RBFOX3 reaching genome-wide significant level . Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK , a region that has been associated with several obesity related traits, and TSPYL5 , regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at  SLC2A9  gene have doubled the effect on SU in women (0.40 standard deviation (sd) in [ABCG2 gene have a stronger effect in men than in women  in ) than th14 sd in ).SLC2A9 variants’ effects on SU may be stronger in severely obese individuals (defined as BMI > 40), with a stronger modulating BMI effect in men than in women [Body mass index (BMI) is strongly positively correlated with SU levels in population-based studies (phenotypic correlations ranging from 0.27 to 0.44 –12), andin women . Both th2), overweight (25 ≤ BMI ≤ 30) and obese (BMI > 30 kg/m2). This allowed investigation of whether stratification revealed new genetic variants influencing SU and to systematically test differences in effects between BMI strata. Interaction between allelic effect and BMI was also investigated using a linear model with introduction of an interaction term and replication attempted in an independent set.Here, we performed a genome-wide investigation for genetic variants influencing serum urate levels in a BMI-dependent fashion, primarily by analysing genome-wide association study (GWAS) stratified by BMI. Stratified analyses are best suited when main effects are very different in magnitude or direction between strata and if the environment factor measured on a continuous scale is not acting linearly. In a discovery set, totalling 41,832 participants, GWAS for SU were performed after stratifying subjects by BMI status categorized into three levels: lean  combined data from 22 population cohorts encompassing 42741 individuals with measured circulating urate levels and BMI. With six additional follow-up studies, all were studies of European descent participants that contributed to the Global Urate and Gout consortium (GUGC) and have thus been previously described in detail [CLK4 locus. Sample sizes for the different sub-analyses performed and urate summary statistics for all studies with break down per BMI and gender stratum are detailed in Two extra studies, the Rotterdam study . Softwa-8 was used. To avoid results driven by one or two populations that are likely to be spurious, meta-analysis results for the lower allele frequency variants (MAF <5%) are reported only if at least four populations contributed and if the contribution of any single study as calculated by the R package “meta” (http://cran.r-project.org/) was not greater than 30%. Annotation to known GWAS hits in the vicinity (window of 150 kb centred on index SNP) of novel potential urate loci was made using the NHGRI GWAS catalogue [The results from all individual GWA scans were combined into a fixed-effects meta-analysis using inverse variance weighting, implemented in the MetABEL R package . From inatalogue , 29-10-2-6.A gene-based test for SU association in the BMI-stratified GWAMA was conducted using the VEGAS software. Briefly, this method assigns SNPs to genes (+/- 50kb of 5’ and 3’ UTRs) and combines the association P-values accounting for linkage disequilibrium between markers assigned to the same gene. Analyses were conducted for each of the nine BMI/gender categories GWAMA results. As 17,787 genes are tested, the Bonferroni-corrected threshold for significance is set at 2.8 10CLK4 variant rs7711186 was sought in six independent studies of individuals of European descent, totalling 1259 individuals, in which the marker was either genotyped or well imputed and, as exploratory foray, in a small sample of individuals of Polynesian descent (N = 290) with prevalent obesity.Replication of the differential effect of the t-test. Test statistics were calculated using the statistic t = (βbmicat1 - βbmicat2)/sqrt), with βbmicat and SEbmicat the meta-analysed SNP effect-estimates and their corresponding standard errors, and r the Spearman rank correlation coefficient between meta-analyzed beta-estimates, in each of the BMI categories compared, across all SNPs. Under the null hypothesis of no difference in effect sizes between BMI strata, the t statistic is expected to follow a Student’s t distribution.The meta-analysed SNP main effects on SU were compared between all pairwise BMI categories  using a Combined-gender and sex-stratified SNP by BMI interaction analyses were also performed in participating discovery studies using linear regression methods.1BMI+β2SNP+β12BMI*SNP+ɛ, with BMI as continuous variable, z the serum urate level adjusted for age, sex  and ancestry principal components expressed as z-score /population standard deviation), β the regression coefficients for the fitted effects, ɛ the error term with normal probability distribution.Urate residuals were generated using the same covariates and standardisation as described for the stratified main effect GWAS. For studies with related individuals, relatedness was accounted for by fitting ancestry principal components (PCs) derived from the genomic relationship matrix rather than fitting it in full within a mixed model for the association test as the iterative processes used for parameter estimations of the mixed models often did not converge in a pilot run using family-based populations. The number of PCs to account for, varying from study to study and best determined by examination of scree plots , was left to the decision of each study analyst. Each study GWAS was performed on imputed genotype dose using the following model: z~μ+β1 and β2) were not reported for studies that used Quicktest, as this later only reported the interaction term (β12). Meta-analyses of the interaction effects (β12) coefficients were carried out using MetABEL as described for the stratified main effect GWAS, with a higher MAF cut-off (5%) for each individual study. To avoid results predominantly driven by one population that are likely to be spurious, meta-analysis results with individual study contribution greater than 50% as calculated by the meta R package were filtered out.Softwares used by the different studies to implement association testing are reported in As the individual studies genomic control inflation factors (λ) for these analyses were often high , only thA small number of studies were available for follow-up of the linear interaction analysis, totalling 9298 participants . All follow-up studies analyses were carried out in the combined-gender data-set only and use the “model-robust method” that is implemented in the ProbABEL and Quicktest packages. Application of the model-robust method in principle leads to lower genomic control inflation for the interaction term . To incr12) coefficients were carried out using MetABEL as previously described for the discovery cohorts. The overall inflation coefficient for this follow-up meta-analysis was 1.018 and 1.006 for the combined discovery and follow-up studies interaction term meta-analysis.Meta-analyses of the interaction effects (βhttp://cran.r-project.org/) available upon request to the package’s authors. After lift over to build37, SNPs were annotated to genes when they were located within gene regions using annotations from the NCBI Gene database  and the SNPs  were ordered according to their location in the genome. Pathways (n = 229) were downloaded using KEGG.db (http://www. genome.jp/kegg/) and the SNPs and genes assigned to the pathways. SNPs with GWAMA p-values less or equal to 5% were considered associated with trait and associated SNPs within a pathway counted. This count was compared to the distribution of counts obtained from 10,000 circular permutations of the SNPs’ GWAMA association p-values with respect to the SNPs locations. In circular genomic permutation the genome is considered circular and ordered from chromosome 1 to 22 and restarting at chromosome 1 [The pathway analysis was carried out using a SNP-based circular permutation method implemented in an extension of the R package “genomicper”  and all but three  of the eleven loci reported in two earlier, non-stratified, SU genome-wide association meta-analyses (GWAMA) of size roughly comparable to the present analyses [-5 in any of the nine meta-analyses are listed in A1CF (lean-combined-gender), HLF (obese-combined-gender) and NRG4 (obese-men) are among the 18 novel, validated and replicated, urate loci in a large recent SU GWAMA  [SLC28A1 (lean-men category), encoding a sodium/nucleoside co-transporter present in kidney. MYO18D and ADAMST17  have been previously listed as suggestive loci for serum urate levels in a small study of African American participants [The stratification process did not yield any novel genome-wide significant signal at the SNP level . However, this effect was not reproduced in a replication set  in the lean-combined-gender stratum despite the larger number of individuals in the latter.Some modulation of effect sizes depending on BMI status is suggested by close inspection of the most strongly associated SNPs in each stratum . For exaSLC2A9 and ABCG2 comparisons in the combined gender analysis as the proportion of male and female is not the same across BMI categories and the effect sizes of the variants are sex-sensitive. Taking a Bonferroni corrected significance threshold for the number of independent SNPs analysed in different settings (0.05/(14*3) = 0.0012), only one locus, ABCG2, showed a statistically significant difference in effect size between obese and lean men  making the magnitude of effect in obese men similar to that seen in women (95% CI 0.125 to 0.221 in lean women). SNPs at three additional loci reached nominal significance  for all nine comparisons, after quality control for low frequency variants, are reported in diff lower than 10-5. The lowest P-values were from the lean-obese and lean-overweight comparisons, all in loci not previously associated with urate and displaying different direction of effects in the lean and obese/overweight strata  in the men lean-overweight contrast. The second most significant difference, Pdiff = 9.13 x 10-8, was also in the men lean-overweight contrast for a variant 5’ of the gene TSPYL5, a gene coding the testis specific Y-encoded-like protein 5 that has been recently suggested to regulate estradiol produced by adipocytes [ARL5B-PLXDC2 and LASS3 for the men  and RBFOX3 for women and combined gender (Pdiff~ 4 x 10-7) which had suggestive