We assessed the normal variation of H2O2 release in lymphoblastoid cell lines (LCL) in a family-based 3-generation cohort (CEPH-HapMap), and in 3 population-based cohorts . Substantial individual variation was observed, 45% of which were associated with heritability in the CEPH-HapMap cohort. We identified 2 genome-wide significant loci of Hsa12 and Hsa15 in genome-wide linkage analysis. Next, we performed genome-wide association study (GWAS) for the combined KORA-GenCord cohorts (n = 279) using enhanced marker resolution by imputation (>1.4 million SNPs). We found 5 significant associations (p<5.00×10−8) and 54 suggestive associations (p<1.00×10−5), one of which confirmed the linked region on Hsa15. To replicate our findings, we performed GWAS using 58 HapMap individuals and ∼2.1 million SNPs. We identified 40 genome-wide significant and 302 suggestive SNPs, and confirmed genome signals on Hsa1, Hsa12, and Hsa15. Genetic loci within 900 kb from the known candidate gene p67phox on Hsa1 were identified in GWAS in both cohorts. We did not find replication of SNPs across all cohorts, but replication within the same genomic region. Finally, a highly significant decrease in H2O2 release was observed in Down Syndrome (DS) individuals (p<2.88×10−12). Taken together, our results show strong evidence of genetic control of H2O2 in LCL of healthy and DS cohorts and suggest that cellular phenotypes, which themselves are also complex, may be used as proxies for dissection of complex disorders.Natural variation in DNA sequence contributes to individual differences in quantitative traits. While multiple studies have shown genetic control over gene expression variation, few additional cellular traits have been investigated. Here, we investigated the natural variation of NADPH oxidase-dependent hydrogen peroxide (H Identified genetic loci, however, explain only a small fraction of the overall observed phenotypic variability even when assessing very large sample sizes 2O2) release is a quantitative cellular phenotype with known implications in multiple diseases 2O2 release, as measured at the extra-cellular level, includes reactive oxygen species (ROS) generation, and superoxide metabolism and degradation, all fundamental components of innate immunity. Causative associations between the phagocyte NADPH-dependent oxidase NOX complex and multiple diseases are known Hydrogen peroxide , which is membrane permeable. Superoxide dismutase (SOD), catalase, and peroxidases are associated with ROS metabolism and degradation. In this study, we measure ROS production and H2O2 release by detecting H2O2 release at the extra-cellular level.The major source of ROS production in lymphoblastoid cells is NOX2-dependent. NOX2-deficient lymphocytes from chronic granulomatous disease (CGD) patients produce very low levels of ROS. Expressed co-factors of NOX2 and NOX homologues in lymphoblastoid cell lines include p222O2 release in EBV transformed lymphoblastoid cell lines (LCL) as an example. We aim at identifying human genetic factors underlying the variability of H2O2 release, and thus the susceptibility to infectious diseases, through a whole genome linkage and association analysis in the general healthy population. Furthermore, we evaluated levels and differences of H2O2 release in LCL derived from Down Syndrome individuals, and identify regulatory loci which may provide insights into the biological pathways of H2O2 variation.The aim of this study is to evaluate the use of a cellular quantitative phenotype as a proxy to complex phenotypes by taking H2O2 release in LCL of four population-based cohorts: KORA, GenCord, HapMap, and DS (2O2 release (arbitrary units) of 0.51 in the KORA cohort , 0.47  in GenCord, 1.22  in HapMap, and 0.23  in DS individuals . To quantify the degree to which this observed variation was individual-specific and reproducible, we assessed phenotypes in 21 LCL of unrelated HapMap individuals in two independent experiments. We found a 28-fold inter-individual difference, and high reproducibility . The phenotypic distribution of H2O2 release in the DS cohort differed from all other healthy cohorts, with a significantly different distribution compared to the HapMap , the GenCord (p<3.11×10−9), and the KORA cohort . We observed extensive variation in H2O2 release that ranged from 0.001 to 6.035 (arbitrary units). To estimate what fraction of this natural phenotypic variation could be attributed to genetic components, we performed a variance-component linkage analysis 2) of 45%. This finding suggests that 45% of all H2O2 release variation can be attributed to genetic factors in the cell lines used herein. In a linkage analysis of LCL of these same individuals, we identified two significant and two suggestive LOD scores on chromosomes Hsa1, Hsa12, Hsa15 and Hsa19  and on Hsa15 with LOD score 4.81 . However, marker density in this linkage analysis was not sufficient to assign candidate genes. Furthermore, the linked region on Hsa12 is very gene-rich. The identified locus on Hsa15 is located 115 kb upstream of the transcription start site of MEIS2. The suggestive hit on Hsa1 had a LOD score of 4.17 , the second suggestive linkage signal was identified on Hsa19 with a LOD score of 3.26 .We measured Hnd Hsa19 Figure 2File S3). After applying quality filters for imputed SNPs (R>0.9), a total set of 1.4 out of 2.1 million SNPs was included for GWAS. We performed principal component analysis to detect outliers for each cohort separately, and excluded one sample from the GenCord cohort. Next, we assessed whether the use of multiple cohorts and imputation of SNPs did not lead to population substructure, or clustering of called versus imputed SNPs by performing both principal component analysis and multidimensional scaling for the combined three cohorts. For the PCA analysis, a random selection of 500 K SNPs, and a centered approach was used. No evident substructure was detected by visual inspection among the three cohorts (File S4), and by analysis of proportions of variance of components 1 to 18. The cumulative proportion of variance of the first 10 components explained only 0.039 of the observed variation (data not shown), and increased slowly with increasing components.We used the KORA and GenCord cohorts as an initial combined cohort for GWA analysis and we used HapMap (N = 58) as a replication cohort (see discussion). As all three cohorts were genotyped on different platforms, we obtained a common and larger set of SNPs by imputation using the HapMap Phase II genotypes as a reference  and 54 suggestive associations with p-values of less than 1.00×10−5 , followed by Hsa10 with 4 SNPs at position 52,264 kb . Among these top associations (p<1.00×10−5), we could not find association on Hsa12 and Hsa15 within the genetic regions linked in the CEU-CEPH family-based cohort, but we found an overlap with a known candidate gene, p67phox  on Hsa1. Three SNPs are located 913 kb downstream of the candidate gene p67phox on Hsa1 , with the strongest signal for SNP rs1535133  and rs12528870 on Hsa6  is not well characterized yet. Among the other strong statistical signals, we identified association signals within the genomic regions of the linked regions. We found a cluster on Hsa12 (p<1.4×10−10) with 12 SNPs at position 4,690 kb close to KCNA6 and GALNT8, and on Hsa21  with 11 SNPs at position at 43,587 kb close to SIK1 and HSF2BP (KCNA6 (voltage-gated potassium channel) mediates the voltage-dependent potassium ion permeability of excitable membranes (UniProtKB for KCNA6_HUMAN). The role of GALNT8  is not described in the literature. SIK1  has a transient role during the earliest stages of myocardial cell differentiation and/or primitive chamber formation SIK1 and three other kinases were identified to be members of a novel mitogen-activated kinase pathway (MAPK) We used 60 unrelated LCL of Caucasian origin from the CEPH-Hapmap cohort as a replication cohort. We determined the H−6), Hsa12 , and on two loci on Hsa15 . Of the known candidate genes expressed in LCL, p67phox  overlapped with two top association signals in the KORA-GenCord GWAS and in the HapMap GWAS . We did not detect strong association findings in KORA-GenCord samples that overlapped with the linkage findings. Neither did we find the same replicated SNPs nor genetic regions among the two GWA studies within a distance of 1 Mb. We identified replication between the top linkage findings for four loci in the HapMap samples on Hsa1 . A similar pattern was observed when comparing top HapMap signals with p-values of all KORA-GenCord. This pattern suggests enrichment for repeated associated loci across GWAS, but this enrichment is not significant.2O2 distribution, we combined all samples after median centered normalization, and estimated the relative contribution of each of the co-variates in an univariate linear regression model with a cluster effect on the population. We found that none of the covariates had a significant effect on the trait distribution. We found the following relative order of correlation: Time of establishment of cell lines , status of immune system , individuals' age , and gender . To estimate whether a statistical model with adjustment for all co-variate was different than a model for the main variate only, we performed a randomized set of 100'000 SNPs using a multivariate linear regression model with adjustment for all co-variates and a cluster effect on the population. We compared the results of this model with an univariate model with no adjustment for co-variates. We found no significant differences between the two models for the 100'000 randomly selected SNPs. This analysis suggested that covariates did not represent a confounding factor.To assess the impact of potential co-variates  on H2O2 release, and evidence for a genetic basis to the observed variation in LCL of the cohorts used herein. This finding is in line with several studies reporting large natural phenotypic variation, such as for gene expression level variation, many of which were repeatedly shown to be shaped by genetic polymorphisms 2O2 release in more than 500 individuals in 4 healthy cohorts and in a DS cohort confirm previously reported differences in H2O2 release of primary neutrophils of healthy individuals and individuals with chronic granulomatous disease The most essential outcome of this study is the finding of extensive natural phenotypic variation in healthy individuals for HSIK1 on Hsa21. SIK1 is encoding a kinase known for its involvement in cardiomyogenesis by regulating cardiomyoblast cell cycle SIK1 in the combined KORA-GenCord GWA.In the study of the Kuhns DB, et al., 10–20 fold differences in primary neutrophils from peripheral blood of healthy individuals were detected in comparison to individuals with chronic granulomatous disease. Also, large inter-individual differences were detected, and correlated to genotypes. This correlation was reported to be a strong predictor for overall survival of CGD patients. Our genetic study of natural variation in H2O2 release revealed 45% of heritability, and we detected genomic regions on Hsa1, Hsa12 and Hsa15 as significant and replicated regions in more than one cohort. Results from GWAS in HapMap samples indicated an associated genetic locus close to the gene 2O2 release, an endophenotype more closely related to sequence variation  may be a complex trait in itself. The detection of multiple genome-wide significant loci suggests a polygenic trait. The strongest linkage results indicate loci on Hsa1, Hsa12, Hsa15 and Hsa19, two of which (Hsa1 and Hsa15) were also significantly associated in either the KORA-GenCord or HapMap GWAS, or both. However, we did not detect the same associated SNPs in the three cohorts, but replication within the same genomic regions. This may indicate either a lack of power to detect genetic loci due to the relatively small sample size, or reflect the complex genetic architecture of H2O2 with multiple loci contributing small proportions to the observed phenotypic variation. Our study design focused on the detection of common alleles with a genetic effect of more than 5% given an allele frequency of 0.15, and with our study design, we have 80% of power to detect such a quantitative trait locus while using 300 individuals H2O2 trait, and suggests that despite differences in the origin of cohorts, cell line establishment and age of the cell lines the underlying genetics is the predominant factor. Further evidence for a strong genetic effect, rather than differences in cellular phenotypes in different cohorts, comes from a study by Dimas, et al. 2O2 release may underlie different cellular covariates than gene expression levels, leading to population-specific differences that we could not fully correct for. We investigated multiple genetic models for assessing the best method for normalization of the phenotypic distribution. We expected all cohorts to show similar distributions of H2O2 release, as all individuals were of European ancestry, and we measured all phenotypes under standardized and highly controlled conditions. No substantial differences among cohorts could be attributed to population substructure