Setosphaeria turcica is a fungal pathogen that causes northern corn leaf blight (NCLB) which is a serious foliar disease in maize. In order to unravel the genetic architecture of the resistance against this disease, a vast association mapping panel comprising 1487 European maize inbred lines was used to (i) identify chromosomal regions affecting flowering time (FT) and northern corn leaf blight (NCLB) resistance, (ii) examine the epistatic interactions of the identified chromosomal regions with the genetic background on an individual molecular marker basis, and (iii) dissect the correlation between NCLB resistance and FT.α=0.05, amplicon wise Bonferroni correction) associated with FT, NCLB, and NCLB resistance corrected for FT, respectively. These markers explained individually between 0.36 and 14.29% of the genetic variance of the corresponding trait.The single marker analyses performed for 8 244 single nucleotide polymorphism (SNP) markers revealed seven, four, and four SNP markers significantly (The very well interpretable pattern of SNP associations observed for FT suggested that data from applied plant breeding programs can be used to dissect polygenic traits. This in turn indicates that the associations identified for NCLB resistance might be successfully used in marker-assisted selection programs. Furthermore, the associated genes are also of interest for further research concerning the mechanism of resistance to NCLB and plant diseases in general, because some of the associated genes have not been mentioned in this context so far. Setosphaeria turcica  is a fungal pathogen that causes northern corn leaf blight (NCLB) in maize. NCLB is a serious, omnipresent foliar disease fo foP valur family and suppr family,50.r = 0.53, Figure23The results of our study indicated that FT and NCLB resistance are correlated across all heterotic pools  or two (Lancaster and Iodent) of the genes that where identified in the whole set of genotypes in the 1 region,53,54. OFTresistance were found in the SSS and Iodent pools, but not in the Flint and Lancaster ones  is predicted to be sufficient.SNPs significantly associated with NCLB and NCLBIn addition to the above described reasons for the imperfect congruency of the identified associations across heterotic pools are on one side sampling effects but on tFT rather than NCLB, because the former is corrected for FT, the detected SNPs explain even a higher proportion of the genetic variance compared to the latter, and the correlation with population structure is lower for the former than the latter.The significant SNP-FT associations identified in our study explained about 15% of the genetic variance Table. This vaThe proportion of the explained genetic variance was generally much higher in the individual pools than in the entire germplasm set Table, 3. PartAlthough we observed for FT highly significant epistatic interactions, these explained only a low proportion of the genetic variance compared to the main effects and, therefore, might be disregarded in marker-assisted selection for this trait. However, this was not true for NCLB as the epistatic interaction explained partly a higher proportion of the genetic variance than the main effects. Thus, taking epistasis into account for this trait should increase the efficiency of marker-assisted selection  and were assigned by breeders to four heterotic pools, namely Flint, Lancaster, SSS, and Iodent.per se performance in routine plant breeding trials, at different numbers of locations (2-7), with different experimental designs  and numbers of replicates (1-3). The experiments were either naturally infested or artificially infested with Setosphaeria turcica according to standard protocols[In the years 2000 to 2009, these genotypes were evaluated for their rotocols. All entA subset of 1 487 inbred lines randomly selected from the phenotyped inbreds regarding FT and NCLB were analyzed with 359 SSR and 8 244 SNP markers (for details see). The SSZea mays Genome Browser - Release 2.0.All markers were mapped in the IBM population. ChromosGenotyping of the SSRs was performed by Limagrain Verneuil Holding  using standard protocols. Genotyping of the SNPs was performed by Biogemma  using an Illumina Infinium iSelect chip.Phenotypic data were analysed based on the following mixed model: yijklm is the phenotypic observation for the ith maize inbred line at the jth environment (year-location combination) in the mth replicate of the lth block in the kth trial, μ the intercept, gi the genetic effect of the ith maize inbred line, uj the effect of the jth environment, gi ∗uj the genotype-by-environment interaction, tjk the effect of the kth trial in the jth environment, bjkl the effect of the lth block in the kth trial of the jth environment, rjklm the effect of the mth replicate of the lth block in the kth trial of the jth environment, and ejklm the residual. θj was a dummy covariate of value 1 in environments with several trials and of value 0 alternatively, βjk a dummy covariate of value 1 in environments with several trials and blocks and of value 0 alternatively, and ρjkl a dummy covariate of value 1 in environments with several trials, blocks, and replicates and of value 0 alternatively.where Mi for each of the 4 149 inbred lines across all trials, we regarded gi as fixed and all other effects as random.Our study was based on data from 10 years and 23 locations spread over Europe, resulting in a total of 45 environments and, thus, the environmental factor was regarded as random. Error variances were assumed to be heterogeneous among environments. For calculating the adjusted entry mean μ, all effects including gi were regarded as random. Heritability on an entry mean basis was calculated for the phenotyped and genotyped inbred lines according to[For estimation of variance components, except rding to for unbaFTwas calculated according to[FT resistance value. Negative values indicated susceptible plants and positive values resistant plants.NCLBrding to. A regreVariance components were determined by the REML method. The mixed model analyses were performed with ASREML release 2.0. All othSingle marker analysis: In the second step of our approach, we used the adjusted entry means for FT, NCLB, and NCLBFT to test their associations with each of the 8 244 SNP markers, using the QK method[K method: Mip is the adjusted entry mean of inbred i carrying the pth allele, mp is the effect of the pth allele of the SNP marker under consideration,ith entry, vu the effect of the uth column of the population structure matrix D, and εip the residual. The variance-covariance matrix of the vector of random effectsKwas a 1487×1487 matrix of kinship coefficients that define the degree of genetic covariance between all pairs of inbreds, andεip was assumed to bewhere Q was calculated based on SSR markers using the software STRUCTURE[z + 1 columns of the Q matrix add up to one. Thus, only the first z columns were used as Dmatrix in our study, to achieve linear independence and, thus, avoid singularities. The kinship matrix K was calculated as described by[T  SNP effects for each of the three traits. We dealt with the multiple testing problem by applying a Bonferroni and amplicon number based Bonferroni correction[D matrix was calculated. Negative values were set to zero.Based on the Wald statistics, we performed a test for the presence of significant , as fixed cofactor in the analyses, when examining all remaining SNP markers for their association with the phenotype. For each of the three traits, this prodedure was repeated due to the high computational burden only two times and, thus a maximum of three SNPs could be selected.Detection of epistasis: For each of the three traits, we performed a screen for epistatic interactions between the significant SNPs from the single marker analysis as well as multiple forward regression and all other SNP markers. The multiple testing problem was considered using the two different Bonferroni corrections.The association analyses of SSR markers were performed with ASREML release 2.0, whereasBS conceived the study. DVI performed all analyses and drafted the manuscript. BS, JPM, and AEM revised the manuscript. All authors read and approved the final version of the manuscript.P values for association analysis of flowering time within the different hererotic pools .Figure S1. Genome-wide  The ten colors represent the ten chromosomes. The horizontal, doted and dashed-doted lines correspond to a nominal 5% significance threshold with Bonferroni and amplicon-wise Bonferroni correction, respectively. Significant P values are represented by a star.Click here for fileP values for association analysis of northern corn leaf blight resistance within the different hererotic pools .Figure S2. Genome-wide  The ten colors represent the ten chromosomes. The horizontal, doted and dashed-doted lines correspond to a nominal 5% significance threshold with Bonferroni and amplicon-wise Bonferroni correction, respectively. Significant P values are represented by a star.Click here for fileP values for association analysis of flowering time corrected northern corn leaf blight resistance within the different hererotic pools .Figure S3. Genome-wide  The ten colors represent the ten chromosomes. The horizontal, doted and dashed-doted lines correspond to a nominal 5% significance threshold with Bonferroni and amplicon-wise Bonferroni correction, respectively. Significant P values are represented by a star.Click here for fileFigure S4. Significant epistatic interactions between the most significantly associated SNP for flowering time (FT) and northern corn leaf blight (NCLB) resistance and all other SNP markers for the entire set of 1487 maize inbred lines. pG is the proportion of the explained genotypic variance.Click here for fileP values for association analysis of flowering time for the entire set of 1487 maize inbred lines on chromosome 8 in the Vgt1 region.Figure S5.  Significant P values are represented by a star.Click here for fileFigure S6. Deviance of the QK mixed model association mapping method applied to northern corn leaf blight resistance of the entire germplasm set of the 1487 genotypes depending on threshold T. For details, see Materials and Methods.Click here for file


