We propose a method in which GBS data can be conveniently analyzed without calling genotypes.F2 families are frequently used in breeding of outcrossing species, for instance to obtain trait measurements on plots. We propose to perform association studies by obtaining a matching “family genotype” from sequencing a pooled sample of the family, and to directly use allele frequencies computed from sequence read-counts for mapping. We show that, under additivity assumptions, there is a linear relationship between the family phenotype and family allele frequency, and that a regression of family phenotype on family allele frequency will estimate twice the allele substitution effect at a locus. However, medium-to-low sequencing depth causes underestimation of the true allele substitution effect. An expression for this underestimation is derived for the case that parents are diploid, such that F2 families have up to four dosages of every allele. Using simulation studies, estimation of the allele effect from F2-family pools was verified and it was shown that the underestimation of the allele effect is correctly described. The optimal design for an association study when sequencing budget would be fixed is obtained using large sample size and lower sequence depth, and using higher SNP density (resulting in higher LD with causative mutations) and lower sequencing depth. Therefore, association studies using genotyping by sequencing are optimal and use low sequencing depth per sample. The developed framework for association studies using allele frequencies from sequencing can be modified for other types of family pools and is also directly applicable for association studies in polyploids.The online version of this article (doi:10.1007/s00122-014-2300-4) contains supplementary material, which is available to authorized users. Lolium Perenne L.). This species is extensively used as forage and turf grass in Europe and is the most valuable forage and turf grass species in temperate climates 2, 2p(1 − p) and p2. This leads to the well-known expression E[g] = 2pa and genetic variance for individuals at the biallelic locus, assuming that parents are unrelated and non-inbred : First, we develop notation for a one-locus model in diploid individuals, which will apply to parents in a perennial ryegrass breeding program. For a biallelic locus, genotypes are denoted as A alleles (out of 4) in parents shows genotype segregation in the F2 family at a ¾ allele frequency for the A allele . We propose here to define an F2-family genotype as either the average allele dosage in the pool, which will be from 0 to 2 in steps of ½, or as the allele frequency within that family, in quarters. Table Next, we determine an “average family genotype” with the ultimate aim to associate family phenotypes with such average family genotypes. Figure p:Table p)4, 4p(1 − p)3, 6p2(1 − p)2, 4p3(1 − p) and p4.From expression , it can GF2 = ½ (GP1 + GP2). We assume that the family phenotype (the collective performance of the group) is the same as the average phenotype in a family . Thus, the genotypic value of an F2 pool can be expressed in the same way as for a biallelic genotype for an individual:From Table The genetic variance across families explained by the F2 pool genotypes works out to be: This is half of the variance explained by genotypes measured on an individual. This expression for the variance assumes no covariance between the parental genotypes, i.e. assumes that parents are not related, but the expression could be extended to include such a covariance to account for relationship between the parents.In general, the allele effect at a locus is estimated by regressing phenotypes on the genotype covariate. The expectation of this regression can be derived by expressing the phenotype as the sum of genotypic value and an environmental term:For the case of F2 pools, the genotypic value included in the phenotype is This shows that the use of F2 pool frequencies will obtain twice the estimate of allele effect. However, the standard error on the estimate using F2 pools will be larger than when individuals could have been used, which is a consequence of having only half the variance across pools compared to individuals. Expression  assumes ST total sequencing reads are obtained, with S1 reads showing one SNP allele and S2 reads showing the other SNP allele. This allows to directly obtain an estimate of the genotype in the form of an allele-frequency estimate, for instance arbitrarily for the first allele:When using GBS, genotypes will be subject to measurement error which leads to underestimation of the allele effect. For the use of GBS, we consider an approach where genotypes are not explicitly called, but allele frequencies obtained from sequencing are directly used for an association study. For a genotype obtained by GBS, consider that gF2 values in Table This genotype estimate will be subject to measurement error due to binomial sampling, which depends on population allele frequency. However, as we show below, the final expression for the bias in the allele effect estimate does not depend on population allele frequency. The average binomial sampling variance on these genotype estimates is determined by the underlying allele frequencies within the F2 families, which are the This allows to derive the expected estimate of allele effect from regressing F2 phenotypes on F2 pool allele frequencies, from the model:a2. Equation .The procedure was repeated 1,000 times; mean and standard deviation of the estimated regression coefficients were reported.In the general case, the allele effect at a locus can be estimated by regressing phenotypes on the allele frequency. This regression can be described by expressing phenotype as the sum of genotypic values of F2 frequency pool and environmental term. In this simulation study, the allele effect was set as 1 and assumed that there is no other genetic variation except environmental standard deviation. The explained variance from the one-locus model  at threeP values for both observed and true frequencies.We performed simulations to study the optimal design for association study in one-locus model. Here, we varied sample size, sequencing depth, allele frequency and environmental standard deviation. We repeated the same previous steps 1–2 and used regression of F2 phenotypes on F2 pool genotype expression  to obtaiTo see how the power changes by including sequencing and genotype calling errors in simulation studies. Here, we simulate the situation in which power to detect a single gene associated with a marker in the presence of 10 % sequencing errors per read and genotype calling at 5 % level of significance. We used variable sequencing depth across the families, assuming a Poisson distribution with an average depth 3, 7, 15 and 30× for the family sizes 4,000, 2,000, 1,000 and 500, respectively. This whole procedure was replicated 1,000 times and the number of significant cases counted.In a second simulation study, two loci in Linkage Disequilibrium (LD) were generated, where one locus was the causal but unobserved locus affecting the phenotype, and the second locus was an observed marker locus used for analysis. In this simulation, the level of LD between causal and marker locus was varied at three levels to show the impact of observing a linked locus instead of the causal locus. The SNP density, and thus average LD between SNPs and causal loci, can be modified in GBS by choosing different restriction enzymes. But also here, when the sequencing budget is fixed, choosing for higher marker density should go at the expense of either sample size or average sequencing depth.A and B with alleles A1, A2 and B1, B2, and haplotype frequencies of A1B1 = x11, A1B2 = x12, A2B1 = x21, A2B2 = x22. This gives allele frequencies for allele A1 as p1 = x11 + x12, allele A2 as p2 = x21 + x22, allele B1 as q1 = x11 + x21 and allele B2 as q2 = x12 + x22. LD was expressed here as the correlation (r) between genotypes, but in order to simulate haplotypes we needed the basic measure of LD as a covariance (D), where the relation between r and D is . Further, we also computed the same for true underlying frequencies in the families, which showed almost the same trend at different levels of sample size and allele frequencies. A full version of the simulation results can be seen in supplementary material Table 1.To study the optimal design for an association study, we performed simulation studies where we varied sample size, sequencing depth and allele frequencies in one locus model. The simulation results for this setup can be seen in Fig. Figure By adding more environmental variance, i.e., using an environmental standard deviation of 10, we obtained the expected result, .i.e., the power to detect a single gene decreased. Complete results of this simulation study are available in supplementary material Table 2.The results from Fig. Overall, this simulation results show that the power to detect a single gene associated with a marker is highest when using the larger sample size at the expense of sequencing depth, similarly in the situation of fixed sequencing capacity it would be more advantageous to use low sequencing depth and maximize the number of samples.Like many other sequence data sets, GBS datum is also contaminated with some noise. To realize this situation, we also performed simulations to see how the power to detect a single gene associated with a marker varies in the presence of some additional errors. Here, we employ  equal sequencing efforts to obtain power in the presence of three errors .Results Fig.  show thar) stronger to weak] versus sequencing depth . Like in the one-locus model power study, we used three levels of allele frequencies of 0.1, 0.3 and 0.5, with environmental standard deviations of 2 and 4, and a sample size 2,000. The results of this simulation can be seen in Table To optimize the SNP density given a fixed sequencing budget, we performed simulation studies in a two-locus model, where we consider the case that a marker is linked to a causative mutation. In this power study, we varied SNP density [LD  at different levels of LD. Results Fig.  indicateThese results revealed that within the constraints of a fixed sequencing budget, higher power can be obtained using higher SNP density, leading to higher LD with causal loci, i.e. by choosing a more frequently cutting restriction enzyme in the GBS technique Fig. .Fig. 6PoWe have developed theory and models to perform association studies in F2-family pools where the “pool genotype” is obtained as an allele-frequency estimate from GBS. The GBS technology is getting more and more interesting due to dropping prices for sequencing and lends itself flexibly to genotype either individuals or pools. Use of GBS on pools is especially attractive, because GBS produces allele-frequency estimates , and could in principle be assessed and included in statistical models when also the parents are genotyped. However, typically pollen barriers of other tall crops are placed between the F1 plots to minimize pollination from other fields. Also, other factors may change or distort the allele frequencies in the pools, for instance genetic drift, selection, and linkage to self-incompatibility loci. We argue that a strong point in the approach to sequence pools is that any such distortion is directly measured and will be taken into account. An approach to call genotypes would be less robust against such distortions, because it would force the frequencies to quarters, even when that may not be correct.Sequencing is not perfect, but current Next-Generation Sequencing technology allows to accurately compute an individual base-call error rate Below is the link to the electronic supplementary material.


KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P<0.0027) compared to the respective maternal alleles. Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles (P<0.01). Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity.The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies . Two SNPs replicated: the paternal rs2471083-C allele . These studies assume that the effect of genetic variants is the same regardless of whether they are inherited from the mother or the father. In our study, we have developed a new approach that allows us to investigate variants whose impact depends on their parental origin (parent-of-origin effects), in unrelated samples when the parental origin cannot be inferred. This is feasible because at genetic markers at which such effects occur there is increased variability of the trait among individuals who inherited different genetic codes from their mother and their father compared to individuals who inherited the same genetic code from both parents. We applied this methodology to discover genetic markers with parent-of-origin effects (POEs) on BMI. This resulted in six candidate markers showing strong POE association. We then attempted to replicate the POE effects of these markers in family studies . Two of our candidates showed significant association in the family studies, the paternal and maternal effects of these markers were in the opposite direction. Since GWASs predominantly include unrelated individuals, the parental origin of the alleles cannot be determined, hence genetic effects influenced by the parental origin of the alleles are typically not considered. Here we present a novel approach that is able to detect POEs using genome-wide genotype data of unrelated individuals. We chose BMI as our target trait, due to previous findings 2>0.1) were excluded from further investigations. Sex-specific association summary statistics were then meta-analysed. No sex specific difference in effects were observed, therefore all reported results are sex-combined.We applied our POE test, which compares the phenotypic variance of the heterozygous genotype group to the variance observed in the homozygous groups, to all SNPs genome-wide. The test, which is applicable to unrelated individuals, assumes that an increased variance in the heterozygous group arises because the heterozygous group consists of two subgroups  each with different means see . Differe−6 and three SNPs in imprinted regions with P<5×10−4 , one SNP fulfilled both criteria. See Our criteria to select SNPs to take forward to the replication stage resulted in the selection of six independent SNPs: four lead SNPs with POE P-value <5×10vs 0.963, POEP = 9.34×10−7; replication PAT P = 0.00264) and rs3091869 [T/C] (GWAS discovery BMI variance (het vs. hom): 1.046 vs 0.957, POEP = 4.7×10−6; replication PAT P = 0.00245) successfully replicated. In particular, we found that heterozygous individuals who carry the rs2471083-C allele paternally have 0.11 (SD unit) higher BMI on average than those carrying the C-allele maternally (P = 0.00264). Heterozygous carriers of the paternal rs3091869-C allele have 0.11 (SD unit) lower BMI on average than those carrying the maternal C-allele (P = 0.00245). KCNK9 and SLC2A10).The replication stage utilised five family-based studies see –S3 to teFTO gene (0.34%) By combining the effect difference estimates KCNK9. Mutations in this potassium channel gene cause Birk-Barel syndrome, a maternally transmitted syndrome of mental retardation, hypotonia, and unique dysmorphism, resulting from genomic-imprinting KCNK9 knock-out mice exhibited more fragmented sleep episodes SLC2A10, a glucose transporter involved in arterial morphogenesis. SNPs in low LD (in CEU 2r = 0.05) with rs3091869 have been shown to alter body fat distribution Notably, rs2471083 is located 105 kb upstream of the imprinted gene We tested these two confirmed SNPs in 705 trios with paediatric (extreme) obese offspring in which parental origin of the alleles was known in up to 255 individuals 2 = 0.998) alleles can be observed in the expression levels of their respective genes (KCNK9 or SLC2A10). To test this we carried out quantitative PCR (qPCR) experiments using lymphoblastoid cell lines (LCL) of the CEPH families. These cell lines have been used extensively to identify imprinted genes KCNK9 and 240 expression values from 24 (mat:pat = 16∶8) for SLC2A10 were available for analysis. We fitted a linear mixed model to test for association between expression levels  and allelic origin. The paternal T allele of rs2471083 was associated with lower KCNK9 expression levels , and the paternal T allele of rs3092611 was associated with higher SLC2A10 expression values . To ensure there was no systematic bias in our experiments giving rise to spurious POE associations we repeated the qPCR experiments for two housekeeping genes GAPDH and HRPT1. Both analyses gave non-significant POE P-values (P>0.3).We evaluated whether the parent of origin effect of the rs2471083-T and rs3092611-T  was (i) associated with the two respective SNPs  in 262 unrelated individuals from the TwinsUK cohort and (ii) associated with BMI in two independent cohorts: 79 BMI discordant (difference >0.5 SD) monozygotic twin pairs from the TwinsUK cohort and a sample of 412 unrelated individuals from the EPIC-Italy cohort. None of these analyses showed significant association  phenotypes and heterozygous genotypes confer relative risk/protection, depending on the parental origin. Polar overdominance, has been observed in humans for type2 diabetes Our novel approach revealed two SNPs, located near the genes KCNK9 and SLC2A10 in LCLs were also influenced in a parent-of-origin manner. The expression of these genes is highest in the brain  indicating a potential neuronal involvement. Expression levels of KCNK9 and SLC2A10 in living brain cells might have been more informative and robust, however, such information is not available. The applied qPCR method was optimised to ensure that the expression levels measured in LCLs were representative only of the target transcript and amplification efficiency was assessed to be sensitive enough to allow the detection of even small changes in gene expression. Interestingly, rs2471083 alleles, regardless of their parental origin, show marginally significant association (P = 0.03) with KCNK9 expression levels in the hippocampus (http://www.broadinstitute.org/gtex/). Our methylation analyses did not reveal any evidence that the POEs were driven by differences in inherited paternal and maternal methylation. Neither of our two SNPs tag common copy number variants (CNVs) , show regulatory activity, we queried RegulomeDB (http://regulome.stanford.edu). None of these SNPs were annotated to have more than minimal binding evidence (RegulomeDB score below 4).RT-PCR experiments revealed that gene expression levels of 2 mice by a two-component mixture distribution fitting of the heterozygous genotype group and further two components for the homozygous groups A previous study proposed to detect POE in inbred FFor our confirmed SNPs multiple lines of evidence show that the parent-of-origin effects are real, most convincingly clear replication in independent family data of parent-of-origin associations of the hit SNPs with both BMI and gene expression levels. Further, the GWAS discovery associations are very unlikely to be artefacts of the factors discussed above: (i) there is no evidence of overdominant, additive, recessive or dominant effects , hence the signals cannot be driven by gene-environment interactions or be an artefact of the scale on which the phenotype is measured (ii) no SNP within 500 kb has any detectable marginal effect on BMI thus the association cannot be driven by haplotype-specific marginal effects  results , furtherSome of the negative results of the other SNPs carried forward to the replication phase in the family data could be explained by lack of power. The power to replicate POE associations in family-based studies is dependent on the available number of heterozygous individuals  and thus increases with minor allele frequency (MAF). Therefore, it is unsurprising that the two SNPs which replicated had relatively high MAF (>27%).DLK1 (rs1802710) because this SNP showed polar overdominance for obesity in children P = 0.32) was visible in our study. Previously reported BMI-associated loci Linkage studies have identified four regions exerting POE on BMI  et al.Previous work comparing strength of associations of mother-offspring BMI with father-offspring BMI did not reveal intrauterine influence on obesity in children FTO, contributing substantially to the heritability of BMI. Our follow-up experiments demonstrated parent-of-origin specific gene expression modulation, but failed to link methylation activity of these loci to BMI values. Inevitably for newly discovered loci, further studies are warranted to determine how these variations functionally influence obesity in humans. The reliance of our approach on difference in phenotypic variance means that it cannot be extended to binary outcomes. Since there are other phenomena which can give rise to significant POE association, we recommend that top hits from our method are followed up in family studies, where parental origin of alleles can be inferred. In addition, our variance based POE test for GWAS data is naturally much less powerful than actually testing the mean values of the two heterozygous subgroups in trios. However, GWASs of unrelated individuals are several-fold more numerous and typically much larger than studies with a trio design, hence our methodology provides a great advance in parent-of-origin research by providing means to exploit all available GWAS data of unrelated individuals in order to identify parent-of-origin effects on continuous phenotypes.In summary, our findings indicate that POEs may play a role in adult obesity. The two identified SNPs have strong parent-of-origin effect on BMI, close to that of the All participating studies were approved by the respective institutional Ethics Committees. All study participants gave written consent including for genetic studies.y) is influenced by the alleles of a particular SNP and the effect depends on the parental origin of these alleles. In the presence of a parent-of-origin effect the heterozygous genotype group is split into two subgroups, depending on the parental origin of the A and B alleles. We assume that the phenotype of any individual in the A/A genotype group is modelled by If we denote the alleles of a bi-allelic SNP by “A” (reference) and “B”  the possible genotypes are A/A, A/B and B/B. Standard GWASs estimate the effect of the alternative allele dosage on the phenotype in question. In this work we are interested in associations in which a phenotype  separately for men and women. Standard variance tests (such as the F-test) are, however, sensitive to deviations from the Gaussian distribution.In the presence of a marginal association a phenotype transformation could alter the genotype group variances and introduce bias into the test Therefore, we used a robust version of the Brown-Forsythe test. Briefly, we first centred the phenotype values (at zero) in each genotype group to avoid inflated variance in the presence of marginal effects in the group of all homozygote individuals. We denote these centred phenotypes by Here http://www3.unil.ch/wpmu/sgg/quicktest/). The robustness of this test to deviations from normality has been studied in Finally, the POE P-value is assigned based on the test statistic −6 or (2) POE P-value <5×10−4 and within 500 kb of previously reported imprinted regions according to the Catalogue of Parent of Origin Effects database (http://igc.otago.ac.nz/home.html). At loci which met either criteria, a lead