Takifugu rubripes using Illumina HiSeq2000 platform to identify gene-associated SNPs in the swimbladder. A total of 30,312,181 unique-mapped-reads were obtained from 44,736,850 raw reads. A total of 62,270 putative SNPs were discovered, which were located in 11,306 expressed genes and 2,246 scaffolds. The average minor allele frequency (MAF) of the SNPs was 0.26. GO and KEGG pathway analysis were conducted to analyze the genes containing SNPs. Validation of selected SNPs revealed that 54% of SNPs (26/48) were true SNPs. The results suggest that RNA-Seq is an efficient and cost-effective approach to discover gene-associated SNPs. In this study, a large number of SNPs were identified and these data will be useful resources for population genetic study, evolution analysis, resource assessment, genetic linkage analysis and genome-wide association studies.Single nucleotide polymorphisms (SNPs) have become the marker of choice for genome-wide association studies in many species. High-throughput sequencing of RNA was developed primarily to analyze global gene expression, while it is an efficient way to discover SNPs from the expressed genes. In this study, we conducted transcriptome sequencing of the swimbladder of  Next-generation sequencing-based RNA-Seq analyses have dramatically changed the way to investigate the functional complexity of transcriptome in many organisms Recently, RNA-Seq has also been used as an efficient and cost-effective method to comprehensively identify SNPs from transcribed regions in the genomes of several fish species. By sequencing of the pooled RNA samples from multiple individuals of channel catfish and blue catfish, a set of quality SNPs were identified including 342,104 intra-specific SNPs for channel catfish, 366,269 intra-specific SNPs for blue catfish, and 420,727 inter-specific SNPs between channel catfish and blue catfish Takifugu rubripes, widely distributed in the Asia, is one of the most important aquaculture species in China. In our laboratory, some SNPs makers associated with growth traits have been identified from the growth-related genes including Leptin, Melanocortin 4 Receptor (MC4R), Insulin-like growth factor (IGF), Myogenic factor 5 (Myf5), Growth hormone releasing hormone (GHRH), Myogenic factor 6 (Myf6) T. rubripes is also widely used as a model system in many scientific fields, especially in the evolutionary studies. The fugu genome has been completed, which is among the smallest vertebrate genomes. It has proven to be a useful ‘reference’ genome for identifying genes and other functional elements in human and other vertebrate genomes, and for understanding the structure and evolution of vertebrate genomes T. rubripes should provide valuable resources for swimbladder research, lung research and evolution studies of fish swimbladder and mammalian lung.The swimbladder in teleost fish is a specialized organ that regulates buoyancy. The homology of the fish swimbladder and mammalian lung has been well recognized based on morphological and embryological evidence. However, the molecular evidence of homology of swimbladder and the mammalian lung was not sufficient T. rubripes using Illumina HisSeq2000 platform to identify gene-associated SNPs. A total of 62,270 putative SNPs were discovered, which were located in 11,430 genes and 1,612 scaffolds, and the average minor allele frequency (MAF) was 0.26. These SNPs should provide useful resources for evolution, population genetic study, resource assessment, genetic linkage analysis and genome-wide association studies.In this study, we sequenced the transcriptome of the swimbladder of T. rubripes. A total of 30,312,181 unique-mapped-reads were obtained from 44,736,850 raw reads after being mapped to the fugu T. rubripes fifth genome assembly from Ensembl database. The genome distribution of the uniquely mapped reads was assessed based on the RefSeq-defined gene models. As expected, the majority of reads (60%) were mapped onto exonic regions, while a large propotion of reads were mapped onto intergenic regions , exon, intron, and up_stream (−1 k). As shown in T. rubripes.Minor allele frequency (MAF) is an important factor for SNP locus evaluation. MAFs of SNPs were calculated from the sequence data. As shown in T. rubripes genome assembly from Ensembl database. On average, 3.6 SNPs per gene were identified. A total of 11,306 expressed genes containing SNPs were identified in the swimbladder with the cutoff values of PRKM setting as 0.08. As shown in SNPs distribution is important for consideration of coverage when using SNP makers. The distribution of SNPs in the genes was analyzed. Expressed short reads were mapped to a total of 17,249 genes based on the fifth fugu Gene Ontology (GO) annotation was further performed for the annotated genes in terms of biological process, molecular function and cellular component. Distribution of the genes in different GO categories at level 2 is shown in Besides GO analysis, KEGG pathway analysis was also carried out for the annotated genes, which is an alternative approach to categorize gene functions with the focus on biochemical pathways. A total of 3,808 expressed genes were assigned with one or more KEGG annotation and were mapped to KEGG pathways . Of thesTight junction is essential for epithelial morphology and function of swimbladder. Tight junctions serve to form seals among epithelial cells, creating a selectively permeable barrier to intercellular diffusion claudin 4/5/6/7/9 genes were identified in the swimbladder of zebrafish claudin genes were highly expressed, including claudin 5a, 5b and 7d. In the human airway, claudin 1, 3, 4, 5 and 7 are expressed in both bronchi and bronchioles. Claudin 5 is localized exclusively in the apical-most region of the tight junctions. Altered Claudin expression pattern can change the paracellular permeability characteristics of the epithelium. Claudin 5 overexpression increases the solute permeability clandin 1 were associated with nickel contact sensitization in individuals without ear piercings, contact sensitization to fragrances, and with both organic compounds and nickel contact dermatitis in human, respectively clandin 1 can alter susceptibility to HCV infection In this study, our KEGG pathway analysis indicated the tight junction existed, including 141 expressed genes containing SNPs. Wnt genes containing SNPs were identified and the expression levels of wnt 7b, wnt 5a and wnt 11 are higher  , DKK3 , DKK4 (rs2073664), sFRP4 (rs1802073 and rs1802074), SMAD7 (rs12953717), and DAAM2 (rs6937133 and rs2504106) Wnt10b were identified in a sample of 1,029 Korean female subjects, which were in almost complete linkage disequilibrium In this study, 8 e higher . Wnt sigIhh (ENSTRUG00000012233 and ENSTRUG00000013525) and Ptc1 (ENSTRUG00000014514) containing SNPs from the swimbladder transcriptome. The role of Hh (Hedgehog) signaling pathway in lung development is very crucial in human, mouse, chicken and Xenopus laevis Shh)-related gene, Indian Hedgehog (Ihh)-related gene and their receptors, Patched 1(Ptc 1) and Ptc2SHH) gene is located in the 7q36 region, which is known to play an important role in embryo patterning, lung development and connection with sexual orientation. A SNP site (rs9333613) was found to be associated with male sexual orientation Ihh is a good candidate gene for association study of developmental disorders mainly affecting skeleton development. The previous study showed that the SNP sites of Ihh were associated with equine bone developmental disorders We observed the expression of two homologues of As the SNPs reported in the present study were identified through bioinformatic analysis, the results were needed to evaluate for the validation rate. A total of 48 SNPs were randomly selected for validation by PCR amplification and Sanger sequencing This study was approved by the Animal Care and Use committee of Key Laboratory of Mariculture & Stock Enhancement in North China's Sea at Dalian Ocean University. All surgery was performed under sodium pentobarbital anesthesia, and all efforts were made to minimize suffering.Takifugu rubripes (length 20cm) were sampled from Dalian Tianzheng Industrial Co., Ltd . The swimbladders of these fish were collected and pooled. Tissues were placed into RNAlater (Ambion), stored at room temperature for 24 h, and then moved to −80°C for storage until RNA isolation. Total RNA was extracted from the pooled swimbladder using the TRIzol R Reagent  by following the manufacturer's protocol. The quantity and quality of total RNA was measured using an Agilent 2100 Bioanalyzer.A total of 45 Total RNA was sent out for next generation sequencing provided by Beijing Institute of Genomics, Chinese Academy of Sciences. cDNA libraries were constructed from mRNA from swimbladder. cDNA libraries were prepared using the Illumina TruSeq RNA Sample Preparetion Kit (Illumina) according to the TruSeq protocol. After KAPA quantitation and dilution, the libraries were clustered 3 per lane and sequenced on an Illumina HiSeq 2000 instrument with 100 bp paired-end reads.T. rubripes fifth genome assembly by BWA program. During the mapping phase, up to five mismatches were allowed. The expression levels  for each gene were calculated using uniquely mapped reads by in-house Perl script according to the equation:The reads were mapping to the fugu The cutoff value of gene expression was calculated for each sequencing sample, genes with RPKM greater than cutoff value were defined as expressed genes BWA and SAMtools  software were used to align reads to the fugu genome assembly (version 5.0) for detecting SNPs http://www.genome.jp/kegg/kass/). The bi-directional best hit (BBH) method was used to obtain KEGG orthology assignments.Gene Ontology (GO) and KEGG pathway analyses were conducted to the genes containing SNPs. GO annotation analysis was performed using Blast2GO, an automated tool for the assignment of GO terms. The annotation result was categorized with respect to Biological Process, Molecular Function, and Cellular Component at level 2. In order to gain an overview of gene pathway networks, KEGG analysis was performed using the online KEGG Automatic Annotation Server (KAAS) Click here for additional data file.Table S2Primers used for SNP validation in the study.(DOC)Click here for additional data file.


Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests  and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.http://bioinformatics.ust.hk/hidden_pattern_finder.zip.The software is freely available at  Random Jungle  on a genome-wide scale to identify genetic variants associated with diseases. Researchers have proposed many methods to investigate association patterns of complex diseases. Two recent reviews ,2 presenm Forest ), is to m Forest ,9. BEAM m Forest  allows eX1 and X2, and one independent variable Y. We can fit two regression models (or logistic regressions for case-control data), However, there is another type of association pattern, which often occurs within local regions in the genome and may not be detected by these methods. This association pattern involves multiple correlated SNPs with neither strong marginal effects nor strong interaction effects. But they can jointly display strong associations. Here we use some simple regression models to explain this association pattern. Suppose we have two dependent variables, X1 correlates with X2, fitting the model Y ~ β1X1 + β2X2 may identify a new association pattern with β1 and β2 (named as bivariate regression coefficients) being significantly larger than X1 and X2. The marginal coefficients β1 and β2, respectively. In the second scenario  is the population correlation between X1 and X2. The marginal coefficients depend on their bivariate regression coefficients as well as the variable correlation, as we illustrated in Figure where ient , ρX, X2 is tβ1 and β2 . The experimental results show that these associations widely exist in GWAS. In this work, we only handle the unfaithfulness issue involving two SNPs while the unfaithfulness can exist among a large number of markers. The detection of associations involving three or more SNPs is too time-consuming and beyond the scope of this work.The simulation study is designed to compare our proposed method with other three methods for detecting associations in the presence of unfaithfulness. These three methods include the marginal association test , Lasso ,13 and B• Marginal association test is used in almost every GWAS due to its simplicity and effectiveness.L1 penalty to eliminate irrelevant variables. Fast algorithms are available for Lasso. Thus, it can simultaneously analyze a huge number of variables. It is very popular in genetics [• Lasso is a shrinkage and selection method for  linear regression. It imposes a sparsity constraint  and uses genetics ,14-16.• BEAM has the capability of detecting both marginal associations and interactions in large-scale data sets. It uses first order Markov chain to accommodate the correlation between adjacent SNPs.X1 and X2 by using β1 > 0, β2 < 0 and ρ > 0 as illustrated in Figure 1 > 0, β2 > 0 and ρ < 0 the unfaithfulness also happens. This corresponds to a situation that the minor alleles of both X1 and X2 increase the diseases risk but X1 and X2 are negatively correlated, as illustrated in Figure The details about the parameter settings in simulation are provided in the method section. In our simulation study, we only handle the unfaithfulness involving two associated variables The results in Figure r2 increases. Although our proposed method also degrades its performance when LD increases, it maintains a relatively high power for strong LD (r2 = 0.7).Another interesting point is that the statistical power of existing methods decreases as the linkage disequilibrium (LD) We have applied our method to analyze the data sets  from the WTCCC . WTCCC sTable P-values, the unadjusted joint P-values, the marginal coefficients and joint bivariate coefficients for these associations. The other details are listed in the supplementary document  was reported in [P-values for these three SNPs based on the single-locus association test are 0.053, 0.245 and 0.216, respectively. All three SNPs reside in the intron of gene MCOLN2. The protein Mucolipin-2, encoded by gene MCOLN2 and also known as TRPML2 , has been confirmed to have strong associations with bipolar disorder in a family-based association study [Among associated SNP pairs identified from the BD data set, we find two suspicious SNP pairs,  and . The unadjusted Figure log10P values given by the joint regression . We have not found evidence to directly connect gene FSIP1 with the coronary artery disease. However, the LD analysis identifies a well studied gene THBS1 (thrombospondin-1), which is centromeric to gene FSIP1 and has been confirmed to increase the risk of coronary artery disease in many studies [We identify four suspicious associations involving five SNPs. The unadjusted  studies -23. It wlog10P given by the joint regression. Figure r2) in that region. Figure log10P of single SNP analysis. Figure Here we also show the enriched signals obtained from the imputation. Figure P-value of this association is 1.326 × 10-12. The unadjusted single-locus P-values of rs1058318 and rs2252745 are 0.074 and 0.840, respectively. SNP rs1058318 resides in the intron region of gene GNL1 and SNP rs2252745 resides in the intron region of gene PPP1R10. Both genes are located in the MHC region and adjacent to each other. Gene GNL1 belongs to the HLA-E class. The locus in HLA-E has been strongly associated with type 1 diabetes [Most identified associations from the T1D data set are linked with the MHC region. The MHC region at chromosomal position 6p21 encodes many genes (such as HLA-DQB1 and HLA-DRB1) that have been associated with type 1 diabetes ,24 by usP-value is 2.442 × 10-15. The unadjusted single-locus P-values for rs2300390 and rs12482676 are 0.460 and 0.061, respectively. Both SNPs reside in the intron of gene RCAN1. Gene RCAN1 mainly functions as a regulator of calcineurin. Calcineurin participates in many cellular and tissue functions. Its abnormal expression is associated with many diseases including hypertension [Among associations identified from the HT data set, we find one suspicious pair involving SNP rs2300390 and SNP rs12482676. The unadjusted Currently, we have difficulties to connect the identified associations of CD, RA and T2D to publicly available findings from other association studies. Their biological implications need to be further explored.P-value of Hardy-Weinberg equilibrium ≥ 0.0001), the number of remaining SNPs is 291,964.We further analyze the Crohn's Disease data set , in whicWe apply our method to this data set and do not find any significant associations masked by unfaithfulness. Our explanation is that Illumina chip uses the tagSNP design and the correlation among SNPs is less than that of Affymetrix 500 K chip used by WTCCC. This result indicates that it is unlikely to detect associations masked by unfaithfulness using the tagSNP design.In order to check if imputation helps in identifying significant association masked by unfaithfulness, we focus on the SNP regions in which we have identified associations from the WTCCC CD data set  is given as follows [In the high dimensional feature space, many features could correlate with each other by chance, which leads to the existence of unfaithfulness and poses a great challenge on feature selection and association analysis. In this work, we only handle the unfaithfulness issue involving two variables (SNPs), while the unfaithfulness can exist among a huge number of variables. The relationship between the marginal coefficient ( follows :(2)ρ is the population correlation between Xq and pX. If p βis. In addition, the number of involved variables could be very big. To exclude the effect of unfaithfulness in feature selection, Fan and Lv [C > 0 such that p = 1,..., s, and then proved that the truly associated variables will be among those having the highest marginal coefficients.where n and Lv  had to mX1 and X2 by using β1 > 0, β2 < 0 and ρ > 0 as illustrated in Figure β1 > 0, β2 > 0 and ρ < 0, the unfaithfulness also happens. This corresponds to a situation that the minor alleles of both X1 and X2 increase the diseases risk but X1 and X2 are negatively correlated, as illustrated in Figure In our simulation study, we only handle the unfaithfulness involving two associated variables X1 and X2 is to first fit the regression model (or logistic regressions for case-control data) Y ~ β1X1 + β2X2 + β12X1X2 and then test the significance of β12. There is no direct connection between β1 (or β2) and β12 In the analysis of unfaithfulness, the relationship between marginal coefficients  is given in Equation (2). The interaction term plays no role here. Therefore, it is not appropriate to use interaction models to detect associations masked by unfaithfulness.In this work, we only deal with a two-locus association pattern involving the unfaithfulness. There are many works ,5,6,30 dX and Y using model Z which is independently associated with X and Y. Specifically, we have X and Y, it is necessary to account for the confounding effect by using model Y ~ yzZ β+ yxXβ. In other words, confounding is more like the situation illustrated in Figure Suppose we are studying the relationship between two variables Y ~ β1X1 + β2X2, both β1 and β2 are not zero, as illustrated in Figure The unfaithfulness is different. For model p Xand q Xwhich are positively correlated. When q Xincreases the disease risk (q β> 0) and p Xacts as a protective locus (p β< 0), unfaithfulness happens. The identified associations and their coefficients listed in Table There are two possible biological interpretations. The first interpretation is illustrated in Figure p Xand q Xwhich are negatively correlated. When both p Xand q Xincrease the disease risk (p β> 0 and q β> 0), unfaithfulness also happens. This case may be particularly interesting when analyzing SNPs with low allele frequencies [p Xand q Xare low and thus the mutations happening at these two loci are relatively recent. We can further assume the haplotype a - a does not exist . This implies these two loci are negatively correlated. Unfortunately, we do not identify this type of associations. Possible reasons include: (1) The current genotyping chip is designed based on the "common disease/common variant" model [The second interpretation is illustrated in Figure quencies . Supposet" model ,34, the GWAS is considered as a powerful approach to detecting genetic susceptibility of common diseases. Such studies require the genotypes of a huge number of SNPs across the genome, each of which is tested for association with the phenotype of interest. This is generally referred to as the direct test of association, in which the functional mutation is presumed to be genotyped. An alternative approach is to take advantage of the correlation between SNPs. This approach genotypes a subset of SNPs, called tagSNPs, which are in high linkage disequilibrium with other SNPs . The assIn this work, we analyzed the WTCCC data generated by the Affymetrix 500 K chip and a Crohn's disease data set generated by the Illumina chip. The Affymetrix 500 K chip spaces SNPs along the genome and the Illumina chip uses the tagSNP design. LD becomes less apparent in the Illumina data set and we did not find any association masked by unfaithfulness. This result suggests that it is very difficult to detect these associations by using the tagSNP design. If more SNPs could be genotyped in the future GWAS, we would detect more unknown associations.The phenomenon named "unfaithfulness" has been discussed as a mathematical concept in the literature. In this work, we answered the question whether there exist associations masked by unfaithfulness in genome-wide association studies. We developed a simple and fast method to examine all SNP pairs and demonstrated that our method is applicable to analyze genome-wide SNP data sets. We conducted experiments on both simulated data and seven real data sets from WTCCC and identify many associations masked by unfaithfulness. As expected, these identified associations only occur in local area. This implies that only the local search is needed to find such associations.To date, we can only connect some identified associations to publicly available results from other association studies. As independent data set is limited as this moment, we have difficulties to replicate these findings. The biological interpretation of many findings remains unclear. It would be of great interest if their biological functions could be investigated. In addition, we only handle the two-locus associations in the presence of unfaithfulness. Detecting such associations for three or more loci is still an open problem.n samples, we use l Xto denote the l-th SNP, l = 1,···, ℒ and Y to denote the class label (0 for control and 1 for case). SNPs are bi-allelic genetic markers. Capital letters  and lowercase letters  are often used to denote major and minor alleles, respectively. For simplicity, we use {0, 1, 2} to represent the three genotypes {AA, Aa, aa}, respectively.Given a data set with ℒ SNPs and p Xand qX, four logistic regression models are typically involved to test associations masked by unfaithfulness:Considering a pair of loci andI(V = v) is the indicator function that takes the value 1 if V = v is true and 0 otherwise. In order to make the representation of both logistic regression models and log-linear models (introduced later) in a compact and consistent way, we use the notation adopted in [where opted in  and rewrandp Xand q Xin Equation (8), Equation (9) and Equation (10) are merely the labels and do not represent the exponents. The term p Xat category i. Throughout this paper, we use ℳ to denote logistic regression models. We will use M to denote log-linear models. The log-likelihood function of a logistic model ℳ is denoted as Lℳ, and its maximum likelihood estimation (MLE) is denoted as M is denoted as ML, and its maximum likelihood estimation (MLE) is denoted as 1 is a logistic regression model whose log-likelihood function and MLE are denoted by Lℳ1 and Please note that the superscripts 1⊕2 is significantly different from ℳ0 when both ℳ1 and ℳ2 are not. The likelihood ratio test is often used to conduct such tests. To test the difference between ℳ1⊕2 and ℳ0, the following three steps are involved:Our goal is to test if ℳ1. Fit a logistic regression model defined in Equation (10) and obtain the log-likelihood 2. Compute the log-likelihood P-value using the χ2 test on the value 2  and ℳ0 involves the following three steps:Similarly, the test of difference between ℳp X(or qX) and obtain the log-likelihood 1. Fit a logistic regression model defined in Equation (8) (or Equation (9)) to measure the main effect of 2. Compute the log-likelihood P-value using the χ2 test on the value 2  and Y. The size of the contingency table is I × J × K, where I = 3, J = 3, K = 2. We use ijk nto denote the observed count in the cell  in the contingency table , and the block independence model corresponding to the null logistic regression model ℳ0. Let HM, the partial independence model P Mand the block independent model B Mevaluated at their MLEs The equivalence between log-linear models and logistic models are given in Table Based on the equivalence, the deviance and the deviance In Equation 11) and Equation (12), 1 and Equandi, j, k, then do IPF as follows:Iterative Proportional Fitting (IPF)  can be um = 1, 2,...) to zero accordingly. Our experimental results show that this solution works well in practice . Then the test statistics can be efficiently computed. As a result, we are able to test every pair of loci to search for associations masked by unfaithfulness in GWAS. Table The updating formulas may only be ill-defined if This approach involves the following steps:P-values can be adjusted by either the classic Benjamini-Hochberg method for controlling false discovery rate (FDR) or the Bonferroni correction for controlling family wise error rate (FWER).• Step 1. For all of ℒ SNP markers, we first filter out those SNPs with significant main effects using Equation (12) since we are only interested in those markers without significant main effects. The ℒ P-values can be adjusted for controlling either FDR or FWER.• Step 2. For the remaining ℒ' SNPs without significant main effects, we check every pair using the Equation (11). Again, the P-value thresholds in both Step 1 and Step 2 are specified by users. The default setting of the threshold is 0.1. The multiple factor for Bonferroni correction is ℒ'(ℒ' - 1)/2, where ℒ' is the number of SNPs after removing those SNPs with significant marginal effects. Since the number of SNPs with significant marginal effects only accounts for a small fraction of the entire SNP set, we have ℒ'(ℒ' - 1)/2 ≈ ℒ(ℒ - 1)/2.The p(D|iG) denote the probability of an individual being affected given its genotype i G, and let iG. Based on the definition of the odds of a diseaseLet p(D|iG) of the genotype i Gcan be calculated usingthe penetrance p(D) and genetic heritability h2 are given asThe disease prevalence The odds table of our simulation model is given in Table θ11 = θ12 = a θand θ21 = θ22 = bθ. The parameter a θ> 1 means that the minor allele "a" increases the disease risk. This corresponds to the bivariate regression coefficient β1 > 0. Similarly, b θ< 1 implies β2 < 0. In the presence of linkage disequilibrium (linkage disequilibrium measure Δ > 0), unfaithfulness arises. To simulate this situation, we further set a θ= θ and b θ= 1/θ. In the simulation, the prevalence p(D) and the heritability h2 are controlled by the parameters α and θ. We first specify the disease prevalence p(D) and genetic heritability h2. Then we numerically solve the parameters (α and θ) based on the above equations. We set p(D) = 0.1 and h2 = 0.02. The details are given in the supplementary document , we present the details of simulation. We also give a brief introduction to log-linear models which are used in the main article. Finally, we provide full lists of the results identified from the WTCCC data sets.Click here for file


FABP1), known to reduce low density lipoprotein and tri-glyceride levels in Europeans. The strongest signal identified by all three metrics was a 1.7 Mb region on Chr2q21. This region contains the genes LCT (Lactase) and MCM6 (Minichromosome Maintenance Complex Component) involved in lactase persistence, and the gene Rab3GAP1 , which contains polymorphisms associated with total cholesterol levels in a genome-wide association study of >100,000 individuals of European ancestry. Sanger sequencing of DNA from six MKK samples showed that the GC-14010 polymorphism in the MCM6 gene, known to be associated with lactase persistence in Africans, is segregating in MKK at high frequency (∼58%). The Cytochrome P450 Family 3 Subfamily A (CYP3A) cluster of genes, involved in cholesterol metabolism, was identified by Fst and iHS as candidate loci under selection. Overall, our study identified several specific genomic regions under selection in the Maasai which contain polymorphisms in genes associated with lactase persistence and cholesterol regulation.The Maasai are a pastoral people in Kenya and Tanzania, whose traditional diet of milk, blood and meat is rich in lactose, fat and cholesterol. In spite of this, they have low levels of blood cholesterol, and seldom suffer from gallstones or cardiac diseases. Field studies in the 1970s suggested that the Maasai have a genetic adaptation for cholesterol homeostasis. Analysis of HapMap 3 data using Fixation Index (Fst) and two metrics of haplotype diversity: the integrated Haplotype Score (iHS) and the Cross Population Extended Haplotype Homozygosity (XP-EHH), identified genomic regions and single nucleotide polymorphisms (SNPs) as strong candidates for recent selection for lactase persistence and cholesterol regulation in 143–156 founder individuals from the Maasai population in Kinyawa, Kenya (MKK). The non-synonmous SNP with the highest genome-wide Fst was the TC polymorphism at rs2241883 in Fatty Acid Binding Protein 1( The Maasai are a pastoralist, Nilotic people living primarily in southern Kenya and northern Tanzania. An economy traditionally based on herds of cattle, sheep, and goats led to a diet rich in lactose, fat, and cholesterol consisting largely of milk, meat, and blood. Although their cholesterol intake is high (600–2000 mg/day), and 66% of their calories come from fat, their total serum cholesterol levels average 135 mg/100 ml 14C. The eleven subjects in the treatment group were fed 2 gm of crystalline cholesterol per day in addition to the basic diet. Blood and fecal samples were collected at the start of the study, weekly for 8 weeks and at the end of 9, 16 and 24 weeks. Using the radioactive tracer to quantitate/normalize the measurements, the data were analyzed to characterize metabolic patterns, namely, the amounts of dietary cholesterol absorbed, synthesized and excreted. The study found that, in spite of the additional 2 gm/day ingestion of cholesterol in the experimental group, there were no significant differences in serum cholesterol, phospholipids, triglyceride levels and lipoprotein patterns between the experimental and control groups. Both groups had identical turnover rates for cholesterol, with no evidence for cholesterol storage in the experimental group. In a similar study in American subjects, Mattson et al Additional clues emerged from a controlled experiment increased risk of diseaseIt is widely accepted that there is a strong genetic component in the risk of hypercholesterolemia, atherosclerosis and heart disease MCM6) gene, known to confer adult lactase persistence in East Africans Motivated by this possibility, we performed a genome wide scan for selection in 143–156 founder individuals from the Maasai of Kinyawa, Kenya (MKK) using the HapMap 3 Two of the methods used to detect selection (Fst and XP-EHH) require a genetically similar reference population. A comparison of Fst among HapMap populations shows that the MKK and African-Americans from South-west USA (ASW) have the lowest average Fst (0.0145), followed by MKK and the Luhya in Webuye, Kenya (LWK) (0.017), while Fst between MKK and Yoruba from Nigeria (YRI) is significantly higher (0.027) . HoweverTo understand the degree of admixture in the populations ASW, CEU, LWK, MKK and YRI, we used STRUCTURE To further quantify the genetic similarity of MKK, LWK, ASW and YRI to the six ancestral populations, we assigned a six component vector to each of these populations, whose coordinates were the fraction of the ancestral components represented in them. A comparison of the cosine similarity of these vectors showed that the largest overlap was between MKK and LWK (0.18), followed by MKK and ASW (0.16). Based on their closer proximity to MKK in the PCA plot, as well as closer cosine similarity, we chose the LWK as the appropriate reference population for the Fst and XP-EHH analysis.B (E) based on the Fst distribution of inter-genic SNPs. This identified 1,232 SNPs with pB<8.6E−6 and pE <0.001  and LWK (n = 110) as in B . Within E <0.001  which we2≥0.25 with at least one other SNP in the cluster  measures the probability that two randomly selected haplotypes sharing the same allele at a SNP are identical up to genomic distance x. At each SNP, we computed the unstandardized Integrated Haplotype Score Recent selective sweeps amplify beneficial mutations and reduce haplotype diversity due to the hitchhiking effect. The Extended Haplotype Homozygosity 2≥0.25). The most significant regions identified as candidates for selection in MKK are in Following the protocols in The third method used to identify selective sweeps in MKK was the Cross Population Extended Haplotype Homozygosity statistic (XP-EHH) 2≥0.25 criterion as before) to identify putative regions under selection in MKK (Using the LWK cohort (n = 90) as the reference population for MKK (n = 143), XP-EHH was calculated for 1,373,755 SNPs that passed various filters . Followin in MKK . The mosThe metrics we use probe for different signatures of selection, and hence, genomic regions which are identified by more than one metric are more likely to be true positives. Using a concordance between at least two of the metrics, we identified seven genomic regions as strong candidates for selection . There wFABP1, alternative name LFABP)  cholesterol levels in Germans We found that the non-synonymous SNP with the highest genome-wide significant Fst was rs2241883 in the gene Fatty Acid binding Protein 1, Liver (e LFABP) . The SNPLCT) gene, which encodes the Lactase protein, as well as the gene MCM6, which contains intronic regulatory regions for LCTThe largest cluster under selection in Maasai, identified by all the metrics, was a 1.7 Mb region on Chr2q21 ,2,3,4. TMCM6 , which are known to be associated with lactase persistence in Africans and Europeans GG = 1, nGC = 3, nCC = 2). We estimated the frequency of the beneficial (C) allele in the MKK samples to be pC = 0.58+/−0.14  at five loci in RAB3GAP1, not found in the HapMap data for the MKK, is associated with total cholesterol levels in a GWAS of >100,000 individuals of European descent increase in total cholesterol levels in the samples with European descent.The selected locus on Chr2q21 contains polymorphisms that have been associated with cholesterol levels in various GWAS studies LCT associated with total cholesterol and Low Density Lipoprotein C (LDL-C) levels LCT were associated with total cholesterol and LDL-C levels, with stronger associations in males than females. This study also found that the G allele at the synonymous SNP rs2304371 in the LCT gene was associated with highest LDL-C levels in males. The same SNP was identified by our methods as a selection candidate  locus was identified as a candidate for selection by Fst and iHS , CYP3A5 (involved in oxidation of fatty acids and steroids in the liver), CYP3A7  and CYP3A43 (involved in testosterone metabolism). The CYP proteins