = 3 × 10−5) reported by Bucheton et al. [p value <1 × 10−5 associated with the LOD score of 5.65, providing genome-wide significance for a visceral leishmaniasis susceptibility locus on Chromosome 1p22. Similarly, an empirical p value <1 × 10−4 was associated with the LOD score 3.74 for a locus at 6q27. Hence, we are confident that these two regions of the genome carry susceptibility genes for visceral leishmaniasis in this region of Sudan, and we have supporting evidence that a gene at 6q27 may also contribute to susceptibility to visceral leishmaniasis in Brazil [Another important component of our study was that, in our analyses, we also took into account consanguinity and over-relatedness of parents within the pedigrees, which can lead to type I errors and inflated LOD scores ,23. It in et al.  for Chron et al. , who spen et al. . In addin Brazil .DR1, which encodes the downregulator of transcription 1/TBP-binding negative cofactor 2, and inhibits transcription by binding to the TATA box binding protein [GLMN, also called FKBP-associated protein FAP48), which is antiproliferative for T cells[(GFI1), which influences myeloid differentiation [(Drosophila) (DLL1) and proteasome subunit beta-type 1 (PSMB1) are also at 6q27. Proteasome function is important in degradation of proteins by antigen-processing cells, which use the Notch pathway to instruct T cell differentiation [L. donovani [Interesting candidate genes are located at 1p22, including  protein  (TBP) lo protein . Other 1r T cells, and grontiation . The Notntiation . Specifidonovani . IdentifL. donovani s.s., L. infantum and L. archibaldi [http://www.qiagen.com). Ethical approval for the study was obtained from the Institutional Review Board of the University of Khartoum, Khartoum, Sudan. Informed consent for sample collection was obtained from adults, and from the parents of children <18 y old.The study was carried out in two villages, El-Rugab and Um-Salala, on the eastern bank of the River Rahad  in the heart of the endemic area in eastern Sudan . They archibaldi . Parasit  for 360 out of 400 microsatellite markers typed for the primary genome scan. Additional families were used to complement the data at later stages of the study, but these did not have sufficient power to independently replicate linkage. Multicase families with VL were ascertained from epidemiological and medical records of the Institute of Endemic Diseases and Médecins Sans Frontières. Families were pursued where records indicated two or more cases in the family. Households generally comprised a senior male with his sons, together with their (multiple) wives and families. When available, both fathers and mothers of families with VL cases were interviewed to determine pedigree structure, with DNA collected from those parts of the pedigree that were informative for linkage. A total of 69 nuclear families from 57 pedigrees were used at different stages of the study . Of thesAll of the individuals classified as affected in these families were diagnosed with clinical VL that responded positively to specific anti-leishmanial treatment. In this respect our affection status may be less severe than that used by Bucheton et al. , who rep  were used for analysis of the primary genome scan. Refined mapping was performed on these families by successful genotyping and PedCheck  of 46 additional microsatellite markers in regions positive for linkage at p < 0.05. To complement data from the primary genome scan, 32 markers in positive regions from the ABI medium density map, and the 46 additional markers, were successfully genotyped and PedChecked  in additional families  of these markers that were successfully genotyped  and verified using PedCheckfamilies . Tests fhttp://www.appliedbiosystems.com) were designed to detect SNPs M02, M13, M40, M42, M78, M89, M118, M144, M145, M148, M171, M181, M215, and M224 as defined by Underhill et al. [SNaPshot assays using the ABI PRISM SNaPshot Multiplex System  where there was one missing parent, all had a minimum of two offspring and most  had three to six offspring. Two families with three  and four (El-Rugab) offspring had both parents missing. Allele frequencies for the microsatellite markers were calculated separately for each stratified analysis in SPLINK , which uses unrelated individuals in the pedigrees to calculate frequencies. Information content for markers was estimated