SNP (with the strongest POE association) was identified; other markers within 1 Mb or in LD (2r>0.1) were excluded from further investigations. In total 2,673,768 HapMap imputed and genotyped SNPs were analysed, of which 29,457 were considered as lying in imprinted regions, criterion (2). Using the procedure of Gao et al. We applied our POE test genome-wide to all HapMap imputed markers in a set of cohorts and results were combined across cohorts using fixed-effect inverse-variance weighting meta-analysis. SNPs were selected for replication if they met at least one of the following two criteria: (1) POE P-value <5×10We tested our findings in family-based studies using a simplified parental asymmetry test 2-corrected BMI residuals as phenotype. The equality of phenotypic means in the two groups was tested using a Student t-test. When significant differences were detected we also estimated the difference between paternal and maternal effect sizes, which is simply the difference between the phenotype averages in the paternal- and maternal- groups.As at the discovery phase, we used sex-, age- and ageIn order to estimate paternal If there is an additive marginal genetic effect influencing the trait certain transformations may inflate the phenotypic variance of the heterozygous group. Let In the following we show that for any If we assume the phenotype follows a Gaussian distribution Without loss of generality one can assume that Using the special form of It is easy to see that for a fixed As http://ccr.coriell.org/), and cultured as previously described 5′-ACCACAGAAGTCAGTAGACGAG-3′; reverse primer: 5′- GTGACATTGGGAGCATGGGA-3′) and a 146-bp region on chromosome 20 that spans the rs3092611 polymorphism . PCR amplification was performed in a 25 µl reaction using GoTaq polymerase (Promega). After purification of PCR products , sequencing reactions were carried out by using 1 µl of each of the 3.2 µM sequencing primers and 0.5 µl of BigDye Terminator v1.1 (Applied Biosystems). Following on-column purification (EdgeBio), sequencing products were run on an ABI-3130 XLS sequencer (Applied Biosystem). To synthesize cDNA, 2 µg of total RNA was retrotranscribed using the Superscript III reverse transcriptase (Invitrogen/Life Technologies) according to the manufacturer's instructions and a mix of random hexamers and oligo-dT that facilitate the detection of poorly expressed genes. To validate primers for qPCR, we first performed a series of test amplifications by using a defined range of primer concentrations (50–200 nM). We then loaded 10 µl of each qPCR product on 1% agarose gels to check the specificity of the amplification product, which should correspond to a 113-bp (KCNK9) and 148-bp (SLC2A10) fragment. To test KCNK9 and SLC2A10 PCR efficiency a standard curve made of five serial dilutions of brain and lung cDNA were used, respectively, since the two genes are known to be highly expressed in these organs. We obtained a standard curve slope of −3.49 for KCNK9 and of −3.37 for SLC2A10, corresponding to 94% and 98% PCR efficiency. For more details see Supplemental Data S1.Lymphoblastoid cell lines were derived from peripheral blood leukocytes of 95 members of 11 CEPH families KCNK9 and SLC2A10 transcripts. As negative controls, housekeeping genes  were also tested for parent-origin-effect to exclude the possibility that the observed difference in KCNK9 and SLC2A10 expression levels was due to the sample preparation process. Raw Ct values were inverse-normal quantile transformed and a linear mixed model was fitted (using the R function lmer) modelling the technical replicates as random effects and parental origin as a fixed effect.The output of the analysis was threshold cycles (Ct), i.e. the number of cycles at which the fluorescent signal of the reaction crosses a pre-determined threshold value. Since standard quantification methods  introduce a considerable amount of experimental noise for very lowly expressed genes, raw Ct values were used to perform an absolute quantification of Figure S1QQ-plot of the POE test P-values for SNPs in imprinted regions (left) and for the whole genome (right).(PNG)Click here for additional data file.Figure S22 = 0.998). The corresponding QQ-plots appear on the right hand side. Neighbouring methylation probes are strongly correlated therefore expected P-values were computed by estimating the effective number of tests For expected P-values we computed the effective number of tests Left hand side plots describe SNP-methylation associations (mQTLs), where each point is a methylation probe. X-axis represents their physical position and y-axis the −log10 association P-value with the target SNP, whose location is indicated by the dashed line. Note that rs3092611 was used as a proxy for rs3091869 (r(PNG)Click here for additional data file.Figure S32 = 0.998). The corresponding QQ-plots appear on the right hand side. Neighbouring methylation probes are strongly correlated therefore expected P-values were computed by estimating the effective number of tests For expected P-values we computed the effective number of tests Left hand side plots describe methylation associations with BMI in MZ twins. Each point is a methylation probe, X-axis represents their physical position and y-axis the −log10 association P-value with BMI. Location of the target SNP is indicated by the dashed line. Note that rs3092611 was used as a proxy for rs3091869 (r(PNG)Click here for additional data file.Figure S4POE association results for previously reported imprinted BMI linkage regions. Second dashed line corresponds to the Benjamini-Hochberg 5% FDR threshold.(PNG)Click here for additional data file.Figure S5−5 in Speliotes et al. POE association P-value QQ-plot for the top 58 independent SNPs with marginal BMI-association P-value <10(PNG)Click here for additional data file.Table S1Description of the BMI distribution in the participating cohorts.(XLSX)Click here for additional data file.Table S2Brief summary of the participating cohorts.(XLSX)Click here for additional data file.Table S3Information on the genotyping methods in the participating cohorts.(XLSX)Click here for additional data file.Table S4Replication of the 6 discovery SNPs in family studies. Extended version of (XLSX)Click here for additional data file.Table S5Parental asymmetry test results for our 6 candidate SNPs in adolescent population.(XLSX)Click here for additional data file.Table S6Analysis of the effect of gender- and age-correction and phenotype transformations on the POE association results.(XLSX)Click here for additional data file.Table S7Genotyping results and parental allele determination for members of the CEPH families.(XLSX)Click here for additional data file.Table S8Association results between methylation- and BMI- differences among MZ twins of the TwinsUK study.(XLSX)Click here for additional data file.Table S9POE association P-values for the 32 SNPs associated with BMI in the largest-to-date meta-analysis (XLSX)Click here for additional data file.Table S10−5 in the largest-to-date meta-analysis POE association P-values for the top 58 independent SNPs with BMI-association P-value <10(XLSX)Click here for additional data file.Data S1Supporting information contain text on the expression- and methylation analysis, the effective sample size derivation in mother-offspring vs trio studies, POE look-ups for SNPs with marginal BMI association, study descriptions and acknowledgements.(DOCX)Click here for additional data file.


For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta‐analysis has emerged as the method of choice to combine results from multiple studies. Many meta‐analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta‐analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two‐stage meta‐analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta‐analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype‐specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type‐I error rate, and our approach is more powerful than inverse variance weighted meta‐analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose‐associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates. In recent years, genome‐wide association studies (GWAS) have identified multiple common variants associated with disease and disease‐related traits. In a typical GWAS, association between a trait and genetic variants is tested one variant at a time, and variants with weak association routinely fail to be detected, especially in small cohorts. Therefore, meta‐analysis is often used by large consortia to increase statistical power  are inferred from Gj, the q×1 genotype vector of the jth subject, using statistical algorithms such as the expectation‐maximization (EM) algorithm  haplotype analysis method for unrelated samples. We incorporate random effects to account for family structure, making the approach applicable to family‐based cohorts, unrelated samples, or a mix of the two. We assume that a total of Xo= denote the overall design matrix of size n×(p+K), and define the overall variance matrix as Ω=σa2Φ+σe2I. The parameters α and βk  are estimated as (XoTΩ^−1Xo)−1XoTΩ^−1Y, where Ω^ is evaluated at the maximum likelihood estimates σa2^ and σe2^, which can be obtained using the lmekin function in R's coxme package  by increasing the number of samples used to obtain effect estimates via meta‐analysis, providing more precise estimates, as reflected in the smaller standard errors.While our approach cannot serve as the only tool for the discovery of associated variants and regions, it is a complementary tool to single‐variant and gene‐based tests. Mahajan et al. [2015] demonstrated the usefulness of haplotype analysis in their investigation of the effect of <0.1%) are recommended to be collapsed to ensure computation stability. Despite these limitations, our approach has the potential to shed some light on the relationship between traits and multiple associated SNVs in a region.Our approach has some limitations. The variants included in the haplotype analysis must be genotyped or imputed in all cohorts. In other words, all cohorts must include the same set of variants in their analysis. Moreover, when using imputed genotypes, best‐guess genotypes must be used because the approach does not currently handle genotypes in the form of dosage. The EM algorithm currently employed for inferring haplotypes works best for a moderate number of variants (< 15), and very rare haplotypes (frequency


