PfastBAT < 5 × 10−8. The gain of power is due to multiple small independent association signals at these loci (e.g. the THRB and FOXP1 loci for schizophrenia). The method is general and can be applied to GWAS data for all complex traits and diseases in humans and to such data in other species.We propose a method (fastBAT) that performs a fast set-based association analysis for human complex traits using summary-level data from genome-wide association studies (GWAS) and linkage disequilibrium (LD) data from a reference sample with individual-level genotypes. We demonstrate using simulations and analyses of real datasets that fastBAT is more accurate and orders of magnitude faster than the prevailing methods. Using fastBAT, we analyze summary data from the latest meta-analyses of GWAS on 150,064–339,224 individuals for height, body mass index (BMI), and schizophrenia. We identify 6 novel gene loci for height, 2 for BMI, and 3 for schizophrenia at  Secondly, the resampling-based methods are computationally demanding as a consequence of a large number of permutations or simulations. While the simulation-based approach (i.e. VEGAS) is significantly less resource-intensive, both VEGAS and PLINK have computing efficiency inversely proportional to s. There are several powerful and/or efficient methods that have been recently developed101112Due to the polygenic nature of most human complex traits and diseases, the effect sizes of individual genetic variants are usually very small, limiting the statistical power to detect them, even in large samples24χ2-statistics over the SNPs using summary data from GWAS and LD correlations between SNPs from a reference sample with individual-level genotypes. We compared fastBAT with VEGAS and PLINK using real genotype data  with real and simulated phenotypes in two scenarios (Methods): I) when individual-level genotype data are available ; II) when individual-level genotype data are unavailable . The correlation between PLINK and VEGAS was slightly smaller (r2 = 0.9989) due to the limited number of simulations in the initial run for VEGAS  but the difference was negligible. For the analysis with real height phenotype data in scenario II (r2 = 0.9862) because of the use of LD from HapMap2 CEU panel to approximate that in the ARIC data. Results from the analysis of the simulated trait in scenario I , despite the sample size of the ARIC cohort  being >80 times larger than that of HapMap2 CEU (n = 90). This suggests that the set-based test is robust to sampling errors in LD estimation10Details of the fastBAT method can be found in Methods. In brief, fastBAT calculates the association p-value for a set of SNPs (e.g. SNPs ±50 Kb of a gene) from an approximated distribution of the sum of nario II , there wenario I  also shoFurthermore, we show using simulations of unlinked SNPs (Methods) that if there is only one causal variant, the p-value from a set-based test is expected to be larger (less significant) than that of the top associated SNP from single-SNP based tests . The gair2 > 0.9) in particular if the causal variants tend to be enriched in genomic regions with lower LDr2 threshold of 0.9 or 0.99) fastBAT method is slightly more powerful than the original fastBAT method in two different simulation scenarios   and genetic architecture of the trait  for the LD-pruned fastBAT method under the null hypothesis that there was no genetic effect  . We re-rhe trait . In pracc effect .r2 threshold value of 0.9 for LD pruning within a set. We identified 50 novel genes loci for height, 8 for BMI and 29 for abbreviated SCZ at a genome-wide significance level  (PfastBAT < 2 × 10−6) in the gene-based analysis and there was no genome-wide significant SNP (PGWAS > 5 × 10−8) within ±1 Mb of the gene. We hypothesize that the reason why the SNPs at these gene loci did not reach genome-wide significance level in GWAS is because of the lack of power, although the sample sizes of those studies were very large (PGWAS < 5 × 10−8) in the latest GWAS. We then performed the same analysis using fastBAT without LD pruning, Pascal-Sum and Pascal-Max, and counted the number of replicated “novel” genes as above (r2 threshold of 0.9 (the default method in the GCTA-fastBAT software tool) discovered the largest number of replicated “novel” genes.We applied fastBAT to summary data from the latest meta-analyses of GWAS for heighth trait) . A novelry large , and we ry large , where wry large  and idenas above . We founP < 5 × 10−8). While a few of these results are likely due to sampling, e.g. PfastBAT just passed the threshold whereas PGWAS of the top associated SNP was slightly below the threshold, there were genes for which PfastBAT was orders of magnitude smaller than PGWAS of the top associated SNP. These include THRB and FOXP1 for height, SCAMP4 for BMI, and FOXP1 and ZNF365 for SCZ. We have shown by simulations above that if there is only one causal variant at a locus, PfastBAT is expected to be larger than PGWAS of the top associated SNP (FOXP1 was associated with both height (PfastBAT = 1.7 × 10−9) and SCZ (PfastBAT = 3.8 × 10−12), consistent with previous evidence that de novo mutations in FOXP1 cause intellectual disability, autism, and language impairment in humansFOXP1 in autism patientsFoxp1 deletion impairs neuronal development and causes autistic-like behaviour in miceOf the novel genes identified by fastBAT using the latest GWAS data, 6 genes for height, 2 for BMI, and 3 for abbreviated SCZ passed the commonly used GWAS threshold p-value  or traits for which there are genes with large effects (e.g. endophenotypes). We have implemented fastBAT with a user-friendly interface as part of the GCTA software package (http://cnsgenomics.com/software/gcta/fastBAT.html). Though the fastBAT method itself is fast, the GCTA-fastBAT implementation has been further optimized by using the efficient EIGEN library (http://eigen.tuxfamily.org) for linear algebra calculation, and by using the parallel computing technique OpenMP for multi-threaded computing. We benchmarked the computational efficiency by simulations (6 permutations) and VEGAS . We further demonstrated by simulations and analysis of real data that in comparison with the recently developed method Pascal , fastBAT has greater accuracy for extremely small p-values and faster running time with lower memory requirement . These represent 1.8%, 4.8% and 3.9% of the total number of genome-wide significant genes identified for height, SCZ and BMI respectively. Of the novel associations, 6 genes for height, 2 for BMI, and 3 for abbreviated SCZ even passed the commonly used GWAS threshold p-value (i.e. PfastBAT < 5 × 10−8)  . For the × 10−8) , which a × 10−8) .et al.et al. study also reported that the single-variant tagging (squared correlation between a sequence variant and its best tagging imputed variant) is much lower, 81% for common and 25% for rare variants, for 1KGP imputation based on Illumina CoreExome arrays. We therefore performed analyses to investigate the loss of power due to imperfect imputation for fastBAT. We used the simulation strategy as described in Yang et al., where we extracted from the UK10K-WGS data the variants that are on Illumina CoreExome arrays, and performed 1KGP imputation based on these variants (PfastBAT) based on WGS data and that based on 1KGP-imputed data. On average across 100 simulation replicates, the mean r2 was 94.2% for common variants and 33.1% for rare variants  using summary data .In the analyses above, we investigated the properties of fastBAT and compared it with the prevailing methods using simulations based on real genotypes data from SNP-array based genotyping or whole genome sequencing (WGS). In the analyses of real data, however, we used GWAS summary data from SNP-array genotyped data being imputed to reference panels, e.g. height and BMI data from HapMap2-based imputation and SCZ data from 1KGP-based imputation. A recent study by Yang variants . We simuvariants . These rhttp://cnsgenomics.com/software/gcta/fastBAT.html). Using this method, we identified novel associations using summary data from the latest meta-analyses of GWAS for height, BMI and abbreviated SCZ. Since the method only requires single-SNP based association p-values and a reference sample for LD estimation, it can be applied to both quantitative trait and case-control studies in humans and other species.In summary, we propose a fast and efficient set-based association test (fastBAT) and implemented it in a user-friendly software tool  where R is the LD correlation matrix for the SNPsT = Σ z2i, i.e. sum of chi-squared statistics of all SNPs. Since the T statistic does not have an explicit cumulative density function, PLINK or VEGAS calculates the gene-based p-value by contrasting the observed T value to an empirical distribution generated from resampling under the null hypothesis (PLINK uses permutations and VEGAS uses simulations)T can be expressed by a quadratic form of the vector z, i.e. T = zTIz with I being an identity matrix. The distribution of a quadratic form of multivariate normal variables can be approximated by the Satterthwaite or Saddlepoint methods22pchisqsum function in R.Let n = 90). In addition, we also simulated phenotypes without any genetic effect to investigate the inflation (or deflation) of the test-statistics for fastBAT under the null.We used GWAS data on 7,663 unrelated individuals (SNP-derived genetic relatedness < 0.025) from the Atherosclerosis Risk in Communities (ARIC) studym unlinked SNPs for 10,000 individuals, where m was the number of SNPs within 50 Kb from UTRs of each gene from the genotype data of the ARIC study, excluding genes with less than 10 SNPs (resulting in 450 genes). Allele frequencies of the SNPs were generated from a uniform distribution, p ~ U, and the genotypes of each SNPs were generated from a binomial distribution, x ~ B. We randomly sampled mc SNP as causal variants, and generated the effect sizes of the causal variants from a standard normal distribution, b ~ N. The phenotypes were simulated following the method used in the GCTA simulation function, i.e. e is the residual. We simulated the residual variance such that each causal variant explained 0.4% of phenotypic variance. We repeated the simulation 10 times for each of the 450 sets under two scenarios, I) one causal variant per gene, and II) three causal variants per gene.To compare the power between set-based test and single-SNP based test, we simulated genotypes of a set of m unlinked SNPs for 10,000 individuals. For each of the 450 sets, we simulated only one causal variant per set. We then added an additional SNP to each set in two scenarios: I) adding a SNP that is in perfect LD with the causal variant, or II) adding a SNP that is in perfect LD with a non-causal SNP selected at random. We then performed fastBAT analysis using these two addition sets and compared the results with the original set.We used the strategy as described above to simulate r2 threshold. As one SNP might be correlated with many others, we counted the number of times each SNP appeared and used this to remove the smallest number of SNPs that still kept the pairwise correlation below the threshold. We then used the remaining SNPs for a fastBAT analysis. We call this method LD-pruned fastBAT.For each set of SNPs (e.g. SNPs centered around a gene), we had a correlation matrix representing LD between all SNPs. We recorded all SNP pairs with squared correlation greater than the LD r2 threshold used for LD pruning. The simulations were based on whole genome sequencing (WGS) data from the UK10K projectet al.We used simulations to quantify empirically the gain or loss of power for LD-pruned fastBAT as a function of the PfastBAT of each gene into a χ2 value with 1 degree of freedom. The power was measured by the mean We performed a gene-based fastBAT analysis of the simulated phenotypes with 20 different LD cutoff values, from 1 to 0.1  for three different SNP panels: all common sequence variants from UK10K, HapMap2 SNPs, and HapMap3 SNPs. As described above, we defined a gene-based set as the SNPs within 50 Kb from the UTRs of a gene. For the ease of calibration, we transformed −6, and MAF ≥ 0.01 were included in the analysis.We performed a gene-based fastBAT analysis (SNPs within 50 Kb from the UTRs of a gene as a set) for height, BMI and SCZ using summary data from the latest meta-analyses of GWAS . We usedTo detect multiple independent signals at the novel gene loci, we performed conditional analyses with GCTA-COJO4How to cite this article: Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep.6, 32894; doi: 10.1038/srep32894 (2016).