in ALLEGRO. All LOD scores reported are multipoint. Simulations (100) performed within ALLEGRO using data for a typical set of six linked polymorphic microsatellite markers  showed that the primary genome scan family set  had >96% and >97% power to detect a major gene at an allele-sharing LOD score = 2.07; p = 0.001 for El-Rugab and Um-Salala, respectively.Nonparametric multipoint linkage analyses were performed in ALLEGRO , with reree size . The Spamily set  across b , which maximizes the parametric LOD score with respect to penetrances and disease allele frequency, and is a further development of GENEHUNTER-IMPRINTING  based on the original GENEHUNTER version 2.1 [Multipoint analysis was performed under trait models with and without imprinting. To allow for imprinting, two penetrance parameters  were specified for individuals who are heterozygous, one for paternal origin and one for maternal origin of the disease allele. The four penetrance parameters are f+/+, fm/+, f+/m, and fm/m, where + specifies the wild-type allele and m the mutant allele, and the paternally inherited allele is listed first. In conjunction with the MOD score, the analysis yields the penetrances and disease allele frequency (p) of the best-fitting trait model. The estimate of the disease allele frequency obtained by MOD-score analysis has the largest variance of all trait-model parameters [D, and an index of imprinting I. D is positive (dominant model) if both heterozygote penetrances equal fm/m, negative (recessive model) if both heterozygote penetrances equal f+/+, and zero (semi-dominant or additive) if the average of the two heterozygote penetrances is halfway between the two homozygote penetrances. I is positive  or negative  if one heterozygote penetrance equals f+/+ and the other fm/m, and zero (no imprinting) if both heterozygotes penetrances are equal. A comparison of the difference in peak MOD scores obtained with or without imprinting provides an additional test for presence of imprinting [Parametric linkage analysis was performed using GENEHUNTER-MODSCOREsion 2.1 . Multiporameters , and canrameters . The MODprinting . Simulatprinting  and Hodgprinting  show thaprinting  propose printing  evidenceA second evaluation of parent-of-origin effects or imprinting was determined by comparing sharing of maternally derived alleles identical-by-descent with paternally derived alleles identical-by-descent in affected relative pairs using the program MERLIN  that allS from the ratio of the expected (0.25) proportion of affected sibpairs sharing zero alleles identical-by-descent under no linkage to the observed proportion across all pedigrees, as determined using affected sibpair linkage analysis in GENEHUNTER .To estimate the contribution that specific loci make to the genetic component of disease susceptibility, we estimated  the locuP0, P1, and P2 of two individuals sharing 0, 1, and 2 alleles identical-by-descent over the 360 microsatellite markers successfully genotyped in the primary genome scan, or 78 markers successfully genotyped in all families during refined mapping. Unrelated individuals should have probabilities 1, 0, and 0, respectively. Full sibs have P0 sharing probabilities of 0.25; half sibs plus first cousin 0.375; grandparent-child, avuncular or half-sib 0.50; double first-cousins 0.5625; first cousins or half-avuncular 0.75; half first cousins 0.875; and second cousins 0.9375. We therefore considered any relationship between parents in the families with P0 sharing probabilities <0.95 to be indicative of a consanguineal marriage, with predicted relationships as outlined in p values associated with the observed LOD scores for linkage to VL susceptibility.The program PREST  was usedhttp://www.who.int/tdr/diseases/leish/default.htm. Pregap and Gap4 programs are available at http://www-gap.mcs.st-and.ac.uk/Download/index.html. Information about the Applied Biosystems medium density map and microsatellite markers is available at https://products.appliedbiosystems.com/ab/en/US/adirect/ab?cmd=catNavigate2&catID=600770. Details of the SPLINK program are available at http://www-gene.cimr.cam.ac.uk/clayton/software/splink.txt.World Health Organization data on global distribution, prevalence, and incidence of visceral leishmaniasis can be found at Table S1(63 KB DOC)Click here for additional data file.Text S1(30 KB DOC)Click here for additional data file.http://expasy.org/sprot) accession numbers for the proteins discussed in this paper are DLL1, O00548; DR1, Q01658; GFI1, Q99684; GLMN, Q92990; PSMB1, P20618; and TBP, P20226.The Swiss-Prot database (