Genome-wide association studies (GWAS) have successfully discovered hundreds of associations between genetic variants and complex traits. Most GWAS have focused on the identification of single variants. It has been shown that most of the variants that were discovered by GWAS could only partially explain disease heritability. The explanation for this missing heritability is generally believed to be gene-gene (GG) or gene-environment (GE) interactions and other structural variants. Generalized multifactor dimensionality reduction (GMDR) has been proven to be reasonably powerful in detecting GG and GE interactions; however, its performance has been found to decline when outlying quantitative traits are present. This paper proposes a robust GMDR estimation method (based on the L-estimator and M-estimator estimation methods) in an attempt to reduce the effects caused by outlying traits. A comparison of robust GMDR with the original MDR based on simulation studies showed the former method to outperform the latter. The performance of robust GMDR is illustrated through a real GWA example consisting of 8,577 samples from the Korean population using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) level as a phenotype. Robust GMDR identified the KCNH1 gene to have strong interaction effects with other genes on the function of insulin secretion. A genome-wide association (GWA) study has become a common approach for testing the association between a single nucleotide polymorphism (SNP) and a complex trait of interest . There hRitchie et al. proposed a Multifactor Dimensionality Reduction (MDR) method for detecting GG interaction . This meSince the initial introduction of the original MDR and GMDR methods, numerous extensions of MDR have been developed. Calle et al. proposed model-based MDR (MB-MDR) for the parametric extension of the MDR method . RecentlThese MDR methods have brought many remarkable successes in GG interaction analysis. However, the effects of outlying trait observations on the results of MDR methods have not been studied. Outlying trait observations are those traits that diverge from the global distribution of traits. Bennett et al., Most et al., and Yang et al. discussed the treatment of outliers in GWAS and proposed simple approaches, such as removing outlying observations and transforming traits, including log-transformation or inverse-normalization , 9, 10. The Korea Association Resource (KARE) is a project for gathering large-scale GWA analyses in the Gyeonggi Province of South Korea . Two comnth order are required, n SNPs are selected and a 3n contingency table is created. Each cell of the contingency table contains the number of cases and controls with the same genetic factor combinations. The second step calculates the ratio of the number of cases to the number of controls in each cell, and uses this ratio to determine whether a cell is “high risk” or “low risk”. If the ratio of case to control in a cell exceeds the threshold (which is commonly specified as 1), then those cells are labeled “high risk”; otherwise they are labeled “low risk”. This process enables each cell to be classified as either “high risk” or “low risk”. In the third step, the ability of selected SNP sets to perform these classifications based on the case / control ratio are evaluated through accuracy and balanced accuracy (BA) measures, where BA is a measure for the prediction ability of the model and is defined as follows:The evaluation of genetic effects using the MDR method requires several steps. The first step involves selecting genetic factors according to their effects on the phenotype. If GG interactions of the The larger the BA produced by the model, the more accurate the model’s prediction ability . These mThe problem of over-fitting is usually avoided by performing a 10-fold cross-validation. It is also possible to use the cross-validation consistency (CVC) as a measure for selecting SNP combinations. This selection is based on the number of times the same SNP combination is selected as best out of 10 cross-validated samples. Alternatively, the SNP combination with the largest CVC measure can also be selected as the best SNP combination. BA and CVC measures have been widely used in MDR analysisAlthough MDR has been used extensively in GG interaction analysis, it has several limitations. First, the MDR method is only capable of handling qualitative traits such as case and control data, because the method classifies each cell by using the case / control ratio. Second, it is not possible for MDR to adjust for other individual covariates such as environmental factors. Therefore, using MDR to analyze traits that are affected by environmental factors may produce biased results. In an attempt to overcome these limitations, Lou et al. proposed the Generalized Multifactor Dimensionality Reduction (GMDR) method , each styi denote the phenotype of individual i, which can be either quantitative or qualitative. Let E(yi) = μi. Consider the following generalized linear model (GLM):l(μi) is the link function, α is the intercept, and xi is a vector that expresses possible genotype combinations of interest. zi is a vector representing environmental factors, and β and γ are coefficient vectors.Let The first step when using GMDR is to fit the null model only with environmental factors under the null hypothesis, i.e., β = 0. The second step is to classify each SNP combination using the residuals from the null model. For each cell of the contingency table, the sum of the residuals is computed. Cells with a positive residual sum are classified as “high risk”; otherwise, they are classified as “low risk”. Subsequent GMDR cross-validation steps are performed similar to those of MDR.i has an independent normal distribution with equal variance.It is noteworthy that, in this GMDR procedure, the distinction between high and low classification is based on the sum of the residuals. Thus, a few outlying observations may have a large impact on the performance of GMDR, which provided the motivation for considering robust GMDR. Although GLM can handle both quantitative and qualitative traits, our efforts were focused on a quantitative trait with outlying observations. Thus, an identity link function was used with the supposition that yThe effect of outlying observations can be reduced by considering robust scores such as an L-estimator and an M-estimator type score. The L-estimator is a linear combination of order statistics that have to be measured. A well-known example of the L-estimator is least trimmed square (LTS) . LTS regk is the number of samples that is actually used in the regression and n is the total sample size. γi(β) is the ith residual for a fixed β that is ordered according to the absolute value of the residuals. Bickel and Lehmann considered trimmed expectations to be the only ones which are both robust and whose estimators have guaranteed high efficiency [ith sample should be used, and it is possible to adjust T to determine the extent to which outliers would have to be trimmed.Here, ficiency . LTS is The second robust GMDR uses an M-estimator type score, which is derived as the minima of the sums of the functions of the data. Least-squares estimators and many maximum-likelihood estimators are examples of M-estimators. The M-estimator was first derived for the purpose of introducing it into robust regression by Huber . Tukey sSolving the M-estimator requires differentiation of the ρ function, which usually requires selection of an appropriate shape for the biweight function.Using the biweight function, our M-estimator GMDR defines the threshold residual score as:In this formula, the threshold T is used to shrink all residuals exceeding T. As this function does not require differentiation; the biweight function is modified by simply reducing the weight caused by extreme loss.First, we simulated the phenotypes from the Normal distribution. In this case, samples were generated by using the following model.The effects of MAFs were checked by considering two MAF values: 0.1 and 0.3. Next, outlying samples were generated by using the following modelA series of designs were created by using different settings for the purpose of data simulation, and the settings are summarized in Second, we performed simulation studies for the skewed distribution. The samples were generated from the gamma distribution given in (10). The MAF of SNPs was set to 0.3. The outlying observations were generated by the shifted Gamma distribution in (11). As the Gamma distribution has a long tail to the right, outlying observations were only generated for the right-hand side of the distribution. A series of designs are summarized in For these Normal and Gamma distributions, we considered two sample sizes of 1,000 and 3,000 to evaluate the effect of sample size, and assumed the number of SNPs to be 100 and 1000. Ordinary GMDR, L-estimator-based GMDR, and M-estimator-based GMDR were compared in terms of their power of detecting a true SNP pair. We used Eq  for calcThe number of iterations was 1,000 when the number of SNPs is 100, while it was 100 when the number of SNPs is 1000. The best SNP pair was selected by using the average CVC through a 10-fold cross validation.SNP1 and SNP2, exists. To detect this pair, we simulated 500,000 null datasets by permuting the response variables. Because there were at most 100 SNPs in each dataset, the randomly selected rate of each SNP pair is at least 1/499,500. We counted how many times the pair of SNP1 and SNP2 was selected as the best model. We found all three methods selected the pair of SNP1 and SNP2 at most twice when the number of SNPs is 1000, which shows that the three methods have a very low FDER.We also computed the false detection rate (FDER) , which wThe effect of threshold values were investigated by using the robust GMDR methods with various threshold values, i.e., T = 1.5, 1.75, and 2. As shown in our simulation results in Figs Our proposed robust GMDR was applied to KARE data with a HOMA-IR phenotype. Before the GMDR analysis, a single SNP analysis was first performed to reduce the computational burden on GMDR. A single SNP analysis was performed for all SNPs.i  represents individuals; whereas, j  represents SNPs. All SNPs are ranked in the order of ascending p-values. SNPs are filtered out again by the linkage disequilibrium (LD) coefficient r2. Only one SNP was selected among the SNP pairs with r2 ≥ 0.8. Thus, the SNPs can be treated independently. After filtering SNPs via LD, we set the total number of selected SNPs as n / log(n) ≈ 585 by the sure independence screening (SIS) criterion for the purpose of performing the GMDR analysis [Noteworthy is that analysis .The same model that was used for selecting SNPs was used for the GMDR analysis. Sex, age, area, and BMI were used as environmental covariates. The regression models for GMDR are given as follows:T was assumed as 1.5, 1.75, or 2 in the L-estimator and M-estimator. The residual scores were then calculated based on this model. The threshold value of We performed the network analysis with the top 100 SNP pairs with the highest BAs. Each node represents an SNP and each edge connecting two nodes represents an SNP pair. We defined the hub genes as genes with at least four edges. For a selected SNP pair, we investigated the high /low risk prediction results of individual cells. When searching for hidden heritability, it is important to use an efficient method to establish the effects of GG interactions. To this effect, GMDR is a powerful method. However, using a simple example, it is demonstrated that the power of the GMDR method may decline in the presence of outlying observations. This problem was addressed by proposing the robust GMDR: L-estimator GMDR and M-estimator GMDR.yi, a parametric approach, such as maximum likelihood estimation, would be recommended.Simulation studies indicated that outlying observations caused the power of ordinary GMDR to decline critically. In contrast, the L-estimator and M-estimator GMDR methods were found to perform reasonably well even when outlying observations existed. When the samples are generated by assuming a Normal distribution, the L-estimator GMDR appears to outperform the other methods when the proportion of outlying observations is large. Otherwise, M-estimator GMDR and L-estimator GMDR perform similarly. If the samples are generated by assuming a Gamma distribution and there are outlying observations, L-estimator performs best. The results of the simulation study indicate that the power of GMDR with a correct distributional assumption is shown to be much larger than with an incorrect distributional assumption. Therefore, if we know the distribution of In our robust GMDR analysis, we used BA as an evaluation measure for the prediction ability. BA uses less information than continuous evaluation measures when used for quantitative traits. However, as the GMDR approach is based on transforming quantitative traits into binary high/low responses, incorporating the continuous scale information directly into the robust GMDR framework is complicated. On the other hand, QMDR does not use binary responses; instead, it uses t-statistics to measure for quantitative traits . Althougnd order genetic models with a normal distribution by using the following formula (15).Without loss of generality, we could consider all kinds of 2Then, we could consider the variance of y to be represented by the following equations.h2 as:With this equation, we could calculate the narrow sense heritability


