We propose a nonparametric association analysis combining both family and unrelated case-control genotype data. Under the assumption of Hardy-Weinberg equilibrium, we formed an affected group to compare with a group of unaffecteds.Comparison with traditional case-control chi-square test and transmission-disequilibrium test shows that this new approach has noticeably improved power. All analysis was based on the simulated rheumatoid arthritis data provided by Genetic Analysis Workshop 15. In the situation of population stratification, we also suggest an approach to update the genotype data using principal components. However, the Genetic Analysis Workshop 15 simulation data does not simulate population stratification. All analysis was done without knowledge of the answers. Traditional linkage analysis has achieved great success in the genetic dissection of mendelian diseases caused by a single gene with large effect. However, it is well known that association analysis has more power than linkage analysis for complex diseases such as rheumatoid arthritis (RA) . Nowadayr affected siblings with genotype x1,..., xr; s unaffected siblings with genotype y1,..., ys, and parents with genotype xm and xf Here, genotypes x and y denote the number of a particular allele whose allele frequency is p. Suppose in the data there are l families with both unaffected parents, m families with one affected parent (say the mother), n families with both affected parents, and additionally unrelated cases wi, i = 1,..., u, and v controls zi, i = 1,..., v. The allele frequencies of the two groups are given by:The RA data set was simulated according to familial patterns and other environmental effects. Each of the 100 replicates has 1500 nuclear families consisting of one affected sibling pair (ASP) and their parents, and 2000 unrelated unaffected individuals as controls. Markers include 730 microsatellite markers, 9187 evenly distributed SNPs on 22 autosomal chromosomes, and 17,820 dense SNPs on chromosome 6. In the analysis, we used the first 200 families and the first 200 people of the 2000 controls. To include unrelated cases in the analysis, we randomly picked one of the two affected siblings from the next 200 families. Our final data set includes 200 families, 200 unrelated cases, and 200 controls. Among the 200 selected families, there were 56 families with a single parent and two families with both parents affected. In the most general setting, we form one group of all affected individuals consisting of affected siblings, affected parents, and unrelated cases, which was compared with a group of all unaffected individuals consisting of unaffected siblings, unaffected parents, and unrelated controls. Depending on the number of affected parents, there are three possible groupings for a family with Var(pa - pu) = Var(pa) + Var(pu) - 2Cov. Assuming Hardy-Weinberg equilibrium, each term is given below:We then use a normal test statistic s result . In partp is the estimated average allele frequency of all subjects in the data. For our final data, r = 2; s = 0; l = 140; m = 56; n = 2, and u = v = 200.And ith locus for jth individual by gij, i = 1,..., M and j = 1,..., N. Let ith locus and X = (xij) the matrix normalized by subtracting ui from each row and dividing by k largest eigenvalues λ1,..., λk with corresponding eigenvectors v1,..., vk The lth eigenvector vl =  gives the lth principal component as lth marker and jth individual.In the situation of population stratification, we suggest an approach to adjust the genotype data using principal components before the above procedures are applied. Unfortunately, the RA data was simulated without a population stratification effect, therefore we only give brief idea of this method here. The rationale of this approach is that across the genome there should be a consistent pattern among allele frequency differences, and that pattern is summarized by principal components to which many markers contribute. We sketch the procedures below. Details may be found in Price et al. . First, p-values are far less than the Bonferroni corrected p-value 0.05/9187 = 5.44 × 10-6. We used the software Haploview [D' scores among SNP6-152, SNP6-153, and SNP6-154 are above 0.93, suggesting strong LD, and the D' between SNP6-155 and the rest was less than 0.38. Next, we applied a case-control chi-square test to the unrelated 200 cases and controls, and a family-based test  to the family data. As a comparison, we also applied our test zfam only to the family data. All the test results were consistent, and are summarized in Table z are strictly larger than the square sum of the corresponding chi-square test and TDT. For the family data, the value of our statistic zfam is also bigger than the value of TDT test statistic. These suggest that the proposed combined test has improved power. Also, as expected, the values of test statistic z are much larger than the test statistic zfam, which is restricted only to families, because more information from the unrelated case-control sample is used.Because population stratification was not simulated in GAW15, we did not adjust the genotype data using principal component procedures. We directly applied the test to the 9187 SNPs, and identified four SNPs whose α = 0.05, we observed 483 SNPs with p-values less than 0.05, giving a slightly higher type I error rate of 0.0525, which might be caused by correlation with disease loci. Thus, we excluded all the 674 SNPs on chromosome 6, and then observed 433 SNPs with p-value less than 0.05, with a corresponding type I error of 0.0508  = 1.85 × 10-6. In particular, the markers 3439, 3442, 3437, 3436, 3440, 3430, and 3426 have the largest test value. Together with the LD patterns from Haploview, we conclude that the most likely interval for a major gene is between 49.4262 cM and 49.5184 cM on chromosome 6.The type I errors of the proposed test are reasonable and comparable to the other two tests, which are listed in Table α = 0.05, the type I error rate reached 0.055. On the other hand, our new proposed test might lose power without the random mating assumption.Under the assumption of Hardy-Weinberg equilibrium, the proposed approach has improved power by combining families of different structures with unrelated subjects, and it also give a potential way to resolve the issue of population stratification. Compared with the traditional TDT test, the proposed test can combine all the available families and may have better power than the TDT because the TDT excludes a certain proportion of families. Under the assumption of no population stratification and low disease prevalence in parents, another simpler test that Risch and Teng describe is to regard all parents from families as unaffected, with the remainder of this test being the same as ours . HoweverRecently Epstein et al.  describeThe author(s) declare that they have no competing interests.


A new method for constructing confidence intervals for the location of putative genes regulating expression levels (quantitative traits) is proposed. This method is suitable for the "intermediate" fine-mapping step usually performed between the initial whole-genome screening and the follow-up fine mapping step as a means of reducing the size of the region where the latter is performed. Assuming the existence of a single quantitative trait locus (QTL) in the region/chromosome identified by the genome scan, the method constructs a confidence region for its true position by testing each location in the chromosome to see if it can be the trait locus. We applied our method to the gene expression data from Problem 1 of Genetic Analysis Workshop 15 (GAW15) data, focusing on 25 genes that have previously been shown to share common regulating factor(s) on chromosome 14. Our results pointed to the same region on chromosome 14 for 13 of the gene expressions studied, not only partially reproducing the results of the previous analysis, but also yielding 95% confidence regions for the regulatory quantitative trait loci. Moreover, we identified three regions, one on each of the chromosomes 3, 9, and 13, which potentially harbor additional common QTLs for several of the original gene expressions. The identification of variants /genes that contribute to variation of a quantitative trait is of great importance for understanding these traits. Although there is a wealth of methods available for detecting such quantitative trait loci (QTLs), most of them are designed to provide evidence in favor of or against the existence of a QTL. As such, these methods can only point to the broad genomic regions where the putative loci lie, but they do not readily provide confidence regions for the actual positions of the trait genes. Such confidence intervals are useful for designing follow-up studies, after preliminary linkage signals have been detected, because they can significantly reduce the number of candidate genes, thereby making follow-up studies more time- and cost-efficient. Finally, an additional challenge that almost all current approaches are faced with is the need for multiplicity adjustment for the number of hypothesis tests performed. This is a rather formidable task to undertake, given the complex dependencies that data from genetic studies experience.We propose a new method that can be used in the "intermediate" fine-mapping step ,2 betweeWe applied our method to the gene expression (GE) data of 194 individuals from the Centre d'Etude du Polymorphism Humain (CEPH) Utah families of European descent (GAW15 Problem 1). From a total of more than 3500 GEs available, we decided to focus on 25 that have been suggested to be (co-)regulated by variants located on chromosome 14 [τ in the genome consider testing the following hypotheses:For an arbitrary location τ* is the true location of the QTL controlling the expression level of a continuous phenotype. If we perform the above hypothesis test at an α-level, then it is easily seen that the collection of all loci τ that the null hypothesis is not rejected forms an 1 - α confidence region for the location of the QTL. In addition to allowing us to derive confidence regions with known statistical properties, this formulation of the null and alternative hypothesis helps circumvent the need for multiplicity adjustment for the number of locations tested.where n sib-pairs, and let yi be the squared difference of the expressions of the two siblings in the ith family. In addition, define πi to be the proportion of alleles shared identical by descent (IBD) by the sib pair at the trait locus. HE [In order to test the above hypotheses, we made use of the method of the squared differences (SQD) of phenotypic values of sib pairs introduced by Haseman and Elston (HE) . In brieocus. HE  proved tβ1 is simply -2πi is substituted by πi(Gi), the expected number of alleles shared IBD given the marker genotypes (Gi) of the family. Based on this observation, one can easily test Eq. (1) by simply testing the following hypotheses for each locus τand assuming null dominance effect, or fairly large sample size, the coefficient d Cardon  demonstrβτ 1is the regression coefficient of the SQDs y values, on the πτ(G) values, the observed IBD sharing by the sib pair at the locus τ. Thus, in order to construct the confidence region, we only need to estimate the additive effect of the trait locus. Such an estimate can be obtained from the data themselves after a linkage signal has been established, i.e., the maximum test statistic used in the preliminary analysis