play an important role in drug metabolism and in the synthesis of steroids from cholesterol On Chromosome 7, a 261 kb wide region spanning the entire Cytochrome P450 Subfamily 3A  gene, a fatty acid binding protein expressed in liver. This locus was not detected by iHS or XP-EHH, suggesting either an increased local recombination rate or a more ancient selective sweep. The T94A polymorphism was strongly associated with lower levels of plasma triglycerides and LDL-cholesterol levels in a study of 826 individuals from Northern Germany LFABP due to a disruption in ligand binding LFABP knockout mice fed a high cholesterol, high saturated fat diet were protected against diet-induced obesity and lower levels of hepatic triglycerides compared to control mice, despite the absence of discernible differences in energy levels, food intake, or mal-absorption of fat induced obesity LFABP may function as a metabolic sensor in regulating lipid homeostasis” LFABP pathway may be partly responsible for the homeostatic regulation of blood cholesterol in Maasai Using Fst, the most significant non-synonymous SNP was the polymorphism rs2241883 located at 88.42 Mb on Chromosome 2 , Table 1LCT and MCM6 genes. The SNP known as G/C-14010 was found to have the most significant association with the lactase persistence phenotype in Kenyan Nilo-Saharan and Tanzanian Afro-Asiatic populations, as well as in a meta-analysis of all the populations combined. Tishkoff et al observed the C-14010 allele to occur at 32% frequency in Kenyan populations. As this SNP is in the upstream regulatory region of the gene LCT, the authors also studied the effect of this polymorphism on expression using luciferase assays in intestinal cells. They found that the C-14010 allele leads to a significantly higher expression. Furthermore, an iHS analysis of the haplotype background on which the SNP occurs indicated that the SNP is under selection in Kenyans and Tanzanians. We found that in the MKK samples from HapMap the C-14010 allele is segregating at high frequency (0.58). Thus, our results confirm the findings of Tishkoff et al, that C-14010 contributes towards selection for lactase persistence in the MKK samples from HapMap.We found evidence for a strong recent selective sweep in a ∼1.7 Mb region on Chr2q21 ,2,3,4. Tincrease in cholesterol levels in Europeans. This might reflect the possibility that Europeans and Maasai have different sets of functional polymorphisms at this locus responsible for lower cholesterol levels: indeed it is known that the Maasai have an African polymorphism associated with lactase persistence, different from the one found in Europeans. It could also be that in the Maasai, the SNPs identified in our study are not themselves functional, but linked to functional variants that are not genotyped. Given the extended linkage disequilibrium (LD) in this region due to a selective sweep in both Europeans and the Maasai, this last possibility is especially important. The differing effects of the SNPs identified in the Maasai, as compared with the Europeans, could arise from the effects of differing modifier alleles at different loci in this region. These possibilities emphasize the difficulties associated with identifying true functional polymorphisms because of potential population specificity of SNP based studies. However, given the GWAS findings, and the strong signal of selection in MKK seen in our analysis, the LCT locus is a candidate region for identifying genotypic variants associated with cholesterol regulation in the Maasai.In addition to lactase persistence, the GWAS studies of CYP3A  family of genes which are involved in cholesterol metabolism and steroid biosynthesis CYP3A5, a gene involved in fatty acid oxidation in liver, as well as CYP3A7, a gene encoding a CYP enzyme expressed in fetal livers. Variants in CYP3A5 have been shown to reduce the efficacy of certain statins, drugs used to lower cholesterol biosynthesis We also identified a 261 kb locus on Chr 7 (the CYP3A locus) to be under selection using Fst and iHS , 2, 4. TAPOF) gene, involved in cholesterol transport and esterification DGAT2) (ITGB8) gene (CFDP1)  . This geB8) gene  implicat (CFDP1) . A GWAS Our results identified several genes and loci involved in cholesterol metabolism as selection candidates in the Maasai. Thus, our findings suggest that the Maasai are adapted for a high-cholesterol and high-fat diet. The traditional diet of the Maasai is rich in saturated fats and cholesterol, and low in carbohydrates. Similar ketogenic diets are often used to treat epileptic seizures in children Maasai social customs may also favor genetic selection against diseases of the elderly. Maasai are both polygynous and gerontocratic, and older men routinely marry nulliparous young women Field studies showed that, in spite of a high fat and high cholesterol diet, the Maasai have low levels of cardiac disease and atherosclerosis. In this paper, we present results from a genome wide scan of the HapMap 3 SNP data using the Fst, iHS and XP-EHH statistics to identify genomic regions under selection in the Maasai. We identify regions containing genes involved in lactose and lipid metabolism which are under selection in the Maasai. Our analysis suggest that the identified regions harbor known and novel genetic polymorphisms responsible for the unusual lipid metabolism, cholesterol homeostasis, protection against cardiac diseases and adult lactase persistence in the Maasai.http://hapmap.ncbi.nlm.nih.gov/. No consent was required.The data analyzed was public SNP data from the HapMap Consortium http://snp.cshl.org/. Using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/), we filtered to retain only SNPs common to all populations.HapMap 3 release 3 SNP genotype data for founders from the Maasai in Kinyawa, Kenya (MKK) (n = 156), the Luhya in Webuye, Kenya (LWK) (n = 110), African-Americans in Southwest USA (ASW) (n = 53), the Yoruba in Ibadan, Nigeria (YRI) (n = 147), and Utah residents of Northern and Western European ancestry (CEU) (n = 112) was downloaded from http://hapmap.ncbi.nlm.nih.gov/downloads/phasing/2009-02_phaseIII/HapMap3_r2/. The data was phased using IMPUTE++ http://hapmap.ncbi.nlm.nih.gov/downloads/genotypes/2010-05_phaseIII/00README.txt). Genetic maps were downloaded from http://hapmap.ncbi.nlm.nih.gov/downloads/recombination/2008-03_rel22_B36/rates/to obtain the genetic map position of the SNPs in cM.Hapmap 3 release 2 autosomal haplotype data for the MKK (n = 143) and LWK (n = 90) was downloaded from Using PLINK, genotype data for MKK, LWK, YRI, ASW and CEU was filtered to exclude SNPs with minor allele frequency <1% or SNPs where more than 1% of the genotype data was missing. Restricting the samples to founders resulted in 1,325,342 common SNPs for 578 individuals. We further restricted the genotype data to a random subset of ∼1% of these SNPs  and ran the “no admixture” model in STRUCTURE B), and an empirical p-value that compared the Fst of a SNP to the Fst distribution of intergenic SNPs. Gene positions were from the human genome build 37 (GRCh37/hg19) available at http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/knownGene.txt.gz. To avoid linkage with genes and promoter regions, we define intergenic regions as those that are at least 50 kb away from the start or stop site of a gene. For the remaining genic or near-gene SNPs, we calculated an empirical p-value (pE) given by the fraction of intergenic SNPs with greater Fst. This procedure identified 1,232 SNPs with pB <8.6E−6 and pE <0.001 that are the top candidates for selection using Fst  and with minor allele frequency >5% in either population (LWK and MKK). We then computed Fst using the method of sing Fst . These Ssing Fst  using thsing Fst .Autosomal haplotype data for 991,737 SNPs in MKK with minor allele frequency >10% were used to calculate raw iHS scores as in Voight et al http://hgdp.uchicago.edu/Software/xpehh.tar. The XP-EHH scores were fit to a normal distribution, which identified the threshold for genome-wide significance to be XP-EHH ≥4.796 . The SNP that exceeded this threshold were chosen as top candidates for selection by XP-EHH . XP-EHH scores were calculated using the code at y XP-EHH . These Sy XP-EHH  using thy XP-EHH .2 as an estimator of linkage disequilibrium. We used the criteria that for a SNP to be included in a cluster, it must have genotype R2≥0.25 with at least one other SNP in the cluster (the justification for this choice of cutoff is given in The SNPs identified as candidates for selection by each of the above methods were clustered using genotype R2≥0.25 and 0 if R2<0.25. The problem of finding linked clusters of SNPs then translates to identifying the connected components of the graph described by this adjacency matrix. This computation was performed in Python using the NetworkX package (http://networkx.lanl.gov/).More concretely, for the SNPs identified by the methods above, we used PLINK to extract a file of raw genotype data from the HapMap genotype data file for MKK. These files contained a matrix of genotype values, whose columns were labeled by SNPs and rows labeled by individuals. We imported this genotype matrix into the statistical package R, to calculate a SNP x SNP Pearson correlation matrix. This correlation matrix was then used to construct a SNP x SNP adjacency matrix whose entries are 1 if Rhttp://frodo.wi.mit.edu/primer3/), and checked for absence of homologies to other parts of the human genome using BLAT. The details of the primers, the loci sequenced and the samples used are in Forward and reverse primers for Sanger sequencing were chosen using Primer3 (Table S1B<8.6E−6 and pE <0.001). Significance was assessed using an exact permutation test  and an empirical test based on the Fst distribution of intergenic SNPs (pE : column R). Columns H-M list the number of individuals with each genotype  in MKK and LWK.1,232 genic or near-gene SNPs identified by Fst as top candidates for selection (p(XLS)Click here for additional data file.Table S22 between SNPs. Clusters with maximum XP-EHH score >3 were identified as being MKK associated. Also listed are the maximum Fst score and the maximum XP-EHH score of any SNP in the genomic extent of the cluster.Genomic regions identified as selection candidates in MKK using Fst and clustering. SNPs having empirical p-value <0.001 with respect to the distribution of intergenic Fst scores were clustered into regions of high linkage disequilibrium using genotype R(XLSX)Click here for additional data file.Table S3SNPs identified as selection candidates using the iHS metric. Sliding windows of 50 SNPs each were scored for fraction of SNPs with |iHS| >2. SNPs with |iHS| >2 that occur in the top 0.02% of non-overlapping genomic windows are listed.(XLS)Click here for additional data file.Table S42 between SNPs with |iHS| >2).Genomic regions identified as selection candidates in MKK using the iHS statistic. Sliding windows of 50 SNPs each were scored for the fraction of SNPs with |iHS| >2. The top 0.02% of non-overlapping windows were identified as candidates for selection. These windows were then merged on the basis of linkage disequilibrium (estimated using genotype R(XLS)Click here for additional data file.Table S5SNPs identified as candidates for selection in MKK using the XP-EHH statistic, with LWK as the reference population. All SNPs listed have scores exceeding the threshold for genome-wide significance .(XLSX)Click here for additional data file.Table S62≥0.25 with another SNP in the cluster. This identified contiguous genomic regions as candidates for selective sweeps. Clusters that overlapped in genomic extent were merged. Column F and G list the number of significant SNPs occurring in the genomic extent of each cluster, and their rsids.Genomic regions identified as selection candidates in MKK using the XP-EHH statistic, with LWK as the reference population. SNPs with genome-wide significant scores  were assigned to a cluster if they had genotype R(XLSX)Click here for additional data file.Table S7Common regions and SNPs identified to be under selection in MKK by our analysis  and by the International HapMap Consortium (using the CMS test). Only those SNPs identified by the HapMap Consortium which were also identified by our analysis  are listed.(XLSX)Click here for additional data file.Appendix S1Details of STRUCTURE calculation.(DOC)Click here for additional data file.Appendix S2Details of Fst calculation, p-values and SNP clustering for Fst and XP-EHH.(DOC)Click here for additional data file.Appendix S3Details of iHS calculation.(DOC)Click here for additional data file.Appendix S4Details of XP-EHH calculation.(PDF)Click here for additional data file.Appendix S5Details of Sequencing in LCT/MCM6 locus(DOC)Click here for additional data file.Appendix S6Plots of Fst, XP-EHH and iHS for all chromosomes.(PDF)Click here for additional data file.