The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01<MAF<0.05), and 83,856 rare variants (MAF<0.01). We describe a novel method for estimating the proportion of the rare variants that are sequencing errors using parent-progeny duos. We then used mixed model methodology to estimate the proportion of variance captured by these different classes of variants for fat, milk and protein yields, as well as for fertility. Common sequence variants captured 83%, 77%, 76% and 84% of the total genetic variance for fat, milk, and protein yields and fertility, respectively. This was between 2 and 5% more variance than that captured from 600k SNPs on a high density chip, although the difference was not significant. Rare variants captured 3%, 0%, 1% and 14% of the genetic variance for fat, milk and protein yields, and fertility respectively, whereas pedigree explained the remaining amount of genetic variance (none for fertility). The proportion of variation explained by rare variants is likely to be under-estimated due to reduced accuracies of imputation for this class of variants. Using common sequence variants slightly improved accuracy of genomic predictions for fat and milk yield, compared to high density SNP array genotypes. However, including rare variants from transcript regions did not increase the accuracy of genomic predictions. These results suggest that rare variants recover a small percentage of the missing heritability for complex traits, however very large reference sets will be required to exploit this to improve the accuracy of genomic predictions. Our results do suggest the contribution of rare variants to genetic variation may be greater for fitness traits. Genome-wide common variants from large scale single nucleotide polymorphism (SNP) genotyping have been used successfully in the last decade to map associated mutations in genome wide association studies (GWAS), and predict future phenotypes for complex traits with genomic prediction. However polymorphisms reaching genome wide significance in GWAS do not explain all of the heritability of complex traits . While One hypothesis for this “missing heritability” is limited linkage disequilibrium between markers and causative mutations, particularly causative mutations with low minor allele frequency  , 7], , . HoweverThe proportion of missing heritability from genotype or sequence data can be estimated in dairy cattle populations, as excellent pedigree recording over long periods of time is available (in our case tracing back to the 1940s), as well as wide spread recording of complex trait phenotypes, such as milk production and fertility. This information enables accurate estimation of genetic variance and heritability. We will use the term ‘missing heritability’ to describe the phenomenon that occurs in these populations when estimates of genetic variance from genomic relationships between individuals calculated from dense SNP chip markers are less than the additive genetic variance estimated from deep pedigree data in these populations. We define the proportion of missing heritability as a population parameter, Substantial whole genome sequence (WGS) data is now available for dairy cattle populations—the 1000 bull genomes project includes key ancestors, that have now been sequenced, from a range of dairy breeds, allowing imputation of variant genotypes, including rare variants, into reference populations genotyped with SNP arrays .WGS data has the advantage that rare alleles can actually be in the data set, so that the proportion of variance explained by the variants is not constrained by LD between markers and the causative mutations. However one of the challenges of using such data is to differentiate between true rare variants and sequencing errors. The primary errors are substitution errors, at rates of 0.5–2.5% per variant , 14]. A. A14]. AThe proportion of variance explained by rare variants in cattle also has important practical implications, as genomic prediction  is now rThe aim of this study was to test whether the hypothesis that sequence variants from coding and potentially regulatory regions, including rare variants can account for the missing heritability previously reported, and to estimate to what extent they can contribute to increasing the predictive ability of genomic selection. First, two strategies to filter ‘true’ rare variants from sequencing errors are proposed. Then, the genetic variance captured by sequence variants in three classes  was estimated for productive traits and fertility. Finally, the gain in accuracy of genomic prediction from using sequencing variants compared to high-density (HD) SNP genotypes was evaluated using mixed models with genomic relationship matrices.A data set of 3311 Holstein sires, genotyped with either the Bovine SNP50k SNP array or Bovine HD 777k SNP array were imputed to sequence variant genotypes using the Holstein and Jersey animals with WGS data from the 1000 Bull Genomes Project , using BWith very large numbers of base pairs sequenced, the number of sequencing errors are likely to be considerable. In an attempt to determine what proportion of the rare variants were real and what proportion were sequencing errors, we used Mendelian inheritance patterns in 38 Holstein cattle parent-offspring duos that were in the 1000 bull genomes data set. We investigated what proportion of variants that were heterozygous in the sires were heterozygous in the sons. For rare variants the expectation is close to 0.5, and the expectation increases as MAF increases as there is an increasing probability that the rare allele is also inherited from the dam. When MAF was very low, the proportion of variants where the sire was heterozygous and the son was also heterozygous was much lower than expected, We used lactation average production of fat (FY) and protein (PY), in kg, and milk (MY), in litres, as production traits, and the calving interval (days) as fertility trait. The genetic variance estimated for yield of fat, milk and protein and fertility from the pedigree, BovineHD array and the three classes of sequence variants , each fitted individually (e.g. separate models), are shown in P<0.005) compared with the model with only common sequence variants. The models including common variants and pedigree had higher likelihood than the models with common and rare variants. Further, incorporating pedigree, common and rare sequence variants together, resulted in the most likely model for FY (P<0.05). For fertility, rare variants significantly improved the likelihood compared to the model with only common variants (P<0.005), whereas the pedigree did not show any additional improvement. Uncommon variants did not significantly improve the log-likelihood of any model. Using only those rare variants validated from sire-son transmission did not increase the likelihood either (results not shown).Next we tested if the variances explained by the different classes of variant in transcript and potential regulatory regions were significantly different. Common sequence variants captured most of the genetic variance for all traits when pedigree, common, uncommon and rare variants were fitted jointly in the model for production traits , accountThese results imply that, although most of the genetic variance can be explained by common sequence variants in these regions, the pedigree still explains some additional genetic variability of the traits studied here, and rare variants explain more variation for fitness traits such as fertility.We evaluated the accuracy of genomic prediction that could be achieved with GBLUP , 21], w, w21], wAll models incorporating genomic information were more accurate than the model using only pedigree. Accuracy of genomic prediction using the BovineHD SNP genotypes or sequence variants (regardless of class of variant) were very similar, Rare variants did not increase the predictive ability in comparison to common sequence data regardless of whether they were validated through duos or not.In our dairy cattle population, relationships defined by SNPs and also by sequence variants in annotated regions captured less genetic variance than pedigree. One potential issue here is that the genetic variances estimated by markers and pedigree may not be strictly equivalent because using pedigree estimates the genetic variance among founders, whereas the genetic variance estimated from the SNP genotypes or sequences are estimates of the genetic variance in the modern (genotyped or sequenced) population. It should be noted nonetheless that we do in fact have many of the founders of the population either genotyped with BovineHD and in the sequence data set . FurtherWe overcome this limitation by fitting the pedigree and sequence variants from transcript and regulatory regions simultaneously, and tested the hypothesis that the additional variance component is significant by investigating the change in log likelihoods. We found that WGS explained only part of this missing heritability . The recThese figures (3% and 14%) are expected to be under-estimates because we only included sequence variants in gene coding and flanking regions within 2 kb of annotated regions. Causal variants for complex traits probably occur throughout the genome , 26, 27 Here, we demonstrated that rare variants are capturing variation different from that captured by common variants, and possibly include variation from rare deleterious effect. It is desirable to recognize rare mutations to drive them to higher frequency in populations under artificial selection if they are favorable or remove them if they are deleterious.This is the first time, to the best of our knowledge, that mixed models accounting simultaneously for pedigree, common and rare sequence variants have been used to estimate the heritability explained by each. Many studies in humans have reported that the genetic variance explained by SNPs is only 1/3 to ½ that estimated from pedigree using analyses that fitted only one variance component at a time. Yang et al  showed tWe would not expect the gap between genetic variance estimated from pedigree and WGS to be as great in Holstein cattle as in humans. Holsteins have a smaller recent effective population size (around 100) than human populations and hence fewer rare variants are expected in the Holstein population due to inbreeding, which flattens the allele frequency spectrum. The lower recent effective population size also leads to longer range LD and hence, in cattle, a rare causal variant may be predicted from a linear combination of SNPs spread over a large genomic region.y) was y = T(1-e-kN), where T was the total number of rare variants, N was the number of duos available and k depends on the allele frequencies of rare variants. The value of T was estimated at 2.19 million of total rare variants in the Holstein genome, and 29,930 rare variants in transcript and up or down-stream regions like those used here. However, we didn’t observe larger accuracies when using only these validated rare variants. It would be necessary to have around 150 duos to confirm most of the rare variants present in these regions in the Holstein population under study. In this study, only about 70% of total rare variants were validated. However, even a 50% increase in the number of true rare variants would only be expected to increase the modest