Currently, there is limited knowledge about the genetics underlying pigmentary traits in East Asian populations. Here, we report the results of the first genome-wide association study of pigmentary traits (skin and iris color) in individuals of East Asian ancestry.N = 305). Quantitative measures of iris color  were extracted from high-resolution iris pictures (N = 342). We also measured the color differences between the pupillary and ciliary regions of the iris . DNA samples were genotyped with Illumina’s Infinium Multi-Ethnic Global Array (MEGA) and imputed using the 1000 Genomes Phase 3 samples as reference haplotypes.We obtained quantitative skin pigmentation measures (M-index) in the inner upper arm of the participants using a portable reflectometer . One of these markers, rs2373391, which is located in an intron of the ZNF804B gene on chromosome 7, was replicated in one of the Chinese samples (p = 0.003). For iris color, we observed genome-wide signals in the OCA2 region on chromosome 15. This signal is driven by the non-synonymous rs1800414 variant, which explains 11.9%, 10.4% and 6% of the variation observed in the b*, a* and L* coordinates in our sample, respectively. However, the OCA2 region was not associated with iris heterochromia.For skin pigmentation, we did not observe any genome-wide significant signal. We followed-up in three independent Chinese samples the lead SNPs of five regions showing multiple common markers  with good imputation scores and suggestive evidence of association (Additional genome-wide association studies in East Asian samples will be necessary to further disentangle the genetic architecture of pigmentary traits in East Asian populations. Human pigmentation diversity is primarily driven by the type, amount and distribution of melanin in the skin, hair and iris. Pigmentation is a polygenic trait, and the last decade has witnessed numerous efforts to elucidate the genetic architecture of pigmentation through association and functional studies . There hHERC2 gene , which is close to OCA2, are strongly associated with blue eyes, and have also been associated with light skin pigmentation in GWAS.  and rs74653330 (Ala481Thr) have been associated with light skin and eye color  in MC1R has also been associated with pigmentation in East Asia  from high-resolution pictures of the iris. The samples were genotyped with Illumina’s MEGA array, and imputed with the 1KG Phase 3 reference samples. We followed up the main signals observed in our GWAS in two independent East Asian samples. This study provides important insights about the architecture of skin pigmentation in East Asian populations.Recruitment of study participants was carried out at the University of Toronto (Canada). All participants ranged between 18 and 35 yr of age and were recruited using online and print advertisements directed towards the University of Toronto student community. A personal questionnaire was administered to each participant to determine their age, sex and whether or not they had been diagnosed with any pigmentation-related diseases or disorders.Biogeographical ancestry was determined using information from the personal questionnaire, which inquired about the ancestry, place of birth and first language of each participant’s maternal and paternal grandparents. The sample used in this study comprised individuals who had grandparents from China, Japan, South Korea or Taiwan. In the few cases where information about grandparents was not available, we used information about both parents to assess biogeographical ancestry. The total number of individuals included in the study was 425. The study was approved by the University of Toronto Research and Ethics Board (Protocol Reference #27015), and all participants provided written informed consent. A 2-ml saliva sample was obtained from each participant using the Oragene-DNA (OG-500) collection kit . All participants were instructed not to eat, drink or smoke for at least 30 min prior to obtaining the sample to ensure maximal sample purity. DNA was isolated from each sample using the protocol provided by DNA Genotek and eluted in 500 ml of TE  buffer. Prior to genotyping, 23 DNA samples were excluded due to poor DNA quality. The final number of samples genotyped was 402.M-values have darker pigmentation). Measurements were taken three times on the inner skin of the upper right arm and pigmentation was reported as the mean of the three measures, after excluding outliers. High-resolution pictures of the right iris of each participant were taken with a Fujifilm Finepix S3 Pro 12-megapixel DSLR mounted on a Nikor 105-mm macro lens. To control for lighting and exposure, photographs were taken with a coaxial biometric illuminator to deliver a constant and uniform source of light to each iris at 5,500 K (D55 illuminant). All photographs were taken under the same setting , and melanin levels were reported as M index  markers, 4/removal of markers with allele frequencies <0.01, 5/removal of markers not present in the 1000 Genomes reference panel, or that do not match on chromosome, position and alleles, 6/removal of A/T or G/C SNPs with MAF >40% in the 1000 Genomes East Asian reference samples, and 7/removal of SNPs with allele frequency differences >20% between the study sample and the 1000 Genomes East Asian reference sample. After these QC steps, we retained 377 samples and 520,076 markers.Genotyping was carried out with Illumina’s Infinium Multi-Ethnic Global Array (MEGA) at the Clinical Genomics Centre  using standard protocols. The MEGA array, which includes approximately 1.7 million markers, was designed to capture common genome variation in diverse population groups. Four samples were included as blind duplicates, and the concordance rate was in all samples higher than 99.99%. We used the program GenomeStudio to carry out the basic QC steps recommended by Illumina. After this initial QC step, approximately 1.4 million were retained for further analyses. The number of autosomal markers included was approximately 1.36 million. We performed additional QC steps to remove samples and markers, according to the following criteria, Sample QC: 1/removal of samples with missing call rates <0.9, 2/removal of samples that were outliers in Principal Component Analysis (PCA) plots, 3/removal of samples with sex discrepancies, 4/removal of samples that were outliers for heterozygosity, and 5/removal of related individuals (pi-hat >  0.2). Marker QC: 1/removal of markers with genotype call rate <0.95, 2/removal of markers with Hardy-Weinberg After performing the QC steps described above, the samples were phased using the program SHAPEIT2 and imputed at the Sanger Imputation Service, using the Positional Burrows-Wheeler Transform (PBWT) algorithm , and theWe used the program EIGENSOFT to perform PCA and evaluate population stratification after pruning markers in high LD and removing regions showing high LD or genomic complexity.M-values as the dependent variable, and sex and the first four Principal Component Axis as independent variables and saved the standardized residuals. A similar process was carried out for the L*, a*, b* and delta iris values, but in this case, due to deviations from normality, the unstandardized residuals were transformed using the rank-based inverse normal transformation. The M-value residuals and the L*, a*, b* and delta transformed residuals were used as input for the association tests with the program SNPTEST v2 (10 Bayes Factor reporting the ratio of two probabilities: the probability of the data under an unconstrained model (M1), and the probability of the data under a null model (M0) in which there is no effect. For example, a log10 Bayes Factor of 3 indicates that the probability of the data under the model M1 is 1,000-fold higher than the probability of the data under the null model with no genotype effects.As a first step of the statistical analyses, we carried out a linear regression with PTEST v2 , using aOf the 377 samples that were retained after the post-genotyping QC step, some samples had missing phenotype data. The final number of samples with valid skin pigmentation data was 305, and the final number of samples with valid iris color data was 342.p < 10−8) and suggestive signals (p < 10−5) identified in the statistical analyses were annotated using the online SNP-Nexus tool (http://snp-nexus.org/), which provides extensive annotations, including potential effects of non-synonymous coding SNPs on protein function , potential regulatory effects , evidence of evolutionary conservation  and evidence of association with complex diseases and disorders .The genome-wide significant (N = 559). All the participants were male, and the mean age of this sample was 21.13 years. Constitutive pigmentation was measured in the buttocks during the month of September. The second sample was a cohort from Taizhou (N = 568), comprising 404 females and 164 males, with an average age of 44.32 years. Constitutive pigmentation was measured in the upper inner arm in the month of April. Genotyping of the SNPs was carried out using SNaPshot. In both samples, the association of the relevant markers with M-index values was carried out using linear regression, including age, rs1800414 and sex (only for the Taizhou cohort) as covariates. The third sample comprises 346 Han Chinese individuals (College students) for whom constitutive pigmentation was measured in the buttocks and the upper inner arm. The study took place in the month of September. In this case, pigmentation was estimated as the mean of three measurements and reported using L* values . Genotyping was done using Sanger sequencing. The association of the variants with pigmentation was tested with the program PLINK, using sex and age as covariates. Unfortunately, to our knowledge, there have been no studies in East Asia reporting quantitative iris color estimates using the CIELab color space, so it was not possible to follow-up the signals identified for iris color in our East Asian samples.The markers showing the strongest evidence of association with skin pigmentation were followed up in three independent Chinese samples, for which skin pigmentation measures  were also available. The first two samples were collected by the Laboratory of Contemporary Anthropology at Fudan University between 2013 and 2015. Constitutive pigmentation was measured with a DSMII colormeter  and pigmentation levels were reported as M-index values . Pigmentation was estimated as the mean of three measures, after excluding outliers. The first sample was a cohort from the Jinan Military Hospital . p < 5 × 10−8). p-values identified in the GWA study. All the markers listed in the Table have good imputation info scores (info > 0.8), and correspond to regions with multiple significant markers  and good imputation scores. The lead signals for these regions are listed on P-values for all the traits. It can be seen that all the regions show nominal significance in more than one iris color dimension. Additional information about relevant markers within each region is provided in the  markers . The regp < 10−5) were followed up in three independent Chinese samples, for which skin pigmentation measures in the buttocks or inner arm were available . The results of the replication effort are shown in rs2373391 located on the ZNF804B gene on chromosome 7, was replicated in the Physical Examination cohort. In this sample, the A allele was significantly associated with lighter skin pigmentation . This marker also had a concordant effect on the College student sample , but the p-values were not nominally significant. In the Taizhou cohort, in which melanin values were measured in the upper arm, rs2373391 was also not significant and the regression coefficient was close to 0. None of the remaining markers was nominally significant in any of the three replication samples.The lead SNPs showing suggestive evidence of association (SLC24A5 (rs1426654) and SLC45A2 (rs16891982), which have derived alleles with very high frequencies in Europe and very strong effects on skin pigmentation in admixed groups  has a frequency of 27.4% and a good imputation score (info = 0. 