The average individual is expected to harbor thousands of variants within non-coding genomic regions involved in gene regulation. However, it is currently not possible to interpret reliably the functional consequences of genetic variation within any given transcription factor recognition sequence. To address this, we comprehensively analyzed heritable genome-wide binding patterns of a major sequence-specific regulator (CTCF) in relation to genetic variability in binding site sequences across a multi-generational pedigree. We localized and quantified CTCF occupancy by ChIP-seq in 12 related and unrelated individuals spanning three generations, followed by comprehensive targeted resequencing of the entire CTCF–binding landscape across all individuals. We identified hundreds of variants with reproducible quantitative effects on CTCF occupancy (both positive and negative). While these effects paralleled protein–DNA recognition energetics when averaged, they were extensively buffered by striking local context dependencies. In the significant majority of cases buffering was complete, resulting in silent variants spanning every position within the DNA recognition interface irrespective of level of binding energy or evolutionary constraint. The prevalence of complex partial or complete buffering effects severely constrained the ability to predict reliably the impact of variation within any given binding site instance. Surprisingly, 40% of variants that increased CTCF occupancy occurred at positions of human–chimp divergence, challenging the expectation that the vast majority of functional regulatory variants should be deleterious. Our results suggest that, even in the presence of “perfect” genetic information afforded by resequencing and parallel studies in multiple related individuals, genomic site-specific prediction of the consequences of individual variation in regulatory DNA will require systematic coupling with empirical functional genomic measurements. A comprehensive understanding of the contribution of individual genome sequences to disease and quantitative traits will require the general ability to predict consequences of genetic variation in non-protein-coding regions, particularly those involved in gene regulation. Here we tested the power to predict such consequences when presented with “complete” information encompassing the genomic DNA binding site patterns of a well-studied regulatory protein across multiple related individuals, coupled with all individual genome sequences at the binding positions. We find that, while there is reasonable ability to predict the average effects of variation within the consensus recognition sequence of a transcriptional regulator, it is not possible to determine reliably the consequences of variation at any given genomic instance. This suggests that the interpretation of individual genome sequences will require comprehensive complementation with functional genomic studies. A growing number of studies associate variation within regulatory DNA and risk of human disease Functionally significant variation within the genomic recognition sequences for certain TFs appears to be correlated in aggregate with nucleotide-level evolutionary conservation and/or position-specific information content To address this, we apply a novel experimental design to identify comprehensively patterns of genetic variation with heritable effects on the occupancy of the major genomic regulator CTCF We mapped binding sites for CTCF by ChIP-seq in B-lymphoblastoid cells derived from 12 members of a three-generation pedigree . We idencis (−3), and DNase I signal . Thus we conclude that the SNP genotype is associated with differences in occupancy at 325 of 5,828 sites tested.We integrated the genetic and functional data sets to survey genome-wide heritable variation in transcription factor occupancy. We reasoned that the strongest signal of heritable variation would be from segregating variants overlapping the binding site. Thus we performed a linear regression of the ChIP-seq density on the SNP genotype in cis . Of 5,82cis . We test−7, permutation). The remaining 50 sites without significant associations had a regression slope of −1.01 (−5). These results suggest that SNPs directly overlapping the cognate recognition sequence explain most but not all of the heritable variation in this pedigree. Remaining variation in occupancy might be heritable due to sequence variants not considered in our SNP-based analysis or heritable epigenetic variation such as methylation. However, it is quantitatively less significant than the heritability attributable to the direct effect of SNPs on occupancy  were already associated to an underlying SNP (FDR 1%), 65 (4.7%) had allele-specific occupancy (FDR 0.1%), and 197 (14%) were on chromosome X. To test for heritable inheritance of occupancy not explained by these factors, we performed Haseman-Elston sib-pair linkage analysis in aggregate at sites differentially occupied among the 6 grandchildren . The 47 of −1.01 , indicatccupancy .−2) and 1,040 binding sites overlapped multiple SNPs within ±180 bp of the ChIP-seq peak. Excluding these sites, we analyzed the 4,428 binding sites with a single SNP and a single motif match. These SNPs were distributed throughout the resequenced region surrounding each CTCF motif  CF motif .−3 outside the versus 1.3*10−5 inside), consistent with these SNPs being false positives or sites with ambiguity in the true location(s) of protein-DNA interaction. Alternatively, some of these SNPs might affect CTCF occupancy indirectly by perturbing an adjacent co-factor binding site.In contrast, we expected that SNPs associated with occupancy differences would be concentrated in the 44 bp region of protein-DNA contact We compared our results to an allele-specific occupancy test performed at heterozygous sites . DespiteAlthough differences in occupancy were largely associated with SNPs at positions strongly affecting overall binding energy , only 132 of 0.04 indicates that the strength of the motif match alone can not explain the breadth of buffering observed.Strength-dependent buffering could be explained by a model where changes in occupancy are observed only when a SNP causes the affinity of a site to cross a threshold for binding. In this case, the strongest and weakest sites will only be affected by the greatest genetic perturbation, while smaller perturbations would affect binding only at sites of intermediate strength. This would create the impression of epistasis between all positions in the cognate recognition site as any affinity-affecting change could potentially buffer another in vitroin vivo have remained unclear. To explore the power of our data set to discover epistatic interactions, we measured the mutual information between the sequence context per-base in the core motif and whether a SNP at each location affects occupancy . This indicates that approximately 40% of the functional substitutions in the human lineage increased occupancy, which is surprisingly high given that most mutations might be expected to reduce binding energy.Previous work studying the power of comparative genomics has predicted a steep increase in the number of sequenced genomes required to obtain nucleotide resolution, particularly in the absence of perfect conservation Gene-based studies have successfully identified causal variants using current methods for prediction of functional non-synonymous protein variants In summary, our results indicate the existence of widespread recognition site-dependent buffering of polymorphism within regulatory DNA regions. A major implication of our work is that the potential for accurately predicting the consequences of variation affecting regulatory factor recognition sequences is severely limited by complex context dependencies, necessitating empirical assessment using functional genomic approaches. The feasibility of approaches such as the one we describe here has recently dramatically increased owing to coupled advances in next-generation sequencing technology and molecular biology, and continuation of this trend should in the near future enable further systematic investigations into the effect of polymorphism on protein-DNA interaction on a routine basis.The B-lymphoblastoid cell lines from CEPH pedigree 1459 were obtained from Coriell and cultured in RPMI1640 medium (Cellgro), supplemented with 15% fetal bovine serum , 2 mM L-Glutamine, and 25 IU/mL penicillin and 25 µg/mL streptomycin (Cellgro).B-lymphoblasts (5 million cells) were crosslinked with 1% formaldehyde (Sigma), lysed in lysis buffer , and sheared by Bioruptor (Diagenode). The supernatant was further diluted 10-fold with dilution buffer . For each immunoprecipitation, 100 µL Dynabeads  were incubated with 20 µL CTCF antibody  for at least 6 hours at 4°C. The antibody-conjugated beads were then incubated overnight with sheared chromatin. The complexes were washed with IP wash buffer I , high salt buffer , and TE buffer . Crosslinking was then reversed in elution buffer  at 65°C overnight. The DNA was separated from the beads and treated with Proteinase K (Fermentas) and purified by phenol-chloroform extraction and ethanol precipitation.Sequencing libraries were constructed according to Illumina's genomic prep kit protocol as previously described High quality reads were aligned to the reference genome using the Eland aligner. SPP http://meme.sdsc.edu/meme/) to scan for instances of the core 14 bp of the canonical motif −2. We adjusted the target locations to center on matches to the nearest CTCF motif if the motif was within 50 bp, and added flanking targets to capture additional nearby motifs. 5 potential probes were tiled at 15 bp spacing to the 120 bp surrounding each target. We adjusted probe binding energy similarly to Ng et al. m between 60–72°C. We used the Duke Uniqueness 20 bp track (UCSC genome browser) to filter out 5,828 probes with potential for cross-hybridization. We further excluded 145 probes in satellite repeats  or with high blast scores to multiple genomic locations. The final design had 242,380 probes targeting 46,652 CTCF sites.We designed an Agilent 244k SureSelect microarray for targeted resequencing on the 51,686 ChIP-seq peaks identified in the 12 samples. We used fimo (0t-1 DNA (Invitrogen), 2× Hi-RPM Hybridization Buffer (Agilent) and 10× Blocking Buffer (Agilent). Hybridization was performed using the Maui Hybridization System  at 55°C for 48 hours according to “MAUI Mixer LC on Agilent 244K CGH Microarrays” protocol. After hybridization, microarrays were washed with Agilent aCGH Wash Buffers 1 and 2, sealed with Secure-Seal (GRACE Bio-Labs) and placed on heat block (VWR) for elution of DNA. DNA was eluted with 3 mL of 95°C PCR-grade water, concentrated, and amplified with SE primers using the same PCR program as above. The libraries were sequenced to 36 bp on an Illumina Genome Analyzer by the High-throughput Genomics Unit (University of Washington) according to standard protocol.Genomic DNA was extracted from cultured cells, and targeted capture was performed based on Supplementary Protocol 3 of Mamanova et al. 2010 http://picard.sourceforge.net/). SAMtools v0.1.7 Reads were aligned to the human genome (hg18) using bwa 0.5.8 http://hapmap.ncbi.nlm.nih.gov, and matched to capture resequencing SNPs by location. 