FTO (fat mass and obesity associated) gene are associated with obesity. SNP have also been identified in the pig FTO gene, among which some are associated with selected fat-deposition traits in F2 crosses and commercial populations. In this study, using both commercial pig populations and an experimental Meishan × Pietrain F2 population, we have investigated the association between one FTO SNP and several growth and carcass traits. Association analyses were performed with the FTO polymorphism either alone or in combination with polymorphisms in flanking loci.Independent studies have shown that several single nucleotide polymorphisms (SNP) in the human FTO was genotyped by PCR-RFLP and tested for associations with some growth, carcass and fat-related traits. Proportions of genetic variance of four pig chromosome 6 genes  on selected traits were evaluated using single- and multi-locus models.SNP (FM244720:g.400C>G) in exon 3 of porcine FTO on the p arm of pig chromosome 6, approximately 22 cM from RYR1. In the commercial populations, allele C of the FTO SNP was significantly associated with back fat depth and allele G with muscling traits. In the Meishan × Pietrain F2 pigs, heterozygotes with allele C from the Pietrain sows and allele G from the Meishan boar were more significantly associated with fat-related traits compared to homozygotes with allele G from the Pietrain and allele G from the Meishan breed. In single- and multi-locus models, genes RYR1, TGFB1 and FTO showed high associations. The contribution in genetic variance from the polymorphism in the FTO gene was highest for back fat depth, meat area on the musculus longissimus lumborum et thoracis tissues and metabolite glucose-6-phosphate dehydrogenase.Linkage analysis placed FTO influences back fat depth in the commercial populations, while in the Meishan × Pietrain F2 pigs with a CG genotype, heterosis occurs for several fat-related traits.Our results show that in pig,  The abbreviation FTO originates from a study in mice with a fused toes (Ft) phenotype and other abnormalities resulting from a 1.6 Mb deleted region on mouse chromosome 8 that includes this gene. It has been suggested that mouse Fto could be involved in programmed cell death, limb development, craniofacial development and the control of left-right asymmetry . The SNP reported in FTO intron 4 by .To identify polymorphisms in the porcine ron 4 by  was alsoFTO SNP FM244720:g.400C>G and quantitative traits were analysed with SAS®  using SAS® 8.2 Version and the results of the Type III sum of squares calculation for the Meishan × Pietrain pedigree, and the GLM procedure (Type IV) of SAS 9.1 for the commercial populations.Associations between i) For the statistical evaluation of the Meishan × Pietrain F2 family, the following model A was used: with CmCm being: homozygotes with both alleles C of grandmaternal origin; CmGm: heterozygotes with alleles CG, both of grandmaternal origin; CmGp: heterozygotes with alleles CG inherited from both grandparents; GmGp: homozygotes with alleles GG inherited from both grandparents; GpGp: homozygotes with both alleles G of grandpaternal origin (the genotype classes for FTO were created using the procedure Chrompic from the CRI-MAP package [j = the effect of two-month class created from slaughter date (j = 1-6); sexk ; litternol = litter number ; b = linear regression value; s_ageijklm = age at slaughter of animal m; S_age = estimated average of the age at slaughter; eijklm = random residual.where Y package . The matFTO, as well as RYR1, LIPE and TGFB1 - for more information, see [m.l.l.t.). Genes that showed no significant association remained in a combined model if their contribution to the total VR reached about 2%.Models including different genes  with thii) For analyses on commercial pigs, the model included the FTO genotype with/or without the RYR1 CT genotype, a crossbred combination, gender and type of diet as fixed factors, and average of age at slaughter as a regression coefficient. The statistical model B was:; RYRj = the effect of RYR1 genotype ; crossk = the effect of crossbred combination ; sexl ; dietm = the effect of diet ; b = linear regression value; s_ageijklmn = age at slaughter of animal n; S_age = estimated average of age at slaughter; eijklmn = random residual. * means that the association analysis of animals with genotype CC at gene RYR1 did not include the effect of gene RYR1 and ** means that the separate association analysis of crossbred combination PNx(CZLxCZLW) did not include the effects of other crossbred combinations.where: Yt-test.The differences between genotype classes were assessed by Sequence analyses identified two SNP, FM244721:g.307C>T and FM244720:g400C>G that were detected by PCR - restriction fragment length polymorphism (RFLP) using conditions presented in Additional file >G [Pigs of different breeds were analysed for SNP FM244720:g.400C>G  and the difficulty of discriminating between alternative orders by linkage analysis with limited numbers of informative meioses. However, the order of tightly linked loci has no influence on the results of genotype effects in single- and multi-locus analyses.This assignment agrees with earlier mapping results -16,19 ans region ,30. In bFTO g.400C>G genotypes were GG in the grandsire (Meishan) and CC, CG and GG in the granddams (Pietrain breed), respectively. The genotype of the three F1 boars was CG with alleles C and G inherited respectively from the Pietrain and Meishan grandparents. Since the genes FTO and RYR1 are linked (separated by ~22 cM) and since RYR1 is known to affect several carcass traits, it had to be considered as well. The grandsire's genotype at RYR1 was CC and all eight granddams were TT homozygotes [The ozygotes .FTO of the grandparents and parents of the F2 animals combined with the FTO flanking phase information made it possible to identify the grandparental origin of alleles in the F2 animals. The results of the association analysis for the genotype classes according to the grandparental alleles' origin are presented in Table RYR1 was not included as a co-factor in these analyses. If it was included as another factor in the model, the differences between estimates in the one-locus and two-locus models remained (data not shown). We consider that the recombination frequency (0.17) observed between FTO and RYR1 in this population makes it possible to detect specific associations between FTO and the traits analysed, mostly or totally independent from RYR1.The genotypes at FTO genotype classes CmGp and GmGp. In both cases, allele Gp is of grandpaternal origin, and the two classes differ by Cm and Gm alleles originating from granddams (Pietrain). Table m.l.l.t. average back fat depth, shoulder fat depth, fat depth at 10th rib, loin fat depth and fat area on m.l.l.t.) as well as feed consumption in the two classes differ significantly, with higher values for genotype CmGp . The values for fat-related traits in this class are also significantly higher than those in classes CmCm and CmGm .In Table CmGp class most probably represent a heterosis effect, often overdominant in comparison with other classes. However, it may not necessarily be the effect of the FTO genotype itself, since extensive linkage disequilibrium in this population means that in the F2 generation extensive haplotypes are inherited intact from the Pietrain and Meishan grandparents in the observed chromosome region, and thus other linked genes may be responsible for the heterosis effect on the fat traits. The heterosis effect was apparent when allele Cm  was present. However, the chromosome segments from both breeds led to heterosis, but if both FTO alleles (i.e. chromosome segments) come from the Pietrain breed (genotype CmGm), no heterosis occurred. This effect may be the result of the genetic background of the founders and consequently F2 pigs, and may be different in other populations.The high positive effects in the FTO gene and three distally located genes , in single- and multi-locus models for several traits are summarised in Additional file RYR1, TGFB1 and FTO) show strong associations in single- and multi-locus models. The covariance components (interaction effects) of RYR1 and TGFB1 are often very low and indicate that combining both loci does not increase the declared variance proportion. These genes are also similarly associated with some traits, e.g. fat cuts, lean cuts and meat quality traits. The most significant variance proportion is observed for FTO in combination with RYR1, LIPE and TGFB1 in the multi-locus model for back fat depth, whereas for meat area on m.l.l.t., and the metabolic trait G6P in fat tissue, it is highest in the single- and multi-locus models, respectively. TGFB1 shows the most significant gene effects on weight of ham relative to half carcass weight in both model types.Proportions of trait variance associated with the g.400 C-g.276 T and g.400 G-g.276 G. We used SNP g.400C>G for the analysis.Commercial crossbred pigs were evaluated in two analyses: (i) with the crossbred combination PNx(CZLxCZLW) alone and (ii) with the combined populations Table . All aniFTO SNP g.400C>G genotypes were observed. Using model (B) with the genotype at RYR1 included, FTO SNP g.400C>G was significantly associated with average back fat depth (P < 0.0001), fat in the belly 2 (P = 0.002), muscle depth (P = 0.02) and average daily gain (P = 0.02). The highest values for fat traits were observed in animals with genotype CC and for muscle depth in those with genotype GG . For intramuscular fat in ham, the number of pigs analysed was rather low , significant associations were found between the FTO SNP and average daily gain (P = 0.02), average back fat depth (P = 0.02), muscling traits  and feed conversion (P = 0.003). In this case too, higher values for back fat depth and fat depth were observed in animals with genotype CC and CG. For the muscling traits, genotype GG was the most favourable .All three FTO SNP g.400C>G genotypes CC and CG were significantly associated with high values for average back fat depth. A similar situation was observed for associations between genotype GG and high values for muscle depth.Clearly, the quantitative traits studied here are under polygenic control. Tests for associations with a single gene can reveal both the effects on genetic variance of the genotypes at the gene of interest and at the genes in linkage disequilibrium with the gene of interest, only if the animals in the population analysed have similar genetic backgrounds (e.g. pure bred populations) and are subject to standardized environmental influences. These requirements are not always met, mainly for economic reasons. In our experiment, we used the commercial crossbred population PNx(CZLxCZLW) separately, in which the heterogeneity in genetic background is not so high as in the population composed of several crossbreds. However, in both this crossbred population and in the joint population composed of several crossbreds, the 2 pigs from the Meishan × Pietrain family, in which the highest effects on fat-related traits, apparently overdominance, are detected in heterozygotes with allele C inherited from the Pietrain and allele G inherited from the Meishan breed.Effect of genetic background can be observed on F2 pigs, it was important to include in the models the genotypes of linked genes, which are known to have effects on the traits of interest, especially RYR1. We examined the proportion of genetic variance explained by FTO and the linked loci. In this case too, highly significant associations between FTO polymorphisms and back fat depth, and also meat area on m.l.l.t. and G6P were observed. It is hypothesized that other loci on different chromosomes may also be significantly involved in the determination of each trait, as additional proportions of genetic variance (examples are given in [Since extended Pietrain and Meishan haplotype blocks are inherited intact in the Fgiven in ).FTO genotypes and fatness and growth rate traits have been performed with SNP from various regions of the gene [CC and CG having higher effects.Previous association analyses between pig the gene ,16,18,19g.400 C (and g.276 T) is associated with