or to known covariates. The non-normal distribution of phenotypes may probably be due to the relative small sample size, as departure from normal distribution was largest for HapMap (N = 58) and smallest for KORA (N = 199). As shown in 2O2 release is enriched for extreme phenotypes with individuals with either very high or very low phenotypes. To assess the impact of the phenotypic distribution on our results, and to exclude false positive signals due to departure from model assumptions, we have considered three different methods of normalization across cohorts. Also, we compared results from parametric and non-parametric association models. We conducted log transformation, median centered and inverse variance normalization, and median centered normalization only. Log transformation did not lead to a normal distribution. When using median and inverse variance normalization we lost all association signals, leading to the hypothesis that the genetic signal may be associated with the high and low end distribution of the phenotypes. Therefore, we chose to use the median centered approach, while keeping the internal variation of each population untouched. The median centered approach does not fully support the assumption for normal distribution. To assess whether results differed due to model assumptions, we compared these results with a non-parametric regression model using the Spearman rank correlation. We found the same top association signals with both models, with the only difference of slightly lower p-values , showing that results did not depend on model assumptions.Our study design considered the use of a combined GWA of 279 individuals to increase sample size, and to replicate findings in a follow-up cohort of 58 HapMap individuals. Even though all cohorts used herein are of Caucasian origin, and population substructure did not appear to be a confounding factor, differences among these cohorts may have reduced the power to detect the same loci across cohorts. Co-variate analysis showed that the effect of the SNP on association was significant in shaping the H2O2 levels after PMA stimulation, and genetic variation involved in the protein kinase C, through which PMA acts directly, cannot be fully excluded. However, heritability would not have been detected if EBV transformation or PMA stimulation would affect pathways in a random way. Finally, H2O2 release is an endophenotype with multiple levels of metabolism. Our assay relied on extracellular measures of hydrogen peroxide release which is the joint result of ROS production, metabolism, and degradation. Each step of these mechanisms may involve few or many genes, and thus contribute to the complexity of the phenotype.LCL represent a relevant physiological cell type for ROS production phox, p40phox, p47phox, p67phox, and homologues RAC1 and RAC2). These results suggest that variation in other genetic loci have stronger impact on natural variation of H2O2 as compared to the known genes identified in studies of Mendelian disorders. Previous case-control studies investigating the molecular basis of either H2O2 or ROS applied candidate gene approaches with contradictory results. Some studies identified mutations or polymorphisms associated with ROS generation within the NADPH-dependent NOX2 gene or its cofactors, while others found no association phox  haplotypes, rather than single polymorphisms. The associated haplotype was shown to act, at least in part, through an effect on protein expression levels. While this approach shed some light on the contradictory results reported so far on the involvement of NADPH-dependent NOX components in ROS generation, the large observed natural variation could not solely be attributed to the NADPH-dependent NOX2 and its cofactors, leading to the hypothesis that other loci are involved in regulatory mechanisms.We did not find genome-wide significant associations for loci of the NADPH-dependent NOX2 and other NOX related genes. We identified a locus 1 Mb from p67phox (or NCF2) as the closest hit for NOX2 members expressed in LCL  of 10 families  and of 60 unrelated individuals of Caucasian origin from the Coriell Cell Repositories . These are considered as healthy individuals and form part of the Caucasians of European Ancestry (CEPH) and the Haplotype Map (HapMap) Project http://snp.cshl.org/linkage_maps; www.hapmap.org; KORA sample collection: KORA S4  is a sample collection of a population-based project conducted from 1999–2001 for which beta-LCL of healthy adults from the Augsburg region in Germany were established GenCord sample collection: The GenCord  sample collection is a population-based collection of LCL derived from umbilical cords of 200 healthy newborns of Western European origin. The sample collection was approved by the institutional ethics committee, and all mothers provided their informed consent prior to sample collection Filter settings for genotype quality required accuracy of sample call rates of more than 98% were (estimated based on 5 duplicated genotyping of samples). Any SNP with a call rate of less than 99% call rate was excluded, as well as all SNPs called less than in 1% of all samples. Separation of homozygote and heterozygote SNPs was verified and corrected if needed (cluster separation >0.3 was required). The range of excess of heterozygosity was verified and SNPs not falling into the range of −0.1 to −1, and 0.1 to 1 were excluded. MAF was set to >5%; to exclude samples with cryptic relatedness, IBD was set to >0.125; and deviation of Hardy Weinberg equilibrium was defined as <0.05. We verified the specified gender, and visualized a random set of 100 SNPs for quality checking.Down syndrome sample collection: 33 Down syndrome (DS) LCL samples were acquired from the Galliera Genetic Bank of Genoa, Emory University School of Medicine in Atlanta, and CSS-Mendel Institute in Rome 2O2 release due to the multiple origins of these samples, no substructure was detected.Cell culture: The LCL of all three populations were cultured in RPMI 1640 with Glutamax I medium (Invitrogen) supplemented with 10% fetal calf serum and 1% mix of penicillin, fungizone, and streptomycin (Invitrogen). All cell lines were grown and maintained for at least 2 weeks at a constant density of 200–400,000 cells/ml. Cells were washed once with PBS (Invitrogen) to reduce accumulation of cell debris 4–5 days prior to measuring ROS production.AmplexRed ROS measurements: Cell lines were assessed for H2O2 release using AmplexRed (Invitrogen), a quantitative enzymatic assay that detects H2O2 in the culture media. Ninety minutes prior to AmplexRed measurements, cell lines were centrifuged at 1000 rpm for 10 min and transferred to 2,5 ml Hanks' balanced salt solution (HBSS) media (containing 5% of glucose) and kept on ice. 0.5 ml of each cell line was used for measuring cell number by Fluorescent Activated Cell Sorting (FACS). Beads were added at a known concentration to obtain absolute values of cell number. Cells were diluted to a final concentration of 500,000 cells/ml.AmplexRed measurements were performed according to manufacturers' instructions with minor modifications. In brief, 50,000 cells were plated in 0.2 ml volume of HBSS containing 25 mM AmplexRed and 0.005 U/ml horseradish peroxidase. NADPH oxidase activity was stimulated with 0.1 mM phorbol 12-myristate 13-acetate (PMA). In 1 of 5 replicates per individual cell line, 10 mM diphenyleneiodonium (DPI), an NADPH oxidase inhibitor 2O2:Experimental design and normalization of H Each well-plate was set up with 12 individual cell lines. Each cell line was measured in 4 replicates in addition to one replicate that was inhibited with DPI. From the 4 replicates per sample, one was removed based on an outlier detection procedure, and the average of the remaining 3 replicates was used as the phenotypic read-out. We defined the phenotypic read-out as the maximal slope over 5 time points within 30 min of measurements. The standard curve of H2O2 was used to transform relative H2O2 measures to absolute values for each individual plate, thus allowing for comparison among different plates. Experiments for which regression of the standard curve of H2O2 did not reach R2 of 0.99 were excluded. For normalization across plates, the same 2 individuals (GM0755 and GM07341) were measured on every plate and served as a reference for normalization. As a negative control cell line, an LCL of a patient with chronic granulomatous disease (CGD) was tested to ensure specificity of the assay for NOX2-dependent H2O2 detection.Linkage analysis and heritability estimate: Heritability analysis was performed with MERLIN software http://snp.cshl.org/linkage_maps); http://www.vital-it.ch). Mapping positions of these markers correspond to NCBI36/hg18.For genome-wide linkage analysis, the same phenotypes and genotypes were used for multipoint variance component analysis Normalization of phenotypes across populations: We applied a median centered transformation for the phenotypes before combining the data from KORA and GenCord populations. HapMap samples were used as a replication cohort and were analyzed separately; and also analyzed with a median centered transformation.Association studies: Imputation of SNPs across all 3 populations. We imputed SNPs from unphased alleles for 337 individuals by using the BEAGLE 3.0.4 software 2>0.9 to ensure high quality imputation. Of all 2.2 million imputed SNPs, 1.4 million were retained for further analyses.Principal component analysis: Principal component analysis was first performed for samples of each cohort, and we excluded one sample from the GenCord population after visual inspection of the first 10 components. Principal component analysis of a random set of 500,000 SNPs was then used to test for population substructure among all three cohorts. Visual inspection of the first to the 8th component did not reveal evidence for substantial population substructures.Genome-wide association studies: We performed linear regression analyses and an additive genetic model to perform association between H2O2 levels and each SNP using PLINK software p-value of the test of no association, i.e., 1 = 0b where Yi are the H2O2 release for each sample i, Xi are the genotypes coded as 0,1,2 and iε are assumed to be independent normally distributed random errors with mean 0 and constant variance. Mapping positions of GWA markers for KORA/GenCord and for HapMap correspond to NCBI36/hg18.Covariate analysis: We combined all samples from HapMap, KORA and GenCord after median transformation of the H2O2 release phenotype (N = 337), The following covariates were assigned to each individual: The status of the immune system of the individual ; gender; the time of cell lines establishment prior to use in this study ; the age of the individual . A clustering effect was set for each of the three populations. We conducted a general linear mixed effect regression model with a clustering effect for the population and adjustment for immune status, gender, cell line age, and individual age. We performed univariate mixed effect linear regression with a cluster effect on populations for each co-variate. We then used a multivariate mixed effect linear regression with adjustment for all co-variates and a cluster effect on populations for a randomized set of 100'000 SNPs. This model was compared to a univariate mixed effect linear regression using the SNP only.File S1Sample collection identifiers and phenotypes. All individuals used in this study are indicated with their identifier and their phenotype. H2O2 release indicates normalized hydrogen peroxide release levels, units are arbitrary.(DOC)Click here for additional data file.File S22O2 phenotypes.Extensive inter-individual variation and high reproducibility for H (A) H2O2 release for 21 CEPH-HapMap unrelated individuals, each of which was measured in 4 replicates is shown. Each bar corresponds to the normalized H2O2 release of an individual, and standard deviations of three replicates per individual are indicated in red. The coefficient of variance is >28 with a p-value of p<1.00×10−4, indicating that there is 28 times more variation among individuals than among replicates. (B) H2O2 release for the same 21 individuals was measured several weeks later, to estimate the reproducibility of the phenotype. Comparison for individuals of both experimental sets indicates a coefficient of correlation of more than 0.89 .(TIF)Click here for additional data file.File S3Overlap of SNPs among the three sample cohorts and genotyping platforms. This schematic figure displays the number of individuals genotyped for each cohort, and the number of SNPs genotyped per population. The overlap between the circles shows the numbers and percentages of overlapping SNPs among populations and genotyping platforms.(TIF)Click here for additional data file.File S4Principal component analysis indicates no apparent substructure. A random set of 500,000 SNPs of 337 individuals from three cohorts was analyzed using centered principal component analysis (PCA). Here we show PC1 versus PC2. No evident substructure is apparent among cohorts.(PNG)Click here for additional data file.


