Ecoregional differences contribute to genetic environmental interactions and impact animal performance. These differences may become more important under climate change scenarios. Utilizing genetic diversity within a species to address such problems has not been fully explored. In this study Hereford cattle were genotyped with 50K Bead Chip or 770K Bovine Bead Chip to test the existence of genetic structure in five U.S. ecoregions characterized by precipitation, temperature and humidity and designated: cool arid (CA), cool humid (CH), transition zone (TZ), warm arid (WA), and warm humid (WH). SNP data were analyzed in three sequential analyses. Broad genetic structure was evaluated with STRUCTURE, and ADMIXTURE software using 14,312 SNPs after passing quality control variables. The second analysis was performed using principal coordinate analysis with 66 Tag SNPs associated in the literature with various aspects of environmental stressors  or production . In the third analysis TreeSelect was used with the 66 SNPs to evaluate if ecoregional allelic frequencies deviated from a central frequency and by so doing are indicative of directional selection. The three analyses suggested subpopulation structures associated with ecoregions from where animals were derived. ADMIXTURE and PCA results illustrated the importance of temperature and humidity and confirm subpopulation assignments. Comparisons of allele frequencies with TreeSelect showed ecoregion differences, in particular the divergence between arid and humid regions. Patterns of genetic variability obtained by medium and high density SNP chips can be used to acclimatize a temperately derived breed to various ecoregions. As climate change becomes an important factor in cattle production, this study should be used as a proof of concept to review future breeding and conservation schemes aimed at adaptation to climatic events. Climate change (CC) will have broad and comprehensive effects upon agriculture and food security as documented by the Intergovernmental Panel on Climate Change  and willBos taurus breeds to Bos indicus breeds, and particularly breeds from developing countries that are believed to already be better adapted to environmental stresses [Bos indicus breeds exhibit higher levels of tolerance to heat stress when compared to Bos taurus breeds a number of disadvantages in productivity and quality traits, such as delayed puberty, lower vigor of newborn calves, lower growth rates and carcass quality [Some authors have suggested a substantial realignment of species raised by livestock producers as a response to forage, disease, and ambient temperature stresses caused by CC . Howeverstresses  like hea quality , 10, 11 Bos taurus breeds have been adapted to subtropical and tropical environments under natural and artificial selection, thereby improving their resilience to heat stress. N’Dama (Africa), Senepol (Virgin Islands) and more recently the Adaptaur  [Bos taurus beef breeds  have been performed [Bos taurus) among the ecoregions where the cattle were produced, with specific focus on SNPs associated with an animal’s ability to cope with environmental stressors that may confer an ability to adapt to climate change variables. Having such an assessment can be a useful proof of concept in determining this breed’s potential ability to respond to CC, and therefore provide insights on how breeders may wish to utilize currently available genetic variation to achieve particular breeding objectives.The Bovine HapMap Consortium  reportedstralia)  are examerformed  and obseerformed , 17 and erformed . The objNo samples were collected for this study; rather they were collected as part of other studies or program activities not associated with this study. To provide an overview of the samples we detail the following. Cattle blood and semen samples were collected by individual livestock owners and submitted to various research entities for germplasm collection or genomic analysis. These private businesses have no obligation to comply with IACUC protocols as the cattle were their property. The majority of the samples were submitted to the American Hereford Association for genomic analysis. The Association worked with the ARS in performing the genomic analysis. The remaining samples were acquired from cattle breeding companies by the ARS National Animal Germplasm Program as part of their efforts to conserve genetic resources.Classifying climatic zones where livestock are raised better enables our understanding of how environmental conditions can impact productivity , 20. A wClimate data for the delimited