Systemic biomarkers provide insights into disease pathogenesis, diagnosis, and risk stratification. Many systemic biomarker concentrations are heritable phenotypes. Genome-wide association studies (GWAS) provide mechanisms to investigate the genetic contributions to biomarker variability unconstrained by current knowledge of physiological relations.We examined the association of Affymetrix 100K GeneChip single nucleotide polymorphisms (SNPs) to 22 systemic biomarker concentrations in 4 biological domains: inflammation/oxidative stress; natriuretic peptides; liver function; and vitamins. Related members of the Framingham Offspring cohort  had both phenotype and genotype data (minimum-maximum per phenotype n = 507–1008). We used Generalized Estimating Equations (GEE), Family Based Association Tests (FBAT) and variance components linkage to relate SNPs to multivariable-adjusted biomarker residuals. Autosomal SNPs  meeting the following criteria were studied: minor allele frequency ≥ 10%, call rate ≥ 80% and Hardy-Weinberg equilibrium p ≥ 0.001.GEE, 58 SNPs had p < 10-6: the top SNPs were rs2494250 (p = 1.00*10-14) and rs4128725 (p = 3.68*10-12) for monocyte chemoattractant protein-1 (MCP1), and rs2794520 (p = 2.83*10-8) and rs2808629 (p = 3.19*10-8) for C-reactive protein (CRP) averaged from 3 examinations (over about 20 years). With FBAT, 11 SNPs had p < 10-6: the top SNPs were the same for MCP1 , and also included B-type natriuretic peptide  and Vitamin K percent undercarboxylated osteocalcin . The peak LOD (logarithm of the odds) scores were for MCP1  and CRP  concentrations; of note the 1.5 support interval included the MCP1 and CRP SNPs reported above (GEE model). Previous candidate SNP associations with circulating CRP concentrations were replicated at p < 0.05; the SNPs rs2794520 and rs2808629 are in linkage disequilibrium with previously reported SNPs. GEE, FBAT and linkage results are posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.With The Framingham GWAS represents a resource to describe potentially novel genetic influences on systemic biomarker variability. The newly described associations will need to be replicated in other studies. There is intense clinical and research interest in blood and urinary biomarkers to diagnose disease, to risk stratify individuals for prognosis and potential intervention, and to provide insights into disease pathogenesis . Hence, In the present analysis, we examined biomarkers involving four biological systems: inflammation, natriuretic peptides, hepatic function, and vitamins. Circulating inflammatory, natriuretic peptides -5, hepatBecause of their prognostic importance, there has been interest in understanding the environmental and genetic factors contributing to interindividual variability in systemic biomarker concentrations. Prior reports support the heritability of systemic biomarker concentrations reflecting inflammatory processes ,15, natrThe biomarkers were assessed in the Framingham Offspring sample, which is described in the Framingham 100K Overview . Brieflyhttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.Biomarkers were measured on morning specimens after an overnight fast  between 7:30 and 9:00 am. EDTA and citrated blood collection tubes are centrifuged in a refrigerated centrifuge immediately after venipuncture. Serum blood collection tubes sit for 30 minutes after venipuncture to allow for complete clotting. Specimens are processed immediately after centrifugation. Blood samples were centrifuged and frozen at -20° (examination 2 through 4) and -80° (examinations 5 through 7). The measurement of the inflammatory markers is detailed in the inflammatory marker manual at the National Center for Biotechnology Information Inflammatory biomarkers (except CRP) were measured in duplicate with commercially available ELISA kits: R&D Systems , Bender MedSystems (CD40 ligand), Oxis (myeloperoxidase), and BIOMEDICA (osteoprotegerin). High-sensitivity CRP was measured in 2002 and 2004 on examination cycle 2, 6 and 7 specimens with a Dade Behring nephelometer; the less sensitive Hemagen assay was used in 1998 for examination cycle 5 specimens. Natriuretic peptides were