main effect in the obese women stratum.The same tests were also done genome-wide to investigate potential BMI-sensitive SNPs of opposite effect between strata. QQ plots for those analyses . showed t strata .. The vad traits ,26,27, ripocytes . The mosEROL1B-EDARADD and one in the RBFOX3 gene, displayed P-values just below the genome wide significance for a BMI*SNP interaction in the combined-sex analysis  but this threshold is very conservative given that many pathways are interconnected or overlapping and the combined and sex separate analyses are not independent. The most significant pathways were the ribosome pathway (P = 3 x 10-4) in overweight women, glycosaminoglycan degradation in obese men (P = 6 x 10-4) and N-glycan biosynthesis in lean women (P = 6 x 10-4).We used a recently developed pathway analysis method where pathway associations are tested following circular permutations of all the GWAS SNPs P-values  and compN-glycan biosynthesis (KEGG pathway hsa00510- N = 43 genes) is particularly compelling as its ranking amongst associated pathways is stable through the variable sample sized analyses for the same BMI stratum: it ranks top in all the lean meta-analyses , while it is medium-ranked in all the overweight analyses  and amongst the lowest ranks in all the obese strata . The list of genes out of the 43 genes in this pathway with at least one SNP nominally significantly associated with urate levels (P <0.05) in either BMI stratum in the combined-gender analyses are listed in No novel locus with a genome-wide significant main effect on SU was uncovered when performing GWAS within the three BMI strata investigated, suggesting that changes in BMI do not switch on a yet unknown major urate locus. However, many loci reached suggestive level of SU association in a BMI dependent fashion and/or displayed suggestive difference in main effects across BMI categories that may collectively account for a substantial amount of BMI-sensitive SU variation.inter = 0.001, because only one candidate gene was tested. Few scans for GxE interaction have been performed genome-wide to date [inter at best 10-4) interaction effect after testing for a joint effect of the main SNP effect and interaction term with the significant results driven by the main SNP effect [One weakness of this study is its relatively modest size. Gene by environment (GxE) detection requires a larger sample-size than that required for the detection of main effects of comparable magnitude   or amount of physical activity or of metabolic status of the subject.Despite these limitations, this is the largest investigation of the interplay between genetic variants influencing urate and BMI status to date and it provides novel, biologically supported, hypotheses that warrant further investigations.SLC2A9 variant effects by BMI and no support from previous reports [ABCG2 in men, with a fan-shaped interaction pattern and diminution  of the genetic variant effect size in obese compared to lean men on average. The ATP-binding cassette transporter ABCG2 has been established as a high capacity urate transporter, is expressed in renal proximal tubules, liver and intestines, and the hyperuricemia causal Q141K mutation has been shown to reduce urate transport rates [GCKR variant in the obese strata was only suggestive but it is well supported by the equivalent doubling in the lowering effect reported for the GCKR pleiotropic rs780094 T allele for fasting insulin and glucose in high-BMI participants compared to low-BMI participants, supplementary GCKR rs780094 variant urate association [Of the known urate loci, there was weak statistical evidence for modulation of  reports  of a conrt rates . Surrounrt rates . Interesociation .RBFOX3 and EROL1B were the top loci showing interaction with BMI status using linear models (with RBFOX3 index SNP reaching genome-wide significance in the combined discovery and look-up studies GWAMA). Both loci displayed the strongest evidence of a significant difference in SNP main effect when the lean and obese stratified samples were compared  was also selected together with 38 other genes in a gene-centric joint test for significant association with HDL-Cholesterol levels in a dataset combining expression data and GWAS data from independent sources [RBFOX3 knockout in mice  is decreased circulating alkaline phosphatase, human levels of which correlates with BMI [EROL1B encoding for the endoplasmic reticulum oxidoreductin 1LB catalizes the formation of disulfite-bonds in the ER. It represents another good candidate for BMI interaction as it is responsive to the unfolded protein response, a signal triggered by ER stress, levels of which are elevated in state of over-nutrition [compared , analyseity gene . RBFOX3  sources . A metabwith BMI  and metawith BMI , a compoutrition . ER streutrition  and has utrition .B3GNT4, also acts in a complex capping reaction, of Type II Lactosamine for example, establishing a precedent for a link between glycosylation enzyme variation and urate levels. The glycolysis intermediate Fructose 6P is the main precursor of amino sugar, combining with glutamine to form glucosamine-6-phosphate. Dependence on glutamine for both purine and glycoaminoglycan biosynthesis as illustrated by the inhibition of either pathway by the glutamine analogue antagonist DON [The “N-glycan biosynthesis” pathway acting to influence urate levels differentially in lean individuals compared to overweight or obese individuals is intriguing. One of the newly identified urate loci , B3GNT4,nist DON  also intThese links would be important to study further as glucosamine can be prescribed to patients with gout to reduce pain and inflammation but the possibility that it might influence the urate level has not been explored.ST6GALT2 to be highly stimulated by the pro-inflammatory cytokines IL6 and IL8 [Significant changes in N-glycosylation profiles with BMI have been well documented ,51,52. F and IL8  that are and IL8 . It is p and IL8 .Metabolic pathways are highly inter-connected and their dys-regulation underlies many diseases. Accounting for body mass index in analyses provides a tool to link pathways to both obesity and urate homeostasis.S1 FigA CROATIA-Vis and B.ORCADES. Residuals from a mixed linear model adjusting serum urate (SU) levels for age and accounting for relatedness are plotted against each other. As noted in  the line(TIF)Click here for additional data file.S2 FigSLC2A9 variant rs11942223, in LD (r2 = 0.6) with variant rs13129697, was available and used in the figure.In the discovery dataset, rs7711186  was suggestively associated with urate in the men-obese stratum, differentially . Look-up in a small Polynesian study (NZL-Poly) where obesity is prominent is added under the overall meta-analysis value for the replication studies, all of European ancestry (represented by lozenge). *For this Polynesian study only the (TIF)Click here for additional data file.S3 Figbmicat1 - βbmicat2)/sqrt),with βbmicat and SEbmicat the meta-analysis weighted beta-estimates and their corresponding standard errors and r the Spearman rank correlation coefficient between meta-analyzed beta-estimates in the BMI categories compared across all SNPs. Inflation coefficients, λGC, are reported for each plot in the left upper corner.The ordered observed squared t statistic are plotted against the ordered expected statitics of the null, chi2, distribution, where t = (β(TIF)Click here for additional data file.S4 FigThe overall inverse—variance-weighted mean effect per BMI stratum is calculated assuming fixed effect across studies and represented by a lozenge, associated P-value displayed as P. Measure of heterogeneity between studies is reported (I-squared) with associated P-value for significance (p). For study abbreviations and references, see (TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 TableSU unit is in mg/dl, sd stands for standard deviation, N is the number of individuals with BMI and SU measures.(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 TableInflation factors were calculated after filtering out poorly imputed variants and low frequency variants , MAF < 5% for SNP*BMI interaction term analysis (λ**). NA flags analysis not performed. *** indicates that model-robust regression method was used.(XLS)Click here for additional data file.S5 TableOnly the information pertaining to the SNP with the lowest P-value (index SNP) is listed. Lower allele frequency variants (1%<MAF <5%) are reported if the meta-analysis included at least four populations and if the contribution of any single study, as calculated by the meta R package, was lower than 30%. A1, allele for which effect (β) is reported; A2 alternate allele, frq(A1) weighted average effect-allele frequency across studies. Associations reported in the vicinity of the urate index SNP (in a 150kb region centred on the SNP) in the NHGRI GWAS catalogue (29_10_2013 update) are listed; highlighted red, the ones with same index SNP or index SNP in high to moderate linkage disequilibrium (r2 >0.4).(XLS)Click here for additional data file.S6 Table-6).Novel loci are shaded in grey. In bold, gene reaching genome-wide significant association with serum urate levels (P < 2.10(XLS)Click here for additional data file.S7 Tablediff < 10-5 are listed with representative index SNP of lowest P-value . Low MAF SNPs were filtered as in -5) in the BMI stratified urate GWAMA analysis Click here for additional data file.S8 TableStudies with inflation factor greater than 1.2 were not included in the analysis. For the combined-gender analysis, the CoLaus study was analysed as a replication study to balance discovery and replication sets. SNP with low MAF (< 5%) were excluded prior to meta-analysis. Results for the discovery, replication and combined sets are presented. Locus in bold indicates a genome-wide significant interaction effect. Shaded are loci common with (XLS)Click here for additional data file.S9 TableShaded are loci displaying suggestive association in linear interaction model(listed in (XLS)Click here for additional data file.S10 Table(XLS)Click here for additional data file.S11 TableN-glycan biosynthesis step coded 1 = N-glycan lipid-linked oligosaccharide precursor synthesis 2 = high mannose oligosaccharide to an Asparagine residue transfer and N-glycan trimming and branching 3 = more elaborate capping reactions(XLS)Click here for additional data file.