Genome-wide association studies of complex traits often are complicated by relatedness among individuals. Ignoring or inappropriately accounting for relatedness often results in inflated type I error rates. Either genotype or pedigree data can be used to estimate relatedness for use in mixed-models when undertaking quantitative trait locus mapping. We performed simulations to investigate methods for controlling type I error and optimizing power considering both full and partial pedigrees and, similarly, both sparse and dense marker coverage; we also examined real data sets. (1) When marker density was low, estimating relatedness by genotype data alone failed to control the type I error rate; (2) this was resolved by combining both genotype and pedigree data. (3) When sufficiently dense marker data were used to estimate relatedness, type I error was well controlled and power increased; however, (4) this was only true when the relatedness was estimated using genotype data that excluded genotypes on the chromosome currently being scanned for a quantitative trait locus. However, human population isolates and various model organism populations contain individuals with varying levels of relatedness. For polygenic traits, this results in correlations among both genotypes and phenotypes and can produce inflated type I error rates when performing GWAS (Although siblings share an average of 50% of their genome identity-by-descent (IBD), the realized sharing is variable. Genotype data allow estimation of realized sharing , with the polygenic effects ui independent of each other and independent of the residual effect ϵ. We model the polygenic covariances as j, k)th element of Φi is the probability that at polygene i a randomly drawn allele from subject j and a randomly drawn allele from subject k are IBD, the th element of Δi is the probability that at polygene i the two alleles in subject j are both IBD with the two alleles in subject k and that neither subject is autozygous, and i. In general, when inbreeding is present, there are additional variance components present  modeled as a fixed effect and  present . The addH0: a = 0 and d = 0 vs. the alternative H1: a ≠ 0 or d ≠ 0. We use the likelihood ratio test with the trait model Φi and Δi and polygenic variances Φi and Δi are estimated by their expected value given a pedigree, i, where Relationship matrices subsection. This leads us to consider three possible models for the polygenic covariance in the likelihood ratio test, VM1: Our objective is, given the genotype data at a marker, to test whether the marker is a QTL. That is, we test the null hypothesis l the th element of j and k are both homozygous for the same allele, 0.5 when one is homozygous and the other heterozygous or both are heterozygous, or 0 when both are homozygous for different alleles. We define the th element of j and k are heterozygous and 0 otherwise. Our marker based estimates are the mean across L markers used in the estimator, L loci in our estimators. Note that under the assumption that all the additive polygenic variances We obtained relationship matrices as described and implemented in the R package “QTLRel” . To obta26 and a structured population (STR). For the AIL, we assumed that one male and one female offspring from each of 144 Fn (2 ≤ n ≤ 25) breeding pairs was randomly mated with a nonsibling to produce the next generation. The final sample used for mapping consisted of four offspring from each of 144 F25 breeding pairs for a sample size of 576. The STR consisted of subsamples from three subpopulations. The first subsample was from an AIL F26,where one male and one female progeny from each of 48 Fn (2 ≤ n ≤ 25) breeding pairs was randomly mated with nonsiblings to produce the next generation and four offspring of each F25 breeding pair contributed to the subsample. The other two subsamples were generated as follows. A male and a female progeny from each of 96 Fn (2 ≤ n ≤ 12) breeding pairs were randomly mated with nonsiblings to produce the next generation. The F13 breeding pairs were randomly split into two subpopulations of equal size and bred until F26 with the same breeding scheme as above within each subpopulation. The STR sample size was also 576. These pedigrees were created once and were used in replicate simulations.We considered two mapping populations: an advanced intercross line (AIL) FU  and U , respectively, in each replicate simulation. The residual effect was simulated from a normal distribution N . The polygenic QTL approximately accounted for 84% of the total variation. Heritabilities in this range are not uncommonly observed in model organisms and humans . A total of 500 polygenic QTL were evenly spaced on the first five chromosomes. We simulated two possible relationships between the markers and the polygenic QTL :  of the pedigree (Last3); (3) using only the final six generations of the pedigree (Last6); and (4) using the entire pedigree (AllPed).For each variance model, VM1, VM2, and VM3, we considered different estimators that varied in their level of informativeness. For VM1, we obtained estimates of the relationship matrices as follows: (1) using no pedigree (Naive), equivalent to assuming all subjects are independent; (2) using only the final three generations  distribution. In the simulations designed to evaluate power, we placed a QTL with an additive effect of 0.5 and a dominance effect of 0.2 at position After completing the prior set of simulations, we were concerned that certain VM2 conditions failed to adequately control type I error rates. We hypothesized that this was attributable to the sparse nature of the markers, so we conducted simulations in which we varied the density of markers for model VM2. In this set of simulations we only considered the STR and simulated 15 chromosomes of length 200 cM with 500 polygenic QTL that were placed randomly across the first 10 chromosomes. The additive and dominance effects of the polygenic QTL were randomly generated using the same distributions as described previously, whereas the residual error was simulated from a Supporting Information, File S1) and the analyzed data set (File S2) are available at http://palmerlab.org/data/.Finally, we used a published dataset from the 8th generation of a mouse AIL, which were bred from two inbred strains. This dataset consisted of 552 mice genotyped at 895 single-nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait, as described in Results of the type I error simulations are shown in e.g., when scanning chromosome 11 markers on chromosomes 1–11 were used to estimate relatedness). In this case, we found that the type I error rate was too conservative resulting in dramatically decreased power. We attribute this phenomenon to the effect of the QTL being partially captured by markers that are included in the polygenic term. Thus, the effect of the QTL is divided between the fixed and random term in the linear mixed model. This phenomenon has recently been referred to as “proximal contamination” by In an ideal situation the markers used to estimate the relationship matrices would exactly tag the polygenic loci (CL). As shown in Finally, we considered using both pedigree and marker information to model relatedness (VM3). We found that although the use of markers or using partial pedigrees was unable to control type I error, the combination of the two effectively controlled the type I error rate. This approach may result in increased power relative to use of the pedigree alone, but this difference, although suggestive, was not statistically significant in our simulations.i.e., VM2) and when the marker density was inadequate, the type I error rate was inflated. Using both marker and partial or full pedigree data  prevented inflation of the type I error rate, without sacrificing much power. This approach provides better power than using the pedigree alone .As shown in Considering Note that our reported powers will include positive associations even when the “significant” locus is far from the true QTL. The consequence is that the power values are greater than they would be under an approach that requires the association to be close to the true QTL. However, the relative powers of the methods will not be affected by the differences between these two approaches.i.e., VM1) or all markers on the genome . In addition, because including SNPs on the chromosome being scanned in the relatedness estimation is overly conservative , a requirement that may not be met if either familial relatedness or population structure is not properly modeled. When working with model organisms, GWAS is often performed with the use of populations in which individuals are closely related to one another, necessitating a method to estimate the relatedness. This can be done using a pedigree, if available, but could potentially also be performed using observed genotype data. We found that estimates of relatedness that use sufficiently long pedigrees can control the type I error rate. Furthermore, marker-based estimates can also control the type I error rate if the markers are sufficiently dense to accurately estimate the realized relatedness at the polygenes. Perhaps more importantly, we find that an estimator that uses both pedigree information and genotype data gave consistently accurate type I error rates across differing levels of pedigree and genotype informativeness, even when using either pedigree or genotype data that alone would not result in a valid test.GWAS is a powerful tool for dissecting the genetic basis of quantitative traits. However, accurate inference depends on a valid test  recent advances allow for highly accurate estimates of IBD sharing given sufficiently dense marker data (Ideally, we would expect to obtain optimal power by not just excluding markers in LD with the locus being tested but by only using genotypes most informative of IBD sharing at the polygenic loci. Here we used IBS sharing as a proxy for IBD sharing, an approximation that is exact in the AIL and STR populations used here. In populations where IBS is less indicative of IBD (ker data , 2013. W


