Leishmania infection are unclear, although previous studies suggest a role for host genetics. Our hypothesis was that canine leishmaniasis is a complex disease with multiple loci responsible for the progression of the disease from Leishmania infection.The current disease model for leishmaniasis suggests that only a proportion of infected individuals develop clinical disease, while others are asymptomatically infected due to immune control of infection. The factors that determine whether individuals progress to clinical disease following Genome-wide association and genomic selection approaches were applied to a population-based case-control dataset of 219 dogs from a single breed (Boxer) genotyped for ∼170,000 SNPs. Firstly, we aimed to identify individual disease loci; secondly, we quantified the genetic component of the observed phenotypic variance; and thirdly, we tested whether genome-wide SNP data could accurately predict the disease.We estimated that a substantial proportion of the genome is affecting the trait and that its heritability could be as high as 60%. Using the genome-wide association approach, the strongest associations were on chromosomes 1, 4 and 20, although none of these were statistically significant at a genome-wide level and after correcting for genetic stratification and lifestyle. Amongst these associations, chromosome 4: 61.2–76.9 Mb maps to a locus that has previously been associated with host susceptibility to human and murine leishmaniasis, and genomic selection estimated markers in this region to have the greatest effect on the phenotype. We therefore propose these regions as candidates for replication studies. An important finding of this study was the significant predictive value from using the genomic information. We found that the phenotype could be predicted with an accuracy of ∼0.29 in new samples and that the affection status was correctly predicted in 60% of dogs, significantly higher than expected by chance, and with satisfactory sensitivity-specificity values (AUC = 0.63). Leishmania and transmitted by the bite of phlebotomine sand flies. Around the Mediterranean basin, visceral (VL) and cutaneous (CL) human leishmaniasis as well as canine leishmaniasis (CanL) are caused by Leishmania infantum. The current disease model for leishmaniasis suggests that infected individuals may live without progression towards clinical disease manifestation probably due to immune control of the infection.Leishmaniasis is a vector-borne disease affecting humans and animals, caused by parasitic species of the genera Leishmania infection are unclear, but previous studies suggest a large contribution of the host genetic background, as reviewed elsewhere Leishmania infection. In humans, most epidemiological studies et al.Leishmania infection is supported by the fact that the percentage of infected dogs in endemic areas is as high as 60% The factors that determine whether individuals progress to clinical disease following Leishmania infantum infection for humans, and CanL is a disease of great importance in veterinary medicine because of its severity in the dog. Despite the importance of leishmaniasis in dogs, there have been very few genetic studies of this species and these have focused on a few candidate genes Understanding the genomic factors controlling progression to clinical disease in dogs is critical since the dog is the main natural reservoir of The dog has been previously proposed as a comparative animal genetic model for disease mapping. For complex diseases, a strategy with a first genome-wide scan genotyping tens of thousands of single-nucleotide polymorphisms (SNPs) for a few hundreds of dogs from one or few breeds has been suggested Leishmania infection. Our working hypothesis were: (i) that the observed phenotypic variance in the progression of leishmaniasis in infected dogs is partly explained by the genetics of the host; (ii) that CanL is a complex disease with multiple loci involved and an environmental component; and (iii) that genomic information may be used to predict the progression of the disease. We applied both genome-wide association study (GWAS) and genomic selection approaches to a population-based case-control dataset of 219 dogs from a single dog breed (Boxer) genotyped for ∼170,000 single-nucleotide polymorphisms (SNPs) in order to study host genetic susceptibility to progression of clinical leishmaniasis from Leishmania infection. Firstly, we tried to identify loci in the canine genome associated with the disease progression phenotype. Secondly, we investigated the genetic component of the observed phenotypic variance. Thirdly, we examined whether genome-wide SNP data could be used to predict accurately the phenotype.The aim of this work was therefore to carry out a genome-wide study of the genetic contribution to the progression of clinical CanL from Leishmania infection, using a dataset of 115 healthy infected and 104 affected Boxer dogs. All dogs had genotypes for 126,607 SNPs distributed across the genome.A GWAS analysis testing markers individually was performed in order to find loci associated with the progression to clinical CanL from Canis familiaris chromosomes (CFA) 1:39,058,553 bp , CFA 4: 68,238,371 bp  and CFA 20: 30,132,329 bp  . Although healthy infected and affected samples generally clustered together in the MDS plot , genetic stratification was observed in our cleaned dataset based on the genomic inflation factor (λ = 1.29), with C1 capturing twice the stratification captured by C2. The associations on CFA1 and 4 remained when confounding effects were accounted for although significance did not reach the genome-wide level (Table S1). Genetic stratification, corrected by fitting the two first dimensions from the multidimensional scaling analysis (C1 and C2), is likely to explain part of the initial association in Model 1, as Praw values for the ten strongest associated SNPs on CFA 1 and 4 were an order of magnitude higher when stratification was accounted for (Model 2). Nevertheless, associations of C1 and C2 with each of these markers were not significant (data not shown). Inclusion of dog lifestyle as a confounding effect did not affect the significance of the markers. After correction for the confounder effects (Model 3) the inflation factor was reduced to λ = 1.17 and this was not reduced by adding three additional MDS dimensions which altogether captured an extra 5% of the genetic variance in the markers (Table S2).Three statistical models were applied by fitting additional covariates in order to correct for the two confounding effects considered (described in Leishmania infection and susceptibility to leishmaniasis in Homo sapiens (49 loci) and Mus musculus (33 loci) to test in a systematic way if any of these loci showed a stronger association in our canine dataset. When possible, these were mapped to their orthologues in the dog genome, and this was successful for 78 loci . We selected SNPs in the GWAS data contained within these candidate loci and their flanking regions (±1 Mb) and assigned them to sets of non-overlapping candidate regions. This resulted in 4,751 SNPs in 37 sets with a median of 108 SNPs (Dataset S1). Sets were tested one at a time for association with the phenotype controlling for within-set linkage disequilibrium (LD) and multiple testing arising from the number of SNPs in the set as described elsewhere 2 = 0.80 and p = 0.05 were used). Three sets of SNPs on CFA 4, and one each on CFA 9 and 10 showed an empirical set-specific p-value (EMP1)<0.05 (Dataset S1). The same sets showed EMP1<0.01 when r2 = 0.10 and p = 0.01 were applied . The sets on CFA 4 spanned the region 61.2–76.9 Mb which had previously showed the strongest associations in the initial GWAS (Table S1). All the sets contain loci associated with Leishmania infection, and the three sets on CFA 4 included several genes  that lie within a locus involved in lesion development in murine Leishmania major infection We examined candidate loci previously reported to have associations with host response to et al.a priori assumption that only few SNPs are useful for predicting the trait. With the modified BayesB method we used (from now onwards referred just as BayesB for simplicity), Models 1–3 produced a similar genome-wide plot of both estimated marker effects  and the proportion of realisations a given marker was estimated to have a non-zero effect (data not shown), with a most detectable peak on CFA 4:61–77 Mb. This region overlapped with both the strongest association in GWAS and the region in which SNP sets covering candidate genes were significant (EMP1<0.01).Finally, the same dataset used in the GWAS was analysed using genomic selection with the BayesB method Table S3, in all cases heritability was found notably greater than zero and it went down to 0.32 with prevalence equal to 0.01.GWAS methodology is concerned with identifying individual SNPs that may be a causative variant for the phenotype or in LD with such a variant. Despite the failure to detect any such SNP, it was possible to detect genetic variation relating to the leishmaniasis phenotype. Two different approaches were adopted, the first using a modified BayesB methodology Using BayesB the fraction of markers contributing to the genetic variance was estimated as 0.015 (s.e. 0.011), however experience with such methods suggests that this fraction is sensitive to the distribution of allele effects that is assumed (results not shown). The inclusion of an additional MDS dimension (C3) did not change the results compared with Model 3.Cross-validation was used to test the predictive potential of genomic evaluation. Five cross-validation sets (denoted A–E) were produced at random from the full dataset to estimate the predictive benefit when new individuals, which have not been used to estimate the effects of markers and covariates, are genotyped in order to predict their phenotypes. Two approaches to assess the predictive value were adopted: the accuracy to predict the phenotype and the capability to diagnose individuals from genomic information.r) for predicting the phenotype. The Model 1 results suggest that the combined SNP effects predict the phenotype with an accuracy of 0.18 and that, by comparison with Model 2, little accuracy is added by including covariates correcting for genetic stratification  and genetic stratification (Model 2) were included. Statistical significance was observed only when lifestyle was included (Model 3), which confirms the earlier result that lifestyle has predictive value., , Secondly, to test the contribution of the genomic data, the predictions obtained from the BayesB analysis were decomposed into the component from the covariates and the component from the SNPs. The SNP component was then permuted within the cross-validation set as described in the Finally, the magnitude of the benefit from the genomic data was assessed by predictions that excluded all genomic data. Overall accuracy obtained by C1 and C2 as explanatory factors alone was not significant Table 3.Nevertheless, as may be expected from the relatively small data sets, there is considerable variation among the cross validation sets, and confidence intervals within individual cross-validation sets are large. Predictive accuracies were significant in sets C and D, but were not significant in sets A, B and E Table 2,g), notably higher than the expected by chance alone   curves were generated from sensitivity and specificity values for different thresholds and the area under the curve (AUC) was calculated as an indicative of the balance between sensitivity and specificity. AUC values were notably higher than randomness and Model 3 achieved the best performance Figure 1Leishmania infection and how such information may be used to predict disease course. Our first analysis was focused on identifying individual loci in the canine genome which contributed medium to large effects for determining disease development. Different analyses associated CFA 4: 61–77 Mb. The strongest association in the GWAS analysis was for markers in this region, even when we considered confounding factors such as lifestyle and genetic stratification, whose causes are discussed below. However, these associations were not significant when corrected for multiple testing . The lack of genome-wide significance at the individual SNP level may indicate that our study was underpowered for GWAS due to the small sample size of our study. However the size of the study was at the lower end of the range of 100–300 cases and 100–300 controls that has been suggested for GWAS in dogs in complex diseases In this study we have explored the contribution of genetic loci in the dog genome for determining clinical progression of disease following Leishmania and susceptibility to leishmaniasis in humans and mice Dataset S1 and Figure S3).Interestingly, when we tested for association focusing only on SNPs residing within candidate loci related to host response to Figure S4). Chromosome 4: 61–77 Mb is syntenic to a locus that mediates host response to Leishmania major in mice, which includes the candidate genes Il7r, Lifr, C6 and C7Il7r (CFA 4: 75.8 Mb) is of special interest as, although healthy infected and affected samples showed similar MAF