The common disease/rare variant hypothesis predicts that rare variants with large effects will have a strong impact on corresponding phenotypes. Therefore it is assumed that rare functional variants are enriched in the extremes of the phenotype distribution. In this analysis of the Genetic Analysis Workshop 17 data set, my aim is to detect genes with rare variants that are associated with quantitative traits using two general approaches: analyzing the association with the complete distribution of values by means of linear regression and using statistical tests based on the tails of the distribution . Three methods are used for this extreme phenotype approach: Fisher’s exact test, weighted-sum method, and beta method. Rare variants were collapsed on the gene level. Linear regression including all values provided the highest power to detect rare variants. Of the three methods used in the extreme phenotype approach, the beta method performed best. Furthermore, the sample size was enriched in this approach by adding additional samples with extreme phenotype values. Doubling the sample size using this approach, which corresponds to only 40% of sample size of the original continuous trait, yielded a comparable or even higher power than linear regression. If samples are selected primarily for sequencing, enriching the analysis by gathering a greater proportion of individuals with extreme values in the phenotype of interest rather than in the general population leads to a higher power to detect rare variants compared to analyzing a population-based sample with equivalent sample size. Genome-wide association studies effectively identify new common loci and pathways, but a large amount of estimated heritability is still unexplained . It is cIf resources are limited, sequencing the extreme tails of a phenotype distribution in the search for rare variants has been successful, for example, in studies on candidate genes for lipoprotein metabolism -7 or typIn light of these recent findings and the expected role of rare variants, is linear regression still the gold standard? It might be conceivable that an excess of rare variants in the extremes would be better detected by choosing good discriminative cut points for the extreme values using appropriate statistical methods. On the other hand, a lot of information might be lost, leading to diminished power. Is this power loss merely due to a smaller sample size?With these considerations in mind, my aim in this analysis of the Genetic Analysis Workshop 17 (GAW17) data set is to detect genes with rare variants that are associated with quantitative traits using two general approaches: analyzing the association with the complete distribution of values by means of linear regression and performing statistical tests based on the tails of the distribution.In this analysis, I use data prepared for GAW17. These data include 697 unrelated individuals from the 1000 Genomes Project originating from 6 populations  . The genI(number of rare variants within the gene > 0) (= I(rare)). I define rare variants as variants with a minor allele frequency (MAF) less than 5%.Because of the nature of rare variants, analyzing each of them individually lacks adequate power. Several kinds of collapsing methods for rare variants have been proposed. The primary idea is to group rare variants together in such a way that individuals with at least one rare variant are tested against others who do not have any rare variant ,13. In tY is modeled as follows for each gene with rare variants:For the association analysis of the rare variants with all values of the continuous traits, I use linear regression models and collapsed rare variants into one indicator variable. Morris and Zeggini basically proposed this idea in the combined multivariate and collapsing (CMC) method . The expn is the number of individuals, α the intercept and and βrare is the expected increase of the phenotypic trait in individuals with at least one rare variant within the respective genes compared to individuals without any rare variant. To evaluate possible population stratification issues, I performed a population-stratified analysis by running the linear regression separately in three homogeneous groups  followed by a consecutive random effects meta-analysis.where The extreme tails are defined by values smaller than 10% quantiles versus values greater than 90% quantiles . In addition, I created a dichotomized trait (the bottom 90% versus the top 10%). In both cases, each gene can be presented in a 2 × 2 table. Therefore we can apply methods that have been proposed for case-control studies only. I use the following methods and compare them in this analysis:1. Fisher’s exact test on each of the 2 × 2 tables corresponding to the cohort allelic sum test (CAST) method proposed by Morgenthaler and Thilly .2. Beta test proposed by Li et al. , which c3. Weighted-sum (WS) method proposed by Madsen and Browning , which gMore details on the collapsing methods used in this analysis  can be found in Dering et al. .Because the analyses on the categorized trait are based on only 20% of the sample size of the continuous trait, I evaluate the effect of sample size on the categorized trait using the following approaches:1. Enriching the tails: In the analysis of each of the first 100 replicates, additional replicates were sampled from replicates 101–200 by chance to increase the sample size of the top and bottom 10%. This enrichment was performed by adding one or two additional data sets, leading to 40% or 60%, respectively, of the original sample size of the continuous trait.2. Widening the tails: The sample sizes were increased by shifting the limits of the categorized trait Q1 closer to the median: The top and bottom 20% were included to attain 40% of the original sample size, and the top and bottom 30% were included to reach 60% of the original sample size.For Q4, which serves as a control phenotype, the type I error was inflated for the linear regression (0.10) and pretty much preserved to slightly elevated for the other methods (0.02–0.07). For Q2 and especially Q1, the type I error was inflated, with a maximum of 0.16 for the linear regression in Q1. The WS and beta methods yielded similar rates throughout . The population-stratified analysis eliminated the inflation in Q4 (0.04) and diminished it for the other traits (0.11 for Q1 and 0.06 for Q2).FLT1 and from 1.5% to 6.5% for ELAVL4; both of these genes are similarly likely to be detected by all methods. For other genes, there is a high variation in power with regard to method. Overall, linear regression is the most powerful method for almost all genes and both traits, followed by the methods using the dichotomized trait. Comparing the three methods used for the categorized trait, the beta method shows the highest power for almost all genes, followed by the WS method. For Q2, power is generally lower (maximum 45.5% for BCHE), but the overall pattern remains the same. Correction for multiple testing using a simple Bonferroni correction would have detected only FLT1 using linear regression on Q1 with adequate power (91%).Figure I used sample-size-enriched data sets to evaluate the effect of sample size on the extreme phenotype approach compared to the gold standard of linear regression. Because the beta method performed the best of all methods in the extreme phenotype approach and also outperformed the WS method with regard to computing time, I used only the beta method for this analysis step.VEGFC gene in the association analysis of Q1. For clarification of this behavior, a conditional density plot is shown in Figure HIF1A using the first replicate. The curve represents the probability of carrying at least one rare variant in the HIF1A gene conditional on Q1 values, which is highest for values above the 90th quantile with as low as 0 probability for values below the 10th quantile. Moving the cut points to the median also enlarges the sample size, but the information added is rather noninformative and thus the power decreases.Figure I applied two different general approaches to the analysis of rare variants in the GAW17 data set: using all values of the continuous trait distribution in a linear regression and comparing the proportion of rare variants in the upper tail of the distribution (upper 10%) versus the rest (lower 90%) or in the lower tail (lower 10%) using methods reported for the detection of rare variants in case-control studies.Inflated type I errors is an issue in the analysis of the GAW17 data set. Consistent false-positive findings have been reported over different methods and all replicates [The WS and beta approaches come down to a 2 × 2 table for which well-known methods could also be applied. From the three methods applied, the beta test and the WS method are the most powerful, indicating that methods that have been primarily developed for the detection of rare variants should be preferred over a simple but overconservative Fisher’s exact test.Another question handled in this analysis was whether the higher power of linear regression was simply due to higher sample size. Enriching the tails by adding individuals with extreme phenotypes led to an increase in power, but widening the tails led to a decrease. Therefore, it is not the size that matters but the selection of samples. It should be noted, however, that the enrichment method artificially added “genotypic twins” because genotypes were held fixed in the simulated replicates. This would not happen in the real world, and it is not clear to what extent this twinning affected the results.In this analysis a sample size that is 40% as high as the original sample size is sufficient to gain at least the same power as found with linear regression. This result is in line with the findings of Yang et al. , who comIf such an approach is chosen, the question remains, How should extreme values be defined? In this analysis, I chose the cut point of 10% as a trade-off between really extreme values and sample size. Conditional density plots for many genes and replicates indicated that using the bottom and top 10% of the distribution provided a good discrimination between individuals with at least one or no rare variant without losing too much data and therefore information. In general, this decision has to be based on the studied phenotype and the sample size, which is available or can be sequenced.What do these findings mean for sample selection and analysis strategy? If a complete population-based study is available for sequencing, then the statistical analysis should not focus on the extremes alone because a standard linear regression including all values is expected to provide the highest power to detect rare variants. Extreme phenotype approaches, however, should be conducted in addition, because this approach has been successful in real study situations. If samples are selected primarily for a sequencing approach, then enriching the analysis by gathering a greater proportion of individuals with extreme values in the phenotype of interest than in the general population leads to a higher power to detect rare variants compared to analyzing a population-based sample with equivalent sample size. With such a selection strategy using rare-variant-approved methods, such as the beta or WS test, money on sequencing can be saved or spent to include additional samples. However, these collapsing methods can only point to genes or sets of SNPs; detecting the truly functional variants is still necessary.The author declares that there are no competing interests.CL developed the design of the study, performed the statistical analysis and wrote the manuscript.