measured by Shionogi using a noncompetitive high sensitivity immunoradiometric assay  was associated with MCP1 concentrations in unrelated participants [CCL2 association may stem from the current report having a smaller sample size (n = 989), using different genetic markers, and being conducted with an additive genetic model in related participants, as opposed to the prior study using unrelated participants (n = 1602) with recessive and dominant models. In a recent meta-analysis of phenotype-genotype association studies, only about one third (8 of 25) of the associations examined were replicated [Our efforts to compare our findings with associations previously reported in the literature underscore some of the challenges in genetic association studies. The above we , as wellabove we , have prabove we  and a siage peak  in Framiicipants ; the assplicated . There aplicated ,74.th and 6th examinations, which may have introduced a survival bias. In addition, our study was susceptible to false negative findings because of the moderate size of the cohort; we lacked power to detect modest associations. Conversely, similar to most GWAS, the reported associations and linkage may represent false positive findings from multiple statistical testing.The strengths of the present study include a comprehensively characterized community-based cohort, with biomarker phenotypes routinely assessed with careful attention to quality control. However, the cohort was largely middle-aged to elderly, and white of European descent, so the findings may not be generalizable to individuals who are younger or of other ethnicity/racial descent. DNA was collected at the 5The Framingham GWAS and the web posting of the unfiltered results represent a unique resource to discover potentially novel genetic influences on systemic biomarker variability. We acknowledge that the newly described associations will need to be replicated in other studies.bp, base pair; Chr, chromosome;CRP, C-reactive protein; FBAT, Family Based Association Tests; GEE, Generalized Estimating Equations; GWAS, Genome-wide association studies; LD, linkage disequilibrium; LOD, logarithm of the odds (base 10); MCP-1, monocyte chemoattractant protein-1; SNPs, single nucleotide polymorphisms.The authors declare that they have no competing interests.EJB conceived of the FHS inflammation project, secured funding, planned the analyses, drafted and critically revised the manuscript. JD assisted in planning and conducting the analyses, and in writing and critically revising the manuscript. MGL planned the FHS inflammation project including assisting in securing funding, and planned and conducted analyses. KLL assisted in planning and conducting the analyses. SLB measured the vitamin data, assisted in planning the analyses and critically revising the manuscript. DRG participated in the study design and reviewed the manuscript. SK contributed to analyses of C-reactive protein and osteoprotegerin, and reviewed the manuscript. JFK assisted in securing the funding, supervised and organized the performance of the assays and reviewed the manuscript. MJK contributed to collecting the data base and revising the manuscript. JPL provided insights into the liver function test analyses and reviewed and approved the manuscript. JBM secured funding for and oversaw measurement of high-sensitivity TNFα concentrations and reviewed and approved the manuscript. SJR contributed to acquisition of the inflammation data, reviewing, revising and giving final approval to the manuscript. JR provided critical assistance in organizing the inflammatory marker data set, conducted quality control analyses and reviewed and gave final approval to the manuscript. RS was involved in revising the manuscript critically for important intellectual content and gave final approval of the version to be published. JAV assisted in securing funding for the inflammation project and revising the manuscript. TJW contributed to the analysis and interpretation of the data, and revision of the manuscript for important intellectual content. PWFW contributed to data acquisition, revision of the manuscript and final approval of the version submitted. PAW participated in 100K study design and reviewed and approved the manuscript. RSV provided critical input in conceiving the project, securing the funding, planning the analyses and critically revising the manuscript.