and observed heterozygosity values along CFA 4: 61–77 Mb, in both groups three SNPs (CFA 4: 75.7–75.9 Mb) flanking Il7r significantly deviated from HWE  (data not shown). Extended patterns of markers deviating from HWE may indicate copy number variants. Variation in the number of copies between affected and healthy infected cannot be detected through differences in genotype frequencies though it might affect the phenotype. In fact, structural variations have been described for the syntenic region in the human genome SPEF2, CAPSL, UGT3A1 and UGT3A2 in addition to IL7R. In mice, structural variation has also been reported for a shorter region overlapping Ugt3a1Leishmania infection.In addition, from the BayesB analysis, markers in this region of CFA 4 had a larger estimated effect on the phenotype than other genome-wide markers . This design was chosen as the use of a single breed for the first stage of GWAS in dogs will increase power by reducing effects of genetic differentiation between breeds and increasing the degree of linkage disequilibrium e.g. bone marrow) as well as conjunctiva or lesion swabs were received occasionally. With regard to CanL, no pre-screening of the samples sent to the laboratory was done by the veterinary clinics. Dogs affected by CanL included those with a documented history of disease, undergoing a relapse or newly diagnosed. For those showing a disease episode when samples were collected, additional samples were requested one month after treatment or if requested by the referring clinician in order to both confirm the diagnosis and help inform then clinician on treatment response. For healthy infected dogs (see below), additional samples and medical information were requested to confirm the absence of CanL development. Inclusion of additional samples relied on the collaboration of veterinary clinics.Veterinary clinics and dog owners were encouraged to participate in the study, in the case of the latter through their veterinary centre. Two millilitres of EDTA peripheral blood and 2 ml of serum were required. In addition, other tissues (Leishmania immune reactions into the following groups: (i) healthy infected: healthy and >4 years old but with evidence of prior infection and (ii) affected: manifest clinical disease and diagnosed before the age of 4 years. Ages were recorded as age-of-onset of disease for affected and current age at sample collection for healthy infected dogs. Lifestyle , gender, level of relatedness and geographic location of origin were also collected. Leishmania quantitative polymerase chain reaction (qPCR) and anti-Leishmania Enzyme-Linked ImmunoSorbent Assay (ELISA) were performed on all samples from all dogs, although additional results from direct parasite detection and anti-Leishmania immune tests were provided by veterinary clinics for most samples. Leishmania qPCR was performed at the Servei Veterinari de Genètica Molecular, UAB, as described previously Dogs were classified by clinical signs, clinical biochemistry, direct parasite detection and anti-DNA was extracted from peripheral blood and bone marrow samples using either QIAamp® DNA Blood Mini Kit (QIAGEN) or by conventional phenol-chloroform DNA extraction and deproteinization methods. All samples were genotyped using the Illumina CanineHD BeadChip  n individuals was calculated from which n dimensions were extracted using MDS analysis, resulting in a matrix of n-samples by n-dimensions eigenvalues. The fraction of genetic variance explained by each dimension was calculated as the variance for a given dimension along all samples divided by the sum of variances for all the dimensions extracted. The eigenvalues for the first two dimensions (C1 and C2) of the MDS analysis were used as continuous covariates. For simplicity, only C1 and C2 were used because the fraction of additional genetic variance explained by each of the subsequent 217 MDS dimensions extracted was minimal . Although healthy infected and affected samples generally clustered together in the MDS plot , genetic stratification was observed in our cleaned dataset based on the genomic inflation factor (λ = 1.29), with C1 capturing twice the stratification captured by C2. Therefore, C1 and C2 values for each sample were fitted as continuous covariates in the indicated models. CanL is known to be a complex disease with an environmental component and thus dogs living outdoors, more exposed to infection, are believed to more frequently develop the disease. Hence, lifestyle was also included as a factor in the analyses for the models indicated. For Model 3, the inflation factor was reduced to λ = 1.17. In the logistic regression, lifestyle was fitted as a factor (one degree of freedom) using dummy variables for indoors, indoors/outdoors, outdoors and ‘undetermined’. In the genomic selection analyses, lifestyle was considered as a categorical covariate with four levels. Three genetic models varying in whether covariates were fitted were defined to explain the phenotype (y), treated as binary :Two confounding effects were considered and fitted into the statistical analyses: genetic stratification and dog lifestyle. Using PLINK Model 1: y∼SNPsModel 2: y∼SNPs+C1+C2Model 3: y∼SNPs+C1+C2+lifestylegenome) was obtained after 10,000 permutations. Based on the permutations carried out in our dataset, the uncorrected p-value that would reach genome-wide significance (at the 5% level) after correction for multiple testing in our study would be p = 2.08×10−6. Formally, first, for each permutation the maximum statistic across all SNPs was recorded and, second, from this distribution of maximum statistics, the statistic in the top 5% is used to give the p = 2.08×10−6 that would be significant after permutations.Markers were tested for association using the Cochran-Armitage for trend test (Model 1) and logistic regression (Models 2 and 3). Genome-wide significance (PLeishmania and susceptibility to leishmaniasis in Mus musculus and Homo sapiensC. familiaris using Biomart Dataset 1). Set-based association tests were performed as described in PLINK 2 = 0.80, p-value = 0.05; and (2) r2 = 0.10, p-value = 0.01. In both cases, a maximum set size of 10 SNPs (∼10% of the median set size) was used. The Cochran-Armitage for trend test was used and 10,000 within-set permutations were conducted to obtain empirical set-based p-values (EMP1).Candidate loci reported as related to host response to ν = 2) and a scale parameter (S) of 0.001 (weak prior). The Markov chain Monte Carlo (MCMC) of BayesB was run for 160,000 cycles and the first 10,000 cycles were discarded as burn in; 3,000 realisations of sampling were performed with 50 cycles between realisations. Absolute variation between each of the 3,000 sampled values of posterior S and their prior S (either S = 0.001 or S = 0.1) was calculated and a Welch two sample t-test was applied. The same test was applied to the sampled values of (1–π) produced by each of weak and stronger S priors. BayesB produced estimates of the genomic breeding values (GEBV) and of the effects of C1, C2 and each lifestyle category, which were used to calculate fitted values of the phenotype (ŷ) according to the different predictive models (Text S1).Datasets were analysed with the BayesB method S = 0.1 (strong prior), which was 100 times higher than the value used otherwise. In absolute values, the posterior S value changed a 58.7% respect to the weak prior (S = 0.001) whereas this variation was significantly greater, 78.9%, for S = 0.1 . Another effect of giving a stronger prior S was that the posterior proportion of markers with a non-zero effect (1–π) was 100 times lower compared to the obtained for the weak prior  and the fraction of genetic variance was also lower . Moreover, the genome-wide pattern of estimated SNP effects was notably different depending on the prior given. With a weak prior most markers had non-zero but very low estimated effect (10%-quantile = 10−5) whereas a small fraction of SNPs (10%-quantile = 0) had 10-fold estimated effects with a stronger prior. Altogether these results can be explained by a scenario in which fewer SNPs with greater effect contribute to the phenotype when a greater prior variance of the additive parameter is allowed.In order to assess the effect of the weak informative prior distribution used for the variance of the additive parameter on the resulting posterior value, for Model 1 the analysis was repeated with When calculating the genetic relationship matrix (GRM) with GCTA C1, EC2, Elife from samples in the training set and produce GEBV for the testing data samples and then calculate their fitted values, according to the corresponding predictive model.Samples were assigned randomly to one of five training sets (denoted A–E) so that (i) each training set had a size of approximately 4/5 of the full unpermuted dataset and (ii) in each training set the proportions of samples belonging to each phenotype  and lifestyle categories were approximately as in the full dataset. Samples not included in each training set were used as testing data. For each training set, BayesB was run to both estimate Er) was calculated as the correlation between fitted values (ŷ) and true phenotypes (y). In each testing set, r was calculated as a measure of accuracy to predict the phenotype. The overall correlation for the full unpermuted dataset was calculated by combining the predictions across sets. The contribution of markers to the accuracy was analysed in three ways. First, GEBV generated with BayesB were permuted before the calculation of fitted values. In this way, the correspondence between phenotypes and covariates was not altered. Within each model, 100 sets of permuted GEBV, resulting in 100 sets of permuted fitted values, were generated for each set. The empirical p-value for the real data was computed as the fraction of permuted sets with a lower p-value that the real data. Second, genotypes were randomized respective to phenotypes and covariates, which were kept as in the original data. The BayesB analysis was then run and cross-validation applied as explained before. Third, fitted values were calculated using uniquely covariates, i.e. GEBV were not used.Accuracy (g) values were calculated (Text S1) for increasing thresholds of fitted values and ROC curves were generated by plotting specificity against sensitivity. The area under the ROC curve (AUC) was calculated as a measure of similarity between specificity and sensitivity.For Models 1–3, receiver operating characteristic (ROC) curves were calculated as follows. A fitted value threshold was set so that below or above it individuals were predicted to be healthy infected or affected, respectively. Specificity, sensitivity and fraction of correct predictions Click here for additional data file.Figure S2Genetic stratification. (A) relative genetic variance explained by the 219 MDS dimensions extracted; (B) MDS plot for the first two MDS dimensions (C1 and C2) with healthy infected and affected samples coloured differently. The percentage of relative genetic variance explained by each dimension is indicated as well as the genomic inflation factor (lambda).(TIFF)Click here for additional data file.Figure S3Distribution of EMP1 across SNP sets of candidate regions. Sets comprise SNPs in the following regions: 6 (CFA 4:61.2–63.2 Mb), 7 (CFA 4: 70.5–74.5 Mb), 8 (CFA 4: 74.8–76.9 Mb), 19 (CFA 9: 40.0–46.5 Mb) and 22 (CFA 10: 29.6–31.5 Mb).(TIFF)Click here for additional data file.Figure S4Genome-wide plot of the absolute mean SNP effects estimated with BayesB for Model 1 (A), Model 2 (B) and Model 3 (C). The peak on CFA 4: 61–77 Mb (red segment) consistent across Models 1–3 coincided with both the strongest association in GWAS analysis and the region in which SNP sets covering candidate genes were significant (EMP1<0.01).(TIFF)Click here for additional data file.Table S1Strongest associations from each region identified in the GWAS analysis. BICF2P1345879 was not used in models 2 and 3 because, for logistic regression, SNPs were pruned based on LD . Choice of SNPs representing each genomic region was based on the strongest associations in Model 1. Canis familiaris genes (CanFam_2.0) were retrieved using Biomart and associated gene names are given, with the exception of some for which no gene name was available and the Ensembl ID is given instead. The same information is presented for the strongest associations on chromosomes 9 and 10 from the set-based analysis.(XLS)Click here for additional data file.Table S2Genomic inflation (λ) was not affected by fitting additional MDS dimensions as covariates of the model.(DOC)Click here for additional data file.Table S3Sensitivity of heritability (2h) estimation using GCTA to prevalence of the phenotype is shown for Model 3.(DOC)Click here for additional data file.Text S1Fitted values, fraction of correct predictions, sensitivity and specificity calculation.(DOC)Click here for additional data file.Dataset S1Candidate genes analysis: (A) candidate genes and loci described in H. sapiens and M. musculus and retrieved genomic positions in C. familiaris; (B) sets of non-overlapping candidate regions plus their ±1 Mb-flanking regions; (C) results from the set-based association study.(XLS)Click here for additional data file.