higher values for fat deposition, while allele g.400 G (and g.276 G) with higher values for meat traits. However, Fan et al. [FTO, respectively, and average back fat thickness in the Berkshire × Yorkshire F2 population. Zhang et al. [FTO and intramuscular fat, but not with average back fat thickness, leaf fat and average daily gain in the Jinhua × Pietrain F2 population.Associations between the intronic SNP (g.276T>G) and back fat thickness have been reported for Italian Duroc and commercial pig populations  and agren et al.  and Du en et al.  did not g et al.  have repFTO gene cause the observed variation in the traits of interest. However, some of these appear to be in linkage disequilibrium with loci influencing fatness traits. Additional research is required to identify causative mutations in the chromosomal region within and flanking the FTO gene.There is no evidence that any of the mutations identified in the pig FTO gene in pig, several genome scans have identified QTL on chromosome 6 in the region harbouring the RYR1 gene with effects on fatness traits . However, additional genes linked to RYR1 such as FTO may be involved in the phenotypic variation of these traits. For example, Mohrmann et al. [2 pigs from crosses between two commercial pig lines, found QTL located close to RYR1 for several fatness traits  even when adjusting for RYR1 genotypes. Paszek et al. [SW1841 - SW1067 in F2 pigs of Meishan × Yorkshire breeds, and Óvilo et al. [SW1057 and S0087 in an Iberian × Landrace intercross. In addition to the FTO gene, there are several other candidate genes of interest  linked to the RYR1 locus [In addition to association studies involving the n et al. , using Fk et al.  detectedo et al.  have repR1 locus ,35, whicFTO gene was analysed for associations with carcass and growth traits. FTO genotypes differ in their effects on several fat deposition traits and some carcass traits, both in Meishan × Pietrain F2 family and in commercial pig populations. In the commercial populations, genotypes CC and CG were associated with a higher average back fat depth, while genotype GG was associated with a higher value for some muscling traits. In F2 pigs of a Meishan × Pietrain family, heterozygotes with allele C from the Pietrain sows and allele G from the Meishan boar had significantly higher values for fat-related traits in comparison with homozygotes G (from Pietrain) and G (from Meishan). The significance of the FTO polymorphism for these traits was further supported by the single- and multi-locus models. Results from different association studies between FTO polymorphisms and production traits indicate that this gene (and its flanking regions) has a major role in the variation of fatness traits. Further research will be needed to identify candidate/causative mutations in this chromosomal region.SNP FM244720:g.400C>G in pig The authors declare that they have no competing interests.VD, HB and AS were responsible for the concept, design, data analysis, data interpretation and drafting of the manuscript. PH contributed to molecular genetic analyses and sequencing. RS, JČ and MŠ organized the experiments in the Test Station in Ploskov (Czech Republic) and collected all phenotypic data. AH contributed to genotyping and data analysis. HG organized the experiments in Hohenheim, supervised the genotypic and phenotypic data acquisition and revised the manuscript. All authors have read and approved the final manuscript.FTO and three additional genes Information on PCR and RFLP conditions for SNP analysis in . The file shows PCR primers for FTO, RYR1, LIPE and TGFB1, amplicon sizes, MgCl2 concentrations, annealing temperatures, SNP types and restriction enzymes for RFLP analyses [analyses ,36-39.Click here for fileFTO polymorphism in combination with linked genes on SSC6  with several traits analysed for the Meishan × Pietrain F2 pigs Associations of the . Proportions of genetic variance of the four genes  on selected traits evaluated by single- and multi-locus models are shown. (DOCX 28 kb).Click here for file


For most complex traits, only a small proportion of heritability is explained by statistically significant associations from genome-wide association studies (GWAS). In order to determine how much heritability can potentially be explained through larger GWAS, several different approaches for estimating total narrow-sense heritability from GWAS data have recently been proposed. These methods include variance components with relatedness estimates from allele-sharing, variance components with relatedness estimates from identity-by-descent (IBD) segments, and regression of phenotypic correlation on relatedness estimates from IBD segments. These methods have not previously been compared on real or simulated data. We analyze the narrow-sense heritability of nine metabolic traits in the Northern Finland Birth Cohort (NFBC) using these methods. We find substantial estimated heritability for several traits, including LDL cholesterol (54 % heritability), HDL cholesterol (46 % heritability), and fasting glucose levels (39 % heritability). Estimates of heritability from the regression-based approach are much lower than variance component estimates in these data, which may be due to the presence of strong population structure. We also investigate the accuracy of the competing approaches using simulated phenotypes based on genotype data from the NFBC. The simulation results substantiate the downward bias of the regression-based approach in the presence of population structure.The online version of this article (doi:10.1007/s00439-012-1230-y) contains supplementary material, which is available to authorized users. Over 1,000 such associations have been documented to date . In this article, we conduct genome-wide heritability analyses of the Northern Finland Birth Cohort (NFBC) 1966 GWAS data. Standard GWAS analyses of these data have been published previously, and multiple genome-wide significant associations were found  at the time of the measurements. Covariate information includes sex, whether the individuals were taking medication for diabetes, whether they were taking oral contraceptives, whether they were pregnant, their fasting status, and whether their weight was self-measured. We excluded individuals who were pregnant  or taking diabetes medication . We estimated heritability for the nine metabolic traits measured in these data: body mass index (BMI), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, systolic blood pressure, diastolic blood pressure, glucose, insulin, triglycerides, and C-reactive protein (CRP). For analysis of BMI we excluded individuals who had self-measured weight . For triglycerides, insulin, glucose, HDL, and LDL, we excluded individuals who were not fasting at the time of measurement . As in Sabatti et al. , CRP valWe detected IBD segments between pairs of individuals using a pre-release version of BEAGLE Refined IBD. This IBD detection method will be described elsewhere. In brief, it has similar computational performance to BEAGLE fastIBD , while for the IBD-segment-based approaches we estimate it based on the quantity of IBD detected for the pair of individuals.T, where T: the 2nd percentile ]. At each point in the genome we calculated the proportion of pairs of individuals sharing an IBD segment covering the point (the IBD rate). Figure i, i+1) is the IBD rate across the interval between SNPs i and i+1, T in this interval and 0 otherwise, j and k have a detected IBD segment that spans the interval between SNPs i and i + 1, and 0 otherwise. Length  is the genetic length of the interval between SNPs i and i + 1, and the sums are over SNPs i, excluding the final SNP on each chromosome. The factor of two in the denominator is part of the definition of relatedness. Relatedness is twice the probability that a random allele taken from each of two individuals is IBD, or equivalently, ignoring the possibility of inbreeding, relatedness is half the probability that the two individuals are IBD through any pair of their alleles.To calculate the weighted relatedness for a pair of individuals, we compute the following ratio:We also calculated an unweighted relatedness which was obtained by summing the genetic lengths of detected IBD segments for the pair and dividing by twice the genetic length of the genome . The weighted version was used except where otherwise noted.Relatedness values for chromosome X are slightly different. We followed the approach of Yang et al.  and usedA in Eq. f, where f is the inbreeding coefficient for the individual. For the IBD-segment-based relatedness matrix, we estimate f by detecting segments of homozygosity by descent (HBD) using Beagle , but we do not use this approach here.Before applying the method of Zuk et al. , we adjuyj is the simulated trait value for individual j, i indexes the n causal SNPs, αi is the effect of causal SNP i, xij is the number of copies of the reference allele at SNP i in individual j, pi is the reference allele frequency for SNP i, and ej is the environmental effect for individual j. We simulated a trait with n = 100 causal loci each contributing equally to the trait variance, and with total heritability 50 % for the trait. For each simulation replicate, 100 SNPs were randomly selected from the autosomal SNPs. We assigned 0.005 genetic variance to each of these 100 SNPs, which corresponds to We simulated phenotypes using the genotypes of the NFBC data. The simulation follows the additive model We applied the Zuk et al. method , the GCTResults for the autosome-wide analyses are shown in Table Estimates using variance component IBD-segment-based relatedness values are approximately 100 % higher than estimates using the GCTA method for a number of the traits including glucose, HDL cholesterol and LDL cholesterol, although 95 % confidence intervals for the estimates overlap. If reflecting true underlying differences, one plausible explanation for the difference is that rare variants contribute substantially to the heritability of these traits. Effects of rare variants may be included in the IBD-segment-based estimates of heritability whereas they are not well captured by the standard GCTA estimates.The estimates from the method of Zuk et al. are significantly lower than those using IBD-segment based variance components for several of the traits (glucose is the one exception). Zuk et al.  have shop value of 0.028 for the IBD-based analysis and 0.043 for the GCTA analysis, with estimated heritability 2.8 % (se 1.5 %) from the IBD-based analysis and 1.7 % (SE 1.1 %) from the GCTA analysis.We applied GCTA and IBD-segment-based variance components to the X chromosome. The only results that were significant at a 0.05-level were systolic blood pressure with a To investigate further the performance of estimates of heritability in the NFBC data we performed simulations. We used the genotypes from the NFBC and created new traits based on additive genetic and random environmental effects, as described in “Estimates from the Zuk et al. method differ depending on the range of relatedness values included in the regression. The results in Table If population structure is the primary cause of the downward bias in the Zuk et al. estimate, we would expect to find less bias in a population with less structure. We simulated phenotypes using the WTCCC2 control data , using the same phenotype simulation model as for the NFBC data. These data exhibit some population structure, but it is much less than that in Northern Finland. For example, the principal components values overlap significantly for different regions of the UK, whereas there is little overlap in the multi-dimensional scaling values for different regions in Northern Finland  than allele-sharing-based estimates in the variance component approach. In the NFBC data, the reported standard errors of the heritability estimates were more than twice as large with IBD segments as with allele-sharing. We think that this is likely due to the effects of very distant relatedness. Very small segments of IBD  due to very distant relatedness (more than 50 generations) are usually not detectable and thus do not contribute to the IBD-segment-based estimates of