In genomic research phenotype transformations are commonly used as a straightforward way to reach normality of the model outcome. Many researchers still believe it to be necessary for proper inference. Using regression simulations, we show that phenotype transformations are typically not needed and, when used in phenotype with heteroscedasticity, result in inflated Type I error rates. We further explain that important is to address a combination of rare variant genotypes and heteroscedasticity. Incorrectly estimated parameter variability or incorrect choice of the distribution of the underlying test statistic provide spurious detection of associations. We conclude that it is a combination of heteroscedasticity, minor allele frequency, sample size, and to a much lesser extent the error distribution, that matter for proper statistical inference. Phenotype transformations are still very popular in genomic research when drawing inference about genotype-phenotype associations. The two most widely used transformations are natural logarithm and rank-based inverse-normal transformation (INT). Their popularity is underlined by them being easily obtained; logarithm has an automated function in all statistical software packages and INT in many, for example, in SAS and SPSS. Often the only reason to transform a phenotype is “to improve normality”. Many researchers still believe that linear regression is valid only for normally distributed model residuals or even model outcomes. We give a few examples: “the Von Willebrand factor was natural log-transformed to improve normality” Linear regression is frequently presented as an optimal method in the context of normally distributed model residuals. But it was discovered as a semiparametric method based on least squares. The fact that residual normality is not necessary for validity of linear regression in sufficiently large samples is well understood in the statistical literature but it is still not widely known and accepted in genetic epidemiology. The sample size in genetic epidemiology studies is typically in thousands, using established cohorts. If study data are stratified for analyses by race/ethnicity and sometimes additionally by an environmental exposure, the sample size is still at least in several hundreds. The behavior of t-tests with extremely non-normal medical cost data suggests that major limitation is not distributional but whether detecting and estimating a difference in the mean of the outcome answers the scientific question at hand In contrast to two-group comparison tests, there has been little empirical research into the behavior of linear regression. In this paper we first show that in linear regression in the genetic association studies framework transformations are typically not needed to improve adherence of the Type I error rate to the nominal level. Further, we focus on a regression feature unique to genetic data. Genotype, the major covariate of interest characterized by minor allele frequency (MAF), is prone to skewness. Many genetic studies include single nucleotide polymorphisms (SNPs) with low MAF, often as low as 0.05 or 0.01, known as rare variants. New arrays are being developed specifically for fine mapping, targeted at SNPs with lower MAF. For instance, Metabochip is a custom SNP array based on the Illumina iSelect platform and was developed for replication and fine mapping of susceptibility variants associated with several metabolic and cardiovascular traits. The MAF filters there may be set as low as 0.001, resulting in genotype skewness of 22. With rare variants addressing of heteroscedasticity becomes crucial. In this paper we discuss the choice of standard errors (SEs) to construct a test statistic and the choice of the distribution of the test statistic under the null hypothesis that improve adherence of the Type I error rate to the nominal level.We note that techniques involving collapsing or summing rare variants, often referred to as pooling or burden tests To illustrate the situation, denote We consider a test for no association between the genotype G and the mean of the model outcome Testing in a regression model framework requires specification of the distribution of the test statistic under the null-hypothesis. The asymptotic distribution of the test statistic is standard normal, and another is a An improper choice of the distribution of the test statistic under the null-hypothesis has consequences for inference validity: if distributional quantiles are too small, as could with normal quantiles or overestimated effective degrees of freedom of a In moderate to large sample sizes, there exists a third option for specification of the distribution of the test statistic under the null-hypothesis; Parametric bootstrap  can provide a good approximation to the distribution of the test statistic under sampling from the true null-hypothesis model through the distribution of the test statistic under sampling from the fitted null-hypothesis model. However, similarly to any resampling approach, this option typically requires additional coding and may be unfeasible with larger number of models. We do not consider it in our paper.Using simulation, we explore tests of association between a genotype and a phenotype in linear regression in samples of unrelated individuals. For a range of sample sizes and MAFs we consider two types of error distributions with heavy tails - Weibull and We note that for a very small sample size of 200 and rare variants with MAF 0.01 we saw too high Type I error rates when using the robust SEs with normal quantiles, see With heteroscedastic data and some skewness in the genotype, the model-based SEs are not valid estimates of the variability of the estimated parameter and thus using them provides invalid Using robust standard errors is a valid approach, though with a very small sample size of 200 and rare variants with MAF 0.01 this empirical estimators does not perform well, see In non-heteroscedastic data we saw slight power increase with transformations in distributions with heavy tails. On the other hand, in data with heteroscedasticity we saw a large decrease of power with transformations. This is common across MAF, sample size, and all three approaches of combining SEs and quantiles. See In situations where a genotype has a large effect in a population subset, transformations do not increase power, see In Linear regression does not require normal distribution of outcome or residuals for sufficiently large samples where Central Limit Theorem applies. In genetic association models sufficiently large is often under 500, depending on the targeted significance level. Sample sizes of studies involved with genetic analyses are mostly substantially larger. Transformations improving outcome normality do not typically improve inference validity. On the contrary, using them may provide invalid inference, specially with heteroscedastic data. Unintuitivelly, under heteroscedasticity transformations proved even less suitable for heavy tailed errors than for normal errors. With this paper we would like to discourage researchers to use phenotype non-normality as an argument to transform it. Model choice should be driven in the first place by biological plausibility and relevance to the scientific question, not by presumed statistical modeling needs.The extend of the heteroscedasticity problem depends on the skewness of the genotype. Simulations suggest that when ignoring the combination of heteroscedasticity and rare variants statistical inference results may be rather misleading. Specifically, we saw largely inflated Type I error rate, resulting in spurious detection of associations. Robust standard errors must be carefully considered to construct a test statistic. With small sample size t-quantiles with appropriate degrees of freedom might be preferred to normal quantiles, improving adherence of the Type I error rate to the nominal level. We encourage researchers to report in their papers' method sections the choice of standard errors method and the test statistic distribution. The excess of false positive findings that we saw in the simulations due to rare genotype and heteroscedasticity may be one of the many reasons why genetic association findings are difficult to replicate.For each individual First, we generate data with no heteroscedasticity, generating the errors For normal distribution we set We set For large number of simulations under the null hypothesis the empirical Because the quantiles of normal distribution and We demonstrate the methods using Causal Variants Across Life Course (CALiCo) with data from Cardiovascular Health Study (CHS)


Because of the low frequency of rare genetic variants in observed data, the statistical power of detecting their associations with target traits is usually low. The collapsing test of collective effect of multiple rare variants is an important and useful strategy to increase the power; in addition, family data may be enriched with causal rare variants and therefore provide extra power. However, when family data are used, both population structure and familial relatedness need to be adjusted for the possible inflation of false positives. Using a unified mixed linear model and family data, we compared six methods to detect the association between multiple rare variants and quantitative traits. Through the analysis of 200 replications of the quantitative trait Q2 from the Genetic Analysis Workshop 17 data set simulated for 697 subjects from 8 extended families, and based on quantile-quantile plots under the null and receiver operating characteristic curves, we compared the false-positive rate and power of these methods. We observed that adjusting for pedigree-based kinship gives the best control for false-positive rate, whereas adjusting for marker-based identity by state slightly outperforms in terms of power. An adjustment based on a principal components analysis slightly improves the false-positive rate and power. Taking into account type-1 error, power, and computational efficiency, we find that adjusting for pedigree-based kinship seems to be a good choice for the collective test of association between multiple rare variants and quantitative traits using family data. Because of the limitation of single-nucleotide polymorphism (SNP) array-based genotyping technology in detecting rare genetic variants, genome-wide association scans usually focus only on common variants analysis. Advances in DNA sequencing technologies in recent years, however, have allowed the identification and genotyping of rare variants with substantially higher accuracy and rapidly decreasing cost, which makes it practicable to detect rare variants in relatively large populations and thus enables association scans of these variants for human complex diseases. Facilitated by these technologies, investigators have recognized rare variants as one important factor that contributes to human complex-disease-related traits and interest in them is increasing -8.Statistically, because of the extremely low frequency of rare variants in populations, the power of detecting disease-associated individual rare variants usually is poor. To increase the power, investigators have proposed a useful strategy in which multiple rare variants are collapsed into one variant; then, the collective effect of multiple rare variants is tested rather than individual rare variants. Different collapsing methods have been developed -14. BesiTo exploit advantages from both collective tests and family data, we propose to combine a collapsing strategy with the framework of the unified mixed model (UMM)  for the To adjust for both population structure and familial relatedness in association analyses, we use the following linear mixed model:Y = Xb + Qv + Zu + e, (1)Y is the quantitative trait of interest, X is the genotype data, b is the fixed effect(s) of genotype(s) under the test, Q is the population structure variables, v is the fixed effects of Q, u is the random polygenic effects of individuals, Z is the design matrix of u, and e is the random residual error.where Q and Z were incorporated to adjust for population structure and familial relatedness, respectively, and X was usually the genotype data for individual SNPs. Here, we propose to combine the UMM with a collapsing test of rare variants by replacing X with the collapsing variable of multiple rare variants from a given genetic unit. There are different ways to collapse multiple rare variants; here, we choose Li and Leal’s method [Equation (1) was originally proposed as the UMM for association analysis of common variants , in whics method .Our goal is to compare six different methods under the framework of the UMM for the collapsing test: 1) a simple regression with no adjustment for population structure and familial relatedness, denoted REG; (2) adjustment for population structure using principal components of genotypes, denoted PC; (3) adjustment for familial relatedness using a pedigree-based kinship matrix, denoted KIN; (4) adjustment for population structure and familial relatedness using the PC and KIN methods, denoted PC-KIN; (5) adjustment for familial relatedness using a marker-based identical-by-state kinship matrix, denoted IBS; (6) adjustment for population structure and familial relatedness using the PC and IBS methods, denoted PC-IBS. More details about the six methods are presented in Table  a simpleY in the models). For each gene, we collapsed the genotypes with minor allele frequency (MAF) less than 0.01 into a binary (1 or 0) variable according to the presence or absence of at least one rare variant in a subject, based on Li and Leal’s method [X) in the models.We used the 200 replications of data of 697 subjects from 8 extended families simulated from Genetic Analysis Workshop 17 (GAW17) . We choss method . We thenQ). Using the R package Kinship 1.10-23 (http:// cran.r-project.org/web/packages/kinship), we obtained the kinship matrix from the pedigree information and fitted it as Z into the models. Using 5,000 randomly selected makers with MAF > 0.05 and the R package EMMA 1.1.2 [Z into the models.We obtained the top 10 eigenvectors for the 697 subjects using Eigenstrat 2.0  using alMA 1.1.2 , we obtaTo estimate parameters and perform significance tests, we used SAS 9.2 for the REG and PC models, Kinship 1.10-23 for the KIN and PC-KIN models, and EMMA 1.1.2 for the IBS and PC-IBS models. Quantile-quantile (Q-Q) plots under the null and receiver operating characteristic (ROC) curves were investigated and compared between the six models.We compared the six selected methods in terms of two important statistical properties, false-positive rate (FPR) and power, by visualization of Q-Q plots and ROC curves.Q-Q plots Figure  show thaAccording to the ROC curves Figure  and in tBy combining FPR and power, we find that the KIN and IBS models outperform other methods for the rare variant collective association test. The two methods have pros and cons. The KIN model is computationally more efficient than the IBS model but needs pedigree information. The IBS model requires no extra pedigree information but may be computationally intense when the sample size is large. Therefore the KIN model should be a good choice when pedigree information is available, especially for studies with large sample size and strong family structure.The result of PC adjustment for rare variant analysis is inconsistent with that for common variant analysis . An impoThe collapsing method we used here assumes that all the casual variants from a gene have the same effect direction, which may not be true. Some effect direction-sensitive methods can be used, but they usually require a permutation test. Because permutation will destroy family structure, it is still an open question of how to perform the permutation test on family data.The authors declare that there are no competing interests.QZ carried out the statistical analysis and drafted the manuscript. DC participated in the programming of statistical methods. AK prepared the data and assisted in computation. IIB and MAP participated in the design of the study and helped to organize the structure of the manuscript. All authors read and approved the final manuscript.


IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation.Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the  IRF6 gene, which is also a well-known risk factor of non-syndrome cleft lip, was found to strongly predispose the mouth shape in Han Chinese females.Heritability of human facial appearance is an intriguing question to the general public and researchers. Although it is known that some facial features are highly heritable, the exact genetic basis is unknown. Previous studies used simple linear measurements such as landmark distances, to evaluate the facial shape variation. Such approaches, although easy to carry out, may lack statistical power and miss complex morphological changes. In this study, we utilized a new 3D face registration method that enables subtle differences to be detected at high resolution 3D images. Based on this, we tried to test and characterize the associations of 10 candidate genetic variants to common facial morphological variations. Different types of phenotype data were extracted and compared in the association tests. Our results show that geometry based data performed better than simple distance based data. Furthermore, high density geometric data outstood the others in capturing small shape changes and modeling the 3D face visualization. Interestingly, a genetic variant from  BMP, SHH, FGF, GHR and Wnt/β-catenin pathways The human face plays an essential role in everyday life. It hosts the most important sensory organs and acts as the central interface for expression, appearance, communication and mutual identification. Inheritance of facial appearance from parents to their offspring is a constantly intriguing question to the public and scientific community. Indeed, human facial morphology is highly heritable. Twin studies have shown that heritability of facial features is as high as 80% In humans, knowledge of the effects of genetic variation on facial morphology has mainly arisen from studies of congenital craniofacial abnormalities. Non-syndromic cleft lip with or without cleft palate (NSCL/P) is the most common congenital craniofacial defect GHR) were suggested to affect mandible shape in Japanese and Chinese populations et al. found that a SNP in ENPP1, a gene essential in bone physiology, was significantly associated with upper facial height in Chuvashians FGFR1 gene, a genetic marker was found to be associated with the cephalic index in multiple populations Relatively few studies have attempted to associate genetic polymorphisms to common facial morphological variations. Several non-synonymous changes in the growth hormone receptor  to capture the comprehensive facial variation information of ∼30,000 3D points (referred to as marker points hereafter) ENPP1, GHR, FGFR1 and IRF6 were identified and their functional relevance was listed (ENPP1) gene is a key regulator of bone mineralization. Ermakov et al found that the upstream promoter and 3′ un-translated regions in this gene harbor genetic variants associated with the upper facial height and other phenotypes GHR is the growth hormone receptor, which plays essential role in the development. Mutations in this gene induce idiopathic short stature and Laron syndrome, marked by a characteristic facial appearance FGFR1, the fibroblast growth factor receptor 1 plays an important role in facial morphogenesis, and mutations in this gene lead to syndomes associated with facial abnormality, such as the type 1 Pfeiffer syndrome (MIM 101600) and Kallmann syndrome 2 (KAL2) (MIM 147950) FGFR1. The Interferon regulatory factor 6 (IRF6) plays a critical role in keratinocyte development. Genetic variants of IRF6, especially SNP rs642961, were found consistently associated to NSCL/P throughout many candidate gene and GWAS studies IRF6, which seems to have a particularly strong signal in Han Chinese We reviewed the literature for candidate SNPs that may be involved in the morphogenesis of the human face.10 SNPs from 4 genes, s listed . The ectThe genetic effects of these SNPs were examined in a Han Chinese population from Taizhou, Jiangsu province on the east coast of China. The complete work flow is summarized in 260/280<1.6 or total DNA quantity lower than 300 ng). In the end, 856 individuals were successfully processed for their 3D images and have corresponding DNA. We carried out the genetic association study in two stages. The individuals of the original cohort were randomly assigned to 2 panels: panel I included 376 individuals , and panel II included 480 individuals . Tests were carried out separately for different genders. In stage I, all 10 candidate SNPs were genotyped for panel I. Then in stage II, the markers that showed preliminary evidence of correlation were validated using panel II. For stage I analysis, individuals were assigned into 3 possible genotype groups for each SNP. None of these SNPs deviated significantly from the Hardy-Weinberg equilibrium. For the LMD data, the landmark-distances were tested for association with SNP genotypes using the Tukey's honestly significant difference test (Tukey's HSD test). Of the total 105 possible pairwise distances, 6 departure from normal distribution according to Shapiro-Wilk normality test. As normality is required in Tukey's HSD test, these phenotypes were removed from further analysis. For the remaining 99 phenotypes, the raw p values were calculated and corrected for multiple-testing with 10,000 permutations (see Methods). TT) and the other two groups CC and CT. Interestingly, the strongest signals seemed to all point to the area around mouth and lower nose area. The distances between the right/left lip corners and the right/left alare (RLipCn – RAla and LLipCn – LAla) had nominal Tukey test p values between 0.002∼0.004 in both the CC/TT and CT/TT comparisons . 40 individuals were further removed due to the poor DNA quality . This procedure resulted in a single empirical p value for each comparison. The geometric permutation tests indicated that several SNPs had a nominal significance of association in stage I, and these signals were highly consistent between the LMG and DG data  than the DG tests  in both comparisons. Color gradient plots reveal that the major changes occur around the lips  overlapped substantially with those from LMG and DG data, suggesting a general compatibility among the three different schemes. The signals from geometric tests  were stronger than those of LMD, as their p values stood nominally significant at individual SNP level, whereas none of the LMD tests passed the single SNP significance level after permutation correction. Globally, none of the LMG/DG proved significant after Bonfferoni correction assuming 60 independent tests (3 genotypes and 2 genders ×10 SNPs).For the LMG and DG data, we did the test for the whole geometric shapes, in a similar way to that previously described  DG data . To faci DG data . In gene females . Howeverthe lips . The GHRin males  J. Two Sectively . The higIRF6 and ENPP1 respectively exhibited the most prominent signals in stage I tests, and were selected to be revalidated in sample panel II. The same tests as in stage I were carried out either solely with panel II or with the combined panel of I and II together. The LMD data showed strong associations between rs642961 and several distances involving mouth landmarks, e.g. LLipP, ULipP and Stm , and marginally significant in the CT/TT comparison  after correcting for 12 tests (2 SNPs×6 comparisons). The same 4 comparisons were more significant in the combined panel  after correcting for all 60 possible tests with 10 SNPs  lips, than the other two genotypes  occurred between CC and TT in females in the LMG data. In the combined panel, the CC/TT comparison in females had the minimum p value of 1×10−5 (corrected p = 0.00012) in both the LMG and DG data. It should be noted that these p values for mouth region do not indicate any formal statistical significance as they were conditional on the prior information of the genetic association in mouth shape. Nonetheless, the extreme p values suggested there are substantial impacts of genetic variants on normal mouth shape variation. One potential problem that may affect the mouth shape analysis is the stomion point. Stomion is the central point between the upper and lower lips. None-neutral expressions or open mouth may induce altered distances between stomion and other mouth landmarks, therefore confound the association signals. Our image dataset has been carefully screened for such cases. In order to formally test the impacts, we removed stomion from the landmark set, and re-ran the image registration procedure and the LMG/DG analyses for SNP rs642961. As can be seen in To investigate the mouth shape changes associated with SNP rs642961 in more details, we extracted the mouth DG data from the whole face by retrieving a defined set of marker points for the mouth. The 5 mouth landmarks  were also extracted