983). None of the variants reached nominal significance in our East Asian sample, except the SLC24A5rs1426654 non-synonymous variant, which is present at a very low frequency in the sample .We explored in our East Asian sample the allele frequencies and effect sizes of loci identified in European populations. These results are depicted in d groups . The tabd groups . Five ofOCA2 plays in East Asian populations. In our iris color GWA, we observed a genome-wide significant signal in the OCA2 region. The lead SNP in this region was rs76930569, and this marker showed particularly strong associations with the a* and b* color dimensions . However, rs76930569 was not associated with delta, which is a measure that captures iris heterochromia . This indicates that the genetic architecture of iris color is different from the genetic architecture of iris heterochromia. It is important to note that rs76930569 is in very strong linkage disequilibrium with the non-synonymous SNP rs1800414 . Not surprisingly, the p-values of rs1800414 were very similar to those of rs76930569 in our sample, and most probably this non-synonymous SNP is the causal polymorphism driving the association. A recent study (rs1800414 G variant (Arg615) is functional and leads to skin lightening. In our skin pigmentation analysis, the rs1800414 G variant was also associated with lighter skin pigmentation, but did not reach genome-wide significance . The magnitude of the skin pigmentation effect observed in our study is quite similar to the effects described in previous reports in which the M-index was used to describe constitutive pigmentation  in East Asian populations. We were able to confirm the important role that the gene nt study  using cuOCA2 region, no other regions surpassed the genome-wide significance threshold for skin pigmentation or iris color. However, there were several regions harboring multiple common markers with good imputation scores and suggestive p-values . The gene ZNF804B encodes a zinc finger protein, but it has been poorly characterized. Variants within this gene have been nominally associated with a number of traits in previous GWA studies  is a major determinant of the variation observed. We estimated that rs1800414 explains 11.9%, 10.4% and 6% of the variation observed in the b*, a* and L* coordinates in our sample, respectively. Therefore, there is a substantial amount of variation that remains unexplained in our sample. This highlights the need to carry out more studies using quantitative measures of iris color in East Asia. Categorical definitions of iris color would not be useful in East Asian populations, because in such a classification most of the irises would have been categorized as “brown”, but there is a substantial range of L*, a* and b* values within this category.Unfortunately, we could not follow up any of the suggestive regions identified for iris color, because of the lack of studies in East Asia reporting quantitative measures of iris color. In this study, we show that there is a substantial amount of variation in iris color in East Asia , and thaTo our knowledge, this is the first genome-wide association study of pigmentary traits in East Asian populations. The main weakness of this study is the small sample size, which limits our statistical power to identify variants with small effects on skin and iris pigmentation. Another limitation is that constitutive pigmentation was measured in different body sites  in the discovery and replication samples. Ideally, constitutive pigmentation should be measured in the buttocks, because there is less exposure to UV in this body site. However, often it is not possible to obtain pigmentation measures in the buttocks, and constitutive pigmentation is measured in the inner upper arm. In this site, there may be some exposure to UV light, particularly at certain times of the year, and several studies have reported that measurements of pigmentation in the inner arm are not interchangeable with estimates in the buttocks . In the http://biostats.usc.edu/Quanto.html) we estimated that our skin pigmentation association study (N = 305) had good power (>0.8) to identify variants explaining approximately 12.5% of the variance of the trait. This implies that there are no polymorphic variants in our samples with large effects on skin melanin levels. For iris color (N = 342), our study had good power to identify variants explaining approximately 11% of the variance of the trait, and in fact we identified a SNP with an effect of approximately that size. It will be critical to expand the number of genome-wide association studies in East Asian populations in order to be able to identify genetic markers with smaller effects on skin and iris pigmentation. We cannot exclude the possibility that there are variants with strong effects on pigmentation that have been fixed in East Asian populations due to the action of positive selection. This is something that happened in Europe with the well-known SLC24A5rs1426654 A allele, which is the variant with the largest effect on melanin levels reported in human populations  or present at very low frequencies in the East Asian sample , in agreement with numerous reports indicating that this is the locus with the strongest effect on skin pigmentation so far described in human populations  with causal loci in Europe, and the pattern of LD between rs4268748 and the causal loci, or the allele frequencies of the causal loci are different in European and East Asian populations. Supporting this explanation is the detailed analysis of the MC1R region reported for one of the European samples (Rotterdam study) included in MC1R gene (rs1805007 (R151C), rs1805008 (R160W), rs1805009 (D294H)) showed the most significant association with skin pigmentation in the Rotterdam study sample, and replacing the SNP rs4268748 by the high penetrance variants marginally increased the amount of the phenotypic variance explained. These three MC1R high-penetrance variants, two of which have frequencies higher than 5% in Europe (rs1805007 and rs1805008), are not found or are found in extremely low frequencies in East Asia, and this may explain the absence of an association signal in our study. MC1R variant, rs885479 (R163Q), which is common in East Asia  but much less frequent in Europe was nominally associated with light skin in a Japanese sample. As expected, the rs885479 polymorphism was present in our East Asian sample, with a frequency of the derived 163Q allele of 60.1%. However, this variant was not nominally associated with skin pigmentation in our sample .We followed up in our East Asian sample genetic markers in 6 loci that have been reported to influence skin pigmentation in European populations . It will be critical to carry out additional association studies in East Asian populations in order to uncover additional variants with smaller effects on skin pigmentation and iris color.Here we report the first genome-wide association of pigmentary traits (skin pigmentation and iris color) in East Asian populations. We measured these traits using quantitative methods, and we show that there is considerable variation not only in skin pigmentation but also in iris color, in spite of the fact that using categorical classifications, all the irises in this sample would have been categorized as “brown”. We confirmed the important role that the 10.7717/peerj.3951/supp-1Figure S1Click here for additional data file.10.7717/peerj.3951/supp-2Figure S2Click here for additional data file.10.7717/peerj.3951/supp-3Figure S3Click here for additional data file.10.7717/peerj.3951/supp-4Figure S4CDX, Chinese Dai in Xishuangbanna, KHV, Kinh in Ho Chi Minh City, Vietnam, CHB, Han Chinese in Beijing, CHS, Han Chinese South and JPT, Japanese in Tokyo.Click here for additional data file.10.7717/peerj.3951/supp-5Figure S5Click here for additional data file.10.7717/peerj.3951/supp-6Figure S6All these regions harbour multiple markers showing suggestive significance and good imputation scores .Click here for additional data file.10.7717/peerj.3951/supp-7Figure S7Click here for additional data file.10.7717/peerj.3951/supp-8Figure S8All these regions harbour multiple markers showing suggestive significance and good imputation.Click here for additional data file.10.7717/peerj.3951/supp-9Figure S9All these regions harbour multiple markers showing suggestive significance and good imputation scores .Click here for additional data file.10.7717/peerj.3951/supp-10Figure S10All these regions harbour multiple markers showing suggestive significance and good imputation scores .Click here for additional data file.10.7717/peerj.3951/supp-11Figure S11All these regions harbour multiple markers showing suggestive significance and good imputation scores .Click here for additional data file.10.7717/peerj.3951/supp-12Table S1The table includes variants in the genes SLC24A5 and SLC45A2, which have very high frequencies in Europe and have been reported to have very strong effects on skin pigmentation in admixed groups. The table also includes variants that reached genome-wide significance in a recent GWAS of skin pigmentation in European samples . The effClick here for additional data file.10.7717/peerj.3951/supp-13File S1All the regions indicated in the file have more than one genome-wide significant or suggestive marker with good imputation scores (info score >0.8).Click here for additional data file.