relatedness; however, these segments will tend to contribute to allele-sharing-based estimates, albeit in a noisy fashion. In data from larger (more outbred) populations this effect is magnified because there is less recent relatedness. In the WTCCC2 data, the reported standard errors of variance component heritability estimates were approximately 14 times as large using IBD segments as using allele-sharing. This makes the use of IBD-segment-based methods for estimating heritability impractical in population data from a population with large effective size.For several metabolic traits, we found that the IBD-segment-based estimates of heritability from variance components are substantially higher than allele-sharing-based estimates. While these differences were not statistically significant, this suggests that the IBD-segment-based estimates are incorporating genetic signals not captured by the allele-sharing-based estimates. A plausible explanation for this phenomenon is that the difference is due to the effects of rare variants which are not well captured by allele-sharing-based estimates. A substantial role of rare variants for metabolic traits is consistent with results from sequencing studies showing important contributions of rare variants to variation in several metabolic traits including HDL cholesterol . Further, there will be some IBD segments that should fulfill our criteria  but that are not detected due to genotype errors or haplotype phasing errors, for example. Such errors add to noise but do not induce bias, provided that the rates of such errors are evenly distributed across the genome. However, we showed that the rate of detected IBD varies considerably over the genome in our results, and we showed [in Section 1.3 of the Appendix ] that reweighting in the relatedness estimation was necessary to avoid biases when variants of large effect fall in regions of high or low IBD detection. A third type of error is mis-determination of the IBD segment endpoints. Determination of endpoints of IBD segments is difficult, because a small number of genotyped markers may be consistent with IBD beyond the true end of the IBD segment, leading to false extension of the detected IBD segment. On the other hand, errors in genotypes or in haplotype phase may lead to premature truncation of the detected IBD segment. In simulated data, we have found that such effects balance each other out in the IBD detection method used here (unpublished data). Unbiased estimation of IBD segment length for detected IBD segments is important. If IBD segment length is overestimated, heritability will be underestimated, and vice versa.We found that the regression-based method gave strongly downwardly biased estimates of heritability for simulated phenotypes in the NFBC data. Since this method implicitly assumes a homogenous population, we believe that the downward bias is due to the strong population structure in these data. In contrast, a variance component approach was fairly robust to population structure in the absence of environmental confounding in our analyses. It is possible that the variance-component-based heritability estimates in the real data could be upwardly biased due to environmental confounding with population structure, although previous studies suggest that most such environmental confounding effects are corrected by the use of principal components Below is the link to the electronic supplementary material.


As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci  and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these  Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels. The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system, providing valuable insights for both basic research as well as clinical applications. In this study, we borrow the metabolomics GWAS concept for a novel, entirely different purpose. Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample, but it has not yet been elucidated which specific metabolite this signal actually represents. The concept is comparable to a fingerprint: each one is uniquely identifiable, but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs. Obviously, this issue tremendously reduces the usability of a metabolomics analyses. The genetic associations of such an “unknown,” however, give us concrete evidence of the metabolic pathway this substance is most probably involved in. Moreover, we complement the approach with a specific measure of correlation between metabolites, providing further evidence of the metabolic processes of the unknown. For a number of cases, this even allows for a concrete identity prediction, which we then experimentally validate in the lab. Recently, genome-wide association studies (GWAS) on metabolic quantitative traits have proven valuable tools to uncover the genetically determined metabolic individuality in the general population The number and type of the metabolic features that went into these GWAS was mainly defined by the metabolomics techniques used: Gieger et al. unknown metabolites”. An unknown metabolite is a small molecule that can reproducibly be detected and quantified in a metabolomics experiment, but whose chemical identity has not been elucidated yet. In an experiment using liquid chromatography (LC) coupled to MS, such an unknown would be defined by a specific retention time, one or multiple masses (e.g. from adducts), and a characteristic fragmentation pattern of the primary ion(s). An unknown observed by NMR spectroscopy would correspond to a pattern in the chemical shifts. Unknowns may constitute previously undocumented small molecules, such as rare xenobiotics or secondary products of metabolism, or they may represent molecules from established pathways which could not be assigned using current libraries of MS fragmentation patterns While these previous GWAS focused on metabolic features with known identity, untargeted metabolomics approaches additionally provide quantifications of so-called “The impact of unknown metabolites for biomedical research has been shown in recent metabolomics-based discovery studies of novel biomarkers for diseases and various disease-causing conditions. This includes studies investigating altered metabolite levels in blood for insulin resistance In mass-spectrometry-based metabolomics approaches, the assignment of chemical identity usually involves the interpretation and comparison of experiment-specific parameters, such as accurate masses, isotope distributions, fragmentation patterns, and chromatography retention times Approaching the problem from a conceptually different perspective, we here present a novel functional metabolomics method to predict the identities of unknown metabolites using a systems biological framework. By combining high-throughput genotyping data, metabolomics data, and literature-derived metabolic pathway information, we generate testable hypotheses on the metabolite identities based solely on the obtained metabolite quantifications . No furtThe concept of our approach is based on the following observations from our previous work on genome-wide association studies and Gaussian graphical modeling (GGM) with metabolomics: We showed that GWAS with metabolic traits can reveal functional relationships between genetic loci encoding metabolic enzymes and metabolite concentration levels in the blood The manuscript is organized as follows: We first conduct a full genome-wide association study on 655,658 genotyped SNPs with concentrations of 225 unknown metabolites using fasting blood serum samples from a large German population cohort (n = 1768) http://cmb.helmholtz-muenchen.de/unknowns.All GWAS and GGM results, unknown metabolite classifications and pathway annotations are available as spreadsheets and in .graphml format in In the first step of our analysis, we conducted a GWAS with the concentrations of known and unknown metabolites, testing a total of 655,658 genotyped SNPs from the KORA cohort for association. Thus, in addition to the unknown metabolite data, we included the association data for known metabolites from our previous study 2≥0.5) with the respective lead SNP. Seven of the 15 loci  have not been described in GWAS with metabolic traits before and thus represent new genetic loci of metabolic individuality. Interestingly, genetic variants in strong LD with CYP2C18 have been reported to associate with warfarin maintenance dose In total, we observe 34 distinct loci that display metabolite associations at a genome-wide significance level . Out of cis-eQTLs  in GTEx. All identified eQTLs with p-values below 10−5 are listed in In our previous GWAS with metabolic traits, we observed that metabolites associating with genetic variants in or near enzymes are likely to be functionally linked to these proteins. A SNP with detectable effects on the metabolome will, for instance, alter expression levels of mRNAs, or affect the properties of the respective enzymes  through modifications of the protein sequence. As an example of the latter case, the SNP rs4343 in the angiotensin converting enzyme (ACE) encoding gene was found to be associated with the activity of the enzyme sulfotransferase family, cytosolic, 2A, dehydroepiandrosterone DHEA-preferring) strongly associates with the concentrations of the unknown metabolites X-11440 and X-11244 . The enzyme encoded by SULT2A1, a bile salt sulfotransferase, converts steroids and bile acids into water-soluble sulfate conjugates for excretion N-acetyltransferase 8. In this case, we may speculate that the unknowns represent similar substrates or products of the N-acetylation processes linked to this enzyme. Finally, we can link the results obtained here with results from other GWAS on metabolic traits. For example, the unknown metabolite X-13431 associates with a genetic variant in the ACADL  gene. This locus does not associate with any other metabolite in the present study, but was previously reported to associate with the medium-chain length carnitines C9 and C10:1 Based on the observation that SNPs in or in the vicinity of enzymes are mostly associating with functionally related metabolites in case of the knowns, we used the GWAS data to derive hypotheses on the potential identity of the respective unknowns. For instance, the SNP rs296391 in close proximity to the SULT2A1 gene , which we have previously shown to be able to reconstruct pathways involving directly related metabolites from cross-sectional blood serum metabolomics data We selected four high-scoring sub-networks in the GGM to show that this concept is indeed applicable to real data. The first two of these sub-networks consist of a series of intermediate compounds from purine metabolism, including guanosine, inosine, xanthine derivatives and urate . In thesThe third sub-network comprises three androsterone sulfate variants, which belong to the class of steroid hormones . We obse1 score 0.72) far beyond random . Detailed results can be found in The next step in our analysis was the integration of the GGM and GWAS approaches with general pathway information from external databases, in order to generate concrete predictions for the unknowns' metabolic pathway memberships. As a feasibility test, we first asked whether the local neighborhood of a metabolite in the GGM can be used to correctly predict its metabolic class. Using a majority-voting based classifier and subsequent permutation testing, we detected significant classification abilities  the GO functional terms for the associated gene of all genome-wide significant GWAS hits, and (3) the KEGG pathways on which the associated genes lie. To the best of our knowledge, there is presently no consistent mapping between annotations from the different data sources available for both metabolites and genes, so we here had to perform the only non-automatic step in the analysis: By manual interpretation of different functional classes  The  classes , we deri classes . For 98  classes . A list In the following, we selected several unknowns that were forwarded to detailed analysis and experimental validation. Five cases were obtained from the set of 16 high-confidence predictions in the previous section, since the combined evidence from GWAS and