SIRT1 and PDGFD.Genetic markers with rare variants are spread out in the genome, making it necessary and difficult to consider them in genetic association studies. Consequently, wisely combining rare variants into “composite” markers may facilitate meaningful analyses. In this paper, we propose a novel approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator technique. We applied this method to the Genetic Analysis Workshop 17 data, and our results suggest that this new approach is promising. In addition, we took advantage of having 200 phenotype replications and assessed the performance of our approach by means of repeated classification tree analyses. Our method and analyses were performed without knowledge of the underlying simulating model. Our method identified 38 markers (in 65 genes) that are significantly associated with the phenotype Affected and correctly identified two causal genes,  Although genome-wide association studies based on the common disease/common variant assumption have identified many disease-causing genetic variants, the variants usually explain only a small percentage of disease risk . This suGiven the discrepancy between relatively high common disease prevalence and low MAF of a rare variant single-nucleotide polymorphism (SNP), it is biologically unlikely that the bulk of disease risk is attributable to a single rare variant. Instead, it is more likely that multiple rare variants increase disease risk . AccordiSeveral methods for exploring associations between multiple rare variants and disease risk have been proposed -6. A priWe present a method that attempts to remedy some of the current problems with two chief improvements: First, SNPs are combined only when they show a joint effect on the response; and, second, SNPs that are not in a gene are grouped according to their positions on a chromosome. We applied this method to the GAW17 data, and our preliminary results suggest that this approach is promising. Our method and analyses were performed without knowledge of the underlying simulating model.n = 21,355) of them have MAF < 0.05, 74.0%  of them have MAF < 0.01, and 38.5%  of them have MAF < 0.001. See the GAW17 simulation materials for additional details on the data [Our analysis focuses on the GAW17 case-control data (with outcome phenotype Affected), which consists of 697 unrelated individuals from 17 different population (race or ethnicity) groups. Four other risk factors are included in the data: race or ethnicity, age, sex, and smoking status. There are 200 phenotype replicates based on the same genotype information. Among the 24,487 SNPs in the data, 87.2%  and evaluating population structure by means of stratification analysis. These steps were taken as a necessary precaution using PLINK .p < 0.0001 [HWE was evaluated to determine whether the genotype frequencies obtained in the data match those expected under HWE conditions. Consequently, we excluded from further analyses all SNPs with MAF > 0.05 and all SNPs that failed the HWE test at < 0.0001 . We alsoThe crux of our method is to produce composite markers using the least absolute shrinkage and selection operator (LASSO) procedure, which represents SNPs in a predefined group. This is a two-step process: First, given a sufficiently large number of rare variant SNPs within a gene, we group rare variant SNPs within their gene. Then all remaining SNPs that are of limited number in any given gene are grouped according to their positions on a chromosome. Furthermore, SNPs are combined only when they show a joint effect on the response using the LASSO method .Specifically, we first run the LASSO procedure on SNPs within the same gene and then again on the sparse SNPs within a group that spreads over different genes. A 0–1 dummy variable is created for each SNP based on the presence or absence of the rare variant. Then, linear combinations of the selected dummy variables are considered by using the LASSO procedure. Even though most of the dummy variables are 0, their linear combination is far more likely to be nonzero. In fact, we include only those linear combinations that are nonzero in at least 5% of the subjects. This ensures that the new markers are not rare.Covariates can be readily incorporated into the LASSO technique by treating them as additional predictive variables. If particular covariates are known to be significantly associated with the phenotype, then they will not be penalized when running the LASSO procedure. Otherwise, the covariates are also penalized and subject to selection. In our analysis of the GAW17 data, the covariates  were not penalized. The Akaike information criterion (AIC) was used to select the best LASSO tuning parameter  corresponding to the best-fitting model. The entire LASSO path was computed using the R package glmnet .The GAW17 data consist of 200 phenotype replications based on the same genotype information; this affords us the ability to conduct our marker selection method on one of the replicates and to use the remaining 199 replications to evaluate the performance of our method. We generated new markers on the first phenotype replication according to the SNP screening process, as introduced in the Methods section.To take advantage of the remaining 199 phenotype replications based on the same genotype information, we assessed our new markers using an association analysis with the phenotype Affected. Any appropriate association method could have been used, although we opted to evaluate performance by conducting classification tree analyses  on the rThe classification analysis considered all acceptable markers produced by our method and adjusted for sex, age, smoking status, and race or ethnicity covariates. Thus for each replicate we constructed a classification tree using the same 493 markers and the phenotype Affected; 199 classification trees were constructed in total. We used the R package rpart for this analysis and applied the default setting of the rpart function. We took advantage of all the simulated replicates to evaluate the effectiveness of our method. In practice, our method can be used with or without replicates. When there are no additional phenotype replications, markers can be constructed and then used to build a tree. To evaluate the significance of the identified markers, one can generate additional data under the null hypothesis by randomly permuting affection status. This has been shown to be an effective technique in previous studies . In the Heuristically, more frequently appearing markers across all of the 199 association analyses have a higher chance of contributing to the phenotype. For each of the 199 constructed association analysis trees, the null hypothesis is that the outcome phenotype, Affected, is independent of the markers, conditional on the covariates. Our screening process, as described in the Methods section, produced 493 nonrare linear combinations of the dummy variables (made from SNPs). Thus under the null hypothesis of no association (independence) between phenotype and any marker, each of the 493 generated markers has an equal chance of being selected to split a node in a tree. The probability that a marker will be selected at any particular split is 1/493. In addition, under the null hypothesis of independence, whether the marker is selected as a splitting variable in a previous split does not effectively change the probability that the same marker will be selected as a splitting variable in subsequent splits.ith tree for i =  as is, and denote the total number of splits selected by all 199 trees as S, 1/493). Although there are several ways to adjust for the false-positive rate resulting from multiplicity, we used the Bonferroni correction to err on the side of caution. Thus using a Bonferroni-corrected significance level of 0.05, we find that the genome-wide-significance level is 0.05/493. Therefore a particular split is considered genome-wide significant if it meets or exceeds the quantile value of 0.05/493 under Bin; therefore 14 occurrences across the 199 analyses is the critical value.Denote the size (number of splits) of the SIRT1 and PDGFD, are correctly identified [Our method identified 38 newly generated markers (composed of 65 genes) that are significantly associated with the phenotype Affected. The mean number of markers used per tree was 11.37 with a standard deviation of 3.98. In Table We propose a novel approach to analyzing rare variant data by incorporating LASSO variable selection. This is a relatively easy-to-implement approach. The results from analyzing the GAW17 data suggest that the new approach can be useful for analyzing rare variant data.We made use of all phenotype replicates provided by GAW17; the first replicate was used to generate markers, and the remaining replicates were used to evaluate the performance of the proposed method. In a real data analysis, however, the goal is typically to apply, not evaluate, the method. Chen et al.  presenteOur method and analyses were performed without knowledge of the underlying simulating model. It should be noted, however, that the false-positive rate from the GAW17 data analysis was high because of the low level of signal, in particular, the low MAF of the rare variant SNPs.The authors declare that there is/are no competing interests.EN, JSB, RC, YH, and YJ carried out the analyses. JSB and YH drafted the manuscript. HZ conceived and led the analysis, and revised and finalized the manuscript. All authors read and approved the final manuscript.