exceeds a certain pre-chosen threshold. Assuming that the total number of sib pairs (n) is sufficiently large, we can randomly split the data into two groups of n1 and n2 = n - n1 pairs. Using the data from the first group, we can estimate the additive genetic variance as τmax, the location where the maximum test statistic of the whole genome screening occurred. Then, based on the data from the second group and for each location τ on the genome, we fit the regression in Eq. (2) to obtain the coefficients where τ should be included in the confidence region for the location of the putative trait contributing gene.to determine whether Our data consisted of GE measurements on 3554 genes for 14 three-generational CEPH families, averaging 14 members per pedigree for a total of 194 people. We chose to analyze the GEs of 25 genes that have been suggested to be (co-)regulated by one or possibly multiple SNPs located on a small genomic region of 5 cM toward the end of chromosome 14 . Howeverβ1 of less than -3.09 (pointwise significant level of 0.001). In the second stage, we focused only on the chromosome(s) that were identified on the preliminary analysis and applied our proposed method to construct 95% confidence regions (CRs) for the true location of the identified putative QTL. Although the sample size of 376 sib pairs seems to be sufficiently large for each stage of the analysis, splitting them into two groups as we described earlier would lead to too small a sample size for either. Thus, we decided to use all the sib pairs for both estimating the additive variance and constructing the CRs. We expect this to result in relatively conservative intervals with true coverage probability higher than their nominal value.Each GE of interest was analyzed according to the following two-stage protocol. First, using the HE test statistic we scanned the whole autosomal genome, 22 chromosomes, to uncover chromosomes that potentially harbor regulating factors. In an attempt to hold the false-discovery rate at a low level, we selected for further analysis only those chromosomes that had a minimum observed standardized score for trans-acting co-regulators in that region. Furthermore, the CRs on three additional chromosomes  experience a significant overlap, suggesting the potential existence of additional co-regulating factors for several of the GEs investigated.Of the 25 GEs studied, only 20 of them revealed signals in the preliminary scan on at least one chromosome. For the majority 14) of the GEs, the genome screening suggested the existence of a single chromosome housing a putative QTL, while for half 3) of the remaining ones, it identified two chromosomes. For the last three of them it pointed to joint regulatory loci in three different chromosomes. In Figure 4 of the  of the rcis-regulating factors because for almost all of the GEs, the constructed CRs are located on different chromosomes than the one on which the gene under investigation resides. The only exception is gene FHIT, which is located on chromosome 3 about 70 cM from the beginning of the chromosome. As we can see from the figure (green line) its CR includes the segment  on the same chromosome, suggesting a possible cis-regulating locus. Finally, although the CR for gene LSM3  included genomic segments on chromosome 3, these segments were at least 70 cM away from its location, and hence do not provide strong support for a cis-acting regulator.Our results do not provide strong evidence to support the existence of We have described a new method that can be used as a tool in the "intermediate" fine-mapping step of linkage analysis studies for providing confidence regions for the location of putative QTLs after linkage signal has been detected. Application of our method to the gene expression data from the CEPH families demonstrated that it can successfully narrow the locations of regulatory regions of expression levels of genes. By focusing on 25 GEs, we were able to corroborate the previous finding of a 5-cM regulatory region on chromosome 14 for multiple gene expression traits. Furthermore, we found some evidence for the existence of three additional regions that may also contain loci that co-regulate the expression levels of several of the genes studied.Although we are highly encouraged by the concordance between our results and those of previous analyses on the same data, the resulting intervals were slightly wider than what one might desire, averaging about 25 cM. A contributing factor might be that we estimated the additive variance component of the identified QTL from the data themselves. As such, this estimate was correlated with the data, leading to slightly wider intervals. Using two independent samples, one for the estimation of the additive variance and one for the construction of the CRs, will help moderate this effect and likely lead to greater precision. However, in a real situation it may not be possible to obtain two independent samples. In such a case, one may employ alternative approaches to estimate the value of the additive variance, such as variance-components estimation methods . We inteThe current formulation of the method assumes negligible dominance variance at the trait locus identified by the preliminary scan. Although in many instances this additive model may be a good approximation , there mAn issue with this two-step procedure is the fact the additive genetic variance is estimated only after a linkage signal has been established. Thus, it is very likely to be biased upwards, especially when the sample size is relatively small ,10,11. TFinally, throughout this current paper we assumed the existence of at most one trait contributing locus on the region/chromosome identified from the preliminary analysis, and used our proposed method to construct a CR for its true location. If this assumption is violated, the coverage probability of the resulting interval is not guaranteed to be close to the nominal value for any of the putative trait loci in the region. In fact, the actual coverage of the derived CR would depend on both the distance between the trait loci and their relative contribution to the quantitative phenotype . An exteThe author(s) declare that they have no competing interests.


Using the North American Rheumatoid Arthritis Consortium genome-wide association dataset, we applied ridged, multiple least-squares regression to identify genetic variants with apparent unique contributions to variation of anti-cyclic citrullinated peptide (anti-CCP), a newly identified clinical risk factor for development of rheumatoid arthritis. Within a 2.7-Mbp region on chromosome 6 around the well studied HLA-DRB1 locus, ridge regression identified a single-nucleotide polymorphism that was associated with anti-CCP variation when including the additive effects of other single-nucleotide polymorphisms in a multivariable analysis, but that showed only a weak direct association with anti-CCP. This suggests that multivariable methods can be used to identify potentially relevant genetic variants in regions of interest that would be difficult to detect based on direct associations. Rheumatoid arthritis (RA) is an autoimmune disease causing inflammation and soft-tissue swelling of mainly diarthrodial joints. The disease can lead to considerable loss of mobility due to pain and joint destruction. A newly identified autoantibody, anti-cyclic citrullinated peptide (anti-CCP), is strongly correlated with persistent RA and is a better predictor of erosive outcome than rheumatoid factor immunoglobulin M (IgM) . ElevatiComplex human diseases such as RA have complicated genetic architectures . AssociaRidge regression (RR)  was intrIn this study, we explored the ability of RR to identify genetic associations with anti-CCP, a quantitative clinical predictor of RA development. We performed a series of RR analyses using a range of tuning parameters and compared the results with those of single-factor analysis. Focusing on regions of interest around HLA-DRB1 and TRAF1-C5, RR was able to identify a SNP that was strongly associated with anti-CCP variation. This association was strongest in a multivariable analysis accounting for the effects of all other variants in the region, and largely vanished in univariable analysis. The fact that this SNP is not directly correlated with anti-CCP suggests that one or more additional genetic variants in our dataset may also be contributing to the variation of anti-CCP, but that the dataset available to us is not sufficiently large to identify these additional factors with high confidence.HLA-DR cluster on chromosome 6 and the 100-kbp TRAF1-C5 region on chromosome 9 [The phenotype and geonotype data provided by the North American Rheumatoid Arthritis Consortium (NARAC) were analyzed, including 868 RA cases and 1194 controls. The analysis dataset includes 867 unrelated RA cases with anti-CCP measured as a quantitative outcome. 545,080 SNPs were genotyped using the Illumina 550 k chip [Z-score for the null hypothesis of no direct association. Population genetic parameters for all SNPs were calculated, including MAFs, genotype frequencies, and either a chi-square test or the Fisher exact test for departures from expectations under Hardy-Weinberg equilibrium (HWE).Pearson correlation was used for detecting associations between individual genetic variants and anti-CCP, using the Fisher transform and the asymptotic standard deviation to form a Y is the n-vector of trait values, X is the n × m design matrix whose ith row contains the genotype of sample i on genetic variants j = 1,2,...,m, β is the m-vector of effect estimates for the genetic variants, and λ is the tuning parameter. The genetic marker and trait data are centered so no intercept is included when fitting the regression. The expected value of Fλ = X'X + λ I and M = X'X. The covariance matrix of Z-scores λ = 0, the Z-score is the same as what would be obtained using OLS. When λ → ∞, the Z-scores converge to values that are proportional to the univariable Pearson correlation coefficients between each independent variable and Y.In RR, the estimated coefficients of the linear model solve a penalized least-squares criterion with the penalty being proportional to the squared L2-norm of the coefficients: λ > 0, the Z-score Zλi need not have mean zero under the specific null hypothesis βi = 0 - this is only guaranteed under a global null hypothesis of no effects among all modeled genetic variants. However, for small values of λ, the degree to which the Z-scores are off-center is small, and can be accommodated by re-standardizing the Z-scores. This is accomplished by considering a quantile-quantile plot of the observed Z-scores against corresponding standard normal quantiles. Based on an assumption that the middle 90% of the Z-scores correspond to zero effects, we fit a simple linear regression to the points on the quantile-quantile plot. The slope of this line estimates the standard deviation of the null Z-scores, which tends to be slightly greater than 1 due to the lack of centering. By scaling the Z-scores with the reciprocal of this standard deviation, any lack of centering of the null Z-score means is presumed to be eliminated.A complicating factor is that when Z-scores for several nonzero values of λ. The Z-scores are then re-standardized as described above, and univariable Z-scores are also calculated. Bonferroni corrections of the Z-scores are used to account for multiple comparisons. Randomization of the trait vector Y is used to assess