Whereas it is well established that plasma lipid levels have substantial heritability within populations, it remains unclear how many of the genetic determinants reported in previous studies (largely performed in European American cohorts) are relevant in different ethnicities.ICAM1. We compared the architecture of genetic loci associated with lipids in both African Americans and European Americans and found that the same genes were relevant across ethnic groups but the specific associated variants at each gene often differed.We tested a set of ∼50,000 polymorphisms from ∼2,000 candidate genes and genetic loci from genome-wide association studies (GWAS) for association with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) in 25,000 European Americans and 9,000 African Americans in the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe). We replicated associations for a number of genes in one or both ethnicities and identified a novel lipid-associated variant in a locus harboring We identify or provide further evidence for a number of genetic determinants of plasma lipid levels through population association studies. In many loci the determinants appear to differ substantially between African Americans and European Americans. Plasma concentrations of lipids and lipoproteins  are heritable risk factors for cardiovascular disease As promising as these observations may be, several critical questions remain – How many of the previously reported loci are genuine causal determinants of plasma lipid levels? Are there more loci associated with plasma lipids that are discoverable with techniques besides genome-wide association mapping? Do any of these loci confer effects on lipids in an ethnic-specific manner? To address these questions, we utilized the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe) comprising more than 40,000 individuals from nine prospective cohorts with measured lipid phenotypes and genotype information obtained using the “ITMAT-Broad-CARe” array, or IBC array, with ∼50,000 polymorphisms from ∼2,000 candidate genes/loci We initiated this study with specific hypotheses: (1) Common DNA sequence variants in previously reported genes and loci, as well as additional novel loci, are associated with plasma lipids; (2) At some of these loci, the specific variants related to plasma lipids differ between ethnic groups. To test these hypotheses, we performed association analyses of the polymorphisms on the IBC array in 25,000 European Americans and 9,000 African Americans in CARe.All participants in each of the CARe cohorts gave informed written consent. The Institutional Review Boards (IRBs) of each CARe cohort  have reviewed and approved the cohort’s interaction with CARe. The study described in this manuscript was approved by the Committee on the Use of Humans as Experimental Subjects (COUHES) of the Massachusetts Institute of Technology.A full description of the CARe Study is found elsewhere All DNA samples passing initial quality checks were interrogated with the IBC v2 chip for genotyping of 49,320 total single nucleotide polymorphisms (SNPs). Samples with overall genotyping success rate <95%, individual SNPs with genotyping success rate <95% within a cohort, monomorphic SNPs, and SNPs mapping to multiple genomic loci were removed. An inbreeding coefficient was calculated for each sample as a measure of heterozygosity, with those samples exceeding 4 standard deviations from the mean  being removed. In cases of identical DNA samples, the sample with the lowest genotyping success rate was removed. Samples that shared 5% or more of their genome with many other samples were also removed. Additional outlier samples as determined by multidimensional scaling were also removed. SNPs for which genotype missingness could be predicted by surrounding haplotypes were removed, as were any SNPs found to be associated with chemistry plates. Pedigrees and SNPs not conforming to Mendelian expectations were identified and, where appropriate, samples were removed. These analyses were all performed with PLINK P values. The Hardy-Weinberg P value for each index SNP in each cohort is listed in Because different ethnic groups were represented, with the expectation of differing genotype frequencies and admixture, no filters were applied for minor allele frequency or Hardy-Weinberg r2>0.98).About 2,000 ancestry-informative markers were included in the IBC v2 chip 2. Each set of residuals was standardized to a mean of zero and a standard deviation of one. The standardized residual served as the phenotype in genotype-phenotype association analyses. Generation of residuals was performed with the R statistical package .We modeled the lipid phenotypes in the following ways. LDL-C was calculated according to Friedewald’s formula: LDL-C = total cholesterol – HDL-C – (TG ÷ 5). If a TG value was >400 mg/dL, LDL-C was treated as a missing value. For individuals on lipid-lowering therapy, the LDL-C value was multiplied by 1.42 to model a 30% reduction in LDL-C on therapy. This represents the average expected reduction in LDL-C with a first-generation statin, the most commonly used lipid-lowering medication during the study periods of most of the cohorts P = 1×10−6 to be the threshold for statistical significance, applying a Bonferonni correction for the number of SNPs tested .For each of the three traits, we used linear regression to test SNP-phenotype associations in each stratum of cohort and ethnicity assuming an additive genetic model, using the ten calculated principal components as covariates: phenotype ∼ genotype + PC1+ PC2+ PC3+ PC4+ PC5+ PC6+ PC7+ PC8+ PC9+ PC10. These association analyses were performed in PLINK. Genotype-phenotype associations within each ethnic group were assessed by weighted z-score-based fixed-effects meta-analysis and effect size estimates  generated by inverse-variance weighted meta-analysis, both using METAL . Genomic control correction was applied to each cohort individually prior to meta-analysis, and a final genomic control correction was applied to each ethnicity’s meta-analysis dataset. For all cross-ethnic comparisons, only SNPs available in all cohorts  were considered. We considered We performed sensitivity analyses for two cohorts for which there were significant numbers of related individuals–CFS and FHS–to account for family relationships, using a linear mixed effects (LME) model to analyze the lipid residuals, with the SNP genotype treated as a fixed effect, and a random effect according to the degree of relatedness within a family To identify SNPs exerting sex-specific effects, we added sex as a covariate in the above linear regression model and tested for a formal SNP×sex interaction; the results were meta-analyzed as described above. Similarly, to test for SNP×SNP interactions among the most strongly associated SNPs at each of the identified loci, we added the minor allele count for each SNP as a covariate, in turn, into the above model (with sex removed), testing for a formal interaction between each SNP pair combination.R2 values for each SNP included in the conditional analyses, as well as for the combination of independently significant SNPs at each locus, were calculated by replicating the relevant linear regression model in the largest cohort (ARIC for both ethnicities) using Statistics Package for Social Sciences (SPSS) software , with principal components excluded.For those loci containing IBC-significant SNPs in both ethnicities, we performed SNP conditional analyses. These were conducted by adding SNP allele counts as extra covariates to the initial linear regression model described above. The conditional analyses were performed iteratively, with additional independent SNPs identified at each step in each locus added to the model. Adjusted SORT1, CILP2/PBX4, GALNT2, MLXIPL, TRIB1, ANGPTL3, APOB, APOE, HMGCR, LDLR, PCSK9, ABCA1, APOA1/APOA5, CETP, LIPC, LIPG, LPL, GCKR, and MVK-MMABP<1×10−6). Importantly, there was no overlap between the GWAS cohorts and the CARe cohorts. We also found that SNPs at 10 loci replicated in the CARe African American cohorts–SORT1 (sortilin), APOB (apolipoprotein B), APOE (apolipoprotein E), LDLR (LDL receptor), PCSK9 (proprotein convertase subtilisin/kexin type 9), ABCA1 , APOA1 (apolipoprotein A-I), CETP, (cholesteryl ester transfer protein), LIPC (hepatic lipase), and LPL (lipoprotein lipase)  . All of FADS1 (fatty acid desaturase 1), PLTP (phospholipid transfer protein), LCAT (lecithin-cholesterol acyltransferase), ANGPTL4 (angiopoietin-like 4), ABCG5/ABCG8 , LPA [lipoprotein(a)], NPC1L1 [NPC1 -like 1], HPR (haptoglobin-related protein)–were shown to have genome-wide-significant lipid-associated SNP variants in GWAS reported subsequent to the fabrication of the IBC array  had previously been reported in a candidate gene study; indeed, the same SNP that was IBC-significant (rs3211938) was shown in that prior study to be a nonsense coding variant that resulted in CD36 deficiency in a homozygous individual and was associated with increased HDL-C levels (P = 0.00018) and decreased TG levels (P = 0.0059) in an African American cohort We also found significant associations for a number of IBC SNPs chosen for their proximity to candidate genes selected from the literature. Of these, eight loci–BC array  [3]. AllICAM1 (intercellular adhesion molecule 1) in CARe African Americans  for the ICAM1 SNP rs5030359 is not only IBC-significant but also genome-wide-significant. This variant is of low frequency in African Americans (just under 1%) and is virtually absent in European Americans. Interestingly, this variant demonstrated the largest effect size for any trait + ethnicity combination (β = –0.52).Finally, one locus harbors an IBC-significant variant that has not previously been reported to be associated with lipid traits–mericans . Of noteLDLR locus, with β = –0.23 (in standard deviation units) and MAF = 0.12 in European Americans, β = –0.20 and MAF = 0.14 in African Americans for LDL-C  can be attributed solely to the different sample sizes . Another example is rs1748197 in the ANGPTL3 (angiopoietin-like 3) locus, which has β = –0.053 and MAF = 0.34 in European Americans, β = –0.042 and MAF = 0.34 in African Americans for TG (P = 6.23×10−7 vs. P = 0.012).Comparing each