With the advances in high-throughput genotyping technology, the study of quantitative trait loci (QTL) has emerged as a promising tool to understand the genetic basis of complex traits. Methodology development for the study of QTL recently has attracted significant research attention. Local phylogeny-based methods have been demonstrated to be powerful tools for uncovering significant associations between phenotypes and single-nucleotide polymorphism markers. However, most existing methods are designed for homozygous genotypes, and a separate haplotype reconstruction step is often needed to resolve heterozygous genotypes. This approach has limited power to detect nonadditive genetic effects and imposes an extensive computational burden. In this article, we propose a new method, HTreeQA, that uses a tristate semi-perfect phylogeny tree to approximate the perfect phylogeny used in existing methods. The semi-perfect phylogeny trees are used as high-level markers for association study. HTreeQA uses the genotype data as direct input without phasing. HTreeQA can handle complex local population structures. It is suitable for QTL mapping on any mouse populations, including the incipient Collaborative Cross lines. Applied HTreeQA, significant QTLs are found for two phenotypes of the PreCC lines, white head spot and running distance at day 5/6. These findings are consistent with known genes and QTL discovered in independent studies. Simulation studies under three different genetic models show that HTreeQA can detect a wider range of genetic effects and is more efficient than existing phylogeny-based approaches. We also provide rigorous theoretical analysis to show that HTreeQA has a lower error rate than alternative methods. The goal of quantitative trait locus (QTL) mapping is to find strong associations representing  causal genetic effects between observed quantitative traits and genetic variations. There are several mouse resources such as the Collaborative Cross (CC)  are used to measure the significance of association between a phenotype and every single nucleotide polymorphism (SNP) in the genome. These single marker−based methods usually do not consider the effects of (both genotyped and ungenotyped) neighboring markers and hence may fail to discover QTL for complex traits. To address this limitation, cluster-based methods, such as HAM However, e.g., TreeLD , which is not true for many large mammalian resources, including the PreCC lines, or that a preprocessing step phases each genotype into a pair of haplotypes. However, haplotype reconstruction itself is a nontrivial process that is both time-consuming . We study two phenotypes. One is the white head spot, which was originally observed on one of the CC founders, WSB/EiJ. Because there are no white head-spotted mice found in F1 crosses of the CC founders, the phenotype is believed to be a recessive trait. Among the 184 mice, there are four with white head spot. Another phenotype we study is the average daily running distance for mice of 5 to 6 days old. This is a typical measurement for mouse activity. The phentotypes are supplied as supporting information, File S1.We use the genotypes of 184 partially inbred mice from the CC lines  models. We include the overdominant model because we observe that heterozygous individuals sometimes exhibit extreme phenotypes. This phenomenon cannot be captured by an additive or recessive model.N is the standard normal distribution, and p is the minor allele frequency. In the additive model, iQ takes values −1, 0, and 1 for homozygous wild-type, heterozygous type, or homozygous type, respectively. For recessive and overdominant models, we usep′ is the fraction of individuals that are homozygous mutants. In a recessive model, iQ takes 1 for heterozygous mutant and 0 otherwise. All causative SNPs are removed from the genotypes before analysis. We represent results of a wide range of realistic contributions of genetic variations by testing five genetic variation settings of π: 0.05, 0.1, 0.15, 0.2, and 0.25.To simulate phenotypes, we adopt the method used in We simulated genotypes of 170 independent individuals. Under each genetic effect model, we generated 100 independent test cases under each setting. In each case, there are 10,000 SNPs and one causative SNP is randomly picked among the SNPs with minor allele frequency greater than 0.15.m individuals and n SNPs. We use S1, S2, …, nS} to represent the phased SNPs. The unphased genotypes can be represented as an m × n matrix k-th row corresponds to the genotype of the k-th individual and the l-th column corresponds to the l-th SNP marker m haplotypes can be represented as a 2m × n matrix k-th and (2k + 1)-th rows correspond to the haplotypes of the k-th individual. In the haplotype matrix We follow the convention of using primed notation for unphased genotype data. Suppose that there are interval along the genome consists of a set of consecutive SNPs. It corresponds to a submatrix Cu,vu-th column and the v-th column. A perfect phylogeny tree is the tree representation of the evolution genealogy for an interval in the genome . For a given interval, a perfect phylogeny exists if and only if the interval is a compatible interval. If a compatible interval is not a subinterval of another compatible interval, it is called a maximal compatible interval.An interval Cete test . That isThe multistate perfect phylogeny tree  is a natGiven an interval u,vu,vS′(H) columns. Given an interval, the following theorem states the necessary and sufficient condition for the existence of a tri-state semi-perfect phylogeny  of the same genotype (e.g. D) may be associated with different nodes in the tree. We will show later that this decoupling will weaken the power of detecting nonadditive genetic effects. However, this tree shares common induced subtrees with the tristate semi-perfect phylogeny tree. Removing the nodes associated with the decoupled haplotypes will result in Another important observation can be made by comparing the perfect phylogeny tree constructed on the haplotypes to the genotype matrix. When the genotype matrix contains a small percentage of heterozygosity, the tristate semi-perfect phylogeny tree shares a substantial common structure with the perfect phylogeny tree on the haplotypes. x edges partitions the tree into x + 1 subtrees. For example, removing the two edges labeled with An edge in a phylogeny tree connects two disjoint subtrees. Removing t individuals, we have p groups, and the ith group contains it individuals. We use ijX to represent the ith element in the jth group, jth group, and G, the F-statistics is defined asThe statistical correlation between a partition and the phenotype can be examined by the F-statistics. Assuming that for a total of P-value of F(G) can be calculated in the following way. If the phenotype values from each group follow a normal distribution, an F-test is applied to obtain the corresponding P-value. Otherwise, a permutation test is needed. The P-value is defined as nPerm is the number of permutations and n is the number of times when the F-statistics of the permuted phenotype is larger than F(G).The corresponding P-value is reported. The corresponding P-value is used as the nominal (uncorrected) P-value of the association between the compatible interval and the phenotype.We examine all possible partitions generated by removing edges in the tree. The partition that generates the most significant P-value. The corrected P-value is the proportion of the permuted data whose P-values are more significant than that of the original data. We refer to such a corrected P-value as the permutation P-value. The basic routine of HTreeQA is summarized in Appropriate multiple testing correction is crucial for QTL studies. In HTreeQA, we apply the widely used permutation test to control family-wise error rate . In eachWe outline two alternative approaches for local phylogeny-based QTL mapping methods and discuss their pros and cons.HTreeQA: We compute compatible intervals by using integer linear programming and construct a tristate semi-perfect phylogeny tree for each compatible interval. Then we follow the procedure described above to find significant associations.Running TreeQA on phased data: We first phase the genotypes using any standard phasing algorithm and then apply TreeQA on the resulting haplotypes. Each haplotype is assumed to have the same phenotype value as the original genotype.The second approach has an inherent drawback. It decouples the two haplotypes of the same genotype. As a result, the two haplotypes may reside in remote branches of the tree, which limits the ability to test certain genetic effects in QTL mapping. For example, the phenotype in Population stratification is an important issue in QTL analysis. Spurious associations may be induced by the stratification if it is not addressed properly . The subpopulation structure is visible at the chromosome level. For example, there are several deep branches in the phylogeny tree of the selected PreCC mice built on Chromosome 10  with a median segment length of 10.46 Mb  on Chromosome 1 as shown in 3 region . A laterAmong the selected methods, only HTreeQA identified another QTL with FWER = 0.05, in the region of 16 M to 25 Mbps (8-12.5 cM) on Chromosome 12. The QTL falls into an unnamed QTL region at 11 cM on Chromosome 12 reported in  will impair their ability in detecting associations between the phylogeny and the phenotype. The extent of its effect varies for different genetic models, especially with regard to heterozygous samples. It affects the additive model the least and overdominant model the most. For a homozygous sample, the nodes corresponding to the two haplotypes carry the same allele, and thus their phenotypes always belong to the same allele group. This may cause minor inflation of the QTL signals because the two haplotypes are treated as independent samples by these methods. For a heterozygous sample the two haplotypes carry different alleles and therefore their corresponding nodes and phenotype are in two allele groups. Under the additive model assumption, one allele group contains all homozygous samples with high phenotype values, and the other contains all homozygous samples with low phenotype values. The heterozygous samples have medium phenotype values, which are added to both allele groups. This may cause minor deflation of the QTL signals. This is why all selected methods have comparable performance. TreeQA slightly outperforms others because its local phylogeny trees can well model the local population structure and separate QTL signals from genetic background.However, under the assumption of overdominant model, heterozygous samples may have extreme phenotype values . These extreme phenotype values will always be in both allele groups; therefore, the phylogeny representation for phased data cannot explain the overdominant effects at all. This is why the traditional phylogeny-based methods like BLOSSOC and TreeQA fail under such a model. Note that HTreeQA does not require phasing. The tristate semi-perfect phylogeny tree has a partition that separates the heterozygous samples from the homozygous samples and thus it is able to detect an overdominant effect. Under the recessive model assumption, the heterozygous allele carries the same effect as one of the two homozygous alleles. Thus, the impact of assigning haplotypes of the heterozygous samples to the two allele groups is greater than that under the additive model and is not as great as that under the overdominant model. Again, this does not affect HTreeQA. Overall, HTreeQA has the best performance in recessive models and overdominant models.We present the running time for each selected method on a machine with Intel i7 2.67-GHz CPU and 8-G memory. We tested all methods using a dataset containing 180K SNPs and 184 individuals. HTreeQA is proven to have an overall lower error rate than TreeQA and other similar approaches in . It can We propose a novel approach for local phylogeny-based QTL mapping on genotype data without haplotype reconstruction. We analyze the incipient CC and show that there is no significant global population structure but visible local population structure. Such local population structure may bias the QTL mapping if it is not addressed properly. The notion of a tristate semi-perfect phylogeny tree is introduced to represent accurate genetic relationships between samples in short genomic regions. As a generalization of the perfect phylogeny tree (defined on haplotypes), a tristate semi-perfect phylogeny tree treats the heterozygous allele as the third state. It provides the power of modeling a wide range of genetic effects and delivers unbiased and consistent performance. It also guarantees a lower theoretical error rate of statistical tests than the perfect phylogeny based approach. This is a significant advantage over any previous methods that have strong bias toward an additive model. It is also worth noting that HTreeQA is much more computationally efficient than any alternative approach.