The honey bee is an economically important species. With a rapid decline of the honey bee population, it is necessary to implement an improved genetic evaluation methodology. In this study, we investigated the applicability of the unified approach and its impact on the accuracy of estimation of breeding values for maternally influenced traits on a simulated dataset for the honey bee. Due to the limitation to the number of individuals that can be genotyped in a honey bee population, the unified approach can be an efficient strategy to increase the genetic gain and to provide a more accurate estimation of breeding values. We calculated the accuracy of estimated breeding values for two evaluation approaches, the unified approach and the traditional pedigree based approach. We analyzed the effects of different heritabilities as well as genetic correlation between direct and maternal effects on the accuracy of estimation of direct, maternal and overall breeding values . The genetic and reproductive biology of the honey bee was accounted for by taking into consideration characteristics such as colony structure, uncertain paternity, overlapping generations and polyandry. In addition, we used a modified numerator relationship matrix and a realistic genome for the honey bee.For all values of heritability and correlation, the accuracy of overall estimated breeding values increased significantly with the unified approach. The increase in accuracy was always higher for the case when there was no correlation as compared to the case where a negative correlation existed between maternal and direct effects.Varroa or production and behavioural traits. In particular, the study is of great interest for cases where negative correlation between maternal and direct effects and uncertain paternity exist, thus, is of relevance for other species as well. The study also provides an important framework for simulating genomic and pedigree datasets that will prove to be helpful for future studies.Our study shows that the unified approach is a useful methodology for genetic evaluation in honey bees, and can contribute immensely to the improvement of traits of apicultural interest such as resistance to  A colony trait (e.g. honey and wax production) in the honey bee is comparable to maternally influenced traits in mammals such as birth and weaning weight; thus, it can be partitioned into the additive genetic effect of the queen  and the additive genetic effects of the progeny workers (direct genetic effects). The queen mediates its effect through heritable characters like egg laying rate or pheromone production in the hive whereas workers affect a trait through their hoarding behaviour or production of and responsiveness to pheromones. Until now, genetic evaluation in the honey bee has been implemented using a pedigree based BLUP-animal model with maternal and direct genetic effects . In the We performed a simulation study to investigate the impact of the unified approach on the accuracy of estimated breeding values in honey bees. Similar to the case of the honey bee, other species also have a situation where genetic evaluation needs to account for maternal effects and uncertain paternity, e.g. weaning weight in beef cattle is a maternally influenced trait. Besides, cows can be exposed to more than one male in a herd and pasture paddock within the same breeding season, thus generating uncertainty on paternity assignments and adversely affecting the accuracy of breeding value predictions . In thisThis study consisted of two main steps. In the first step, a dataset was simulated for a honey bee population, which involved the modelling and simulation of the population structure, genome, correlation between maternal and direct effects, heritability, true breeding values, genetic, phenotypic and residual variances. The simulated dataset was close to realistic scenarios and in agreement with the genetic and reproductive peculiarities of the honey bee. In the second step, genetic evaluation was performed using the unified approach and the traditional pedigree based BLUP approach.A random mating population was simulated for 1000 generations to obtain a base population in mutation-drift equilibrium with linkage disequilibrium (LD) . The simFive additional overlapping generations were simulated from the base population. Each of the generations consisted of 500 potential-dam queens and 250 drone-producing queens. From these 500 potential-dam queens, 10% were randomly selected as dam queens. The 50 selected dam queens produced 500 potential-dam queens  in the pedigree. Generations following the base population were overlapping and mating was polyandrous as in the normal breeding population. These characteristics are described in more detail in the following section.As a consequence of polyandry in honey bees, offspring have an unclear paternal descent. To overcome the problem of representing the paternal descent, Bienefeld et al.  suggesteA colony is formed by a queen and its progeny comprising several thousand workers. Since it is impossible to include all workers of a colony for genetic evaluation, an average worker was constructed that represented all workers of a colony. It was assumed that one average worker existed for each potential-dam queen/dam queen in the pedigree.th generation and queens constituting a dummy sire were taken from the (n-1)th generation i.e. one generation preceding the dam queens  in the base generation and all potential-dam queens (and the corresponding average worker) in all but the last generation. Genotyping information was available for all dam queens in the base generation and all potential-dam queens.N.We simulated a realistic genomic dataset which helped to assess the impact and applicability of the unified approach to the honey bee. A diploid genome consisting of 16 linkage groups was simulated for every queen . A totalrqw) was obtained by estimating the correlation between the maternal and direct true breeding values.Studies in honey bees  and direct effects (TBVw) for a queen were calculated using the formula TBVqi and TBVwi are the maternal and direct true breeding values for the ith queen, respectively. qqij and qwik are QTL genotypes of the ith queen at the jth and kth QTL controlling the maternal and direct effects, respectively and has a value of 1 or −1 for the homozygous genotypes or 0 for the heterozygous genotype. aj and ak are allele substitution effects at the jth and kth QTL.Maternal and direct true breeding values were simulated for all dam queens of the base population and all potential-dam queens from generations 1–5. True breeding values for maternal . The way the value for residual variance (σe2) was chosen is explained in the later section.The overall true breeding value of a queen was the sum of its maternal and direct true breeding values. The phenotype of each queen was obtained by adding the overall true breeding value of a queen to a residual value drawn from a normal distribution σq2) and direct (σw2) effects were obtained by calculating the variance of the simulated maternal and direct true breeding values, respectively. The covariance between maternal and direct effects (σqw) was obtained by calculating the covariance between the maternal and direct true breeding values.Variances of maternal (σg2) would become σq2 + 0.25σw2 + σqw (the latter from 2 × 1 × 0.5 × σqw). However, for the sake of easy comparison and interpretation, the overall breeding value was taken as a sum of the direct and maternal breeding values of a queen and the total genetic variance was taken as a sum of variance of maternal effects, direct effects and twice the covariance between them, and can be expressed as σg2 = σq2 + σw2 + 2σqw.Usually a breeding value is defined as twice the expected deviation of an individual's progeny from the mean, or twice the ‘transmitting ability’ of an individual . If we chm2) and direct (hd2) effects. In our study, we simulated a fixed maternal heritability of 0.15, 0.25 and 0.35  that can be expressed as a ratio of the variance of maternal effects to the phenotypic variance and is given as follows:A colony trait in honey bees is determined by the heritability of maternal (σp2) was obtained from the expression σe2) was obtained by subtracting the total genetic variance (σg2) from the phenotypic variance (σp2) i.e. σe2 = σp2 − σg2. The ratio of variance of direct effects to the phenotypic variance provided a measure of the heritability of direct effects, as given below.After rearranging, we get, Table A BLUP-animal model with maternal and direct effects , with a y is a vector of records of the colonies, b is a vector of fixed effects, u1 is a vector of random direct effects, u2 is a vector of random maternal effects, e is a vector of random residual effects, X is an incidence matrix relating observations to the corresponding environment, Z1 and Z2 are the incidence matrices relating observations to the corresponding direct effects and maternal effects, respectively.where A) constructed from pedigree information and (2) the unified approach (UNI_BLUP) based on a combined relationship matrix (H) constructed from pedigree and genomic information.Estimation of breeding values was done using the following two approaches: (1) the traditional BLUP approach (PED_BLUP) based on a numerator relationship matrix (A) were calculated according to the method proposed by Bienefeld et al. [Pp) of 0.367 to account for polyandry. This value is currently used for Germany-wide genetic evaluation of the honey bee populations where all mating sites are managed according to unified guidelines (with respect to number of drone-producing colonies and their relationship). The details for constructing the A matrix recursively are given in the Additional file A matrix for all 5275 individuals in the pedigree. The A matrix was partitioned into A11, A22, A12 and A21 where subscripts 1 and 2 represent genotyped and non-genotyped individuals, respectively. The inverse of the partitioned A matrix can be expressed as [Elements of the numerator relationship matrix  was constructed for the 2550 queens with genotyping data. Different methods have been developed to derive the G matrix [G matrix was obtained from ZZ’/2 ∑ pi(1 − pi), where Z is equal to M − P, M is the matrix specifying marker alleles inherited by each individual and P is equal to 2(pi − 0.5) with pi being the frequency of second allele at locus i in the base population. In order to avoid a singular G matrix [Gw) was constructed using a weighing factor (w), given as Gw = wG + (1 − w)A11. Christensen and Lund [w) could be interpreted as the relative weight on the polygenic effect. Aguilar et al. [w was taken as 0.99 [In the honey bee pedigree, a dummy sire and an average worker represent a group of individuals and thus, it is not possible to get individual genotyping data. Moreover, it is not possible to obtain genotyping information from all queens in the population. Using the unified approach is advantageous for honey bees as genomic information for genotyped queens can be integrated with pedigree information from genotyped as well as non-genotyped individuals resulting in a combined relationship matrix G matrix ,25. We cG matrix . The G mG matrix ,24, a moand Lund  suggester et al.  reported as 0.99 .H-1), described by Legarra et al. [The inverse of the combined relationship matrix  as compared to the PED_BLUP approach for all values of heritability and correlation between maternal and direct effects. The increase in accuracy by UNI_BLUP was approximately 0.1 (or 29%) for most of the cases.Similar to juvenile queens, the accuracy of overall estimated breeding values for all queens was higher with the UNI_BLUP approach (P < 0.05) than the PED_BLUP for all values of heritability and correlation between maternal and direct effects. The percentage increase in accuracy for the case of no correlation between maternal and direct effects at maternal heritabilities of 0.15, 0.25 and 0.35 was approximately 9.6%, 6.5% and 4.0%, respectively. In case of a negative correlation of −0.46, the percentage increase in accuracy was approximately 13.5%, 10.9% and 9.0% at maternal heritabilities of 0.15, 0.25 and 0.35, respectively.From these results we can conclude that the UNI_BLUP approach performed better than the PED_BLUP and the accuracy of overall estimated breeding values increased considerably with the UNI_BLUP approach.Table Both low heritability and negative correlation contribute to a lower genetic variance which leads to a decrease in the accuracy. The accuracy of overall estimated breeding values was reduced as a result of negative correlation in comparison to the case where