Integration of data from genome‐wide single nucleotide polymorphism (SNP) association studies of different traits should allow researchers to disentangle the genetics of potentially related traits within individually associated regions. Formal statistical colocalisation testing of individual regions requires selection of a set of SNPs summarising the association in a region. We show that the SNP selection method greatly affects type 1 error rates, with published studies having used methods expected to result in substantially inflated type 1 error rates. We show that either avoiding variable selection and instead testing the most informative principal components or integrating over variable selection using Bayesian model averaging can help control type 1 error rates. Application to data from Graves' disease and Hashimoto's thyroiditis reveals a common genetic signature across seven regions shared between the diseases, and indicates that in five of six regions associated with Graves' disease and not Hashimoto's thyroiditis, this more likely reflects genuine absence of association with the latter rather than lack of power. Our examination, by simulation, of the performance of colocalisation tests and associated software will foster more widespread adoption of formal colocalisation testing. Given the increasing availability of large expression and genetic association datasets from disease‐relevant tissue and purified cell populations, coupled with identification of regulatory sequences by projects such as ENCODE, colocalisation analysis has the potential to reveal both shared genetic signatures of related traits and causal disease genes and tissues. In addition, comparison of disease and eQTL data has the potential to reveal both the likely disease causal gene in regions where a number of candidate causal genes exist, and the relevant tissue type where tissue‐specific eQTLs exist. However, dependence between genotypes at neighbouring SNPs, caused by LD, means that determination of colocalisation is not obvious, as there may exist distinct but neighbouring causal variants for each trait that are mutually associated.In recent years, genome‐wide association studies (GWAS) have facilitated a dramatic increase in the number of genetic variants associated with human disease and other traits such as gene expression. Understanding the means by which these variants exert their effect will aid the design of the detailed functional followup studies already underway. Although the causal variants are not commonly known, multiple traits have been mapped to the same genetic loci, raising the possibility that the same variants affect multiple traits either directly or with one trait mediating the other. For example, genetic susceptibility to type 2 diabetes across 12 loci appears mediated by the genetic influence on body mass index , generated. This effectively compares the P value at the test SNP k conditional on SNP k′ to that conditioning on all other SNPs in the region. However, note that because this method summarises evidence for colocalisation by a rank only, there is no statistical inference attached. Thresholds for interpreting ranks would be expected to depend on SNP density and LD patterns.Concerned that LD would make interpretation of the conditional test difficult, Nica et al.  extendedq SNPs are chosen that are deemed somehow to jointly be good predictors of one or both traits. Regressing Y and Y′ against these columns of X and X′ respectively produces estimates, b1 and b2, of regression coefficients β1 and β2, with variance‐covariance matrices V1 and V2, respectively. Since sample sizes are large, the combined likelihood may be closely approximated by a Gaussian likelihood for , assuming V1,V2 are known and that Cov=0. Assuming equal LD in the two cohorts, i.e., that the correlation structure between the SNPs does not differ, Plagnol et al.  for distinct unlinked causal variants. Its distribution is increasingly skewed towards 1 as the LD between distinct causal variants increases. This makes sense if one considers that the case of two distinct variants in some LD lies partway between the extreme cases of distinct unlinked causal variants and a single common causal variant, which is equivalent to distinct causal variants in complete LD. The effect of using the most associated SNPs for testing compared with using the true causal SNPs is to reduce the skew towards higher rank scores as the k and k′ defined above [Plagnol et al., b1, b2) if the estimation occurs in the same dataset as the selection. We show here that neither method maintains control of the type 1 error rate  when only tagging genotypes are available and effect sizes are large (relative risk ∼ 1.3). This is because, in this situation, lasso may select SNPs that are apparently weakly associated (either truly or through random noise) at which, as demonstrated in Supplementary Fig. S2, effect estimates are more strongly biased.For the proportional approach, two strategies have been applied. Either colocalisation has been tested using the pair of SNPs We propose two alternative approaches to proportional testing, either of which can help control type 1 error rates.The first is to use a set of principal components that summarise the genetic variation in the region, instead of individual SNPs. However, it is not obvious how many components are required. As more components are selected, more information about the genetic variation in a region is captured, and hence we are more likely to accurately capture the signal of any causal variants. However, successive components add decreasing amounts of information whilst still adding another degree of freedom. At some point the negative effect of increasing degrees of freedom will outweigh the positive effect of increasing information, and we were concerned that the optimal test may depend heavily on the threshold used to determine the number of components selected. Instead, power seemed broadly acceptable once components capturing 70–90% of variation were selected . In our 49 test regions, 70% of the variance could be captured by selecting an average of 7 (range 2–18) or 9 (range 3–44) components under a tagging or complete genotyping approach.r2 between the causal variants, and is maximum when the two are completely unlinked (r2=0). When using PCs, the power is reduced reflecting the loss of information in not knowing these causal variants, but the loss is greatest for complete genotyping scenarios, suggesting that we may be selecting too many components in the case of complete genotyping and emphasising the difficulty in choosing one optimal threshold for all studies and regions.We found type 1 error rates were controlled across the range of thresholds explored, but show for illustrative purposes the results when we fixed the threshold at ⩾90% of genetic variation Fig. . As mighP values may then be averaged, weighting by each model's posterior probability, in a Bayesian model averaging framework and generating a posterior predictive P value. Because of its computational burden for simulations, we only consider the BMA approach under a tagging genotyping scenario. This demonstrates good control of the type 1 error, even tending to be mildly conservative, as has previously been reported when posterior predictive P values are interpreted similarly to standard P values [Meng, Our second proposed approach is to test the proportional null hypothesis using all possible two SNP models. The resulting es Meng, . Despite∼5%) into a mainly European population, is barely detectable at the sample sizes of 1,000 used, and indicates that the method is robust to small departures from the assumption of equal linkage disequilibrium. Of course, as with any genetic analysis, it remains sensible not to rely on this property, but to formally examine the evidence for population structure and exclude obviously outlying samples.The proportional colocalisation test assumes identical patterns of LD in the two datasets so that the effect of a shared causal variant is proportional across any set of SNPs. To explore its sensitivity to this assumption, we considered sampling haplotypes for one dataset from a subset of European populations, and for the other dataset from a either a mixture of European populations or a mixture of European and African populations. As might be expected, for strongly admixed datasets, the control of type 1 error rate is lost, with type 1 error rates up to eightfold that seen under the case of no mixing Fig. . Howevervs. not colocalising variants cannot capture the complexity of this situation, but it is instructive to explore the test's expected behaviour in order to allow proper interpretation with real data where the number, and sharing configuration of causal variants is unknown. Figure So far, we have only considered the case of a single causal variant for each trait. But the proportional test makes no assumption about the number of causal variants, only that their effects are proportional. Figure So far, also, we have assumed that it was enough to consider only the universe of two SNP models when applying our BMA approach. The motivation for this was that a two SNP model leads to a one degree of freedom test, and might therefore be expected to maximise power. We examined the effect of averaging over either all two SNP or all three SNP models in the context of the above multiple causal variant simulations. This shows that type one error rates are similar, that power is similar for two SNP models, but that power can be increased by averaging over three SNP models compared to two SNP models, particularly when there are really three or more distinct causal variants Fig. .PTPN22 is associated with rheumatoid arthritis and T1D [Barrett et al., IL2RA are associated with T1D and multiple sclerosis [Maier et al., Existing evidence suggests that a single locus may contain variants that predispose to any one of multiple diseases, e.g., the nonsynonymous C1858T SNP in P<1.1×10−6, a permutation derived threshold specific to this study) and at a nominal significance threshold of P<0.05 for HT. Six of these display no evidence against colocalisation , the exception being 2q33.2/CTLA4/ICOS in which the ppp value for the BMA approach is 6×10−3 (two SNP models) or 8×10−5 (three SNP models). In this region, the profile of the single SNP P values do differ , but it would require larger sample sizes to confidently conclude that the two diseases have different causal variants in this region given the number of tests completed.We first examined the seven regions where a significant single SNP effect has been identified in both diseases, i.e., at