It has been well established that theoretical kernel for recently surging genome-wide association study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several confounding factors of which population stratification is the most prominent. Whilst many methods have been proposed to correct for the influence either through predicting the structure parameters or correcting inflation in the test statistic due to the stratification, these may not be feasible or may impose further statistical problems in practical implementation.We propose here a novel statistical method to control spurious LD in GWAS from population structure by incorporating a control marker into testing for significance of genetic association of a polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of structure prediction which may be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Utility and statistical properties of the new method were tested through an intensive computer simulation study and an association-based genome-wide mapping of expression quantitative trait loci in genetically divergent human populations.The analyses show that the new method confers an improved statistical power for detecting genuine genetic association in subpopulations and an effective control of spurious associations stemmed from population structure when compared with other two popularly implemented methods in the literature of GWAS. This gives rise to an urgent need for methods of adjusting for both population structure and cryptic relatedness occurring due to distant relatedness among samples with no known family relationships.Linkage disequilibrium (LD) based association mapping has received increasing attention in the recent literature To avoid the problems raised from population stratification, family-based association studies have been proposed, such as the transmission-disequilibrium test (TDT), which compares the frequencies of marker alleles transmitted from heterozygous parents to affected offspring against those that are not transmitted et al.Numerous methods have been proposed to overcome the problems caused by population structure without the need for family based samples. Among the most widely used are the genomic control (GC) et al.Rather than using a panel of unlinked markers to exploit the cryptic population structure, a single null marker can be used to correct for bias of the test statistic in association studies. Wang Method 1) was compared with two alternative methods: single marker regression without population structure correction (Method 2) and multiple regression analysis with incorporation of known individual ancestry information (Method 3).The expression quantitative trait locus (eQTL) analyses have recently shown that variation in human gene expression levels among individuals and also populations is influenced by polymorphic genetic variants m. Let us consider three bi-allelic loci: one affects a quantitative trait (Q) while another two are polymorphic markers devoid of direct effect on the trait. We call, for convenience, one of the markers the test marker (T) which is to be tested for association with the QTL, and the other as control marker (C), assumed to be not associated with both the QTL and the test marker . Two alleles are denoted by A and a at the putative QTL, T and t at the test marker, and C and c at the control marker. Three genotypes at the QTL, AA, Aa and aa, are assumed to affect the quantitative trait by d, h and –d respectively. Trait phenotype of an individual (Y) is assumed to be normally distributed with mean depending on its genotype at the QTL and residual variance X and Z, which are the number of alleles T and C respectively. In subpopulation i (i = 1 or 2), the allelic frequencies of the QTL, test marker and control marker are denoted by The method analyzes a structured randomly mating population produced through instant admixture of two genetically divergent subpopulations. The proportion of subpopulation 1 in the mixed population is denoted by m is the proportion of subpopulation 1 in this mixed samples, the superscripts (1) and (2) refers to the subpopulations, t-test can be used to test for significance of the regression coefficient t-distribution with v degrees of freedom and non-centrality parameter t-variable with the same degrees of freedom. The value of v equals n−3 and the non-centrality parameter is given by For phenotype of a quantitative trait and each of the test markers, we fitted the following model: the genotype In practice, we propose the following procedure to select the control marker for a given test marker. Firstly, any marker but the test marker would be candidate for the control marker if it has or isan autosomal location on different chromosomes from the test marker,less missing genotype data than a prior given proportionFor each marker passing the above screening, one calculates the expected and observed variances fromThe method fits a simple regression model for detecting LD between the trait phenotype and a test marker as we proposed previously iP  as described in the following multiple regression modelThe method regresses the trait phenotype on genotypic value of a test marker  made a high proportion of false positive inference of the marker and QTL association when the LD was actually absent (populations 1–4) but failed to detect truly existing LD between the two loci (populations 5–9). The method is thus inappropriate to be used for genetic association analysis when population structure was present. Performance of Method 3, which incorporates membership of individuals to constituent populations as a covariate in multiple regression analysis, depends on the extent by which individuals are correctly allocated to their belonging populations. For example, the method lost its statistical power to detect the truly existing LD (populations 5–9) or made false positive inference of genetic association when on average a quarter of individuals under analysis were wrongly allocated to subpopulations (populations 1–4). These results show that the present method provides a powerful test for linkage disequilibrium between polymorphic markers and QTL and an effective control of population structure in the test.Each simulation was repeated 100 times and simulation data was analyzed using the three different methods described above. We tabulated in Method 1 is the key underpinning for the method to be able to control influence of population structure in the genetic association test. To investigate effect of the control marker on efficiency of the association test, we explored performance of the method when population structure is actually absent or when different control markers are used in the presence of population structure. Method 1 by using the control marker either with a constant allele frequency between two subpopulations or with varying allele frequencies. It demonstrates that the type I error is well controlled and the disequilibrium is efficiently detected by the method using a control marker even when population structure does not actually exist (a). In addition, when population structure is present (b), the method bears a high chance to make a false positive inference and to lose its detecting power if the control marker selected to be implemented in the analysis has a small difference in allele frequency between the subpopulations. However, the risk can be effectively controlled and the reduced power can be recovered when using the control marker with a large allele frequency difference. All these suggest that implementation of control markers with a non-trial difference in allele frequency will not cause any significant problem of false positive/negative inference when population stratification is actually not existent. In presence of population structure, we propose selection of a marker with largely divergent allele frequencies as the control marker.Use of control markers in http://www.ncbi.nlm.nih.gov/geo (Gene Expression Omnibus: GSM5859). The expression arrays were analyzed using the Affymetrix MAS 5.0 software and the hybridization intensity was log2-transformed into expression phenotype. The study focused on 4,197 genes that are expressed in lymphoblastoid cell lines. Of the 4,197 genes, 1,097 were detected to be significantly differentially expressed between the CEU and CHB+JPT samples . All markers with an allele frequency of ≥5% were included, giving more than 2.2 million and 2.0 million common SNP markers for the CEU samples and CHB+JPT samples respectively. Comparison between the CEU and CHB+JPT samples provided genotype data for 1,606,182 unique SNP markers among all 142 individuals (60 CUE and 82 CHB+JPT samples).The gene expression and SNP datasets were collected from Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines of unrelated individuals of European-derived , and Asia-derived . The datasets were originally developed by Spielman et al cis to trans regulation and different levels of heritability. Some of these quantitative phenotypes show population specified expression or heterogeneity of underlying genetics. These enable the method to be tested under different genetic backgrounds. Finally, re-analysis of the same datasets recently published allows a direct comparison of analysis with the method developed in the present study with that implemented in the published analysis.We selected and re-analysed the gene expression and SNP datasets in the present study for several reasons. Firstly, these samples were collected from the populations whose genetical diversification was well verified In 2005, The International HapMap Project reported that the CHB and JPT samples' allele frequencies were generally very similar, but different to the allele frequencies of CEU samples . We firsker data .cis-regulated eQTL was operationally defined by the presence of significant association with a SNP in the region 500 kb upstream of the start of the transcript to 500 kb downstream of the 3′ end; otherwise, the eQTL was classified as trans-acting. Method 1 developed in the present study, Method 2 the simple regression analysis employed by Spielman et al in Method 3 the multiple regression analysis) from the Europe derived, Asia derived samples and their mixed respectively. It can be seen that the eQTL analysis results from the CEU and CHB+JPT samples are comparable between Method 1 and 2 in terms of the number of detected eQTLs and estimated locations of these eQTLs, suggesting a comparable predictability of the two methods in the absence of population structure. In the mixed sample, 64% of eQTL detected by the multiple regression analysis (Method 3) with use of full population membership information can be recovered by the method developed in the present study (Method 1), confirming the predictability of the latter in the presence of the population structure. We explored the predictability of Method 3 when individuals were randomly assigned to the Europe derived sample (CEU) with probability of 58% or to the Asia derived sample (CHB+JPT) otherwise. The analysis showed that only 12%  of eQTL detected by the method with the partial population membership information was consistent with those detected by the same method with the full membership information, suggesting that the predictability of the method depends heavily on certainty of the membership information and that the method may generate a large proportion of false positives when the information is not complete.We implemented the three methods described above to perform association mapping of eQTL using the gene expression and SNP marker datasets. The analysis was carried out on the CEU and CHB+JPT samples separately or jointly. An eQTL in the present analysis was defined as an independent peak in the p-value profile across a given chromosome. Peaks occurring within 5 Mb of adjacent peaks were taken as a single eQTL peak because of insufficient evidence to declare the existence of multiple eQTL peaks over such narrow intervals cis-regulated genes cis-regulators from the three samples, there were a large number of spurious association signals predicted from the simple regression analysis (Method 2) with the mixed sample  in the CEU and CHB+JPT samples separately. In addition to 16 previously detected cis- acting factors, Method 1 detected 35 novel cis- eQTL and all the eQTL explained 20∼70% of variability in expression of the genes regulated (trans-regulators detected by our method from the mixed sample against the Gene Ontology (GO) Molecular Function annotation database (http://www.geneontology.org/) and found that 101 (42%) trans-eQTLs predicted were mapped into the category of transcriptional factors, 82 (33%) trans-regulators played a role in signal pathway activity. In total, 75% trans-regulators predicted by the present method were previously known to play a role in gene regulation. All these reveal a significantly improved statistical power of the present method in detecting the true genetic associations.The POMZP3 and HSD17B12 (on the human chromosome 7 q11.23 and chromosome 11 q11.2 respectively) are two well-characterized and gulators . Firstlyegulated . We compcis- eQTL detected from the mixed samples is larger than that from the component samples separately whilst a much larger number of trans- eQTL are detected in the component samples than in their mixed. This observation may reflect the fact that an increase in size of the mixed sample has enhanced the statistical power to detect cis- eQTL and thus led to an increased number of cis- eQTL detected. However, if linkage disequilibria between genes regulated and their trans- regulators are in opposite directions between different populations, the LD may be counter-balanced in the merged population, and thus decrease the number of the trans- eQTL to be detected. Despite a relatively small number of cis eQTLs detected, the cis-regulated effects were generally stronger than those in trans, with about 14% (7/51) cis-acting eQTL having a determination coefficient It is interesting to note that the number of ficient  , consistLinkage disequilibrium based association mapping has been advocated as the method of choice for identifying chromosomal regions containing disease-susceptibility loci or loci affecting other complex quantitative traits of interest The new method developed in this study is tested and compared with other most popularly implemented methods in the literature of genetic association studies through intensive computer simulation studies and analysis of large scale and high quality gene expression and SNP datasets for mapping expression QTL. These analyses strongly support outperformance of the new method for its significantly improved statistical power to detect genuine LD between any polymorphic markers and putative trait loci and its effectiveness in controlling spurious association due to population stratification. Worthwhile, although the multiple regression analysis based on a mixed linear model does also provide a control of the influence of population stratifications, its efficiency depends heavily on accuracy of prediction of the population structure and on accurate allocation of individuals' membership to the constituent populations. Any bias in the structure prediction and uncertainty in the membership allocation may lead to severe consequence on its analytical efficiency. It has been argued that several factors may substantially influence or even disable the prediction of population structure Wang et al has proposed use of a single null marker to correct for population structure in a candidate gene based association analysis using case and control samples Figure S1Comparison of allele frequencies between populations for all SNP markers genotyped in the International HapMap Project. The colour in each bin represents the number of SNPs that display each given set of allele frequencies.(TIF)Click here for additional data file.Table S1Parameters defining two subpopulations that are merged to produce admixed populations.(DOC)Click here for additional data file.Table S2Predicted and observed proportions of significant tests of linkage disequilibrium between a test marker and a putative QTL in different simulation populations from Method 1 in which the control marker implemented into the analyses had either (a) no population structure, and has a constant allele frequency difference of 0.4 at control marker locus or (b) population structure exist, and has varied allele frequency differences at control marker locus.(DOC)Click here for additional data file.Table S3The 51 cis-eQTLs predicted by Method 1 from the mixed sample.(DOC)Click here for additional data file.


Man1a2 and H2-ab1, that reside at nonimprinted loci but that show parent-of-origin effects. We show that expression of multiple genes becomes dysregulated in a sex-, tissue-, and parent-of-origin-dependent manner. We provide evidence that nonimprinted genes can generate parent-of-origin effects by interaction with imprinted loci and deduce that the importance of the number of imprinted genes is secondary to their interactions. We propose that this gene network effect may account for some of the missing heritability seen when comparing sibling-based to population-based studies of the phenotypic effects of genetic variants.The number of imprinted genes in the mammalian genome is predicted to be small, yet we show here, in a survey of 97 traits measured in outbred mice, that most phenotypes display parent-of-origin effects that are partially confounded with family structure. To address this contradiction, using reciprocal F1 crosses, we investigated the effects of knocking out two nonimprinted candidate genes,   •Heritability of murine complex traits has a significant parent-of-origin effect•Many mouse quantitative trait loci show parent-of-origin effects•Gene knockouts induce parent-of-origin-like expression changes in reciprocal crosses A surprisingly large proportion of traits exhibiting inheritance patterns based on parent of origin indicates a network of interactions between imprinted and nonimprinted genes. The results suggest that these interactions may account for some of the missing heritability seen when comparing sibling-based to population-based studies of the phenotypic effects of genetic variants. Parent-of-origin effects, in which the phenotypic effect of an allele depends on whether it was inherited from the mother or father, have well-established roles in animal growth  and beha+ T cells, which do not contain known imprinted genes, to investigate how these effects arise.In this study, in order to estimate the effect of parent of origin on phenotype, we reanalyze data from a mouse heterogeneous stock (HS) previously phenotyped for 97 traits. HS mice are descended from eight inbred progenitor strains  and maintained for over 50 generations  so that We first show that parent of origin makes a significant contribution to the heritability of most complex traits but is confounded with family structure. We considered 97 traits we had previously analyzed in the HS . Here, ws maternally and haplotype t paternally. We used these phased probabilities to partition the kinship between each pair of HS animals according to parent of origin. Kinship is defined here as the genome-wide average number of shared founder haplotypes. We write + to symbolize coinheritance of an allele from parents of the same sex , − for coinheritance from parents of the opposite sex, and ± for coinheritance regardless of parental sex.Our analysis is based on the fact that, if two individuals (not necessarily siblings) share an allele, then either it was inherited from parents of the same sex or from parents of the opposite sex. Parent-of-origin effects can then be evaluated by comparing the heritabilities associated with these modes of inheritance. For each mouse and at each locus and for each pair of HS founder haplotypes  of the traits, we found expected A.We then repeated the analysis using only SNPs within 3 Mb of known imprinted genes . Because, in the HS, linkage disequilibrium hin 2 Mb , these Smutation  for the It is well known that siblings share more alleles than nonsiblings. However, it is less appreciated that this excess of shared alleles derives from common parents and so is confounded with parent-of-origin and with parental effects . The effsiblings B, whereasiblings C, and acHere, allele sharing is defined in terms of the ancestral haplotypes of the HS, but this result applies in any population, for diallelic markers in Hardy-Weinberg equilibrium see . At a SNThus, parent-of-origin allele sharing tracks sibship membership, and the excess of We next investigated whether individual QTLs showed parent-of-origin effects. Here, we define an iQTL to mean a QTL exhibiting an additive parent-of-origin effect. We reanalyzed 837 autosomal QTLs for the 97 traits that we had previously mapped in the HS  for pareWe found that standard single-locus tests for parent-of-origin effects, when applied to simulated nonimprinted complex traits, produce many false-positive iQTL calls. This error rate increases with the number of QTLs contributing to the simulated trait, even when a mixed model  is used We found 138 iQTLs (16%) with an FDR of 20%, and 304 (36%) at FDR 25% . Thus, tDlk and Dio3a  overlaps five QTLs for diverse phenotypes, four of which are iQTLs at FDR <25%. The region chr7 (58.6–62.5 Mb) contains the Snord116 cluster syntenic to the human Prader-Willi locus, and overlaps four iQTLs for body weight. However, many iQTLs are not associated with known imprinted regions. For example, another four iQTLs for body weight overlap the locus chr3 (97–101 Mb), yet this contains no known imprinted genes. An iQTL for percentage of CD4+ T cells occurs within the major histocompatibility complex (MHC) on chromosome 17. Across all phenotypes, there is no significant enrichment of iQTLs at imprinted genes, although body weight does show slight enrichment .Some iQTLs overlap known imprinted genes. For example, the imprinting control region between cis eQTLs, and called a cis eQTL imprinted if it was within 5 Mb of its gene location  and where the test for an additive parent-of-origin effect had logP > 5 in a mixed model. None of the simulated nonimprinted traits reached this level of significance, so we expect fewer than one of the imprinted expression QTLs (ieQTLs) to be a false positive , Rasgrf1, Grb10, and Snord113 (Meg3 locus)—are known imprinted genes or in known imprinted control regions. Some of these overlap with the iQTLs we identified, including a probe for a Snord116 repeat within the Prader-Willi locus (logP = 22) presumably responsible for the parent-of-origin effects on body weight we observed at the locus. Mice with a paternally inherited Snord116 deletion show severe postnatal growth retardation, whereas maternally inherited deletions are normal (H13 (logP = 8.8) and Nnat (logP = 12.6) on chromosome 2 overlap with a single iQTL for body weight. Interestingly, although these ieQTL effects are very significant, the body weight effects are slight. Grb10 has imprinted behavioral and physiological effects  is potentially imprinted based on expressed sequence tag data  and H2-ab1 , to confirm function. All laboratory procedures involving mice were conducted under UK Home Office authority after approval by Local Ethical Review at University of Oxford. We distinguish between two types of reciprocal cross: where a homozygote KO is crossed with a wild-type (WT), or where a heterozygote KO is crossed with WT. In the former design, all F1 offspring are heterozygous, and parent-of-origin effects are found by comparing the phenotypes of individuals grouped according to the parent transmitting the KO allele. In the latter . The Man1a2 KO has no direct effect on body weight (p < 0.169) but has two distinct epigenetic effects, which persist throughout life (−23). Second, there is a parent-of-origin effect, where the weight of offspring carrying a KO allele depends on whether it is inherited from the mother or father (p < 0.006).Body weight grows approximately quadratically with age, with males heavier than females -H2bm12/KhEgJ, which contains three amino acid substitutions in the β1 exon . In the heterozygous experiment, 48% of the 73 offspring tested were genotypically WT. We measured CD4+ T cells in spleen and blood. In spleen, there was a very significant difference in CD4+ T cells between KO and WT F1 mice (−12) and a sex difference (p < 0.006). There was a parent-of-origin effect , but no parental effect  and hippocampus .We sequenced RNA from 30 F1 mice from the We list in H2-ab1 itself is differentially expressed only in the hippocampus of heterozygous females. We conclude that the H2-ab1 KO exerts a parent-of-origin effect in cis and trans.Four general results emerge. First, distinct but overlapping sets of genes are differentially expressed genome-wide depending on sex, tissue, and genotype. In all the comparisons, other genes from within the MHC (and not solely immune system-related genes) are differentially expressed. ∗] in Second, where a gene is differentially expressed, its expression jumps approximately 2-fold up or down, particularly for genes for which expression can be estimated accurately. This is consistent with silencing  of one allele A–5D. ManThird, differentially expressed genes are under tighter transcriptional control, i.e., variation in expression between mice with the same sex and parental genotype is lower than average . Variatiance see .Figure 6−6). However, this enrichment might also be because we have greater power to detect differential expression in genes with low dispersion.Our data suggest that the dispersion of key genes is under organismal control because cancer-related genes  an unexpectedly large component of heritability is attributable to parent of origin, and (2) KOs of two nonimprinted genes under iQTLs produce complex phenotypic and transcriptomic parent-of-origin effects. The interpretation and implications of these findings depend on the way we decompose sources of heritability, which we discuss first.The HS population contains individuals with differing degrees of relatedness, including both siblings  as well as much more distantly related individuals (sharing less than 5%). We exploit this feature to decompose parental origin effects that remain hidden to designs using unrelated individuals. When we estimate the contribution to heritability attributable to alleles shared by two animals that comes from parents of the same sex , we find that it is twice that attributable to shared alleles descended from parents of the opposite sex (one from a mother and one from a father). The former is strongly confounded with family effects because the greater relatedness among siblings derives from identical haplotypes inherited from the same parent. An important implication is that heritability of traits estimated from relatives (in particular twins) will likely exceed those from population-based studies, and this would in part explain why genome-wide SNP-based heritability estimates, and those obtained by summing the effects attributable to detected loci, will underestimate heritability . These effects are due to maternal or paternal consequences of the H2-ab1 KO. In contrast, in heterozygous KO mice, the genes that are differentially expressed are largely distinct from the WT set -12/KhEgJ  mice werExtended Experimental Procedureshttp://mus.well.ox.ac.uk/poe.Data are available from The heterogeneous stock (HS) of mice is descended from eight known inbred strains. For every HS mouse, each chromosome is a mosaic of the founder haplotypes . The mosaic can be derived from genotypes at a dense set of markers using a hidden Markov model (HMM) in . We modiL in individual i, the parental genotypes will convey varying degrees of certainty regarding the phase of the observed unphased genotype Depending on the genotypes of the parents, at some loci it is possible to determine the phase of the offspring genotype. Thus at the locus L-1 to phased state L, given the phased genotype L. The backward recurrence is defined similarly, from which we can compute the probability The forward HMM recurrence  is nested within the imprinted model (2), and model (2) contains twice the number of haplotype parameters as model (1). In principle the evidence for imprinting can be tested at each locus Instead we used the simulations to derive the empirical distribution of a statistic ntervals ). The mig±2+Iσe2 . Then we10 p value of the imprinted QTL fit at locus L (i.e. model (2)) and L (model (1)), and where L runs over each interval across the QTL. The null distribution of The evidence for an iQTL was summarized by the statistic We required parent of origin effects to be additive, which reduced the parameter space considerably . We also tested for iQTL using the most general formulation for an iQTL (data not shown). However, comparisons with our simulated QTLs showed that we had a lower false discovery rate (FDR) when the additive iQTLs were called rather than in the more general model.cis eQTL), and therefore have a simpler genetic architecture, we compared observed ieQTLs to simulations where each trait had a single QTL explaining 5% of the variance. For these simulations we did not observe the same confounding of parent of origin with family effects and therefore did not need to use the statistic We used the hippocampus Illumina microarray gene expression data in ad libitum. All experiments were carried out in accordance with UK national (Home Office) and institutional guidelines.All mice were housed and procedures carried out at the WTCHG. Mice were maintained in a specific pathogen free facility in individually ventilated cages, and given food and water Exploratory data analysis showed that body weight had an approximately quadratic dependence on age. We analyzed weights from all mice at all time points in a single linear mixed model in order to parameterize the growth curves. Repeated observations on a given animal are correlated, so a random effect for each mouse was included in the model. A series of nested models of increasing complexity was fitted, in order to dissect the effects of sex, parent and genotype on weight. Thus, using the R formula terminology, a model that describes weight Mixed models were fitted using the lmer function in the R lme4 package and differences between nested models in + T cells were measured in blood and spleen and Double cortin (DCX) stained neurogenesis were measured in hippocampus using the protocols in (CD4ocols in . Tests fAll RNA sequencing was performed at the WTCHG Oxford Genomics Centre.Hippocampus and lung RNA was extracted from 30 of the H2-ab1 mice from the second experiment (heterozygote x wild-type). Total RNA was isolated from frozen lung using RNeasy kit, and from frozen hippocampus using RNeasy micro kit . RNA concentrations were determined using a NanoDrop spectrophotometer, and lung RNA quality, purity and integrity were assessed using an Agilent 2100 Bioanalyser . Hippocampus RNA quality was not routinely assessed on the Bioanalyser because the quantities were too low. Three or  four mice from each combination of sex, F1 genotype and parental genotype were used.RNA was barcoded, pooled and sequenced on a lane of a Hiseq2000 using 51bp paired end reads. mRNA-seq library construction was as follows: Total RNA quantity and integrity were assessed, using Quant-IT RiboGreen RNA Assay Kit  and Agilent Tapestation 2200 R6K. mRNA enrichment was achieved by processing 5μg of total RNA using the Magnetic mRNA Isolation Kit from NEB (S1550S) with minor modifications. Generation of double stranded cDNA and library construction were performed using NEBNext Ultra Directional RNA Library Prep Kit for Illumina® (E7420L) according to manufacturer specifications.Upon ligation of Illumina Adapters (Multiplexing Sample Preparation Oligonucleotide Kit) each library was size selected with two Ampure Bead bindings . The following custom primers (25 μM each) were used for the PCR enrichment step:Multiplex PCR primer 1.05′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′Index primerINDEX]CAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3′5′-CAAGCAGAAGACGGCATACGAGAT[Barcoding indices used were the eight base tags developed by .Amplified libraries were analyzed for size distribution using the Agilent Tapestation 2200 D1K. Libraries were quantified by quantitative RT-PCR using Agilent qPCR Library Quantification Kit and a MX3005P instrument (Agilent) and relative volumes were pooled accordingly. Finally, a second quantitative RT-PCR was performed to measure the relative concentration of the pool compared to a previously sequenced mRNA library in order to determine the volume to use for sequencing. Sequencing was performed as 50bp paired end read on a HiSeq2000 according to Illumina specifications.http://bioconductor.org) packages Genomic Ranges, Rsamtools, GenomicFeatures, rtracklayer, leeBamViews. Count data are in After standard quality control reads were aligned to the mouse reference genome (mm10) using TopHat  with def


MPPED2, DDX1, SLC47A1, CDK12, CASP9, and INO80. Morpholino knockdown of mpped2 and casp9 in zebrafish embryos revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. By providing new insights into genes that regulate renal function, these results could further our understanding of the pathogenesis of CKD.Chronic kidney disease (CKD) is an important public health problem with a genetic component. We performed genome-wide association studies in up to 130,600 European ancestry participants overall, and stratified for key CKD risk factors. We uncovered 6 new loci in association with estimated glomerular filtration rate (eGFR), the primary clinical measure of CKD, in or near  MPPED2, DDX1, SLC47A1, CDK12, CASP9, and INO80. CDK12 effect was stronger in younger and absent in older individuals. MPPED2, DDX1, SLC47A1, and CDK12 loci were associated with eGFR in African ancestry samples as well, highlighting the cross-ethnicity validity of our findings. Using the zebrafish model, we performed morpholino knockdown of mpped2 and casp9 in zebrafish embryos and revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. These results further our understanding of the pathogenesis of CKD and provide insights into potential novel mechanisms of disease.Chronic kidney disease (CKD) is an important public health problem with a hereditary component. We performed a new genome-wide association study in up to 130,600 European ancestry individuals to identify genes that may influence kidney function, specifically genes that may influence kidney function differently depending on sex, age, hypertension, and diabetes status of individuals. We uncovered 6 new loci associated with estimated glomerular filtration rate (eGFR), the primary measure of renal function, in or near  For the stratified analyses, in addition to identifying loci that were significant within each stratum, we performed a genome-wide comparison of the effect estimates between strata of the four risk factors. A complete overview of the analysis workflow is given in P value≤10−6 and high between-study homogeneity (I2<25%) in the CKD45 