to compose the mouth LMG data. The landmark-distance analyses were not repeated as they remained the same despite the extraction of the mouth data. Geometric permutation tests were conducted as before for the mouth LMG and DG data. In general, the results seemed to be much more significant than the corresponding whole face comparisons . In paneTT and CC genotypes , and the TT/CT comparison was also highly significant  on this PC. The first PC from the LMG data revealed similarly strong differences in the TT/CC  and TT/CT  comparison. The large differences between TT and the other two genotypes and the little difference between CC and CT suggested that this locus may follow a dominant model. To formally test this, we constructed an additive model and a dominant model based on the standard linear model (see Methods). The additive model did not suggest any statistical significance, whereas the dominant model was highly significant both with the LMG  and the DG data . Based on the dominant model, the genotypes of rs642961 explained a substantial proportion of the total variance  in PC1. Interestingly, when we tested these two models in a combined panel that included both males and females, the additive model remained insignificant, and the dominant model also became only marginally significant . This suggests that the effect of TT is female specific. To extract the facial pattern that best distinguishes TT from the other genotypes, we further carried out a simple linear discriminant analysis (LDA). As a hyperline that transects the mean points of TT and CC groups would best separate these two groups, this line was defined as a new data axis onto which individual data points were projected to generate hyperline (HL) scores. The HL scores were plotted against the PC1 scores to visualize data distribution (2 = 0.97). The TT distribution differed substantially from that of CC and CT. Specifically, the average PC1 score of 0 found 18 of the 19 TT individuals at the minus side; similarly, the average HL score of 0.444 had 18 out of 19 TT individuals at the minus side. To visualize the mouth shape changes, we transformed the mean shape  on both the LMG and DG data. In the DG data, the first PC mode best distinguished the ribution . As can an shape  by addin1+ shape . The who1+ shape .To the authors' knowledge, this study is the first to use high resolution face image registration to test the genetic association for common facial variation. Human face is a highly complex geometric surface. The simple inter-landmark distances used in previous studies may have over-simplified the common variation of human faces. As the high throughput acquisition of high content 3D image data becomes easier, methods based on shape geometric information, especially of high definition, become increasingly necessary to enable comprehensive and fully quantitative analyses of the complex facial traits. Based on high density 3D face registration, we compared three different schemes of phenotype during tests of genetic association, including LMD, LMG and the high resolution geometric data DG. We found that, in general, the three schemes produced consistent signals for the candidate SNPs. In the stage I test, the LMD method had only moderate association signals, mainly due to the large number of tests. The 15 landmarks gave rise to 105 possible tests in each genotype comparison . One strThe two geometric schemes were generally found to give stronger association signals, implying better statistical power for the geometric methods. This may be due partially to the fact that the geometric tests were carried out in one step, which avoided a complex test structure. Interestingly, the LMG data of only 15 landmarks showed highly consistent test signals with that based on DG data. This suggests that these 15 landmarks capture the majority of the normal facial morphological variation. When only shape difference is to be tested, the LMG method seems to provide better efficiency  and potentially higher test power. However, the strong consistency between LMG and DG in the association signals attributed to rs642961 may be partially accounted for by the high landmark density around the mouth area (5 out of 15 chosen landmarks). Features with fewer landmarks would confer lower power in the LMG tests. On the other hand, the DG data has other unique advantages for shape change inference and modeling. We also show here that the point-wise distance distribution between the mean faces can highlight the areas of shape changes in high definition , which cIRF6 gene have been found to predispose to the risk of NSCL/P IRF6 gene and common facial variation has not been established. This is the first study that provides strong evidence that rs642961 also affects normal facial shape variation. In particular, TT individuals may have more protrusive and thicker lips  from self-reported Han Chinese were sampled from Taizhou, Jiangsu province. Age ranges were between 17 and 25 years. 2∼3 ml of saliva was collected from each participant for DNA extraction. Individuals with obvious health problems or any history of facial surgery were excluded from the study.Genomic DNA was extracted from saliva following a modified Phenol–chloroform protocol www.3dmd.com/3dMDface) was used to collect high-resolution 3D facial images from participants. This system captures 3D facial images at a speed of ∼1.5 milliseconds and a geometry accuracy of 0.2 mm RMS.The 3dMDface system  transformation. The mesh points of the reference face were then projected to the sample surface to find their one-to-one correspondents. The projection points were then used to re-define the mesh of the sample facial surface We applied a new approach to achieve high density point-wise registration across all 3D facial images s = , where ix, iy, iz stand for the x, y, z coordinates of the ith point. There are n points in total. For two shapes s and s′, the squared Euclidean distance for the ith point is,Assuming each shape is represented as a vector: p values for multiple-testing. Briefly, the genotypes were reshuffled among the participants for 10,000 times and the Tukey's test was similarly carried out. The lowest p value from each permutation was combined to derive a null distribution. The empirical raw p values were then ranked against the null distribution to give the corrected permutation p values For the LMD data, in order to correct for the large number of sub-tests within each SNP, we performed a permutation procedure. For each of the 99 traits, raw p values were first calculated with Tukey's HSD test. A permutation procedure was used to correct the raw p value.For the LMG and DG data, genotypes were randomly reshuffled among the individuals, and the PPD distances were calculated for the permutated genotype groups. 5000 permutations were carried out in stage I analyses and 100000 permutations in stage II analyses due to the much more significant P values. The PPD distribution under permutation was compared to the observed PPD value. The proportion of the permutation sets that had PPD values smaller than or equal to the observed PPD was taken as the nominal one-sided The prcomp function in the R statistics package was used for PCA analysis. An un-scaled PCA analysis was carried out, assuming equal variance for all points.CC and CT as 0 and TT as 1. Model test and analyses were conducted with the R statistics package.We established both the dominant model and the additive model based on the standard linear model. The additive model was implemented by encoding genotypes as 0, 1 and 2. The dominant model was built by assuming Figure S1Mean facial shape comparisons for all 10 SNPs using Panel I. The mean shapes of different genotype groups were compared pair-wisely. The point-wise distances are shown as color gradients. A higher intensity of color gradient indicates greater point-wise distance. The first genotype group average face as the reference face . The white color indicates no difference between reference face and compared face. The cold(or warm) colors indicate that the average shape of the reference face in a comparison is inside (or outside) of the compared face. Genotypes with sample size less than 20 were marked in red, as their average face shapes and corresponding comparisons were less reliable.(TIF)Click here for additional data file.Figure S2Age distribution in Taizhou population.(TIF)Click here for additional data file.Table S1The geometric permutation test of all the 10 SNPs in Panel I.(DOC)Click here for additional data file.Table S2Validation of the rs642961 signals based on LMD data.(DOC)Click here for additional data file.Table S3The results of the geometric permutation test of rs7773292 in the stage II analyses.(DOC)Click here for additional data file.Table S4Validation of the association signals in rs642961 after removing the stomion points.(DOC)Click here for additional data file.


Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects.highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits  related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes  were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3).This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype The six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway. Genome-wide association studies (GWAS) have broadened our knowledge on architectures of disease susceptible loci for many common disease of public health importance. A general approach for GWAS follows a strategy to investigate the correlations between single genetic variants and single traits within a univariate framework. The GWAS have not considered complicated genetic nature such as pleiotropy that occurs due to potential genetic correlation between different traits ,2. Thus,Patterns of pleiotropic effects have been observed more with an increasing number of variants identified through GWAS . For insAs we have mentioned the examples above, previous work has shown that ignoring pleiotropic effects may cause imprecise phenotype definition of heterogeneous samples or even spurious associations. A bias in sampling cases and controls characterizing single traits might be propagated since the sampling errors tend to be correlated if the single traits were correlated. This may confound the interpretation of results. Although any loss of power occurred by selection of samples can be recovered by increasing the sample size, the sample size of the GWAS has cost constraints. With large sample sizes of several thousand cases and controls there has been usually limited study power to detect alleles of modest effect sizes  .In this regard, incorporation of the multiple phenotypes to the GWAS can be an alternative way to unravel missing heritability in the GWAS and to find pleiotropic genes. Even though the GWAS of multivariate phenotypes are known to enhance the power of the GWAS such an approach has not been well established.To perform multiple phenotypes based GWAS, application of traditional GWAS approaches has suffered penalties from multiple testing problems caused by testing multiple genome-wide scans of single traits separately. This may diminish the power of GWAS due to elevating heterogeneity and bias in samples. Statistically classic multivariate methods have been applied to GWAS of multivariate phenotypes to tackle in an effective manner. Such methods are likelihood-based mixed effects model (LME) ,10 and gO’Brien model  and its lowLDLhighTG, which means a negative relation between low levels of LDL and high levels of TG. Our preliminary results revealed that effect sizes (odds ratios=1.44-2.38) of genetic loci associated with the multivariate phenotype were higher than genetic loci identified in the initial GWAS, while their p-values were less significant than those in the initial GWAS. Those loci cannot be detected within a single trait based framework.Much work have not investigated what types of single traits can be correlated to induce multivariate phenotypes. In this context, we aimed to discover novel multivariate phenotypes from large scale epidemiological data by a data mining approach and develop a scheme to GWAS of multivariate phenotypes. In our previous work , we repohighLDLhighTG combining elevated low density lipoprotein cholesterol (LDL-C) levels and elevated triglyceride (TG) levels, which have an important clinical implication in metabolic syndrome (MS).Here, we present a more sophisticated scheme for refining association rules to extract patterns of phenotypic associations and to visualize them graphically. As a case study, we describe the results of GWAS for multivariate phenotype highLDLhighTG.An association rule which expresses patterns of multivariate phenotypes encoding partial correlations between single traits specifies quantitative descriptions of the single traits. Association rules can provide explicit boundaries of the single traits of multivariate phenotypes for optimal selection of both cases and controls. This work contributes a methodology for exploration in GWAS analysis of multiple phenotype We investigated 350K variants in 8,512 individuals in Korea Association Resource (KARE) for performing GWAS of multivariate phenotypes and mining patterns of them. Additional details of quality-control and imputation procedures have been reported in . Gene anThe problem of discovery of multivariate phenotypes from a set of single traits is transformed into finding frequent patterns of associations of single traits. In our approach we employed Association Rule Mining (ARM)  to discoThe strength of the association rules can be measured in terms of their support (s) and confidence (c). The support of a rule (X → Y) is the probability that cases in a database contain both X and Y. The confidence of a rule is the probability that cases contain X can also contain Y. Note that the head of a rule, Y, is restricted to be one of the single traits which we are interested in and X is a set of traits partially correlated with Y. Interesting rules are manually extracted.We used 10g Oracle Data Miner (ODM) which implemented the APRIORI algorithm  to compuIf we set threshold too high for support , there may miss many interesting patterns involving the low support with high confidence. Such low support traits may correspond to rare associations of sing traits, but whose patterns are still interesting to know. Most patterns mined characterize common correlations between traits, which are already well known. Domain experts such as biologists may have different interesting traits. Our strategy to find interesting rules is that we set low support threshold, generate as many rules and filter them by user interestingness measurements and interesting traits as user constraints. High confidence rules can misinform and do not consider the support of the traits appearing in the rule head. One way to resolve this problem is to apply a metric known as lift :Lift=P/P(X)×P(Y)Y in the rule consequence. Lift was originally called interest which measures how many times more often X and Y occur together than expected if they are statistically independent.which calculates the ratio between the rule’s confidence and support of the single trait The following example shows rule form constraints to find interesting association rules containing single traits such as high levels of TG (TG5) and low levels of LDL(LDL1):R1: ((TG5⊂LHS) ∨ (TG5⊂RHS)) ∧ ,R2: ((LDL1⊂LHS) ∨ (LDL1⊂RHS)) ∧ .igraph package on R 2.12.0. A set of interest association rules represented with an undirected edge weighted graph where vertices represent traits and edges indicate relationships (i.e. associations) between other traits in rules. Vertex size is proportional to degrees of vertices.We employed graph-based techniques to visualize interesting association rules using i, yj) which can be a mixture of continuous and nominal traits. The multivariate phenotype can be a conjunction of predicates of single traits, as expressed one or more association rules.A multivariate phenotype(y) is defined as bivariate traits  is performed by following tasks:(1) Test a genome-wide scan for a multivariate phenotype (y): GWAS(y)i), GWAS(yj)(2) Test genome-wide scans for singleton traits: GWAS over those from a single trait (yi) as follows:(3) Calculate mOR(4) Prune SNPs by filtering strategies (see section strategy for pruning SNPs)j)(5) Repeat tasks 2~4 for other single trait (y(6) A final set of SNPs is generated by the intersection of two sets of SNPs survived from filtering steps which prune SNPs more likely to affect either of single traits.i and yj) are excluded by mORpS (vide infra). The mORpS measures the strength of association of a multivariate phenotype against those of single traits.SNPs identified for an association test of a multiple phenotype may include genetic variants more likely affecting each of single traits which should be excluded. Tasks 1 to 4 are subject to filtering steps to identify genetic variants influencing the multivariate phenotype y while the genetic variants much more likely to affect single traits  P-values for an association test of multivariate phenotype ≤ 5×10Pyi ≥ 1 and mORpyj ≥ 1(2) mOR-5 and p-values of clumped SNPs ≤ 10-4 in order to evaluate formation of LD.(3) P-value of an indexed SNP ≤ 10-7 which is not corrected) since we consider a small sample size for cases and controls due to combination of phenotypes. mORPS ≥1 means that it is 10 times as likely that SNPs are associated with a multivariate phenotype against a single one.There have been no generalized method to deal with the multivariate phenotypes and the cut-off p-value for a significant association in GWAS is not well defined. The cut-off p-value for the multivariate phenotype is set to be less stringent than usual GWAS which mostly set a Bonferroni corrected p-value of 0.05  related to MS, were consistent with the fact that MS increases T2D and cardiovascular diseases (CVD) .One of the noteworthy findings is the association between low DS for the measure of BMD and high TG levels. The associations between low DS and a cluster of MS defined by the four common traits i.e. obesity, hypertension, hyperlipidemia and insulin resistance with high glucose levels and dissociation with insulin levels  were in concordance with newly reported work  that exaHigh levels of LDL were shown positive relations with BMI, glucose levels and plasma lipids including TCHL, TG, and NONHDL as well as negative relations with DS. We did not find associations between high LDL levels and insulin levels. Interestingly, highLDL have positive relations with single traits related to renal function such as Blood Urea Nitrogen (BUN) and Sodium in Urine (SONA).Among multiple phenotypic associations with high TG, we considered the phenotypic associations which subdivided samples into feasible sizes of cases and controls for GWAS. We focused on contradictory relationship between high TG levels  have more power in distinctively classifying samples of highLDLhighTG into cases and controls  and high LDL-C levels (Rules 15-18). The rules were sorted and selected by their confidences.As seen from above, there exist complicated associations among single traits. Selection of cases and controls based on single traits without considering those associations may increase confounding effects in samples. Compared with single traits based selection of cases and controls, multivariate based approach can have more power to distinguish cases from controls.highLDLhighTG and 15 are located in six genes  (Table PAK7 on 20p12.2 is in a missense mutation by substitution of arginine (CGA) by proline (CCA). Clinical association has not yet been found with this variant. It is located within a LD block (530kb) with other four SNPs, of which two (rs6140956 and rs6133716) are in intronic region of C20orf103. It is worth to note that C20orf103 contains a frameshift mutation at rs72238296, which is 755 bases upstream of rs6140956 in the same gene  detected across three SNPs   with high r) Figure  of TRPM3d Figure . This obent work  investigBCL2 gene on chromosome 18 (18q21.33) were identified. BCL2, which is involved in a number of cancers including melanoma, breast carcinomas and etc., was recognized as important modulators of cardiac myocyte apoptosis. A distinct support for relevance of BCL2 to cardio vascular disease (CVD) was reported by recent finding [BCL2 expression.Two SNPs of  