Flow is a psychological state of high but subjectively effortless attention that typically occurs during active performance of challenging tasks and is accompanied by a sense of automaticity, high control, low self-awareness, and enjoyment. Flow proneness is associated with traits and behaviors related to low neuroticism such as emotional stability, conscientiousness, active coping, self-esteem and life satisfaction. Little is known about the genetic architecture of flow proneness, behavioral inhibition and locus of control – traits also associated with neuroticism – and their interrelation. Here, we hypothesized that individuals low in behavioral inhibition and with an internal locus of control would be more likely to experience flow and explored the genetic and environmental architecture of the relationship between the three variables. Behavioral inhibition and locus of control was measured in a large population sample of 3,375 full twin pairs and 4,527 single twins, about 26% of whom also scored the flow proneness questionnaire. Findings revealed significant but relatively low correlations between the three traits and moderate heritability estimates of .41, .45, and .30 for flow proneness, behavioral inhibition, and locus of control, respectively, with some indication of non-additive genetic influences. For behavioral inhibition we found significant sex differences in heritability, with females showing a higher estimate including significant non-additive genetic influences, while in males the entire heritability was due to additive genetic variance. We also found a mainly genetically mediated relationship between the three traits, suggesting that individuals who are genetically predisposed to experience flow, show less behavioral inhibition (less anxious) and feel that they are in control of their own destiny . We discuss that some of the genes underlying this relationship may include those influencing the function of dopaminergic neural systems. Flow is a psychological state of high but subjectively effortless attention accompanied by a sense of automaticity, high control, low self-awareness, and enjoyment. It typically is experienced during the performance of challenging activities that are matched in difficulty to one's skill level Behavioral inhibition, a stable temperamental trait characterized by fearful reactivity to novelty, has also repeatedly been associated with internalizing disorders such as anxiety It has also been argued that the enjoyment associated with a flow experience may serve as a reward signal important for intrinsic motivation to perform a task Finally, personality as well as flow proneness has been shown to be moderately heritable. While the heritability of personality – typically reported to range between .4 and .6 - has been established by numerous twin studies Here, we aimed to explore the genetic architecture of the relationship between flow proneness (FP), behavioral inhibition (BI) and locus of control (LOC), utilizing a genetically informative sample of adult twins.The present study used data from a recent and extensive web-based survey – the SALTY study – sent out to approximately 25,000 twins born between 1943 and 1958 registered with the Swedish Twin Registry (STR), one of the largest registries of its kind. Participation rate was about 45%–11,369 individuals – of those 92 individuals had a missing zygosity score and were therefore deleted, leaving 11,277 individuals – 3,375 full pairs and 4,527 single twins aged between 51 and 66 . For further details on the present sample and a comparison of respondents to non-respondents on a number of background characteristics see Dawes et al. The study was approved by the Regional Ethical Review Board in Stockholm (Dnr 2008/1735-31/3). All participants have given written and informed consent.The Swedish Flow Proneness Questionnaire (SFPQ) was used as an indicator for flow proneness in daily life. The SFPQ is a 22-item self-report measure developed to estimate an individual's proneness to experience flow The Locus of Control Scale (LOC) was used to assess locus of control. The LOC has been developed to provide an estimate of an individual's perception of control over event outcomes internal locus of control which is associated with a feeling of control over one's own destiny and the belief that outcomes in life are a result of one's own skills, behaviors, and efforts. On the contrary, a low score on the LOC indicates an external locus of control which is associated with the feeling that outcomes in live are beyond one's control and mainly attributable to external influences.The Adult Measure of Behavioral Inhibition (AMBI) was used to assess behavioral inhibition. The AMBI The three variables were converted to z-scores with a mean of zero and a standard deviation of one. Scores more than three standard deviation below or above the mean were winsorized – 22 individuals for LOC and 14 individuals for BI and FP, respectively A trivariate ADE Cholesky decompositions was fitted initially, with FP, BI, and LOC. Then, the model fit of the reduced models  was compared to the full or less restricted models to determine the most parsimonious model. Finally, a trivariate GE model was fitted, to estimate the total genetic effect (broad-sense heritability) with confidence intervals All three traits were normally distributed. The covariates age and sex had a significant effect on BI with females and older people being less impulsive (more inhibited). Sex also had a significant effect on LOC and FP with women being more likely to have an external LOC (low score) and reporting slightly higher flow proneness. In order to control for participation bias, we checked whether the means for the variables were different between those individuals who only did the paper-based survey and those who also filled in the online survey Means and standard deviations for males and females for all three variables are shown in Model fitting results of the trivariate analyses are shown in The trivariate GE Cholesky decompositions showed a good fit to the data , indicatThe univariate sex-limitation model for BI  showed tThe present study explored genetic and environmental influences on the relationships between FP, BI, and LOC in an adult twin sample. The phenotypic correlations between the three variables were low but significant and confirmed our hypothesis – individuals with an internal locus of control and a low behavioral inhibition were more likely to experience flow. Furthermore, the associations between FP, BI, and LOC appeared to be mainly genetically mediated. This suggests that the same set of genes predisposing individuals to feel in control of their destiny  and to be less inhibited (more extravert), also predisposes them to be more prone to have flow experiences. All three variables showed moderate heritabilities, with some indication of dominant genetic influences.The presence of dominant genetic effects on all three studied traits was suggested both by the DZ correlations being less than half the MZ correlations, and by the trivariate modeling results, indicating a DE model as the most parsimonious. This is in line with evidence suggesting non-additive genetic influences on human personality traits 2 = .29–.35) compared to the heritability for overall flow proneness in the present study (h2 = .41). This may be due to the higher Cronbach's alpha for the full scale (.83) compared to the sub-scales (.61–.72). A lower Cronbach's alpha indicates a higher measurement error which would be reflected in inflated E-estimates, i.e. lower heritability.However, given that limited power resulted in non-significant estimates for most A and D influences in the full ADE Cholesky decompositions, we continued with a multivariate GE model in order to estimate total genetic effects with confidence intervals Trivariate modeling showed that, in line with the rather low phenotypic correlations between the variables, the genetic  influences on each trait were largely specific, with only little of the total genetic variance being explained by shared genetic influences. However, although shared genetic influences only explained little of the total genetic variance, they could explain almost the entire relationship (i.e. phenotypic correlation) between the three traits resulting in moderate genetic correlations.r = .41). Therefore, genetically based individual differences in dopaminergic function could explain some of the relationship between FP, BI and LOC.Some of these common genes may be involved in dopaminergic functions. It is well known that dopaminergic neural systems play an important role in reward processing Finally, univariate sex-limitation modeling for BI showed that there were not only differences in heritability of the trait between males and females , but also that unlike in males, in females dominant genetic influences played a significant role. In addition, given that male DZ twin correlations were more than half the MZ correlations, there also may be some common-environmental (C) influences on BI in males. To our knowledge, this is the first genetically informative study with sufficient power to specifically explore sex differences in BI. However, some indication for sex differences in the architecture of personality-related traits has been shown in past research As always, it is important to recognize the limitations of the present study. Our results cannot be generalized beyond the sample used – here consisting of middle-aged Swedish twins – so results may vary for different ethnicities and age groups. Correlations between the three variables were lower than expected, which, in combination with a relatively small sample size for the FP variable, resulted in limited power to distinguish between A and D effects in the full ADE Cholesky decompositions. This is further reflected in the relatively wide confidence intervals of the twin correlations and the variance component estimates. The limited sample size did also not allow for exploration of potential sex differences in the genetic architecture of the relationship of three variables. However, twin correlations for the five zygosity groups showed no indication for sex differences for FP and LOC, with the twin correlations being similar across the sexes in MZ and all three DZ groups. Furthermore, difficulties to derive precise estimates for genetic and environmental influences on the relationship between traits have been reported when based on samples of MZ and DZ twins only Table S1Demographic information of individuals who only completed the paper-based questionnaire (not the online part) compared to those individuals who also filled out the online questionnaire.(DOCX)Click here for additional data file.Table S2Model fitting results for the univariate sex-limitation model of BI including same-sex twin pairs only.(DOCX)Click here for additional data file.