ecoregions was obtained from the National Oceanic and Atmospheric Administration , 23, 24 The average monthly THI was then used as an indicator of the level of heat stress an animal might encounter during any month using the classification of: normal: <75; alert: 75–78; danger: 79–83; and emergency: >84 . The perThere has been previous research on Hereford documenting the extent of genotype-environmental interactions in cool arid and warm humid environments for a number of characteristics and their ability to adjust to new environmental conditions , 28. MorBos indicus), genotyped with the 777K high density SNP chip, were acquired from US gene bank or Brazil [Data from 577 Herefords genotyped with either 50K or 777K Bovine SNP chips  were acquired and initially evaluated. A total of 491 samples were part of the “2,000 Bull Project”  where ear Brazil  were usehttp://www.illumina.com/documents/products/technotes/technote_topbot.pdf) to guarantee uniformity in the reporting allele frequency calls. Additional quality controls were carried out to exclude samples with call rates (CR) < 0.85, Minor Allele Frequencies (MAF) < 0.05, Hardy Weinberg Equilibrium (HWE) < 0.001; and linkage disequilibrium (LD) r2 > 0.5 in 50 marker windows. The final data set for genetic structure study was carried out with 14,312 SNP markers.The preliminary working data set was formed by using all Hereford samples mentioned. Data were pruned to 45,066 SNP markers common to all samples and converted to A/B format following the recommendations from Illumina Inc. , and response to environmental stresses , 38 wereTreeSelect  a prograThe approach used to estimate average monthly THI indicated that animals raised in CA and CH are generally not exposed to conditions classified as “dangerous”. However, animals raised in TZ and WA and WH are exposed to conditions in the “danger” classification for approximately 60 and 120 days per year, respectively .Analysis using 14,312 SNPs revealed by the minimum estimated error generated by multiple ADMIXTURE runs was observed with K = 6 . In geneEstimated migration rates among regions  indicatePedigree data supplied by AHA were used to compute average coefficients of relationship within and among the five ecoregions  and thusEstablishment of a genetic substructure within a breed using the large SNP panel was followed by an evaluation of 66 selected SNPs previously reported to be associated with production traits  or ability to respond to environmental stressors .Principal coordinate (PC) analysis with the 66 SNPs showed that the first (66.1%) and second (18.7%) PC accounted for 84.8% of the variation. TreeSelect results  among thFor additional perspective WA and WH subpopulations were compared to Nelore cattle with TreeSelect . Of the Bos taurus breeds [Climate change brings new challenges for maintaining and increasing livestock productivity. Suggestions that CC will result in changes of species and/or breeds used by livestock producers have raised awareness , 8. Heres breeds , high avs breeds , 33.inter alia, temperature and humidity found across CA-CH, WA, WH and TZ ecoregions. Therefore, obtained results based upon molecular marker data support previous findings based on quantitative analysis of production traits which identified genetic by environmental interactions [Within breed population substructures have been previously reported for cattle breeds , and usuractions  and adapractions , 28 in tEstimated averages of monthly THI  indicateAnalysis of pedigree data and geographic distribution of breeders across the US show that the potential for significant gene flow across ecoregions frequently is high for Herefords. Data preparation procedures were performed to minimize such confounding effects. For example, cattle with a large genetic relationship to the Line 1 and outside CA (where Line 1 was developed) were removed from the analysis, as well as, animals with large genetic relationships with animals located in different ecoregions.Bos taurus than Brahman cattle [Allele frequency differences observed between Nelore vs WH and WA were relatively large suggesting that while WH and WA allele frequencies approached frequencies observed in Nelore, they were not at parity . Despiten cattle .The TZ is in a central location between temperature and humidity gradients, in addition to other correlated environmental factors . HereforThese results show that a breed with a relatively high average genetic relationship and generally associated with production in temperate zones have a range of genetic variability potentially useful in their adaptation to