index SNP in each of the lipid loci in European Americans to African Americans, we noted quite varied patterns of association. In some cases, the effect size estimates and minor allele frequencies (MAFs) are quite similar in two ethnicities. An example is SNP rs6511720 in the or LDL-C . The relANGPTL4 locus, with β = 0.048  in European Americans and β = –0.00060  in African Americans; rs4846918 in the GALNT2  locus, with β = 0.069  in European Americans and β = 0.016  in African Americans for HDL-C; and rs2515629 in the ABCA1 locus, with β = 0.0079  in European Americans and β = 0.11  in African Americans for HDL-C. These findings indicate considerable heterogeneity in the architecture of lipid-related loci in the two ethnicities.Other index SNPs diverged considerably in the two ethnicities. Some of the SNPs displayed considerable differences in their estimated effect sizes despite similar MAFs. Notable examples include rs2278236 in the LIPC for HDL-C, LDLR for LDL-C) was the same SNP the most highly associated for the trait in each ethnicity. At an additional four loci , the most highly associated SNP in one ethnicity was among the most highly associated SNPs in the other ethnicities. In the remaining five loci , the most highly associated SNPs in the ethnicities did not overlap.We took advantage of the dense genotyping in each locus on the IBC v2 array to perform more detailed comparisons of the 11 loci that harbored IBC-significant SNPs for both ethnicities . In onlyCETP with HDL-C in European Americans) per locus. Remarkably, only one locus (SORT1) harbored a single independent SNP in each ethnic group.For each ethnicity we performed conditional analyses with the most highly associated SNPs in each of the 11 loci to uncover any additional, independently associated SNPs (at IBC significance) in the same gene regions . We perfPCSK9 locus, the most highly associated SNPs (rs11591147 in European Americans and rs11806638 in African Americans) are more than 10 kb apart and show much weaker associations (and are much less common) in the other ethnicity  and others are distant.For many gene regions, the independent SNPs did not colocalize in the two ethnicities. For example, in the thnicity . SimilarLCAT with HDL, APOE with TG–the strength of association of SNPs is greater in African Americans than in European Americans  in European Americans (MAF = 0.00047 vs. MAF = 0.12), since the effect size estimates are similar in the two ethnic groups (β = 0.33 in European Americans vs. β = 0.26 in African Americans). For LCAT, rarity accounts for the second independent SNP in African Americans  but not the most highly associated SNP . No second independent SNP for LCAT was identified in European Americans.Notably, for two loci/trait combinations–mericans . For APOR2) in the respective traits explained by each SNP and the combination of the independently significant SNPs at each locus. The greatest proportion of variance explained by a single SNP in African Americans was 3.7% (rs17231520 for HDL-C); for European Americans it was 2.5% (rs17231506 for HDL-C). These SNPs are both located in the CETP locus on chromosome 16. Unsurprisingly, the CETP locus explained the greatest proportion of variance, in any trait, for both African Americans and European Americans . For African Americans, the combination of all independently significant SNPs at the 11 loci that harbored IBC-significant SNPs for both ethnicities explained 4.5%, 7.7%, and 1.9% of the variance in LDL-C, HDL-C, and TG, respectively. For European Americans, 6.2%, 6.0%, and 4.1% of the variance in LDL-C, HDL-C, and TG were explained, respectively.P value (0.002) was generated for the interaction between SNPs rs10455872 (LPA) and rs934197 (APOB) for LDL-C in African Americans, but was far from IBC-significant and withstands a Bonferroni correction of only 25 tests (there were 120 SNP×SNP interactions tested for LDL-C among African Americans).We investigated whether any SNP×SNP interactions existed between any of the most significant SNPs identified at each of the 19 above-mentioned loci for their respective traits; none were identified , S7, S8.PLTP) generated a P value of 1.51×10−4 for HDL-C among European Americans and was highly significant among women (P = 7.11×10−12) but not men (P = 0.12). The P value of this interaction was two orders of magnitude smaller than for any of the other SNPs for any lipid trait, with the exception of rs439401 (P = 1.30×10−3 for TG among European Americans). Expanding the search to all SNPs on the IBC chip failed to identify any further IBC-significant SNP×sex interactions , and 10 of the loci replicated in the combined CARe African American cohorts . It is likely that with genotyping in additional African American individuals, increased power will ultimately allow replication of many of the remaining nine loci. We identified an additional 10 loci associated with one or more lipid traits at Bonferroni-corrected statistical significance. Eight of these loci were mapped in GWAS studies subsequent to the design of the IBC array, and one locus had been identified in a prior non-GWAS study. The remaining locus, These findings give a high degree of confidence that most, if not all, of the reported GWAS lipid loci harbor authentic determinants of plasma lipid levels deserving of further functional investigation, and that many of the loci are relevant not just in European American populations but more generally in global populations.We were able to use the dense SNP genotyping in loci available via the IBC array to analyze lipid-associated loci in an unprecedented level of detail, particularly in African Americans. Our analyses demonstrate that at many loci there are major differences in genetic architecture between European Americans and African Americans. These differences manifest in at least three ways.PCSK9 that are of low frequency in African Americans, but absent in European Americans, have been shown to result in a robust reduction in LDL-C levels and coronary heart disease risk CD36 is much more highly associated with HDL-C in African Americans (P = 2.60×10−12) than in European Americans (P = 0.030), with a large discrepancy in MAFs (8.3% vs. 0.01%)  than in European Americans . These variants may represent causal variants, as is the case with the nonsense coding variant rs3211938 in CD36First, some of the most highly associated SNPs in one ethnicity are rare or absent in the other ethnicity. This is a well-established phenomenon; for example, truncation mutations in . 0.01%) . SimilarABCA1 locus   . In caseCETP locus we identified at least 3 independently associated SNPs (with HDL) in African Americans and 4 independently associated SNPs in European Americans , consistent with what was observed in the previous study. Other loci in which specific variants appear to have larger effect sizes in African Americans than in European Americans include PCSK9 for LDL-C , GCKR for TG , APOA1-C3-A4-A5 for TG , and LCAT for HDL-C (β = 0.11 vs. β = 0.069), among others. Similarly, there are a number of loci in which specific variants appear to have larger effect sizes in European Americans than in African Americans  and in high linkage disequilibrium, suggesting that they are marking the same causal variant  and are effectively in perfect linkage disequilibrium. In contrast, for the same 6 SNPs in African Americans, they are in varying degrees of linkage disequilibrium, with rs12740374 standing out as having the strongest association . Thus, this analysis nominates rs12740374 as a strong candidate for the causal variant in the locus. Of note, a recent study identified rs12740374 as a causal variant through functional experimentation in cell-based reporter assays In the  variant . In EuroLDLR and LIPC loci, respectively, rs6511720 and rs2070895 were the most highly associated SNPs in both ethnicities, suggesting that each is either a causal SNP or tightly linked to a causal SNP Click here for additional data file.Figure S2Graphical plots of SNPs in each lipid-associated locus. The x-axis indicates the basepair position on the indicated chromosome. The y-axis indicates the negative log of the P value of association with the indicated lipid trait. Red stars indicate the P values of association of the SNPs in European Americans, black circles in African Americans. Each of the best independently associated SNPs in each locus is circled and labeled.(DOC)Click here for additional data file.Table S1CARe participant characteristics.(DOC)Click here for additional data file.Table S2Hardy-Weinberg P values for lipid-associated SNP variants.(DOC)Click here for additional data file.Table S3SNP×SNP interactions between the most significant SNPs at each LDL-C-related locus among European Americans.(DOC)Click here for additional data file.Table S4SNP×SNP interactions between the most significant SNPs at each LDL-C-related locus among African Americans.(DOC)Click here for additional data file.Table S5SNP×SNP interactions between the most significant SNPs at each HDL-C-related locus among European Americans.(DOC)Click here for additional data file.Table S6SNP×SNP interactions between the most significant SNPs at each HDL-C-related locus among African Americans.(DOC)Click here for additional data file.Table S7SNP×SNP interactions between the most significant SNPs at each triglyceride-related locus among European Americans.(DOC)Click here for additional data file.Table S8SNP×SNP interactions between the most significant SNPs at each triglyceride-related locus among African Americans.(DOC)Click here for additional data file.Table S9SNP×sex interaction tests for the most significant SNPs at each LDL-C-related locus.(DOC)Click here for additional data file.Table S10SNP×sex interaction tests for the most significant SNPs at each HDL-C-related locus.(DOC)Click here for additional data file.Table S11SNP×sex interaction tests for the most significant SNPs at each triglyceride-related locus.(DOC)Click here for additional data file.Table S12Top ten significant SNP×sex interactions for each lipid trait among European Americans.(DOC)Click here for additional data file.Table S13Top ten significant SNP×sex interactions for each lipid trait among African Americans.(DOC)Click here for additional data file.