study‐wide significant levels for GD . If the posterior probability was spread broadly over the model space and P*(m) varied considerably across models, the distribution of ppp would be far from uniform, with too few observations in the tails. The fact that we observe only mildly conservative type 1 errors, even in the weakest associations considered here , probably reflects our requirement in simulating our datasets that some nominal level of association must be observed for at least one SNP in a region. This means that, for our simulated data, the posterior probabilities tend to be mainly focused on a small subset of models, and these models tend to contain sets of SNPs related by LD so that P*(m) does not vary substantially across the set of models on which most of the posterior probability is concentrated. Whether caution is needed in applying this approach in smaller datasets depends somewhat on a researcher's view. Certainly, it is unlikely that the ppp distribution would be uniform if small samples with weak associations were used, and therefore power to detect departure from colocalisation would be reduced. However, the alternative to colocalisation being tested is not just that of no colocalisation, but implicitly that of distinct causal variants in two traits. If there is weak or only limited evidence for association with either trait, then it does seem appropriate that one cannot reject a null of colocalising causal variants in favour of an alternative distinct causal variants. On the other hand, if evidence for association has been independently established, a researcher may be sure that there are causal variants for each trait although there is only weak evidence in the currently available datasets, in which case they may wish to explore calibrating the ppp distribution, for example by means of simulation [Hjort et al., Our proposed solution is to use proportional testing and either avoid variable selection altogether by using the PCs that capture the majority of genetic variation in the region, or integrate over the variable selection using BMA. Either method maintains type 1 error, and the BMA approach appears more powerful than the PC approach, although both have reduced power compared to the hypothetical scenario of being able to test the causal variants themselves. Recall from equation P<5×10−8 and we used a 0.1cM window around the most associated SNP in our AITD analysis. If regions were defined more broadly, one might expect the BMA method to be relatively unaffected, as SNPs beyond the boundary of the current regions would not show much evidence for association. On the other hand, we expect the power of the PC approach would decrease, as the number of components, and therefore the degrees of freedom of the test, would increase without any compensatory increase in information.Regions are typically defined after GWAS studies according to genetic distance in order to describe the physical region within which a causal variant tagged by the association is expected to lie. T1DBase uses a definition of 0.1cM surrounding any single SNP with We have proposed alternative approaches to colocalisation testing, but how should a researcher choose that approach and the parameters specific to that approach? Our view is that we would use PCs as a first pass if many loci are under consideration, to prioritise regions for more detailed analysis. The optimal number of components is unknown, but a number that captures something in the region of 70–90% of genetic variation seems acceptable. However, if evidence for association is strong in both datasets, BMA is a more powerful approach and there seems little reason beyond computational expense to prefer averaging over the universe of two SNP models to that of three SNP models. However, in large regions, a relatively modest number of SNPs can cause the number of three SNP models to be infeasible. For example, 100 SNPs can generate 4,950 two SNP models but 161,700 three SNP models. We would not suggest exploring four SNP models because that seems likely to spread the posterior probabilities very thinly, and we haven't explored combining two and three SNP models because it is unclear what prior weight should be given to a two SNP compared to a three SNP model.P values are unlikely to detect any departure from colocalisation for reasons described above, but here the Bayesian approach does allow us to use make some inference using the posterior distribution of η. Given the relatively smaller number of HT cases (462) compared to GD , it might be expected that many of the loci only associated with GD have failed to be reach significance for HT due to lack of power. Estimates of power to detect association with HT under the assumption of equal effects in GD and HT are broadly similar across the seven regions associated with both diseases and the six regions associated with GD only (Supplementary Table S1), but these are likely to be over optimistic due to the expected bias in the GD effect size estimates. However, for five of these six regions, the evidence suggests that failure to detect association with HT is more likely to be due to a discrepancy in effect size, with the effect considerably larger in, and possibly specific to GD, compared to HT, rather than a lack of power. However, while the data suggest that LPP may have a stronger effect on GD compared to HT, and that CCR6 may be an undetected HT locus, the small sample size for HT prevents us from drawing this conclusion with any confidence.As an example application of our proposed approaches, we analysed 13 loci associated with the autoimmune thyroid diseases GD and HT. Reassuringly, inference was broadly similar regardless of method, and we and showed that in the seven regions where a locus has been associated with the two diseases, the data are generally consistent with common causal variants exerting an equal effect on each disease. In regions previously significantly associated with only GD, posterior predictive There is a pressing need for more widespread use of formal colocalisation testing. Researchers are turning to eQTL data to interpret GWAS results by simply considering whether an eQTL SNP is associated with any disease [Nicolae et al., GSDMA as the most likely causal gene for asthma in the 17q21 region [Hao et al., GSDMA expression were relatively weakly associated with asthma, and, vice versa, that the SNPs most strongly associated with asthma showed relatively weaker levels of association with GSDMA expression compared to the strongest signals. The asthma association on the 17q21 region was one of the first cases of explicitly using expression data to interpret disease association, with the association with asthma initially attributed to ORMDL3 based on expression data from EBV transformed cell lines [Moffatt et al., GSDMB from a reanalysis of the same data [Moffatt et al., In the case of network analysis, results can be difficult to reconcile with simple representations of the data. For example, integration of lung expression and asthma genetic association data led to the identification of Given the tissue‐specific nature of many eQTLs identified to date [Dimas et al., The author has no conflict of interest to declare.Disclaimer: Supplementary materials have been peer‐reviewed but not copyedited.Supporting InformationClick here for additional data file.


NOP9 gene and language-related phenotypes under a parent-of-origin model. Replications of these findings are particularly challenging because the availability of parental DNA is required.Specific language impairment (SLI) is a common neurodevelopmental disorder, observed in 5–10 % of children. Family and twin studies suggest a strong genetic component, but relatively few candidate genes have been reported to date. A recent genome-wide association study (GWAS) described the first statistically significant association specifically for a SLI cohort between a missense variant (rs4280164) in the n = 106 informative families) including children with language and reading difficulties and a nuclear family cohort (n = 264 families) selected for dyslexia.We used two independent family-based cohorts characterised with reading- and language-related traits: a longitudinal cohort (NOP9 locus in both cohorts: minimum P = 0.001 for phonological awareness with a paternal effect in the first cohort and minimum P = 0.0004 for irregular word reading with a maternal effect in the second cohort. Allelic and parental trends were not consistent when compared to the original study.We observed association with language-related measures when modelling for parent-of-origin effects at the NOP9 locus and its role in language-related traits. A systematic evaluation of parent-of-origin effects in genetic association studies has the potential to reveal novel mechanisms underlying complex traits.A parent-of-origin effect at this locus was detected in both cohorts, albeit with different trends. These findings contribute in interpreting the original GWAS report and support further investigations of the  Specific language impairment (SLI) is a common neurodevelopmental disorder, estimated to affect 5–10 % of preschool-aged children . An SLI CMIP and ATP2C2 at 16q24 [CNTNAP2 at 7q36 [TM4SF20 at 2q36.3 [A strong genetic component has been suggested by family studies, with up to one third of affected children having parents with language or literacy impairment . This obat 16q24 , CNTNAP2 at 7q36  and TM4St 2q36.3 .KIAA0319 dyslexia candidate has shown association with language skills in independent studies [SLI presents significant comorbidity with other neurodevelopmental disorders, particularly with dyslexia . Dyslexi studies , 17. Can studies .http://genome.gov/gwastudies/). One of the main limitations is the availability of sufficiently large and well-characterised cohorts [ROBO2 gene through the analysis of epidemiological samples [Genome-wide association studies (GWASs) for language phenotypes have been sparse so far and have been less successful when compared to results obtained for other disorders  with paternal parent-of-origin effects. The strongest maternal effect was observed for an intergenic variant on chromosome 5p13 but did not reach statistical significance within a GWAS context . The association on chromosome 5 was followed up in a sample of 313 language-impaired children and their mothers selected from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. While association under a maternal parent-of-origin effect was observed at this locus, the opposite allelic trend was seen. The paternal effect for NOP9 was not followed up in the ALSPAC cohort since paternal DNA was