maternal and genetic effects had no correlation  the traditional BLUP approach based on pedigree data and (2) the unified approach based on both pedigree and marker data. In this study, we investigated the accuracy of overall, direct and maternal estimated breeding values as well as the influence of heritability of the trait and the genetic correlation between maternal and direct effects on the accuracy.It has been reported in honey bees that most economically important traits have low to medium heritability -29. TherG matrix. Likewise, Christensen and Lund [Unlike previous studies ,7,34, ouand Lund  reportedIn our study, the accuracies of maternal and direct estimated breeding values for the pedigree based approach (PED_BLUP) with maternal and direct heritability of 0.15 were 0.38 and 0.41 at no correlation and 0.21 and 0.19 at a correlation of −0.46, respectively. In an earlier pedigree based study by Roehe and Kennedy , the accA complexity associated with the estimation of breeding values for maternally influenced traits is that negative correlation between maternal and direct effects severely impedes the response to selection ,36. AddiTo provide a comparison between genetic evaluation methods based on the unified approach and the pedigree based approach, we modelled a complex scenario by taking into consideration characteristics such as varying heritability and correlation between maternal and direct genetic effects, uncertain paternity and other genetic and reproductive peculiarities of the honey bee. To the best of our knowledge, this is the first study that describes the use of molecular marker data for genetic evaluation in honey bees by employing the unified approach. The study provides background knowledge about the simulation of a genomic and a pedigree dataset in honey bees for genetic evaluation, therefore, it can serve as an important framework for future studies. Studies in other species ,34 have BLUP: Best Linear Unbiased Prediction; LD: Linkage Disequilibrium; MAF: Minor Allele Frequency; QTL: Quantitative Trait Loci; SNP: Single Nucleotide PolymorphismThe authors declare that they have no competing interests.PG conducted the study and wrote the manuscript. NR and KB conceived the study, participated in discussions and helped to draft the manuscript. AS and TC participated in discussions and helped to draft the manuscript. All authors read and approved the final manuscript.Details for constructing the honey bee’s numerator relationship matrix recursively.Click here for file


Oxidative stress related genes modify the effects of ambient air pollution or tobacco smoking on lung function decline. The impact of interactions might be substantial, but previous studies mostly focused on main effects of single genes.We studied the interaction of both exposures with a broad set of oxidative-stress related candidate genes and pathways on lung function decline and contrasted interactions between exposures.1), FEV1 over forced vital capacity (FEV1/FVC), and mean forced expiratory flow between 25 and 75% of the FVC (FEF25-75) was regressed on interval exposure to particulate matter <10 µm in diameter (PM10) or packyears smoked (a), additive SNP effects (b), and interaction terms between (a) and (b) in 669 adults with GWAS data. Interaction p-values for 152 genes and 14 pathways were calculated by the adaptive rank truncation product (ARTP) method, and compared between exposures. Interaction effect sizes were contrasted for the strongest SNPs of nominally significant genes (pinteraction<0.05). Replication was attempted for SNPs with MAF>10% in 3320 SAPALDIA participants without GWAS.For 12679 single nucleotide polymorphisms (SNPs), change in forced expiratory volume in one second (FEV1/FVC decline by 3.8% (pinteraction = 2.5×10−6), and rs12190800 in PARK2 attenuated FEV1 decline by 95.1 ml pinteraction = 9.7×10−8) over 11 years, while interacting with PM10. Genes and pathways nominally interacting with PM10 and packyears exposure differed substantially. Gene CRISP2 presented a significant interaction with PM10 (pinteraction = 3.0×10−4) on FEV1/FVC decline. Pathway interactions were weak. Replications for the strongest SNPs in PARK2 and CRISP2 were not successful.On the SNP-level, rs2035268 in gene SNCA accelerated FEVConsistent with a stratified response to increasing oxidative stress, different genes and pathways potentially mediate PM10 and tobacco smoke effects on lung function decline. Ignoring environmental exposures would miss these patterns, but achieving sufficient sample size and comparability across study samples is challenging. SNP-level interaction signals were integrated onto upper biological levels to identify significantly interacting genes and pathways. The impact of PM10 exposure on lung function decline was contrasted to tobacco smoking by comparing patterns of associations at the gene- and pathway level, as well as interaction effect sizes for the strongest interacting SNP within genes.All participants gave written informed consent. The study was approved by the Overall Regional Ethics Commission for Clinical Medicine  and the responsible cantonal ethics committees of each study centre .SAPALDIA is a population-based cohort study established in 1991 to assess the effects of long-term exposure to ambient air pollution on respiratory health, with a first follow-up examination in 2002. Participants were residents from 8 communities throughout Switzerland aged 18–60 years at baseline. Details of the study design and methodology were published elsewhere see Figure S1). Participants without genome-wide data served as replication sample.The current work is based on up to 669 non-asthmatic participants with genome-wide data fulfilling quality control criteria and complete data on sex, age, height, PM10- and smoking exposure (1) were obtained. Forced expiratory flow between 25 and 75% of the FVC (FEF25–75) was recorded.Spirometry was performed without bronchodilation. Identical spirometry protocols and devices  were used in 1991 and 2002 1, the ratio FEV1/FVC and FEF25–75 between 1991 and 2002, as measures of airway obstruction, calculated by subtracting the first measurement from the second (measurement at SAPALDIA2– measurement at SAPALDIA1).In the present study we studied the decline of FEVSmoking information was assessed by questionnaire. At each examination, never smokers were defined as having smoked less than 20 packs of cigarettes or 360 g of tobacco in their life, ex-smokers as having quit smoking at least 30 days before the interview, and current smokers as those who reported active smoking 3) * years. Annual average exposures were calculated by using exposure estimates from Gaussian Dispersion models on a 200m×200m grid throughout Switzerland for years 1990 and 2000, and interpolating historical trends from fixed air pollution monitoring stations. Participants were assigned individual annual exposure estimates via their geo-referenced residence addresses, taking account of residence changes during follow-up. Details on exposure modeling are given elsewhere Similarly to calculating packyears, interval PM10 exposure was defined by summing individual average home outdoor exposure to PM10 over each year of follow-up, giving estimates in  <5% or low imputation quality (Rsq<0.5). A total of 2′168′681 SNPs withstood QC, and genome-wide data was finally available in 669 non-asthmatic individuals with environmental exposure data.Strict quality control (QC) was applied by excluding samples with <97% genotyping success rate, non-European origin, cryptic relatedness or sex-inconsistencies, as well as SNPs with Hardy-Weinberg equilibrium p-value<10CRISP2, and rs12190800 in PARK2) with MAF>10%. Genotyping was done using the iPLEX Gold MassARRAY  on the whole SAPALDIA study population including the analysis sample, as the costs for manual sample selection outweigh those of additional genotyping. The replication sample consisted of 3320 successfully genotyped participants with complete data for covariates and all three lung function parameters .Replication genotyping was attempted for two interacting SNPs . Resulting gene lists were further enriched by literature reviews www.ingenuity.com), 14 molecular pathways related to oxidative stress and environmental exposures of interest were identified  see Table S1). Gene specific allele dosage files in MACH format were used for analysis.Gene regions were defined by retrieving transcription start and end positions in the ‘gene track’ of the UCSC browser  and interval PM10 exposure  . To asseThe interaction of genetic variation and exposure to PM10 or tobacco smoke on lung function decline was assessed in different stages.http://www.genabel.org) First, SNP level analyses on decline in FEV1, FEV1/FVC and FEF25–75 were done for each gene separately using multiple linear regression in ProbABEL v0.1.3 (We used a slightly modified version of the Adaptive Rank Truncation Product (ARTP) method described by Yu and colleagues SNP-level analyses were run 10000 times, always after having newly permuted gene-specific SNP-allele-dosages across participants. SNP-level interaction p-values of the observed and permutated datasets were used for calculating gene- and pathway-level p-values. According to Yu et al. Emerging patterns of interaction were compared between exposures at the pathway- and gene-level. In pathways with nominally significant interactions, gene-level p-values were plotted against each other to identify the relative contributions to the pathway signal.For the SNP with the strongest interaction signal in each nominally significant gene regression analyses were repeated with exposure centered to the median. Effect estimates were scaled to represent an exposure contrast of one interquartile range (IQR), and interaction effect sizes were compared between PM10 and tobacco smoke exposure. For SNP rs2035268 in gene SNCA, which was one of the top interaction signals in FEV1/FVC decline, genotype specific estimates for PM10 and packyears exposure were calculated to exemplify the effect modification by genotype. To this purpose, imputed allele dosages were coded as genotypes as follows: dosage <0.5 genotype TT, 0.5≤ dosage <1.5 genotype GT, and dosage ≥1.5 genotype GG. Reparametrization of exposure variables into genotype specific ones was employed to avoid model-overspecification and instable estimation in small genotype strata (rs2035268: MAF 5%).Power calculations were done using QUANTO software see Table S2).Regarding the distribution of sex, age and lung function according to categories of smoking and PM10 exposure, our study sample on average presented decreasing lung function values and accelerated lung function decline with increased smoking Table 2.