finding  that PPANRIP1 was reported to have an association with HDL [NRIP1 as a cofactor for LXR in different ways, namely serving as a coactivator in lipogenesis and as a corepressor in gluconeogenesis [NAV1 on chromosome 1q32.1, a human homolog of a C. elegans gene, unc-53, is expressed in adult heart and the developing brain. Clinical association has not been established with it. Our results warrant that variants associated with highLDLhighTG should be evaluated for further study.with HDL . Recent ogenesis . NAV1 onIt is important to emphasize that LD structures for the six genes across three populations  are distinct. The pattern of the strongest LD was observed in JPT+CHB among the three. Whereas, the weak pattern of LD was appeared to be in CEU , while p-values of six SNPs (p ≥ 0.05) were on the borderline statistical significance. Five SNPs in NIRP1  and NAVI (rs665770 and rs529581) were more reproducible in both regional subcohorts and gender groups.ps Table . Nine ofPAK7 and NRIP1 were more effective in women; BCL2, TRPM3 and NAV1 were more reproducible in men. highLDLhighTG was more detectable in women than man . PAK7 and NRIP1 may lead to the gender specific susceptibility in concordance with previous work [Reproducibility of gender difference in the 15 SNPs were as follows: ous work  reportinhighLDLhighTG were apparently more significant than those for single traits highLDL and highTG  or LD blocks than at SNPs. We examined QTLs and their associated phenotypes for the six genes identified using Phenotype and Disease Association track group in UCSC genome browser. The QTLs and their associated phenotypes were extracted from rat and mouse QTLs from RAT DB and MGI (Mouse Genome Information) Table .NRIP1 share QTLs commonly associated traits such as BMD and a cluster of common traits defining MS. Those common traits in MS shared by the six genes are blood pressure, non-insulin dependent diabetes mellitus, renal function, cardiac mass, and body weight. The phenotypic associations of high TG and high LDL levels with low BMD examined through rat and mouse QTL associations except NRIP1 have mapped in the regions of QTLs associated with BMD. Furthermore it can be more support that TRPM3 was mapped to OMIM phenotypes such as osteosclerosis hardening bones, epilepsy, amyotrophic lateral sclerosis (ALS), of which association with CVD was reported in a recent work [BCL2 and TRPM3 have in common with associating ALS; PAK7 and NAV1 have in common with similar phenotypes Alzheimer disease (AD) and Parkinson disease (PD) where a cross-talk between MS and AD was reported [The six genes except ent work . Differereported .highLDLhighTG have phenotypic associations with common traits in MS. The common traits in MS, particularly hyperlipidemia, may be linked to pathogenic associations with osteosclerosis and neurodegenerative disorders including AD and PD influenced by pleiotropic genetic factors. Thus, the genetic markers identified in our work can have pleiotropic effect on MS, BMD and neurodegenerative disorders.In summary, our results suggest that the genetic markers identified with multivariate phenotype highLDLhighTG using STRING, a database of predicted protein-protein interactions (PPI). We obtained 5 different networks of genes interacting with each of five genes by confidence of association evidence (≥ 0.5). Each of the gene networks  in AD and PD pathways, while BCL2 and NIRP1 shared Huntington’s disease as well as AD. In particular, three genes i.e. BCL2, PAK7, and NIRP1 shared pathways in cancer and other pathways, supporting our hypothesis that multivariate phenotypes have common etiology pathways when they are affected by pleiotropic genetic factors.We explored possible functional relationships between five of six genes associated with s Figure  was mappWe have showed a methodology to identify genetic markers associated with multivariate phenotypes derived from patterns of phenotypic associations discovered by ARM. An application of a large scale mining approach to anthropometric and biochemical traits has not been previously reported.The patterns of phenotypic associations were visualized with connected graphs which were informative to draw relations between plasma lipid levels with BMD and a cluster of common traits  related to MS. These putative patterns of the phenotypic associations were in concordance with the fact that MS increases T2D and CVD . More inhighLDLhighTG from the association patterns of two single traits high LDL and high TG, which subdivided samples into feasible sizes of cases and controls for GWAS. Multivariate phenotype highLDLhighTG increased the strength of phenotypic associations with the correlated single traits by additive effect of multiple single traits. Out of 17 traits linked to single traits high LDL and high TG, four traits  have more power in distinctively classifying samples of highLDLhighTG into cases and controls when the single traits were combined into the multivariate phenotype.For the identification of pleiotropic genes, we derived multivariate phenotype highLDLhighTG identified 15 SNPs in six genes . While p-values (9.6 × 10-5 ≤ P ≤ 1.2 × 10-4) of genetic variants in the six genes were less significant than those identified in general GWAS due to limited sizes of the sample those genes were not detectable within univariate framework of the GWAS. Effect sizes (odds raios=1.34-1.60) of the variants ranged between modest and intermediate sizes were comparable to those in the general GWAS. Relevance of the six genes to CVD in MS was apparently explained in previous work.Our approach to GWAS of multivariate highLDLhighTG represents one of pleiotropic patterns related to MS and the six genes associated with it can influence the pleiotropic effects on MS. In particular, we showed possibility that hyperlipidemia may be linked to pathogenic associations with osteoporosis and neurodegenerative disorders including AD and PD by incorporating associations of OMIM phenotypes with PPI networks for the six genes. We have found that four genes i.e. BCL2, NAV1, NIRP1 and TRPM3 may share AD and PD pathways where interacting genes with them are involved. Three genes i.e. BCL2, PAK7, and NIRP1 also share common pathways in cancer.According to analysis based on rat and mouse QTL DB, our results suggest that the six genes were mapped to QTLs associated with common traits related to MS, supporting that PAK7, C20orf103, TRPM3 and NAV1 have not yet been found although characteristics of genomic features for the four genes are also suggestive to investigate further study. Our work emphasize that multivariate phenotypes based GWAS can identify pleiotropic genes that share common etiology pathways.Clinical association studies for 4 genes i.e. SHP developed the concept and the method. JYL implemented a module for visualizations of association rules with networks and participated in drafting the paper. SK interpreted the results for the point of view of biology. SHP drafted the paper and JYL and SK were contributed to finalize the draft.The authors declare that they have no competing interests.Description of traits. A table shows 52 traits and their description and measurement.Click here for fileBasic characteristics for traits. Baseline characteristics according to means and standard deviations.Click here for fileAssociation rules encoding high TG and high LDL levels. Representative association rules encoding high TG and high LDL.Click here for fileFeatures for genomic regions and LD structures.Click here for file


Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11<p<2.8×10−23). Three of these—trimethylamine, 3-amino-isobutyrate, and an N-acetylated compound—were measured in urine. The other—dimethylamine—was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region . Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. The study's longitudinal twin design allowed detailed variance-components analysis of the sources of population variation in metabolite levels. The mQTLs explained 40%–64% of biological population variation in the corresponding metabolites' concentrations. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analysing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addition to the heritability contribution from the corresponding mQTL effects.We have performed a metabolite quantitative trait locus (mQTL) study of the  Physiological concentrations of metabolites—small molecules involved in biochemical processes in living systems—can be measured and used to diagnose and predict disease states. A common goal is to detect and clinically exploit statistical differences in metabolite concentrations between diseased and healthy individuals. As a basis for the design and interpretation of case-control studies, it is useful to have a characterization of metabolic diversity amongst healthy individuals, some of which stems from inter-individual genetic variation. When a single genetic locus has a sufficiently strong effect on metabolism, its genomic position can be determined by collecting metabolite concentration data and genome-wide genotype data on a set of individuals and searching for associations between the two data sets—a so-called metabolite quantitative trait locus (mQTL) study. By so tracing mQTLs, we can identify the genetic drivers of metabolism, characterize how the nature or quantity of the corresponding expressed protein(s) feeds forward to influence metabolite levels, and specify disease-predictive models that incorporate mutual dependence amongst genetics, environment, and metabolism. Genetic variation at eQTLs can be incorporated into network models that help define dependence between genotypes, molecular traits, environment, and physiological states metabolites, small molecules involved in biochemical processes in living systems, which can be measured in samples such as biofluids and tissue extracts using 1H nuclear magnetic resonance spectroscopy (1H NMR) Biocrates platform.  studies have proved a powerful aid to functional genomics, with many thousand genetic loci now highlighted that affect RNA transcription levels or splicing in human tissues cis eQTL of each mRNA to be focused on a relatively small, gene-centred region. Moreover, most known eQTLs are cis-acting single-nucleotide polymorphisms (SNPs), lying usually within tens of kb of the genes whose expression they influence a priori. An mQTL study tests variation in each metabolite for association with genome-wide genetic variation. As such a large number of tests is performed, effect sizes must be substantially larger to be reach statistical significance. Thus, as well as being potentially rarer, mQTLs are typically more difficult to detect than eQTLs of equivalent effect size.Metabolites are mechanistically further removed from the genome than are mRNAs, creating an important qualitative distinction between metabolite QTL (mQTL) and eQTL studies. The mRNA-to-gene mapping is a useful property of eQTL studies, allowing the search for a effect size of an mQTL is defined to be the proportion of population variation in metabolite concentration that is explained by genetic variation at the corresponding mQTL SNP.A number of recent studies have reported mQTLs for serum metabolite concentrations in humans 1H NMR-detectable metabolites in urine or plasma that are strongly influenced by common single-locus genetic variation?’ To this end, we performed an mQTL-discovery study using 1H NMR to analyse plasma and urine samples from multiple cohorts . The current paper's secondary aim was to provide further support for the findings of The primary question addressed by our study is: ‘Are there We collected plasma and urine samples from participants across two cohorts—MolTWIN and MolOBB—as part of the MolPAGE programme. The MolTWIN cohort comprised 142 female twins of Northern European descent, who donated samples longitudinally. The MolOBB cohort comprised 69 participants in the Oxford Biobank (OBB) 1H NMR spectra on plasma and urine samples; Biocrates-platform metabolite concentration data on plasma samples; and genome-wide SNP data  axis is known as a chemical shift and is quoted in parts per million . In order to address both multiple testing and the kinship of twin pairs, we used a permutation-based procedure, constraining the genome-wide false-discovery probability to be less than 0.001 for each metabolite peak's genome-wide scan.We detected, and then replicated, four metabolites driven largely by SNP variation (We proceeded to identify as many of the mQTL-driven metabolites as possible using a combination of: the web-based human metabolome database N-acetylated compound(s): X.NH.CO.CH3, with X unknown; we denote this set of one or more metabolites as N-ACu. We were unable to annotate N-ACu unambiguously despite conducting a number of additional experiments, including: seven experiments in which we spiked candidate compounds into selected urine samples and then re-measured the 1H NMR spectra; solid phase extraction experiments on urine samples in which we attempted to separate out N-ACu and thus aid its identification; and 2-dimensional 1H-13C heteronuclear single quantum coherence NMR spectroscopy experiments on selected urine samples.The mQTL at 2p13.1 associates with concentrations of one or more urine metabolites that we partially identified as We went on to characterize more accurately each metabolite's associations with SNPs within 200 kb of the hit regions. We used a linear mixed-effects model to account for: the sharing of genes and environment across twins, the collection of multiple samples longitudinally from some subjects, and the technical replication of each biological sample  effects . The individual-visit and common-visit components of variation modelled the longitudinal fluctuations between sample-donation visits that were respectively non-shared and shared by twins in a pair (the common-visit effect was included in the model because twins visited the clinic in pairs).For 1H NMR metabolite levels.The proportions shown in 1H NMR mQTLs). Our results qualitatively extended the findings of not explained by the mQTL itself  eQTLs have been discovered in fibroblasts , we have raised the possibility that a qualitatively similar relationship with PYROXD2 expression will be observed in the tissue that truly mediates the mQTL effect (likely to be liver or kidney).We examined these particular gene expression data (i.e. measured in fat and blood cells) because they had been acquired already on MolTWIN cohort members. In performing this analysis, we were not suggesting that variation in gene expression in fat has a direct impact on the concentration of TMAu or DMAp. However, a substantive proportion of eQTLs modulate expression in a similar way in different tissues AGXT2 . AGXT2 is known to be expressed in human liver and kidney. An eQTL for AGXT2 was reported in liver  with rs37369 in subdomain 1, and rs37370 in subdomain 2. We extracted functional predictions (SIFT Two of the most significant mQTL SNPs for BAIBu were rs37369 (T/C at 0.09/0.91) and rs37370 (C/T at 0.08/0.92), with ons SIFT  and Phylons SIFT . We usedWe analysed the 15 mQTL associations reported in Illig et al. 1H NMR, whilst Illig et al. 1H NMR 1H NMR spectroscopy is untargeted, quantifying the most abundant 50–100 metabolites in a biofluid, typically those above 10 micromolar in concentration 1H NMR data, of which five were also targeted by the Biocrates platform . So, the sets of metabolites considered by the two studies are minimally overlapping and therefore complementary.The current paper has extended recent studies 1H NMR and the Biocrates platform. This study design permitted a detailed decomposition of population variance in metabolite concentration . For the newly discovered 1H NMR mQTLs, this proportion varied between 40%–64%. For the 13 currently replicated mQTLs discovered by Illig et al. The MolTWIN plasma and urine samples were collected longitudinally from twins, and analysed with technical replication using ntration . We estiThe current study's twin design allowed us to quantify the proportion of biological variance in metabolite concentration that was attributable to familial factors . For Illig et al.'s replicated mQTLs, the ‘non-SNP’ familial variation  was considerable, explaining on average 44% (range 9%–70%) of biological variation in the corresponding metabolic trait . On aver1H NMR-detectable urine and plasma metabolites, using the MolTWIN 1H NMR data of the current study, though without incorporation of the genotype data. We decomposed biological population variation into components, including that explained by familial sources and that explained by longitudinally stable sources. On average (across 1H NMR peaks), familial sources explained 42% (IQR 32-52) of variation in plasma metabolite concentrations and 30% (IQR 17-39) of variation in urine ones. Longitudinally stable sources explained 60% (IQR 51-72) of variation in plasma metabolite concentrations and 47% (IQR 35-60) of variation in urine ones. The substantive widespread presence of familial and stable variation across the urine and plasma 1H NMR metabolomes has implications for the design and interpretation of metabolite biomarker-discovery studies In a separate study 1H NMR mQTLs (discussed in detail below) have experienced recent positive selection in European populations Interestingly, two of the 1H NMR-based Chenomx platform. Only one of the three mQTLs identified in the current paper—that of BAIBu—was identified in 1H NMR. In contrast, Suhre et al. investigated genetic drivers of a targeted subset of the urine 1H NMR metabolome with a substantially greater sample size than that of the current study, and thus had statistical power to detect relatively weak genetic effects. The untargeted nature of the current study allowed the detection of two strong mQTL drivers of urine metabolite concentrations—TMAu and N-ACu—that were not targeted by the Chenomx platform used in N-acetylated compounds . Also, the unambiguous annotation of BAIBu was assisted by input from Suhre et al. 3.CH.Y, with Y unknown but containing CH or CH2 and an electronegative substituent.During the revision stage of the current paper, an article by Suhre et al. PYROXD2 has experienced recent positive selection in European populations PYROXD2 and increased concentration of TMAu and DMAp. Further work will be necessary to clarify the mechanisms linking: DMAp and TMAu levels; PYROXD2 gene expression; and genetic variation in LD with rs7072216 . The signature of selection at PYROXD2 is indirectly suggestive of biomedical relevance; we also note that the set of genes showing evidence for positive selection is enriched for genes involved in oxidoreductase activity Genetic variation at N-oxide (TMAO) is formed endogenously in the liver via the N-oxygenation of TMA by the flavin-containing monooxygenase (FMO) protein family, and particularly by FMO3 There have been a number of studies that have examined the sources of variation in physiological concentrations of methylamines and their derivatives, e.g. FMO3 disrupt conversion of TMA to TMAO, resulting in high physiological levels of TMA and an accompanying fish-odour phenotype It may prove productive to relate the TMAu mQTL finding to the rare recessive genetic disorder trimethylaminuria, in which mutations at NAT8 is a likely candidate for mediating the association between SNP variation and N-ACu (urine concentration of N-acetylated compound(s)), since NAT8's encoded enzyme specifically catalyzes N-acetylation—NAT8's enzyme is cysteinyl-conjugate N-acetyltransferase, CCNAT The N-ACu mQTL lies within a large 500 kb haplotype block , and theNAT8  reduces acetylation efficiency, thus leading to toxin-induced kidney injury. The N-ACu mQTL SNP rs9309473 is in strong LD  (N-ACu), and with increased susceptibility to renal dysfunction Scheinfeldt et al. AGXT2 with BAIBu levels is consistent with the role of AGXT2's encoded enzyme, mitochondrial aminotransferase, which is expressed primarily in the kidney and catalyzes the reaction of BAIB with pyruvate to form 2-methyl-3-oxopropanoate and alanine (EC 2.6.1.40). It had also been previously suggested that altered BAIB homeostasis might contribute to hyper-β-amino-isobutyric aciduria, a relatively common Mendelian metabolic disorder in humans The BAIBu mQTL was also identified by Suhre et al. AGXT2 locus and BAIBu concentration.We found the non-synonymous SNP rs37369 . The MolOBB study received ethical approval from Oxfordshire REC C (08/H0606/107).www.twinsUK.ac.uk): a longitudinal epidemiological study of 11,000 twins , for which extensive clinical, anthropometric, lifestyle, and demographic information, and a wide range of biological measurements have been collected The 142 participants in the current study were recruited from the UK Adult Twin registry at St. Thomas' Hospital . Both twins in a pair always visited on the same day, and each visit was scheduled at either 10:00 or 14:00 .The 69 participants in the current study were selected from the Oxford Biobank 1H NMR analysis were collected in heparin tubes, whilst samples for Biocrates-platform analysis were collected in EDTA tubes. Blood samples were kept on ice for 20 min prior to centrifugation (16060 × g) at 4°C for 10 min, and subsequent storage at −80°C.Fasting blood and urine samples were collected at all clinic visits of each participant. Spot urine samples were centrifuged (16060 × g) at 4°C for 10 min before being stored at −80°C. Fresh blood was collected in a 9 mL tube through venepuncture. Samples for DNA was extracted from whole-blood samples using GeneCatcher  according to manufacturer's protocol. Genome-wide SNP genotypes were measured on a total of 166 individuals: 70 from the MolOBB cohort, and 96 from the MolTWIN cohort . The genotyping assay used was the Illumina 317K BeadChip SNP array .−4; SNPs at which HWE was rejected were omitted from the study. After quality control, the genotypes of ungenotyped MZ twins were copied from their corresponding genotyped twin. The final data set prior to imputation comprised 69 MolOBB members and 142 MolTWIN members genotyped at 296,017 autosomal SNPs.Quality control on the genotyped subjects was performed in a way similar to those described previously by the Wellcome Trust Case Control Consortium Measured genotypes were used to impute an additional 2,245,627 SNPs using the HapMap-CEU population (release 22) as reference Total RNA was extracted from adipose tissue biopsies with TRIreagent  and quantified using a NanoDrop. For whole-blood samples, PAXgene tubes were used, and RNA was extracted according to the manufacturer's protocol . RNA was labelled using the MessageAmp II 96-well amplification kit . Labelled RNA was hybridized onto Affymetrix HGU133 Plus2 arrays, washed, stained, and scanned for fluorescence intensity according to manufacturers protocols .http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/CDF_download.asp) and used to group probes into sets, each set corresponding to an Ensembl-annotated gene, resulting in 18,394 such genes represented in the array data. See Data were preprocessed using the RMA method without background correction  PYROXD2 gene, and used in the current paper's analysis of the mQTL for TMAu and DMAp.Expression data were extracted at the EDTA plasma samples were vortexed after thawing and centrifuged at 4°C for 5 min at 10,000 x g prior to loading of 10 µL of supernatants onto the 96-well kit plate. Processing of the AbsoluteIDQ kit followed the protocol specified by the manufacturer, including the following automated steps on the Hamilton ML Star robotics platform : (i) drying plasma samples under a nitrogen stream, (ii) derivatization of amino acids with 5% phenylisothiocyanate reagent (20 µL), (iii) drying of samples, (iv) extraction of metabolites and kit internal standards with mM ammonium acetate in methanol (300 µL), (v) centrifugation through filter plate , vi) dilution with 600 µL MS running solvent. 