variance ascribed to them by 50%. Further research is needed to distinguish rare variants from sequencing errors using an alternative method to the parent-offspring duos strategy, because rare variants validated in this way track familiar relationships as well as possible rare causal variants.The larger genetic variance captured by WGS data did not translate into higher accuracy of genomic prediction. That is, including the rare variants did not improve the predictive ability of the models. There are at least 4 possible reasons for this: 1) The increase in variance explained is small; 2) the small size of the training population limits the accuracy with which the effect of rare variants can be estimated; 3) long LD in Holsteins means that common SNPs can trace signals from a rare QTL, and 4) rare variant genotypes are poorly imputed. For 3), this is particularly the case here as our validation population was only one generation removed from the training or reference population. We would expect greater benefit from the sequence data if the prediction was tested in a validation population less closely related to the reference population , 31]. T. T31]. TAs part of the 1000 Bull Genomes Project Run 3, a total of 429 individuals of 15 breeds were re-sequenced with Illumina and SOLiD technology. In many breeds the sequenced ancestors were key ancestors that captured the most genetic variation, chosen by algorithms similar to Druet et al., .e.g. SNP overlapping with indel). In addition, we also filtered variants for proximity: when an indel was within 10 bp of another indel, the indel with a lower QUAL score was removed; when any variant was within 3 bp of another variant, the lower QUAL variant was removed. The filters were implemented by extending the python VCF file parser PyVCF (https://github.com/jamescasbon/PyVCF/).Raw re-sequence reads were filtered based on chastity score and trimmed based on quality scores. Fastq files were then aligned to the reference bovine genome assembly UMD3.1 using BWA . All BAMi.e. totalling 2,785,440).The Genome Analyzer Tool Kit  was usedA data set of 3311 Holstein bulls were genotyped either with the Illumina Bovine 54K SNP array or the Illumina Bovine HD SNP array (777K SNP). After quality control of the genotype data following Erbe et al., , 43,425 2 genotypic correlation >0.999) was removed. This pruning was carried out with PLINK software  wa wa49] waith component of the y vector is the phenotypic value of the ith animal used for prediction. Then, 1 was an n-row vector of ones (n being the number of records), and μ is the overall mean; g was the vector of additive genetic merits of the animals, assumed to be multivariate normal as G the genomic relationship matrix of all n animals calculated as in Yang et al., [Z is an (n×n)-incidence matrix, whose rows consist of unit vectors with one component being 1 and all the others zero, indicating the respective positions of animals in the g-vector of genetic values. Finally, D a diagonal matrix with weights on the residual variance according to the number of effective numbers of the sire at calculating his phenotype [G was calculated as in Yang et al., [In this model, the s g~N0,σgG, with Ghenotype . The covs g~N0,σgG, with GG matrix:Three different models per trait were used in which the only difference was the construction of the 1GBLUP-HD. constructed with 580,125 common SNPs (MAF>0.05) from the BovineHD Illumina Beadchip,2GBLUP-Seq. constructed with the 675,062 common variants from pruning the sequence data for LD>0.999 and MAF<0.05.3G matrix was assumed to be the numerator relationship matrix made up from pedigree relationships. The pedigree contained 8978 animals tracked up to 7 equivalent generation in the Holstein data set. This is usually named the A matrix and the model is equivalent to the commonly known as a BLUP model in animal breeding.BLUP-PED. the Variance components were estimated via restricted maximum likelihood using ASReml 3 .Since the common-variants complex-traits theory is well-established, we tested if the inclusion of pedigree and rare variants further improved the statistical fitting through log likelihood ratio test asWe assumed a significance threshold of P<0.005 for the The predictive ability of different sorts of genomic information was assessed using cross validation. The data set was split into reference and validation sets. The former included 2832 Holstein sires, whereas the latter was created with the 465 youngest animals in the data set.The underlying statistical model to predict data in the validation set included a genomic and a polygenic effect  as,y=wμg was the genomic component assumed to be distributed as G (described in models 1–2 above). Then, Z being a corresponding incidence matrix. All other components were as described previously. Model 3 above with corresponding reference and validation sets was considered as the benchmark model.In this model, Additionally, three joint analyses of common and uncommon or rare variants from sequence data were implemented using the following model:4y = wμ+Zseqgseq+Zuncommonguncommon+Zu+e5y = wμ+Zseqgseq+Zrv−setgrv−set+Zu+e6y = wμ+Zseqgseq+Zrv−validatedgrv−validated+Zu+egseq, guncommon, grv-set and grv-validated were the vector of additive genetic merits for the common, uncommon, rare variants from sequence data, and rare variants from duos, respectively, assumed to be multivariate normal as G. being the corresponding genomic relationship matrix for each set of variants constructed as described above, and where Predictive ability was assessed using the metrics of Pearson correlation, slope of the linear regression and predicted mean squared error between predicted and observed daughter yield deviations in the validation set.S1 Fig(TIF)Click here for additional data file.S2 FigDensity distribution for imputation accuracy is shown in the plot for each group of MAF.(TIFF)Click here for additional data file.S3 Fig(***P<0.001). On the lower diagonal, the bivariate scatterplots, with a fitted line.The histograms and corresponding density functions are plotted in the diagonal. On the upper diagonal the value of the phenotypic correlation plus the result of a correlation test (TIFF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.


Genotyping chips for rare and low-frequent variants have recently gained popularity with the introduction of exome chips, but the utility of these chips remains unclear. These chips were designed using exome sequencing data from mainly American-European individuals, enriched for a narrow set of common diseases. In addition, it is well-known that the statistical power of detecting associations with rare and low-frequent variants is much lower compared to studies exclusively involving common variants. We developed a simulation program adaptable to any exome chip design to empirically evaluate the power of the exome chips. We implemented the main properties of the Illumina HumanExome BeadChip array. The simulated data sets were used to assess the power of exome chip based studies for varying effect sizes and causal variant scenarios. We applied two widely-used statistical approaches for rare and low-frequency variants, which collapse the variants into genetic regions or genes. Under optimal conditions, we found that a sample size between 20,000 to 30,000 individuals were needed in order to detect modest effect sizes (0.5% < PAR > 1%) with 80% power. For small effect sizes (PAR <0.5%), 60,000–100,000 individuals were needed in the presence of non-causal variants. In conclusion, we found that at least tens of thousands of individuals are necessary to detect modest effects under optimal conditions. In addition, when using rare variant chips on cohorts or diseases they were not originally designed for, the identification of associated variants or genes will be even more challenging. Since the introduction of Genome Wide Association Studies (GWAS), a large number of common single nucleotide variants (SNVs) have successfully been associated to many complex diseases . Howeveret.al. [et.al. [et.al. identified a significant cardio-protective variant which was common in an isolated population, however this variant is assumed to be rare in outbred European populations [The importance of considering allelic variants in coding regions, as well as budgetary and practical restrictions for whole exome sequencing in large studies, motivated the construction of the “exome chips” , 14. A net.al.  found onet.al. ), two ad [et.al.  identifi [et.al. . With a ulations .The design of the exome chip was based on pooled exome sequencing data of 16 contributing studies , which cSince the power to detect an association between a single SNV and a phenotypic trait decreases with decreasing minor allelic frequency, there has been a need for new statistical tools for analysing low-frequent and rare variants. These variants often occur at different locations throughout the considered genes. Therefore, methods for this type of variants have been developed, which aim to collapse variants along a meaningful biological unit  into one test statistic. This includes methods which contrast the mean number of observed variants between cases and controls, such as Weighted Sum Statistic (WSS)  and ReplWith respect to the increasing use of exome genotyping chips, we aimed to investigate the sample size requirements for association studies using these chips. The power for different statistical approaches for analysing low-frequent and rare variants has been investigated and compared to each other by others , 40, 41.We developed a simulation pipeline, which relies on simulations based on all variants of the underlying chip design, thereby capturing the entire allele frequency spectrum and underlying dependencies between the variants. In this paper, we mimicked the structure of the Illumina HumanExome BeadChip array, but the available pipeline can also be applied to any other (future) chip designs.et.al.[As a starting point for the simulations in this paper, we simulated a data pool of genotypes for 200,000 unrelated individuals using the approach described in Basu et.al., with soet.al., we repret.al..et.al. [To construct case-control phenotypes, we used the same approach as Madsen et.al. , 37 fixaWe considered two different scenarios for the structure of the simulated causal genes. In the first scenario, 100% of the SNVs in each analysed gene were causally linked to the phenotype. In the second scenario, the same genes were analysed, but only 50% of the SNVs within each gene where causally linked to the phenotype, thus decreasing the signal to noise ratio.Beta kernel). The WSS test is an adaptive sum test, for each unit, it calculates a weighted sum for all individuals, and then permutes the ranking of those sums, if the cases are consistently ranked on top, this will correspond to a low p-value. The weight for each variant is determined by the MAF and the case-control ratio. The two statistical methods used here were chosen as representatives for two common classes of methods for rare variant analysis; variance contrasting tests and sum tests. In both methods, all the genes are tested independent of each other. The weights applied to all variants have similar structure for both SKAT and WSS. In both methods, the weighing is such that common alleles will receive a low weight, while empirically rare variants will have a high weight.To assess the sample size required to obtain sufficient power, we applied two widely used statistical methods for rare variants: SKAT  and WSS We investigated the power performance by drawing sample sizes of 10,000 (10k), 20k, 30k, 60k, and 100k individuals from the genotype pool described above. The simulated case-control ratio was 1:1. To assess the power under the different