whether the overall significance level is maintained after considering the results for several λ values.Our main interest is to identify genetic variants associated with the trait that do not show direct associations in univariable analysis. We will do this by considering RR Z-scores for 328 SNPs in the HLA-DR and TRAF1-C5 regions were calculated using RR with tuning parameters λ = 1, 10, 100, and 1000. We found that coefficient estimates and Z-scores varied continuously with λ, and these values are representative of the results that occur for other λ values. As expected, the Z-scores were slightly over-dispersed, with standard deviations estimated from their central values ranging from 1.02 to 1.1. The Z-scores were then rescaled by the reciprocals of these factors. The Z-scores based on Fisher-transformed Pearson correlation coefficients were also calculated for each SNP. For each value of λ, the SNPs were ranked in absolute value and compared to a Bonferroni threshold of 3.8. The maximal Z-scores among the univariable model and the four RR models  were used to rank the 328 SNPs  in univariable analysis of anti-CCP, and its Z-score varied smoothly with a high value exceeding 5 (when λ = 1). No SNPs were significant for larger values of λ, or for the univariable analysis. SNP rs2844533 is located at 31,458,781 bp on chromosome 6, about 25 kbp upstream of HLA-B gene, and about 1 Mbp downstream of HLA-DRB1 gene.The Z-scores exceeding the Bonferroni threshold in approximately 1 in 20 data sets, as expected (data not shown). This suggests that our re-standardization was successful at creating statistics that are well approximated by a standard normal null distribution. We repeated the analysis under Box-Cox transforms of the trait value of the form (yκ-1)/κ, and found that for κ ranging from 1 (no transformation) to 0.55 (approximately a square root transformation), rs2844533 continued to be the top-ranked SNP and was significantly associated with the trait after Bonferroni adjustment.Randomization of the trait vector, followed by the same analysis as performed with the actual trait values, produced p > n .Even when genes contribute additively to a quantitative trait, the genes having strongest additive effects can often be identified with greater power by using multivariable techniques, rather than scanning for direct associations. A major reason for this is that when several correlated variants are present, each introduces heterogeneity that partially masks the other variants' effects. However, traditional multivariable analysis using OLS regression is ineffective for large numbers of predictor genes, particularly when substantial correlations are present. RR operates on a continuum between univariable analysis and OLS multiple regression, potentially allowing the trade-off between univariable and multivariable analysis to be made optimally in a particular setting. For predictive modeling with a high dimensional and/or substantially correlated covariate vector, RR outperforms OLS regression . It can λ values from 1 to 10 correspond to effective degrees of freedom of 280 to 326, compared to 328 for OLS regression) focusing on a candidate region of interest extracted from a GWAS data set. As demonstrated here, an important application of RR will be to help in determining whether multiple disease loci are located in a candidate region following a genome-wide scan, such as the strong LD region within 15q25.1 associated with lung cancer [Here we used RR to identify a strong association in a mildly ridged, multiple regression fit (Sample size is a critical issue when using multiple regression analysis for genetic mapping, particularly in the case of a complex disease where individual genetic variants only explain a small portion of total variance . In prinBy considering a sequence of RR analyses and rigorously calibrating the estimates of genetic effects, we identified a single SNP showing a strong association with a quantitative trait related to RA. The most plausible explanation for this finding is that multiple genetic variants in our data set are contributing to anti-CCP variation in a way that obscures the direct relationships between genetic variants and the trait. While univariable analysis and multiple regression fit using least squares yielded no associations with rigorous significance levels, the RR procedure allowed us to identify one SNP with high confidence. In this case we were unable to identify the other genes presumed to be associated with anti-CCP variation; however, our inability to identify these synergistic factors does not detract from the potential importance of the SNP we did find.CCP: Cyclic citrullinated peptide; GAW: Genetic Analysis Workshop; GWAS: Genome-wide association studies; HWE: Hardy-Weinberg equilibrium; IgM: Immunoglobulin M; LD: Linkage disequilibrium; MAF: Minor allele frequency; NARAC: North American Rheumatoid Arthritis Consortium; OLS: Ordinary least square; RA: Rheumatoid arthritis; RR: Ridge regression; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.YVS and KAS participated in the design and coordination of the study, performed the statistical analysis, and drafted the manuscript. JZ, N-HC and SLRK participated in the design of the study. All authors read and approved the final manuscript.


Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes pre-corrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiple-marker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance.The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis. Here we describe a comprehensive set of GWA analyses to detect quantitative trait loci (QTL) in the simulated population in order to compare the commonly used methods of linkage, transmission disequilibrium test (TDT), and single marker association with more experimental models including multiple marker and haplotype associations and epistasis. Based on the comparisons we aim to derive a generic strategy for GWA studies on general pedigreed populations.With recent advances in genotyping technology, high density marker maps are becoming commonly used to map the genetic loci controlling complex trait variation. Most large-scale genome-wide association (GWA) studies published to date, such as those conducted by the Wellcome Trust Case Control Consortium , used caThe simulated population consists of 4665 individuals across four generations. From the first generation, 15 sires, each mated 10 dams that produced 10 progeny per full-sib family. Each individual was phenotyped for one continuous trait and genotyped with 6,000 Single Nucleotide Polymorphism (SNP) markers without missing values. The SNP data were phased and treated as evenly spaced across six 100 cM chromosomes.P-value of 2*10-4, was used as the threshold for linkage analyses. For all single-QTL association studies, Bonferroni correction of 5916 tests was used to derive the 5% genome-wide threshold resulting in the nominal P-value of 8.45*10-6, or 5.08 in the -log10(P) transformation (logP). That threshold was used consistently across the GWA analyses in this study to detect markers that significant by their marginal effects (denoted as qSNP). Although the Bonferroni correction is known for being too conservative, it is easily implemented and much less computer-intensive than permutation tests. Furthermore, the resulting P-value threshold is in line with many published GWA studies.Haploview  was usedFigure de facto QTDT, where the allelic association is evaluated within the nuclear families only. Using the within-family component solely in evaluating the allelic association is robust to admixture in the population. Secondly, without partitioning the mean effect of a locus into the between- and within-family components, testing of the total association was also carried out. Such a test is not a TDT, although it is implemented in the QTDT software, and it is a less conservative test compared to QTDT when population stratification can be ignored.The pedigree was divided into 450 nuclear families. At first, a variance components linkage analysis [The first stage of GRAMMAR  was adopUsing the pre-corrected phenotypic values, we evaluated the joint effect of multiple SNPs within a three marker sliding window. Markers were fitted as individual linear covariates within a multiple regression framework to test for their joint association. Using the same sliding window method haplotypes were estimated from 3 adjacent SNPs with the software "haplo.stats" in R . A progr1+SNP2+SNP1 *SNP2+e) with the NULL model (y = μ+e); the second test compares the full model with the two-SNP model (y = μ+SNP1+SNP2+e) (i.e. epistasis test). Only pairs that were significant for the first test enter the epistasis test. When either SNP1 or SNP2 is a qSNP, the first test is changed to ensure the full model is better than the single SNP model (y = μ+qSNP +e) before the epistasis test. When both SNP1 and SNP2 are qSNPs, only the epistasis test is needed. To avoid spurious interactions between closely located SNPs an arbitrary minimum distance of 10 cM was applied to any interacting SNP pairs.A four-stage approach was used to analyse epistasis based on the pre-corrected phenotypes where SNP genotypes were fitted as fixed factors: 1) single SNP regression to identify qSNPs (see above); 2) detect qSNP × qSNP pairs ; 3) deteK qSNPs are identified from N available SNPs in stage one, the number of the first tests is in the order of N2, K*N and K2 for the non-qSNP × non-qSNP, qSNP × non-qSNP and qSNP × qSNP pairs, respectively. The number of pairs that are significant for the first test is used to derive the 5% genome wide threshold for the epistasis tests.The 5% genome wide thresholds were derived for the nested tests using Bonferroni correction based on the number of tests (assuming independent tests). Suppose Forward linear regression was used to integrate the mapping results in the order of a) qSNP × qSNP pairs; b) qSNP × non-qSNP pairs; c) non-qSNP × non-qSNP pairs and d) qSNPs using their corresponding thresholds. The epistatic pairs were fitted first because they also capture the marginal effects of the qSNPs that were involved in these pairs. QTL (pairs) were included in the model in order of decreasing significance. QTL or QTL pairs were dropped when their individual P value was smaller than their corresponding threshold.2 between adjacent markers was 0.2 and decreased linearly with map distance. However, much higher LD was observed when looking at all pairwise r2 values within a 20 marker window – the mean maximum r2 between two SNPs was 0.62.The uncorrected trait data was approximately normally distributed using a Q-Q plot. It ranged from -5.36 to 8.67, with mean and standard deviation of 1.36 and 2.10 respectively. No differences in the distribution were observed due to sex. The estimated heritability was 29.6%. The LD between adjacent SNP pairs was generally low; the mean rLinkage analysis revealed strong evidence of QTL on chromosomes 4 and 5 Figure . Tests fThe additive single SNP analysis identified 9 QTL peaks by visual inspection Figure . In totaBecause the single-marker GRAMMAR identified the same peaks as QTDT, we concluded that the benefit of the robustness to admixture of QTDT is negligible in the current dataset. GRAMMAR is more flexible for modelling epistasis and performing the joint analysis. Therefore, GRAMMAR was chosen over QTDT for the subsequent analyses that follow.The multiple marker analysis identified a total of 9 QTL peaks in the same locations as