Haptoglobin is an acute phase inflammatory marker. Its main function is to bind hemoglobin released from erythrocytes to aid its elimination, and thereby haptoglobin prevents the generation of reactive oxygen species in the blood. Haptoglobin levels have been repeatedly associated with a variety of inflammation-linked infectious and non-infectious diseases, including malaria, tuberculosis, human immunodeficiency virus, hepatitis C, diabetes, carotid atherosclerosis, and acute myocardial infarction. However, a comprehensive genetic assessment of the inter-individual variability of circulating haptoglobin levels has not been conducted so far.Haptoglobin gene (HP) as a strong genetic predictor of circulating Haptoglobin levels , explaining 45.4% of its genetic variability . The functional relevance of rs2000999 was further demonstrated by its specific association with HP mRNA levels . Finally, SNP rs2000999 was associated with decreased total and low-density lipoprotein cholesterol in 8,789 European children .We used a genome-wide association study initially conducted in 631 French children followed by a replication in three additional European sample sets and we identified a common single nucleotide polymorphism (SNP), rs2000999 located in the Given the central position of haptoglobin in many inflammation-related metabolic pathways, the relevance of rs2000999 genotyping when evaluating haptoglobin concentration should be further investigated in order to improve its diagnostic/therapeutic and/or prevention impact. Hp1 and Hp2 encoded by the Haptoglobin (HP) gene located in chromosome 16 and resulting in three common isoforms: Hp1-1, Hp2-2 and Hp2-1  Human haptoglobin (Hp) is an acute phase inflammatory glycoprotein essentially synthesized by the liver and up-regulated by cytokines HP rs72294371 ‘common polymorphism’  infection HP ‘common polymorphism’ has also an effect on various other infectious diseases Hp levels and orphism’  have beeIn Type 2 diabetes patients, the Hp2-2 phenotype has been suggested to confer greater risk of cardiovascular events Despite these findings, the basis of Hp level inter-individual variability is still unknown. To identify genetic variants modulating physiological levels of Hp, we analyzed genome-wide association study (GWAS) data generated in European children for whom no age-related disease may influence Hp concentrations. We also assessed the association between the identified variants and cardiovascular risk factors .Patterns of family correlations for serum Hp concentrations were assessed following both unadjusted values . Model 1P<0.001) of the total variance. Shared (i.e. within families) and random environmental variances were 11.6 and 62.4% respectively  in 656 nuclear bi-parental families  from the STANISLAS Family Study (SFS) cohort . Model 2ectively .HP gene . In order to assess the redundancy between these two signals, we ran conditional regression analyses for both SNPs adjusted for each other and found that rs2000999 alone drove the association observed at the HP locus  block on chromosome 16 that includes the HP gene . Using ttotalN = 1,434), and a familial subset  of the SFS cohort , we genotyped the latter in the GWAS first stage children (SFS cohort) by using a PCR-based method and a gel reading. Only a subset of 341 out of 631 samples was successfully genotyped after independent readings by two readers. In this sample set, we found no evidence for LD between HP ‘common polymorphism’ and rs2000999 (r2 = 0.135) nor with the other SNPs that were genotyped by the Illumina array within the 218 kb LD block that includes both HP ‘common polymorphism’ and rs2000999 . Furthermore, the HP ‘common polymorphism’  and rs2000999 (MAF = 0.20) were both highly associated with Hp levels, as expected . When both variants were included in the same regression model, we found that they significantly and independently contributed to the increased Hp levels (HP rs72294371 ‘common polymorphism’P = 0.001 and rs2000999P = 5×10−5) indicating that the association with rs2000999 would be novel and not redundant with the HP ‘common polymorphism’. However, it is noteworthy that despite strong efforts, we did not succeed by far in genotyping all samples. We used two other technologies: a pre-designed TaqMan copy number assay (Applied Biosystems) and a PCR-based method with another design than previously used. Unfortunately, we did not find a good concordance (<70%) between the three methods. We conclude that given the state of art, we cannot definitively conclude that the present signal of association is not related to the HP ‘common polymorphism’ genotype.In order to assess the degree of independence of rs2000999 from the HP gene expression in subcutaneous adipose tissue sample from 194 non-obese subjects ascertained from the Swedish SibPair cohort .In order to validate our main results, we secondary assessed the effect of SNP rs2000999 on r cohort . We founP = 0.002; P = 0.0008; We finally assessed by additive model the effect of SNP rs2000999 on total, HDL and low-density lipoprotein (LDL) cholesterol, Apolipoproteins A1 and B in five independent European pediatric cohorts totaling 8,789 children. Total cholesterol was ln-transformed and we normalized the LDL cholesterol by computing the square root. All measurements were adjusted for gender, age (excepting the NFBC1986) and z-score BMI. Our data showed that rs2000999, with A as allele effect, was associated with total cholesterol  which shares more than 90% nucleotide sequence homology with HPHP ‘common polymorphism’ We first determined in 656 nuclear families that 26% of the Hp plasma level variance was under genetic control. Then, using a GWAS in 631 children from the same population and replicating in three independent populations, we identified rs2000999 as the major genetic determinant of Hp levels. This genetic variant alone explained 45.4% of the genetic variance of this trait . SNP rs2000999 is located in the intronic region of Our study shows that SNP rs2000999 also modulated expression levels of the Hp mRNA in human adipose tissue suggesting that this SNP (or a SNP in very strong LD with this one) is indeed functional. It is noteworthy that SNP rs2000999 has been previously reported to associate with total cholesterol in 4,200 adults from the EUROSPAN consortium Increased plasma levels of several inflammatory markers correlate with higher incidence and prognosis of various cardiovascular diseases Interestingly, the effect of rs2000999 on Hp levels is more important in our discovery cohort which includes healthy children having low Hp concentration (0.65 g/L±0.39). As shown in the analyses for other diseases In addition, the effect of aging and of the environment is minimized in children. Then, by using healthy pediatric populations, we were able to assess more accurately the effect of the SNP rs2000999 on Hp levels.HP ‘common polymorphism’. Three different methods were evaluated to genotype the HP ‘common polymorphism’ in our whole GWAS sample set. Unfortunately, we found no concordance between the three methods, which underlie a major difficulty to carry out an accurate genotyping of this polymorphism. Even if this difficulty was not clearly discussed and not published to our knowledge, it is admitted in the scientific field and it should also be present in the clinical diagnosis setting. In contrast, SNP rs2000999 can be accurately and easily genotyped.We tried to assess the degree of independence between rs2000999 and the Our findings should be further replicated in non-European adults, especially in those affected by infectious diseases. More generally, rs2000999 should be assessed in cohorts of patients affected by the large variety diseases associated with Hp levels. It is not a trivial task, as Hp is a trait that has been infrequently measured in cohorts used for genetic studies. Given the major effect of rs2000999 on Hp gene expression and on Hp levels, Mendelian randomization approach would be of interest to test the causative effect of this SNP on infectious and non-communicable phenotypes in order to assess its clinical relevance.All the populations involved in the present study were recruited in accordance with the latest version of the Declaration of Helsinki for Ethical Principles for Medical Research Involving Human Subjects. All participants and their parents gave a written informed consent. Genetic studies protocols were approved by the local ethics committees for the protection of subjects for biomedical research: the Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale (CCPPRB).The SFS is a 10-year longitudinal survey involving 1,006 volunteer families of European ancestry whose members were free of chronic disease (cardiovascular or cancer) with recruitment taking place from 1993–95 th percentile for age and sex according to a French cohort We studied obese children  successfully genotyped for whom at least BMI, total, HDL and LDL-cholesterol, Apolipoprotein A1 and B measurements were available.2 difference in BMI). Gene expression and genetic variation were analysed in 194 non-obese subjects from the SibPair cohort.The SibPair cohort comprises 154 nuclear families (732 subjects) from Sweden, each containing an obesity-discordant sib pair (at least 10 kg/mP<0.001), low genotyping call rates (<95%) or low minor-allele frequencies (<1%). We retained 318,237 SNPs for analysis. Genomic control λGC was 1.01.Genomewide genotypes were generated for the 631 unrelated SFS children using the Illumina Human CNV370-Duo array We used the Applied Biosystems SNPlex™ technology to replicate the association of genome-wide significant genetic variants in the SFS replication set, obese children and GENDAI, NFBC1986 and Verona cohorts.http://www.appliedbiosystems.com). Allelic discrimination was performed by capillary electrophoresis analysis using an Applied Biosystems 3730xl DNA Analyzer and GeneMapper 3.7 software. Genotyping call rate was above 95% in all populations studied and genetic variants were in HW equilibrium (p>0.001).SNPlex is based on the Oligonucleotide Ligation Assay (OLA) combined with multiplex PCR target amplification and was carried out as per the manufacturer's instructions (HP ‘common polymorphism’ in the 631 children of the discovery cohort (SFS cohort) in order to determine any linkage disequilibrium with regard to genome-wide significant variants identified in the analysis. Only genotypes that were concordant following a double blind genotyping call by two independent readers were retained for statistical analyses (N = 341). Two additional genotyping methods for HP ‘common polymorphism’ were used in order to validate the above-method: a custom TaqMan copy number assay (Applied Biosystems) following the manufacturer's recommendations and another PCR-based method using the following oligonucleotide primers : 5′-CTCTCCTTTCTCCCTTCCTGTC-3′ and 5′-TTTATCCACTGCTTCTCATTGT-3′. We didn't obtain correspondence between the banding patterns and the Hp genotypes.We used a PCR-based method Blood samples were collected between 8:00 and 9:00 am or 11:30 and 12:30 pm by venipeuncture after overnight fasting. Hp protein levels were measured in blood plasma samples by high sensitivity immunophotometry analyses using the BN™II Siemens analyzer  and Siemens reagents and following the manufacturer's instructions.Total cholesterol, HDL-cholesterol and apolipoproteins A1 and B were assayed using enzymatic methods  and LDL-cholesterol was calculated using the Friedewald formula Intra-familial correlations were estimated by using maximum likelihood techniques 2G, σ2H and σ2E, respectively.Variance component analysis was applied in order to assess the relative contributions of genetic, common household factors and individual specific environment in familial aggregation of serum haptoglobin concentrations. The variable used to estimate variance component was adjusted for age and BMI, separately for fathers, mothers, sons and daughters. The analysis was conducted by using a multivariate normal model for pedigree analysis as described by Lange and colleagues 2G, σ2H and σ2E with a model including only σ2H and σ2E or σ2G and σ2E, respectively. In addition, possible effects of covariates (age and BMI) and genome-wide significant variants' allelic frequency on these variance components were tested.The hypothesis of no polygenic component or no household effect was checked by comparing a model including σComparison of nested models was based on the likelihood ratio criteria. Eventually, the best parsimonious model was selected. The percentage contributions of the three components, additive genetic factors (heritability), household factors and residual environmental, to residual phenotypic variance (after adjustment for covariates) were determined.http://cran.r-project.org/web/packages/rmeta/index.html). No major heterogeneity in effects was observed (P<0.02). The same mixed model and the same software were used to analyse the association of genetic variants with lipid traits.We carried out genome-wide association and replication analyses on Hp levels using linear mixed regression models under the additive genetic model with one degree of freedom, adjusting for age, gender and BMI and using PLINK HP was measured in subcutaneous adipose tissue To investigate the effect of genome-wide significant variants on gene expression , we usedlme4) with p-values obtained from the t-statistic.We used a linear mixed model  to assess association of SNPs with gene expression. Log-transformed expression level was regressed on the random-effect term, which accommodates the family pedigree structure, and on the fixed-effect terms i.e. sex, age, BMI level and the SNP of interest . Analysis was carried out using the R function lmer (package lme4) that generates Monte Carlo Markov Chain samples from the posterior distribution of the parameters of a linear mixed model. The prior on the fixed effects parameters is taken to be locally uniform while the prior on the variance-covariance matrices of the random effects is taken to be the locally non-informative prior. Based on 100,000 samples drawn from the posterior distribution, we calculated the smallest p such that the (1−p) credible interval does not contain the value 0. This parameter was finally used to assess the p-value obtained from the t-statistic: if smaller than p, it was considered anticonservative and its value discarded.Significance of the fixed effects was further investigated in the Bayesian set-up using the R function mcmcsamp (package Data S1HP rs72294371 ‘common polymorphism’ flanking sequence. Source: 1000Genome. (http://browser.1000genomes.org/Homo_sapiens/Variation/Summary?r=16:72090747-72091766v=rs72294371vdb=variationvf=13544249).(DOC)Click here for additional data file.Data S2Supplementary Methods. 1. Screening of latent population substructure. 2. Conditional analysis. 3. Meta-analysis. 4. Gene-expression analysis.(DOC)Click here for additional data file.Table S1Conditional regression within the recombination hotspot around rs2000999. UNADJ: unadjusted p-values, SNP: single nucleotide polymorphism, add: additive model.(XLS)Click here for additional data file.Table S2Association of rs2000999 with HDL-cholesterol and Apolipoproteins A1 and B. N: sample size; MAF: Minor Allele Frequency; β: beta coefficient for the effect allele A.(DOC)Click here for additional data file.