GGMs provides rich functional annotations that allow to derive possible compound candidates. Moreover, in order to demonstrate the power of GGMs in the absence of genetic associations, we selected one further case (HETE) where publically available pathway information was systematically exploited. Experimental validations were performed by running pure candidate compounds on the LC-MS/MS platform. For cases where no pure compound was available, we determined exact molecule masses and revisited the retention times and fragmentation spectra.We investigated six metabolic scenarios in-depth and attempted experimental confirmation of the respective predictions . In the Our first scenario, DIPEPTIDE, presents the prediction and successful validation of three unknown metabolites involved in short-peptide metabolism , left. I12H16N2O4. While the formula still matches more than 1,200 molecular structures, the prediction of this unknown as a dipeptide leaves only two candidate molecules, namely phenylalanylserine (Phe-Ser) and serylphenylalanine (Ser-Phe). Both variants were obtained from a commercial source and run on the LC-MS/MS platform. The retention index For experimental validation, we first checked the plausibility of the candidates with respect to the fragmentation spectra and determined the exact masses. The accurate mass determined for X-14208 is 252.11172±0.001 Da, supporting the chemical formula C−26) with rs296391, which lies in strong LD in the SULT2A1 gene locus. SULT2A1 encodes for a member of the sulfotransferase family 2A, dehydroepiandrosterone-preferring, further strengthening the metabolic context. Based on the GGM and GWAS results, we hypothesized that X-11244 is a steroid sulfate related to androstane.In the second scenario, STEROID, we investigated an unknown metabolite (X-11244) for which both GGM and GWAS data strongly indicate an identity related to steroid-hormone compounds: X-11244 is tightly linked via GGM edges to dehydroepiandrosterone sulfate and two other unknowns, which in turn connect to epiandrosterone sulfate and androsterone sulfate , middle.19H30O8S2. Querying ChemSpider for this chemical formula yields only four results, one of which corresponds to an androstene disulfate variant (ChemSpider ID 21403154). Analysis of several disulfated androstenes demonstrated similar retention times and fragmentation spectra. Among the tested variants, 4-androsten-3β,17β-disulfate showed the best match. Given that other isomers are also possible, which cannot necessarily be chromatographically resolved, we annotated X-11244 more generically as ‘androstene disulfate’.Experimentally, the primary loss of a fragment with a nominal mass of 98 and the presence of an ion at 97 m/z observable in the fragmentation spectrum of X-11244 indicate the presence of at least one sulfate group in this unknown . The exacis-5,8,11,14-eicosatetraenoic acid , bilirubin metabolism (BILIRUBIN), and a connection between ascorbate metabolism and catechol-O-methyltransferase (ASCORBATE), are described in We developed and validated a novel integrative approach for the biochemical characterization of “unknown metabolites” from high-throughput metabolomics and genotyping datasets. Our method allows for the functional annotation of previously unidentified metabolites and, as a consequence, enhances the interpretability of metabolomics data in genome-wide association studies and biomarker discovery. For the first time, we systematically evaluated genetic associations of unknown metabolites, thereby discovering seven new loci of metabolic individuality. By classifying a series of unknown metabolites, we gained new insights into the functional interplay between genetic variation and the metabolome both for previously reported and new loci. Furthermore, several of the unknown compounds that we identified as well as their newly associated loci were independently reported in disease-related studies. In the following, we discuss three genetic loci and their associated phenotypes.catechol-O-methyltransferase enzyme. COMT is responsible for the inactivation of catecholamines such as L-dopa and various neuroactive drugs by O-methylation The first example is a recent biomarker study, where Milburn et al. angiotensin-converting enzyme, is an exopeptidase which cleaves dipeptides from vasoactive oligopeptides, and plays a central role in the blood pressure-controlling renin-angiotensin system The second example relates to the ACE gene locus, which is a known risk locus for cardiovascular disease, hypertension and kidney failure. The protein encoded by the ACE locus, UDP glucuronosyltransferase 1) and represents a bilirubin-related substance. Moreover, we experimentally validated X-11421 and X-13431, which display a strong association with ACADM , as acylcarnitines containing 10 and 9 carbon atoms, respectively. The identification of these latter two unknown metabolites as medium-chain length acylcarnitines is coherent with reports by Adams et al. The third example is an explorative study to detect biomarkers for insulin sensitivity. Gall et al. In summary, we integrated high-throughput metabolomics and genotyping data from a large population cohort for elucidating the biochemical identities of unknown metabolites. To this end, we applied metabolomics genome-wide association studies and Gaussian graphical modeling in order to link these unknown metabolites with known metabolic classes and biological processes. For six specific scenarios, we went from systematic hypothesis generation over detailed investigation and identity prediction to direct experimental confirmation. Similar validations may now be undertaken for the remaining predictions that we report in It is to be noted that our method does not specifically require genotyping data. Even metabolomics measurements alone, analyzed through the GGMs, may provide sufficient information for the classification and even precise identity prediction. The unknowns with GGM evidence but without GWAS hits in One limitation of our approach is the requirement for associations with functionally described loci or known metabolites. Certain metabolite groups might thus systematically not be identifiable. For instance, if the identity of a whole class of biochemically related molecules is unknown , then the GGM associations between those compounds will not aid in identity elucidation. The 118 unknown compounds for which we could not derive any classification might represent such cases. Thus, our functionally oriented method should be regarded as a complementary extension to the existing identity determination methods.Accordingly, our approach can be extended in several directions. It can be combined with method-specific, automated techniques that further exclude sets of metabolites. Previously mentioned methods relying on mass-spectra Beyond the application to metabolite identification, our study demonstrates the general potential of functional metabolomics in the context of genome-wide association studies. The comprehensive metabolic picture provided by GGMs in combination with GWAS allows for the detailed analysis of metabolic functions, chemical classes, enzyme-metabolite relationships and metabolic pathways.−6, minor allele frequency MAF>1%. In total, 655,658 SNPs were left after filtering.We used data from n = 1768 fasting serum samples used in a previously published genome-wide association study on a German population cohort. Details of the sample acquisition and experimental procedures can be found in −10 meet genome-wide significance, corresponding to a significance level of α = 0.05. SNP-to-gene assignments were derived via linkage disequilibrium (LD) from HAPMAP 2≥0.8 with the query SNP. A detailed description of the GWAS procedure can be found in In order to avoid spurious false positive associations due to small sample sizes, only metabolic traits with at least 300 non-missing values were included and data-points of metabolic traits that lay more than 3 standard deviations off the mean were excluded by setting them to ‘missing’ in the analysis (leaving 273 known and 213 unknown metabolites). Genotypes are represented by 0, 1, and 2 for major allele homozygous, heterozygous, and minor allele homozygous, individuals respectively. We employed a linear model to test for associations between a SNP and a metabolite assuming an additive mode of inheritance. Statistical tests were carried out using the PLINK software (version 1.06) 2≥0.5 with the respective “lead SNP”. Lookups of eQTLs were performed for all significant SNPs . Detailed results up to a p-value of 10−5 can be found in Lookups of previously known associations between phenotypes and genetic variants were performed using the GWAS catalog 474, see  using thFor the GGM calculation, we require a full data matrix without missing values. From the original data matrix containing n = 1768 samples and 517 metabolites (thereof 292 knowns and 225 unknowns), we first excluded metabolites with more than 20% missing values (column direction), and then samples with more than 10% missing values (row direction). The filtered data matrix still contained n = 1764 samples with 355 metabolites (217 knowns and 138 unknowns). Remaining missing values were imputed with the ‘mice’ R package X and Y on the remaining variables in the data set, the partial correlation coefficient between X and Y is given by the Pearson correlation of the residuals from both regressions. Since our dataset contains more samples than variables, full-order partial correlations can be conveniently calculated by a matrix inversion operation. A significance cutoff of α = 0.05 with Bonferroni correction was applied. A detailed description of the GGM calculation procedure can be found in Gaussian graphical models are induced by full-order partial correlation coefficients, i.e. pairwise correlations corrected against all remaining (n-2) variables. GGMs are based on linear regressions with multiple predictor variables. When regressing two random variables Age, gender and SNP effects were removed by adding the respective variables and SNPs states to the data matrix. For each pair of variables under investigation, Gaussian graphical models remove the effects of all remaining variables on this correlation (due to the above-mentioned linear regression approach). That is, adding a variable to the data matrix will automatically result in the removal of confounding effects of this variable on the correlations of all other variables. Note that age, gender and SNPs were not investigated as an actual node in the network but merely used for the correction procedure. For the later analysis steps, we then only considered metabolite-metabolite edges in the network. SNP states were coded as numerical values of 0, 1 and 2 (see previous section), such that the linear regressions that underlie the GGM correspond to an additive genetic model cf. . Gender 2”, and “SO4” etc. in our analysis, since such metabolites unspecifically participate in a plethora of metabolic reactions. For each enzyme catalyzing one or more reactions in our pathway model, we retrieved functional annotations from two independent sources: (i) GO-Terms from the Gene Ontology Metabolic reactions were imported from three independent human metabolic reconstruction projects: (1) H. sapiens Recon 1 from the BiGG databases All imported metabolic pathways along with metabolite database identifiers, excluded compounds and pathway annotations can be found in Dataset S1ZIP archive containing Excel sheets and .graphml files for the GGM and GWAS results, as well as detailed pathway annotations.(ZIP)Click here for additional data file.Figure S1GGM sub-network with bilirubin variants.(PDF)Click here for additional data file.Table S1Systematic classifications for 106 unknown metabolites.(XLS)Click here for additional data file.Text S1Detailed GGM modularity analysis results.(PDF)Click here for additional data file.Text S2Assessment of the majority voting classification approach.(PDF)Click here for additional data file.Text S3Supporting experimental data and description of the unknown identification scenarios CARNITINE, BILIRUBIN, and ASCORBATE.(PDF)Click here for additional data file.


Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20∼50 SNPs reported by the remaining individual GWA studies explained 3∼5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent. Heritability of normal variation in human height is estimated to be ∼80%–90% The above mentioned GWAS and meta-analysis were based on large cohorts primarily of European descent sampled from Europe and North America, although a few have included other ethnicities The study population was derived from a genetic study of metabolic syndrome Genome-wide SNP genotyping was performed using the Affymetrix Human SNP Array 5.0 following the manufacture's protocol. Genotype calls were determined using the CRLMM algorithm http://www.genome.gov/gwastudies/) could be recovered from the top 100 significant regions (defined as 250 kb on either side of associated SNPs) using each of these two methods. Standardized height adjusted for age and gender and their interaction term was used in all analyses. P-values and effect size estimates assuming additive allelic effect are reported throughout the manuscript. All statistical analyses were performed in R v2.12 and the GenABEL package (v.1.6) The study sample included 533 unrelated and 771 related individuals from 238 families. Therefore, two different methods were used to account for the relatedness – the genomic control (GC) 2) was then estimated based on a linear regression model using the scores as predictor and age-, gender-, and their interaction term adjusted height residuals as outcome.To assess the overall fitting of previously identified height-associated loci in our study sample, we estimated the percentage of variance explained using genetic scores that combine information from different panels of known loci reported in previous studies. The unweighted genetic score was computed as an individual's number of height-increasing alleles, and the weighted genetic score was constructed as We estimated and compared the variance explained based on different panels of height-associated SNPs and their effect size estimates extracted from published large-scale GWAS, which included 1) 180 genome-wide significant SNPs reported by Lango Allen et al. −55, r2 = 28.0%) and in males was 0.195 cm  and females (N = 739), and males (177.7±7.5 cm) were significantly taller than females (164.4±7.1 cm). Regression analysis showed that adult height was negatively associated with age in both genders with a larger age-effect in females. Estimated height decrease per year in females was 0.239 cm (P = 9.05×10−11). The effect size estimates of 58 SNPs were in different directions (points in the 2nd and 4th quadrants in −16) correlated . The 9 GC regions include several well known height loci, including EFEMP1, UQCC and HMGA2. Of particular interest is the UQCC locus ; the minor allele (T) was associated with an average increase of height 1.93 cm. Re-analysis of the region conditional on rs6058227 failed to completely eliminate the signals of the other SNPs, suggesting the possibility of multiple causal variants within the region . Interestingly, the GC method recovered 9 previously known loci  comparede region . SimilarWe presented an estimation of variance explained by previously reported height-associated loci in a relatively isolated population based on a genome-wide scan of 2.5 M genotyped and imputed SNPs in 1304 individuals. Given our relatively small sample size, we did not aim to identify novel loci; instead, our purpose was to evaluate whether height-associated SNPs identified by GWA studies in large population cohorts share similar effects in an isolated and homogeneous population.We evaluated the percentage of variance explained by genetic scores based on previously reported panels of height-associated SNPs and their effect size estimates. Under the assumption of additive effect between loci, the weighted genetic score captures the majority of genetic effects at multiple loci. The corresponding explained variance can be used as an indicator of the overall agreement of the effect size estimates between reference studies (from which the effect sizes/weights were developed) and our current study. The results indicate that a comparable fraction of variance can be explained in our samples if the genetic score was constructed from a population of European descent. In our sample the variance explained by the 180 height-associated SNPs reported by Lango Allen et al. Using the SNPs reported by the three Nature Genetics studies Our results indicate that the empirical performance of the GC method was better than the MG approach by recovering a larger number of previously-identified height loci among the top ranking regions. This implied that the P-values reported by GC or association tests ignoring relatedness may better reflect the real order of significance than the complex approach of MG. Recent simulation studies indicate that GC or conventional tests are appropriate methods to study genetic association in randomly ascertained, moderate-size pedigrees for both quantitative Of the 9 height-associated loci described in As in the study by Lango Allen et al. In summary, we have evaluated the portability of height-related loci and their estimated effect sizes reported in large cohorts to an isolated population with a homogeneous genetic background. We found proportions of explained height variability were comparable to multiple reference GWAS in populations of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.Figure S1Correlation of height with age in males and females.(TIF)Click here for additional data file.Figure S2Minor allele frequencies of the 180 height-associated SNPs were highly correlated between our study and the reference study by Lango Allen et al .(TIF)Click here for additional data file.Figure S3Genetic scores follow normal distribution.(TIF)Click here for additional data file.Figure S4QQ plot of GC (A) and MG (B) association test.(TIF)Click here for additional data file.Figure S5Manhattan plot (the red bars indicated the nine replicated GWA regions).(TIF)Click here for additional data file.Table S1Frequencies and effect sizes of the 180 height-associated SNPs reported by Lango Allelen et al. [Nature 467 (7317): 832-8].(PDF)Click here for additional data file.