−6) detected an interaction between SNP rs2035268 in gene synuclein alpha (SNCA) on chromosome 4 q21 and PM10 on FEV1/FVC decline (pinteraction = 2.5×10−6). Compared to the baseline TT genotype, each G-allele was associated with a 3.8%  2.2 to 5.4%) higher decline per 83.4 µg/m3*year PM10 exposure (IQR) over 11 years. Further, rs12190800 located in gene Parkinson disease protein 2 (PARK2) on chromosome 6 q25.2 interacted with PM10 on FEV1 decline. Compared to the TT-genotype each C-allele entailed an attenuation of 95.1 ml (95%-CI 60.1 ml to 130.1 ml) in FEV1 decline per IQR of PM10 (pinteraction = 9.8×10−8). Exposure and outcome specific regression estimates for all 12679 SNPs are given in Data S1.A SNP-level analysis correcting for 12679 tests . After correcting for performing 152 gene-level tests (αBonferroni = 0.05/152 = 3.29*10−4), the interaction between gene cysteine-rich secretory protein 2 (CRISP2) located on chromosome 6p12.3 and interval PM10 exposure on FEV1/FVC decline remained significant (pinteraction = 3.0×10−4). A marginally significant interaction was seen for gene SNCA on chromosome 4q21 with the same outcome and exposure (pinteraction = 4.0×10−4). Interactions observed for packyears exposure did not withstand multiple testing corrections.In the gene-level analysis, nominally interacting genes differed between PM10 and packyears exposure across the parameters of lung function decline Table 3.Table S3.P-values of interaction for all tested genes are given in “mitochondrial dysfunction” and “methane metabolism” interacted nominally (α = 0.05) with PM10 on FEV1/FVC decline (p = 0.017) and FEF25–75 decline (p = 0.029), respectively. A further interaction signal was observed for pathway “apoptosis” and packyears exposure on FEV1-decline (p = 0.051). Inspecting the interaction p-values of pathway-specific genes revealed that the pathway signals mostly arose from single genes  or single genomic loci  . P-values of interaction for all tested pathways are given in Table S3.Pathways 1/FVC decline, where significant and marginally significant gene-level interactions have been detected for genes CRISP2 and SNCA, respectively. Estimates for decline in FEV1 and FEF25–75 are presented in Table S5.The comparison of interaction effect sizes for PM10 and packyears exposure was based on regression estimates for the strongest interacting SNP only within each nominally significant gene. CRISP2 accelerated FEV1/FVC decline by 1.1% per IQR change in PM10 exposure over 11 years .For FEV1/FVC by 1%, and in FEV1 by 50 ml (data not shown). Respective estimates for PM10 were non-significant. SNP main effects remained non-significant and their beta estimates largely unaffected by the exclusion of interaction terms.In models including only main effects but no interaction between SNPs and exposure, an IQR of 9.8 packyears was significantly associated with accelerated decline in FEVCRISP2 SNP rs360563 (MAF of 49.8%) and rs12190800 in PARK2 (MAF 16%), but their interaction with PM10 exposure on FEV1/FVC and FEV1 decline could not be confirmed in the remainder of the SAPALDIA population . Thereby, MAFs in the replication sample corresponded to those in the discovery sample, and both SNPs were in Hardy-Weinberg equilibrium.Replication genotyping was done for To the best of our knowledge, this is the first study assessing gene-environment interactions on lung function decline using analysis methods that accumulate interaction effects along a broadly defined set of candidate genes and pathways. Our results suggest that different oxidative stress genes could be involved in mediating the adverse effects of ambient air pollution and tobacco smoke exposure on lung function decline.stratified oxidative stressmodel while studying the biological effects of particulate matter exposure on human and mouse cell lines exposed to solutions of Diesel exhaust particles (DEP) and concentrated ambient air particles (CAP) sampled in a highly polluted area BCL2, CASP6, MAP2K1, NFκB1, TGFBR2, TP53). Only two such genes showed interaction signals with PM10 exposure , and many of the others related to scavenging or production of ROS . These observations are consistent with the stratified oxidative stress model. The observation of larger interaction effect sizes at the level of SNPs for FEV1 and FEF25–75 decline, as well as the frequent presence of significant main effects further support higher oxidative stress levels induced by tobacco smoke than PM10 exposure.We can currently only hypothesize about the reason for observing different patterns of interaction between the two environmental exposures. A possible explanation would be that ambient particulate matter pollution and tobacco smoke, although sharing many constituents, also differ in their composition, which possibly affects the overall and relative relevance of the different pathways. A probably more important explanation is that levels of oxidative stress imposed by ambient PM10 exposure are much lower than those induced by active tobacco smoking. Experimental studies have shown that different levels of oxidative stress trigger dose-dependent, specific activations of pathways on the cellular level in response to the oxidant burden Another important observation was that the effect of genetic variation related to oxidative stress appeared to be mediated predominantly by the interactions with environmental exposures, as hardly any SNP main-effects were observed. This is in line with the findings of genome-wide studies on lung function performed to date see Table S1), and the well-known gene-deletions in glutathione S-transferases are difficult to tag by SNP-genotyping as they represent copy number variations. This makes it difficult to interpret respective results. On the other side, a comparison of imputed SNP data for rs360563 (gene CRISP2) with genotypes measured during replication in the initial study sample showed a high concordance indicating high imputation quality (see Table S6). The absence of strong interactions on the pathway level is likely due to our primary focus on function while selecting candidate genes, which limited pathway coverage. But genes in a pathway may also differently interact with exposure, or compensate for each other. Further, regulatory genomic regions could be located farer away than the chosen flanking segments of 20 kilobases. Detecting interactions in pathways is thus more challenging.Our study had several limitations. First, the limited number of non-asthmatic adults with available genome-wide data restricted our power to detect associations at the gene and pathway levels. In this context, we faced the problem of finding studies with genome-wide genotyping and comparable data on both phenotypes and environmental exposures. This issue is particularly imminent regarding ambient air pollution exposure. As a consequence, small sample size did not allow us to identify further strong interaction signals to follow-up, while the observed ones could not be replicated in the remainder of the study population. Our gene and pathway level results are thus of more exploratory nature. Limited power is also known to inflate effect estimates when the strongest association signals are selected for further follow-up Click here for additional data file.Figure S2Scheme of analysis steps in the ARTP-method. The ARTP method developed by Yu and colleagues Order p-values from single SNP analysis in ascending order, 2. Calculate products of ranked p-values at different truncation points depending on gene length (light and dark green arrows in the graph), 3. Adjust product p-values using permutation distribution (1st and 2nd yellow arrow from the right), 4. Select the minimum of the adjusted products (red arrow) and readjust (1st yellow arrow from the left). The readjusted product minimum represents the gene-level p-value. For each permutated dataset, an adjusted product minimum can be calculated as well. The procedure can then be repeated using the resulting, original and permutation gene-level p-values to yield p-values of the pathway.(TIF)Click here for additional data file.Table S1Characteristics of selected oxidative-stress related genes and mapping to candidate pathways.(XLS)Click here for additional data file.Table S2A-C p-value for comparisons of characteristics between baseline and analysis sample; PB-C p-value for comparisons of characteristics between follow-up and analysis sample; a Chi-squared tests for proportions, two sample T-tests for means, and ranksum-test for medians, b n = 650 with complete baseline and follow-up data, c in ever-smokers only.Comparison of study sample to non-asthmatic participants lost to follow-up, and those followed-up w/o genome-wide data. P(XLS)Click here for additional data file.Table S3Interaction P-values by lung function decline parameter and exposure for a) all tested pathways and b) genes.(XLS)Click here for additional data file.Table S41-decline (n = 669). The table shows the effect estimates of the strongest interacting SNP in each nominally significant gene . SNP-estimates are based on an additive model. Beta-estimates are in units of milliliters for FEV1, and represent declines per effect allele and/or for an exposure contrast of one interquartile range (IQR) over 11 years. All estimates are taken from the same interaction model. Positive values mean that the respective decline is attenuated, opposed to acceleration with negative values. Rows are sorted according to ascending interaction p-values. gen: genotyped SNP; imp: imputed SNP; All1: allele 1 , All2: allele 2 ; FreqAll1: frequency of allele 1.Effect estimates of the strongest interacting SNP from each nominally significant gene on FEV(DOC)Click here for additional data file.Table S525–75-decline (n = 650). The table shows the effect estimates of the strongest interacting SNP in each nominally significant gene . SNP-estimates are based on an additive model. Beta-estimates are in units of milliliters per second, and represent declines per effect allele and/or for an exposure contrast of one interquartile range (IQR) over 11 years. Positive values mean that the respective decline is attenuated, opposed to an acceleration with negative values. gen: genotyped SNP; imp: imputed SNP; All1: allele 1 , All2: allele 2 ; FreqAll1: frequency of allele 1.Effect estimates of the strongest interacting SNP from each nominally significant gene on FEF(DOC)Click here for additional data file.Table S6Comparison of genotyped and imputed data for SNP rs360563 in gene CRISP2. *imputed allele dosages have been rounded to corresponding integer values of mutant alleles as follows:dosage <0.500> = 0.5 and <1.51> = 1.52N = 665 corresponds to the current analysis sample with available genotyping data.(DOC)Click here for additional data file.Methods S1Details on the ARTP method specifications and power calculations.(DOC)Click here for additional data file.Text S1Overview of the SAPALDIA study team as of July 2011.(DOC)Click here for additional data file.Data S11, percentages for FEV1/FVC, and milliliters per second for FEF25–75, Betas represent declines per effect allele and/or for an exposure contrast of one interquartile range (IQR) over 11 years. All estimates are taken from the same interaction model. Positive values mean that the respective decline is attenuated, opposed to acceleration with negative values. Rows are sorted according to chromosome and position. All1: allele 1 , All2: allele 2 ; FreqAll1: frequency of allele 1, MAF: minor allele frequency. n: number of observations in the model. Beta_int/se_int/p_int: beta estimate/standard error/p-value of the SNP*environment interaction term.Outcome and exposure specific regression results of all 12679 SNPs. Effect estimates are derived from multiple linear regression models specifying SNP main effects, interval PM10 and packyears exposure (centered to the median) and an interaction between SNP and either PM10 or packyears exposure. An additive genetic model was assumed. Adjustments were made for sex, age and height at follow-up, packyears smoked up to baseline, population ancestry, and study area. Beta-estimates are in units of milliliters for FEV(XLS)Click here for additional data file.


The traits included hemoglobin concentration (HGB), hematocrit (HCT), RBC count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). Patients were participants in the electronic MEdical Records and GEnomics (eMERGE) network and underwent genotyping of ~1.2 million single-nucleotide polymorphisms on the Illumina Human1M-Duo array. Association analyses were performed adjusting for age, sex, site, and population stratification. Three loci previously associated with resistance to malaria—HBB (11p15.4), HBA1/HBA2 (16p13.3), and G6PD (Xq28)—were associated (P ≤ 1 × 10−6) with RBC traits in the discovery cohort. The loci replicated in the replication cohort (P ≤ 0.02), and were significant at a genome-wide significance level (P < 5 × 10−8) in the combined cohort. The proportions of variance in RBC traits explained by significant variants at these loci were as follows: rs7120391 (near HBB) 1.3% of MCHC, rs9924561 (near HBA1/A2) 5.5% of MCV, 6.9% of MCH and 2.9% of MCHC, and rs1050828 (in G6PD) 2.4% of RBC count, 2.9% of MCV, and 1.4% of MCH, respectively. We were not able to replicate loci identified by a previous GWAS of RBC traits in a European ancestry cohort of similar sample size, suggesting that the genetic architecture of RBC traits differs by race. In conclusion, genetic variants that confer resistance to malaria are associated with RBC traits in African-Americans.To identify novel genetic loci influencing interindividual variation in red blood cell (RBC) traits in African-Americans, we conducted a genome-wide association study (GWAS) in 2315 individuals, divided into discovery ( Disorders involving red blood cells (RBCs) are common and associated with adverse health outcomes  has yet to be reported. Identifying common genetic variants influencing RBC traits may offer insights into iron metabolism and erythropoiesis in African Americans, and such variants may also modify disease severity in conditions such as sickle-cell disease and thalassemia. It is well known for example, that sickle-cell heterozygotes have less severe incidences of Plasmodium falciparum infections than those with normal adult hemoglobin. Other abnormal hemoglobins, G6PD deficiency and pyruvate kinase deficiency also confer some degree of resistance against falciparum malaria (reviewed by Genome-wide association studies (GWAS) have revealed multiple loci that influence interindividual variation in RBC traits in individuals of European and Asian ancestry (www.gwas.org) was established by the National Human Genome Research Institute to develop and implement approaches for leveraging biorepositories associated with EMR systems for large-scale genomic research . Genotype data were cleaned using the quality control (QC) pipeline developed by the eMERGE Genomics Working Group . These markers provide 76% coverage of the genome of an African population at i.e., the first two principal components [PCs]). We adjusted for genetic ancestry via the first two PCs generated by principal component analysis (PCA), a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables  into a set of values of linearly uncorrelated variables called PCs. For SNPs in the X chromosome, alleles A and B were coded  in males and  in females, and additionally sex was included as a covariate. We estimated the regression coefficient (β), and the proportion of variance in a RBC trait explained by a variant . The statistical power for a sample size of 1904 in the discovery cohort to detect a quantitative trait locus that explained ~2% variance in a RBC trait, was 80% at a significance level of 5×10−8.When multiple measurements of a RBC trait were available for an individual patient, we chose the median value and the corresponding age for the genetic analyses. We performed association analyses by using linear regression implemented in PLINK  in whom relevant comorbid conditions, specific medications, and blood loss were absent. To do this, we employed a previously developed algorithm based on billing codes and natural language processing of unstructured clinical notes to exclude RBC traits values affected by comorbidities, medications, or blood loss (www.gwas.org), and the values of RBC traits before and after implementing the algorithm are provided in Supporting Information, Table S1).Because RBC traits can be affected by a wide array of medical conditions, we also performed analyses in the subset of patients (ood loss . The phePatterns of linkage disequilibrium (LD) were analyzed based on HapMap Phase II YRI for chromosome X  and the We selected the most significant SNPs in 15 loci associated with six RBC traits  in individuals of European ancestry (Table S2. The strongest correlation was between HGB and HCT (r = 0.95), and the weakest was between MCHC and RBC count (r = −0.03).The characteristics of study patients, including the discovery and replication cohorts, are summarized in P < 1 × 10−6. We selected the 12 most significant SNPs based on the LD pattern  associated with six RBC traits in the discovery cohort, and tested their association in the replication cohort. After adjustment for multiple testing by Bonferroni correction , we identified three loci that were significant in the replication cohort and remained significant (P < 5 × 10−8) in the combined cohort: chromosome 11p15.4 (with MCHC), 16p13.3 , and chromosome Xq28    , was associated with MCHC in the combined cohort  on chromosome 11p15.4, located 14.5 kb downstream of the ITFG3) was associated with MCV , MCH , and MCHC  in the combined cohort (HBZ-HBM-HBA2-HBA1-HBQ1), which contains three functional genes , is present at this locus (HBA1 is ~87.2 kB upstream of rs9924561). Another SNP (rs13336641) adjacent to the α-globin gene cluster was associated with MCV  and MCH . The SNPs rs13336641 and rs9924561, although ~147 kb apart, are in LD (r2 = 0.46 in 1000 genomes YRI).A SNP (rs9924561) in an intron of the integrin α FG-GAP repeat containing 3 gene , MCV , and MCH  in the combined cohort  in the d cohort . The mini.e., hematologic disorders, medications, and blood loss) that can affect RBC traits, based on a previously developed algorithm . The traits HCT, HGB, RBC count and MCH, MCV, RBC count were the main contributors to PC1 and PC2, respectively. The main driver of the third PC was MCHC , which is similar to the genome-wide association analysis of this particular trait.The first three PCs explained 99% of the total variance among the correlated RBC traits. For the genomic association analyses, we used only the first and second PC  including rs9924561 that was identified for RBC count, HGB and HCT traits separately, were associated with PC1 . Nineteen SNPs either on chromosome 16p or Xq23 (P < 5 × 10−8) were associated with PC2 including rs9924561 and rs1050828 previously identified for MCV, MCH, and MCHC traits separately . The finding on chromosome 11 was not replicated in the PCAs.Two SNPs on chromosome 16p (P = 7 × 10−4), MCV (P = 1 × 10−3), and MCH (P = 3 × 10−3)] and rs7775698 on chromosome 6q23.3 [associated with RBC (P = 1 × 10−2) and MCH (P = 1 × 10−3)] were replicated in our study sample after correction for multiple comparisons. At both loci, the minor allele frequency, and the direction of association were similar between patients of European and African ancestry. The inability to replicate other loci may have been due low statistical power given the relatively small sample size of 2315 African Americans ; and/or a different genetic architecture underlying RBC traits in the two ethnic groups.SNPs at two loci [rs218237 on chromosome 4q12 [associated with RBC  confer resistance to malaria in Africans  at chromosome 11p15.4 was associated with MCHC in the present study. The HbS allele  leading to sickle-cell anemia, is in LD with rs7120391 (r2 = 0.37). MCHC is thought to be important in sickle-cell disease because sickling rates are strongly dependent on hemoglobin concentration  in the 5′ regulatory region of the human α-globin gene cluster, including two HBA genes (HBA1 and HBA2). Variants in HBA leading to α+ thalassemia are protective against severe malaria , HCT , RBC count , and MCV . G6PD deficiency is one of the most common enzymopathies in humans (nearly 300 million affected people worldwide), paralleling the distribution of malaria (G6PD has been shaped by recent positive selection (We found that a nonsynonymous SNP (rs1050828) in  malaria . Previoun = 3012)  to be associated with RBC traits. These loci were not associated with RBC traits at a genome-wide significance level in the present study. Only two of the 15 loci that we previously identified in 12,486 patients of European ancestry in the eMERGE network (ITFG3 on chromosome 16p13.3 were replicated (P < 5 × 10−8). One explanation could be that the discovery cohort in the present study is underpowered to detect such associations given the relatively small sample size. The variance explained by 23 loci associated with RBC traits in a prior GWAS  confer resistance to malaria. None of the three loci identified in a GWAS for RBC traits in a European ancestry cohort of similar sample size (n = 3012) (In conclusion, using a GWAS approach, we identified three loci associated with RBC traits in individuals of recent African ancestry. Variants in genes residing in these loci (