The phyletic pattern observed in extant genomes is the result of ancestral gain and loss events along the phylogenetic tree. Here we present CoPAP (coevolution of presence–absence patterns), a user-friendly web server, which performs accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization. We developed a platform for comparing different algorithms that detect coevolution, which includes simulated data with pairs of coevolving sites and independent sites. Using these simulated data we demonstrate that CoPAP performance is higher than alternative methods. We exemplify CoPAP utility by analyzing coevolution among thousands of bacterial genes across 681 genomes. Clusters of coevolving genes that were detected using our method largely coincide with known biosynthesis pathways and cellular modules, thus exhibiting the capability of CoPAP to infer biologically meaningful interactions. CoPAP is freely available for use at  A phyletic pattern  is a binary-coded data set in which presence (‘1’) versus absence (‘0’) of homologous characters is denoted across species. This 0/1 matrix is equivalent to a gap-free multiple sequence alignment, in which rows correspond to species and columns correspond to binary characters. Phyletic pattern representation is useful for evolutionary analysis of various types of data including gene families , restricMethods for evolutionary analysis of phyletic patterns have progressed from the traditional parsimony  to likelPrevious studies have shown that genomes evolve under various constraints, which are reflected in correlated evolutionary histories. Examples include coevolving sites within a protein  and coevHere we present CoPAP (Coevolution of Presence–Absence Patterns), a user-friendly web server which is the first publically available web server for coevolutionary analysis of phyletic data. The main features and novelties of our web server are as follows: (i) usage of efficient probabilistic methods, capable of analyzing evolutionary interactions across hundreds of genomes (see case study below); (ii) implementation of various evolutionary models including complex mixture models, which can accurately capture gain–loss dynamics; (iii) visualization and analysis of the inferred coevolutionary network using Cytoscape  with addThe CoPAP input is a phyletic pattern provided as a 0/1 matrix. A phylogenetic tree is either provided as input by the user or estimated from the phyletic pattern by the neighbor joining (NJ) algorithm . For NJ,CoPAP infers coevolutionary interactions and computes statistical significance using simulations. For methodological details see  as well The inference of coevolutionary interactions is dependent on ancestral mapping of gain and loss events along the tree. The accuracy of such mapping depends on the underlying evolutionary model . The simUsing simulations we evaluated the CoPAP methodology and compared it with the explicit models for pairwise coevolution as implemented in BayesTraits  and withWe used CoPAP to analyze 4258 bacterial clusters of orthologous genes (COGs) across 681 bacterial genomes. Phyletic patterns were retrieved from eggNOG  and the CoPAP identified 5605 significant interactions . Out of the 4258 COGs analyzed, almost 40% (1664) were found to be involved in strong coevolutionary interactions. CoPAP automatically produces graphical representation of the global properties of the coevolution network. http://tree.bio.ed.ac.uk/software/figtree/ and Archaeopteryx (CoPAP allows users to easily inspect presence–absence patterns for genes of interest with respect to their underlying phylogeny using FigTree eopteryx . Figure The reconstructed coevolutionary network is available for download as a detailed text file. Additionally, CoPAP provides advanced network visualization and analysis by automatically loading the network to the Cytoscape platform . Figure The observation that by-and-large clusters of coevolving genes are annotated with similar biological functions strongly supports the validity of this approach to extract meaningful biological interactions. This observation also suggests a crucial role for coevolutionary analysis in uncovering dependencies and associations between evolving genes. The publically available web server we present here is suitable for analyzing various binary-coded data and thus, has the potential to facilitate further biological understanding with the discovery of additional coevolutionary networks.


Eight SNP-transcript pairs of highest significance (lowest p = 1.27×10−51) withstood multiple-testing correction in the second stage and modulated CYP27A1 gene expression. Additionally, we show that C9orf72 appears to be the only gene in the 9p21.2 locus that is regulated in cis, showing the potential of this approach in identifying causative genes in association loci in ALS. This study has identified candidate genes for sporadic ALS, most notably CYP27A1. Mutations in CYP27A1 are causal to cerebrotendinous xanthomatosis which can present as a clinical mimic of ALS with progressive upper motor neuron loss, making it a plausible susceptibility gene for ALS.Amyotrophic lateral sclerosis (ALS) is a progressive, neurodegenerative disease characterized by loss of upper and lower motor neurons. ALS is considered to be a complex trait and genome-wide association studies (GWAS) have implicated a few susceptibility loci. However, many more causal loci remain to be discovered. Since it has been shown that genetic variants associated with complex traits are more likely to be eQTLs than frequency-matched variants from GWAS platforms, we conducted a two-stage genome-wide screening for eQTLs associated with ALS. In addition, we applied an eQTL analysis to finemap association loci. Expression profiles using peripheral blood of 323 sporadic ALS patients and 413 controls were mapped to genome-wide genotyping data. Subsequently, data from a two-stage GWAS  were used to prioritize eQTLs identified in the first stage . These prioritized eQTLs were carried forward to the second sample with both gene-expression and genotyping data . Replicated eQTL SNPs were then tested for association in the second-stage GWAS data to find SNPs associated with disease, that survived correction for multiple testing. We thus identified twelve  Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness caused by loss of central and peripheral motor neurons. Symptoms typically have a localized limb or bulbar onset and progress to other muscle groups of the body. Denervation of respiratory muscles and dysphagia leading to respiratory complications are the most common causes of death. There is no cure for this rapidly progressive disease.UNC13A and 9p21.2 as susceptibility loci for sporadic ALS C9orf72 was the basis of this linkage signal Approximately 5% of patients have a family history of ALS Despite these large study samples, GWAS have been able to explain only little of the genetic variation in ALS It has been established that gene expression levels can be mapped to genomic variation as a quantitative trait in order to detect so-called expression quantitative trait loci (eQTLs) A schematic overview of our study design is shown in All participants gave written informed consent and approval was obtained from the Institutional Review Board of the University Medical Center Utrecht. The present study was conducted according to the principles expressed in the Declaration of Helsinki.flipscan  was performed in PLINK v1.07 −4), or with differing heterozygosity or missing rates between cases and controls were excluded. Duplicate samples, samples with a genotyping rate <95%, samples without gender information, or samples where the genotypic gender did not match the phenotype file gender were excluded. LD-based SNP pruning was used to determine a subset of SNPs in approximate linkage equilibrium. This subset of SNPs was used to identify related samples, which were subsequently removed (pi-hat >0.2). The software package EIGENSTRAT was used to detect population substructure by principal components analysis Genome-wide genotype data were derived from a previously published GWAS of sporadic ALS in seven countries  2 transformed 2 transformed. All probe sequences were aligned to the NCBI build 