Whole-genome association studies typically focus on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. As such, the objective of this study was to perform a pathway-based association analysis in Holstein dairy cattle in order to identify relevant pathways involved in bull fertility. The results of a single-marker association analysis, using 1,755 bulls with sire conception rate data and genotypes for 38,650 single nucleotide polymorphisms (SNPs), were used in this study. A total of 16,819 annotated genes, including 2,767 significantly associated with bull fertility, were used to interrogate a total of 662 Gene Ontology (GO) terms and 248 InterPro (IP) entries using a test of proportions based on the cumulative hypergeometric distribution. After multiple-testing correction, 20 GO categories and one IP entry showed significant overrepresentation of genes statistically associated with bull fertility. Several of these functional categories such as small GTPases mediated signal transduction, neurogenesis, calcium ion binding, and cytoskeleton are known to be involved in biological processes closely related to male fertility. These results could provide insight into the genetic architecture of this complex trait in dairy cattle. In addition, this study shows that quantitative trait pathways inferred from single-marker analyses could enhance our interpretations of the results of genome-wide association studies. The decline in reproductive performance in dairy cattle is a serious concern of farmers and the dairy industry worldwide Lucy, . Most reFGF2 and STAT5A genes are associated with estimated relative conception rate in Holstein bulls. In addition, Li et al.  in the Holstein breed. Recently, some studies have addressed bull fertility using a genome-wide association approach. In this sense, Blaschek et al. , were used in the study. In brief, SCR is the expected difference in conception rate of a sire compared with the mean of all other evaluated sires. SCR values from the 1,755 bulls ranged from −10.66 to +6.80%, and the number of breedings per bull ranged from 303 to 111,402. SCR data were obtained from seven consecutive evaluations between August 2008 and December 2010. For bulls with multiple evaluations, the most recent SCR evaluation was used in the analysis. Genotypes of the 1,755 bulls for the Illumina BovineSNP50 Bead Chip were also provided by USDA-ARS Animal Improvement Programs Laboratory. SNPs with minor allele frequencies below 5% were removed. After data editing, 38,650 markers were available.j is the fixed effect of the jth AIPL-USDA SCR evaluation , SNPik is the number of copies of one allele of the SNP  carried by the ith bull , βk is the regression coefficient for the SNP considered , sirel represents the random additive genetic effect of the lth sire  of the ith bull, and eijkl represents the random residual for each observation. For the analysis using SNP genotype as a categorical variable, the same statistical model was used replacing βk SNPik with SNPik where SNPik is the fixed effect of the kth genotypic class  for the SNP considered carried by the ith bull.Associations between SNP genotype and SCR were analyzed by using the SNP allele count as a linear covariate, as well as by using the SNP genotype as a categorical variable. The following mixed model was used to analyze SNP genotype as a linear covariate:l and eijkl were assumed as A represents the matrix of additive relationships  between sires in the pedigree and W is a diagonal matrix of order 1,755 with its elements representing reliabilities of the SCR values. Associations between SNPs and SCR were tested using a likelihood ratio test, by comparing the aforementioned models to a reduced model without the SNP effect. The analyses were performed using the pedigreemm package  and InterPro (IP) databases were used to define functional sets of genes. The GO database  and control the false discovery rate, the procedure proposed by Benjamini and Hochberg  was adopP-value ≤ 0.05) using SNP genotypes as linear covariates or categorical variables, respectively. In total, 4,450 different SNPs showed significant associations with bull fertility in one or both models used for the analysis.In a previous study, we performed a genome-wide association study for bull fertility using a total of 38,650 SNPs spanning the entire bovine genome  showed significant enrichment  of genes associated with bull fertility. This entry, with a total of 37 genes, showed 16 significant genes, which is 10 more than expected under the null hypothesis. Genes statistically associated with SCR  within each significant category  are listed in File S1 in Supplementary Material.Twenty GO terms showed significant overrepresentation of genes statistically associated with SCR. Table Rho protein signal transduction (GO:0007266) is part of Ras protein signal transduction (GO:0007265) and Regulation of Ras protein signal transduction (GO:0046578) is part of Regulation of small GTPase mediated signal transduction (GO:0051056). Furthermore, Ras protein signal transduction is part of Small GTPase mediated signal transduction (GO:0007264), which in turn is part of Intracellular signaling cascade (GO:0007242). Interestingly, GO:0046578 and GO:0051056 negatively regulate GO:0007265 and GO:0007264, respectively. Overall, these results point out that an intracellular signaling pathway involving small GTPases and their negative regulators was significantly associated with bull fertility. Interestingly, a recent microarray study showed that Regulation of small GTPase mediated signal transduction term was significantly enriched with genes differentially expressed between human sperm that achieved successful fertilization and sperm that failed the intracytoplasmic sperm injection  is part of Neuron differentiation (GO:0030182), which in turn is part of Neurogenesis (GO:0022008). One well-known connection between neurons and male fertility is provided by the hypothalamic–pituitary–gonadal axis. Indeed, the maintenance of spermatogenesis depends upon stimulation of the testes by the gonadotropic hormones, follicle-stimulating hormone, and luteinizing hormone, both produced by the pituitary gland in response to gonadotropin-releasing hormone from the hypothalamus  is part of Pyrophosphatase activity (GO:0016462), which in turn is part of Hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides (GO:0016818). Nucleoside-triphosphatases are enzymes that catalyze the hydrolysis of a nucleoside triphosphate in a nucleoside diphosphate and phosphate. Pyrophosphatases are enzymes that catalyze the hydrolysis of a pyrophosphate bond between two phosphate groups. Both types of enzymes, which belong to the family of hydrolases, participate in purine and thiamine metabolism  term showed a significant overrepresentation of genes associated with bull fertility  and Cytoskeletal part are part of Cytoskeleton (GO:0005856). The close relationship between cytoskeleton and spermatogenesis is well documented. Sertoli cells are characterized by their well-developed cytoskeleton, which is responsible for the collective organization of the seminiferous epithelium , Secretion (GO:0046903), (Table Extracellular matrix part (GO:0044420), and Plasma membrane part , Table  and Extrll GO:003940, SecrHaloacid dehalogenase-like hydrolase (IPR:005834), showed significant overrepresentation of genes associated with SCR. IPR:005834 entry contains a group of hydrolase enzymes including l-2-haloacid dehalogenase, epoxide hydrolases, and phosphatases. These enzymes are involved in diverse cellular functions such as membrane transport, signal transduction, metabolism, and DNA repair , which in turn will increase the number of functional categories and provide even further insight into the genetic architecture of bull fertility.In this study, we used genome-wide association SNP results to infer quantitative trait pathways associated with bull fertility. We have identified 20 GO categories and one IP entry that showed significant overrepresentation of genes statistically associated with SCR in Holstein dairy cattle. Interestingly, several of these functional categories, such as small GTPases mediated signal transduction, neurogenesis, calcium ion binding, and cytoskeleton are involved in biological processes closely related to male fertility. These findings could contribute to a better understanding of the genetic architecture of this complex trait in dairy cattle. In addition, pathways enriched with genes that are significantly associated with male fertility could be used in marker-assisted selection in dairy cattle breeding schemes. Thus, the quantitative trait pathway approach used in this study could complement genome-wide association studies for investigating quantitative traits in livestock species.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.http://www.frontiersin.org/Livestock_Genomics/10.3389/fgene.2012.00307/abstractThe Supplementary Material for this article can be found online at


Up to 22.9 million sequence variants were analyzed in combined samples of 45,576 and 37,341 individuals with serum B12 and folate measurements, respectively. We found six novel loci associating with serum B12  or folate levels (FOLR3) and confirmed seven loci for these traits . Conditional analyses established that four loci contain additional independent signals. Interestingly, 13 of the 18 identified variants were coding and 11 of the 13 target genes have known functions related to B12 and folate pathways. Contrary to epidemiological studies we did not find consistent association of the variants with cardiovascular diseases, cancers or Alzheimer's disease although some variants demonstrated pleiotropic effects. Although to some degree impeded by low statistical power for some of these conditions, these data suggest that sequence variants that contribute to the population diversity in serum B12 or folate levels do not modify the risk of developing these conditions. Yet, the study demonstrates the value of combining whole genome and exome sequencing approaches to ascertain the genetic and molecular architectures underlying quantitative trait associations.Genome-wide association studies have mainly relied on common HapMap sequence variations. Recently, sequencing approaches have allowed analysis of low frequency and rare variants in conjunction with common variants, thereby improving the search for functional variants and thus the understanding of the underlying biology of human traits and diseases. Here, we used a large Icelandic whole genome sequence dataset combined with Danish exome sequence data to gain insight into the genetic architecture of serum levels of vitamin B 12 (B12) and folate in Icelanders and Danes. We found 18 independent signals in 13 loci associated with serum B12 or folate levels. Interestingly, 13 of the 18 identified variants are coding and 11 of the 13 target genes have known functions related to B12 and folate pathways. These data indicate that the target genes at all of the loci have been identified. Epidemiological studies have shown a relationship between serum B12 and folate levels and the risk of cardiovascular diseases, cancers, and Alzheimer's disease. We investigated association between the identified variants and these diseases but did not find consistent association.Genome-wide association studies have in recent years revealed a wealth of common variants associated with common diseases and phenotypes. We took advantage of the advances in sequencing technologies to study the association of low frequency and rare variants in conjunction with common variants with serum levels of vitamin B Individuals with deficiencies in these vitamins can develop anemia and, in the case of B12 deficiency, serious neurological problems. In adults, epidemiological studies have also suggested that subclinical B12 or folate deficiencies are associated with increased risk of cardiovascular disease 12 and folate are in addition to nutrition influenced by several biological processes including absorption, transportation and cellular uptake, as well as processing of precursors into active molecules. Heritability, utilizing di- and monozygotic twins, is estimated to be 59% and 56% for B12 and folate