scenarios, we randomly selected a set of 100 genes. The distribution of allelic frequencies of this subset was similar to the corresponding allelic frequency distribution of all SNVs on the chip . The meaWe provide two types of simulations without adding an effect on the simulated genotypes . First, we aim to characterize the implemented statistical methods with respect to their ability to detect false positive findings. To achieve that, we used the 50 simulated datasets for 60k samples including 100 genes described above and assigned case-control status randomly. For each simulated dataset, we evaluate the percentage of false positives and present the mean percentage across all simulated datasets. In this simulation, we choose a 5% threshold for the p-values of each gene. A genome-wide threshold could have been simulated here as well, but would require a much larger number of null genes and thus dramatically increase the computational burden. Second, we wanted to show the genome-wide performance of the tests with no underlying effect present for the underlying chip structure considered in this paper. Thus, we simulated 10 datasets including all genes  for two different numbers of individuals , assigning the case-control status arbitrarily.The simulations and power assessment where done using the computer program R 3.2.1 , with thThe simulation program can be received from the authors by request.Both of the statistical methods keep the Type-I error level, with SKAT being slightly more conservative than WSS. The corresponding estimated mean percentages of false positives were 0.0465 (SKAT) and 0.0503 (WSS) when applying the 5% Type-I error threshold see . In ordeTo assess the power of the underlying chip under the non-null distribution, we simulated an increasing effect size (PAR) for different sample sizes based on two statistical approaches (SKAT and WSS). We first investigated a scenario where all variants within a gene were causal. In this scenario, both SKAT and WSS reached a power of 80% with a PAR less than 1.4% (SKAT) and 2.4% (WSS) per SNV, and SKAT converged to maximum power for 1.5% for sample sizes above 10k . SKAT anWhen only 50% of the SNVs within each unit were causal, a much slower convergence was observed for both methods . For 10kIn order to assess how many individuals would be needed for a given power, we plotted power as a function of sample size . Under tIn order to investigate the relationship and distribution of the effect sizes GRR and PAR of the causal variants, we plotted a histogram of the GRR for different PAR . For a fIn this work we addressed the utility of genotyping chips for rare variants under optimal conditions, illustrated by simulating the content of the Illumina HumanExome BeadChip array under different scenarios. Given a homogenous population (as was used for the design of the chip), we found that to detect a true association with 80% power, for a PAR around 1.5% on each causal variant in the presence of noise, a sample size of at least 20k individuals were needed under optimal conditions. Thus, the chip performance was acceptable for large (PAR > 1%) effects even in relatively small cohorts (10-20k). For small effect sizes (PAR < 0.5%) in the presence of noise, a balanced case-control study with a total sample size of 30k to 50k individuals would be required.et.al. the association seemed to be driven by two alleles, which represent 10% of the variants in this specific gene on the exome chip [Our assumption of sample homogeneity of conferred risk for the SNVs in this analysis is not likely to be met in most association studies. This is mainly due to population specific rare variants. We also assumed that all rare coding causal variants were deleterious. Although some variants may be protective, the majority of rare coding alterations are believed to be either harmful or have low phenotypic effect , thus maome chip . One of ome chip . 40% of ome chip .An important issue is the enrichment of SNVs associated with certain diseases in the design of the exome chip. Many common complex disease groups were not represented in the cohorts used to design the chip, leaving the possibility that rare variants which may be strong risk factors for these diseases were not included on the chip. The rest of the SNVs included may be neutral or have very small phenotypic effects. This will most likely result in sub-optimal performance in studies of diseases that were not considered when designing the exome chips.Although the difference in mean power between SKAT and WSS analyses was not substantial, SKAT consistently outperformed WSS, which is in line with previous studies . FurtherThe simulation pipeline developed here could be adapted to different chip designs. This program is only dependent on allele frequencies and positions, since the linkage disequilibrium (LD) between variants was modelled with a distance function. Furthermore, the algorithm is flexible in its implementation, so it can be applied to assess the performance of any other chip design, under different scenarios. In some simulation studies for assessment of rare variant methods, a genome wide p-value cut-off was not used , 41 and There is no standard algorithm for simulating effects on genetic variants, this has led to a situation where the reported results can vary depending on the implemented methods and assumptions. Two popular approaches for emulating effect sizes are Odds Ratio (OR) modelling and Risk Ratio (RR) modelling. Although these approaches are quite different, when the number of observed genotypes is small, both the OR and the RR will be approximately the same.When the GRR was empirically estimated from the simulated data set, they were consistently lower than expected from the equation used to generate them  Eq 1. ThThe collapsing methods tests each gene (unit) and the power presented in Figs While recent studies using exome chips have identified associations between low-frequent or rare variants and disease, the identified variants have not yet contributed substantially to explaining “The missing heritability”. Few of the studies have reported variants with minor allele frequency below 0.5% to be associated with disease , 26–28. In our study we have only considered “perfectly called” variants, i.e. we have not introduced any errors in the genotype calling algorithms. This may be an important issue for rare and low-frequency genotyping chips, where calling the variants has proved to be challenging .In conclusion, we found that a very large sample size, in the order of tens of thousands is needed to detect modest effects under optimal conditions. For effect sizes less than 0.2% PAR, around 100,000 individuals should be studied to have enough power to reach genome wide significant results.S1 Table(DOCX)Click here for additional data file.S1 Fig(A) Histogram of the distribution of the allele frequencies on the exome chip, plotted on log 10 scale. The histogram is split into three bins, depending on their allele frequency. The lines indicated the allele frequencies of the causal alleles in each scenario. The blue line represents the allele distribution of the SNVs selected in the scenario where 100 of the SNVs within each causal gene were causal. The orange line represents the allele distribution of the causal SNVs, where 50% of the SNVs within the casual genes are causal. (B) Histogram of Genotype Relative Risk (GRR) for all causal variants in the 100% scenario, for two different Population Attributable Risks (PAR). This is the GRR used to construct the phenotypes for those two PARs.(TIF)Click here for additional data file.S1 Algorithm(DOCX)Click here for additional data file.S1 Source Code(TAR.GZ)Click here for additional data file.


The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.ANGPTL4 and High Density Lipoproteins  which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with ‘nonsynonymous’ and ‘loss of function’ consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease. Genome wide association studies (GWAS) have had a profound influence on the number of disease associated common variants detected across the genome, although recently a greater emphasis has been placed on the impact that rarer variants can have on disease, 2. The Rare variant association studies have had varying degrees of success in recent years, 6. The Filtering in this manner can have limitations for several reasons. Firstly, even if a variant is annotated correctly, this does not necessarily mean that it will have a deleterious effect . Secondly, it is also possible that other types of variants within the coding region of the gene (i.e. silent mutations) can potentially have a deleterious effect but may get filtered out. Lastly, filtering variants according to their consequence is limited to coding regions, therefore making annotations for filtering non-coding regions (i.e. intronic regions of a gene or intergenic regions of the genome) an attractive commodity, particularly with the current influx of whole genome sequence (WGS) data.The Combined Annotation-Dependent Depletion (CADD) method ohttp://www.uk10k.org), we have undertaken low frequency and rare variant analyses to evaluate whether filtering variants according to CADD scaled C-Scores identifies association signals not detected when filtering according to variant consequence. Our hypothesis was that incorporating non-coding bioinformatics annotations based on predicted variant functionality would be a valuable asset for future studies which conduct these types of approaches.Using WGS data from the UK10K project . VVariants responsible for a ‘loss of function’ according to VEP . VCFtools was used again to condense these regions down to just those variants.http://cadd.gs.washington.edu/info).Variants with a CADD C-Score ≥15 (i.e. the 5% most damaging variants predicted across the genome). This is a suggested cutoff by the developers of CADD to identify potentially pathogenic variants as it is the median value for all possible canonical splice site changes and nonsynonymous variants  cutoffs of MAF≤5% and MAF≤1%. Importantly, filtering by variant consequence was confined to coding regions, whereas filtering by CADD definitions also allowed the inclusion of potentially deleterious non-coding variants which reside in intronic regions of genes. The results of this analysis, as well as those in subsequent analyses of this study, were not adjusted for multiple comparisons. This was because we were interested in the comparison of filtering approaches in terms of identifying association signals, rather than evaluating whether these signals are real, which is important for analyses regardless of filtering method. Furthermore, the same number of analyses were conducted for each filtering method as this would have otherwise incorporated bias into the study.We collapsed variants together across candidate genes and analysed them using SKAT with theLIPG and HDL (P = 0.02) as well as between PVR and LDL (P = 0.02) after filtering to only include nonsynonymous variants. For the loss of function variant analyses, CD300LG, BCAM and ANGPTL4 were associated with HDL, LDL and TG respectively . Using CADD to filter variants provided evidence of association between three of the previously mentioned genes and traits, as well as between ANGPTL and HDL (P = 0.02). The majority of these effects appeared to be driven by rare variants, as evidence of association was observed after applying a MAF cutoff of 1%. The only exception to this was the association between LIPG and HDL after filtering to include nonsynonymous variants, which did not provide strong evidence of association using this cutoff (P = 0.07). Tables Using a MAF cutoff of 5%, we observed evidence of association between L P = 0.0 as well We also identified