unavailable. Replicating these associations is particularly challenging because testing parent-of-origin effects require parental DNA, which are not normally available in a case-control study design.The first GWAS conducted specifically in an SLI cohort was reported recently for a cohort of 278 families . AlthougNOP9 locus in both cohorts but with different allelic trends and parental effects.Here, we report the first follow-up study for the results of this GWAS for language impairment using a longitudinal cohort of 106 informative families well characterised with language-related phenotypes . Given the overlap between SLI and dyslexia, we tested for association also with reading measures and analysed an additional cohort of 264 families selected primarily for dyslexia. Analysis of quantitative measures of reading and language ability, under a parent-of-origin model, showed statistically significant associations for rs4280164 at the n = 46), children with preschool speech and/or language difficulties (n = 26) and typically developing children (n = 41). DNA from additional family members generated a total sample size of 332 individuals. Exclusion criteria were a non-white European ethnicity and/or a non-verbal IQ score below 70. Any child with a diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) was excluded from the cohort prior to sample collection. Saliva samples were extracted from children and parents using Oragene kit (prepITL2P) .We investigated a longitudinal cohort (referred to as the York cohort), consisting of 106 informative families characterised with reading and language measures . The proThe York cohort included a significant proportion of children with a family history of reading difficulties in addition to children with language problems. Accordingly, we analysed an additional cohort selected for a dyslexia diagnosis. This ‘dyslexia cohort’, composed of nuclear families, has been described previously , 28. EveDNA was not available for 39 of the 212 (18 %) and 59 of the 528 (11 %) parents in the York and dyslexia cohorts, respectively.r = 0.8 between vocabulary and grammar at school age.From the range of available language measures characterising the York cohort, we selected those most closely related to traits previously used in genetic analyses for language- and reading-related traits , 29–31. At later ages (5 ½ and 6 ½ years old), reading measures included single-word reading (READ), Test of Word Reading Efficiency (TOWRE) , WechsleIn contrast to the York cohort, which included longitudinal measures, the dyslexia cohort was assessed only once for study participants with an age range of 6 to 25 years and language was not measured. We analysed the reading-related measures described previously in genetic studies of this cohort . This inNOP9, chromosome 14) and rs10447141 (on 5p13) were genotyped using TaqMan assays  on a ViiA7 qPCR instrument  in the York cohort, and as part of a multiplex Sequenom assay in the dyslexia cohort. All assays passed standard quality control tests for call rate, Mendelian error and Hardy-Weinberg equilibrium. Furthermore, genome-wide genotyping data, which were available for a subset of the siblings , showed a 100 % concordance with genotypes generated for the entire cohorts in this study. Principal component analysis (PCA) of the genome-wide genotyping data was used to assess population stratification. All samples retained for the analysis showed no evidence of population stratification, thus allowing for a total association model, within and across families.Markers rs4280164 .We conducted a replication study for the al model . We perfp13 loci , respectWhen parent-of-origin was incorporated into the analysis, we observed association for a range of measures but with an inconsistent pattern. The original study reported a paternal effect at rs4280164 where the G allele was associated with an SLI status . In contP = 0.02) but strongest associations were observed for NWR  and PA-is . The rs10447141 marker showed only marginally significant associations for LSK under a paternal origin effect (P = 0.04).In the parent-of-origin model, the association with AFW was attenuated (P = 0.045).Given the associations we observed for PA-is in the York cohort, which included a significant proportion of children with a family history of reading difficulties, we also investigated 264 families from the ‘dyslexia cohort’. Association analysis under a standard model showed only a marginally significant association for rs4280164 with PD (P = 0.0004). The trend of association was also inconsistent with previous observation; A was the risk allele under a maternal effect. No association was observed for rs10447141 with standard or parent-of-origin models in the dyslexia cohort. Paternal and maternal effects were confirmed to be statistically independent  under a conservative assumption of independence across all tests: PA-is in the York cohort  and OC-irreg in the dyslexia cohort .Although the phenotypes are correlated with each other and cannot be considered independent, our results need to be interpreted in the light of multiple testing. Two associations reached the significance level adjusted for multiple test comparisons (NOP9 gene) and another on chromosome 5 (rs10447141), in language impairment susceptibility under a parent-of-origin model [NOP9 gene yielded significant associations when modelling for parent-of-origin across a range of measures in both cohorts but with inconsistent allelic and parental effects. The original study reported that risk was conferred by the major allele (G) with a paternal effect. Conversely, the A allele showed association in the dyslexia cohort but with a maternal effect. The results in the York cohort are intriguing: the G allele was associated with poor language skills with a maternal effect, whereas a paternal effect for the A allele was observed with some reading measures. While it would be tempting to speculate that different alleles affect different traits (e.g. reading or language) under different parental effect, caution should be used in interpreting the results until further replication analyses are conducted. It is also worth noting that opposite allelic trends were observed for the association described at the chromosome 14 loci in the two cohorts  tested in the original study [We report the first follow-up analysis of a GWAS implicating two loci, one on chromosome 14 . The rs4280164 minor allele (A) has a frequency of 22–24 % (18 % reported in HapMap for European population) and corresponds to a missense variant leading to a substitution (S308N) in the protein encoded by NOP9. The minor allele, which gave the strongest associations in both the York and dyslexia cohorts . An eQTL effect is reported across different tissues, the strongest of which  is observed for the neighbouring LTB4R gene in the thyroid. This observation does not offer an immediate interpretation of how the SNP could affect language-related phenotype; however, it is worth noting that eQTL databases depend on the available tissues and the foetal brain, which would be the most relevant to our phenotype of interest, is not immediately accessible.The differences in parent-of-origin could be intrinsic to our study design and be the result of random fluctuation due to small sample sizes. The original study included 278 affected children, and the present study was based on cohorts of 106 and 264 families leading to a range of 107 to 111 and 443 to 491 informative individuals, respectively. Undoubtedly, sample size is a limitation of the present study, especially in the York cohort, which could explain the inconsistent trend. However, the strongest association was observed in the largest cohort (e.g. dyslexia cohort) for the OC-irreg phenotype  present a mono-allelic expression and a large proportion of those contribute to the variability of human traits, but NOP9 was not tested in this study [Our findings could also reflect the complex nature of the mechanisms underlying parent-of-origin effects that can result from different phenomena. Imprinting has been demonstrated for less than 1 % human genes, but its contribution to trait variation could be higher than expected . While tosome 14 , 44, 45.osome 14 , diabeteosome 14 , 47, bodosome 14  and pubeosome 14 . Recentlis study . Parent-is study , leadingis study . It has is study . The remTherefore, while we cannot exclude that the association patterns we report are due to chance effects, our results encourage further investigations of the NOP9 locus in the context of language abilities.NOP9 locus for both language and reading-related phenotypes in two independent cohorts. Although allelic and parental trends are not in line with the findings of the original GWAS, the results suggest that this locus might be implicated in neurodevelopmental phenotypes through a complex parental inheritance mechanism. To elucidate such mechanisms, it will be necessary to further study parent-of-origin effects at this locus in additional cohorts selected for reading and language impairment. Our study, once again, highlights the difficulties in conducting replication studies across heterogeneous datasets; therefore, it will be important to define common core tests that would permit direct comparison of different cohorts. We propose that parent-of-origin effects should be evaluated more systematically in association studies for language disorders as well as other complex traits since this model has the potential to elucidate some of the missing heritability.In summary, we report a follow-up study for the first GWAS for SLI conducted under a parent-of-origin model by testing the top two associated markers . Our stu