levels, respectively, indicating that there is a substantial genetic component to the population diversity in these physiological variables 12 and folate can thus give insights into the interplay of diet, genetics and human health. Genome-wide association studies (GWAS) have yielded some sequence variants influencing B12 levels 12 levels have been convincingly reported for four loci, FUT2, MUT, CUBN and TCN1 in European populations MS4A3, CLYBL, FUT6 and 5q32 in a Chinese population MTHFR A222V variant was demonstrated prior to the GWAS era FIGN and PRICKLE2) One-carbon metabolism (OCM) is a process whereby folate transfers one-carbon groups in a range of biological processes including DNA synthesis, methylation and homocysteine metabolism The classic GWAS applied commercial chip-based genotyping and imputation of HapMap variants of which a majority were common single nucleotide variants (SNVs) with very few rare variants with minor allele frequency (MAF) <1% 12 and folate by compiling data in up to 45,576 individuals based on sequencing initiatives in Iceland and Denmark. For the first time we apply next generation sequence data to identify sequence variants affecting serum levels of B12 and folate and the present datasets are the largest utilized to date for the analysis of these traits.Here we aimed to identify and characterize associations of SNVs across the allele frequency spectrum with serum levels of B12 and folate serum levels based on 38,229 and 21,708 Icelandic sibling pairs, respectively. Our analysis revealed estimates of 27% for B12 and 17% for folate which are lower than previously reported We estimated the heritability of B12 and folate levels we compiled data from two sequencing initiatives in Iceland and Denmark. In Iceland, a large population-based resource has been generated applying WGS and highly accurate imputation of the sequence information into a large fraction of the population 12 and folate measurement, respectively, using highly accurate long-range phasing based imputation 12 and 8,428 individuals with measurement of serum folate . Analyses were performed in three steps; sequence variants were analyzed in the Icelandic and Danish samples separately, then by combining in a meta-analysis the overlapping sequence variants identified in both study samples. Loci that associated significantly with B12 or folate levels from these studies were fine mapped using the Icelandic WGS data imputed into chip genotyped individuals and the same data set was used to identify additional signals at each of these loci trough conditional analysis. Finally, the full Icelandic data of 22.9 million SNVs were used in GWAS to identify additional loci represented by non-coding variants or rare coding signals not genotyped in the Danish design. Genome-wide significance (GWS) level in the study was set at P<2.2×10−9, based on Bonferroni correction for the 22.9 million SNVs  were novel and six were previously reported either in populations of European or East-Asian ancestry FOLR3) and confirmed the reported MTHFR locus  or nonsense (n = 1) mutations with only the FUT6 locus having a stronger non-coding signal (rs708686) than the missense P124S mutation . None of the INDELs detected at the 11 B12 loci associated more strongly than the lead SNVs. However, when reassessing each of the two folate-associated loci we detected a two nucleotide insertion  encoding a common (MAF 10.0%) frameshift mutation in exon 3 of FOLR3, that associated more strongly with folate levels than the intronic SNV rs652197 identified in the initial scan . Upon further inspection, we found that the ancestral sequence contained the insertion indicating the occurrence of a two base deletion in humans. The deletion with an allelic frequency of 90% in Iceland creates a premature stop codon at amino acid position 107 compared to the full-length protein consisting of 245 amino acids. Coding variants are thus lead signal of both folate loci (FOLR3 and MTHFR).Since only coding variants were in the combined analysis we used the Icelandic WGS-based data to screen for stronger non-coding signals at the loci identified in meta-analysis of coding variants. Interestingly, the strongest signal at 10 of the 11 Bmutation . As onlyMMACHC that associates with B12 found in the Icelandic data being at the extreme (http://evs.gs.washington.edu/EVS/). For TCN1 and FUT6 previously reported to associate with serum B12 levels we confirmed the association, yet with different SNVs than reported. At the TCN1 locus the strongest associated SNV in the Icelandic data was rs34324219 (r2 = 0.28) non-coding rs526934 was more weakly associated  identified in the combined analysis of Icelandic and Danish data associated more strongly  for which previous association with serum folate has been reported The lead SNVs included both rare, low frequency and common variants with MAFs ranging from 0.2% to 48%  and 2. O extreme . This va34324219  encodingsociated . At the strongly  than thestrongly . For the12 levels in individuals of East-Asian ancestry (MSRA and 5q32) the variant was either not present in the Icelandic data or at very low frequency . The secondary signal at the CUBN locus was shown for a group of correlated markers represented by rs56077122 (located in an intron of the neighboring TRDMT1)  and an intergenic variant whereas one secondary signal in the TCN2 locus, represented by rs5753231, was located immediately 5′ to TCN2  . In TCN1 to TCN2 , Table 3HFR gene , Table 312 or folate levels explained is estimated to be 6.3% for B12 and 1.0% for folate (Of the identified variants (lead and secondary) the fraction of variance in serum Br folate .12 or folate loci affect the expression of the target gene we analyzed genome-wide expression QTL (eQTL) data from white blood cells  and adipose tissue (n = 673) from Icelanders with information on 22.9 million SNVs 12 or folate signals that are coding (MUT (P = 9.1×10−59 in white blood cells and P = 2.5×10−16 in adipose tissue) and the frameshift INDEL in FOLR3   . Of the 12 genes described here i.e. MMACHC, MMAA, MUT, CD320, TCN2 and CUBN have been described in connection with rare conditions of methylmalonic aciduria and megaloblastic anemia that all relate to defects in B12 metabolism . In addition, epidemiological studies have suggested a link between reduced B12 and folate levels and the risk of common conditions such as cardiovascular diseases 12 or folate variants on these conditions we analyzed the association with coronary artery disease (CAD), stroke, colon cancer, prostate cancer and Alzheimer's disease in data obtained from deCODE's phenotype database. As outlined in 12 or folate levels did not consistently affect the risk of the diseases tested; the B12 or folate increasing allele for some variants was weakly protective and for others weakly at risk, and only two loci (CUBN associated with CAD and MTHFR with stroke) were statistically significant (P<0.0018) but with opposite effects on these diseases. B12 or folate deficiencies can lead to increased serum homocysteine 12 or folate loci tested only two associated significantly with homocysteine levels, with the B12 or folate increasing allele decreasing the homocyteine levels as expected  and increased mean corpuscular hemoglobin (MCH). None of the identified variants associated significantly with MCV and MCH    indicate12-associated loci, FUT2, has previously been associated with reduction in liver enzymes including alkaline phosphatase (ALP) FUT2 in cell surface glycobiology as determinant of the Lewis antigen blood group. To evaluate pleiotropic effects of the identified B12 and folate variants, we screened the deCODE phenotype database, which contains information on the majority of common diseases and their associated risk factors (n = 400), applying both multiplicative and recessive genetic models (P = 3.5×10−6 after Bonferroni correction). We found that the FUT2 variant associated strongly with serum levels of ALP (P = 1.1×10−73) and also with psoriasis (P = 4.3×10−3) as previously reported. We also detected a strong association with serum levels of cancer antigen 19-9 (P = 1.1×10−146), lipase (P = 2.2×10−24) and suggestive association with bone mineral density (BMD) (P = 1.3×10−5) with the B12-increasing allele decreasing ALP levels, increasing the serum levels of the cancer antigen 19-9 and lipase and increasing the risk of developing low BMD (osteoporosis)  and of the folate-associated variant in MTHFR with thoracic aortic aneurysm (TA). In both cases the effect of the B12- or folate-increasing allele was protective (12 and folate levels as reduced levels of B12 and folate have been linked to the development of aortic aneurysm One of the Bporosis) . An incrotective . These a12 and folate. We discovered five novel loci that associate with serum B12 levels and one novel locus for folate levels and replicated the six reported B12 loci and one folate locus. In addition, we identified five novel secondary independent signals at both the new and previously reported loci. The fraction of variance in serum B12 or folate levels explained by the identified variants is estimated to be 6.3% for B12 and 1.0% for folate  has potential links with these processes  are known and two (MUT and MMAA) are suggested to interact in vivoHere we performed association analyses of up to 22.9 million SNVs, identified through WGS and WES, in up to 45,576 individuals to identify and characterize genetic variation influencing population diversity in serum levels of Br folate . Of the rocesses . Only CLrocesses . Moreove12 and folate variants we replicated some of the previous associations of the FUT2 gene and detected novel suggestive association with increased risk of osteoporosis (low BMD) potentially mediated through diminished bone remodeling as a consequence of reduced ALP activity. We also detected suggestive associations of the FUT6 and the MTHFR variants with AAA and TA, respectively. However, we did not demonstrate association of any of the variants with the cardiovascular diseases, CAD and stroke, colorectal cancer, prostate cancer or Alzheimer's disease and only two of the variants associated with homocysteine levels. Although to some degree impeded by low statistical power for some of these conditions, these data suggest that sequence variants that contribute to the population diversity in serum B12 or folate levels do not modify the risk of developing these conditions.By screening the deCODE database for pleiotropic effects of the BAll participants gave written informed consent. The studies were conducted in accordance with the Declaration of Helsinki II and were approved by the local Ethical Committees  ref. VSNb2008060006/03.1).12 and folate levels were assessed in blood samples from Icelanders at the Landspitali University Hospital Laboratory or at the Icelandic Medical Center (Laeknasetrid) Laboratory in Mjodd (RAM), between the years 1990 and 2011. B12 and folate levels were normalized to a standard normal distribution using quantile normalization and then adjusted for sex, year of birth and age at measurement. For individuals for which more than one measurement was available we used the average of the normalized value.For the Icelandic samples, serum B12 and folate, respectively. Health2006 is a population-based epidemiological study of general health, diabetes and cardiovascular disease of 3,471 individuals aged 18–74 years 12 and folate, respectively. In Inter99 serum B12 and folate were measured by a competitive chemiluminescent enzyme immunoassay  as previously reported 12 and folate were measured by chemiluminescent immunoassay .The Danish data were generated in two population-based study samples recruited in Copenhagen. The Inter99 cohort is a randomized, non-pharmacological intervention study for the prevention of ischaemic heart disease, conducted on 6,784 randomly ascertained participants aged 30 to 60 years at the Research Centre for Prevention and Health in Glostrup, Denmark in silico genotyping.In the Icelandic part, SNVs were identified through the Icelandic WGS project. A total of 1,176 Icelanders were selected for sequencing based on having various neoplasic, cardiovascular and psychiatric conditions. All of the individuals were sequenced to a depth of at least 10×. The generation of genotypic data in Iceland is detailed in earlier reports P<0.05) in a sequencing-based association study were selected. Finally, we selected 599 synonymous variants in 192 loci previously associated with common metabolic traits at GWS. Genotype data was obtained for 18,744 SNVs. Quality control of samples included removing closely related individuals, individuals with an extreme inbreeding coefficient, individuals with a low call rate, individuals with a mislabeled sex and individuals with a high discordance rate to previously genotyped SNVs. 15,989 individuals passed all quality control criteria. The SNVs were filtered based on their MAF (>0.5%), genotype call rate (>95%), Hardy-Weinberg equilibrium (P>10−7) or cross-hybridization with the X-chromosome. 