244 of the 27,808 genotype calls in common (0.88%) were discordant.We performed two validations of the SNP calls from our targeted resequencing. First, we performed Sanger sequencing on PCR products from genomic DNA . We testhttp://code.google.com/p/mosaik-aligner/) with the options “-bw 13 -act 20 -mhp 100 -m unique -mm 4 -minp 1.0”, requiring a unique mapping considering up to 4 mismatches. Reads with more than 2 mismatches were then discarded. Reads with identical 5′ ends were presumed to be PCR duplicates and were excluded using Picard v1.22 MarkDuplicates. Smoothed density tracks were generated using bedmap (http://code.google.com/p/bedops/) to count the number of tags overlapping a sliding 150 bp window, with a step width of 20 bp. Density tracks were normalized for sequencing depth by a global linear scaling to fix an arbitrary value of 25 as the 50th percentile of bins with more than 15 reads. We identified instances of the canonical motif  within 15 bp of the center of the resequencing target, keeping the motif with the best p-value. We measured occupancy by the maximum normalized ChIP-seq tag density over the 14 bp motif.For each individual, a custom human genome was created from hg18 including autosomes, unscaffolded contigs, mitochondrial DNA, a Y chromosome for males, and the Epstein-Barr virus genome (gi|9625578|ref|NC_001345.1). Each genome was personalized to reflect SNPs identified by resequencing, using IUPAC codes to represent heterozygous position. ChIP-seq data was mapped using MosaikAligner v1.1.0021 . Using a Benjamini-Hochberg FDR strategy confirms a similar cutoff for FDR 1% . To independently confirm our FDR methodology, we considered the proportion of significant SNPs within the region of protein-DNA contact . We used recombination rate data We used 31,128 SNPs identified in our resequencing as markers to generate a map of identity-by-descent  and sequence context at 14 positions in the core CTCF motif using the mutualInfo function of the R package bioDist. To estimate the significance of the mutual information values, we applied a bootstrap method for each pair of positions tested. We calculated p-values from 2,000 iterations of resampling per pair of positions . To accoWe downloaded phyloP based on a 44-way vertebrate alignment from the UCSC Genome Browser. SNPs were ranked in decreasing order of the phyloP score at the location of the SNP, thus ranking sites with the most indication of purifying selection the likely to be disrupted by a SNP. To measure the predictive power of models of protein-DNA binding energy, we used a position weight matrix to compute the difference in log-odds score between the two alleles of each SNP. Sites with the largest difference between alleles at the location of the SNP were ranked as most likely to be disruptive. Plots were generated using the R package ROCR http://genome.ucsc.edu, version hg18), and have been deposited in GEO (GSE30263). Resequencing data are available in the SRA (SRP009457), and the capture resequencing array design in GEO (GPL14147).ChIP-seq data are viewable in the UCSC Genome Browser  Example site showing differential occupancy among the 6 grandchildren (B) The alleles transmitted to each grandchild illustrate identity by descent (IBD). PF, paternal grandfather; PM, paternal grandmother; MF, maternal grandfather; MM, maternal grandmother. Note that the grandchildren who share alleles from the same grandparent have more similar occupancy; compare the ChIP-seq signal in (A) for the grandchildren who inherited an allele from the paternal grandmother (PM) for  to the signal for those who inherited an allele from the paternal grandfather .  Aggregate Haseman-Elston linkage analysis sites exhibiting differential occupancy in the 6 grandchildren, analyzing occupancy at (C) a positive control of 47 autosomal sites where already associated with a SNP or (D) the remaining 50 autosomal sites not significantly associated with a SNP. A negative slope indicates that sib pairs with more similar inheritance at the binding site also exhibit more similar occupancy at that site, thus implying that the variation in occupancy is heritable. (E) Permutation analysis to quantify significance of slopes from . Both sets of sites (arrows) show significant heritability compared to permuted data, and the signal is weaker for sites not directly implicated in the association analysis (right arrow).(EPS)Click here for additional data file.Figure S2Comparison of association and allele-specific analyses. (A–B) Y-axis indicates the number of SNPs tested for (A) association with occupancy and (B) allele-specific occupancy at a given position (x-axis) relative to the known CTCF motif (below). Color indicates alleles of the SNPs at each position. Gray shading indicates the extent of protein-DNA contact. Although SNPs were found distributed uniformly throughout the resequencing window, note that in (B) fewer SNPs are testable overall and at the flanks given the need for heterozygous SNPs with high ChIP-seq coverage, despite several additional replicates. We observed a slight reduction in SNPs directly over the core motif, corresponding to the most evolutionarily conserved region. (C–D) SNPs significantly associated with differences in ChIP-seq occupancy (C) and sites demonstrating allele-specific occupancy (D). Color indicates higher-occupancy allele. Both analyses demonstrate an enrichment of significant SNPs over the region of protein-DNA contact, though to a greater extent in (C). (E–F) Same as (C–D), but color indicates SNPs for which the higher-occupancy allele  also has a higher log-odds score in the canonical motif and gray indicates SNPs that affect occupancy, but the higher-occupancy allele has a lower score in the motif. In comparison to the allele specific occupancy (F), the regression analysis (E) identifies more sites (325 vs. 181), has a higher concentration of significant SNPs within the region of protein-DNA contact (85% vs. 66%), and has a higher proportion of SNPs matching the expected binding energetics (84% vs. 68%).  are reproduced from (EPS)Click here for additional data file.Figure S3SNPs not affecting occupancy show no relationship between the magnitude of the perturbation and the strength of motif match. Y-axis, magnitude of perturbation, represented by the difference in log-odds scores between the two alleles. X-axis, log-odds score of motif match, stronger matches to the right, label represents lower limit of bin.(EPS)Click here for additional data file.Figure S4Positive predictive value of PWM model to predict the effect of SNPs on occupancy. X-axis measures stringency of cutoff, represented by the difference in log-odds scores between alleles. At a log-odds difference cutoff of 7.1, 41% of predictions represent true positives.(EPS)Click here for additional data file.Figure S5cis with ChIP-seq occupancy (A) Histogram of p-values for all tested binding sites (B) Histogram of FDR-adjusted q-values (C) Effect size of SNPs associated with differences in occupancy (FDR 1%), measured by r2 of a linear regression (D–F) Allele-specific occupancy at heterozygous sites (D) Histogram of p-values for all tested binding sites (E) Histogram of FDR-adjusted q-values (F) Effect size of SNPs associated with allele-specific occupancy (FDR 0.005%), measured by the log of the ratio of the counts of reads mapping to the higher and lower alleles.Statistical identification of association with differential occupancy and allele-specific occupancy. (A–C) Association of SNPs in (EPS)Click here for additional data file.Figure S6Hierarchical clustering confirms strength-dependent buffering. (A) Normalized ChIP-seq density for 379 CTCF sites (Y-axis) with polymorphism in the core region of protein-DNA contact (positions 0–13) in the six grandchildren (X-axis). Hierarchical clustering resolves three clusters (labeled at right). (B) However, the percentage of polymorphisms that significantly affect binding is higher in cluster 3 (42%) than in cluster 1 and 2 (33% and 27%). (C) The sites comprising the three clusters are distinguished by their overall ChIP-seq intensity, with cluster 1 being the weakest sites, cluster 3 being intermediate, and cluster 2 being the strongest sites; compare to (EPS)Click here for additional data file.Figure S7Statistical significance of the mutual information between sequence context and SNPs affecting occupancy. P-values estimated by bootstrap; two interactions with p<0.0025 were considered significant Click here for additional data file.Table S1Summary of ChIP-seq data mapped to customized genomes with Mosaik. Tags used in analysis indicates the uniquely aligned tags remaining after removing duplicates. Enrichment indicates the proportion of tags mapping to a ChIP-seq peak, representing the enrichment over background and indicating the quality of the data. Biological replicate structure: Replicates a and b were used in the regression analysis. 4 further available replicates  were used to add power for the allele-specific occupancy analysis, but were not used elsewhere. * Replicate “b” of GM12870 was only used for the linkage analysis.(TXT)Click here for additional data file.Table S2Pearson correlation of two replicates per individual of ChIP-seq data. Correlation performed after normalization at signal at ChIP-seq binding peaks.(TXT)Click here for additional data file.Table S3Summary of resequencing data mapped using bwa. Tags used in analysis indicates the uniquely aligned tags remaining after removing duplicates. On-target percent indicates the fraction of tags mapping back to within ±100 bp of a resequencing target.(TXT)Click here for additional data file.Table S4Survey of the effect of SNPs at transcription factor binding sites - 36,834 CTCF sites. SNPs are broken down by location relative to motif in the first two columns, either within the region of protein-DNA contact or outside it. The third column summarizes SNPs regardless of location relative to the motif; sites with multiple overlapping SNPs are counted once.(XLS)Click here for additional data file.Table S5Location of ChIP-seq binding positions in CEPH Pedigree 1459 called by SPP.(TXT)Click here for additional data file.Table S6Resequencing SNP calls in CEPH Pedigree 1459. chrom, chromStart, chromEnd ID, Strand, the 0-indexed hg18 location of the SNP. numNonRefAllelesGrandparents, number of nonreference alleles in 4 grandparents; genotype, nonreference / reference allele; GM12864-74, called genotypes with 00 indicating missing data.(TXT)Click here for additional data file.Table S7Sites tested in association analysis. chrom, chromStart, chromEnd, Strand, the strand-oriented hg18 coordinates of 134 bp window around the motif. peakID refers to the ChIP-seq peak; flag, nonzero indicates that the SNP was excluded from the analysis; position location of the SNP relative to motif; positionFlag, TRUE indicates position can not be reliably determined. allele.1, allele.2, and chimpAllele, the stronger, weaker and chimp alleles of the SNP. Slope, y.intercept, q.value refer to the regression. Signal.het, ChIP-seq density of the heterozygote class from regression model.(TXT)Click here for additional data file.Table S8Sanger validation of SNPs from capture resequencing. 33 sites tested exhibit no discordance between capture resequencing and Sanger validation. chrom, chromStart, chromEnd, Strand, the strand-oriented hg18 coordinates of the SNP; SNP ID, from (TXT)Click here for additional data file.Table S9Validation of SNPs from capture resequencing using HapMap genotypes. 4,246 sites tested exhibit 244 discordant calls  between capture resequencing and public HapMap genotypes. chrom, chromStart, chromEnd, Strand, the strand-oriented hg18 coordinates of the SNP; SNP ID, from (TXT)Click here for additional data file.