PLoS Biology published a research article reporting evidence of an association between a gene called GAD2 and susceptibility to obesity . Although the genetic data were suggestive rather than conclusive, the editors and reviewers supported publication because unambiguous evidence in association studies is notoriously difficult to obtain and obesity is such a major burden on public health. Enthusiasm was heightened because GAD2 lies in a region of human Chromosome 10 that is thought to contain a gene influencing obesity, and GAD2 itself is involved in the synthesis of a neurotransmitter implicated in the regulation of food intake. Publication would allow others to test just how important the connection between GAD2 and obesity might be—and the enticing prospect was that insight into the role of GAD2 in this disease might lead ultimately to new therapeutic approaches.In December 2003, PLoS Biology, we publish a follow-up to the original study, from a separate team of researchers based at the University of California at San Francisco . Swarbrick et al. looked at different and much larger patient populations and analyzed variation within GAD2 more comprehensively, but disappointingly, the new study was not able to replicate the initial finding.In this issue of Despite the negative conclusions, however, the reviewers were no less enthusiastic about the publication of this paper than the first. One of the reasons for this is related to a problem that has beset the field of complex disease genetics for several years—a tendency towards the publication of studies that show an association.Diseases are termed complex when their etiology is influenced by many factors, both genetic and environmental. These diseases include obesity, diabetes, mental illness, and many more common ailments. But finding the genes that are involved with these diseases is an extremely difficult problem: individual genes have only a limited effect, which means that large patient populations need to be studied; specific genes might have effects only in certain ethnic groups; and statistical artifacts can be hard to eliminate. These and other problems mean that success stories are few and far between, whereas false leads have been plentiful. High-profile journals, in particular, have therefore tended to publish the positive results, whereas negative data often end up in specialist literature or, much worse, don't get published at all.Because the identification of a complex disease gene is so difficult, the first positive report is rarely definitive—replication studies that use independent populations with sample sizes large enough to detect the expected effects are necessary to bolster (or undermine) the case. A bias towards publishing positive results might therefore perpetuate the false impression that a gene is indeed associated with a disease. In a field this complex, such bias is entirely unhelpful.PLoS Biology, we've taken the view that well-designed, and high-powered replication studies can be just as worthy of publication in the journal as the initial finding of a genetic association—and PLoS's community journal PLoS Genetics takes a similar view. The new paper on GAD2 demonstrates this point—the study was judged by the reviewers to be sufficiently large and well conducted to provide a robust test of the hypothesis that variation in GAD2 is involved—above a certain minimum effect—in susceptibility to obesity. The negative conclusion suggests that researchers now need to consider possible reasons for the variability in results such as sample or phenotype differences, or else examine the possibility that other variants in the Chromosome 10 region might be involved in obesity. It might ultimately be that the initial finding simply does not hold in larger samples. That replication papers are important for the field is supported by the publication of another large study in type 2 diabetes , which included replication data, and has already been cited 28 times.Replication studies are therefore vital to the field of human genetics, and at Would editorial opinion of the paper by Swarbrick et al. have been any different if the result had been positive? We think not, because the first study left sufficient room for doubt, and a positive result could have been just as significant. In general, then, we will judge a replication paper on its merits, and consider whether the submitted work provides a major advance on the previous genetic findings.PLoS Biology and PLoS Genetics are both highly selective journals. By themselves they cannot solve the problem, and will only publish the most significant association studies—whether the results are positive or negative. Furthermore, the accumulation of genetic association data is likely only to accelerate in the coming years, thanks to major collaborative efforts such as the SNP Consortium (http://snp.cshl.org) and the International HapMap Project (http://www.hapmap.org). Public availability of these rich resources will stimulate genome-wide efforts to map the genes underlying any number of complex disorders. Data will abound, and they need to be published and also made publicly available. Most of the individual studies will not provide definitive evidence of associations, but as the data build, meta-analyses will become more and more informative.But PLoS is currently exploring the possibility of providing further publication opportunities to extend the venues that are available for association studies. As with all PLoS articles, the data and the associated papers would be freely available to read and reanalyze. With the introduction of more such open-access publications and with support from the community, it would be possible to eliminate bias in the publication of association studies for good.