CC. Differences between Hereford and Nelore suggest a wide range of livestock breeds may have capacity to adapt to varying climatic conditions . Our resNatural selection and/or correlated responses to selection for production characteristics are likely to allow populations within a breed to become adapted to specific environments . AlthougBos indicus breeds. Conversely, considering that subpopulations found within WA and WH are lower ranking in certain production traits , it may be beneficial to select from a base population formed with animals from TZ, as this subpopulation may enable a more balanced selection strategy.The presence of varying allelic frequencies in different ecoregional subpopulations suggests an array of options for breeders to use as they work to adapt their herds to CC variables. Faster responses to selection for adaption to CC may be achieved using a base population composed of animals from WA or WH ecoregions, as suggested by results obtained in the comparisons with This study suggest that a breed dispersed across a wide range of ecoregions may have untapped ability to respond to existing environmental differences which may potentially become increasingly larger as CC becomes more prominent. The results indicate the importance of maintaining various subpopulations within a breed to serve as reservoirs of diversity, which may be crucial for facilitating such changes.S1 Table(DOCX)Click here for additional data file.S2 TableThe loci were evaluated in the “A/B” format. The “A” allele was used for frequency calculations between ecoregions at each locus.(DOCX)Click here for additional data file.


A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model.P < 0.05).We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits  in Holstein  and Jersey  cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP  contains supplementary material, which is available to authorized users. In general, genetic variation in complex or quantitative traits is considered to be governed by a large number of loci with small to moderate effects, which are individually undetectable by genome-wide association studies (GWAS) with stringent significance thresholds –5. A betDrosophila melanogaster populations [The genomic best linear unbiased prediction (GBLUP) assumes that all genomic markers contribute equally to variability of a trait  and ignoulations . Compareulations . A possiThe GFBLUP model is computationally intensive for evaluating many genomic features . TherefoTo date, there are many genes that are yet neither functionally characterized nor mapped to any biological databases –16, in pEscherichia coli (E. coli) is a common mastitis-causing bacteria [E. coli induce acute inflammatory responses [Mastitis, an inflammatory condition of the mammary gland, is often caused by invading pathogens. It is the most costly disease in the dairy industry due to treatment cost, reduction in milk production and milk quality, and in some cases culling of the affected cows . Gram-nebacteria , and theesponses . Genes wesponses , 23–26. esponses . Since lesponses , we hypoesponses , we furtIn the current study, mastitis and three milk production traits  from Nordic Holstein  and Jersey  cattle were analyzed using imputed sequence genotype data (~15 million SNPs) and hepatic transcriptome data from an IMI study. Our main objectives were to apply the GFBLUP model and SNP set test: (1) to investigate the genomic variance explained by transcriptomic regions that are responsive to IMI; (2) to improve the accuracy of within-breed and across-breed genomic prediction using GFBLUP compared to GBLUP; and (3) to investigate the relationship between the predictive ability of GFBLUP and the significance of genomic features based on the SNP set test.E. coli LPS (0111:B4)  dissolved in 10 mL of a 0.9% NaCl solution, while the left front quarter was used as a control and challenged with 10 mL of 0.9% NaCl solution only. Clinical signs, data on production traits together with milk and blood parameters associated with LPS infection were recorded throughout the trial and confirmed that mastitis inflammation was induced. Liver biopsies collected 22 h before and 3, 6, 9, 12 and 48 h after LPS infection in three cows were used for RNA extraction. Sampling procedures for liver biopsies were described previously [The IMI experimental design and collection of liver biopsies were reported previously , 29. In eviously . Finallyftp://ftp.ensembl.org/pub/release-86/gtf/bos_taurus) was counted using the function Feature-Counts in the Rsubread package with default settings. The average mapping