We show that the statistical power of a single single-nucleotide polymorphism (SNP) score test for genetic association reflects the cumulative effect of all causal SNPs that are correlated with the test SNP. Statistical significance of a score test can sometimes be explained by the collective effect of weak correlations between the test SNP and multiple causal SNPs. In a finite population, weak but significant correlations between the test SNP and the causal SNPs can arise by chance alone. As a consequence, when a single-SNP score test shows significance, the causal SNPs contributing to the power of the test are not necessarily located near the test SNP, nor do they have to be in linkage disequilibrium with the test SNP. These findings are confirmed with the Genetic Analysis Workshop 17 mini-exome data. The findings of this study highlight the often overlooked importance of long-range and weak linkage disequilibrium in genetic association studies. In a typical genome-wide association study, single single-nucleotide polymorphism (SNP) association tests, such as score tests ,2, are uIn this study, we show that the causal SNPs contributing to the power of a single-SNP score test are not necessarily located near the test SNP, nor do they have to be in genuine LD with the test SNP. We term this phenomenon the hyper-LD effect. The hyper-LD effect is a consequence of weak correlations between the test SNP and multiple causal SNPs. Score tests performed at rare SNPs are particularly prone to this hyper-LD effect. In this study we highlight the often overlooked importance of weak correlations between distant SNPs in association studies.correlation refers to sample correlation in a finite population, and the term LD refers to the expected value of the sample correlation between alleles at different SNPs.We first derive formulas for computing the power of the score tests in the presence of multiple causal SNPs. We then give theoretical explanations of the hyper-LD effect. We focus our discussion on quantitative trait models, but, by using an argument similar to that in Appendix 1 of Chapman et al. , our resJ diallelic causal SNPs at positions indexed by j = 1, …, J:We consider a quantitative trait model with Y is the vector of trait values; μ is a constant vector of baseline mean trait values; vectors kZ, k = 1, …, K, represent measured covariates, such as age, sex, and an indicator of whether an individual is a smoker; the coefficients kα represent the effects of the covariates on trait values; the jX, j = 1, …, J, are vectors of genotypes; and the coefficients jβ represent the allele effect sizes. To model an additive genetic effect, the genotypes are coded as 0, 1, or 2 according to the number of minor alleles present. Furthermore, we assume that there are no gene-gene or gene-environment interactions and that all individuals in the study are truly unrelated.where kZ, k = 1, …, K. The score statistic u[τ is:Let atistic u for testτ, the variance of u is estimated by:Under the null hypothesis of no association between the trait values and the test SNP Z =  and YYs is the sample variance of the residual trait values (1 is a vector of ones). The score statistic u measures the covariance between the genotype vector τX and the trait value vector Y after adjusting for measured covariates. If a covariate kZ is correlated with τX, then the covariance between τX and Y will decrease after adjusting for kZ. The effect of covariate adjustment is also reflected in the variance estimate v  of the test is determined by the noncentrality parameter λ:If there are one or more causal variants, as in the trait model given by Eq. (1), Under the trait model given by Eq. (1),and the noncentrality parameter takes an intuitive form:N is the number of individuals in the sample,where js1,j2 is defined as:and τjr is the correlation coefficient between (the genotype vectors at) the test SNP τ and the causal SNP j, adjusted for the measured covariates. We refer to jh as the direct effect of SNP j; j. The term τX be the sum or weighted sum of the genotype vectors at the collapsed SNPs.n et al.  to the cEquation  extends n et al.  by lettiWith the help of Eq. (6), we can now summarize the theoretical findings of this study:τjr ≠ 0) with the test SNP.1. The power of a single-SNP score test reflects the cumulative effect τjr = 0.1) between the test SNP and the causal SNPs can collectively give rise to significant power in a score test. For example, 10 causal SNPs, each having a direct genetic effect jh = h and a correlation coefficient τjr = 0.1 with the test SNP would result in a noncentrality parameter of (N − 1)h2, which is not 10 but 100 times greater than what it would be if there were only a single causal SNP with the same effect and correlation.2. In the presence of multiple causal SNPs, individually weak correlations τjr values will be greater than 2.33/N1/2 (= 0.088 when N = 697) by chance. In a genome-wide association study, 1% corresponds to a large number of SNPs.This implies that even between SNPs in linkage equilibrium, about 1% of the τjr will have a positively skewed distribution and thus will be more prone to random fluctuations.4. When either the causal SNP or the test SNP is a rare SNP , the correlation coefficient 1 and a rare SNP2 with the same allele effect size (β1 = β2), the common SNP will contribute more to the power of the score test performed at the rare SNP than vice versa (r21h1 >r12h2): the correlation coefficient is symmetric (r12 = r21), but the common SNP can explain more trait variation (h1 >h2).5. Between a pair of a common SNPFindings 1 to 3 explain the causes of the hyper-LD effect: the phenomenon where the statistical significance of a score test can be explained by the collective effect of weak correlations between the test SNP and distant causal SNPs. Findings 4 and 5 explain why score tests performed at rare SNPs are particularly prone to the hyper-LD effect.p-value smaller than 10−4 in any of the populations, leaving 22,757 SNPs for our analyses.In this section, we confirm our theoretical findings with the Genetic Analysis Workshop 17 (GAW17) mini-exome data. We performed a power analysis and examined the correlations between SNPs in the GAW17 data. We focused exclusively on the unrelated individuals data set, which consists of 697 individuals from seven populations and their genotypes and phenotypes. At each of the 24,487 SNPs, we used the software GenABEL [kZ in Eq. (1)).The power analysis was performed using the quantitative risk factor Q1. The true simulation model  was knowjβ taken to be the true values in the simulation model [α = 0.05 so that the simulated power was strictly between 0 and 1 at most SNPs. The analytical power explains 97.0% of the variance in the simulated power. The strong agreement between the analytical power and the simulated power confirms our theoretical finding 1: The power of a score test can be explained by the cumulative effect of all SNPs that are correlated with the test SNP. Note that the direct genetic effects of the test SNPs cannot explain the simulated power: Among the top 100 SNPs with the highest simulated power, only 7 are true causal SNPs; the other 93 do not have direct genetic effects on the trait values.Figure on model . We estiτjr > 0.1 with SNP C12S706. At SNP C12S706, the cumulative effect of the causal SNPs resulting from correlations is jh of all but one causal SNP (the one exception being SNP C13S522 with jh = 0.23). The power of the score test reflects this cumulative effect. The fact that single-SNP score tests can show high power at SNPs not located near any causal SNP makes single-SNP tests unreliable as a tool for mapping trait genes.We next present a concrete instance of the hyper-LD effect. In this instance, none of the causal SNPs that contribute to the power of the score test are located near the test SNP. The score tests show high power (simulated power = 1) at a cluster of SNPs  on chromosome 12. These SNPs are not causal SNPs in the simulation model. In fact, none of the causal SNPs are located on chromosome 12. Note that the power of the score tests at this cluster of SNPs is still well explained by Eq. (6). For instance, 13 causal SNPs have correlations τ in the GAW17 data, we counted the number of SNPs j having τjr > 0.1 with τ and plotted this number against the minor allele frequency (MAF) at SNP τ in Figure We explained  that in the presence of multiple causal SNPs, even small correlations between the test SNP and causal SNPs can be significant to the power of a single-SNP score test. For each SNP To demonstrate that weak but significant correlations between SNPs can arise by chance alone, we simulated another set of 22,757 SNPs that are in linkage equilibrium with each other: The SNP genotypes were simulated to be independent and in Hardy-Weinberg equilibrium, with MAFs matching those of the SNPs in the GAW17 data. Again, for each simulated SNP, we counted the number of SNPs having correlation coefficients greater than 0.1 with it. The results are shown in Figure τjr > 0.1 with it. The results are summarized in Table τjr > 0.1 with one or more causal SNPs as a result of weak LD or simply by chance. For example, 8,435 independently simulated SNPs have correlations τjr > 0.1 with at least one of the causal SNPs just by chance.For each SNP in the GAW17 data and in the simulated data, we also counted how many of the 39 causal SNPs in the GAW17 data had correlation coefficients We have demonstrated a phenomenon that we call the hyper-LD effect in which the statistical significance of a score test can be explained by the collective effect of weak correlations between the test SNP and distant causal SNPs. Tests performed at rare SNPs are particularly prone to this hyper-LD effect. In the presence of multiple causal SNPs, the results of single-SNP score tests can be dominated by the hyper-LD effect and thus can provide misleading information for mapping trait genes if they are misinterpreted.We emphasize the importance of weak and long-range correlations between SNPs in association studies. These long-range correlations can be due to genuine LD or random fluctuation or both. The magnitude of the random correlations arising by chance will decrease as the population size increases (Eq. (10)), but genuine LD between distant SNPs resulting from processes that reflect population history will persist. We speculate that more causal SNPs will be present in a larger population. If the number of causal SNPs is larger, even weaker correlations will be significant to the power of the association tests. So even in large populations, the hyper-LD effect will still be of concern.Possible approaches to alleviating the hyper-LD effect include increasing the study population size, effectively increasing the MAFs by collapsing rare variants, using gene-set or pathway analysis, and combining information from family-based linkage or association analysis. The effectiveness of these approaches needs to be investigated in future studies.The authors declare that there are no competing interests.YD supervised the study and drafted the manuscript. YD, GM, LS, and RD participated in the study design. All authors participated in the analysis of the data and revision of the manuscript. All authors read and approved the final manuscript.