36 reference genome using UCSC’s Genome Browser function BLAT retired and these probes were excluded as well , leaving 37,118 gene-expression probes.Genome-wide gene expression data were obtained from 805 Dutch individuals (357 patients and 448 controls), who were also genotyped on either the HumanHap 300K, HumanCNV 370K or HumanHap 550K platforms in the previously described GWAS For the genetic mapping of gene expression, the subset of Dutch individuals with both genome-wide genotype and expression data was tested for population substructure by principal components analysis of genomic data using EIGENSTRAT For the GWAS data, association with disease was tested in a logistic model using gender, dummy-coded nationality and the first eight principal components in order to correct for ancestry as covariates. To determine the number of principal components to be included in the logistic regression model, the first ten principal components from the EIGENSTRAT For all analyses involving expression data, Surrogate Variable Analysis (SVA) was used to account for heterogeneity in gene expression due to known and unknown environmental, technical or demographic factors Cis eQTLs were defined as SNPs modulating transcript expression levels within a region of 1Mb surrounding a probe’s genomic midpoint cis effects may, however, occur due to SNPs that are located within a transcript probe or that are in linkage disequilibrium (LD) with SNPs mapping within a transcript probe cis effect SNPs and SNPs mapping to a transcript probe in either of the HapMap phase III release 2 or 1000 Genomes Pilot 1 CEU panels. 21,863 SNP-transcript combinations (pairwise LD threshold 2r >0.2) were excluded from analysis. Similarly, we removed 24,170 SNP-transcript combinations with an InDel overlapping with a transcript probe, according to the Database of Genomic Variants  cis left for analysis. The number of possible combinations in cis was used for Benjamini-Hochberg false discovery rate (FDR) calculations. Significant cis effects were those SNP-transcript pairs that had significant p values at an FDR of 5% after 10,000 permutations. Permutations were performed swapping case/controls labels so that each subject is assigned the genotype vector of another random subject, while the expression matrix is unchanged. This prevents the underestimation of the null distribution, thereby preventing the detection of false-positive eQTLs, as described previously For the eQTL analyses, SNP genotypes coded as an additive genetic model were tested for association with gene expression by linear regression using disease status, age, gender, surrogate variables (18 in the discovery set and 19 in the replication) and riluzole use as covariates. cis effects in the eQTL discovery set. Recent studies on the genetics of gene expression have shown that disease-associated loci from GWAS are greatly enriched for eQTLs In order to link the identified eQTLs to disease, we made a selection of significant Only these SNP-transcript pairs were used for follow-up in the replication data. Patient characteristics for the expression replication dataset are presented in cis SNP-transcript pairs was tested in the GWAS replication data by logistic regression using gender, dummy-coded nationality and the first two EIGENSTRAT principal components (these were significantly correlated to case/control status) as covariates. Association test results were clumped based on LD (2r >0.5) using PLINK, so that SNP p values could be obtained for independent eQTLs. eQTLs with a replication pGWAS <0.05 after Bonferroni correction for the number of independent (LD-based clumped) loci were considered to be significant  with genome-wide significance (p = 1.7×10–8) after Bonferroni correction for 268,952 SNPs. A Manhattan plot of genome-wide results is shown in In the GWAS discovery set, 2,261 ALS cases and 8,328 patients remained after quality control measures with genotypes for 268,952 SNPs. Details of included study populations are shown in cis with discovery pGWAS <0.05 (cis eQTLs GWAS <0.05. We first determined the number of cis eQTLs in the set of SNPs with pGWAS <0.05 (n = 755). Then, we randomly selected a subset of 14,167 SNPs with pGWAS >0.05, matched for minor allele frequency to the set of SNPs with pGWAS <0.05 (in 5% frequency bins). Subsequently, we determined the number of eQTLs present in each of these sets of SNPs, using 100,000 permutations. By determining how often more than the initial number of eQTLs were observed, we showed that there was evidence for enrichment for eQTLs in the set of disease-associated SNPs .From the eQTL discovery results, we selected the 1,108 SNP-transcript pairs (755 eQTL SNPs) in AS <0.05 . To confcis were significantly replicated  cis effects might fine-map these loci. For the UNC13A locus (SNP rs12608932), multiple-testing correction was applied for 41 possible SNP-transcript pairs in cis (as determined by a genomic distance of <500kb between the SNP and a probe’s midpoint). One SNP-transcript pair had a nominal p value <0.05, the transcript of which mapped to gene PGLS (pEQTL = 0.01). However, when using a 5% Benjamini-Hochberg FDR for the locus as multiple-testing correction, no SNP-transcript pairs reached statistical significance. For the chromosome 9p21.2 locus, we looked for cis eQTLs within a 130kb LD block comprising previously associated SNPs (rs2814707 and rs3849942). Multiple-testing correction for the testing of 328 SNP-transcript pairs was applied using a 5% FDR. Two SNP-transcript pairs reached the threshold for statistical significance and were associated with C9orf72 isoform a expression levels  block comprising a total of ten genes , which can present with progressive upper motor neuron signs and is a known clinical mimic for primary lateral sclerosis CYP27A1 have been reported in a patient with atypical CTX and frontotemporal dementia characteristics CYP27A1 a plausible candidate gene for ALS.We searched MEDLINE, Gene Ontology and OMIM databases to identify links to known pathways in ALS pathogenesis for The strengths of our study are the meticulous pruning of expression probes as present on the expression array, with regard to non-specific mapping in the human transcriptome, or harboring SNPs that might interfere with hybridization of probes to the array, resulting in false-positive eQTLs cis eQTL in lymphocytes were identified in a study using lymphoblastoid cell lines cis eQTLs in human brain tissue studies appeared to be present in our data  The focused analysis of variants in the chromosome 9p21.2 locus, which was previously associated with ALS cis eQTL for sporadic ALS, which modulates CYP27A1 expression and additionally points to C9orf72 in the chromosome 9p21.2 locus as the gene involved in ALS pathogenesis. To further identify eQTLs relevant to ALS, the concomitant analysis of epigenetic and other level -omic data, e.g. proteomic or metabonomic can be used, as recently shown in a model organism In summary, our genome-wide study of the genetics of gene expression has identified one Text S1GWAS quality control.(DOC)Click here for additional data file.Figure S1Manhattan plot of autosomal SNP association p values in the GWAS discovery set.(PDF)Click here for additional data file.Figure S210  versus the expectation under the null for the genome-wide association results in the GWAS discovery set.Quantile-quantile plot of observed −log(PDF)Click here for additional data file.Figure S3Plots for SNP genotype vs. expression level correlations for eQTL SNPs modulating CYP27A1 expression levels.(PDF)Click here for additional data file.Figure S4Plots for SNP genotype vs. expression level correlations for eQTL SNPs modulating C9orf72 expression levels.(PDF)Click here for additional data file.Table S1Expression study populations.(PDF)Click here for additional data file.Table S2GWAS populations and genotyping platforms.(PDF)Click here for additional data file.Table S3Results for replicated eQTLs associated with CYP27A1 expression levels.(PDF)Click here for additional data file.Table S4cis eQTL overlap with previous studies.(PDF)Click here for additional data file.Table S5Details of quality control of genome-wide genotype data.(PDF)Click here for additional data file.


Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked. Identifying an associated region requires that trait-associated SNPs be ranked above null SNPs, while identifying the causal variant requires that, among associated SNPs, associations due to causality are ranked above indirect associations due to other factors, e.g. linkage disequilibrium (LD). GWAS and imputation studies typically report the top-ranked SNP for each associated locus, and follow-up studies typically attempt replication for these top-ranked SNPs  investigated the difficulty in overcoming the stochastic effect of high LD among causal and non-causal SNPs et alZaitlen −4) GWAS SNP to support the evidence at a genome-wide significant imputed SNP. However, these strategies lead to two important issues that have received little attention in the context of causal SNP identification: (1) the effect of the re-use of successful GWAS data and (2) the effect of genotyping error rates that differ between sequenced or imputed SNPs.Studies of complex traits now underway are leveraging the cost efficiency of integrating GWAS, low- and high-coverage sequencing, and imputation to achieve sample sizes in the tens of thousands et alThe re-use of GWAS data that had contributed to the identification of an associated region for post-GWAS analysis can adversely affect accurate causal SNP identification. For example, the simulation study of Wiltshire High error rates and differences in error rates, due to differences in coverage, read length and depth, minor allele frequency (MAF), GC content, local sequence structure, and other sequence-specific factors, are common to NGS SNPs and are well-recognized obstacles to analysis Differential genotyping accuracy between studies has been shown to reduce power of meta-analysis in the imputation setting In this report, we first demonstrate that:Localization success rate decreases as the correlation between the tag and causal SNP increases.Selection at the tag SNP exacerbates this problem by increasing the magnitude of the association evidence at the tag SNP itself and at other neighboring SNPs in higher LD with the tag relative to the causal SNP.Differential genotyping or imputation error between SNPs further decreases localization success rate, with or without the tag selection.This problem can be exacerbated by increasing sample size, if genotyping accuracy at the causal SNP is lower than at neighboring SNPs.We develop an analytic description of how these factors influence the probability of localization success and evaluate this probability for a range of plausible parameter values. We then show how to properly adjust for the adverse effects of these factors with a re-ranking procedure. We evaluate the performance of the method with extensive simulation studies under a wide range of realistic scenarios, and we demonstrate the practical use of re-ranking with an application to the NCBI BPC3 aggressive prostate cancer GWAS with imputation M sequenced (or imputed) SNPs, i, i  = 1, …, MS, in the region surrounding a significant GWAS tag SNP G are ranked by the magnitude of their association statistics in order to identify the causal SNP C. SiT is the Wald test statistic at a sequenced SNP iS; G and iS (r2 is the well-known pair-wise correlation measure of LD between two SNPs); iS , that is the excess in the expected value of the test statistic G induced by selection based on its small p-value (or high rank). We call this phenomenon the selection effect (ΔG is zero if the region was not selected via a tag SNP that achieved the given significance or ranking criterion in the same sample). Our proposed re-ranking statistic for a sequenced SNP iS isLet C which could be: one of the sequenced or imputed SNPs iS, i  = 1,…, M; the GWAS tag SNP G although this is unlikely; or neither if the genomic coverage was incomplete. Let the tag SNP G be coded such that the coded allele is positively correlated with the causal allele. Let n observations. We assume that the distribution of the Wald test statistic at the causal SNP, Without loss of generality, let C and the tag SNP G.  The value of Let SiT at the sequenced SNP iS, conditional on the value of the observed test statistic G, isG, the selection effect at the GWAS tag SNP G carries through to each sequenced SNP iS in proportion to the correlation G and iS. The combination of attenuation due to LD and upward selection bias at the tag, G, distorts the association evidence so that SNPs in high LD with the tag are more likely to be top-ranked. We call this phenomenon the tagging effect, and use an estimate to remove bias from the conditional expected value of The conditional distribution of the test statistic iS appear in both the numerator and denominator of called or estimated genotype of the sequenced SNP (in contrast to true genotype of the sequenced SNP). Assuming the probability of genotyping error is independent of the actual genotype, then . It is clear that, without correction, smaller Siρ (higher genotyping error) and smaller iS would be top-ranked. We call this phenomenon the genotyping accuracy effect.Third, differential call rates among SNPs . The inclusion of low LD value is motivated by the fact that correlation between the causal SNP and the best tag is often lower than expected. The coverage of GWAS platforms tends to be overestimated for both sequenced and imputed SNPs , we first consider the simplified case of 2 SNPs, one causal (from sequencing or imputation) and one tag (from GWAS) with correlation between the two SNPs ranging from Analytical evaluations of tag], approaches E[Tcausal]  and the causal , compared with no selection, regardless of the LD between the two SNPs (two SNPs . Consequtwo SNPs . Resultstwo SNPs  and S3.Gρ = 1), while the causal SNP was not, and if the genotype error at the causal SNP resulted in Cρ = 0.80 (the blue dash-dotted curve), then the localization success rate would be reduced by an additional 10%–30% as compared to perfect genotyping accuracy (the black solid curve). Low genotyping accuracy at the causal SNP reduces the expected value of it's test statistic, leading to decreased localization success rate . For exaCρ<Gρ and CGr is large), the proxy SNP may capture the association better than the causal SNP. As a result, this proxy SNP will out-rank the causal SNP more than 50% of the time. In this case, the localization success rate would be less than 50%, and would decrease further as sample size increases , the localization success rate drops from 47% to 26% as sample size increases from 100 to 10,000. Lower cρ would lead to even lower localization success rates (results not shown). This pattern is similar for the rare SNP and more common SNP cases  The bootstrap estimator can be applied whether the region of interest was selected by rank or by p-value threshold. Unlike the threshold-based likelihood and Bayesian methods 2r estimate http://www.utstat.toronto.edu/sun/Software/BR2/), which provides the essential quantity of The above analytical results demonstrate the need to correct for the joint effects of selection, tagging and genotyping accuracy on the localization success rate. The practical implementation of the proposed re-ranking statistic in We conducted extensive simulation studies to empirically evaluate the performance of the re-ranking method under five general scenarios :Scenario 1: GWAS used for discovery, and sequencing/imputation used for fine-mapping around GWAS “hits” using the same GWAS sample. Scenario 1 is a GWAS-focused design based on the WTCCC Type 1 Diabetes substudy data. A significant region is identified by a significant GWAS tag SNP (p<5×10−7) and followed by fine-mapping with post-GWAS data (sequenced or imputed SNPs) in the region surrounding the tag SNP. The SNP with the largest test statistic in the region is selected as the best candidate causal SNP. Data is simulated as follows.GWAS Data and Tag SNP: In order to generate realistic data, we used the individual level genotypes from the WTCCC T1D sub-study as the GWAS data (1963 cases and 2938 controls); by fixing the genotypes we preserve realistic LD correlation structure over the entire genome. Among the reported WTCCC T1D significant regions, we randomly selected 12q24 109.82–111.49 Mb as the region of interest and designated rs11066410 (MAF 4.8%) as the GWAS tag SNP. In Scenario 1, the GWAS data are the WTCCC T1D substudy including GWAS genotyping data on all 4901 subjects. By fixing the genotypes we preserve realistic LD structure over the entire genome. We then simulate phenotype datasets that are significant at the tag SNP, rs11066410. Other tag SNPs in the region could be also significant, however in order to tease out the effect of tagging from other factors such as LD structure between sequencing SNPs in the region and MAF, we selected simulation datasets conditional on significance at rs11066410.Sequencing/Imputation Data and Causal SNP: We simulated sequencing data for the region of interest with 10 post-GWAS SNPs, among which one is the causal SNP with OR = 1.5. We varied the correlation between the tag and the causal SNP from r = 0.78  to 1 . We introduced random error into post-GWAS SNP genotypes at per-allele rates 2%, 1.5%, 1%, 0.5%, 0.25% or 0%, so that the average Siρ in each case was 0.82, 0.86, 0.90, 0.95, 0.97 or 1, respectively. Each sequencing SNP is in LD with the causal SNP as well. The correlation between the sequencing SNPs and the causal SNP ranges from 