Genetic Analysis Workshop 17 provided simulated phenotypes and exome sequence data for 697 independent individuals (209 case subjects and 488 control subjects). The disease liability in these data was influenced by multiple quantitative traits. We addressed the lack of statistical power in this small data set by limiting the genomic variants included in the study to those with potential disease-causing effect, thereby reducing the problem of multiple testing. After this adjustment, we could readily detect two common variants that were strongly associated with the quantitative trait Q1 (C13S523 and C13S522). However, we found no significant associations with the affected status or with any of the other quantitative traits, and the relationship between disease status and genomic variants remained obscure. To address the challenge of the multivariate phenotype, we used propensity scores to combine covariates with genetic risk factors into a single risk factor and created a new phenotype variable, the probability of being affected given the covariates. Using the propensity score as a quantitative trait in the case-control analysis, we again could identify the two common single-nucleotide polymorphisms (C13S523 and C13S522). In addition, this analysis captured the correlation between Q1 and the affected status and reduced the problem of multiple testing. Although the propensity score was useful for capturing and clarifying the genetic contributions of common variants to the disease phenotype and the mediating role of the quantitative trait Q1, the analysis did not increase power to detect rare variants. Although genome-wide association studies in population samples may provide information on disease associations with relatively common genetic polymorphisms, they give only a tiny glimpse of the underlying functional variants and networks that might contribute to disease processes. Especially, rare variants in gene coding regions that are not in linkage disequilibrium with common variants are not well represented in these studies. Therefore exome resequencing projects have emerged to fill this knowledge gap; these studies focus on the detection of rare variants in coding regions of the genome. In addition, phenotypes are often influenced by many underlying quantitative traits and environmental exposures that might contribute to disease risk. These factors might be present in case subjects as well as in control subjects, therefore complicating the detection of risk factors. Covariates might introduce bias into the analysis if they are not evenly distributed between case subjects and control subjects. As confounding factors they might also obscure the relationship between risk factors and disease. New statistical approaches are clearly needed to address these analytical challenges.Propensity score analysis is a relatively novel statistical approach to dimension reduction, bias detection, and risk estimation in studies where multiple covariates are present . In clinThree major applications of the propensity score have emerged. In the first approach the propensity score is used to reduce bias in observational case-control analyses if covariates are not evenly distributed between the case and control groups. In this context, case and control subjects could be matched one to one on the propensity score to create more homogeneous groups for comparison. This approach often leads to a significant reduction in sample size through elimination of unmatched cases . In the In this study, we explore the use of the propensity score as a means to reduce the multiple dimensions of the phenotype in a case-control design using the exome sequencing data provided in the framework of the Genetics Analysis Workshop 17 (GAW17). Because the outcome is directly influenced by multiple quantitative traits, we use the propensity score to summarize the information on the covariates and to create a new quantitative trait or outcome variable, the probability of being affected given the covariates. Then, we compare this multivariate approach with the univariate approach in a case-control association analysis after reducing the multiple-testing problem even further by selecting only potential disease-causing genomic variants.In the framework of GAW17, exome data for 697 unrelated individuals, a simulated phenotype, and environmental exposure were provided to explore and compare analytical methods for the detection of rare variant/disease associations [p) of being affected given the covariates  were considered the independent variables. The propensity score e(ix) for subject i  was defined as the conditional probability of being affected  versus nonaffected  given a vector of observed covariates ix:We use the propensity score to summarize the disease-related covariates as the predicted probability  and C13S522  . The correlation between Q1 and the propensity score was 0.67 (p < 0.0001). No significant associations were found with the affected status itself or with any of the other quantitative traits after correction for multiple testing.In the case-control association analysis, limitations and advantages of the different approaches became readily apparent. Selecting only nonsynonymous SNPs that were present in at least two individuals in the case or control group increased the power of our analysis by reducing the multiple testing issues. Following this approach, quantitative trait analysis for Q1 detected strong and significant associations with two SNPs: C13S523  and C13S522  mediated through Q1 , our approach was unable to detect additional rare variants. The analysis missed 69 true disease-related SNPs with MAFs between 0.17 and 0.00071, and β values between 1 and 0.03, including nine additional SNPs in the gene FLT1.Case-control association analysis with the propensity score as the quantitative trait correctly identified the association with C13S523 (A common approach to rare genomic variants is the selection of variants that are present only in case subjects. Taking this approach, we found 421 SNPs that fulfilled this criterion; 16 of those were present in only one case subject. Out of 405 SNPs present in at least two case subjects, only 5 SNPs in five different genes were associated with disease according to the model and 400 SNPs were false positives. The most obvious contributing factor to the inflation of rare variants is ethnic admixture in this data set. Even though we attempted to correct for this problem using principal components analysis, it could not be completely eliminated.Composite phenotypes that are strongly influenced by multiple quantitative traits with specific genetic risk factors are a frequently encountered phenomenon in genetic studies of common complex disorders. Often these traits are present in case subjects as well as in control subjects, and they might introduce bias or even be confounding factors in the estimates of disease associations. A joint estimate of these covariates is rarely included in genome-wide association studies.Propensity score analysis has emerged as an approach to dimensionality reduction. Because the contributing quantitative traits are present in both case and control subjects, a probabilistic approach that summarizes the multiple risk factors is appropriate, particularly because the genetic risk factors predominantly influence the affected status through the quantitative traits. Using propensity score analysis, we were able to detect two SNPs associated with the affected phenotype that were obscured when only the affected phenotype was used as the outcome, and we were able to clarify the relationship between the quantitative traits and the phenotype.The application of the propensity score in the more traditional sense as a means to reduce bias was limited in this data set. After all, potentially confounding covariates were not the focus of this simulation. In fact, most covariates were directly and causally related to the affected status. Attempts to match case and control subjects on the propensity score either by one-to-one matching or by stratification resulted in a significant reduction in sample size as a result of a large number of unmatched observations. The resulting loss of power made the detection of significant associations impossible. Using the propensity score as a covariate after inclusion of all known contributing variables eliminated all the genetic contributions to the affected status mediated by the quantitative traits.Still, using the propensity score as a dimensionality reduction tool has several advantages over multivariate regression. Multivariate regression models are often concerned with finding parsimonious models using only a limited number of covariates to avoid overparameterization. In the propensity score estimation, the number of covariates that can be included is not limited by the model. Interactions and nonlinear terms can easily be incorporated.A commonly encountered problem in case-control association studies is false-positive association resulting from nonrandom differences between case and control subjects that are not related to the presence of the disease itself ,10. PopuSequencing data sets still have a relatively small sample size that limits the power of a study to detect associations with rare variants in a traditional case-control design. Therefore, it might be useful to identify genomic variants that are more likely to cause disease, such as nonsynonymous variants and truncating and non-sense mutations in coding regions, through resequencing approaches. Focus on those variants would decrease the multiple testing problem and increase the power to detect disease-associated variants with large effect. However, realistic expectations should be in place when dealing with these data. Case-control association designs might not be the appropriate approach to rare variants, particularly under genetic heterogeneity. Family-based resequencing approaches might be more appropriate under these conditions.Propensity score analysis could be a useful tool in genetic case-control association analyses. Even though we admit that this simulated data set had limitations for the meaningful use of this method, our study demonstrates an application to the dimensionality reduction of phenotypes that are influenced by multiple correlated traits with strong genetic risk factors. This approach might give some advantage in settings in which issues related to multiple testing arise. Potential problems include the selection of covariates. Our approach did not increase the power to detect rare variants, which remains a problem that is difficult to address in case-control studies.The authors declare that there are no competing interests.CML performed the propensity score analysis and created the figure. JFS performed the regression analysis for the variable selection and the regression analysis using propensity scores as co-variates. BK performed the genetic case-control association analysis, supervised the work of CML and JFS, and wrote the paper. All authors participated in the study design, the discussion and interpretation of the results. All authors have read and approved the final manuscript.