P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10−6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with  Rapid progress in next-generation whole-exome and whole-genome sequencing technologies provides new opportunities for detection of rare genetic variants that control complex traits. However, statistical methods using single-variant association tests that are commonly adopted in genome-wide association studies are generally underpowered for rare variants. The statistical power of association analysis increases when the genetic variants in a genomic region are tested all at once, not individually , 2.Several approaches have been proposed for region-based association analysis of rare variants. These include burden tests based on collapsing rare variants within a region –6, the kMethods using the FDA approach have additional advantages. Their main rationale is reduction of the influence of noise and/or observation errors . MoreoveThe key of all gene-based methods is to combine information about the association between the trait and genotypes of every genetic variant to calculate a single value of the statistical test for the entire region. This pooling can be achieved by merging genotypes for collapsing-based methods , combiniFor methods where information of different genetic variants about a tested hypothesis is summarized, the variant weights can be introduced to the model. Good choices of weights can improve power. The weights are prespecified using any kind of data  that is estimated without using the outcome and reflects the relative contribution of each variant to the test statistics . WeightsWeights have been introduced to the models assuming random genotype effects: collapsing-based and kernel-based methods. However, none of the models assuming fixed genotype effects use weights.In this paper, we introduce weights to a functional linear regression model of fixed genotype effects described for testing an association using both independent and structured samples. We estimate the statistical properties of our new method using Genetic Analysis Workshop 17 mini-exome independent and family data  and a wim genetic variants with known physical locations ti . Let the genetic variants be ordered as t1<…<tm and scaled from  to .Consider a genomic region containing n individuals, let y denote an (n×1) vector of known trait values, X denote an (n×(1+c)) matrix, in which the first column is a vector of units and the other columns are c covariates, and G denote an (n×m) matrix of genotypes of m variants. Here, Gij is equal to the number of minor alleles of the i-th individual for the j-th variant with the location tj.For a sample of n individuals is expressed as:α is a fixed ((1+c)×1) vector of regression coefficients, whose first element measures the intercept and the others measure the effects of c covariates; β is an (m×1) vector of regression coefficients describing the fixed effects of m genetic variants; h is an (n×1) random vector of polygenic effects distributed as N, and ε is an (n×1) random vector of errors distributed as N, where σ2g and σ2e are the respective components of the total variance σ2 = σ2g + σ2e of the trait. Here R and I are an (n×n) relationship and identity matrices, respectively. Model  = Xα + Gβ and a covariance matrix Ω = σ2gR + σ2eI. If the sample consists of unrelated individuals, then R = I and Ω = σ2I.The traditional linear regression model of multiple additive effects for an arbitrarily structured sample of α+Gβ+h+ε.Here α in×1) unknown vector of genetic variant functions (GVFs), and t in .In the framework of the FDA approach, discrete genotypic values of ordered variants  and effects of the variants are interpreted as continuous data . In thisKG and Kβ basis functions, respectively. According to [ϕ(t) = (ϕ1(t),…,ϕKG(t))T is a (KG×1) vector of basis functions that are used to smooth the genotypes; Φ is an (m×KG) matrix with an element Φij = ϕj(ti); ψ(t) = (ψ1(t),…,ψKβ(t))T is a (Kβ×1) vector of basis functions that are used to smooth the genetic effects; and, finally, Kβ×1) vector of model regression coefficients.By applying FDA, GVFs and BSF can be described by sets of rding to , G~(t) aSubstituting the expressions for m×Kβ) smoother-matrix W is formed from two sets of basis functions, ϕ(t) and ψ(t), intended for smoothing genotypes and their effects, respectively. It depends on the type and number of the predefined basis functions, as well as on the positions of genetic variants in the region. In fact, the matrix W converts the (n×m) design matrix, where each row is a set of m individual’s real genotypes, into a new (n×Kβ) design matrix where each row is a set of Kβ linear combinations of m real genotypes. So, models  in model (β of size (m×1) in model   m×Kβ) sm size Kβ× in modelUsually, identical sets of basis functions  are used for GVFs and BSF. In this case, the model with both genotypes and their effect smoothed becomes equivalent to that without genotypes smoothing (the beta-smooth only model) . Howevery follow a multivariate normal distribution with the mean vector E(y) = Xα + GWβF and the covariance matrix Ω = σ2gR + σ2eI. In this case, two hypotheses are compared, H0: βF = 0 versus H1: βF ≠ 0. The number of parameters of interest is Kβ in model (m in model (Model  assumes in model  and m inΘ is an (m×m) diagonal matrix of weights for m genetic variants. The new smoothing matrix ΘW in model (n×Kβ) design matrix. Before the weighing procedure, each element of the design matrix (GW) represents some linear combination of m real genotypes. In the new design matrix (GΘW), the influence of each genetic variant in this combination is controlled by weight prespecified for this variant. For the variants with higher weights, their impact in the design matrix is higher than that in model  with the prespecified parameters a1 and a2 evaluated at MAF for the j-th variant [The stronger the differences between weights of causal and non-causal variants, the higher the power of an association test. A simplest a priori supposition about what variants are causal is that deleterious mutations are expected to be rare. In this case, the weights are defined by the minor-allele frequency (MAF), for example, as the beta distribution density function √ variant .K)).We compared two standard beta-smooth only models: 15 B-spline or 25 Fourier basis functions. Such numbers of B-spline and Fourier basis functions have been recommended by Fan et al.  and testNI), where G is a matrix of genotypes for variants selected to be causal, β is a vector of additive effect sizes of genetic variants, R and I are the relationship and identity matrices (R = I for the population sample), h2 is heritability .We used genotypes of Genetic Analysis Workshop 17 (GAW17 ) family-β = 0). We analyzed gene regions with > 25 polymorphic exome genetic variants of the GAW17 data sets to avoid overparametrization under chosen Kβ = 25  and simulated 5×105 replicates to obtain 3×107 regional P values. The type I error was estimated as the proportion of P values that are less than alpha, with alpha ranging from 0.05 to 2.5×10−6.To estimate the type I error, we simulated the trait without effects of genetic variants ( 25 (see  for detaP ≤ α. The type I errors are very close to the declared levels.Gβ in the above formula). The following scenarios were considered: 1) the proportion of causal variants in the regions is 0.05, 0.1, or 0.2; 2) the proportion of effects that have the same direction is 0.5, 0.8, or 1; 3) either all genetic variants or only rare variants with MAFs ≤ 0.03 used to select causal variants; 4) for each causal variant j, the effect size was simulated as (i) |βj| = log(s)|log10(MAFj)|/2 similar to [s being equal to 2, 3, 5, or 7  or as (ii) s being equal to 0.01, 0.02, 0.03, 0.05, or 0.1 .For power estimation, we selected gene regions that contained ≥ 30 polymorphic genetic variants and ≥ 10 rare variants with MAFs ≤ 0.03 . In each replicate, we randomly selected one of these regions for simulation of the region-specific genetic component of the trait : 1) a1 = a2 = 0.5 and 2) a1 = 1; a2 = 25. To varying degrees, these functions give more weights to rare variants |log10(MAFj)|/2. Increasing the weights of rare  variants allows increasing the power of functional linear models. For the test using the weighting function with the parameters a1 = 1 and a2 = 25, the power is higher than the powers of the unweighted test for all the scenarios. For family data, the test using the weighting function with a1 = a2 = 0.5 has the power higher than does the unweighted model, but lower than does the weighted model with a1 = 1 and a2 = 25. For population data, the effect of weighting is less than that for the family sample, and weighting with the parameters a1 = a2 = 0.5 seems to have advantage over the unweighted model only for large effect sizes. This pattern is consistent for both types of basis functions.The same effect of weighting was observed for scenarios, where the effect size was simulated as (1-MAFj) . In this(1-MAFj) .βj| = log(s)|log10(MAFj)|/2, we did not observe an increase in power  were excluded from analysis. We estimated the effect of weighting on the scenarios where causal variants were selected from both rare and common variants. The weighted models demonstrate an increased power only for the scenarios where the effect size was simulated as (1-MAFj) . For thein power . For allAs can be seen, for most scenarios, the models using Fourier basis show a higher power than the models using B-spline basis. This can be in part explained by a different number of basis functions: 15 for B spline and 25 for Fourier bases. However, we compared the power of these two models using the same numbers of basis functions and demonstrated that the models using Fourier basis have a higher power for both 15 and 25 basis functions . Therefoa1 = 1; a2 = 25 or a1 = a2 = 0.5. No loci reached a common sense Bonferroni-corrected significance level in an exome-wide screen of the BP traits. Two loci reached P < 10−5 for DBP: P = 8.2×10−6 for the VMP1 gene (encoding vacuole membrane protein 1) with the B15 model and the weighting function parameters a1 = 1; a2 = 25, and P = 9.1×10−6 for the MC1R gene with the unweighted F25 model. The unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively, for the VMP1 gene. MC1R has already been found to be associated with heart failure . For VMP1, an association with lipoprotein-associated phospholipase A2 activity (a marker of increased cardiovascular risk) has been shown [We analyzed blood pressure traits measured in the ORCADES sample , 26. Theen shown .P < 0.1 in at least one analysis. SDHB, KCNJ5 and SLC12A1 always, KCNJ1 and KLHL3 in most cases) had lower P values with weighted than unweighted models. The unweighted model was always better for WNK4, although there was no large difference between three models: unweighted, weighted with a1 = a2 = 0.5 and weighted with a1 = 1; a2 = 25. The Fourier and B-spline models showed similar behavior—as did the models with two types of weights.For positive control, we looked at 28 known Mendelian BP genes [We proposed a new weighted functional linear model for gene-based association analysis and demonstrated that the power of existing methods can be increased by introducing weights to functional linear models.Our new model is the first weighted model with fixed genotype effects for region-based association analysis. Although weighting of predictors into the complete multiple linear regression model is meaningless, we showed how weights can be introduced into reduced models such as FLM. We propose that our weighting procedure can be generalized to other models of the same class, e.g. to principal component analysis based models. To date, no attempt has been made to increase their power with the help of weights assigned to different genetic variants in a way similar to what was successfully done for the models using collapsing and variance component approaches , 21, 22.The weights are defined as allele-specific coefficients that control the relative importance of each variant to test the association. Only some of the variants in the region are causal, and the rationale for introducing weights is to increase the impact of exactly these variants in the test statistics.wj = Beta with the prespecified parameters a1 and a2 evaluated at the MAF for the j-th variant. The beta density is flexible and can accommodate a broad range of scenarios. By setting 0 < a1 ≤ 1 and a2 ≥ 1, the weight of each rarer variant can be increased and the weight of each common variant, decreased. Normally the a1 = 1 and a2 = 25 values are used in the kernel-based methods, because in this case the weight of each rare variant is increased, while for variants with MAF 1%–5% still put decent nonzero weights [a1 = 1 and a2 = 25 had the highest power under many simulation scenarios, but this benefit was not obvious on real data. This could be explained by the difference in sample sizes and, therefore, MAFs. As it can be seen in a1 = 1 and a2 = 25 differentiate well between MAFs from 0.1 to 0.001, but assign almost equal weights to all MAFs < 0.001. On the contrary, model with a1 = a2 = 0.5 assigns increasingly higher weights to lower MAFs.In addition to the common disease—common variants hypothesis, the common disease—rare variants hypothesis has been proposed to explain missing heritability . The lat weights . We haveβ) of rare and common variants was large. When the effect size of common variants is not small, weighted models can be ineffective. However, in this case genetic variants can easily be identified by single point association analysis. Regional association analysis has been specially proposed to identify rare genetic variants.The effectiveness of the weighted FLM also depends on the effect sizes of common variants. We demonstrated that weighting by MAFs increased the power only in those scenarios where the difference between effect sizes , while all FDA-based models showed P values from 0.05 to 2.5×10−5 for this gene. FDA-based and kernel-based methods gather different relevant information. They model fixed and random effects, respectively, and often identify different loci. To put together the advantages of different tests, new methods for their combining have been proposed [Good choices of weights can improve power. However, different weights may be optimal for different regions, as we have demonstrated with real data. The same behavior can be observed for weighted SKAT models , 33. Theproposed , 34. OurThe proposed weighting via MAFs is the simplest and does not require any additional research activity. Even so, it still appears to gain power when its assumption holds. Good a priori knowledge about what genetic variants are more likely to be causal would allow for even better efficiency. If a priori information is available, for example, some variants are predicted as functional, damaging or loss-of-function via Polyphen-2  or otherS1 FigOther model parameters and notations are as in (TIF)Click here for additional data file.S2 FigProportion of causal variants is the proportion of all variants within the region . Other model parameters and notations are as in (TIF)Click here for additional data file.S3 FigProportion of causal variants is the proportion of all variants within the region . Other model parameters and notations are as in (TIF)Click here for additional data file.S4 FigUnweighted FLM was used on familial data. B: B-spline basis functions; F: Fourier basis functions. The effect size for the j-th variant was modeled as |βj| = log(s)|log10(MAFj)|/2 using rare variants. Other model parameters and notations are as in (TIF)Click here for additional data file.S5 FigThe differently weighted FLM based on the B-spline basis functions was used. The notations of the models are the same as in (TIF)Click here for additional data file.


Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution. Genome-wide association studies (GWAS) have become a key tool to discover genetic markers for complex traits; however, environmental factors that interact with genes are rarely considered. Here, the authors conduct a GWAS of obesity traits, and find that smoking may alter genetic susceptibilities. Recent genome-wide association studies (GWAS) have described loci implicated in obesity, body mass index (BMI) and central adiposity. Yet most studies have ignored environmental exposures with possibly large impacts on the trait variance1It is often noted that currently smoking individuals display lower weight/BMI and higher waist circumference (WC) as compared to nonsmokers45791212A comprehensive study that evaluates smoking in conjunction with genetic contributions is warranted. Using GWAS data from the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, we identified 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity, assessed by BMI and central obesity independent of overall body size, assessed by WC adjusted for BMI (WCadjBMI) and waist-to-hip ratio adjusted for BMI (WHRadjBMI). By accounting for smoking status, we focus both on genetic variants observed through their main effects and GxSMK effects to increase our understanding of their action on adiposity-related traits. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.P-value<5 × 10−8 while Approach 4 used Bonferroni adjustment after screening. Lead variants >500 kb from previous associations with BMI, WCadjBMI, and WHRadjBMI were considered novel. All association results are reported with effect estimates oriented on the trait increasing allele in the current smoking stratum.We meta-analysed study-specific association results from 57 Hapmap-imputed GWAS and 22 studies with Metabochip, including up to 241,258 (87% European descent) individuals  while accounting for current smoking (SMK)  and nine having significant GxSMK interaction effects  are >500 kb from a previously-identified WCadjBMI-associated variant (rs16957304); however, after conditioning on the known variant, our signal is attenuated , indicating that this finding is not novel. For WHRadjBMI, 32 loci were identified in Approach 1 (SNPadjSMK), including one novel locus near HLA-C, with no additional loci in Approach 2 , 58 loci reached GWS in Approach 1  after conditioning on at least one nearby height and hip circumference adjusted for BMI (HIPadjBMI) SNP, but association signals remained independent of other related SNP-trait associations. For WHRadjBMI, our GWAS signal was attenuated by conditioning on two known height variants (rs6457374 and rs2247056), but remained significant in other conditional analyses. Given high correlations among waist, hip and height, these results are not surprising.We used GCTAEPHA3 and 1 near INADL. For WCadjBMI, 2 novel loci were identified near RAI14 and PRNP. For WHRadjBMI, five novel loci were identified in secondary meta-analyses near BBX, TRBI1, EHMT2, SMIM2 and EYA4. A comprehensive summary of nearby genes for all novel loci and their potential biological relevance is available in Several additional loci were identified for Approaches 1 and 2 in secondary meta-analysis , the variant minor allele (G) exhibits a decreasing effect on BMI in current smokers (βsmk=−0.047) but no effect in nonsmokers (βnonsmk=0.002). Previous studies identified nearby SNPs in high LD associated with smoking ADAMTS7)CHRNA5-CHRNA3-CHRNB4 gene cluster has been associated with lower BMI in current smokersCHRNA5-CHRNA3-CHRNB4 genes encode the nicotinic acetylcholine receptor (nAChR) subunits α3, α5 and β4, which are expressed in the central nervous systemApproach 3 directly evaluated GxSMK interaction SNPint; . For priGRIN2A) with opposite effect directions by smoking status was identified for Approach 3  an effect in one stratum (smokers or nonsmokers) and a less pronounced but concordant effect in the other stratum, or (ii) an effect in the larger nonsmoker stratum and no effect in smokers . In contPbinomial=1.2 × 10−4).When examining the smoking specific effects for BMI and WCadjBMI loci in our meta-analyses, no significant enrichment of genetic effects by smoking status were noted. . HoweverPSNPadjSMK=1 × 10−8 to PSNPadjSMK=0.1  for BMI at each threshold, with more variance explained in smokers. For WCadjBMI, the difference was significant for SNP sets beginning with PSNPadjSMK≥3.16 × 10−4, and for WHRadjBMI at PSNPadjSMK≥1 × 10−6. In contrast to BMI, SNPs from Approach 1 explained a greater proportion of the variance in nonsmokers for WHRadjBMI. Differences in variance explained were greatest for BMI (differences ranged from 1.8 to 21% for smokers) and lowest for WHRadjBMI (ranging from 0.3 to 8.8% for nonsmokers).We calculated the variance explained by subsets of SNPs selected on 15 significance thresholds for Approach 1 from jSMK=0.1 , Fig. 4.These results suggest that smoking may increase genetic susceptibility to overall adiposity, but attenuate genetic effects on body fat distribution. This contrast is concordant with phenotypic observations of higher overall adiposity and lower central adiposity in smokers46We conducted thorough searches of the literature and publicly available bioinformatics databases to understand the functional role of all genes within 500 kb of our lead SNPs. We systematically explored the potential role of our novel loci in affecting gene expression both with and without accounting for the influence of smoking behaviour , an intronic variant in the GTeX and  and is hCD47 (CD47 molecule), near rs670752 for WHRadjBMI , is significantly decreased in obese individuals and negatively correlated with BMI, WC and Hip circumferenceExpression of Several novel loci harbour genes involved in unique biological functions and pathways including addictive behaviours and response to oxidative stress. These potential candidate genes near our association signals are highly expressed in relevant tissues for regulation of