variants with a CADD C-Score ≥15 across the genome and aggregated them together across all gene regions according to UCSC definitions (reference genome hg19). Variants were then collapsed together and analysed with 7 cardiovascular traits  using SKAT after applying a MAF cutoff of 5%. We repeated this process in a second set of analyses using a MAF cutoff of 1% to investigate rarer variation. This process was repeated except filtering to include variants which led to a ‘nonsynonymous’ or ‘loss-of-function’ consequence (i.e. regardless of CADD C-Score) according to dbSNP annotations (build 137) in two other separate sets of analyses. Only regions which had at least 2 variants remaining after filtering were eligible for analyses, as a single remaining variant would offer no added value when analysed using SKAT compared to using a single variant test.Gene-based p-values from the CADD filtered analyses were matched with p-values from the ‘nonsynonymous’ filtered analysis and the results were–log10 transformed and plotted for each trait and MAF cutoff. This meant that only genes which had at least 2 variants within their region after filtering in both sets of analyses (i.e. ≥ 2 ‘nonsynonymous’ & ≥ 2 variants with a CADD C-Score ≥15) were plotted on these graphs. Due to the frequency of points on these plots, overall trends would have been very challenging to identify using scatter plots. We therefore used hexbin plots for this task , which aGene-based p-values from all CADD filtered analyses were also matched with those observed from the loss-of-function analyses. However, simple scatter plots were sufficient for this data as there were far fewer than in the comparison with nonsynonymous filtering. It is also worth clarifying that these loss-of-function variants may also have been included in the nonsynonymous analysis, as variants can be classed as both . As before, there was no overall trend to suggest that filtering using either approach provided stronger evidence of association with cardiovascular traits. Again there were results which represented a lack of concordance between filtering approaches, suggesting that evidence of association with traits would only be observed when filtering with one method but not the other. Scatter plots for these analyses can be found in The previous analysis was repeated except using two alternative bioinformatics tools rather than CADD. These were FATHMM-MKL  and DANNFATHMM-MKL and DANN analyses were conducted separately. Analyses were undertaken as before, collapsing variants by gene regions and analysing them with each cardiovascular trait in turn after applying a MAF cutoff of 5% and 1%. Gene regions were analysed twice using the SKAT-O and MiST tests and only regions which had at least 2 variants after filtering were eligible for analysis. Hexbin and scatter plots were again used to display results compared to the ‘nonsynonymous’ results and the ‘loss-of-function’ results respectively. The FATHMM-MKL plots suggested similar inferences to the CADD based analyses, whereas certain DANN analyses using SKAT-O (particularly for TG) appeared to have more concordance with results using nonsynonymous annotations. Plots from all these analyses can be found in ANGPTL4 and HDL (P = 0.02). We have also undertaken extensive genome wide analyses to determine how filtering variants using bioinformatics annotations compares to filtering by variant consequence within gene regions, when conducting low frequency and rare variant collapsing analyses. These results were plotted as hexbin and scatter plots, which suggested the different approaches to filtering variants yield different sets of associated loci. The consequence of this meant that certain association signals were only detected when filtering by annotations from the bioinformatics tools and not according to variant consequence, and vice versa. This suggests that future studies should benefit from this alternative approach to identify association signals potentially overlooked using conventional methods, due to the inclusion of non-coding regions which may harbour potentially deleterious variants.We have conducted a candidate gene study to evaluate whether incorporating variant annotations using a non-coding bioinformatics tool can aid rare variant analyses over filtering by variant consequence. Filtering according to CADD provided evidence of association between genes and lipids similar to filtering by variant type, as well as moderate association between The reason why this novel approach to filtering variants may identify association signals not detected when filtering by variant consequence could be influenced by several factors. Firstly, this could be due to the inclusion of predicted deleterious synonymous variants which therefore improves the statistical power of analyses. Furthermore, using these annotations may lead to removing predicted neutral nonsynonymous variants  which reduces the amount of statistical noise incorporated into analyses. The ability to adjust the inclusion threshold for variants when using prediction algorithm annotations is also advantageous to studies. In this study, we have used a CADD C-Score of ≥ 15 as our inclusion threshold, which is suggested by the authors of CADD to identify potentially pathogenic variants as it is the median value for all possible canonical splice site changes and nonsynonymous variants . HoweverIn this study we have predominantly used the SKAT-O and MiST tests as they have been reported to be amongst the most consistently powerful collapsing methods according to evaluations of gene-based tests . HoweverDespite the advances in low coverage sequencing techniques over the last few years, the majority of rare variant analyses applied to these data have been underwhelming, 31. RecThe fundamental question when filtering variants according to predicted function concerns the reliability of the bioinformatics resource used. Clearly, the success of any analysis which relies on these resources hinges on their accuracy. Further advances in the accuracy of variant prediction will have a beneficial impact on association studies which incorporate annotations as we have illustrated in this study. Moreover, filtering variants based on prediction algorithms can be undertaken for analyses conducted in non-coding regions of the genome, which would not be possible when filtering according to variant consequence. The majority of GWAS hits discovered to date fall within non-coding regions, 38, altFiltering low frequency and rare variants using knowledge based on molecular function and pathogenicity should help identify strong evidence of association not detected using conventional filtering approaches. Follow up analyses which evaluate these signals will be beneficial in the identification of potential mechanisms and causal variants for complex disease.The UK10K consortium has two main project arms. In this study, we have used data from the cohorts’ arm which was designed to investigate the contribution of genome wide genetic variation to a range of quantitative traits. This arm contains individuals from two intensively studied cohorts of European ancestry, ALSPAC  and TwinsUK:http://www.bristol.ac.uk/alspac).ALSPAC is a population-based cohort study investigating genetic and environmental factors that affect the health and development of children. The study methods are described in detail elsewhere, 40  Committee, South East London, REC 2. Written informed consent was obtained from parents for all measurements made.http://www.twinsuk.ac.uk/).The TwinsUK registry is a cohort of volunteer adult twins from all over the United Kingdom. InitialInformed consent was obtained from participants before they entered the study and ethical approval was granted by the National Research Ethics Service (NRES) Committee, Westminster, London.DNA Samples from 4,030 UK10K study participants  were subjected to low coverage (6-8x average read depth) whole-genome sequencing (WGS). Sequencing was performed at both the Wellcome Trust Sanger Institute (WTSI) and the Beijing Genomics Institute (BGI). DNA (1–3μg) was sheared to 100–1000 bp using a Covaris E210 or LE220 . Sheared DNA was size subjected to Illumina paired-end DNA library preparation. Following size selection (300–500 bp insert size), DNA libraries were sequenced using the Illumina HiSeq platform as paired-end 100 base reads according to manufacturer’s protocol.Data that passed quality control (QC) was aligned to the GRCh37 human reference used in phase 1 of the 1000 Genomes Project. Reads were aligned using BWA (v0.5.9-r16). Of the Missing and low-confidence genotypes in the filtered VCFs were refined out using the imputation procedure in BEAGLE 4 with defThe final sample size for association analyses comprised of 3,621 individuals .Height was measured to the nearest 0.1cm using a Harpenden stadiometer  and weight was measured to the nearest 0.1kg using Tanita electronic scales. Body Mass Index (BMI) was calculated as (weight (kg))/(height (m))2. Blood Pressure was measured with a Dinamap 9301 vital monitor completed by trained staff using the appropriate cuff size. Two readings of both systolic and diastolic blood pressure (SBP & DBP respectively) were taken when the study participants were at rest and the mean of each were used as a measurement in our analysis. Both these measurements were taken from the age 9 clinic .Non-fasting blood samples were also taken from participants who attended the age 9 clinic . Plasma lipid concentrations , triglycerides (TG) and high density lipoprotein cholesterol (HDL)) were measured by modification of the standard Lipid Research Clinics Protocol with enzymatic reagents for lipid determination. Low den2. Brachial blood pressure was measured using an automated cuff sphygmomanometer  Ltd, Henfield, UK). SBP and DBP were measured three times, two of which were highly correlated (0.90 for SBP and 0.92 for DBP) and averaged to get our final phenotype measurements.Height was measured to the nearest 0.5cm using a wall-mounted stadiometer and weight (light clothing only) was measured to the nearest 0.1kg using digital scales. Body Mass Index (BMI) was calculated as (weight (kg))/(height (m))Blood samples were taken after at least 6 hours of overnight fasting. The samples were immediately inverted three times and left to rest for 40 minutes at 4°C to obtain complete coagulation. The samples were then centrifuged for 10 min at 2000g and serum was removed. Four aliquots of 1.5 ml were placed into skirted micro centrifuge tubes and then stored in a -45°C freezer until sampling. A colorANGPTL4, BCAM, CBLC, CD300LG, HNF4A, LDLR, LIPC, LIPG, LPL, PCSK9 and PVR. Analyses were repeated after applying each variant filtering method, as well as applying two different minor allele frequency cutoffs (MAF≤5%) and (MAF≤1%).We used CADD to obtaiWe also identified variants with a CADD C-Score ≥15 across the genome and aggregated them together across all gene regions according to UCSC definitions (reference genome hg19). Variants were then collapsed together and analysed with 7 cardiovascular traits  using SKAT after applying a MAF cutoff of 5%. We repeated this process in a second set of analyses using a MAF cutoff of 1% to investigate rarer variation.Gene-based p-values from the CADD analyses were matched with p-values from the ‘nonsynonymous’ filtered analysis and the results were–log10 transformed and plotted using hexbin plots for each trait and MAF cutoff. This meant that only genes which had at least 2 variants within their region after filtering in both sets of analyses (i.e. ≥ 2 ‘nonsynonymous’ & ≥ 2 variants with a CADD C-Score ≥15) were plotted on these graphs. This was undertaken using the ‘hexbin’ package in R  This proS1 File(PDF)Click here for additional data file.