16,192 SNVs passed all filters FOLR3 rs652197 in Danish samples was done by KASPar SNP Genotyping System .In the Danish part of the study 16,192 SNVs for genotyping were selected from a WES study of 2,000 individuals 12 and folate with SNVs. Let in silico genotypes and serum B12 and folate measurements into smaller clusters. Here we chose to restrict the cluster size to at most 300 individuals.A generalized form of linear regression was used to test for association of serum BThe maximum likelihood estimates for the parameters P-value among all SNVs not yet included in the model and if it had a P<5×10−8. In the last step none of the SNVs remained significant at this threshold.For the multivariate regression analysis we only used Icelandic individuals which have been genotyped using the Illumina chip-genotyping platform. The multivariate linear regression analysis was performed conditioning for a given marker by adjusting for the estimated allele count based on imputation of this marker. The GC correction factor was the same as used for the unadjusted association analysis. A forward selection multiple logistic regression model was used to further define the extent of the genetic association. Briefly, all imputed SNVs located within an interval around the lead SNVs were tested for possible incorporation into a multiple regression model. In a stepwise fashion, a SNV was added to the model if it had the smallest http://pngu.mgh.harvard.edu/purcell/plink/). All P-values were corrected by GC. Inflation factors (λ) were at acceptable levels: B12: Inter99: 1.027, Health2006: 1.014 and folate: Inter99: 1.024, Health2006: 1.010.Association analysis of each SNV in the Danish data was performed using linear regression assuming an additive model. Principal component analysis was performed using the covariance matrix and the first principal component and sex were included in the model as covariates. All quantitative traits were quantile normalized to a normal distribution prior to analysis. Association analyses were done using PLINK software  P-value and direction of effect adjusted for the number of individuals in each sample.For all SNVs with data from more than one study sample  we performed meta-analyses of summary association data where we estimated the combined effect in a fixed-effects meta-analysis using the METAL software  as a function of genomic position (NCBI Build 36). Only SNVs with P<0.01 are plotted. The lead SNV with the lowest combined P-value is indicated by the rs-number. Estimated recombination rates (HapMap CEU) are plotted to reflect the local LD structure. Gene annotations were obtained from RefGene.Regional plots of the 11 loci associated with serum B(PDF)Click here for additional data file.Figure S2P-values  as a function of genomic position (NCBI Build 36). Only SNVs with P<0.01 are plotted. The lead SNV with the lowest combined P-value is indicated by the rs-number. Estimated recombination rates (HapMap CEU) are plotted to reflect the local LD structure. Gene annotations were obtained from RefGene.Regional plots of the two loci associated with serum folate. Genotyped and imputed SNVs passing quality control measures are plotted with their meta-analysis (PDF)Click here for additional data file.Table S1Clinical characteristics of the Icelandic samples. Data are mean ± standard deviation or median (interquartile range). For individuals for which more than one measurement was available we used the average of the normalized value.(PDF)Click here for additional data file.Table S2Clinical characteristics of the Danish samples. Data are mean ± standard deviation or median (interquartile range).(PDF)Click here for additional data file.Table S312 or folate loci in the Icelandic data. For each of the identified B12 or folate loci presented in FUT6 locus were rs708686 located 5′ of FUT6 gives the strongest signal.Overview of the most significantly associated SNV for each of the identified B(PDF)Click here for additional data file.Table S412 levels in GWAS. *These markers are only present in East-Asia. References: 1. Lin X, Lu D, Gao Y, Tao S, Yang X, et al. (2012) Genome-wide association study identifies novel loci associated with serum level of vitamin B12 in Chinese men. Hum Mol Genet 21: 2610–2617. 2. Hazra A, Kraft P, Lazarus R, Chen C, Chanock SJ, et al. (2009) Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum Mol Genet 18: 4677–4687. 3. Hazra A, Kraft P, Selhub J, Giovannucci EL, Thomas G, et al. (2008) Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat Genet 40: 1160–1162.Association results in the Icelandic data for SNVs previously reported to associate with B(PDF)Click here for additional data file.Table S5* Not previously shown at genome-wide significance. References: 1. Tanaka T, Scheet P, Giusti B, Bandinelli S, Piras MG, et al. (2009) Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am J Hum Genet 84: 477–482. 2. Hazra A, Kraft P, Lazarus R, Chen C, Chanock SJ, et al. (2009) Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum Mol Genet 18: 4677–4687.Association results in Icelandic data for SNVs previously reported with suggestive association with folate levels in GWAS. (PDF)Click here for additional data file.Table S612 levels (2.2×10−9> P<10−6).Suggestive loci in the Icelandic or the Icelandic and Danish data associated with serum B(PDF)Click here for additional data file.Table S7−9>P<10−6).Results in the Icelandic data for loci with suggestive association with serum folate levels (2.2×10(PDF)Click here for additional data file.Table S812 or folate levels for signals with P<5×10−8. Conditional analyses were performed using imputed sequence data from chip-genotyped Icelanders with information on serum B12 or folate levels. Results for SNV #1 (lead SNVs) at each loci are unconditional on other SNVs. Analysis of SNV #2 is conditional on SNV #1 and SNV #3 is conditional on SNV #1 and #2. The LD between the SNVs at each locus was estimated from the sequence information of the 1,179 whole genome sequenced Icelanders.Results from stepwise conditional analyses using the Icelandic data at loci associated with serum B(PDF)Click here for additional data file.Table S9Cis12 and folate SNVs on the expression of the target gene in white blood cells and adipose tissue.-effect of the B Correlation between SNVs that associate with increased B12 or folate and mRNA expression in blood and adipose tissue from 1,001 and 673 individuals, respectively. The correlations are tested by regression analysis adjusted, for age, sex and differential cell counts (blood only), and inverse normal transformed relative expression values on the estimated genotype dosage. aNo other SNV shows significantly higher correlation with the expression in adipose or blood of MUT than rs1141321. bThe INDEL chr11:71527804 is the most significant cis variant for FOLR3. cFor TCN2 there are cis variants both in blood and adipose tissue that have stronger correlation than rs5753231 with its expression, while having little effect on B12 levels.(PDF)Click here for additional data file.Table S1012 and folate associated markers with potential co-morbid conditions in Icelanders.Association results for Ba Effect size and effect allele frequency from the Icelandic population. Associations at P<0.001 are shown in bold. EAF, effect allele frequency; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin.(PDF)Click here for additional data file.Table S1112 and folate variants with diseases and traits in deCODE database.Association results for the B Shown are the strongest association results for the folate and B12 variants, genome-wide significant or suggestive, with diseases and traits in deCODE's database. EAF, effect allele frequency; BMD, bone mineral density; AAA, abdominal aortic aneurysm; TA, thoracic aneurysm; 1The annotation is based on the RefSeq hg18, 2The reference alleles based on Build 36 hg18 are shown in bold, 3The low BMD phenotypes are defined as those BMD values that are below −1 standard deviation (SD) from the mean.(PDF)Click here for additional data file.Text S1Supplementary (PDF)Click here for additional data file.


Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and  The causal relationship between genetic polymorphism within a species and the phenotypic differences observed between individuals is of fundamental biological interest. The ability to predict genetic risk factors for human disease and agronomically important traits like growth rate and yield in plants require an understanding of both the specific loci that underlie a phenotype, and the genetic architecture of a trait. This relationship between phenotype and genotype has been of major interest at least since Mendel postulated the existence of ‘internal factors’ that are passed on to the next generation.Forward genetics, in which many individuals that differ in genotype are screened for phenotypes of interest, has been a hugely powerful tool to address such questions. In general, the raw genetic differences being screened are obtained either by mutagenesis or sampled from a natural population. Any phenotypic differences identified are connected back to the underlying causative loci via various mapping approaches including Quantitative Trait Locus (QTL) mapping. In this perspective we consider a complementary and powerful tool for connecting the genotype-phenotype map, Genome-Wide Association Studies (GWAS).Arabidopsis have been dissected in this way ).Gene-by-Gene (GxG) interaction or Epistasis The non-additive interaction of two or more loci (see [73]). Allelic combinations between sites may result in a higher (positive epistasis) or lower (negative epistasis) phenotype than expected from the effect size at each locus alone.Heritability The proportion of phenotypic variance attributed to variance in genotype (broad sense heritability) in a particular environment (see [74]). The contribution from additive genetic variants (i.e. excluding dominance and epistasis) is the narrow sense heritability  which can be estimated from the regression of offspring phenotypic values on parental phenotypes.Linkage disequilibrium (LD) The non-random co-occurrence of two or more alleles. LD naturally occurs between loci in close proximity, and is broken down by recombination. Higher than expected LD can be maintained, even across different chromosomes, by selection or population structure.Mixed models A statistical model that contains both fixed and random effects, used to estimate correlations between phenotypes and genotypes, while taking into account the relatedness between individuals.Phenotypic variance A measure of the spread of trait values within a population. Phenotypic variance results from genetic (see heritability) and environmental factors. The proportion of phenotypic variance explained by a single locus is a product of its effect size and allele frequency.Pseudo-heritability An estimate of narrow sense heritability from the mixed model. This is the fraction of phenotypic variance that can be explained by the genetic relatedness between individuals (as estimated by a genome-wide kinship matrix from SNP data).Synthetic association The association of a non-causative marker with a given trait, driven by linkage to one or more causative markers and/or an unmeasured source of error.The authors declare that they have no competing interests.AK and AF wrote and edited the manuscript together. Both authors read and approved the final manuscript.