Genome-wide association studies have successfully identified many common variants associated with complex human diseases. However, a large portion of the remaining heritability cannot be explained by these common variants. Exploring rare variants associated with diseases is now catching more attention. Several methods have been recently proposed for identification of rare variants. Among them, the fixed-threshold, weighted-sum, and variable-threshold methods are effective in combining the information of multiple variants into a functional unit; these approaches are commonly used. We evaluate the performance of these three methods. Based on our analyses of the Genetic Analysis Workshop 17 data, we find that no method is universally better than the others. Furthermore, adjusting for potential covariates can not only increase the true-positive proportions but also reduce the false-positive proportions. Our study concludes that there is no uniformly most powerful test among the three methods we compared , and their performances depend on the underlying genetic architecture of a disease. In the past several years, genome-wide association studies (GWAS) have successfully identified many common single-nucleotide polymorphisms (SNP)  associated with complex human diseases. Despite the findings from GWAS, a large portion of the remaining heritability cannot be explained by these common variants . The impSome of the proposed pooling methods first specify a fixed threshold for the MAF and then perform association tests on the set of variants with MAFs smaller than that threshold ,6. The wWe use the GAW17 data for 697 unrelated individuals with variants on 22 autosomal chromosomes. There are 24,487 SNPs located in 3,205 genes on these chromosomes. We use the start and end positions (in base pairs) of each gene to pick the SNPs falling within the boundaries of that gene. We analyze all the phenotypes available: Q1, Q2, Q4, and the binary trait (Affected). All the 200 simulated replicates were studied. To evaluate the performance of the four tests , we requested the simulation answers and compared the answers with the results obtained from the four tests.http://genetics.bwh.harvard.edu/rare_variants/) of Price et al. [All the analyses were performed using the VT test software  and 0 otherwise. For the WS test,where ip is the allele frequency of the ith variant.where z-score:For the VT test, a T, where SE represents the standard error. Let zmax be the maximum z score among all possible values of T; then, the significance of zmax is assessed by permutation of phenotypes. Suppose that we perform p permutations and therefore have zmax,1, zmax,2, …, zpmax,, which are the maximum z scores obtained at their optimal thresholds T1, T2, …, pT, respectively. To ensure the validity of the VT test, the software allows T1, T2, …, pT for permuted data to be different from the optimal threshold T for the original data. The VT software then compares zmax with zmax,1, zmax,2, …, zpmax, to determine its statistical significance.is computed for each allele frequency threshold p-value was calculated based on 100,000 permutations. To increase the computation speed, the VT test uses linear regression instead of logistic regression to analyze all phenotypes, including the binary trait. To understand the influence of adjustment for covariates, we compared the results when ignoring all covariates with the results obtained when adjusting for Age and Smoking status. We first obtained the residuals by regressing the phenotypes on Age and Smoking status, and then the residuals were regarded as the adjusted phenotypes and were analyzed by the VT software.When we performed the T1, T5, WS, and VT tests with the VT software, each α, we estimate the type I error rate using:The phenotype Q4 is not related to any of the 3,205 genes, so we use this part of the results to evaluate type I error rates. Given a significance level I(·) is the indicator function, ip,r is the p-value of the ith gene in the rth replicate, gT is the set formed by all genes, and |gT| is the number of genes in gT. Note that the set gT varies with different methods. Because the T1 test considers only genes that have at least one SNP with MAF less than 1%, the total number of genes considered by the T1 test is 2,485. Similarly, the T5 test considers only genes that include at least one SNP with MAF less than 5%, and the total number of genes considered by the T5 test is 2,881. The WS and VT tests are performed for all genes without prespecifying a threshold, so the total number of genes considered by both of these tests is 3,205.where Table α, we estimate the true-positive proportion using:The phenotypes Q1, Q2, and the binary trait (Affected) are related to some genes, so we use the results on these three phenotypes to evaluate the true-positive proportions and the false-positive proportions. Figure and the false-positive proportion using:gS is the set formed by disease genes, |gS| is the number of genes in gS, and {gT\gS} is the set formed by genes unrelated to the disease. The set of gS is provided by the underlying simulation model.where From the results, all methods perform better for continuous traits than for the binary trait. For continuous traits, these methods perform better for Q1 than for Q2. This is reasonable because Q1 has higher residual heritability. For phenotypes Q1 and Affected, the areas under the ROC curves  increased when the phenotypes were adjusted for Age and Smoking status. However, adjusting for these two covariates did not have any influence on the ROC curves for Q2. This is also reasonable because Q2 is not influenced by any covariate, according to the underlying simulation model.FLT1 and C4S1878 (MAF = 16.50%) in gene KDR. However, the powers of the four tests are all high for detecting FLT1 and KDR, because the two genes include many functional variants. Excluding C13S523 from FLT1 or excluding C4S1878 from KDR makes no difference to the final results. The VT test is inferior to the T5 test because of the inclusion of the higher threshold (>5%), which increases noise and reduces power. When analyzing the phenotype Q2, we found that the VT test was the most powerful method for detecting gene VNN1. However, both the T1 test and the T5 test performed poorly in detecting VNN1, because one of the two functional variants in VNN1 is relatively common (MAF = 17%). Therefore, when analyzing Q2, the VT test is slightly better than the other methods. For the binary trait (Affected), all tests have similar (poor) performances. This is because the binary trait was determined by a model including noise (Q4). Not surprisingly, analyzing this trait is more challenging than analyzing Q1 or Q2.In this study, we evaluated the performance of three methods (four tests)—fixed-threshold, weighted-sum, and variable-threshold methods—for detecting rare variants. The main difference between these methods is the selection of a threshold to discriminate rare from common variants. Based on the simulation model for Q1, most true signals are variants with MAF < 5%, so the T5 test is the best method. The only two exceptions are C13S523 (MAF = 6.67%) in gene Based on our results, we found that inflated type I error rates were caused by potential confounders not adjusted for in the models Table . If a phWe evaluated the performance of three methods  in pooling signals of multiple rare variants in a gene. Based on our analyses for the GAW17 data, we find that no method is universally better than the others. Furthermore, adjusting for potential covariates can not only increase the true-positive proportions but also reduce the false-positive proportions. Our study provides an overall evaluation of the three popular pooled association methods with the GAW17 exome simulation data. This can provide insights to determine a strategy for analyzing exome sequencing data.The authors declare that they have no competing interests.W-YL participated in the design of the study, performed the statistical analysis, and drafted the manuscript. NY, GG, and NL participated in the design of the study and revised the manuscript. BZ participated in the statistical analysis and revised the manuscript. All authors read and approved the final manuscript.


We evaluated the prevalence and incidence rates of Leishmania spp. before and after the change in the protocol. In addition, based on our results, we propose a new alternative that is less expensive for the screening and confirmation of CVL. Plasma samples were obtained from a serobank from dogs evaluated in a cross-sectional study  and in a cohort study of susceptible animals (n = 447), followed for 26 months. Serology testing was performed using ELISA, IFAT, and DPP. The incidence and prevalence of CVL were determined by using the protocol of the Visceral Leishmaniasis Control and Surveillance Program until 2012 (ELISA and IFAT using filter paper) and the protocol used after 2012 (DPP and ELISA using plasma). The prevalence was 6.2% and the incidence was 2.8 per 1,000 dog-months for the protocol used until 2012. For the new diagnosis protocol for CVL resulted in an incidence of 5.4 per 1,000 dog-months and a prevalence of 8.1%. Our results showed that the prevalence and incidence of infection were far greater than suggested by the previously used protocol and that the magnitude of infection in endemic areas has been underestimated. As tests are performed sequentially and euthanasia of dogs is carried out when the serological results are positive in both tests, the sequence does not affect the number of animals to be eliminated by the Control Program. Then we suggest to municipalities with a large demand of exams to use ELISA for screening and DPP for confirmation, since this allows easier performance and reduced cost.The techniques used for diagnosis of canine visceral leishmaniasis (CVL) in Brazil ELISA and IFAT have been extensively questioned because of the accuracy of these tests. A recent change in the diagnosis protocol excluded IFAT and included the Dual-Path Platform (DPP) Leishmania infantum (Leishmania chagasi), which is endemic in South America, Central America, the Mediterranean basin, and parts of Asia In recent decades visceral leishmaniasis (VL) has become a major public health problem in Brazil, affecting approximately 3,379 individuals per year, with an annual incidence rate of 1.9 cases per 100,000 L. infantum, and canine VL (CVL) is considered to be an emerging and re-emerging disease, as indicated by an increase in the number of seropositive dogs and by the geographical expansion of the disease Dogs are highly susceptible to leishmaniasis infection and represent the major source of infection because of intense skin parasitism, independent of their clinical presentation To control the spread of disease, the Brazilian Ministry of Health through the Visceral Leishmaniasis Control and Surveillance Program (VLCSP) has instituted various measures including early diagnosis and treatment of human cases, identification and culling of seropositive infected dogs, control of insect vectors, and health education Until 2012, CVL had been diagnosed using IFAT, a method recommended for confirming positive cases detected by ELISA. These serological tests were performed using serum or blood samples collected on filter paper Leishmania spp. infection in a large canine population in an endemic area of Brazil. In addition, based on our results, we propose a new alternative that is less expensive for the screening and confirmation of CVL.Herein, we report the results of a baseline canine survey followed by a cohort study using the conventional serological methods used by the VLCSP until 2012 (ELISA and IFAT with filter paper) and after 2012 (DPP and ELISA with plasma) to evaluate the prevalence and incidence of The study was approved by the Committees of Ethics in Animal Experimentation of the Universidade Federal de Ouro Preto (protocol no. 083/2007), of the Universidade Federal de Minas Gerais (protocol no. 020/2007), and of the City Council of Belo Horizonte (protocol no. 001/2008). All procedures in this study were according to the guidelines set by the Brazilian Animal Experimental Collage (COBEA), Federal Law number 11794. Owners of the dogs participating in the project were informed of the research objectives and were required to sign the Informed Consent Form before sample collection.2) of Belo Horizonte. According to zoonosis control management, the canine population comprised 20,883 dogs. With an expected positive rate of 5% to 10% for CVL in the study area 95% confidence interval (CI) and an estimated precision rate of 1.5%, the appropriate sample size for the study was 1,226 dogs. The field work was performed in close collaboration with the Municipal Health Service, and the data were collected during the canine survey census conducted by health agents as a routine procedure of the VLCSP.Plasma samples were provided by the serobank of the Clinical Research Laboratory of the School