Because of combining the genetic information of multiple loci, multilocus association studies (MLAS) are expected to be more powerful than single locus association studies (SLAS) in disease genes mapping. However, some researchers found that MLAS had similar or reduced power relative to SLAS, which was partly attributed to the increased degrees of freedom (dfs) in MLAS. Based on partial least-squares (PLS) analysis, we develop a MLAS approach, while avoiding large dfs in MLAS. In this approach, genotypes are first decomposed into the PLS components that not only capture majority of the genetic information of multiple loci, but also are relevant for target traits. The extracted PLS components are then regressed on target traits to detect association under multilinear regression. Simulation study based on real data from the HapMap project were used to assess the performance of our PLS-based MLAS as well as other popular multilinear regression-based MLAS approaches under various scenarios, considering genetic effects and linkage disequilibrium structure of candidate genetic regions. Using PLS-based MLAS approach, we conducted a genome-wide MLAS of lean body mass, and compared it with our previous genome-wide SLAS of lean body mass. Simulations and real data analyses results support the improved power of our PLS-based MLAS in disease genes mapping relative to other three MLAS approaches investigated in this study. We aim to provide an effective and powerful MLAS approach, which may help to overcome the limitations of SLAS in disease genes mapping. Association studies are widely used to identify genetic variants underlying complex human diseases, such as osteoporosis The limitations of SLAS promote the development of MLAS approaches. Because MLAS can simultaneously consider the genetic information of multiple loci, it is expected that MLAS were more powerful than SLAS in disease genes mapping. Multilinear regression is one of the major multivariate analyses approaches, and has been applied to MLAS Several methods have been proposed to deal with large dfs in multilinear regression. The first one is tagSNPs-based multilinear regression Recently, Taylor and Tibshirani proposed the tail strength measure (TSM) for assessing the overall significance levels of multiple hypotheses tests in microarray studies In this paper, we present a MLAS approach based on partial least-squares (PLS) analysis, while avoiding large dfs. As an extension of multiple linear regression, PLS generalizes and combines the features of PCA and multilinear regression The type I error rates of the four MLAS approaches under various scenarios investigated in this study are normal and not shown to simplify our presentation. The power comparison results of the four MLAS approaches under the epistatic model are presented in Large dfs is one of the major issues with MLAS. To deal with this problem, we propose a PLS-based MLAS approach, while avoiding large dfs. Simulation study based on real data from the HapMap project suggests that our PLS-based MLAS generally outperformed other three popular MLAS approaches under various scenarios investigated in this study. PLS is suitable to handle the data with many independent variables as well as multicollinearity among the variables −7) in previous genome-wide SLAS of lean body mass, most of them still attained small p values (PLS-based MLAS can easily be applied to genome-wide association studies (GWAS). To investigate the performance of PLS-based MLAS for GWAS, we conducted a genome-wide MLAS of lean body mass using a real sample consisting of 973 unrelated USA whites. To the best of our knowledge, this is the first multilocus GWAS of lean body mass. Besides TRHR detected in previous genome-wide SLAS of lean body mass p values . BiologiTo illustrate the performance of our approach, we developed PLS-based MLAS for quantitative traits in this study. However, PLS-based MLAS can easily be extended to qualitative traits under logistic regression model. Covariates can also be incorporated into PLS-based MLAS due to the flexibility of regression analyses. Additionally, because permutations are used to evaluate the significance level of testing statistic, our PLS-based MLAS do not depend on specific statistical assumption, for instance the normality assumption of target traits. The computational cost of PLS-based MLAS is also acceptable for real studies. For instance, our genome-wide MLAS of lean body mass using PLS-based MLAS needed about 21 days (running on Dell computer cluster with four Intel Xeon 1.6 GHz processors and 4G memory).An issue with PLS-based MLAS is how many PLS compoments we should include into analyses. Some methods developed for PCA can be used here. For instance, we can select top m of the ordered PLS components that explain certain proportions of total genotypic variance  Taylor and Tibshirani originally proposed TSM to assess the overall significance levels of multiple hypotheses tests in microarray studies In summary, we present a simple and flexible MLAS approach with small dfs. Simulation study and real GWAS data analyses results support the improved performance of our PLS-based MLAS in disease genes mapping relative to other popular MLAS approaches investigated in this study. We aim to provide an effective and powerful MLAS approach, which may help to overcome the limitations of SLAS in disease genes mapping.All studies were approved by the Institutional Review Boards of University of Missouri-Kansas City. Informed-consent documents were written by all study participants.i denote the quantitative trait value for subject i , and Xij denote the genotype of subject i at SNP j . In this study, we coded Xij to be 0, 1 or 2, representing the copy number of minor allele for subject i at SNP j. Other genotype coding scenarios can also be used, such as Xij  = 0 or 1 for genetic dominant or recessive models, if desired. The extended Tukey's 1-df interaction model proposed by Chatterjee N et al. i and genotype Xij. In this model, the SNPs with large marginal genetic effects are assumed to have large interactive genetic effects. Total interactive effects of all SNPs are measured by weighted sum of the product of marginal effects of each pair of SNPs through an interaction parameter γ in the extended Tukey's 1-df interaction model, defined byj denotes the regression coefficient for SNP j. γ measures total interactive effects of all SNPs within candidate genetic regions. ei denotes the residual environmental effect for subject i, and is assumed to follow a normal distributation with mean 0 and variance 0: β = 0  under multilinear regression.Consider a sample of n unrelated subjects with k genotyped SNPs. Let YAlthough the interactive effects of multiple SNPs are modeled as an interaction parameter γ in the extended Tukey's 1-df interaction model j denote the residual phenotype vector, and Xj denote the residual genotype matrix for the jth PLS component. wj represents the first left singular vector of crossproduct matrix Sj  =  XjTYj . uj denotes the scores of Xj along the jth PLS component. During each iteration, the scores uj was first calculated byInstead of directly using genotypes as regressor, we propose to regress the PLS components derived from genotypes on target traits under multilinear regression model. A standard iterative process implemented by the pls package of R was used to derive PLS components from genotypes in this study j of Yj and loading bj of Xj at the jth PLS component were then defined byThe loading aj+1 and genotype matrix Xj+1 for j+1 PLS component were calculated byFinally, the residual phenotype vector Yij  denote the score of subject i at the jth PLS component. PLS-based multilinear regression can be defined asSuppose top m of ordered PLS components were further included into multilinear regression analysis. Let Pj denotes the regression coefficient for the jth PLS component. ei denotes the residual environmental effect for subject i, and is assumed to follow a zero mean normal distributation with variance Where α is intercept. β(1)≤p(2) ≤…≤p(k) denote the ordered p values of the k SNPs. TSM can be defined asWe also investigated the performance of TSM for MLAS. Suppose k genotyped SNPs within a candidate genetic region. Association tests can be conducted at each SNP, and p(i) should follow a beta distribution with expected value i/(k+1), and TSM should have an expected value 0. Otherwise, p(i) will deviate from its expected value i/(k+1), and result in a positive value of If none of SNPs within the candidate genetic region is associated with target traits, pTaylor and Tibshirani showed that TSM approached normal distribution under large k (Equation 6), which could be used to determine the significance level of TSM statistic http://hapmap.ncbi.nlm.nih.gov/downloads/index.html.en). There were total 262,658 SNPs at chromosome 6. To simulate genes with various genetic structures, we randomly selected 10,000 genetic regions from chromosome 6. Each region contained 14 consecutive SNPs with 0.2≤MAF≤0.5 and pre-assigned ranges of D' between adjacent SNPs  denotes the copy number of high risk allele for subject i at SNP j. Without loss of generality, we supposed that there was an interactive effect between the high risk alleles of SNP j and SNP u under the epistatic model.γju denotes the interactive effect between SNP j and SNP u, and equate 0 in the additive model. For the epistatic model, Xiju was assigned 1 if the genotype vector of SNP j and SNP u was either of ,  or , and 0 otherwise. ei denotes the residual environmental effect of subject i, and follow a zero-mean normal distribution with variance Genetic epistatic and additive models were used for quantitative phenotype simulations. Let YThe simulated genotype  and phenotype data were simultaneously analyzed by tagSNPs-based MLAS, PLS-based MLAS, PCA-based MLAS and TSM-based MLAS. The F test was used here to compare the performance of various MLAS approaches. For tagSNPs-based MLAS, 3 of 12 SNPs (in the epistatic model) or 13 SNPs (in the additive model) were first selected as tagSNPs using hmmlsselect program 5,000 replicates were conducted for each parameter setting. In each replicate, 800 individuals were simulated. Power and type I error rates were calculated respectively as the proportions of positive association results  obtained from the simulated genes with and without genetic effects in 5,000 replicates. All our data simulations and analyses were implemented with statistical package R To investigate the efficiency of PLS-based MLAS, we applied it to a real GWAS data consisting of 1,000 unrelated US whites. The characteristics of this sample have been detailed in previous single locus GWAS of lean body mass