those identified by the single-marker analyses. Overall, the multiple marker method identified a total of 320 individual significant SNPs. The Haplotype analysis identified the same 9 QTL peaks as the multiple-marker method with a total of 338 significant SNPs.0 of only a single qSNP effect. Following this test, 3040 pairs were significant and the Bonferroni corrected logP threshold for the epistasis test was 4.78. For the pairwise analysis of non-qSNP pairs the logP threshold against H0 of no QTL effect was 8.51. Following this test, 99634 pairs were significant and the Bonferroni correct LogP threshold for the epsitais test was 6.3. Two significant non-qSNP × non-qSNP pairs were detected: 1) SNPs 540 and 3219 on chromosomes 1 and 4, respectively; 2) SNPs 1257 and 3689 on chromosomes 2 and 4 respectively. It was interesting that the adjacent SNPs of 1257 (i.e. 1256 and 1258) were both qSNPs with an r2 of 0.75 whereas the LD between 1257 and 1256 (0.26) or 1258 (0.46) was much lower. No significant interactions with any of the 108 qSNPs were found.The 108 qSNPs identified in Stage one were used for epistasis analyses. The thresholds for the epistatic analyses were as follows: For the 108 qSNPs, the logP threshold for their pair-wise analyses was 5.08. For the interaction between qSNPs and non-qSNPs the logP threshold was 7.01 against the HThe 108 qSNPs and the two epistatic pairs were integrated using the forward selection regression method to remove possible redundancy among those SNPs. The two epistatic pairs and 9 qSNPs remained significant in the final model and jointly explained 12.3% of the phenotypic variance Table . HoweverP  and re-tested the interactions following the same algorithm. In addition to the two pairs detected, we listed 7 new pairs: 2 qSNP-qSNP , 2 qSNP × non-qSNP  and 3 non-qSNP × non-qSNP . After integration with the 108 qSNPs, all pairs and 6 remaining qSNPs jointly explained about 15% of the phenotypic variance. Further effort is required to determine the appropriate thresholds for use in GWA epistasis analyses.Bonferroni derived threshold adjust for the number of tests, but assume they are independent. As markers are correlated these thresholds will be too stringent and this will be particularly the case when dealing with pairs of correlated markers as is done in epistatic analyses. To explore to potential effects of this we relaxed each of the thresholds used in stages 2 to 4 of the epistatic analyses by 1 log , which is approximately three times faster compared to a standard desktop, the Merlin step of the QTDT software took 2 hours per chromosome while the actual QTDT analyses took about 4 hours per chromosome. A 'genome scan' using the single SNP GRAMMAR method took only 6 minutes on a standard desktop PC. Using the same machine, the 3-marker window analyses took 16 minutes, while the 3-marker haplotypes analyses took approximately 1 hour. The epistatic analyses for n SNPs require (n-1)*(n/2) calculations with additional terms to be estimated. These analyses ran for several days using a background queue at the ECDF facility therefore no reliable estimate of calculation time was available.Using the ECDF computing facility Using several methods in analysing GWA can be useful in gaining confidence on the QTL identified. GRAMMAR is much faster to run than QTDT and takes into account complex relationships existing in general pedigrees. Furthermore, extending the model in GRAMMAR to study epistasis is reasonably easy and computationally feasible by using parallel or Grid computing.Following presentation of the simulation design, our strategy turned out to be effective and correctly detected 5 out of 6 major QTLs as well as 4 out of 45 smaller QTLs, despite the conservative thresholds employed in this study. The estimates of the genotypic QTL effects in the joint model (after stepwise regression) appear small compared to the simulated values. However, the estimates of the allelic QTL effects from the additive single marker GRAMMAR analyses compare quite well to the simulated values Table . It seemGWA: Genome-Wide Association; QTL: Quantitative Trait Locus; MAF: minor allele frequency; SNP: Single Nucleotide Polymorphism; LD: Linkage Disequilibrium; GRAMMAR: Genome-wide Rapid Analysis using Mixed Models And Regression; QTDT: Quantitative Transmission Disequilibrium Test; qSNP: Quantitative SNP (SNP that is a putative QTL); logP: logarithm to the base 10 of the P valueThe authors declare that they have no competing interests.ACL, JP and WHW carried out the analyses and drafted the manuscript. DJK and CSH coordinated the analyses and the write-up. All authors have read and contributed to the final text of the manuscript.


We also calculated inbreeding coefficients and showed a significant trend for population-wide increasing heterozygosity outside of linkage disequilibrium. We successfully replicated these associations in a demographically similar cohort comprised of a subgroup of 477 Baltimore Longitudinal Study of Aging participants. We also constructed statistical models showing predicted declining rates of autozygosity spanning the 20th century. These predictive models suggest a 14.0% decrease in the frequency of these runs of homozygosity and a 24.3% decrease in the percent of the genome in runs of homozygosity, as well as a 30.5% decrease in excess homozygosity based on the linkage pruned inbreeding coefficients. The trend for decreasing autozygosity due to panmixia and larger effective population sizes will likely affect the frequency of rare recessive genetic diseases in the future. Autozygosity has declined, and it seems it will continue doing so.This research investigates the influence of demographic factors on human genetic sub-structure. In our discovery cohort, we show significant demographic trends for decreasing autozygosity associated with population variation in chronological age. Autozygosity, the genomic signature of consanguinity, is identifiable on a genome-wide level as extended tracts of homozygosity. We identified an average of 28.6 tracts of extended homozygosity greater than 1 Mb in length in a representative population of 809 unrelated North Americans of European descent ranging in chronological age from 19–99 years old. These homozygous tracts made up a population average of 42 Mb of the genome corresponding to 1.6% of the entire genome, with each homozygous tract an average of 1.5 Mb in length. Runs of homozygosity are steadily decreasing in size and frequency as time progresses (linear regression,  Population geneticists use genetic markers to quantify and compare levels of inbreeding in populations and identify disease-associated loci; epidemiologists utilize demographic factors to quantify disease risk modifiers. Our research group sought to investigate the intersection of these two disciplines and examine the way in which demographic trends associated with decreasing levels of inbreeding may influence genomic structure and how this may affect medical genetics research. By examining two age-heterogeneous populations of outbred North Americans, we were able to ascertain genetic changes occurring over the past century that have been likely brought about by recent increases in mobility, urbanization, and population admixture. Using multiple measures of the genomic manifestations of distant consanguinity, we showed significant trends towards decreasing levels of autozygosity and more marginal inbreeding coefficients as study participant birth years neared the chronological present day. We believe this finding is particularly important, as decreasing autozygosity and less homozygosity genome-wide may help to slightly reduce the burden of rare recessive diseases in the future. Rates of travel and migration within North America have increased substantially over the past century due to advancements in infrastructure and technology. It has been hypothesized that this ease of travel and globalization has shaped the demographic structure of both North American and world populations in recent generations. Within the past two centuries, population growth, admixture and expansion have been rapid, causing increasing genetic variation in many populations In our research, we have investigated how demographic trends in the past century have been recapitulated in quantifiable genetic changes, and how this may impact medical genetics and genetic diseases. We focused our study on genome-wide rates of autozygosity and measured tracts of extended homozygosity in two age-heterogeneous samples of North Americans to estimate the genetic effect possibly attributable to demographic change.ld. Our results show that older members of the population have a tendency to possess more homozygous runs, which comprise a higher percentage of the total genomic length, than those found in younger participants. These older participants also exhibit a larger proportion of excess homozygosity based on estimates from inbreeding coefficients.We measured autozygosity in the form of runs of extended homozygosity (ROHs). We analyzed these extended tracts of homozygosity genome-wide, showing a strong positive association between increasing chronological age and increasing rates of autozygosity. These homozygous runs were used to quantify consanguinity in our analysis populations. We also utilized a modified inbreeding coefficient to quantify decline in the proportion of excess homozygosity outside of linkage disequilibrium. This modified inbreeding coefficient, has been calculated using data that has had all neighboring single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium (LD) with each other removed (LD pruned data), and is referred to as FThe homozygous runs representing autozygosity in relatively outbred populations may also be highly relevant in disease gene discovery. The mapping of these regions on a genome-wide scale could help to identify low-frequency variants associated with complex disease ld were suggestive . This illustrates possible generational differences in autozygosity in an outbred population of unrelated individuals  and average ROH length . We thenividuals .ld . Trends showing a positive association between Fld and chronological age were also significant . This association between Fld and chronological age was relatively unattenuated in models adjusted for average ROH length. The weakest associations with chronological age occurred when examining the linear association with average ROH length. The effect size of this association with average ROH length was small, with standardized beta-coefficients varying between 0.06 and 0.07 (t-statistics between 1.77 and 2.12), although still below our a priori significance threshold of p<0.05 for two of the models. All models for the association between chronological age and average ROH length were at least borderline significant .The trend of increasing autozygosity associated with chronological age remained significant in a number of linear regression models, each model incorporating different covariates . In multld has decreased by a factor of 30.5% in our models.The predictive models based on the results of our regression analyses forecasts a multi-faceted trend for increasing autozygosity as years since birth increases . These models, summarized in A subset of participants from the Baltimore Longitudinal Study of Aging (BLSA), were selected to replicate these associations from our analyses of the Coriell Control cohort. These participants were selected based on their similarity to the