analysis  eGFR based on serum creatinine (eGFRcrea) and CKD  in the overall sample, 2) eGFRcrea and CKD stratified by the four risk factors, and 3) CKD45, a more severe CKD phenotype defined as eGFRcrea <45 ml/min/1.73 manalysis , and 11 o strata .MPPED2 gene; rs6431731 near the DDX1 gene), in the diabetes-free sub-group (rs2453580 in an intron of the SLC47A1 gene), in the younger age stratum , and the direction test . In the combined meta-analysis of all 45 studies used in the discovery and replication stages, all six SNPs met the genome-wide significance threshold of 5×10−8, with individual P values ranging from 4.3×10−8 to 8.4×10−18  as compared to the older age group (>65 years of age).While none of the loci identified for CKD45 or the test for between-strata difference analyses replicated, all 6 loci identified from the eGFRcrea overall analysis, stratified analyses, and the direction test did . These 6.4×10−18 . The imp.4×10−18 . Brief dMPPED2, DDX1, SLC47A1, and CDK12) harbored SNPs that achieved statistical significance after correcting for multiple comparisons based on the genetic structure of each region (see Methods for details). MPPED2, and MPPED2, was associated with eGFRcrea in African Americans . While rs12278026 is monomorphic in the CEU population in HapMap, rs3925584 and rs12278026 have a D′ of 1 (r2 = 0.005) in the YRI population, suggesting that these SNPs may have arisen from the same ancestral haplotype.We further examined our findings in 8,110 African ancestry participants from the CARe consortium cis expression of the nearby CASP9 gene in myocytes, which encodes caspase-9, the third apoptotic activation factor involved in the activation of cell apoptosis, necrosis and inflammation . In the kidney, caspase-9 may play an important role in the medulla response to hyperosmotic stress We also performed eQTL analyses of our 6 newly identified loci using known databases and a newly created renal eSNP database (see Methods) and found that rs12124078 was associated with MPPED2 and DDX1) were in regions containing only a single gene, and 1 (CASP9) had its expression associated with the locus lead SNP. Thus, to determine the potential involvement of these three genes during zebrafish kidney development, we independently assessed the expression of 4 well-characterized renal markers following morpholino knockdown: pax2a  nephrin (podocyte) slc20a1a  slc12a3  ddx1 morphants . For mpped2, no differences were observed in expression of the proximal or distal tubular markers slc20a1a and slc12a3 .Of the 6 novel loci identified, 2 , both in the absence and presence of dextran, and observed a significant increase in edema prevalence in casp9 with  and without  dextran challenge but not in mpped2 morphants , which is highly evolutionary conserved and encodes a protein with metallophosphoesterase activity The lead SNP at the P values from 3.7×10−16 to 0.01) and 11 with CKD45  , File S1UMOD had a stronger association in older individuals  and in those with hypertension , and CDK12 was stronger in younger subjects . We tested the interaction between age and rs11078903 in one of our largest studies, the ARIC study. The interaction was significant  and direction consistent with the observed between-strata difference.When we examined these 29 renal function loci by age group, sex, diabetes and hypertension status , and S19Finally, we tested for associations between our 6 new loci and CKD related traits. The new loci were not associated with urinary albumin-to-creatinine ratio (UACR) or microalbuminuria MPPED2, DDX1, CASP9, SLC47A1, CDK12, INO80). In contrast to our prior genome-wide analysis We have extended prior knowledge of common genetic variants for kidney function mpped2 resulted in abnormal podocyte anatomy as assessed by expression of glomerular markers, and loss of casp9 led to altered podocyte and distal tubular marker expression, decreased dextran clearance, edema, and enhanced susceptibility to gentamicin-induced kidney damage. These findings demonstrate the potential importance of these genes with respect to renal function and illustrate that zebrafish are a useful in vivo model to explore the functional consequences of GWAS-identified genes.Despite our hypothesis that genetic effects are modified by CKD risk factors, most of the identified variants did not exhibit strong cross-strata differences. This highlights that many genetic associations with kidney function may be shared across risk factor strata. The association of several of these loci with kidney function in African Americans underscores the generalizability of identified renal loci across ethnicities. Zebrafish knockdown of Despite these strengths, there are some limitations of our study that warrant discussion. Although we used cystatin C to separate creatinine metabolism from true filtration loci, SNPs within the cystatin C gene cluster have been shown to be associated with cystatin C levels With increased sample size and stratified analyses, we have identified additional loci for kidney function that continue to have novel biological implications. Our primary findings suggest that there is substantial generalizability of SNPs associations across strata of important CKD risk factors, specifically with hypertension and diabetes.−1.192 were set to 15, and those >200 were set to 200 ml/min/1.73 m2. CKD was defined as eGFRcrea <60 ml/min/1.73 m2 according to the National Kidney Foundation guidelines 2. Control individuals for both CKD and CKD45 analyses were defined as those with eGFRcrea >60 ml/min/1.73 m2.Serum creatinine and cystatin C were measured as detailed in In discovery and replication cohorts, diabetes was defined as fasting glucose ≥126 mg/dl, pharmacologic treatment for diabetes, or by self-report. Hypertension was defined as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or pharmacologic treatment for hypertension.Genotyping was conducted as specified in e(eGFRcrea), loge(eGFRcys), CKD, and CKD45 overall and 2) loge(eGFRcrea) and CKD stratified by diabetes status, hypertension status, age group (≤/>65 years), and sex. GWAS of loge(eGFRcrea) and loge(eGFRcys) were based on linear regression. GWAS of CKD and CKD45 were performed in studies with at least 25 cases  and were based on logistic regression. Additive genetic effects were assumed and models were adjusted for age and, where applicable, for sex, study site and principal components. Imputation uncertainty was accounted for by including allelic dosages in the model. Where necessary, relatedness was modeled with appropriate methods (see www.eurac.edu/GWAtoolbox.html) A schematic view of our complete analysis workflow is presented in ith study, K is the number of studies, and ith study. The meta-analyses were performed using METAL P value≤5×10−8 for any trait or any stratum were deemed genome-wide significant Meta-analyses of study-specific SNP-association results, assuming fixed effects and using inverse-variance weighting, i.e.: the pooled effect P value<5×10−8 in any trait or stratum that were independent (defined by pairwise r2<0.2), in the primary association analysis. This yielded five SNPs in five independent loci. The same criterion was applied to the CKD analysis, where no SNPs passed the selection threshold. Given the smaller number of cases with severe CKD resulting in less statistical power, a different selection strategy was adopted for the CKD45 analysis: selected for replication were SNPs with discovery P value≤5×10−6, MAF≥5%, and homogeneous effect size across studies (I2≤25%). Four additional SNPs were thereby selected for replication from the CKD45 analysis.In the eGFRcrea analyses, after excluding loci that were previously reported a priori probability that a given effect allele of a SNP has either a positive or negative association with eGFRcrea is 0.5. Because the meta-analysis includes independent studies, the number of concordant effect directions follows a binomial distribution. Therefore, we tested whether the number of discovery cohorts with the same sign of association (i.e. direction of effect) was greater than expected by chance given the binomial distribution and a null expectation of equal numbers of associations with positive and negative sign. The test was only applied for eGFRcrea in the overall analysis. Multiple testing was controlled by applying the same P value threshold of 5×10−8 as in the overall GWAS. Given that no SNP met this criterion, we selected for replication one novel SNP with the lowest P value of 4.0×10−7.In addition to identifying SNPs for replication based on the genome-wide significance threshold from a fixed effect model meta-analysis, we performed a “direction test” to identify additional SNPs for which between-study heterogeneity in effect size might have obscured the overall association that was nevertheless highly consistent in the direction of allelic effects. Under the null hypothesis of no association, the 1−b2)/(SE(b1)2+SE(b2)2)0.5, with b1 and b2 indicating the effect estimates in the two strata and SE(b1) and SE(b2) their standard errors P value≤5×10−5, an association P value≤5×10−5 in one of the two strata, and MAF≥10%. Independent loci were defined using the same criteria as described above. Eleven further SNPs, one per locus, were selected for replication from the between-strata difference test.Based on the results of the stratified GWAS of eGFRcrea and CKD, for each SNP we tested the hypothesis whether the effect of a SNP on eGFRcrea or CKD was the same between strata (null hypothesis), i.e. diabetes versus non-diabetes subjects, hypertensive versus normotensive, younger versus older, females versus males. We used a two-sample test defined as Z = , we estimated the False Discovery Rate as a q-value using the QVALUE Replication was performed for a total of 21 SNPs including 5 from the overall and stratified eGFRcrea analyses, 1 from the direction test on eGFRcrea, 4 from the overall CKD45 analysis, and 11 from the between-strata difference test. Replication studies used the same phenotype definition, and had available genotypes from imputed P values were compared to the genome-wide significance threshold of 5×10−8 to test whether a SNP was genome-wide significant. Between-study heterogeneity of replicated SNPs was quantified by the I2 statistic Finally, study-specific results from both the discovery and replication stage were combined in a joint inverse-variance weighted fixed-effect meta-analysis and the two-sided de novo genotyping in 10,446 samples from KORA F3, KORA F4, SAPHIR and SAPALDIA, the MassARRAY system at the Helmholtz Zentrum  was used, using Assay Design v3.1.2 and the iPLEX chemistry . Assay design failed for rs1322199 and genotyping was not performed. Ten percent of the spectra were checked by two independent, trained persons, and 100% concordance between investigators was obtained. SNPs with a P value<0.001 when testing for Hardy-Weinberg equilibrium , SNPs with call rate <90% (rs500456 in KORA F4 only) or monomorphic SNPs (rs2928148) were excluded from analyses without attempting further genotyping. The call rates of rs4149333 and rs752805 were near 0% on the MassARRAY system. These SNPs were thus genotyped on a 7900HT Fast Real-Time PCR System . Mean call rate across all studies and SNPs ranged from 96.8% (KORA F4) to 99% (SAPHIR). Duplicate genotyping was