GFINDer , a Web system we previously developed that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining.Analysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose, mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To allow performing phenotype analyses of inherited disorder related genes we implemented new original modules within GFINDer modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications. To achieve this we exploited, normalized and structured the information present in textual form in the Clinical Synopsis sections of the Online Mendelian Inheritance in Man (OMIM) databank. Such valuable information delineates numerous signs and symptoms accompanying many genetic diseases and it is divided into phenotype location categories, either by organ system or type of finding.New GFINDer facilitates a genomic approach to the understanding of fundamental biological processes and complex cellular mechanisms underlying patho-physiological phenotypes.Supporting phenotype analyses of inherited diseases and biomolecular functional evaluations,  Understanding clinical phenotypes through their corresponding genotypes is paramount to unveil inherited alterations that can lead to pathological processes and syndromes. However, such comprehension can be very difficult with complex disorders, which frequently present different clinical phenotypes that may result from interactions among multiple and potentially unknown genetic loci. Moreover, considerably different genetic alterations may cause very similar or even the same phenotype ,2. Thus,Nevertheless, useful clinical information related to genetic diseases is generally not easily accessible and is mainly included in free text descriptions. Hence, it is not suitably organized to be used in computational analyses. This limited availability of controlled structured phenotypic information is hampering the development of effective analytical contributions in the field.Recently, some tools have been developed to extract genetic and disease information from free text -11. ThesIn some medical areas, such as oncology, curated phenotypic information of complex genetic disorders is being collected in structured format -14. NeveThus, at present the most important curated, comprehensive, reliable, and updated source of information in human genetics still remains the Online Mendelian Inheritance in Man (OMIM) databank ,16, whicGFINDer, a Web system we previously developed for analyzing dynamically aggregated annotations of user uploaded gene lists [GFINDer modules specifically devoted to the analysis of inherited disorder related genes. These new modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications.To effectively exploit the valuable information in the OMIM Clinical Synopsis section, we first extracted phenotype and their location names and normalized them to create a term vocabulary describing phenotype and phenotype location categories. Then, we hierarchically structured these category descriptions according to increasing detail or topological levels. Finally, within ne lists , we usedAt time of writing, OMIM contained 16,199 entries, 4,570 of them included a Clinical Synopsis section orderly divided into phenotype location categories representing an organ system  or a type of finding . Under a location category of a specific OMIM genetic disease or locus entry, they are listed the phenotype manifestations in that location known to be associated with the entry. In the OMIM Clinical Synopsis sections we found 169 different names of phenotype location categories and 22,609 distinct phenotype descriptions. After location name normalization, we had 94 unique location category terms and 44 synonyms or typographical errors for 32 category terms. This was obtained by combining, for example, "Lab", "Laboratory abnormalities" and "Laboratory" categories into a unique "Laboratory" category, "Neuro" and "Neurologic" into the single "Neurologic" category, and by merging each of the "Ears", "Eyes", "Joints", "Lungs", "Muscles", and "Limbs" category names with its correspondent singular category term, respectively. Phenotype description normalization produced 20,132 unique controlled descriptions, which included 7,898 descriptions internally subdivided into up to six hierarchical levels of increasing detail. For example, the phenotype descriptions "Hearing loss, sensorineural, non-progressive, non-syndromic, congenital, greater mid frequency than low- or high-frequency loss", "Dysplasia, metaphyseal, marked, of tubular bones, in childhood", "Deafness, sensorineural, prelingual, profound", "Temperature insensitivity, distal, in some patients", and "Hemolytic anemia, following ingestion of fava beans" were subdivided and hierarchically structured into six, five, four, three, and two hierarchical levels, respectively  were involved in 462 inherited diseases, and 122  were associated with 679 different phenotypes in 63 locations. Therefore, we used the GFINDer Exploration and Statistics Genetic Disorders module to evaluate the relevant presence of genes associated with specific inherited disease