Coriell cohort used in the initial analyses. The results from the BLSA cohort supported all of our hypotheses tested in the initial discovery cohort.ld .In the BLSA cohort, significant differences in rates of homozygosity exist when comparing participants between the standardized chronological ages of 40–59 years to those aged 80–99 years. These two, more demographically similar, age groups were compared to stringently test replication due to the relatively few BLSA samples in the 19–39 year old cohort (N = 19) that would have limited statistical power for comparisons. Results were consistent to those in the initial analyses of the Coriell Control cohort, with t-tests showing significant differences between age strata for number of ROHs , %ROH , and average ROH length . The younger participants were generally less homozygous than the older strata, with significant differences in FLinear regression models of age-associated decrease in measures of autozygosity were also utilized to test the validity of our initial results. Identical regression models as those used in the Coriell cohort were used in the BLSA cohort. The smaller BLSA cohort successfully replicated all trends found to be significant in the analyses of the Coriell cohort. These models were generally more significant in the replication analyses than in the initial discovery cohort .ld, we show a quantifiable decrease in consanguinity over the twentieth century. Based on data provided in Carothers et al ld found in our discovery population is on the order of individuals transitioning from a single inbreeding loop 4–5 generations prior, to no inbreeding loops within <6 generations. We postulate that the increased mobility, urbanization and outbreeding in North America in the last century has led to less consanguinity (and thus less homozygosity and homogeneity) in younger individuals This research has definitively shown the existence of a trend for decreasing autozygosity with younger chronological age in the North American population of European ancestry. The ROHs we identified, larger than 1 Mb, are clearly representative of autozygosity due to distant consanguinity in our outbred populations, and not chromosomal abnormalities or common copy number variants We have shown a weaker association with chronological age for the measured average ROH length. It is possible that since populations that are becoming more outbred, less consanguinous and more heterogeneous recombination could fracture ROHs into smaller segments, for which a robust measure such as %ROH would be less affected than average ROH size. This could contribute to higher variation in average run length measurements, resulting in increased variation in the measure causing more possible type I error and decreased statistical power when compared to %ROH.ld) may increase the chances of rare recessive genetic diseases. The trends shown in this research may have a larger impact in modifying the epigenetic, epistatic and polygenic pathways that influence many complex traits With extended regions of homozygosity decreasing in size and becoming less frequent, this structural genomic trend may have some latent effect on public health, as well as the recently developed methods for genome-wide association studies (GWAS). Theoretically, an excess of ROHs and excess homozygosity  funded Neurogenetics repository at the Coriell Institute. These samples were collected by Coriell to be used as convenience genetic controls, representative of the North American population of varied European descent. The replication cohort for this study was taken from the Baltimore Longitudinal Study of Aging, a community based longitudinal study of aging currently in its 50http://neurogenetics.nia.nih.gov), an intranet repository for genotype data created on the Illumina platform.Individuals from both cohorts were genotyped concurrently at the National Institute on Aging's Laboratory of Neurogenetics (LNG) using the Illumina Infinium technology . The assays used for genotyping included were the Infinium II HumanHap550 v. 1, Infinium II HumanHap550 v. 3, or a composite of Infinium HumanHap300 and Infinium II 240S. By combining the genotype data from Infinium HumanHap300 and Infinium II 240S assayed participants, to an equivalent level of genomic coverage as the Infinium II HumanHap550 assays, we were able to standardize participant data across a total of 545,066 single nucleotide polymorphisms genotyped on the Illumina platforms. The raw genotype data were stored and quality controlled using GERON genotyping (http://pngu.mgh.harvard.edu/~Purcell/plink/). 14 of the samples from the BLSA cohort were excluded due to X chromosome heterozygosity inconsistent with self-reported sex. Blinded sex-checks were carried out for all samples using the default function for genotypic estimates of gender in the Illumina Beadstudio package . The only conflicting genders reported by the Beadstudio results were identical to those reported by PLINK.All samples from both cohorts were first quality controlled for a minimum of a 97% successful genotype call rate. Any samples failing this initial quality control step were re-genotyped using a new DNA aliquot until a 95% successful call rate was achieved. 13 of the initial DNA samples from our neurological control population were ultimately excluded due to consistent call rates below our inclusion threshold of 97%. Of the 848 participants from BLSA that were genotyped who based on available data were not self-reported African American, 34 samples had call rates below 97%. SNPs with minor allele frequencies less than 5% and departures from Hardy-Weinberg equilibrium  and missingness per SNP greater than 5%, were excluded from further analyses. PLINKv1.0.1 was used to carry out sex-checks based on heterozygosity of the X chromosome genotypes were used to exclude 4 participants from the Coriell cohort whose self-reported sex did not match that presented in the genotypic data  analyses were used to identify repeated samples or distantly related individuals, with an apriori exclusionary criteria of sharing at most a 2.0% proportion of the genotyped SNPs as identical by descent in any pair-wise combinations of samples. This strict exclusionary criteria is necessary, as including any participants that are related within recent generations could bias our analyses and lead to a non-independence of measures of autozygosity. Probands from groups of related individuals were randomly selected for inclusion in the analyses. This IBD filtering called for the removal of one duplicate sample from the Coriell population. This filter eliminated 101 cryptically related individuals from the BLSA. The high number of related individuals in the BLSA is to be expected as a result of recruitment patterns in the study. The BLSA itself is a cohort of volunteers initially recruited from a group of retired Federal scientists, who subsequently recruited friends and family members. Family members of early participants in the BLSA were given priority enrollment during the course of the study.Identical by state distances were generated using multi-dimensional scaling for the remaining samples in an attempt to identify population outliers not already considered during the admission/adjudication of the participants. The distribution was standardized on HapMap samples used to aid in the detection of stratification and outliers. One participant was removed from the Coriell cohort of neurological controls due to ancestry consistent with African samples. There were no identifiable sub-population clusters or outliers apparent within the European American samples in the multidimensional scaling analyses of the Coriell dataset. When the Coriell cohort, BLSA cohort, and HapMap samples underwent combined multidimensional scaling analyses , additional outliers for possible stratification effects were removed from the BLSA cohort, with 222 samples being more than two standard deviations from the combined population mean for any of the four components of the MDS model. BLSA is a relatively ethnically diverse population, compared to the Coriell samples. Beginning in 1990, a conscious effort was made to recruit African Americans, while Asians, Latin Americans and participants from other ethnic groups were also recruited in recent decades. This deviation from mean component measures in BLSA during the quality control process may be due to some slight level of additional cryptic relatedness or population admixture/stratification not seen in the discovery cohort samples. All genotyping quality assessments, sex-checking, IBS and IBD calculations were carried out using PLINK v1.0.1. A summary of quality control results can be found in After the quality control process, we were left with an analytic population of 809 neurologically normal participants genotyped at 476,962 SNPs. These participants were sampled between the ages of 15 and 95 years old . However, records show a 6 year period over which these controls were adjudicated and sampled. There was no statistical correlation between the dates of sample collection and participant age , this suggests no sampling bias with regard to age at collection. This data allowed for the calculation of the participants' estimated current chronological age (within less than1 year) standardized to the year 2008. Chronological age refers to the participant's calculated current age, regardless of death, and ranges between 19–99 years of age (mean age of 61.7 years±16.8). The population is comprised of 57.9% female participants.ld calculations. This may be due to the fact that the BLSA cohort is derived from an urban dwelling population based in Baltimore, MD. The BLSA cohort was comprised of slightly more males than the Coriell cohort, but this should not be a factor in the replication as all analyses were confined only to the 22 autosomes.477 participants from the BLSA were selected for replication purposes after passing similar quality control to the Coriell cohort, including the removal of genotyping failures, population outliers and cryptically related samples. These samples were individually standardized to 450,364 quality controlled SNPs. BLSA samples in the replication population possessed a population standardized mean chronological age of 68.3 (S.D. = 13.7). With regard to autozygosity measures, the BLSA samples were slightly lower than those of the Coriell cohort in both the entire population and the four age strata  and 2. TWe utilized the PLINK v1.0.1 toolkit to identify runs of homozygosity. Primary criteria for inclusion of a genomic region into a homozygous run are the region must be at least 1 megabase in length and contain at least 50 adjacent SNPs (per Mb) with homozygous genotype calls. This robust size and SNP density threshold for inclusion into ROHs allows for the algorithmic exclusion of copy number variants, centromeric and SNP-poor regions. The density requirement of at least 50 SNPs per Mb is based on an apriori genome-wide coverage target of ∼500,000 quality controlled SNPs in analytic populations. This requirement of at least 50 SNPs per Mb is similar to the requirements for ROHs found in the Gibson et al., 2005 analysis of runs of homozygosity in HapMap Phase II data Our metrics for comparing rates of autozygosity among the participants in this study were able to be calculated after identifying the ROHs. Our primary measures of autozygosity include: total percentage of the genome included in ROHs and the average length of ROHs. The total percentage