adiposity and smoking behaviour  that is key for neutralizing reactive oxygen species introduced by tobacco smoke and obesity27INPP4B has been implicated in the regulation of the PI3K/Akt signalling pathwayThe GRIN2A, near rs4141488, controls long-term memory and learning through regulation and efficiency of synaptic transmissionGRIN2A in the prefrontal cortex in murine modelsGRIN2A and obesity-related phenotypes in the literature, yet memantine and ketamine, pharmacological antagonists of GRIN2A activity34r2>0.7) are within active enhancer regions for several tissues, including liver, fetal leg muscle, smooth stomach and intestinal muscle, cortex and several embryonic and pluripotent cell types , a signalling transducer involved in multiple biological processes related to the nervous system, immune system, and other cellular functions (38SLC23A2 (Solute Carrier Family 23 (Ascorbic Acid Transporter), Member 2), is essential for the uptake and transport of Vitamin C, an important nutrient for DNA and cellular repair in response to oxidative stress both directly and through supporting the repair of Vitamin E after exposure to oxidative agents4043In secondary meta-analysis of European women-only, we identified a significant GxSMK interaction for rs6076699 on WCadjBMI . This SNunctions 36. AlterNMNAT1), upstream of WCadjBMI variant rs17396340, is responsible for the synthesis of NAD from ATP and NMNNMNAT protects against damage caused by reactive oxygen species in the brain, specifically the hippocampusCDK6, near SNP rs10269774, and FAM49B, near SNP rs6470765, are targets of the BACH1 transcription factor, involved in cellular response to oxidative stress and management of the cell cycleNicotinamide mononucleotide adenylyltransferease (PRegression<0.05/26=0.0019), only one SNP remains significant: rs12902602, identified for Approaches 2 (SNPjoint) and 3 (SNPint) for BMI, showed association with SQ (P=1.45 × 10−9).In a look-up in existing GWAS of smoking behaviours )r2>0.7) with SNPs previously associated with smoking-related traits , lung cancer, and cardiovascular diseases  SNPs). However, there are additional variants that may be associated with height, but not previously reported in GWAS examining height, including two for WHRadjBMI near EYA4 and TRIB1, and two for WCadjBMI near KIF1B and HDLBP (PRegression<0.002).Given high phenotypic correlation between WC and WHR with height, and established shared genetic associations that overlap our adiposity traits and height12PSNPint<0.05) for GxSMK interaction on BMI (P<0.005). No smoking-associated SNPs exhibited GxSMK interaction. Therefore, we did not see a strong enrichment for low interaction P values among previously identified smoking loci.Finally, as smoking has a negative (weight decreasing) effect on BMI, it is likely that smoking-associated genetic variants have an effect on BMI in current smokers. Therefore, we expected that smoking-associated SNPs exhibit some interaction with smoking on BMI. We looked up published smoking behaviour SNPs49n on BMI , but onlPSNPadjSMK, PSNPjoint, PSNPInt>5E−8) following meta-analysis with our GIANT results: rs12629427 near EPAH3 (Approach 1); rs1809420 within a known locus near ADAMTS7 (Approach 4) remained significant for interaction, but not for SNPadjSMK; and rs336396 near INPP4B (Approach 3). For WCadjBMI, 3 SNPs were not GWS  following meta-analysis with our results: rs1545348 near RAI14 (Approach 1); rs4141488 near GRIN2A (Approach 3); and rs6012558 near PRNP (Approach 3). For WHRadjBMI, only 1 SNP from Approach 4 was not significant following meta-analysis with our results: rs12608504 near JUND remained GWS for SNPadjSMK, but was only nominally significant for interaction (PSNPint=0.013).We pursued validation of our novel and interaction SNPs in an independent study sample of up to 119,644 European adults from the UK Biobank study . We founA possible limitation of our study may be the definition and harmonization of smoking status. We chose to stratify on current smoking status without consideration of type of smoking  for two reasons. First, focusing on weight alone, former smokers tend to return to their expected weight quickly following smoking cessation713DOCK3, ARFGEF2 and TMEM38B) and two for WHRadjBMI PGRS<1.6 × 10−7, PGRSDiff=4.3 × 10−5), by 27% for WCadjBMI  and by 168% for WHRadjBMI . Therefore, despite potential limitations, much is gained by accounting for environmental exposures in GWAS studies.To assess how much additional information is provided by accounting for SMK and GxSMK in GWAS for obesity traits, we compared genetic risk scores (GRSs) based on various subsets of lead SNP genotypes in various regression models (Methods section). While any GRS was associated with its obesity trait . While many of our newly identified loci support the hypothesis that smoking may influence weight fluctuations through appetite regulation, these novel loci also have highlighted new biological processes and pathways implicated in the pathogenesis of obesity.CHRNA5-CHRNA3-CHRNB4 gene cluster. One novel BMI-associated locus near INPP4B and two novel WCadjBMI-associated loci near GRIN2A and PRNP displayed significant GxSMK interaction. We were also able to identify significant GxSMK interaction for one known BMI-associated locus near ADAMTS7 and for five known WHRadjBMI-associated loci near LYPLAL1, RSPO3, MAP3K1, HOXC4-HOXC6 and JUND. The majority of these loci harbour strong candidate genes for adiposity with a possible role for the modulation of effects through tobacco use.Importantly, we identified nine loci with convincing evidence of GxSMK interaction on obesity-related traits. We were able to replicate the previous GxSMK interaction with BMI within the PSNPadjSMK) by adjusting for current smoking status. Our analyses did not allow us to determine whether these discoveries are due to different subsets of subjects included in the analyses compared to previous studies1P-values that are at least one order of magnitude lower than in previous GIANT investigations, despite smaller samples in the current analysisP values for variants identified in Approach 1 are at least two orders of magnitude lower than previous findings. Thus, adjustment for smoking may have indeed revealed new loci. Further, loci identified in Approach 2, including nine novel loci, suggest that accounting for interaction improves our ability to detect these loci even in the presence of only modest evidence of GxSMK interaction.We identified 18 new loci in Approach 1 , which may be expected given that there are a greater number of variants with higher effect estimates in smokers. For WCadjBMI, there was no enrichment for stronger effects in one stratum compared to the other for our significant loci; however, there was a greater proportion of explained variance in WCadjBMI for loci identified in Approach 1 (SNPadjSMK) in nonsmokers. For WHRadjBMI, there were significantly more loci that exhibit greater effects in nonsmokers, and this pattern was mirrored in the variance explained analysis. The large difference between effects in smokers and nonsmokers likely explains the sub-GWS levels of our loci in previous GIANT investigationsBBX, KIF1B, SOX11 and EPHA3) and, like other obesity-associated genes, may be involved in previously-identified pathways linked to neuronal regulation of appetite , adipo/angiogenesis (ANGPTL3 and TNF) and glucose, lipid and energy homeostasis .Our results support previous findings that implicate genes involved in transcription and gene expression, appetite regulation, macronutrient metabolism, and glucose homeostasis. Several of our novel loci have candidate genes within 500 kb of our tag variants that are highly expressed and/or active in brain tissue , were measured in all studies; waist and hip circumferences were measured in the vast majority. For each sex, traits were adjusted using linear regression for age and ageOur study highlights three traits of interest: BMI, WCadjBMI and WHRadjBMI. Height and weight, used to calculate BMI kg m−, were meThe participating studies have varying levels of information on smoking, some with a simple binary variable and others with repeated, precise data. Since the effects of smoking cessation on adiposity appear to be immediate78Studies with GWAS array data or Metabochip array data contributed to the results. Each study applied study-specific standard exclusions for sample call rate, gender checks, sample heterogeneity and ethnic group outliers . For eacTo obtain study-specific summary statistics used in subsequent meta-analyses, the following linear models  were run separately for men and women and separately for cases and controls for case-control studies using phenotype residuals from the models described above. Studies with family data also conducted analyses with these models for men and women combined after accounting for dependency among family members as a function of their kinship correlations. We assumed an additive genetic model. The analyses were run using various GWAS software P value (<10−6), (iii) imputed SNPs with low imputation quality . To test for issues with relatedness or overlapping samples and to correct for potential population stratification, the study-specific standard errors and association P values were genomic control (GC) corrected using lambda factors /2) were excluded. Lead SNPs that met significance criteria were selected based on distance (±500 kb), and we defined the SNP with the lowest P value as the top SNP for a locus. SNPs that reached genome-wide significance (GWS), but had no other SNPs within 500 kb with a P<1E-5 (lonely SNPs), were excluded from the SNP selection process. Two variants were excluded from Approach 2 based on this criterion, rs2149656 for WCadjBMI and rs2362267 for WHRadjBMI.Before selecting a lead SNP for each locus, SNPs with high heterogeneity -8 level. These variants were then carried forward for a test of interaction, comparing the SMK-stratified specific regression coefficients in the second step (SNPscreen).The right side of P<5 × 10−8 were considered GWS. In Approach 4 (SNPscreen) variants for which the P value of the test of interaction is less than 0.05 divided by the number of variants carried forward were considered significant for interaction. We performed analytical power computations to demonstrate the usefulness and characteristic of the two interaction Approaches.In Approaches 1–3 variants significant at http://locuszoom.sph.umich.edu/) . For each plot, LD was calculated using a multiethnic sample of the 1000 Genomes Phase I reference panels1251Regional association plots were generated for novel loci using the program Locuszoom  study consisting of 9,713 individuals of European descent and 580 individuals of African American descent, and the Framingham Heart Study (FramHS) consisting of 8,481 individuals of European ancestry, both studies imputed to HapMap r22. However, because our primary analyses were conducted