Sequencing studies of exonic regions aim to identify rare variants contributing to complex traits. With high coverage and large sample size, these studies tend to apply simple variant calling algorithms. However, coverage is often heterogeneous; sites with insufficient coverage may benefit from sophisticated calling algorithms used in low-coverage sequencing studies. We evaluate the potential benefits of different calling strategies by performing a comparative analysis of variant calling methods on exonic data from 202 genes sequenced at 24x in 7,842 individuals. We call variants using individual-based, population-based and linkage disequilibrium (LD)-aware methods with stringent quality control. We measure genotype accuracy by the concordance with on-target GWAS genotypes and between 80 pairs of sequencing replicates. We validate selected singleton variants using capillary sequencing.Using these calling methods, we detected over 27,500 variants at the targeted exons; >57% were singletons. The singletons identified by individual-based analyses were of the highest quality. However, individual-based analyses generated more missing genotypes (4.72%) than population-based (0.47%) and LD-aware (0.17%) analyses. Moreover, individual-based genotypes were the least concordant with array-based genotypes and replicates. Population-based genotypes were less concordant than genotypes from LD-aware analyses with extended haplotypes. We reanalyzed the same dataset with a second set of callers and showed again that the individual-based caller identified more high-quality singletons than the population-based caller. We also replicated this result in a second dataset of 57 genes sequenced at 127.5x in 3,124 individuals.We recommend population-based analyses for high quality variant calls with few missing genotypes. With extended haplotypes, LD-aware methods generate the most accurate and complete genotypes. In addition, individual-based analyses should complement the above methods to obtain the most singleton variants.The online version of this article (doi:10.1186/s12859-015-0489-0) contains supplementary material, which is available to authorized users. With rapid advances in sequencing technology, large-scale sequencing studies enable discovery of rare polymorphisms. Exome and targeted sequencing studies are especially popular in the studies of complex traits. These designs focus on small genome regions likely to be enriched for functional variants -3, achieVariant calling algorithms fall into three major categories depending on how information from shotgun sequencing data is aggregated across individuals and genomic positions . The firTo compare the performance of the three types of algorithms in large-scale sequencing datasets with high coverage, we analyzed 7,842 European individuals, each sequenced at 202 targeted genes . The aveWe found that at a fixed number of variant sites, IBC identified a larger proportion of extremely rare variants of high quality, particularly singletons, while capturing most of the common polymorphic sites that were identified by the other callers. We replicated the result in the additional high-coverage dataset and by using different variant caller implementations. However, IBC genotypes at common variants were of the lowest quality by all measures. They were the least concordant with GWAS genotypes and within sequencing replicate pairs. Moreover, the IBC call set contained 4.72% missing genotypes, due to low coverage or low quality calls. In the PBC set, the percentage of missing genotypes dropped to 0.47% by using a population allele frequency prior. PBC also showed improved heterozygous concordance with on-target GWAS genotypes as well as between replicates. Without flanking markers, LDC achieved similar genotype accuracy with PBC, while further reducing the missing genotypes to 0.17%. With extended haplotypes from flanking GWAS markers, LDC achieved the same level of missing genotypes (0.17%) and the highest genotype concordance among all callers.et al. [To understand the strengths and limitations of individual-based, population-based and LD-aware variant calling methods, we analyzed sequence read data from 7,842 unrelated European individuals. The next-generation sequencing data was part of a large-scale targeted sequencing experiment generated for the purpose of identifying variants associated with 12 common diseases and cardiovascular and metabolic phenotypes, previously described in Nelson et al. . This exhttp://bio-bwa.sourceforge.net) [http://picard.sourceforge.net/). We recalibrated base quality scores using Genome Analysis Toolkit (1.0.5974) from the Broad Institute [Among the 7,842 individuals considered, 80 were independently sequenced twice. All 7,842 individuals had been previously typed on one of Illumina  or Affymetrix  chips for genome-wide association studies (GWASs). Prior to variant calling, we aligned reads using BWA 0.5.9 (rge.net)  with humnstitute . We combnstitute  . These likelihoods are calculated per genomic position with aligned reads. Base quality scores of the reads are refined using the base alignment quality (BAQ) adjustment to account for base calling error rates and mapping uncertainty [π(genotype) to generate posterior probabilities Pr(genotype|reads). We considered 3 categories of calling algorithms that reflect how information is aggregated across individuals and positions.We used likelihood-based models for genotype and SNP calling, as outlined in Li et al. . For eacertainty . Using Bhttp://genome.sph.umich.edu/wiki/GlfSingle) to call genotypes. By calling also the reference homozygous genotypes, we obtained the union set of all variant sites and genotypes across all individuals.IBC applies an individual-based prior which assumes each allele has a probability θ = 0.001 of being different from the reference. For variant sites, we assigned uniform prior probabilities for transitions and transversions to avoid bias in the evaluation of genotype quality based on transition to transversion ratio (Ts/Tv). By computing the genotype likelihoods using aligned reads per individual, the model assigns the most likely genotype when the posterior probability reaches a threshold of 99%; genotypes with lower posterior probability are marked as missing. We used glfSingle , which also generated variant calls for NHLBI GO Exome Sequencing Project (ESP) and contributed to 1000 Genomes Project analyses [PBC uses a two-step procedure to call variants . First, analyses ,22,31.In this study, we used a posterior probability threshold of 99% for the most likely genotype, which was the same threshold as for the ESP . To mainR2 between true allele counts and estimated allele counts, as a measure of imputation quality. This caller, previously used in low-pass sequencing studies [http://genome.sph.umich.edu/wiki/ThunderVCF).Starting from a set of variant calls, LDC updates the genotype of each individual at each marker using a Hidden Markov Model derived from the haplotype-based model used in the imputation software MACH . The LDC studies ,22, has We used LDC to refine each of the two PBC call sets described above. We applied the standard setting of 30 iterations and 200 reference haplotypes per iteration. We considered two scenarios with different haplotype information: First, we applied LDC on short haplotypes, which consisted only of the PBC variant calls at the sequences captured in the sequencing experiment. Second, we created long haplotypes by combining PBC variant calls with GWAS-genotypes from flanking markers within 500 kb from both ends of each target gene. In both scenarios, we masked GWAS genotypes within the target regions and used these markers as measures of genotype quality.et al. [R2 quality control criterion, which filters sites with To remove potentially false variant calls caused by technical artifacts, we followed the filtering and support vector machine (SVM) approach used in the ESP  and Zhanet al. . InitialSVM generates a summary score for each site based on the initial quality metrics, classifying good and bad calls with respect to training call sets  [http://www.ncbi.nlm.nih.gov/SNP/), a recent public archive of confirmed variants.We compared 4 sets of 27,500 variants, generated using IBC, PBC, LDC without flanking haplotypes and LDC with flanking haplotypes. First, we evaluated the overall quality of each call set by calculating transition to transversion ratios (Ts/Tv), stratified by variant type as annotated by ANNOVAR   and by mWe then characterized IBC-specific variants and PBC-specific variants by their Ts/Tv and read coverage. Most of the IBC- and PBC-specific variants were singletons. We performed an independent capillary sequencing experiment on 32 IBC-specific and 41 PBC-specific singleton variants, sampled from individuals from the CoLaus study  carryinghe) between our genotype calls from sequencing and the genotypes from GWAS chips at the on-target markers. he is defined as the number of genotypes called as heterozygous in one set but homozygous in the other, divided by the total number of heterozygous genotypes in both sets. (3) he for the 80 sequence replicate pairs, at variant sites where at least one individual per pair is heterozygous. (4) The shared variants between each pair of call sets and calculated the he between every pair of callers.We assessed genotype quality of each call set by four summary statistics: (1) The percentage of missing genotypes from no calls and filtered genotypes (2) The pairwise heterozygote mismatch rates . We generated random samples of 50, 100, 500, 1,000, 2,500 and 5,000 individuals from the original dataset by sequentially adding individuals and used PBC to call variants in each of the samples. For each down-sampled dataset, we calculated the proportion of IBC singletons identified by PBC and recorded the genotype quality of these PBC singletons. We repeated the full random sampling experiment 10 times and averaged the results.To assess if our results were driven by the specific choice of calling algorithms, we applied the individual- and population-based settings of GATK UnifiedGenotyper (version 3.1.1-g07a4bf8)  to our oTo replicate our results in a second dataset with higher sequencing coverage, we considered an additional dataset obtained from the AMD Consortium, which sequenced 3,142 individuals at 57 genes from 10 age-related macular degeneration loci [In the complete call sets of 7,842 individuals, the individual-based single marker caller (IBC) generated 31,970 variants while the population-based single marker caller (PBC) generated 29,147 variants. The LD-aware caller (LDC) modified genotypes from PBC, hence it generated the same number of variants. We filtered each call set separately and ranked the variants using a support vector machine (SVM). We observed 30,297 IBC, 27,690 PBC variants and 27,535 LDC variants with positive SVM scores. To compare call sets for a fixed call rate, we focused on the top 27,500 variant sites from each set. In the IBC set, 59.4% of the calls were singletons (MAF = 0.06%), while 57.7% of the PBC and LDC calls were singletons , while the PBC call set had a substantially lower percentage (0.47%) Table . The LDCWe assessed the quality of the variants included in the four call sets by calculating the transition-to-transversion ratio (Ts/Tv). A Ts/Tv > 2 is expected for intergenic sites; Ts/Tv is typically much higher in coding regions due to purifying selection . In our Comparing Ts/Tv between known variants and novel variants, we observed that known variants (in dbSNP) generally had higher Ts/Tv than novel variants Table . SingletAt exonic