performed in at least 14% of the subjects in each study with a concordance of 95–100% (median 100%). In the Ogliastra Genetic Park Replication Study (n = 3000) de novo genotyping was conducted on a 7900HT Fast Real-Time PCR System , with a mean call rate of 99.4% and 100% concordance of SNPs genotyped in duplicate.For Twenty-nine SNPs, including the 6 novel loci reported in the current manuscript along with 23 previously confirmed to be associated with renal function P value<0.05). Both the interaction term and the terms for the main effects of age and the SNP were included in the model.SNP-by-age interaction, for the one SNP showing significantly different effects between strata of age, was tested in the ARIC study by fitting a linear model on log(eGFRcrea) adjusted for sex, recruitment site, the first and the seventh genetic principal components  in the combined sample  we had 80% power to detect differences as large as 0.035.To assess genome-wide between-strata differences, with alpha = 5×10P value with MAF>0.03 was considered the top SNP in the African ancestry sample. We defined statistical significance of the identified lead SNP in African ancestry individuals based on a region-specific Bonferroni correction. The number of independent SNPs was determined based on the variance inflation factor (VIF) with a recursive calculation within a sliding window of 50 SNPs and pairwise r2 of 0.2. These analyses were performed using PLINK.For each of the 6 lead SNPs identified in our European ancestry samples, we extracted eGFR association statistics from a genome-wide study in the CARe African ancestry consortium For each replicating SNP, we obtained association results for urinary albumin-to-creatinine ratio and microalbuminuria from our previous genome-wide association analysis Significant renal SNPs were searched against a database of expression SNPs (eSNP) including the following tissues: fresh lymphocytes P values for eQTLs were calculated using linear multivariable regression in both cohorts and then combined using Fisher's combined probability test . Different anatomic regions of the kidney were visualized using a panel of 4 established markers: pax2a  nephrin (podocyte marker) slc20a1a  slc12a3  Zebrafish were maintained according to established IACUC protocols. Briefly, we injected zebrafish embryos with newly designed  or previously validated casp9  morpholiWe performed dextran clearance experiments following previously described protocols Embryos were injected with control, mpped2, or casp9 MOs at the one-cell stage. At 48 hpf, embryos were manually dechorionated, anesthetized in a 1∶20 dilution of 4 mg/ml Tricaine in embryo water, and oriented on a 1% agarose injection mold. As previously described Figure S1Flowchart of the project.(TIF)Click here for additional data file.Figure S210P values plot from stage 1 discovery meta-analysis. Plots show the discovery analysis of eGFRcrea in the overall group, with known loci P value = 5×10−8. The unmarked locus is RNASEH2C on chromosome 11, colored in gray despite genome-wide significance. The P value for the current stage 1 discovery for rs4014195 was 2.7×10−9. This locus previously did not replicate P value was 0.8832, yielding a combined stage 1+stage 2 P value of 2.6×10−7. Therefore, we did not submit this SNP for further replication.Genome-wide −log(PDF)Click here for additional data file.Figure S310P values from the discovery analysis of eGFRcrea overall (A) and by strata of the main CKD risk factors (B). The orange line and its 95% confidence interval (shaded area) represent the null hypothesis of no association. In panel (A), results are compared when considering all SNPs (black dots) and when removing SNPs from loci that were already reported in previous GWAS Quantile-quantile plots of observed versus expected −log(PDF)Click here for additional data file.Figure S4MPPED2; (B) DDX1; (C) SLC47A1; (D) CASP9; (E) CDK12; (F) INO80. −log10P values are plotted versus genomic position(build 36). The lead SNP in each region is labeled. Other SNPs in each region are color-coded based on their LD to the lead SNP. Gene annotations are based on UCSC Genome Browser and arrows indicate direction of transcription. Graphs were generated using the stand-alone version of LocusZoom Regional association plots for the six new loci in the European ancestry discovery samples: (A) (PDF)Click here for additional data file.Figure S5Forest plots of the six novel loci in the discovery phase.(TIF)Click here for additional data file.Figure S6P value or with a circle for discovery based on direction test.Results from discovery meta-analysis of eGFRcrea for the six new loci: overall sample and all strata are considered. Reported is the effect size on log(eGFRcrea) and its 95% confidence interval. The stratum where the SNP was discovered is marked with a triangle for discovery based on meta-analysis (TIF)Click here for additional data file.Figure S7MPPED2; (B) DDX1; (C) SLC47A1; (D) CASP9; (E) CDK12; (F) INO80. −log10P values are plotted versus genomic position (build 36). The lead SNP in each region is labeled and identified by a blue arrow and blue P value. The SNP with the smallest P value in the region is indicated by a red arrow. Other SNPs in each region are color-coded based on their LD to the lead SNP . Gene annotation is based on UCSC Genome Browser  and arrows indicate direction of transcription. Graphs were generated using the stand-alone version of LocusZoom Regional association plots for the six new loci in the African ancestry CARe samples: (A) (PDF)Click here for additional data file.Figure S8pax2a , nephrin (C), slc20a1a (D) and slc12a3 (E). (F–J) Ddx1 morpholino(MO)-injected embryos do not show significant changes in renal marker expression. (K) Number of observed abnormalities per number of embryos examined at 400 uM MO injection for renal gene expression analysis.Ddx1 knockdown does not affect kidney gene expression. (A–E) Uninjected control embryos show normal kidney development as demonstrated by in situ hybridization for the renal markers (TIF)Click here for additional data file.Figure S9Casp9 and mpped2 knockdown embryos are more susceptible to gentamicin-induced kidney injury. Compared to control embryos (A), casp9 and mpped2 knockdown embryos develop edema at 103 hpf , suggestive of a renal defect. When injected with gentamicin, a nephrotoxin that reproducibly induces edema in control embryos (B), mpped2 and casp9 knockdown embryos develop edema earlier, more frequently, and in a more severe fashion . Whereas control embryos primarily develop cardiac edema, mpped2 and casp9 knockdown embryos display cardiac (arrowhead), ocular (black arrow), and visceral (white arrow) edema, demonstrating that mpped2 and casp9 knockdown predisposes embryos to kidney injury. (G) Quantification of edema prevalence in control, mpped2, and casp9 knockdown embryos 2, 22, and 55 hours post-injection (hpi) of gentamicin. These numbers are presented graphically in (TIF)Click here for additional data file.Figure S10Comparison of the effect size on eGFRcrea and on eGFRcys for the lead SNPs of known and new loci. Results are based on the largest sample size available for each locus, i.e. the combined discovery and replication sample for the novel loci , the discovery sample only for the known loci . Sign of effect estimates has been changed to reflect the effects of the eGFRcrea lowering alleles. Original beta coefficients and their standard errors for the two traits can be downloaded from the File S1.(TIF)Click here for additional data file.Figure S11Odds ratios (ORs) and 95% confidence intervals of CKD and CKD45 for the lead SNPs of all known and new loci, sorted by decreasing OR of CKD.(TIF)Click here for additional data file.File S1Effect size on eGFRcrea and on eGFRcys for the lead SNPs of known and new loci.(XLSX)Click here for additional data file.Table S1Study-specific methods and full acknowledgments—discovery studies.(DOC)Click here for additional data file.Table S2Study-specific methods and full acknowledgments—replication studies and functional follow-up studies.(DOC)Click here for additional data file.Table S3Characteristics of stage 1 discovery studies.(DOC)Click here for additional data file.Table S4Study-specific genotyping information for stage 1 discovery studies.(DOC)Click here for additional data file.Table S5Characteristics of stage 2 replication studies.(DOC)Click here for additional data file.Table S6in silico replication studies.Study-specific genotyping information for stage 2 (DOC)Click here for additional data file.Table S7Top four SNPs from the CKD45 analysis.(DOC)Click here for additional data file.Table S8P value of the test for difference between strata is reported.Loci identified by the test for differential effects between strata in the GWAS. Results are sorted by trait, group and chromosome. For each SNP, the (DOC)Click here for additional data file.Table S9Imputation quality of replicated SNPs in all discovery and replication studies: median MACH-Rsq and interquartile range (IQR) are reported.(DOC)Click here for additional data file.Table S10Effects of novel and known loci on log(eGFRcrea) in the overall population.(DOC)Click here for additional data file.Table S11Genes nearest to loci associated with renal traits.(DOC)Click here for additional data file.Table S12Imputation Quality (MACH-Rsq) for the best SNPs in the African ancestry samples of the CARe consortium (1.00 refers to genotyped data).(DOC)Click here for additional data file.Table S13Baseline characteristics of the kidney biopsies for the eQTL analysis.(DOC)Click here for additional data file.Table S14Analysis of the new loci for eQTL status in meta-analysis of two cohorts of kidney biopsies.(DOC)Click here for additional data file.Table S15P values.Association of novel and known loci with CKD and CKD45: Odds Ratios (OR), 95% confidence intervals (95%CI) and (DOC)Click here for additional data file.Table S16Association between novel and known loci and log(eGFRcrea) in individuals without and with diabetes and test for difference between strata.(DOC)Click here for additional data file.Table S17Association between novel and known loci and log(eGFRcrea) in individuals without and with hypertension and test for difference between strata.(DOC)Click here for additional data file.Table S18Association between novel and known loci and log(eGFRcrea) in individuals younger and older than 65 years and test for difference between strata.(DOC)Click here for additional data file.Table S19Association between novel and known loci and log(eGFRcrea) in females and in males and test for difference between strata.(DOC)Click here for additional data file.Table S20Effects of novel loci on the logarithm of urinary albumin-to-creatinine ratio (log(UACR)) in the overall sample and by diabetes and hypertension status.(DOC)Click here for additional data file.Table S21Effects (log odds ratios) of novel loci on microalbuminuria (MA) in the overall sample and by diabetes and hypertension status.(DOC)Click here for additional data file.Table S22Association of novel loci with diastolic and systolic blood pressure in the ICBP consortium.(DOC)Click here for additional data file.Table S23Association of novel loci with myocardial infarction in the CARDIoGRAM consortium.(DOC)Click here for additional data file.