in multiple ancestries, each study supplemented the genetic data using HapMap reference populations so that the final reference panel was composed of about 1–3% Asians (CHB+JPT) and 4–6% Africans (YRI for the FramHS) for the entire reference sample. We extracted each 1 MB region surrounding our candidate SNPs, performed joint approximate conditional analyses, and then repeated the steps for the appropriate Approach to identify additional association signals.To determine if multiple association signals were present within a single locus, we used GCTAMany of the SNPs identified in the current analyses were nearby SNPs previously associated with related anthropometric and obesity traits . For all lead SNPs near a SNP previously associated with these traits, GCTA was also used to perform approximate conditional analyses on the SNPadjSMK and SMK-stratified data in order to determine if the loci identified here are independent of the previously identified SNP-trait associations.NUDT3 effect on BMI), medium (BDNF effect on BMI) or large  was used to identify any genes and cis- or trans-eQTLs within 500 kb of our lead SNPs. All genes identified by Snipper were manually curated and examined for evidence of relationship with smoking and/or adiposity. To explore any potential regulatory or function role of the association regions, loci were also examined using several online bioinformatic tools/databases, including HaploReg v4.1 (ref. http://genome.ucsc.edu/), GTeX Portal , and RegulomeDBTo identify genes that may be implicated in the association between our lead SNPs , 2, 3 an4.1 ref. , UCSC Ger2>0.9, calculated in the CEU+YRI+CHB+JPT 1000 Genomes reference panel), or proxy SNPs, were retained for consideration.We used two approaches to systematically explore the role of novel loci in regulating gene expression. First, to gain a general overview of the regulatory role of newly identified GWAS regions, we conducted an eQTL lookup using >50 eQTL studiesSecond, since public databases with eQTL data do not have information available on current smoking status, we also conducted a cis-eQTL association analysis using expression results derived from fasting peripheral whole blood using the Human Exon 1.0 ST Array . The raw expression data were quantile-normalized, log2 transformed, followed by summarization using Robust Multi-array Average−8 to 0.1) from Approach 1 (SNPadjSMK model). First, each subset of SNPs was clumped into independent regions to identify the lead SNP for each region. The variance explained by each subset of SNPs in the SMK and nonSMK strata was estimated by summing the variance explained by the individual lead SNPs. Then, we tested for the significance of the differences across the two strata assuming that the weighted sum of chi-squared distributed variables tend to a Gaussian distribution ensured by Lyapunov's central limit theorem67We estimated the phenotypic variance in smokers and nonsmokers explained by the association signals. For each associated region, we selected subsets of SNPs within 500 kb of our lead SNPs and based on varying P value thresholds  Ever vs Never smokers, (ii) Current vs Non-current smokers and (iii) a categorical measure of smoking quantityTo better understand why our novel variants remained undiscovered in previous investigations that did not take SMK into account, we also conducted a lookup of our novel variants in published GWAS results examining genetic main effects on BMI, WC, WCadjBMI, WHR, WHRadjBMI, and height122 values using a combined ancestry  1000 Genomes Phase 1 reference panelwww.ebi.ac.uk/gwas)492>0.5 with our lead SNP were retained for further interrogation.To further investigate the identified genetic variants in this study and to gain additional insight into their functionality and possible effects on related cardiometabolic traits, we searched for previous SNP-trait associations nearby our lead SNPs. PLINK was used to find all SNPs within 500 kb of any of our lead SNPs and calculate rN=3,457). We compared GRSs limited to previously known lead SNPs (see 0+β1GRS), the adjusted GRS model (TRAIT=β0+β1GRS+β2SMK) and the GRSxSMK interaction model (TRAIT=β0+β1GRS+β2SMK+β3GRSxSMK). Additionally, we used an F statistic to test whether the residual sum of squares (RSS) for the full model including GRSxSMK interaction was significantly different from the reduced model.We calculated several unweighted genetic risk scores (GRSs) for each individual in the population-based KORA-S3 and KORA-S4 studies .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Tables, Supplementary Notes and Supplementary ReferencesBMI association results for all ancestries meta-analyses. Significant SNPs for BMI across all approaches  and strata  for combined ancestries meta-analyses. Summary statistics are presented from stage1 plus stage 2 results.WCadjBMI association results for all ancestries meta-analyses. Significant SNPs for WCadjBMI across all approaches  and strata  for combined ancestries meta-analyses. Summary statistics are presented from stage1 plus stage 2 results.WHRadjBMI association results for all ancestries meta-analyses. Significant SNPs for WHRadjBMI across all approaches  and strata  for combined ancestries meta-analyses. Summary statistics are presented from stage1 plus stage 2 results.BMI association results for European meta-analyses. Significant SNPs for BMI across all approaches  and strata  for European-only analyses. Summary statistics are presented from stage1 plus stage 2 results.WCadjBMI association results for European meta-analyses. Significant SNPs for WCadjBMI across all approaches  and strata  for European-only analyses. Summary statistics are presented from stage1 plus stage 2 results.WHRadjBMI association results for European meta-analyses. Significant SNPs for WHRadjBMI across all approaches  and strata  for European-only analyses. Summary statistics are presented from stage1 plus stage 2 results.Lookup of known main effects SNPS. Lookups of previously identified main effects loci in the current combined ancestries meta-analyses results for BMI, WCadjBMI, and WHRadjBMI.Look up of novel loci from the current analyses in previous GWAS of BMI, WC, WCadjBMI, WHR, WHRadjBMI, height and three smoking behavior traits .


An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD. Heart Disease is the leading cause of death in United States. CoronarySadly, most clinically routine tests such as the 12 lead ECG and serum lipid screening are remarkably insensitive for CHD. For example, in a study of 479 patients admitted for acute chest pain with creatine kinase-MB isoenzyme (CK-MB) and troponin T (TnT) confirmed MI, 12 lead ECG was positive in only 33% and 28% of these patients at admission and post-admission, respectively.[While a variety of approaches, including imaging, mechanical and bio-electrical techniques have been used –10, deveIn the era where the barrier to profile and access omics data is relatively low, the challenge shifts to the mining of more sensitive diagnostic biomarkers. To that end, genome-wide association and exome/genome sequencing studies . Hence, this study was designed to 1) integrate genome-wide genetic and epigenetic data from the FHS obtained using DNA from blood to develop an integrated CHD prediction model, 2) develop a CHD prediction model consisting of conventional risk factors, and 3) compare the CHD prediction performance of these models. While the models in this study are being used to classify individuals based on their current CHD status, they will serve as a proof-of-concept and set the stage for the eventual development of genetic-epigenetic markers for predicting CHD prior to its occurrence (i.e. forecasting).https://dbgap.ncbi.nlm.nih.gov). The participants provided written informed consent to a Framingham Heart Study clinical staff to participate in this study. The University of Iowa Institutional Review Board approved all analyses described in this study.This study was conducted using fully anonymized Framingham Heart Study data obtained through dbGAP (The Framingham Heart Study (FHS) (dbGAP study accession: phs000007) has been described in detail elsewhere., 18 The Based on consent and after removing duplicates, DNA methylation data was available for 2,567 individuals. Genome-wide DNA methylation of the Offspring cohort was profiled using the Illumina Infinium HumanMethylation450 BeadChip.  array. Of the 2,560 individuals remaining after DNA methylation quality control, 2,406  had genotype data. Again, quality control steps have been described in detail in a recent publication. A total For each individual, the following data were extracted from the FHS dataset: age, gender, systolic blood pressure (SBP), high-density lipoprotein (HDL) cholesterol level, total cholesterol level, hemoglobin A1C (HbA1c) level, self-reported smoking status, the use of statins, CHD status and date of CHD established. Since DNA methylation was profiled using biomaterial collected during the eighth examination cycle of the Offspring cohort, all phenotype data obtained during this cycle were used in analyses. An individual was categorized as having symptomatic CHD if they received a positive diagnosis from the Framingham Endpoint Review Committee at or prior to their eighth examination cycle . This categorization criterion reduced the number of individuals from 1,599 to 1,545 .To identify CHD associated genome-wide DNA methylation changes, linear regression analyses were conducted using data from the 1,545 individuals in the training set in R V3.1.2 as delineated in The association between DNA methylation and CHD was determined while controlling for age, gender and DNA methylation laboratory batch effects. Bonferroni correction for multiple comparisons at a genome-wide α = 0.05 was performed for every regression analysis. A total As mentioned in the introduction, the goal of this study was to develop an integrated genetic-epigenetic classifier for symptomatic CHD. To achieve this, training and testing datasets were prepared to develop the classifier and subsequently test its predictive capability. The 1,545 individuals  that survived all quality control steps and had a CHD status at their eighth examination cycle were used for variable reduction, variable selection and model training. Since only 173 individuals were diagnosed with CHD, an undersampling approach similar to EasyEnsemble of the majority class was performed to address the class imbalance. , 23 UsinTo assess the predictive capability of the trained model, data from the 696 individuals removed based on their relatedness were used. Again, CHD status and the eighth examination cycle dates of these individuals were compared to categorize them as either positive or negative for symptomatic CHD. From doing so, the number of individuals was reduced from 696 to 669 . Since only 71 of these 669 individuals were diagnosed with symptomatic CHD, the final test dataset was balanced with 71 