rate across all samples was approximately 68%. Analysis of differential gene expression was conducted using edgeR [Statistical approaches used for analysing RNA-Seq data were described previously . Brieflyng edgeR . A smallng edgeR . Therefong edgeR . After nng edgeR . The relng edgeR . In the ll genes . Statist−2, 10−2, 10−3, 10−6, 10−8, and 10−10) in each of the five experimental comparisons , respectively. In addition, since the biological functions of up-regulated and down-regulated genes can be quite different, each of these 30 genomic features was further divided into four subsets based on four log2(fold-change)s cut-off values . Therefore, another 115 genomic features were built, because five conditions were without DEG. In total, 145 genomic features were defined. The number of DEG in each genomic feature is summarized in Table S1  (i.e. the hepatic transcriptome regions responsive to IMI) that were obtained from the above RNA-Seq analyses were used to define genomic features. First, 30 genomic features were defined using six false discovery rate (FDR) cut-off values (i.e. ≤5×10http://www.nordicebv.info/), and were available for 5056 HOL and 1231 JER cattle. Detailed information of these phenotypes was previously described in [The phenotypes were de-regressed breeding values (DRP) from routine genetic evaluations by the Nordic Cattle Genetic Evaluation  or with minor allele frequency (MAF) <0.01 were further excluded. A total of 15,355,382 and 13,403,916 SNPs remained for the HOL and JER datasets, respectively. The SNP locations were based on the UMD3.1 reference genome (http://www.ensembl.org/Bos_taurus/Info/Index). A SNP was considered to be linked with a genomic feature if its chromosome position was within the open reading frame of DEG in the particular genomic feature.Imputation from Illumina BovineSNP50 BeadChip (50 K) to Illumina BovineHD BeadChip  genotypes for these individuals and further to whole-genome sequence variants was described previously , 37. Bri Project  and addi Project . GenotypMinimac2 . In totaFor within-breed prediction, each of the datasets (i.e. HOL and JER) was divided into training and validation sets based on birth-year of the animal to access prediction accuracy. The birth-year cut-off was 2006 for HOL and 2004 for JER, and the younger animals were assigned to the validation dataset Table . We chos1 is a vector of 1s, For each genomic feature as defined before, SNPs were partitioned into two sets (i.e. within and outside the genomic feature), followed by the GFBLUP model analysis:ribed in . BrieflyThe standard GBLUP model includes only one random genomic effect:The variance components, rocedure  implemenrocedure . The pror) is calculated as the correlation between GEBV and DRP in the validation population. The bias of the genomic predictions with both GFBLUP and GBLUP was evaluated by the regression of DRP on the GEBV, i.e. Genomic breeding values (GEBV) were predicted using both GFBLUP and GBLUP models. In the GFBLUP and GBLUP models, GEBV is Single-marker GWAS analyses for four traits were only conducted in the HOL training population, followed by SNP set test analyses for testing the associations between genomic features and traits. Single-marker GWAS was performed using a two-step variance component-based method, to account for population stratification, as implemented in EMMAX . In the The summary statistic of a genomic feature was calculated as the sum of the test statistics  structure among SNPs should be retained. An empirical distribution of the summary statistics of a genomic feature was therefore obtained by using the following cyclical permutation procedure as described previously , 46. Brihttp://kobas.cbi.pku.edu.cn/home.do) [In order to investigate the biological function of a genomic feature, functional enrichment analysis of DEG in the particular genomic feature was conducted using a web-based tool, KOBAS2.0 (home.do) , where ahome.do)  was used10(P) values of imputed sequence-level SNPs from single-marker GWAS for mastitis and milk production traits on the HOL training population are shown in the Manhattan plots of Figure S1 ) of genomic features based on the SNP set test in the HOL training population was compared with the changes in prediction accuracy of GFBLUP within- and across-breed predictions, respectively.The results for RNA-Seq analyses at different time-point comparisons  are summarized in Table S2  for mastitis, protein, milk and fat yield, respectively  for mastitis, 0.602 (bias = 0.775) for protein yield, 0.635 (bias = 0.862) for milk yield, and 0.607 (bias = 0.808) for fat