20 µL of the final extracts were applied to flow injection analysis mass spectrometry.Samples were analyzed using an API 4000 triple quadrupole mass spectrometer (ABSciex) equipped with an Agilent 1200 Series HPLC and a HTC PAL auto sampler from CTC controlled by the software Analyst 1.5. The standard flow injection method comprising two 20 µL injections (one for positive and one for negative electrospray ionisation mode) was applied for all measurements. Quantification was achieved by multiple reaction monitoring detection in combination with the use of stable isotope-labelled and other internal standards assay refers to the measurement of 163 metabolite concentrations in a biological sample. Of a total of 356 assays across the MolOBB and MolTWIN cohorts, we identified two assays that exhibited anomalously low concentrations of all metabolites (relative to the levels observed in the other assays); we omitted those two assays from further analysis.We performed quality-control checks, including boxplots and principal-component score plots, on the Biocrates-platform data to identify failed assays, where an 2O supplemented with 0.1% (w/v) sodium azide as a bacteriostatic agent and 1.5 mM sodium formate as a chemical-shift reference (δ8.452). Urine was diluted 2 in 1 in phosphate buffer  supplemented with 1 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid  and 0.1% (w/v) sodium azide. Sample aliquots were allocated to 96-well plates (and wells thereon) in a randomized design.Thawed samples were centrifuged at 16060 × g for 10 min. Samples were aliquotted into two technical replicates prior to sample preparation. Plasma was diluted 1 in 4 in physiological saline prepared in 20% D1H) using a 5 mm TXI flow-injection probe equipped with a z-gradient coil, at 300 K, at a spectral width of 12019 Hz, with 96 transients being collected with 8 dummy scans using 64k time domain data points. For both plasma and urine samples a standard 1D spectrum [RD−90°−3 µs−90°−tm−90°−acquire] with selective irradiation of the water resonance during the relaxation delay  and during the mixing time  was acquired. Additionally, for the plasma samples, a spin-echo (Carr-Purcell-Meiboom-Gill) spectrum [RD−90°−(τ/2−180°−τ/2)n−acquire] with a total echo time of 608 ms  and a diffusion-edited spectrum made using a bipolar pulse-pair longitudinal eddy current delay pulse sequence with spoil gradients immediately following the 90° pulses after the bipolar gradient pulse pairs were acquired. Continuous wave irradiation was applied during the relaxation delay at the frequency of the water (or HOD) resonance. Eddy current recovery time (Te) was 5 ms, and the time interval between the bipolar gradients (|) was 0.5 ms. Further details may be found in Each experiment was acquired on a Bruker DRX 600 MHz spectrometer  operating at 600 MHz .Spectra were phased using in-house software . All other data analysis was performed in R The spectra were denoised in the frequency domain using wavelet-based methodology , or was coded as a missing value if no local maximum was present. We chose peak height to be the estimator of concentration as, in addition to its simplicity, it had relatively good robustness properties in the context of spectral artefacts . If the width  of a peak varies substantially across spectra, then peak height may be less precise than area at quantifying concentration. Plots of peaks did not reveal substantial peak-width variation in our data sets.Only common peaks—present in at least 80% of spectra in their corresponding data set—were included in downstream statistical analysis, and only a peak's non-missing data were included at the statistical modelling stage. A missing datum, corresponding to there being no local maximum in the peak's ppm interval, typically occurred for one of two reasons: (a) the corresponding metabolite's concentration was too low to create a local maximum, or (b) a relatively large neighbouring peak overlapped the peak of interest . The omission of type (a) missing values from the analysis potentially decreased statistical power to detect mQTLs driving metabolite concentration variation at levels near or below the level of detection. The omission of type (b) missing values from the analysis increased the robustness of inference (and conserved power) in the face of artefactual effects of overlapping peaks. To illustrate, in Prior to model fitting, we discarded any peaks that were annotated to exogenous metabolites (of ibuprofen or acetaminophen), to a spike-in compound , or to urea . Across the three plasma data sets, 104 peaks were annotated to glucose; we discarded all but one representative glucose peak in each plasma data set.The spectra were normalized using probabilistic quotient normalization t-statistic for the test of the null hypothesis t-statistic observed across all SNPs tested, and this statistic, We tested each metabolite peak in turn for association with 2,541,644 autosomal SNPs. For this stage we averaged and transformed the peak data as follows: (i) we averaged each subject's metabolite peak data across all biological and technical replicates; (ii) for robustness, we mapped the quantiles of the resulting inter-subject distribution to the quantiles of a standard Gaussian distribution. We denote the resulting data vector by t-statistics as before, quantifying the additive genetic association between t-statistic across SNPs, yielding the We characterized the (metabolite peak-specific) null distribution of For each metabolite, we rejected t-statistics exceeded, in absolute value, the metabolite's maximum null test statistic, hit region to be the smallest contiguous region containing all genome-wide significant SNPs.Underlying the observed data at a metabolite peak  was a complex correlation structure, induced by the sharing of alleles, individuals, and samples by different sample aliquots. In the follow-up analysis of hit regions we explicitly modelled this covariance structure while quantifying the metabolite's association with each local SNP in turn (i.e. with each SNP within 200 kb of the hit region and with MAF > 5%).To deal with potential deviations from the Gaussian distributional assumptions, we mapped the quantiles of the empirical data distribution at each peak to the quantiles of a standard Gaussian distribution, yielding the transformed data vector, familial variance (i.e. the variance attributable to genetics and common environment), The current paper's model induced a covariance structure on nt paper , variancFor each SNP within 200 kb of the hit region, we fitted the mixed-effects model both with and without the At the most strongly associated SNP, we went on to fit the model in a Bayesian framework, quantifying the precision of parameter estimates using posterior credible intervals. For this analysis we used directly the log-transformed metabolite concentrations, denoted by For each of the mQTLs discovered in the MolTWIN cohort, we re-tested the association using only data from the MolOBB cohort. Specifically, we mapped the quantiles of the metabolite's concentration data to the corresponding quantiles of a standard Gaussian distribution; we then tested for an additive association with the corresponding SNP's genotype data, including age and gender as covariates in the linear model. Resulting p-values are shown in We used the concentration data directly as output from the Biocrates platform, and calculated metabolic traits from concentration ratios as in The data underlying the current paper's analyses are available for download from an FTP server .Figure S11H NMR spectra that are driven by mQTL variation. In the bottom panel we plotted 50 spectra over a subset of the ppm axis . The top panels are zoomed-in views of peaks from the three mQTL-driven urine metabolites of the current paper. The vertical scale of the bottom panel differs from the vertical scale shared by the top three panels.Peaks in urine (TIF)Click here for additional data file.Figure S2Hit region for DMAp. Top: location of genes, with rectangles denoting the position of exons. Middle: log-transformed p-values ((TIF)Click here for additional data file.Figure S3Hit region for BAIBu. Top: location of genes, with rectangles denoting the position of exons. Middle: log-transformed p-values ((TIF)Click here for additional data file.Figure S4total phenotypic variance, because this was the measure of effect size used in Comparison of estimates of effect size of mQTL SNPs for metabolic traits measured on the Biocrates platform. Effect sizes are compared between Illig et al. (estimates are taken from (TIF)Click here for additional data file.Figure S51H NMR. Effect sizes are compared between the discovery stage (MolTWIN cohort) and the replication stage (MolOBB cohort). The MolTWIN estimates and credible intervals are as shown in total variance in metabolite concentration explained by the SNP using the ratio of sample variances: biological variation in metabolite concentration. This was achieved by dividing by total variance in Comparison of estimates of effect size of mQTL SNPs on metabolite concentrations measured by (TIF)Click here for additional data file.Figure S6Distribution of the ratio of TMAOu concentration to the combined concentration of TMAOu and TMAu (includes both MolTWIN and MolOBB cohorts). Trimethylaminuria controls have relatively high values of TMAOu/(TMAOu + TMAu), typically greater than 0.8 (TIF)Click here for additional data file.Figure S7Plot of spectra for which mQTL-driven metabolites, labelled top, are determined as missing. Missing-peak spectra are plotted in black. For comparison, an arbitrarily selected set of 25 present-peak spectra is plotted in grey. Vertical green lines delimit the corresponding peak's bin ((TIF)Click here for additional data file.Table S11H NMR data)’ of 1H NMR data)’ of Details of statistical association between each mQTL-driven metabolite and the SNPs within 200 kb of its hit region. Genomic locations are given in NCBI build 37 coordinates. Columns labelled ‘Beta,’ ‘S.E. Beta’ (S.E.  =  standard error) and ‘p-value’ (for the test of the null hypothesis that (XLS)Click here for additional data file.Table S2(A) Non-synonymous SNPs in LD with mQTL SNPs. (B) Corresponding residue changes and predicted functional effects of non-synonymous SNPs.(DOC)Click here for additional data file.Table S3Previously discovered eQTLs within 200 kb of mQTL hit regions.(DOC)Click here for additional data file.