variants, all call sets attained Ts/Tv greater than 3, with nonsynonymous variants having lower Ts/Tv than synonymous variants  were IBC-specific, 1,787 (96.59%) being singletons with Ts/Tv 1.97. On the other hand, 1,731 (41.18%) variants were shared between PBC and LDC sets, but not found by IBC. We considered sites in this category as PBC-specific since LDC did not introduce new sites, but only modified genotypes at sites called by PBC. Of these PBC-specific sites, 1,260 (72.79%) were singletons with Ts/Tv 1.08.p-value = 0.258). Relaxing the SVM threshold to 29,000 SNPs per call set, IBC-specific and PBC-specific singletons still had comparable validation rates, at 91.30% (42/46) and 92.45% (49/53) respectively.IBC identified more singletons at low coverage than PBC, even after an additional filtering of all genotypes with less than 7x coverage Figure . IndepenNotably, 99.13% of PBC-specific sites were in the IBC unfiltered (complete) call set of 31,970, including all 471 sites with minor allele count >1. On the other hand, only 177 (9.57%) IBC-specific sites were in the PBC complete call set of 29,147; the majority was undiscoverable using PBC. Therefore, we extended the validation experiment to IBC-specific singleton calls ranked below 29,000, where no singletons from PBC could be sampled from the CoLaus subset. Capillary sequencing showed that these IBC-specific singletons at the lowest ranks had a validation rate of 81.82%  while LDC with flanking markers generated 3,664 (1.25%) more heterozygous genotypes than PBC. Mismatch rates in these extra genotypes varied widely between the two settings; calls from LDC without flanking markers had a mismatch rate of 2.41% while calls from LDC with flanking markers had an error rate of 0.71%  and population-based (G-PBC) call sets. The G-IBC call set contained 34,704 variants with Ts/Tv 2.21, while the G-PBC call set contained 33,696 variants with Ts/Tv 2.23. Each call set contained about 32% singletons: the G-IBC call set contained 11,001 singletons with Ts/Tv 2.13, and the G-PBC call set contained 10,678 singletons with Ts/Tv 1.77. The proportion of singletons was substantially higher in our IBC call set (59.36%) generated using glfSingle and PBC call set (57.73%) generated using glfMultiples, as well as in previous analyses of the same dataset (60.32%)  using thet al. [IBC identified 523 on-target SNPs with more than one non-reference allele. Of these, 513 SNPs  had two non-reference alleles  and 10 had three non-reference alleles. Following the population genetics calculations used in Nelson et al. , we predet al. ,35, we oet al. [Most of the triallelic variants were rare: 205 (38.53%) had two singleton non-reference alleles, and 253 (47.56%) had one singleton non-reference allele and one more common non-reference allele. For the 10 SNPs showing all four alleles, 8 had at least one singleton non-reference allele. Nelson et al.  validateThe computational burden of variant calling increases when the algorithm aggregates more information across individuals and sites. Hence IBC is the fastest algorithm and LDC is the slowest. IBC used about 250 CPU-hours to generate all variants for all 7,842 individuals, while PBC used 400 CPU-hours. For IBC, each individual at a specific genomic region can be analyzed in parallel. For PBC, all individuals have to be considered jointly, but genomic positions are independent and can be analyzed in parallel. In terms of memory usage, IBC consumed negligible memory since it only needed to read in the genotype likelihoods for one position per individual. For PBC, memory consumption increased roughly linearly with sample size. To analyze our dataset with 7,842 individuals, the maximum memory usage was 7.9 Gb. In our down-sampling analyzes, sample sizes of 1,000 and 5,000 consumed 1.1 Gb and 5.3 Gb memory respectively.The LDC model considers all haplotypes jointly, with run time increasing in quadratic scale with the number of haplotypes included in the reference panel, which is the state space of the underlying Hidden Markov Model. Other factors affecting run time included length of each haplotype, number of iterations, and total sample size. We performed LD-aware calling per gene for 15,684 haplotypes at 202 genes, using a reference panel size of 200 for 30 iterations. After running PBC, LDC with flanking haplotypes took about 3000 CPU-hours. Without flanking haplotypes, LDC took about 2000 CPU-hours. To speed up the process while retaining sufficient LD information, LDC can be run in parallel on larger genomic regions, such as a 1Mb region or a chromosome. Memory usage increased linearly with the number of variants in the gene: each gene contained 300 to 2,000 variant sites after adding GWAS flanking genotypes, with the memory required for running LD-aware algorithm ranging from 45 Mb to 300 Mb.We performed all analyses on a Dell C6100 blade server with four discrete dual 6-core Intel Xeon X5660 CPUs at 2.80 GHz. 128 GB RAM and 1 TB of local SATA disk were available on this system.We performed an extensive comparison between calling algorithms of various complexity on a large sequencing dataset capturing exons of 202 drug-targeted genes with mean coverage of 24x. As a result of the capturing process necessary for targeted sequencing, we observed a wide range of coverage per targeted position, echoing the outcomes of other exome sequencing studies aiming at high coverage ,36. ThusExisting calling algorithms aggregate different levels of information from sequence reads. We considered three major groups of likelihood-based models: (1) Individual-based single marker caller (IBC) uses aligned reads at each marker per individual, (2) population-based single marker caller (PBC) uses aligned reads at each marker for all samples to estimate population allele frequency, (3) LD-aware genotype refinement caller (LDC) uses linkage disequilibrium information from loci surrounding each called variant. Many different approaches exist for each model; each uses a variation of individual-based, population-based or haplotype-based priors. Previous studies have shown comparable performance between glfSingle/glfMultiples and earlier versions of the GATK UnifiedGenotyper . By compComparing filtered call sets of identical size  for each caller, IBC discovered more rare variants than PBC. In particular, at lower coverage, IBC was able to identify more high-quality singletons than PBC. We replicated this result twice, in a second dataset with higher coverage and in the original dataset using a different approach of the callers. We observed that the ability of PBC to detect singletons depended on sample size: With increasing sample size, PBC identified fewer singletons, and the quality of the identified singletons decreased. This advantage of IBC over PBC can be partly explained by the fact that in larger samples, singletons have an allele frequency < 0.001. Hence the prior for a site being a singleton is stronger in the individual-based caller and less evidence is required to call a singleton.While we found significant differences between caller-specific sites, IBC and PBC call sets had >99% concordance at the high-quality, non-missing heterozygous genotypes. Our validation experiment confirmed all selected IBC-specific singletons, with very few unconfirmed singletons in the PBC call set. Moreover, most PBC-specific singletons were in the IBC unfiltered (complete) call set. We observed the same trend of IBC generating an augmented set of singletons in high coverage sequencing data (>120x), where IBC almost doubled the number of high-quality singletons at sites with local low coverage (<10x).Furthermore, only IBC was capable of identifying polymorphisms with more than one non-reference allele, which led to discovery of an additional 1.9% of rare alleles in the sample. The excess of triallelic sites over the theoretical prediction of 1.4% is likely the result of heterogeneity of mutation rate due to sequence context and genomic environment. Existing associations between multiallelic variants and disease phenotypes ,39 suggeWhile IBC had strengths in identifying singletons, PBC generated better overall genotype quality. At common variants, PBC genotypes overcame low coverage at specific samples, achieving fewer missing genotypes and higher accuracy than IBC calls. The discordance between IBC and GWAS genotypes was low (0.82%), but more than two times higher than the GWAS discordant rates of the other call sets.LDC achieves even higher genotype accuracy than IBC and PBC by using haplotype information to impute missing genotypes from an existing single-marker call set. Imputation is typically more effective with longer haplotypes. In our study, we created long haplotypes by combining sequencing data with SNPs from previous GWAS genotyping chips. LDC with such flanking haplotypes achieved the highest accuracy and the least missing genotypes. As targeted sequencing studies might not have chip data to generate long haplotypes, we studied if LDC would still improve genotype accuracy with haplotypes based only on the sequencing data. Without flanking haplotypes, LDC had fewer missing data at the common variants over PBC, yet with a slightly higher mismatch rate. In particular, the additional heterozygote genotypes at common GWAS markers had a high mismatch rate of 2.43%, despite an overall mismatch rate of 0.39%. This suggested that using LDC on short haplotypes to impute missing genotypes created a relatively large number of imputation errors. Comparison between sequence replicates further demonstrated that LDC without flanking haplotypes had minimal benefit over PBC. As LDC imposes a considerable computational burden, it seems questionable whether this caller should be used when flanking haplotypes are not available.In summary, while IBC generated high quality unique singletons, as well as multiallelic variants, its resulting call set contained more missing genotypes and genotyping errors at common variants. PBC calls showed a substantial decrease in the number of missing genotypes and errors over IBC calls at these variants. Only when flanking haplotypes were available, LDC calls showed noticeable refinement of PBC genotypes, resulting in a call set with the highest concordance with GWAS genotypes and between experimental replicates. Therefore, IBC had strengths in calling extremely rare variants, while PBC combined with LDC had strengths in calling the more common variants.Based on these results, we recommend a two-fold calling strategy for targeted sequencing studies with medium to high coverage in a large sample. We recommend first to use a population-based single marker caller to generate accurate common variants and most of the rare variants. Second, we recommend using individual-based single marker caller to enrich the call sets with additional singletons. If flanking markers around targeted regions are available, despite the computation burden, we recommend using LD-aware caller to refine and impute population-based calls at high accuracy, resulting in a complete call set.The documentation, dataset, figures and tables supporting the results of this article are included in Additional files