of the genome included in ROHs was calculated by summing the length of each individual ROH per participant. This summed length of identified ROHs was then divided by a factor of 2,645 and subsequently converted to a percent by multiplying the dividend by 100. The division by 2,645 in the generation of the %ROH measure is based on the number of megabases covered by SNPs included in the Infinium HumanHap 550v.1 and 550v.3 assays used to generate our genome-wide datasets. This estimate of coverage of 2,645 Mb was calculated by summing the distance between the first and last available SNP of each chromosomal arm for each of the 22 autosomes. The average length of ROHs was calculated by dividing the total length by the number of ROH segments per participant. Both of these measures are expressed in Mb.ld, we created LD pruned versions of each of the genome-wide datasets. We accomplished this by algorithmically excluding SNPs in LD with neighboring SNPs to create the LD pruned datasets, and then calculating inbreeding coefficients based on the remaining data.As ROHs are associated with regions of high linkage disequilibium, we also attempted to examine rates of homozygosity outside of LD. To calculate the additional measure of FUsing data from all 809 participants in our analysis population , we calculated variance inflation factors (VIF) for each of the possible pairwise combinations of SNPs within a sliding window of 50 SNPs (with 5 SNP overlaps per window). We excluded all SNPs with a VIF>1.05 within each sliding window. This VIF threshold corresponds to a maximum multiple correlation coefficient representative of ∼1% co-linearity of genotype calls with any other SNP in the sliding window of analysis. This stringent variance threshold allowed us to trim the genome-wide dataset for the Coriell cohort to 48,902 SNPs dispersed relatively evenly across the 22 autosomes, and to 34,307 autosomal SNPs in the BLSA cohort.ld). We converted this to a percentage in our tables for ease of comparison, although actual Fld coefficients are used in the predictive models. In our randomly selected populations of unrelated individuals, the Fld values we calculated are a proxy for the occurrence of excess homozygosity on a genome-wide level. The Fld statistic allows for an accurate assessment of autozygosity outside of linkage disequilibrium, without being adversely affected by low SNP density after removing SNPs in regions of LD. The linkage pruned sub-sets of the genotypic data was used for the calculation of observed and expected rates of homozygosity outside of LD as well of the generation of the inbreeding coefficients that comprise the Fld statistic. A discussion of the use of the LD-pruned data to construct ROHs may be found in the supplemental materials in We calculated expected rates of homozygous calls per participant based on HWE expectations of genotype frequencies using the LD-pruned datasets. We then calculated cohort specific observed rates of homozygosity within the LD-pruned datasets, expressed as a summed count of homozygous genotypic calls per participant. Using PLINK, we then calculated single population inbreeding coefficients (F statistics) to summarize the proportion of homozygous genotypes differing from our HWE based expectations per participant. These F statistics, based on calculations carried out on the datasets containing only SNPs not in LD with each other, comprise the summary measure we refer to as the LD pruned inbreeding coefficient  and Fld. Differences in mean measures of autozygosity and Fld between the oldest (estimated current age of 80–99 years) and youngest (estimated current age of 19–39 years in the Coriell cohort) generations were compared using a basic two-way t-test for each of the autozygosity measures.Generational differences were estimated by sorting participants into 4 age strata based on 20 year intervals. Descriptive statistics were calculated again to compare mean variation in measures of autozygosity and Fld and chronological age. Separate regression models were constructed for dependent variables of number of ROHs, %ROH, average ROH length and Fld. These models were initially adjusted for gender only , although to create more parsimonious models, gender was not included. Additional covariates of observed and expected rates of homozygosity outside of LD were added to the second and third sets of models respectively to further scrutinize and follow-up the initial results. Fld was used as a covariate in the fourth model set to account for the possible confounding effect of chance homozygosity outside of LD in the examinations of trends involving measures of autozygosity derived from ROHs. Subsequent regression models evaluating the trend for increasing Fld with chronological age were adjusted for average ROH length. Additional regression models investigating associations in combined cohorts are described in the supplemental materials in Linear regression models were constructed in order to investigate possible associations between chronological age and autozygosity measures. Similar models were used to evaluate the association between F2 as the primary predictor of increasing autozygosity measures (gender adjusted) or Fld. However, none of these models that incorporated age2 showed a stronger association with the measures of autozygosity, as the standardized-beta-coefficients and r2 values were actually smaller than those in the previous models of linear age. These models are not included in the manuscript as they add no additional pertinent information, but are available upon request.A second set of linear regression models were created to investigate the possible multiplicative effect of age, by using ageld. These models provide estimates of time dependent means and confidence intervals for both measures. Percent change over 100 years was estimated for each measure using these models. All estimates of percent change were based on a minimum value of zero except for Fld, when a scalar minimum for the calculation based on the lowest value within the 95% confidence interval of the predictive model (Fld = −0.0031) was used.Linear predictive models of autozygosity decrease over the twentieth century were extrapolated from regression models based on the Coriell discovery cohort. These models estimate decreasing rates of autozygosity and excess homozygosity as time progresses. These are based on the regression coefficients from the original un-adjusted models of chronological age predicting demographic change in the total number of ROHs, %ROH, average ROH lengthand FFigure S1ld decline as participant birth year increases. Regression plots showing trends in declining autozygosity in combined cohorts. Panel A shows decreasing percent of the genome contained in ROH as birth year increases. Panel B shows decreasing Fld as birth year increases. Red lines represent linear trends adjusted for study site. Points represented by the symbol ‘+’ indicate Coriell samples, points represented by the symbol ‘x’ indicate BLSA samples.Regression plots showing trends for %ROH and F(1.56 MB TIF)Click here for additional data file.Table S1Summary of exclusions made in data cleaning process.(0.03 MB DOC)Click here for additional data file.Text S1Supplemental materials.(0.04 MB DOC)Click here for additional data file.


Isolated populations are a useful resource for mapping complex traits due to shared stable environment, reduced genetic complexity and extended Linkage Disequilibrium (LD) compared to the general population. Here we describe a large genetic isolate from the North West Apennines, the mountain range that runs through Italy from the North West Alps to the South.The study involved 1,803 people living in 7 villages of the upper Borbera Valley. For this large population cohort, data from genealogy reconstruction, medical questionnaires, blood, anthropometric and bone status QUS parameters were evaluated. Demographic and epidemiological analyses indicated a substantial genetic component contributing to each trait variation as well as overlapping genetic determinants and family clustering for some traits.The data provide evidence for significant heritability of medical relevant traits that will be important in mapping quantitative traits. We suggest that this population isolate is suitable to identify rare variants associated with complex phenotypes that may be difficult to study in larger but more heterogeneous populations. Common complex traits are caused by multiple environmental and genetic factors each contributing to trait variability. Significant heritability and increased disease risk in relatives are measures of the importance of the underlying genetic factors; however, to predict the risk of a disease in healthy individuals requires detailed knowledge of the risk factors, their effect size and how they interact. The prediction of the genetic risk is still limited by the availability of common polymorphisms that are associated to the risk of a disease in the population. The large genome wide association studies (GWAS) performed to date have provided initial information on the genetic architecture of many diseases, but they have identified variants that individually explain a very small fraction of the genetic variance, to be used for an accurate prediction of the genetic risks th century in the South West of the Netherlands The population structure of a genetic isolate can be quite variable, from the very large populations of Sardinia 1803 adult subjects from Val Borbera, selected based on their ancestry to represent the general population in the valley. We have studied a large set of quantitative traits that are relevant to major clinical domains, spanning from anthropometric measures to blood pressure and serum markers of diseases. The analysis of the heritability of each trait provides a quantitative assessment of the impact of the underlying genetic variation. Additional genetic correlation analyses suggest the presence of a set of genetic determinants that are likely to be relevant to multiple phenotypic traits. The results presented here are preliminary to the identification of genetic determinants underlying variation in quantitative traits, including risk factors for many common disorders.The aim of the present study is to characterize the population isolate of the upper Val Borbera, a large valley localized within the Apennine mountains of Piedmont, in the Northwest of Italy, geographically secluded from the surrounding areas. We present here evidence that the population, due to its isolation, high level of endogamy and lack of immigration in the last centuries, is indeed suitable for the study of complex disorders. As a part of a research program aimed at the identification of genes and variants associated to common disorders, we present here the analysis of a cohort of The Val Borbera is a large valley isolated from the surrounding areas by mountains and by a deep canyon on its western side. In the middle of 1800, the valley was inhabited by >10,000 people, living in seven villages located at about 400 to 800 m of altitude  in the Alessandria province . Due to th century, indicating some immigration. It increased sharply afterward, reached 80% with peaks of 90% in 1800 and started to decrease in the middle of the 1900.A complete genealogy of the population was reconstructed from birth, marriage and death records extracted from city archives starting from 1838 and from 17 (out of 24) parish church archives from approximately 1600. Data were