randomly chosen controls, resulting in the final test dataset of n = 142.The total number of genetic (SNP) and epigenetic (DNA methylation) probes remaining after quality control measures were 403,192 and 472,822, respectively. Due to the large number of variables , we implemented steps as discussed below to reduce the search space and minimized redundancy in the predictors.Linkage disequilibrium based SNP pruning was performed in PLINK with a wTo reduce the number of DNA methylation loci, first, the correlation was calculated between the 472,822 CpG sites and CHD status. CpG sites were retained if the point bi-serial correlation with CHD was at least 0.1. A total of 138,815 CpG sites remained. Subsequently, Pearson correlation between those 138,815 sites was calculated. If the Pearson correlation between two loci was at least 0.8, the loci with a smaller point bi-serial correlation (i.e. less correlated with CHD) was discarded. In the end, 107,799 DNA methylation loci (~23%) remained for model training.To systematically feed variables into the classification model, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the top 1% methylation and SNPs were calculated and used to rank these loci.scikit-learn in Python V2.7.12 [Using a stratified 10-fold cross-validation approach, Random Forest (RF) classifi V2.7.12  on all eTo compare the performance of our integrated genetic-epigenetic model to a model with conventional CHD risk factors as predictors, a similar approach was employed to build the model on the training data and subsequently test it on the test dataset. The filtering and modeling steps are summarized in “strata” and “sampsize” arguments were used to perform stratified sampling of the minority class. This is a simpler implementation of the undersampling ensemble approach described above. The number of trees (ntree) parameter of this alternative RF classifier was tuned. The same n = 1,545 training set and n = 142 testing set were used to train and test this classifier.Additionally, to compare our ensemble approach to a single RF model, an alternative RF model was developed in R using the randomForest package. The To identify statin use associated genome-wide DNA methylation changes, linear regression analyses were conducted using data from the 1,545 individuals in the training set in R V3.1.2 as delineated in The association between DNA methylation and statin use was determined while controlling for age, gender, DNA methylation laboratory batch effects and CHD status. Bonferroni correction for multiple comparisons at a genome-wide α = 0.05 was performed for every regression analysis. A total The clinical characteristics of the 1,545 and 142 subjects used for training and testing, respectively, in this study are given in The mean and standard deviations of other conventional risk factors are also summarized in Regression of CHD to genome-wide DNA methylation using data from the 1,545 individuals in the training set resulted in 11,497 significant methylation sites (2.4%) after Bonferroni correction for multiple comparisons. The top 30 sites are shown in Eight RF models were built on the eight sub-datasets consisting of genetic, epigenetic, age and gender data from the 1,545 subjects in the training dataset. Standard scikit-learn RF parameters were used to determine the important SNPs and DNA methylation loci. Based on the average accuracy and AUC of the eight classifiers and the information gain criteria of each variable, four CpG sites , two SNPs (rs6418712 and rs10275666), age and gender were retained for prediction. Using the tuned parameters, all eight models were re-fitted to the training dataset. The performance metrics (mean and standard deviations) of these stratified 10-fold cross-validated models are shown in An ensemble of the eight models was used to perform CHD classification in the test dataset. An individual was classified as having CHD if at least four of the eight models voted in favor of CHD. Of the 142 individuals (71 with and 71 without symptomatic CHD) in the test dataset, the CHD status of 110 individuals was predicted correctly, resulting in a test accuracy of 77.5%. The confusion matrix of the prediction is shown in To understand the variance of each of the four CpG sites captured by the conventional risk factors, using training set data, regression analysis was performed and the results are summarized in Additionally, the list of genome-wide DNA methylation signatures associated with statin use, controlling for age, gender, batch and CHD status, which was obtained using training set data was compared to the list of the four CpG sites. As shown in To compare the performance of our integrated genetic-epigenetic model to the performance of conventional CHD risk factors in predicting CHD status, another eight RF models were built using age, gender, SBP, HbA1c, total cholesterol, self-reported smoking and HDL cholesterol as predictors. Again, using tuned parameters, the eight RF models were built on the training dataset and tested on the test dataset. The performance metrics (mean and standard deviations) of the eight models are summarized in When the ensemble of the eight models was tested on the test dataset, the test accuracy was 65.4%, which is approximately 12% less than that of our integrated genetic-epigenetic ensemble. The confusion matrix is shown in To determine if our ensemble approach consisting of eight models performs better than a single RF model, as described in the methods, one RF model that includes stratified sampling based on the minority class was built in R. The results are discussed in ADAL gene was the most significantly differentially methylated with respect to CHD status. This gene is part of the adenosine deaminase family.[A better understanding of the relationship of epigenetic changes to the pathogenesis of cardiovascular diseases is essential for the development of improved diagnostics and therapeutics. In our analysis to uncover epigenetic signatures associated with CHD, cg26910465 from the e family. Previouse family.–34, givee family. The incle family. which ine family. Hence, tOne plausible reason for the lack in generalizability is the possible confounding effects of genetic variation. The concept of the potential for genetic confounding of epigenetic signal is widely accepted. ThereforWhile a similar integrated genetic and epigenetic study is not available for comparison, our integrated model clearly outperforms the classifier that uses the conventional risk factors. The conventional risk factors model demonstrates the limited predictive value of these risk factors as indicated by a number of studies.–45 In a As demonstrated in this manuscript, there are several approaches to building classifiers. In comparing the two methods delineated in this manuscript, the ensemble model performed better than the single RF model  with resADAL, JOSD1, and CCT6P1, respectively, while cg12091641 does not map to a specific gene. ADAL has been described above. The gene, JOSD1, has been reported to be upregulated in peripheral blood mononuclear cells in those with asymptomatic myocardial dysfunction.[CCT6P1 is the pseudogene of the Chaperonin Containing TCP1 (CCT) Subunit 6 gene. The protein of this gene is one of the subunits of the CCT complex. While CCT6P1 has not been directly implicated in CHD, the CCT complex has been shown to interact with LOX-1, a receptor involved in atherogenesis.[HUS1, a gene involved in CVD related oxidative DNA repair [It is important to realize that the combination of methylation sites and SNPs in our ensemble  is only one of large number possible combinations that could be highly predictive. The first three CpG sites, cg26910465, cg11355601, and cg16410464, map to genes function. CCT6P1 iogenesis. The firsA repair –53 and iA repair –56Based on the permutation results , we demoOur analyses did not take into account the possible effects of medications. This is notable because the current armamentarium of cholesterol lowering agents can have a dramatic effect on the levels of certain risk factors, such as serum cholesterol, that are associated with risk for CHD. Indeed, the presence of these medications may be the reason why the serum cholesterol level is actually lower in those in the training set who have CHD than in those in the training set without CHD. Unfortunately, it is very difficult to incorporate these types of data into the current analytical approach for several reasons. Even if the subjects self-report of prescriptions were accurate, critical information needed to account for their effects, such as medication compliance and the treatment history length are not available. In the future, having data such as “pill count” and serum drug level information will be critical if we are to fully understand the effect of medical interventions on epigenetic signatures used for disease prediction.Although it is possible that a portion of model variance is secondary to the effects of medications, we will note that to date, an epigenetic signature of statins on peripheral DNA methylation has not been identified. However, our statin use analysis show that none of the four methylation probes in our integrated genetic-epigenetic model are significantly associated with statin use. This may be due to 35% of the non-CHD subjects also taking statins. Nevertheless, it is without question that future studies must be wary of the potential for medication effects to alter predictive capacity.There are several other limitations in our study. First, our study includes only individuals of European ancestry. However, the incorporation of genetic variation in our model allows for the generalizability between ethnic groups. Nevertheless, additional studies are required to demonstrate this. Second, testing of the ensemble models were not performed on an independent cohort. Third, while our approach predicts symptomatic CHD, the goal is to use this study as a proof-of-concept towards building a multivariate model capable of forecasting risk for a future CHD event. Extensive exploration in prospectively biosampled cohorts will be necessary to achieve that goal. Yet, it is important to note that this integrated genetic-epigenetic approach has its advantages. The use of conventional risk factors in calculating risk requires cumbersome testing procedures, the collection of considerable amounts of blood and multiple lab tests. Conceivably, the need for these often cumbersome tests and procedures will be greatly reduced by using a single genetic-epigenetic assay procedure that uses a microgram or less of DNA. More importantly, the pathways associated with specific epigenetic loci with high predictive value could be very useful in guiding therapeutic interventions, management of risk factors and monitoring efficacy of treatments and lifestyle modifications.S1 File(TXT)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(PDF)Click here for additional data file.