Identifying quantitative trait nucleotides (QTNs), the genetic polymorphisms linked to phenotypic variation, has become a goal for many plant ecologists and evolutionary biologists in recent years. But what is the true value of this potentially expensive and labor intensive programme of research? In this review we discuss the ways by which the QTN programme can offer unique insight into the ecology and evolution of adaptation in plants. We cite recent noteworthy examples of QTN work and provide recommendations for refocusing efforts to identify and study the genes underlying ecologically important traits. why it is such a hallowed goal. Here we outline motivations for the QTN programme that offer general insight, regardless of whether QTNs are of large or small effect, and that aid our understanding of the mechanistic dynamics of adaptive evolution. We focus on five areas: (i) vertical integration of insight across different levels of biological organization, (ii) genetic parallelism and the role of pleiotropy in shaping evolutionary dynamics, (iii) understanding the forces maintaining genetic variation in populations, (iv) distinguishing between adaptation from standing variation and new mutation, and (v) the role of genomic architecture in facilitating adaptation. We argue that rather than abandoning the QTN programme, we should refocus our efforts on topics where molecular data will be the most effective for testing hypotheses about phenotypic evolution.The goal of identifying the genes or even nucleotides underlying quantitative and adaptive traits has been characterized as the ‘QTN programme’ and has recently come under severe criticism. Part of the reason for this criticism is that much of the QTN programme has asserted that finding the genes and nucleotides for adaptive and quantitative traits is a fundamental goal, without explaining  A dominant goal of contemporary evolutionary genetics has been to describe, at the molecular level, the loci responsible for adaptations and complex phenotypes. While this goal first gained steam in non-model systems with the widespread adoption of quantitative trait locus (QTL) mapping in the late 1990s, the recent explosion of sequencing technologies is likely to only heighten interest in this goal. New sequencing technologies, analytical approaches and statistical methods now offer the prospect of detecting the nucleotide variants responsible for variation in quantitative traits—an agenda why it is a worthwhile goal. An important consideration for any QTN-based research is: What are the benefits of knowing the specific genes, genetic regions or nucleotides responsible for adaptive phenotypes above and beyond the generic knowledge that there must be some unknown genes, genetic regions or nucleotides that contribute? Here we aim to provide answers to this question, and as such a rationale for the QTN programme.Many, but not all, papers in contemporary ecological and evolutionary genetics take it for granted that describing the molecular basis of adaptations is a worthy goal—or simply assert that it is a fundamental goal—without providing an explanation for We suggest that the challenge posed by Rockman is one of properly interpreting the generality and inference space of QTN results. A dissecting scope and scanning electron microscope both provide enhanced magnification for studying biological features: one is useful for studying external morphology and the other for cellular and sub-cellular structures. The inability to describe cellular morphology with a dissecting scope does not imply that we should discard it, but rather only use it for tasks where it is appropriate. Are the existing QTNs—which Rockman argues are primarily (unrepresentative) large-effect loci—so well characterized that we can stop studying them? Below, we describe a rationale for why we should characterize the genetics, ecology and evolutionary dynamics of QTNs and what we can learn from them. While the smallest-effect, infinitesimal QTN might be beyond our reach right now , we seek to clarify what one can learn from any QTN and why they are worth pursuing.We also endorse vertical integration ; (ii) understanding genetic parallelism and the role of pleiotropy in constraining adaptation; (iii) understanding the maintenance of genetic variation; (iv) understanding the role of standing genetic variation in adaptation; and (v) understanding the role of genomic architecture in adaptation.Numerous reasons to seek QTNs exist, and here we explore in detail those that are most compelling to us. Throughout, we focus on work in natural  systems, seeking to highlight plant examples whenever possible. A summary of the strengths and weaknesses of the various approaches can be found in Table Biologists of many stripes often repeat the famous Phlox drummondii and Phlox cuspidatum. In sympatric populations, an unusual dark red morph of P. drummondii occurs , and work over the decades supported the hypothesis that the phenotype evolved as a result of classical reinforcement (selection for reduced inter-species hybridization)  , 1985.In 2011 Hopkins and Rausher used a candidate gene approach to uncover QTN linked to flower colour in these species. They uncovered one mutation that is tightly associated with pigmentation (pink vs. red) and another for colour intensity (light vs. dark). They then returned to 39 natural populations, sequenced the QTN-containing loci and found nucleotide diversity patterns indicative of recent, strong natural selection at the red colour locus. No signature was found at the intensity locus and thus it seemed red flower colour had been verified as the target of selection . VerticaMimulus guttatus)  . HoweverBoechera stricta There are still very few studies of the molecular basis of parallel phenotypic changes across taxa in wild plants, with one striking exception: the study of evolutionary transitions in flower colour. It has long been noted that within genera, two types of flower colour changes, from pigmented flowers to white flowers and from blue to red flowers, have occurred independently in many angiosperm clades. The molecular genetic basis of these changes has now been determined in at least 10 separate taxa , thereby connecting theory, quantitative genetics and population genetics. For instance, consider the prediction that QTNs maintained by mutation–selection balance will be rare, partially recessive and deleterious. An empirically testable hypothesis is that individuals with extreme phenotypes carry a greater number of mutations affecting that trait, a hypothesis that is now supported by QTN studies of human disease  and yet successfully identify handfuls of loci that explain significant trait variance and overlap previously identified candidate genes (n = 517) identified QTNs for 14 traits that explained on average 36 % of trait variation  identified numerous common major-effect alleles underlying trait variation  populations, to identify the QTNs that are of the most evolutionary and ecological significance. The QTNs that we are able to identify are those that explain a significant portion of phenotypic variance for the trait. The contribution of a given biallelic locus to the additive genetic variance in a trait is given by 2e effect . ConsequThe QTN programme can also clarify the role of aspects of genomic architecture—chromosomal inversions, translocations, ‘supergenes’ in areas of suppressed or restricted recombination—in adaptation . In partEarly work on the genetics of heterostyly is illustrative e.g. . HeterosThere are numerous challenging evolutionary questions regarding the evolution of heterostylous pollination systems. How did these intricate, seemingly co-adapted systems evolve? Are initially unlinked loci captured by chromosomal inversions or translocations, or must the loci already be loosely linked? How do selection and recombination interact in creating and breaking up these supergene complexes? How much pleiotropy is required for diverse features of floral morphology to be inherited together? These questions have received intense investigation from the theoretical population genetics perspective . The conAnother area where the QTN (or QTL) programme is necessary for evaluating the role of genomic architecture in adaptation is in studies on the role of chromosomal inversions in local adaptation. Theoretical work by M. guttatus, which inhabits a broad geographic range in western North America. Inland ecotypes tend to be annual, while coastal ecotypes are perennial, and the two types also differ significantly in flowering time. The divergent flowering time between inland and coastal ecotypes generates strong natural selection when the opposite type is experimentally transplanted: late-flowering, perennial coastal plants fail to reproduce before droughts in inland habitats, while early-flowering, annual inland plants fail to capitalize on the extended growing season provided by the coastal climate. Initial observations (One of the best examples of a locally adaptive inversion is in the yellow monkey flower, rvations . The desThe continuing advance of next-generation sequencing technology and its subsequent drop in price has opened the flood gates for research into the genes underlying trait variation in nature. Concomitant with this rush, some of the deeper reasons why we are interested in this information sometimes get lost, and often the caveats and limitations of QTN work are brushed aside in the wake of excitement over the seemingly endless possibilities. We need to reconvene, refocus and then redouble our efforts to gain insight into the evolutionary process, using genetic data where it can be the most effective and allow greatest testing of evolutionary hypotheses. Refocusing and planning efforts in the QTN programme must take into careful consideration not only these broader contexts, but the experimental resolution of available methods, their inference space and generality , and wheThis work was supported by NSERC Canada and Genome Canada.All authors conceived the ideas, wrote the paper, edited manuscript drafts and responded to reviewer comments.None declared.