of Pharmacy at the Federal University of Ouro Preto and were selected from a baseline survey and prospective cohort study performed from June 2008 to August 2010 in Belo Horizonte, Minas Gerais State, Brazil The follow-up study was initiated 10 months after the baseline  with 447 dogs and blood samples were collected by venipuncture. Evaluations II and III were conducted 16 and 26 months after the baseline, respectively. All dogs included in evaluations II and III underwent the same procedures as used in evaluation I.During the follow-up, losses occurred due to seropositivity in sequential tests, death, change of address, household closed, refusal, and dog escape  and 2. Tg, 20 min), and the plasma was collected and stored at –70°C from 3 to 5 years until it was assayed for the serological tests (DPP and ELISA) in the Laboratory of Immunopathology of the Federal University of Ouro Preto. Serological tests conducted in the Laboratory of Immunopathology were performed blind.A sample of peripheral blood (5 mL) was collected by puncture of the brachiocephalic vein, and an aliquot was transferred to a glass vial containing sufficient anticoagulant (ethylenediaminetetraacetic acid) to achieve a final concentration of 1 mg/mL. Then the blood sample was transferred from the syringe to filter paper and maintained at room temperature until dry out. The filter paper sample was then sent to the Laboratory of Zoonosis of the Municipality of Belo Horizonte and analyzed. The blood sample was centrifuged  for screening and IFAT  as a confirmatory test. This protocol used blood collected on filter paper (eluate) to perform the serological tests. The second protocol used the DPP CVL rapid test (Bio-Manguinhos/Fiocruz) for screening and ELISA (Canine Leishmaniasis EIE Kit) as a confirmatory test. This protocol used plasma for the serological tests. Both protocols followed the manufacturer’s instructions. As an alternative, the third protocol used ELISA (Canine Leishmaniasis EIE Kit) for screening and DPP CVL rapid test for confirmation. The cut-off of the EIE Kit was defined based on the manufacturer’s instructions and by considering the mean of the optical density of the negative controls multiplied by two. IFAT to detect anti-Statistical analysis was performed using Stata software . The prevalence and incidence rates indicated by ELISA, IFAT, and DPP were estimated using 95% CI.Among the 1,226 dogs, 106, 169, and 322 dogs had seropositive results according to ELISA using filter paper (ELISA-FP), DPP, and ELISA using plasma (ELISA-PL), with estimated prevalence rates of 8.6% , 13.8% , and 26.3% , respectively. Among the ELISA-FP–positive results (106), 76 (71.7%) were confirmed by IFAT. Independent of the protocol of diagnostic test used in screening or confirmatory phase (DPP or ELISA-PL), the same number of dogs (99) was found positive for infection in both tests  and 2. AAccording to ELISA-FP and IFAT, 27 seroconversions were observed in both tests within the study cohort, with an overall incidence rate of 2.8 per 1,000 dog-months . However, for DPP and ELISA-PL 51 seroconversions were observed, with an incidence rate of 5.4 per 1,000 dog-months  .In the present study we compared, for the first time, the change of the protocols for the diagnosis of CVL in Brazil. We performed a baseline survey using 1,226 dogs, followed by a cohort study using 447 dogs. Our results showed that the protocol using the DPP and ELISA detected a higher prevalence (8.1%) and incidence  of infected dogs than did the protocol using ELISA and IFAT . Previous studies showed that DPP had good performance, with sensibility ranging from 93% to 100% and specificity ranging from 92% to 100% Trypanosoma cruzi, Trypanosoma caninum, Leishmania braziliensis, and Ehrlichia canis, and false-negative results attributable to other factors T. caninum, Alves et al. IFAT is the most widespread diagnostic method. For many years, IFAT was considered the serological reference test for CVL. However, this technique presented limitations such as low reproducibility, especially when filter paper was used, higher false-positive results because of cross-reactivity with An improvement in the accuracy of the tests used for the diagnosis of CVL is critical for the VLCSP The DPP has advantages such as easy storage, rapidity as a diagnostic method, ease of use, and flexibility in the type of biological samples used  However, based on the current guidelines of the Ministry of Health and the data obtained in this study, we suggest to municipalities with a large demand of exams to use ELISA for automated screening and DPP as confirmatory test. This strategy would allow diagnosis in a laboratory where quality control can be easily implemented, thus facilitating the performance of large-scale screening tests. In addition, the cost of the ELISA reaction is lower (US $1.63) than the cost of the DPP test (US $2.72), when performed on large scale. Moreover, these values are related to the price of the tests only and do not include the transportation of inputs, acquisition and maintenance of equipment or training of specialized technicians.All these methods need to be significantly improved because the results determine whether seropositive dogs in endemic areas will be euthanized. In addition to the direct consequence experienced by the dogs (euthanization), there are also consequences for the dog owners, such as emotional trauma. Furthermore, false-negative results are unacceptable because they may lead to the perpetuation of infection. A possible way to improve canine diagnosis is the use of ELISA with recombinant antigen, such as rK39 and rK26, since they exhibit excellent performance Important features of this study must be highlighted. The analysis assays were carried out with samples obtained in the epidemiological studies  to evaluate prevalence and incidence in a large number of dogs evaluated in an urban endemic area.To avoid diagnosis bias, the assays were performed in blind (DPP and ELISA). Also, we took care not to identify sample results according to previous exams of ELISA and IFAT carried out in filter paper.In conclusion, the new protocol for diagnosis of CVL (DPP and ELISA) shows that the prevalence and incidence of infection are far greater than suggested by the previous protocol (ELISA and IFAT), indicating that the magnitude of infection in endemic areas has been underestimated. As tests are performed sequentially and euthanasia of the dog is carried out when the serological results are positive in both tests, the sequence does not affect the number of animals to be eliminated by the Control Program. Then, we suggest to municipalities with a large demand of exams to use ELISA for screening and DPP for confirmation because of its easy use and reduced costs.


Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research. The aim of genome-wide association studies (GWAS) is to determine genetic patterns that underlie human traits of medical interest. Despite their relative, the combined effect of the identified genetic variants accounts for only a small portion of the heritability for complex traits, such as hypertension or cancer . Rare vaSeveral approaches have been recently proposed to analyze rare genetic variants information. A common way to model is to count their relative abundance in case and control groups . HypotheWe classified each variant on the basis of its MAF supplied by the GAW17 snp_info file; a SNP was classified as a rare variant  otherwise as a common variant. We carried out all analyses using the family-based data set and the first family-based phenotype simulation (fam_phen.1). The GAW17 data set consists of 24,487 genetic variants  located in 3,204 genes. We used the gene_info file to annotate genes harboring putative associated variants. The sample is composed of 697 individuals distributed in eight extended pedigrees, and we performed the analyses using the binary variable, affected versus unaffected.We calculated polygenic heritability estimates for the affection status and quantitative traits Q1, Q2, and Q4 with Sex, Age, and Smoking status as covariates in each model. Using the variance components approach implemented in the SOLAR package , we calcThe GAW17 allele files were formatted for the QTDT software [We obtained heritability estimates for affection status using Age, Q4, and the information provided by rare and common variants in each analyzed gene. We tested four different ways to deal with such information. In the first analysis, the total sum of rare alleles (synonymous and nonsynonymous) was used as a continuous covariate in the polygenic model. In the second analysis, only nonsynonymous rare variants were considered; individuals were coded as 0 for those homozygous for the wild-type allele of a particular variant or as 1 for those presenting at least one rare allele. The third analysis was based on counting the absolute number of minor alleles in each common SNP from a gene and using this variable as a covariate in the polygenic model. In the fourth analysis, the pedigrees were considered separately and each SNP was used as a covariate in a polygenic model. Variants reducing trait heritability in at least one family were selected.p.values of markers to establish a gene-based p–value [VEGAS (versatile gene-based association study) suite combines the HapMap Phase 2 haplotypic information with association  p–value . InitialWe performed linkage analyses using the SOLAR package . To compA reliable analytical approach can disentangle a complex phenotype by presuming that associated genes share functional characteristics and probably belong to the same biological pathways. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database organizes biological pathways knowledge. Initially, we downloaded the latest database release  and determined which genes were annotated in the KEGG database. We found that 1,032 genes were mapped in 189 pathways (a gene could participate in more than one pathway). Annotated genes represent only a third of the genes present in the GAW17 data set. A hypergeometric test were use to determined overrepresented pathways [All analyses were carried out in a computational environment composed of 24 Intel i7 computers summing a total of 192 computing nodes. This cluster is managed by a Linux Rocks distribution specially.p = 0.06, OR 1.04), Smoking status , Q1 , Q2 , and Q4 . Trait heritability varied depending on the model specified; without any covariates the heritability was only 0.10 (SE 0.07), but we observed higher values when adjusting for different covariates, especially for Age and Q4 (0.58) , Q1 , Q2 , Q4 , and current Smoking . In a multivariate logistic regression model (using only independent samples), we tested the association of these variables and Affection Trait. . The results as follows: Age  was detected , that were not detect by any rare variant approach . This first tested QTDT approach presented low sensitivity but a significant increase in the positive predictive value (0.05) because only 19 genes showed evidence of association (p < 0.05) (Table PIK3R3 and PTK2B) were considered significant. The same panel was inspected using a variance components approach with the same regression model applied to the polygenic model (Q4 and Age as covariates). Twenty-one genes and two causal genes were detected. This approach had similar sensitivity and specificity to the first tested QTDT approach  (Table PIK3R3 and PIK3C2B)  were not detected using any of the explored methods  is not realistic for deep-sequencing projects or GWAS where multiple-test correction is mandatory. Table Biological information is progressively being added to regression models, allowing researchers to capture meaningful genetic information . This prGWAS are the leading tool for identification of genetic markers that underlie phenotypes of interest. This information should be combined with rare variants identified by deep-sequencing projects. In this study, we serially tested different ways to deal with the information provided by rare genetic variants, and none was found to be especially superior to another. Our results could be seriously jeopardized by how the data were simulated or the limitation of the SOLAR package to deal with binary traits. But, based on our results, it is possible to pinpoint the necessity of new and more customized methods to deal with rare variants, especially in a family study design. In the next few years, we will be flooded with the data generated by deep-sequencing platforms, and these new methods will play a central role.The authors declare that there are no competing interests.MAAA carried out computational, statistical analysis and drafted the manuscript. ARVRH carried out statistical analysis. PSLO participated in experimental design of the study; JEK: participated in experimental design and the development of the study; ACP: conceived the study and helped to draft the manuscript. All authors read and approved the final manuscript.