We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes . Our results suggest that both linkage and association tests with families show promise for identifying rare variants.Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees ( In contrast to the common variant/common disease hypothesis that dominated the era of linkage-disequilibrium-based genome-wide association studies (GWAS), there is increasing awareness that rare variants of modest to large individual effect contribute to disease liability and may explain a substantial proportion of the so-called missing heritability of common traits. There is therefore great interest in developing statistical methods to detect rare causal variants. Rare variant analysis is complicated by several unique challenges related to sequencing-based uncertainties in variant calling, the large search space of rare variants, and the inherently low carrier rate frequencies of these variants. It has been theorized that both linkage and family-based analysis work well in analyzing rare variants ,2. CombiThe Genetic Analysis Workshop 17 (GAW17) data set was developed to model a real-world rare variant screen using data generated from a mini-exome scan . The genOur initial goal is to evaluate a variety of linkage-based approaches using the within-family identity-by-descent (IBD) information provided. In the absence of knowledge regarding the disease model, we restricted our evaluation to nonparametric methods so as to maximize power . We evali = 1, …, N independent trios and M rare variants in a given gene. We apply the test to markers using a defined rare variant allele frequency threshold (<1% and 3% are illustrated). The cutoff is arbitrary and deserves further exploration. The test statistic has the following numerators:The resolution of linkage analysis is limited by the number of informative meioses within each pedigree (a function of pedigree structure and randomness). We therefore consider family-based association methods to facilitate fine mapping of linked regions. The association test is based on a modified FBAT  statistiiT is the trait, iX is the observed number of rare variant alleles among the offspring for the ith family, μ is the trait offset , and iP is the parental genotype corresponding to the ith family. The numerator is the sum of individual numerators of each of the FBAT statistics for all M SNPs. It represents the contributions for all families over all variants in a given gene to the new FBAT statistics. The test statistic W/[Var(W)]1/2 is a Z-statistic that can be used to test against a one-sided or two-sided alternative.where W has a complicated expression. Even if we assume that the nuclear families within a pedigree are independent, estimating the covariance structure between the SNPs for each family is difficult because of the presence of linkage disequilibrium between variants. For the purpose of this project, we use the empirical variance as the denominator, which gives:The variance of Instead of trios, we can extend the numerator by summing contributions over all nuclear families in all pedigrees:lth offspring of the ith nuclear family in the kth pedigree. We can compute the empirical variance in two different ways, by treating either the pedigrees:where the summand corresponds to the or the nuclear families:as independent units, where the term in braces in expression (4) or (5) is the contribution of the pedigree or the nuclear family. The choice of assumption has important implications for test performance. Assuming that nuclear families are independent gives a biased estimate of the variance if indeed phenotypic correlation exists between nuclear families within a pedigree. Alternatively, assuming that pedigrees are independent gives a conservative estimate of the variance when only a small number of pedigrees are studied, as in the GAW17 family data. This test can also be extended to nuclear families with missing parents by conditioning on a sufficient statistic for transmission instead of parental genotype.KDR (p = 2.0 × 10−8) is the top gene and is most significant in one pedigree; VEGFA (p = 1.4 × 10−5) is among the top significant genes in another pedigree; and FLT1 (p = 5.4 × 10−3 and 1.0 × 10−3) shows up as the top gene in two other pedigrees, but the signal seems to be significantly weaker.We observe striking differences in the performance of the various linkage-based approaches evaluated. Any linkage method that aggregated results across pedigrees failed to identify any of the causal genes among the top candidates. In contrast, when genetic heterogeneity was considered by performing pedigree-stratified analysis, some of the causal genes were identified. The results are summarized in Table p-values corresponding to the true causal genes are summarized in Table p < 0.01) for three genes , and for the fourth gene (KDR), significance was achieved using the higher frequency. Using pedigrees as independent units instead of nuclear families yielded nonsignificant results; given the small number of pedigrees, this was expected.To assess the performance of our modified FBAT statistic, we first screened all variants with a frequency less than 1% for association using a univariate application of the standard FBAT statistic . We found that, with the exception of one disease-causing variant (C4S1884), all the variants demonstrated trends of association (at α = 0.05), although none reached significance after adjustment for multiple tests. We next applied our modified FBAT, performing gene-based tests of all rare variants with frequencies less than 1% or less than 3%. The p < 0.01). As can been seen in Table VEGFA and VEGFC, regardless of allele frequency cutoff, whereas power varies by allele frequency cutoff for FLT1. Power is poor for KDR, regardless of cutoff. Among genes that were modeled as disease causing but for which random sampling resulted in the absence of polymorphic rare variants in our data sets, the false-positive rates are low. Two related genes, HIF1A and HIF3A, have false-positive rates of 0, and the other three genes have rates no higher than 0.02, suggesting high test specificity (not shown). However, a more comprehensive assessment of all genes reveals a substantially higher false-positive rate. Figure PCDHGA2 (rate = 0.245), PSMB8 (rate = 0.475), and TRPC4 (rate = 0.205). The high false-positive rate for KIT can be explained by its close proximity to KDR.To estimate the FBAT statistic’s true- and false-positive rates, we ran our method on the 200 individual phenotype replicates and reported the proportion of times a gene was declared significant (at Rare variants are likely to be private to one or a limited number of families. As a consequence, it is likely that the genetic liability conferred by rare variants will exhibit pronounced genetic heterogeneity, with different individual contributions from numerous variants. It is well recognized that model misspecification, including failure to consider allelic heterogeneity, can severely limit disease-gene mapping efforts. It therefore follows that gene-mapping efforts that focus on rare variants accommodate this reality. In our study, aggregating linkage statistics across all pedigrees yielded negative results, whereas modeling linkage within individual pedigrees performed well. So linkage analysis shows some promise in analyzing rare variants given sufficiently large pedigrees.The modified FBAT is promising. It correctly identifies causal genes that contain polymorphic SNPs in the family sample. However, we found that there were considerable false positives; many factors could be responsible for the high false-positive rates, for example, failure to adjust for multiple testing, linkage disequilibrium between causal and noncausal SNPs, incorrect variance estimation, lack of normality resulting from the restriction to rare variants, and the method used to simulate the replications.With regard to variance estimation, there are only 8 pedigrees and 194 nuclear families, so differences in the two approaches to computing the variance are to be expected. In study designs often seen in actual samples, these differences may not be so important, but clearly, better approaches are needed. Some limited examination of the sensitivity of the false-positive rate suggests that the use of only rare variants does not have a major impact. Furthermore, the simulation structure of the family-based sample makes it difficult to evaluate performance of any family-based methods. First, many of the true causal SNPs are not polymorphic in the family-based sample, making it impossible for both linkage and association analyses to identify the causal genes with those variants. Second, for the proposed family-based methods the random variable is the transmission of genotype. Hence the simulated replicates of phenotypes cannot be used to appropriately evaluate power or validity of such methods.Further research should investigate possible approaches to extend the proposed association test using variable thresholds for identifying rare variants and using available pathway information. Another issue that can be addressed in future research is the assumption that all rare variants act in the same direction, affecting the disease risk; potential ways to address the violation of such an assumption in the context of our method should be tested.Linkage, stratified by pedigree, provides a promising method for identifying rare variants, provided that pedigrees are large. The modified FBAT approach also suggests that it is a promising approach, but the false-positive rates need to be addressed. Although not attempted here, a promising scenario may be to combine the two approaches, using linkage to screen genes or regions and then using the FBAT for testing selected regions. Given the scale of large-scale sequencing, this approach not only may be more powerful but may also provide substantial cost savings. Finally, methods for evaluating power and type I error for linkage and transmission testing need to be designed differently to provide valid estimates for those tests.The authors declare that there are no competing interests.W-K Yip performed the initial cleaning of the data set and linkage analysis; GD developed and applied the novel FBAT statistics for the fine mapping analysis. BARy and NL supervised the project.