0.78 to 0.975.Phenotype: Phenotype datasets significant (p<5×10−7) at the GWAS tag SNP were simulated using a logistic model with causal SNP OR = 1.5.Scenario 2: All GWAS and sequenced/imputed SNPs used for discovery and fine-mapping in the same dataset. Here we assumed that all GWAS and post-GWAS SNPs are used to identify an associated region (p<5×10−7), and the most significant SNP in the region is then identified as the best candidate causal SNP. GWAS tag SNP data were simulated with MAF 5%. Sequencing data were simulated as described in Scenario 1 with parameter values detailed in −7) at any GWAS or post-GWAS SNP, were simulated using a logistic model with causal SNP OR = 2.Scenario 3: Discovery and fine-mapping using different datasets. In this scenario, the region of interest was discovered in a previous study, while sequencing is performed in an independent dataset without conditioning on significance of the GWAS tag SNP in the independent dataset. Genotype and phenotype data were simulated as in Scenario 2, except that phenotype datasets were not selected for significance.Scenario 4: Multiple causal SNPs. To explore the effect of multiple causal SNPs, we re-considered scenario 3 but we assumed there are 11 fine-mapping sequenced/imputed SNPs, among which 2 are causal (both OR = 2).Scenario 5: Missing data. This scenario focuses on the effect of missing data . Genotype and phenotype data were simulated as in Scenario 3, except that genotyping accuracy was perfect. The missing rates were randomly assigned to each SNP so that the missing data proportion was between zero and twice the average error rate.ρ can vary substantially from study to study. For example, for low-coverage studies, it can vary from 0.63 to 0.99 depending on the coverage, MAF and sample size ρ can range from 0.89 to 0.99 depending on the reference panel size ρ also depends on MAF; the same error rate in a lower MAF SNP results in a smaller ρ.The parameter values in ρ is high, SNP-level ρ can vary widely within a single study. Browning and Browning ρ of 0.95, however individual ρ was less than 0.77 for 20% of the SNPs. We show that coverage rates can also vary widely between SNPs (Gρ = 1), because GWAS genotyping has been estimated to be over 99.8% accurate Even when the average een SNPs  by examiOne of the main findings of the simulation study is that GWAS-based region selection or moderate genotyping error can substantially reduce the probability of correctly identifying the causal SNP , S2, conThe simulation study also shows that the proposed re-ranking procedure can recover much of this lost power to identify the causal SNP, increasing the localization success rates by 1.5- to 3-fold in many cases . When ger) and less accurately sequenced/imputed  (For studies using GWAS-based selection (scenario 1), the adverse effects of tagging and genotyping accuracy on localization success rate are strongest when the causal SNP is well tagged   and S1. ρ of 0.89, and this is borne out in our simulations. The re-ranking procedure corrects for both missing data and genotyping error to the same degree.When the discovery sample is also used for fine-mapping, but significance is not required at the GWAS-tag SNP (scenario 2), the genotyping accuracy effect alone could still considerably reduce power to identify the causal variant . When anIn some cases, investigators are more interested in delimiting a set of best candidate causal SNPs instead of a single top SNP. In the supplementary material, we include additional simulation results for this scenario. We define an alternative localization success rate metric as the probability that the causal SNP is in the top 10% of SNPs by rank . Brieflyet al2<0.8 were excluded from the meta-analysis test statistic, leaving a total of 5.8 million GWAS and imputed SNPs.Machiela jZ is the normally distributed test statistic for study j, and jw is the weight for study j, the meta-analysis test statistic used for the standard naïve ranking isj  and the meta-analysis test statistic computed with the observed imperfectly genotyped SNPs (obsZ) isZ scaled for variance of 1.Fine-mapping in the meta-analysis context ranks SNPs by the meta-analysis test statistic. Re-ranking requires that we compute the correlation between the meta-analysis test statistic on the Z-score scale  with and without accounting for genotyping error. Assume et al2>0.2) with the index SNP from each region for analyses  that moves from the naïve rank of 245 to the new rank of 16 after adjustment. This SNP's apparent association is largely driven by data from a single study: the naïve rank in the EPIC study is 10. When we remove this study from the meta-analysis, the naïve rank is 306 and the adjusted rank is 119. No other SNP in the top 10% is this drastically affected when the EPIC study is removed from the analysis. In the meta-analysis context, we recommend examining top SNPs for heterogeneity among studies when re-ranking produces dramatically different results.There is one poorly imputed SNP at 17q24.3  SNPs is less than 0.97. A large number of post-GWAS SNPs in a study may appear to be significant, but when not all were directly genotyped with high accuracy, re-ranking can help select the most probable causal SNPs for follow-up. High density genotyping followed by low-coverage sequencing in the same sample can produce misleading results, as demonstrated by our simulations, so we do not recommend this design for identifying causal variants. Our re-ranking method tends to down-rank the tag SNP. If the tag SNP is suspected to be causal (e.g. based on prior study), we recommend examining the rank of the tag SNP using both the naïve and re-ranked methods when selecting SNPs for further study. Several imputation and sequencing software packages provide accurate estimates of Re-ranking is especially important when study-specific factors exacerbate the effects of GWAS-based selection and genotyping error. Such factors include: high genetic diversity which makes sequencing reads difficult to align under the alternative, i.e. SNPs that are themselves causal or in LD with a causal SNP. Including null SNPs in the re-ranking procedure increases the number of SNPs the causal must out-compete, and so we recommend that only SNPs suspected to be under the alternative be included. In our application we included all SNPs that had squared pairwise correlation (2r) with the index SNP (most significant SNP in the region) greater than 0.2.We emphasize that re-ranking improves the localization success rate when applied to SNPs Existing methods that incorporate genotype uncertainty into tests for association to reduce power lost due to genotyping error or missing data We expect that re-ranking will play an important role as sequencing costs fall and GWAS platform coverage increases. Ultra-high density GWAS platforms are more likely to include tag SNPs in very high correlation with the causal SNP, which increases power to detect indirect association at the tag SNP. However, without re-ranking, strong tagging also decreases power to correctly identify the causal SNP in subsequent low-coverage sequencing. Advances in GWAS and sequencing platforms will allow researchers to drill down into lower MAFs and smaller effect sizes. Both low MAF and small effect size yield lower power, which exacerbates upward bias at the tag Figure S1www.1000genomes.org/data).Distribution of SNP-specific read depth using the 1000 Genomes low-coverage pilot data on 351,456 SNPs from chromosome 1 in the CHB and JPT samples Click here for additional data file.Figure S2th–75th percentiles , and the localization success rates  for association studies (1000 cases and 1000 controls) of one causal SNP  and one tag SNP  with no selection for significance at the tag SNP  or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 .Tagging effect decreases localization success rates with or without the selection effect, rare SNP. The expected values of the association test statistics at a tag SNP (red) and the causal SNP (black), shading from 25(TIFF)Click here for additional data file.Figure S3th–75th percentiles , and the localization success rates  for association studies (1000 cases and 1000 controls) of one causal SNP  and one tag SNP  with no selection for significance at the tag SNP  or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 .Tagging effect decreases localization success rates with or without the selection effect, high frequency SNP. The expected values of the association test statistics at a tag SNP (red) and the causal SNP (black), shading from 25(TIF)Click here for additional data file.Figure S4imperfect genotyping accuracy due to genotyping, sequencing or imputation errors resulting in correlation between the actual and estimated genotypes ρC = 0.80 (blue dash-dotted) to 1 (black solid)) and one tag SNP ; perfect genotyping accuracy with ρG = 1) with no selection for significance at the tag SNP (A) or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 (B).Genotyping accuracy effect further reduces localization success rates with or without the selection effect, rare SNP. Localization success rates for association studies (1000 cases and 1000 controls) of one causal SNP Click here for additional data file.Figure S5imperfect genotyping accuracy due to genotyping, sequencing or imputation errors resulting in correlation between the actual and estimated genotypes ρC = 0.80 (blue dash-dotted) to 1 (black solid)) and