collected from >96,000 records and the genealogy was reconstructed with a custom algorithm designed to manage the large number and assortment of different records from multiple sources across four centuries . Most of the population (89.5%) was included in a large genealogical tree of >50,000 people tracing back up to 16 generations. The population size was determined from the number of births/25 years periods . It seemA total of 1803 people aged from 18 to 102 years were enrolled in the study . This sath century, it increased to ∼1000 in the 18th century and to 1500 in the middle of the 19th century and decreased in the 20th century due to emigration  that included at least 16 generations ranging from 1500 to 1980. The average number of people in each generation, assuming 30 years intervals for each generation, was few hundreds individuals during the 17Using the full genealogy we calculated the kinship coefficient (kc) of the living descendants of the original population: the average kinship was 0.000373 with 3.5% of the entire population presenting a kinship >0. The average inbreeding in the population was 0.000746.We collected and investigated 52 quantitative traits, including blood (n = 41), anthropometric (n = 4), cardiovascular (n = 3) and bone status, as determined by quantitative ultrasound (QUS) parameters, (n = 4) and assessed the effect of age and sex on each trait –S4. As eFurther differences appeared when we compared the mean and standard deviations of all traits divided in four age ranges , each including approximately 25% of the sample , as wellThe large sample size allowed us to accurately measure the heritability of each trait considered. First, a simple model with two variance components  was used on data that were appropriately normalized to accommodate the assumptions of the model see  section.A wide range of heritabilities was observed for each group. All were statistically significant  to 1.46E-62 for height) with standard errors ranging from 0.04 to 0.07 . ConsideWe compared the heritability of each trait with other Italian population data either previously published  and 10th (n = 164) percentiles of the distribution of triglycerides: kc = 7.83E-4 versus 4.79E-4 for the rest of the populations and kc = 8.76E-4 versus 4.73E-4 . Significant enrichments in kc was also found among individuals (n = 176) in the 10th percentile of the waist circumference distribution  ribution : kc = 9.ribution  was overAnalysis of the family clustering of the extreme phenotype groups pinpointed to large multigenerational families (not shown) that are expected to be enriched for rare and major-effect variants. These families may represent an informative set of individuals suitable for quantitative trait linkage analysis.th century.In this paper we report a demographic and epidemiological analysis of a genetically isolated population, settled in Val Borbera, a large valley in the North West Apennines, in Italy. Around 60% of the modern descendants, still living in the 7 main villages of the valley or in the nearby areas, have been recruited based on their ancestry. Analysis of the large genealogy constructed starting from city and parish archives showed not only that endogamy was high in the past, but also that >90% of the participants to the study had 4 grandparents born in the valley and that 87% were connected in a unique large genealogical tree that included up to 16 generations tracing back to the 16We collected information on a large number of traits, including blood, cardiovascular, anthropometric and bone status QUS parameters. Most of the trait distributions presented large differences between age groups and sexes, in keeping with similar data from the general outbred population, and further confirming that genetic isolates are good representation of the general population.vs the other isolates, was clearly related to the significantly higher prevalence of alpha and beta thalassemia alleles among Sardinians as compared to continental Italy The size of the cohort and the availability of a genealogy provided the opportunity to measure accurately the heritability of many traits, to compare the effect of age and sex as well as to look for genetic determinants shared by different traits. Overall, trait heritability was on average higher for blood measurements and for anthropometric traits and lower for cardiovascular and bone status QUS parameters. These results were consistent with previous studies The analysis of genetic correlation coefficients showed overlap of genetic determinants for multiple traits. Many of the traits that shared genetic components were related, confirming the power of the analysis. However, other associations  were obtained in separate sessions, in the early morning. Blood was tested the same day or aliquoted and stored for further analysis.A standard battery of tests see  was perfBone status measurements were done with the DBM Sonic Bone Profiler , which follows transmission of an ultrasound beam through the four phalanges of one hand. Data was transferred and stored anonymously in a MySQL database.http://www.r-project.org]) were used for descriptive and inferential statistics analyses. Non-parametric testsd were used to investigate significance of the differences among independent, not normally distributed values. Gender differences were assessed by the Mann-Whitney U test, whereas the medians among ranges of age were compared using the the Kruskal-Wallis test.SPSS 17.0  and in-house R-2.8.1 scripts , was used to deal with kurtosis and skewness and to normalize each trait distribution.A maximum-likelihood variance components analysis was used to calculate the proportion of the overall variability due to the single covariates a2 and environmental component σe2 of each trait variance due to mean effects of single alleles. Polygenic model as performed by solar (http://solar.sfbrgenetics.org/) quantified heritability as h2 = σa2/σa2+σe2 and provided an estimate of the degree to which the offspring phenotypes are explained by parental phenotypes.A basic model was used to estimate the additive polygenic component σd2 and sibship effect σs2. In dominance model, variability of each trait is explained by Var(Ti) = σa2+σe2+σd2 and the covariance between values for each pair of individuals as Cov = 2ϕi,jσa2+Δijσd2 where Δij represents the probability that individuals i and j share two alleles identical by descent (IBD) based on their kinship coefficients. Genetic dominance model estimated heritability in broad sense as H2 = σa2+σd2/σa2+σe2+σd2. In household model the excess of similarity between siblings may be attributed to common early childhood environment and the variance is defined by Var(Ti) = σa2+σe2+σs2 and the covariance as Cov = 2ϕi,jσa2+Isib σs2 where Isib corresponds to 1 for full siblings and 0 for other pair of individuals on genealogy. Sibship effect model estimated heritability in narrow sense as h2 = σa2/σa2+σe2+σs2.Further models were used to estimate additional variance components as genetic dominance σhttp://www.sph.umich.edu/csg/chen/ghost/) was used to estimate the heritability of each pair of traits, to extract a genetic ρg and an environmental ρe correlation coefficient for each pair of traits A base polygenic model as developed in GHOST 0.0.9 | for all pair of traits, was used as input for the R function hclust (http://www.r-project.org/) to create a dendrogram object with an average agglomerative hierarchical method of clustering which connects trait by trait the most similar ones.A dissimilarity matrix, reported |1–ρhttp://www.stat.uchicago.edu/~mcpeek/software/CCQLSpackage1.3/) to calculate kc for each pair, based on the genealogy Using SPSS, each trait value was fitted by a linear regression curve that considers all significant covariates to estimate the expected values for each individual. The differences between observed and predicted values were calculated for each individual to obtain residual values of each trait considered. All individuals values were divided in percentiles and individuals in every percentile were submitted as input for KinInbcoef 1.0 Click here for additional data file.Figure S2http://solar.sfbrgenetics.org/).Power calculation of significant lod scores that could be obtained with the whole pedigree for traits of different heritability. Continuous line: lod score> = 3, broken line: lod score > = 2. The analysis was done with Solar ((0.11 MB TIF)Click here for additional data file.Table S1individuals/generation.(0.03 MB XLS)Click here for additional data file.Table S2Distribution of the traits.(0.03 MB XLS)Click here for additional data file.Table S3Mean values of the traits.(0.05 MB XLS)Click here for additional data file.Table S4Age ranges.(0.05 MB XLS)Click here for additional data file.Table S5Heritability parameters.(0.05 MB XLS)Click here for additional data file.Table S6Effect of each covariate on heritability.(0.03 MB XLS)Click here for additional data file.Table S7Clusters of traits above o.5 cutoff of Genetic Correlation.(0.03 MB XLS)Click here for additional data file.


Although genome sequences are available for an ever-increasing number of bacterial species, the availability of facile genetic tools for physiological analysis have generally lagged substantially behind traditional genetic models.Methylobacterium extorquens AM1 in order to demonstrate the utility of pCM433.Here I describe the development of an improved, broad-host-range "in-out" allelic exchange vector, pCM433, which permits the generation of clean, marker-free genetic manipulations. Wild-type and mutant alleles were reciprocally exchanged at three loci in Escherichia coli replicon for easy cloning, an IncP oriT enabling conjugal transfer, an extensive set of restriction sites in its polylinker, three antibiotic markers, and sacB (encoding levansucrase) for negative selection upon sucrose plates. These traits should permit pCM433 to be broadly applied across many bacterial taxa for marker-free allelic exchange, which is particularly important if multiple manipulations or more subtle genetic manipulations such as point mutations are desired.The broad-host-range vector for marker-free allelic exchange described here, pCM433, has the advantages of a high copy, general  The availability of genome sequences for hundreds of bacteria has helped reduce the disparity between the quality of physiological analyses available for these organisms and those possible for more traditional model organisms. Although genome-enabled approaches such as proteome and transcriptome analysis are now feasible for a great many systems, many of these non-traditional model organisms still have only limited genetic tools available for genetic manipulations. One particularly important tool is an allelic exchange vector to perform reverse genetic analysis. This generally involves homologous recombination of a selectable marker, such as an antibiotic resistance cassette, into the desired site on the chromosome. Issues arise, however, when the planned number of genetic manipulations outnumbers the number of markers available, or if the effect of mutations such as nucleotide substitutions, is desired.Escherichia coli is to use a combination of in vivo restriction of a donor plasmid with a transiently-expressed, highly active recombination system based on Lambda Red recombinase  and the plasmid has been deposited with Addgene .The sequence of pCM433 has been deposited [GenBank:CJM designed and carried out all experiments and drafted the final manuscript.