phenotypes or locations within the considered cardiovascular system correlated genes (CARDIO) versus the neurobiology related genes (NEURO).To assess capabilities of implemented roarrays  focused Exploration module we observed the distribution of phenotypes and phenotype locations , "Heme" (p = 0.02206), "Heart" (p = 0.03165) and "Cardiac" (p = 0.03562) categories for the CARDIO class, and "Neurologic" (p < 0.00001), "Central nervous system" (p = 0.0002), "Behavioural/Psychiatric manifestations" (p = 0.00347), "Peripheral nervous system" (p = 0.01702) categories for the NEURO class.With the s Figure  within tGFINDer statistical analysis properly highlighted as most relevant in each gene class signs and symptoms logically pertaining to that class  and "Hypertension" (p = 0.02271) phenotypes, whereas the NEURO class included "Dementia" (p = 0.00159), "Myoclonus" (p = 0.00677), "Dysarthria" (p = 0.00884) and "Mental retardation" (p = 0.01557) phenotypes.Finally, we analyzed the phenotypes most represented in the CARDIO versus NEURO class. Focusing only on genes with phenotype annotations and on phenotype categories associated with at least two of the considered genes, s Figure . In factGFINDer Web system.Obtained results demonstrate validity of the approach for the analysis of genetic disorder phenotypes, locations and related genes that we developed, implemented and made available within the GFINDer Genetic Disorders modules implemented for the study of genetic disorder related genes.Our efforts to derive from the OMIM entries a controlled vocabulary of phenotype locations and descriptions enabled us to normalize and structure the valuable OMIM phenotypic data according to the obtained vocabulary and make them suitable for computational use. Although detailed phenotype descriptions could be further homogenized and standardized, their subdivision in hierarchical levels of detail that we performed allows to group specific phenotypes according to their common general traits, without loosing their specific characteristics. So, for example "Mental retardation, moderate" and "Mental retardation, nonspecific" can be both generally considered as "Mental retardation" and at the same time they can be treated as different types of mental defects. This provides the chance to modulate analysis granularity when searching for phenotypic traits shared among multiple diseases or genotypes. It also ensures more significant and clear results when categorical statistical analyses are performed at lower granularity levels of detail. Such interesting feature, proper of the hierarchical structure and hence belonging also to the defined phenotype location hierarchy, is exploited in the new Exploration module, the user can select the detail level of phenotype description, or topological location, at which exploring the genetic disorder phenotypic annotations associated with a considered set of genes, or explore all levels at the same time , the binomial test (which is an asymptotic limit of the first for high number of genes), and the exact Fisher test (based on a two-way table crossing gene classes and phenotype categories) were implemented [p-values relate to relevant phenotype categories for a certain class of genes. However, depending on the number of considered genes and their associated phenotype annotations, the number of performed statistical tests can be high. This can greatly increase the Type-I error associated to the tests, i.e. the probability of obtaining a significant p-value by chance when the null hypothesis is true . This requires corrections on the calculated p-values in order to obtain proper significances.Statistical techniques were used in our approach implemented in lemented ,20,21. AGFINDer several correction methods for multiple tests have been included. The simplest and most strict is the Bonferroni method that can be applied if the performed tests are independent [p-value of each test is considered significant, in such a way that the Type-I error of the whole set of tests is maintained. The correction is the following αcorrected = α / N, where N is the number of performed tests. From a practical point of view this is equivalent to keep the usual threshold α for the performed tests and apply a correction to the observed p-values such that pcorrected = N * p. However, the Bonferroni method greatly reduces the power of detecting a specific hypothesis when the number of tests increases. False Discovery Rate (FDR) and Family-Wise Error-rate (FWE), an extension of Bonferroni method, are milder corrections and they are even suitable when independence among tests does not hold [p-values such that the maximum has rank N and the minimum has rank 1. Then, the correction to be applied is pcorrected = p * N / rank(p), except for the maximum p-value that is not corrected. The latter instead, in the implementation proposed by Benjamini and Hochberg [p-value correction: pcorrected = p * (N - rank(p) + 1). All three methods above illustrated are available in GFINDer. Among them the FDR, which is the mildest of the three and practically consists in defining the maximum acceptable number of obtained false-positive tests, is considered the most suitable correction method to be applied on genomic data.In ependent . It consnot hold . The forHochberg , uses thGFINDer software modules, and wrote this manuscript. OG developed GFINDer software modules devoted to the analysis of inherited disorders, and was involved in their design and testing. MMan visually inspected and normalized the OMIM phenotype vocabulary. KG supervised the statistical analysis aspects of the project. FP provided supervision and funding of the project.MMas was responsible for the overall project conception and coordination, was involved in design and testing of


Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions. Traditional approaches to this problem are 1) the Bonferroni method [p-values using various assumptions about the arrangements of markers. A key feature of this algorithm is that the marker arrangement can be completely user-specified. In particular, it is not necessary to assume that the markers are equally spaced, which is important because the p-value is sensitive to the degree of marker clustering.When many tests are conducted in a genome scan, with or without fine mapping, it is important to correct the observed i method ; and 3) i method ,5 and thi method . The IS We compared the IS approach to the Bonferroni correction and to the method proposed by Feingold et al. , which iBecause our research did not seek to examine the impact of missing data, we utilized all phenotype data in both cohorts and the complete genotype data in the 100 simulated data replicates provided. We used standard clinical criteria ,9 to def) was computed from complete nuclear family information using one marker at a time (single point). The standard ASP test statisticThe genome-wide scan for each replicate consisted of 399 markers. ASP linkage analysis was performed using GENIBD and SIBPAL  for eachN affected sib pairs was computed, and unadjusted p-values were obtained by comparing this statistic with a standard normal distribution [p-values corrected for the 399 tests. The two sampling approaches use the sex-averaged marker map, assuming a Haldane mapping function, to derive correlations in the linkage test results between adjacent markers, and they also assume Hardy-Weinberg equilibrium is in effect. These methods provide an estimate of the exceedance probability, the probability that one or more test statistics exceeded a particular observed value. These methods are based on averaging the results of independent random realizations of the same sampling experiment and give an unbiased estimate of a true exceedance probability whose variance is inversely proportional to the Monte Carlo sample size . IS differs from NS in that samples are drawn conditionally on at least one marker test statistic exceeding the threshold value. Because of this, IS is more computationally efficient when the target exceedance probability is small and the number of tests is large. Additionally, since the IS algorithm performs extremely well for small p-values, when ordinary Monte Carlo sampling tends to break down, we now have at our disposal a tool for quantifying the effect of marker clustering on the true p-value, and for determining the quality of a large deviation approximation given by Feingold et al. [p-values were also computed using the Bonferroni and FBS methods.for ribution . This sad et al. . AdjusteTo examine the rate of false positives for each method, we considered all marker loci on the even-numbered chromosomes, a total of 197 markers, which are known to be unlinked to these traits. To examine the effect of these correction methods on the power to detect trait loci, we considered markers flanking known trait loci. We selected only trait loci that contributed at least 10% to baseline effects of the underlying quantitative trait.p-value of < 0.05, averaged over 100 replicates. Experiment-wise type I error rates  it can produce with a given number of iterations, when the exceedance probability estimated is small and the number of tests is large. In this study, correcting for the 399 tests, the relative efficiencywas at least 189 for an exceedance probability of < 0.05, corresponding to a z ≥ 3.613 .The standard normal approximation to the standard ASP test statistic is valid under the null hypothesis when markers are fully informative. The fact that we obtain unadjusted While power was generally low in these data and may have been affected by the loss of information inherent in our binary trait definitions, we were able to show that the IS  method of correcting for multiple tests exhibited the same or greater power than the FBS and Bonferroni methods, while still adequately controlling type I error. When there is a marked irregularity in the spacing between markers, as would be observed in a genome-wide scan followed by fine mapping in one or several regions, the IS method has been shown to perform significantly better than the FBS method . The incp-value below the desired significance level.The critical values of the NS method, as shown in Table