one tag SNP ; perfect genotyping accuracy with ρG = 1) with no selection for significance at the tag SNP (A) or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 (B).Genotyping accuracy effect further reduces localization success rates with or without the selection effect, high frequency SNP. Localization success rates for association studies (1000 cases and 1000 controls) of one causal SNP Click here for additional data file.Figure S6imperfect genotyping accuracy due to genotyping, sequencing or imputation errors resulting in correlation between the actual and estimated genotypes ρC = 0.95) and one tag SNP  to 0.98 (red dashed); 100% genotyping accuracy with ρG = 1) with no selection for significance at the tag SNP.Figure well-tagged causal SNPs sequenced with low accuracy are unlikely to be correctly identified even as sample size increases, rare SNP. Localization success rates for association studies  of one causal SNP Click here for additional data file.Figure S7imperfect genotyping accuracy due to genotyping, sequencing or imputation errors resulting in correlation between the actual and estimated genotypes ρC = 0.95) and one tag SNP  to 0.98 (red dashed); 100% genotyping accuracy with ρG = 1) with no selection for significance at the tag SNP.Figure well-tagged causal SNPs sequenced with low accuracy are unlikely to be correctly identified even as sample size increases, high frequency SNP. Localization success rates for association studies  of one causal SNP Click here for additional data file.Figure S8th–75th percentiles , and the localization success rates  for association studies (1000 cases and 1000 controls) of one causal SNP  and one tag SNP  with no selection for significance at the tag SNP  or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 .Tagging effect decreases localization success rates with or without the selection effect, 3 SNPs: 1 tag, 1 causal, 1 non-causal sequencing SNP. The expected values of the association test statistics at a tag SNP (red), the causal SNP (black), a non-causal sequencing SNP (green), shading from 25(TIFF)Click here for additional data file.Figure S9th–75th percentiles , and the localization success rates  for association studies (1000 cases and 1000 controls) of one causal SNP  and one tag SNP  with no selection for significance at the tag SNP  or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 .Tagging effect decreases localization success rates with or without the selection effect 5 SNPs: 1 tag, 1 causal, 3 non-causal sequencing SNPs. The expected values of the association test statistics at a tag SNP (red), the causal SNP (black) and the maximum test statistic of the 3 non-causal sequencing SNPs (green), shading from 25(TIFF)Click here for additional data file.Figure S10th–75th percentiles , and the localization success rates  for association studies (1000 cases and 1000 controls) of one causal SNP  and one tag SNP  with no selection for significance at the tag SNP  or selection at the tag SNP requiring the test statistic GT to be significant with p-value<0.05 .Tagging effect decreases localization success rates with or without the selection effect 7 SNPs: 1 tag, 1 causal, 5 non-causal sequencing SNPs. The expected values of the association test statistics at a tag SNP (red), the causal SNP (black) and the maximum test statistic of the 3 non-causal sequencing SNPs (green), shading from 25(TIFF)Click here for additional data file.Figure S11A, B), and re-ranking statistics adjusting for genotyping accuracy  for SNPs in LD (r2>0.2) with rs1016343  or rs34255287 . Circles highlight SNPs whose rank changed considerably after re-ranking. Color indicates pair-wise correlation with the most significant SNP in the region. Shape indicates genotyping accuracy over all 7 cohorts as measured by Naïve test statistics and re-ranking statistics for regions surrounding rs1016343 on 8q24.21 and rs34255287 on 11q13.3 for association with prostate cancer risk. Naïve test statistics ((JPG)Click here for additional data file.Figure S12A, B), and re-ranking statistics adjusting for genotyping accuracy  for SNPs in LD (r2>0.2) with rs7816007  or rs6983267 . Color indicates pair-wise correlation with the most significant SNP in the region. Shape indicates genotyping accuracy over all 7 cohorts as measured by Naïve test statistics and re-ranking statistics for regions surrounding rs7816007 and rs6983267 on 8q24.21 for association with prostate cancer risk. Naïve test statistics ((JPG)Click here for additional data file.Figure S13A), and re-ranking statistics adjusting for genotyping accuracy (B) for SNPs in LD (r2>0.2) with rs382434. Circles highlight SNPs whose rank changed considerably after re-ranking. Color indicates pair-wise correlation with the most significant SNP in the region. Shape indicates genotyping accuracy over all 7 cohorts as measured by Naïve test statistics and re-ranking statistics for regions surrounding rs382434 on 8q24.21 for association with prostate cancer risk. Naïve test statistics ((JPG)Click here for additional data file.Table S1Trends in power and localization success rate due to tagging and genotyping accuracy effect.(PDF)Click here for additional data file.Table S2e for simulation Scenarios 2, 3, 4.Alternative localization success rates(PDF)Click here for additional data file.Text S1Ranking SNPs to identify candidate causal SNPs.(PDF)Click here for additional data file.Text S2Derivation of distribution of GWAS and tag SNP test statistics.(PDF)Click here for additional data file.Text S3Low power exacerbates the selection effect.(PDF)Click here for additional data file.Text S4Tagging and coverage of GWAS platforms.(PDF)Click here for additional data file.


The harvest index for many crops can be improved through introduction of dwarf stature to increase lodging resistance, combined with early maturity. The inbred line Shen5003 has been widely used in maize breeding in China as a key donor line for the dwarf trait. Also, one major quantitative trait locus (QTL) controlling plant height has been identified in bin 5.05–5.06, across several maize bi-parental populations. With the progress of publicly available maize genome sequence, the objective of this work was to identify the candidate genes that affect plant height among Chinese maize inbred lines with genome wide association studies (GWAS).P≤0.0001) and 105 genomic loci harboring coding regions were identified. Four loci containing genes associated with gibberellin (GA), auxin, and epigenetic pathways may be involved in natural variation that led to a dwarf phenotype in elite maize inbred lines. Among them, a favorable allele for dwarfing on chromosome 5 (SNP PZE-105115518) was also identified in six Shen5003 derivatives.A total of 284 maize inbred lines were genotyped using over 55,000 evenly spaced SNPs, from which a set of 41,101 SNPs were filtered with stringent quality control for further data analysis. With the population structure controlled in a mixed linear model (MLM) implemented with the software TASSEL, we carried out a genome-wide association study (GWAS) for plant height. A total of 204 SNPs (P = 8.91e-10) and its confirmation in the Shen5003 introgression lines. Results from this study suggest that, in the maize breeding schema in China, specific alleles were selected, that have played important roles in maize production.The fact that a large number of previously identified dwarf genes are missing from our study highlights the discovery of the consistently significant association of the gene harboring the SNP PZE-105115518 with plant height ( Semi-dwarfism, an important agronomic trait that contributes to crop yield, improves harvest index and nitrogen response, and increases lodging resistance sd1, results from a deficiency of GA20-oxidase activity in the GA biosynthetic pathway RHT) gene, which encodes a DELLA protein involved in the GA signaling pathway d1, d2, d3, d5, d8, d9, An1, DWF1 and DWF4br2 mutant Epi-d1, a spontaneous rice mutant that displays a metastable dwarf phenotype caused by silencing of the DWARF1 (D1) gene, which encodes a GTP-binding protein involved in giberellin signalling Plant height is a complicated quantitative trait that is controlled by a large number of genes. Defects in either signaling or biosynthesis of GA or brassinosteroids (BR) may give rise to a typical dwarf phenotype. Semi-dwarfism in rice, controlled by ph3, which is located on chromosome 5, accounts for 38.6% of the total phenotypic variation To date, using different mapping populations, more than 219 QTLs for plant height have been reported across ten maize chromosomes (2010 December update to Gramene database). One major locus consistently detected between bins 5.05 and 5.06 reduces height of maize inbreds from different bi-parental populations developed from Shen5003 and derived inbreds Teosinte branched1 (Tb1), which explains a large proportion of phenotypic variation in maize plant architecture, was first cloned by time-consuming positional cloning in maize Arabidopsis, Aranzana et al. were able to identify genes controlling natural variation in flowering time and pathogen resistance using genome-wide polymorphisms among 95 accessions Arabidopsis was demonstrated using 250,000 SNPs HvbHL H1 gene was fine-mapped by GWAS using a 1536-feature SNP array based on expressed sequence tags Several approaches such as map-based cloning and association mapping have been successfully used to identify candidate genes. So