Typically, genome-wide association studies consist of regressing the phenotype on each SNP separately using an additive genetic model. Although statistical models for recessive, dominant, SNP-SNP, or SNP-environment interactions exist, the testing burden makes an evaluation of all possible effects impractical for genome-wide data.We advocate a two-step approach where the first step consists of a filter that is sensitive to different types of SNP main and interactions effects. The aim is to substantially reduce the number of SNPs such that more specific modeling becomes feasible in a second step. We provide an evaluation of a statistical learning method called “gradient boosting machine” (GBM) that can be used as a filter. GBM does not require an a priori specification of a genetic model, and permits inclusion of large numbers of covariates. GBM can therefore be used to explore multiple GxE interactions, which would not be feasible within the parametric framework used in GWAS. We show in a simulation that GBM performs well even under conditions favorable to the standard additive regression model commonly used in GWAS, and is sensitive to the detection of interaction effects even if one of the interacting variables has a zero main effect. The latter would not be detected in GWAS. Our evaluation is accompanied by an analysis of empirical data concerning hair morphology. We estimate the phenotypic variance explained by increasing numbers of highest ranked SNPs, and show that it is sufficient to select 10K-20K SNPs in the first step of a two-step approach. Genome-wide association (GWA) studies have a history of mixed successes. Although the investigation of numerous phenotypes has led to the detection of relevant single-nucleotide polymorphisms (SNPs), results of GWA studies fall short in explaining the genetic variance in phenotypes that is expected from twin and family studies -3. DetecFurthermore, in a typical GWA study, a univariate phenotype is regressed on each SNP separately. Although simple statistical models are convenient in terms of computational ease and interpretability, these advantages are countered by potential loss of information. Most commonly only the additive genetic model is tested, and only few (if any) covariate main effects are included to maintain computational feasibility. This approach decreases the power to detect recessive or dominant effects, and excludes exploration of SNP-SNP and SNP-covariate interactions -10. It iGenome-wide detection studies might benefit from a two-step approach where the first step consists of substantially reducing the number of SNPs such that more complex models, or models permitting significance testing, can be applied in a second step. Desiderata for a first step filtering method include  methods Statistical learning (SL) methods are designed to find important predictors in large data sets, and can be applied in cases where the number of predictors is much larger than the number of subjects. Different learning methods such as Random Forests and Bayesian Lasso have recently been applied to genome-wide data -19.The three main differences between standard GWA studies and SL approaches are (1) that in the regression model commonly used in GWAS the association between a SNP and the phenotype is tested separately for each SNP whereas in SL all SNPs are analysed jointly,(2) in standard GWAS a specific genetic model is specified a priori that relates a SNP to the phenotype  whereas SL models are built iteratively in a data driven fashion, and (3) the regression model used in standard GWAS is limited to SNP main effects whereas SL methods can capture SNP-SNP and SNP-covariate interactions.k splits can capture k-order interactions. All variables are considered at each step of the tree building algorithm in the search for the “best predictor”, that is, the variable that best increases homogeneity in the daughter nodes. If trees are grown to k splits, then the inclusion of covariates  results in an automatic search for conditional effects of SNPs and covariates.We propose to use a statistical learning method called Gradient Boosting Machine (GBM) as the first step filter. The focus on the current paper is to provide an evaluation of GBM. GBM is a statistical learning method that fulfills the desiderata of a SNP selection method listed above. Previous research has shown that GBM performs as well or better than Random Forests (RF), a more well-known learning method . We chooGBM can be used to rank-order SNPs according to their cumulative predictive performance. The variable importance measure (VIM) used in GBM is similar to the Gini importance commonly used in Random Forests  VIMs forThe main goal of the study is to evaluate the performance of GBM as a filter. We compare the sensitivity of ranking SNPs by p-value resulting from fitting the standard additive GWA model to Manolio et al.  ranking 2>.95) on chromosome 1 that explained approximately 6% of the variance of a normally distributed liability underlying the observed 3-category hair curliness  [−6). Mutations in WNT10A are related to odonto-onycho-dermal dysplasia, characterized by symptoms including dry and misformed hair.Previous GWA studies of hair morphology have shown large as well as small and suggestive effects, making hair morphology a highly suitable phenotype for a comparison of GBM and standard GWA using empirical data. Hair curliness in Europeans varies widely, with 45% of northern populations having straight hair compared to 40% with wavy and 15% with curly hair . A previ, curly) . This la, curly)  Rs118037, curly) -37. In a, curly)  and was , curly)   to estimate the phenotype variance that is explained by increasing percentiles of ranked SNPs compared to using all SNPs . Again, Parametric models such as the regression model commonly used in GWA studies are unlikely to be outperformed by alternative methods if the parametric model is the correct model for the data. The weakness of fitting an a priori specified model lies in the potential for misspecifications and omissions of effects . However, since twin and family studies indicate that for many phenotypes a large part of the genetic effects are in fact additive (but see ), filterThe main meta-parameters of GBM were chosen based on cross validation that was carried out in a small number of cells of the simulation design. The specific settings were (1) number of trees=3000, interaction depth=5, and shrinkage=0.0001. To evaluate simulation results, SNPs were ranked according to p-value (methods 1 and 2), or VIM (method 3). We then calculated the median rank of the simulated SNPs across data sets, and transform the median ranks into percentiles.−6. In addition, following the example of the International Schizophrenia Consortium [−10. QC resulted in selecting 2,359,291 SNPs and N=2235 subjects for our main analyses.The main sample in the empirical illustration consisted of unrelated individuals selected at random from two adult family samples that comprised a total of 3894 individuals from 2447 families. Genotype data were collected in several waves on different platforms, requiring imputation to combine samples. The data collection, genotyping, and imputation are described in Medland et al. . Qualitynsortium , we perfIn the first part of the simulation, we applied GBM, Robust SNP, and a standard additive GWA model to a simulated SNP embedded in 3000 empirical SNPs unassociated with the phenotype. The simulated SNPs explained 0.15, 0.20, or 0.30% of the phenotypic variance, respectively, and had either MAF=0.5 or MAF=0.1.The second part of the simulation demonstrates the advantage of GBM in the presence of SNP-SNP interaction effects . The res−13, 2.97×10−13, 1.63×10−11, and 1.24×10−11, respectively. No other SNP passed the threshold of 5×10−8.In the empirical illustration using the hair morphology data, we compare top ranked SNPs resulting from additive GWAS and GBM. To address the question what proportion of SNPs should be selected in a first step, we estimated the variance in hair curliness that is explained by increasing proportions of SNPs ranked by VIM compared to SNPs ranked by p-value. Using an additive genetic model in a GWAS reproduced previous results concerning rs17646946, rs11803731, rs4845418, and rs12130862 on chromosome 1. The four SNPs reached genome-wide significance with p-values 3.47×10We applied GBM to the hair curliness data using settings that had cross validated adequately in previous analyses of empirical and simulated data of comparable size. The settings are (1) number of trees in prediction model=3000, (2) shrinkage=0.001, and (3) bag fraction=.5, and (4) interaction depth=1. GBM successfully reproduced the GWAS results of the large effects of rs17646946, rs11803731, rs4845418, and rs12130862 in the 1q21 region on chromosome 1. These four SNPs had the highest VIMs. Rs7349332 located in WNT10A on chromosome 2, which reached genome-wide significance in the study by Eriksson et al. [th-9th, that is, within the 0.000375th percentile. To investigate whether the divergence between the additive genetic model and GBM regarding the signal on chromosome 9 was due to limiting the GWAS to an additive model, we tested rs2784081, which had the highest rank, in a dominant and a recessive genetic model. The recessive model resulted in β=0.89 (se=0.18), with associated p-value 5.89×10−07, whereas the dominant model was not significant . The additive model tested in the GWAS had a p-value of 5.08×10−4. Rs2784081 tags the tumor necrosis factor receptor-associated factor 2 gene, TRAF2, located at 9q34. TRAF2 is mainly known for its role in regulating programmed cell death (apoptosis).The GBM results deviated from the additive genetic model results concerning the second largest signal detected with GBM . GBM impApoptosis in the hair follicle is described in detail in Botchkareva et al. . The act2>.9) with rs2784081 was tested in an additive as well as a recessive model. Neither model resulted in statistical significance . Narrowing the sample to Northern Europeans  in order to match population characteristics more closely to our original sample was equally unsuccessful.We attempted to replicate the results concerning rs2784081 using two different independent data sets. The first data set was a sample of N=23,458 unrelated European individuals. The data stem from an ongoing web-based data collection, which is described in detail in Eriksson et al.  and whicp=0.040.The second data set used in our attempt to replicate the effect of rs2784081 consisted of participants of the Twins UK adult twin registry, which is a volunteer cohort of over 10,000 twins from the general population. The part of Twins UK with available genotype data is described in more detail in Hysi et al. . We randSelecting a proportion of top ranked SNPs in a first step filter provides the possibility of fitting more complex models in a second step. To obtain an indication which proportion of SNPs to select, and to further evaluate GBM as a first step filter we estimated how much variance in hair curliness was explained by the top 1k, 5k, 10k, and 20k of the ranked SNPs. The main objective of this part of our analysis was to obtain an indication of how many top ranked SNPs would be necessary to match the estimate of variance explained by all SNPs. This analysis was done using GCTA ,47. We cOne of the advantages of GBM is that it is not necessary to specify a genetic model a priori. As noted by Lettre et al. , limitinkth order interactions). In practice, complex phenotypes are often measured with multiple items. It is easy to show that using a sum score as a proxy of a multidimensional phenotype can entail a considerable loss of information, and therefore loss of statistical power to detect genetic effects [Note that it is possible to ft GBM models that differ with respect to the order of interaction  and correct for Type I error as described in So and Sham, GBM has the additional advantage of providing SNP rankings that do not exclude epistatic effects, and that can include main and interaction effects involving large numbers of potentially interesting covariates.In conclusion, our simulation showed that GBM performs well even under conditions that are optimally favorable for additive genetic GWA methods, and outperforms standard methods in case interactions are present. Furthermore, the illustration showed that GBM replicated previous GWAS results concerning large effects of four SNPs in Trichohyalin gene in the 1q21 region. Also, SNPs tagging