Canis familiaris, exhibits profound phenotypic diversity and is an ideal model organism for the genetic dissection of simple and complex traits. However, some of the most interesting phenotypes are fixed in particular breeds and are therefore less tractable to genetic analysis using classical segregation-based mapping approaches. We implemented an across breed mapping approach using a moderately dense SNP array, a low number of animals and breeds carefully selected for the phenotypes of interest to identify genetic variants responsible for breed-defining characteristics. Using a modest number of affected (10–30) and control (20–60) samples from multiple breeds, the correct chromosomal assignment was identified in a proof of concept experiment using three previously defined loci; hyperuricosuria, white spotting and chondrodysplasia. Genome-wide association was performed in a similar manner for one of the most striking morphological traits in dogs: brachycephalic head type. Although candidate gene approaches based on comparable phenotypes in mice and humans have been utilized for this trait, the causative gene has remained elusive using this method. Samples from nine affected breeds and thirteen control breeds identified strong genome-wide associations for brachycephalic head type on Cfa 1. Two independent datasets identified the same genomic region. Levels of relative heterozygosity in the associated region indicate that it has been subjected to a selective sweep, consistent with it being a breed defining morphological characteristic. Genotyping additional dogs in the region confirmed the association. To date, the genetic structure of dog breeds has primarily been exploited for genome wide association for segregating traits. These results demonstrate that non-segregating traits under strong selection are equally tractable to genetic analysis using small sample numbers.The domestic dog,  Each dog breed is defined by a specific combination of morphological, behavioral and coat color traits. Many of these phenotypic traits, as well as the underlying mutations, are shared between breeds. Some of these phenotypic traits have no effect on health, while others may be associated with certain medical problems. Brachycephaly  is one oBrachycephaly is characterized by severe shortening of the muzzle, and therefore the underlying bones, and a more modest shortening and widening of the skull Phenotypic traits that are common across many breeds, such as brachycephaly, are likely to be identical by descent, just as many other specific genetic traits have been shown to be shared between different dog breeds This unique structure of dog breeds has been useful in determining the genetic basis for many desirable and deleterious traits, an effort facilitated by new genetic technologies emanating from the sequencing of the canine genome In this study, an across breed mapping approach using SNP arrays and fine mapping techniques was used to identify chromosomal locations that are associated with brachycephaly using comparable sample numbers to studies that have investigated traits within single breeds. This approach takes advantage of the allelic nature of traits between breeds and the extended regions of linkage disequilibrium within breeds as well as the power of strong artificial selection for breed-defining traits 2 test, nominal p value (praw) = 2.89×10−11 and p value corrected by permutation testing (pgenome) = 2.0×10−4) was detected on CFA 3 at 70.2 Mb adjacent to the causative gene   is a change in urinary metabolism caused by a mutation in the SLC2A9 gene 72.4 Mb) . The sec2 test, nominal p value (praw) = 1.31×10−9 and permutated p value corrected for genome-wide search (pgenome) = 5.99×10−4) on CFA 20 at 24.88 Mb   . Althoug2 test, nominal p value (praw) = 2.34×10−10 and p value corrected for genome-wide search (pgenome) = 1.5×10−4 on the basis of 100,000 permutations)  . The assIn order to overcome the issue of breed-specific differences resulting in widespread false positives, several breeds were used that, aside from the trait under investigation, were phenotypically similar to the affected individuals. Affected breeds for brachycephalic head type included Japanese Chin, Affenpinscher, Pekingese, French Bulldog, Pug, Boston Terrier, Boxer, Bulldog and Shih Tzu. Control dogs were dolichocephalic (long muzzle) or mesaticephalic (medium muzzle length); they included Akitas, Bloodhounds, Dalmatians, Coonhounds, Belgian Tervurens, Whippets, Great Danes, German Shepherd Dogs and Black Russian Terriers. A complete list of affected and control dogs are given in supplemental 2 test, nominal p value (praw) = 4.0×10−12 and p value corrected for genome-wide search (pgenome) = 3.0×10−5 on the basis of 100,000 permutations). The genetically complex nature of the brachycephalic head type phenotype may be contributing to the associations across many different chromosomes; however, based on permutation testing, the association on CFA 1 is 1340 times stronger than the next highest association.Using this method, strong genome wide association was identified for multiple SNPs for brachycephalic head type . The bra2 test, nominal p value (praw) = 9.47×10−13 and p value corrected for genome-wide search (pgenome)<2.0×10−4)  .p value of 5.287×10−49. The next most significant p value for brachycephaly was 2.44×10−36.In order to further confirm the chromosome assignment for brachycephaly, a second independent data set was used for genome wide association. This dataset had fewer affected and control breeds but more individuals from each breed  that are much longer than those seen in humans, thereby allowing trait and disease mapping in dogs with very few individuals and many fewer SNPs than are needed in human studies. However, the length of LD in dogs varies by breed and is in fact highly breed-specific and dependent upon factors such as the age of the breed, population-limiting events such as bottlenecks and popular sire effects, and the breed population size p value of <0.05 would identify false positive chromosomal locations. In the three cases where the causative mutation had already been identified, the most significant association was properly assigned and was ∼100 times more likely than the next highest association using permutation testing. The falsely decreased p values were more extreme in the second dataset where fewer breeds and more individuals per breed were used. As new arrays are developed that can assay more SNPs, it may be possible to use across breed mapping for more complex traits. This approach is most effective if many breeds are used or if mixed breed animals are used in addition to purebreds to reduce the false positives due to breed differences in allele frequencies.In all four across breed mapping examples, a Across breed mapping, followed by permutation testing and selection of the best locus (>100 more significant than the next highest), successfully identified genomic regions of association for brachycephaly. Fine mapping confirmed and narrowed the interval and two genes, THBS2 and SMOC-2, were implicated. Brachycephaly has been under strong selection as a breed-defining characteristic and was probably fixed either before or soon after the breeds were artificially created. The signatures of selective sweeps Of the two genes associated with brachycephaly, the most promising candidate gene was THBS2. Thrombospondins (1 and 2) are expressed in bone and cartilage during development and in the adult skeleton The second candidate gene for brachycephaly, SMOC2, was located just outside of the critical interval for brachycephalic head type. Nonetheless, disruption of its expression could still be causative of the phenotype. It is a member of the SPARC (secreted protein acidic and rich in cysteine) family, a group of modular, matricellular proteins As demonstrated by these examples, purebred dogs offer a unique opportunity to identify the genetic etiology of extraordinary phenotypic diversity. Morphological traits such as brachycephaly are characteristic of certain breeds and had a functional purpose at the time that those breeds were created. The fact that such traits were fixed within breeds by strong selection by breeders allows the use of genetic approaches that are not feasible in other species. The small number of samples used for this analysis highlights the power of the canine model system for genetic analysis of traits and implies its potential application to identify other important disease loci in dogs.http://genome.hku.hk/portal/index.php/affymetrix-genechipr-genotyping/sample-preparation) or through the use of Centricon spin columns (Millipore) and eluted in reduced-EDTA TE buffer .Blood samples were acquired from patients of the University of California at Davis William R. Pritchard Veterinary Medical Teaching Hospital. All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies, and all animal work was approved by the Institutional Animal Care and Use Committee (IACUC), approved Protocol for Animal Care and Use #15356. Genomic DNA was isolated using the QiaAmp DNA Blood Mini Kit (Qiagen Inc). DNA samples were purified according to Affymetrix sample preparation guidelines . Microarray work was performed by the UC Davis Cancer Center Genomics and Expression Shared Resource, Sacramento, CA. DNA was amplified and labeled according to the manufacturer's protocols, and arrays were washed and stained on a Fluidics Station 450 and were scanned on a GeneChip Scanner 3000. A complete list of the dogs used is available in supplemental tables S1 and S2 for the two datasets.p value of <0.01 were used for analysis. The program PLINK Only genotype calls with a The second independent dataset was also run on the Affymetrix Version 2 Canine array with 49,663 SNPs (50K). Approximately 10 dogs from each of the breeds used were randomly chosen for analysis. For brachycephaly, three breeds were considered as cases  and 13 breeds  were considered as controls. Genome wide association calculations for single marker chi-square were performed using the software package PLINK To check for a selective sweep, a relative decrease in heterozygosity was evaluated. When determining results for a decrease in heterozygosity relative to controls, all dogs with a call rate of >75% were used. SNPs with a call rate < 95% were removed. The rate of heterozygosity was first calculated for control breeds and case breeds separately and then the ratio between those was used as a measure of relative heterozygosity. The relative heterozygosity was normalized in a way that gives negative values when cases are less heterozygous than controls and otherwise positive values. The normalization was performed by the following formula: ratio = 1 -1/ratio if ratio < 1 and ratio ≠ 0. In other cases, the ratio was diminished by 1.http://www.ncbi.nlm.nih.gov/SNP/). SNPs were selected for each region based on known polymorphisms between the Boxer reference sequence and other breeds. Acceptable, documented SNPs were interrogated in 96 affected dogs from various breeds and 187 controls. These samples were genotyped for the selected SNPs by Sequenom-based MassARRAY genotyping through GeneSeek .SNPs chosen for fine-structure mapping were harvested through the online Entrez SNP database, dbSNP (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi)  . The desRNA was extracted from liver samples from a Boxer and a Dalmatian for brachycephalic head type using the Fast Track 2.0 mRNA Isolation Kit (Invitrogen). cDNA was synthesized from these samples using the SuperScript III First-Strand Synthesis System (Invitrogen). RT-PCR was performed for both THBS2 (liver) and SMOC2 (liver) in order to define the cDNA structure of these genes.PCR was performed on a GeneAmp PCR system 9700 (Applied Biosystems) with 1 µL DNA, 2.5 mM dNTP, 0.5U AmpliTaq Gold Polymerase (Applied Biosystems), 1 µL 10X Buffer II with 1.5 mM MgCl2, and water to 20 µl. Amplification parameters were: 95°C for 12 min, 35 cycles of 94°C for 30 s, primer-specific annealing temperature for 30 s, and 72°C for 45 s, with a final extension of 72°C for 20 min. Products were purified using the QIAquick PCR Purification Kit (Qiagen Inc).Purified PCR products were sequenced with the ABI Prism BIGDye Terminator Cycle Sequencing Kit (version 3.1) and unincorporated dye terminators were removed with the BigDye XTerminator Purification kit (Applied Biosystems). Sequencing products were processed on an ABI Prism 3100 Genetic Analyzer (Applied Biosystems) and visualized using Chromas2 (Technelysium). Sequences were obtained from a Boxer and a Dalmatian control for brachycephalic head type. The available canine genome sequence (Boxer) was used as an additional affected dog for brachycephalic head type. Sequences were submitted to GenBank (accession numbers∶GU123606-GU123608). Sequence alignment was performed using VectorNTI software (Informax).Table S1(0.02 MB XLS)Click here for additional data file.Table S2(0.03 MB XLS)Click here for additional data file.Table S3(0.03 MB XLS)Click here for additional data file.