far, two kinds of genome-wide association panels have been constructed for association mapping studies in maize. The NAM panel, which comprises 5000 recombinant inbred lines derived from crossing the B73 reference line to 25 diverse inbred lines, was constructed to understand the molecular basis of phenotypic variation with great power There is, however, little GWAS information for SNP markers associated with dwarf loci in panels of elite inbred Chinese maize lines and germplasm, particularly for the widely used inbred line Shen5003. Therefore, the objectives of this study were to: 1) carry out a GWAS for plant height including major dwarf loci using 41,101 well-selected SNPs covering the entire maize genome; 2) identify loci for plant height in Chinese elite maize inbred lines under three environmental conditions; and 3) explore the genetics of major alleles at dwarf loci on chromosome 5 in Shen5003 and six of its derivatives. Finally, approaches for identifying candidate genes using GWAS in maize, in comparison with selfing species, are discussed.e−4 and 2945 markers harbored multiple loci on maize chromosomes. Thus, a subset of 44,235 SNPs (80%) with known physical position was generated in this study.Using the MaizeSNP50 BeadChip, a total of 50,195 SNPs (91.1%) were successfully called with less than 20% missing data across the 288 inbreds. Of these 50,195 SNPs, 2784 were monomorphic in all lines. The source sequences of the SNP regions were used for basic local alignment search tool (BLAST) comparisons with the MaizeGDB (B73 RefGen_v1) to update coordinate information. A total of 231 of the 50,195 successful assays did not have a BLAST match below the threshold of 1Seven inbred lines with a high heterozygosity rate (HR) (>10%) across 44,235 markers were discarded. The remaining 281 lines with an average heterozygosity of 0.5% were used for further analysis. For these samples, the reproducibility between replicates across all data points was about 99.9%, which was consistent with high-quality data for the maize B73 line . Subsequently, the error rate (ER) for the genotyping was reduced from 0.0873% to 0.0353% by repeating assays in four line and discarding SNPs with multiple loci .in silico to the maize physical map across ten chromosomes were used to determine the extent of LD in the association panel. As shown in All 44,235 of the SNPs that were mapped P<0.01).The mean heritability for plant height across three environmental conditions was estimated as 70.63%, suggesting that this trait would respond well to artificial phenotypic selection P) was markedly decreased by about half (0.552551/0.973008) within a total of 41,101 SNPs, and the peaks for SNPs on chromosomes were reduced with MLM , among which SNP PZE-105115518 was the most significant (P = 8.91e-10). These 204 SNPs covered 105 genomic blocks harboring coding regions from which more than 225 cDNAs were identified. The data showed that 78.9% of the SNPs were surrounded by cDNAs and that eight SNPs were located in the fragments of genes predicted from the B73 reference sequence. Several hot spots, including bins 1.11, 2.09, 3.02, 4.05, 5.05, 5.06, 6.03, 6.04, 6.05, 9.07, and 10.03 were identified as containing more than three SNPs in the predicted genes (P) = 5.26) was similar to AUXIN RESISTANT 1 in ArabidopsisP) = 9.05) is located within a gene (GI: 100273912) annotated as a hypothetical protein, related to an Arabidopsis DNA glycosylase, loss of function of which causes a semi-dwarf phenotype P) = 5.38), is located upstream of a gene (GI: 194701035) similar to Gibberellin 20 oxidase. One homolog of the candidate gene (GI: 195636889), upstream of SNP PZE-109106743 (−log10(P) = 4.83), was annotated as a tetratricopeptide repeat (TPR) domain protein involved in cell division cycle in rice. Together, these prediction data showed that GA, auxin, and epigenetic pathways may all be involved in natural variation that led to a dwarf phenotype in elite maize inbred lines.A total of 204 SNPs across 10 chromosomes were significantly associated with plant height   . No otheGW2 gene, controlling grain width (GW) and weight, was undetectable via significant associations in the panel of 517 rice landraces tested ZmGW2-CHR4 and ZmGW2-CHR5, were significantly associated with kernel width (KW), or one of three other yield-related traits (kernel length (KL), kernel thickness (KT), and one-hundred kernel weight (HKW)) P≤0.0001) and 105 genomic regions related to plant height were pinpointed with 41,101 SNPs  across three environments. According to previous studies, only four genomic blocks harbored the known candidate genes controlling plant height , and four known loci harboring candidate genes for plant height were located in bins 1.11, 5.05, 6.04, and 9.07, based on previous studies. However, only one SNP, PZE-106064587 in bin 6.04, was identified as closely linked to a predicted gene for a protein similar to gibberellin 20 oxidase. In addition, auxin signalling and epigenetic networks also play important roles in internode elongation. In the maize br2 mutant, height reduction resulted from the loss of function of a P-glycoprotein that modulates polar auxin transport in the maize stalk AUXIN RESISTANT 1 (AXR1). In the case of Arabidopsis axr1, the decrease in plant height is the consequence of a defect in auxin action Arabidopsis DNA glycosylase, loss of function of which results a semi-dwarf phenotype Based on extensive results from dwarf mutants, GA and BR are major factors determining plant height d1, d2, d3, d5, d8, d9, An1, DWF1, and DWF4D8 would affect quantitative variation in maize plant height D8 polymorphisms and plant height were found in the DELLA region, although this region was completely conserved across the tested lines Breeding for semi-dwarfness was a key objective for high yield in the first Green Revolution P≤0.0001) in this study. However, there are cases in which QTLs discovered in bi-parental mapping populations are not detected by GWAS. One reason for this is that the moderate number of founders has limited resolution to dissect low-frequency or subpopulation-specific alleles. For instance in rice, the peak signal for grain width was tied closely to a previously identified domestication gene, GW5GW2 gene GW2 gene, ZmGW2-CHR4, and ZmGW2-CHR5, have been significantly associated with kernel-related traits PSY2 gene in maize To date, more than 219 quantitative trait loci for plant height have been detected (2010 December update to Gramene database), and a total of 204 SNPs were significantly associated with plant height , II, Shunyi, Beijing  and III, Sanya, Hainan Province . These locations were under the control of the Ministry of Agriculture of the People's Republic of China, and the required permission was obtained before these sites were used for planting.For genotyping and SNP-calling, a total of 284 diverse inbred lines  for plant height across three environments was computed according to Knapp (1985) 2h = 2σg/(2σg+2/nσgl+2/nrσe), where 2σg is the genetic variance, 2σgl is genotype-by-environment interaction, 2σe is the error variance, r is the number of replications, and n is the number of locations. The estimates for 2σg, 2σgy, and 2σe were obtained by analysis of variance (ANOVA) using the general linear model procedure of the statistical software SPSS 12.0 (IBM SPSS Inc.). Correlation coefficients were obtained with the program PROC CORR of SAS 8.0 .Broad-sense heritability (SNP genotyping was performed using the MaizeSNP50 BeadChip produced by Emei Tongde (Beijing). The SNP content featured on this chip, using 56,110 evenly spaced markers to cover the whole maize genome based on the B73 reference sequence, was selected from several public and private sources with 984 negative controls. These markers have been successfully validated in more than 30 diverse lines. DNA from 284 lines was extracted by a modified CTAB procedure according to Murray and Thompson (1980) The selected 44,235 SNPs showed a wide distribution in minor allele frequencies, ranging from nearly monomorphic (MAF<0.5%) to equal allele frequency (MAF≈50%) across the 277 inbred lines . About 72r (r2≥0.1) was used to estimate LD between all SNPs with less than 20% missing data on each chromosome via the software PLINK P)≥4 were selected for candidate gene analysis.The linkage disequilibrium measurement parameter P≤0.0001) or supporting intervals (http://gbrowse.maizegdb.org/cgi-bin/gbrowse/maize_v1/). The function of cDNAs were predicted using the blastx program at the National Center for Biotechnology Information (NCBI) database (http://blast.ncbi.nlm.nih.gov/).The sequence available for the maize line B73 provides a reference genome that can be used to analyze candidate genes Figure S1Population structure across 277 lines with 5000 SNPs (MAF≥0.2).(TIF)Click here for additional data file.Figure S2Genome-wide association studies on plant height with general linear model across three environments (MAF≥0.05).(TIF)Click here for additional data file.Figure S3Minor allelic frequency for 44,235 SNPs in maizeSNP50.(TIF)Click here for additional data file.Figure S4SNP distribution across the maize genome (MAF≥0.05).(TIF)Click here for additional data file.Table S1The SNPs, harboring cDNA, were detected with MLM in each environment (−log10(P)≥4, MAF≥0.05).(DOC)Click here for additional data file.Table S2SNP distribution of the Illumina maizeSNP50 within 277 inbred lines.(DOC)Click here for additional data file.