yield. Compared to the GBLUP model, 27, 44, 17 and 13 of the 145 genomic features resulted in higher prediction accuracies with GFBLUP (Δr) were significantly (P < 0.05) positively correlated with –log10(P) of genomic features based on the SNP set test across all four traits , 0.46 (P = 4.4 × 10−9), 0.46 (P = 4.4 × 10−9) and 0.44 (P = 3.6 × 10−8) were found between changes in accuracy and −log10 for mastitis, protein yield, milk yield, and fat yield, respectively. These results demonstrated that the SNP set test could be used as a computationally simple way to develop more predictive GFBLUP models.The results of SNP set tests for all 145 genomic features across four traits in the HOL training population are summarized in Tables S3, S4, S5 and S6  with GFBLUP for mastitis, protein, and milk yield, respectively  with the best-performing genomic feature across the four traits was 0.020, which corresponds to a 3.9% increase compared to GBLUP. These results indicate that the use of gene expression data obtained from one breed may improve marginally the genomic prediction accuracy in other breeds. It should be noted that, for JER, the increase in prediction accuracy with GFBLUP for milk and fat yield was very small  for mastitis, 0.530 bias = 0.760) for protein yield, 0.597 (bias = 0.796) for milk yield, and 0.433 (bias = 0.669) for fat yield. Compared to the GBLUP model, 21, 14 and 2 genomic features resulted in higher prediction accuracy (Δ60 for pr10(P) based on the SNP set test on the HOL training population for mastitis and protein yield , 0.52 (P = 3.1 × 10−11), 0.19 (P = 0.02) and 0.06 (P = 0.5) were found between changes in accuracy and −log10(P) for mastitis, protein yield, milk yield, and fat yield, respectively.The changes in prediction accuracy with GFBLUP on the JER validation population were also significantly positively correlated with −logeld Fig. . Correlar ≥ 0.01) for mastitis, protein, milk and fat yield, respectively  of SNP set test in the HOL training population  with the best-performing genomic feature across four traits was 0.111, which corresponds to a 164.4% increase relative to GBLUP. Compared to within-breed prediction, the relative improvement in genomic prediction accuracy seems to be clearer in across-breed prediction.When the complete HOL population was considered as training population to predict the genomic values of individuals in the JER population, prediction accuracy of GBLUP was very low, i.e. prediction accuracies were equal to −0.058 (bias = −0.343) for mastitis, 0.098 (bias = 0.622) for protein yield, 0.160 (bias = 0.762) for milk yield, and 0.070 (bias = 0.482) for fat yield. Compared to the GBLUP model, 60, 68, 71 and 44 of the 145 genomic features resulted in higher prediction accuracy with GFBLUP  > 2) that were detected in the 48 vs. −22 h comparison resulted in an increase of 0.204, 0.020 and 0.041 in prediction accuracy for protein yield among across-breed, and within HOL and JER predictions, respectively  enriched in innate immune response and negative regulation of endopeptidase activity and protein metabolism  or single chromosomes may not provide enough biological insights into the genetic architecture of a trait , 51, 53.In populations with a high degree of linkage disequilibrium (LD), such as highly selected dairy cattle breeds, the genomic relationship based on genome-wide markers provides accurate information about the genomic variation of the traits , althougAcross-breed genomic prediction accuracies for milk production traits were close to zero, when HOL was used as training population to predict genomic values for JER using the GBLUP approach. This is in agreement with observations in , 56. Whe), but in theory it is possible to include multiple genomic feature effects [The assumption made in the GBLUP model  does not match the real genetic architecture that underlies the traits. It puts equal weights to the elements in the genomic relationship, whereas the GFBLUP allows putting different weights to the individual genomic relationships in the prediction equation according to the estimated genomic parameters . Predict effects , 58, whi effects . TherefoBayesian mixture models, such as BayesR , which it weight , 50. Thet weight . All gent weight , 59.t2 of SNPs within the genomic feature. Another commonly used approach for the SNP set test is based on counting associations exceeding a pre-defined significance threshold within the genomic feature [The SNP set test based on single-marker test statistics derived from GWAS is a computationally fast way to evaluate a large number of genomic features . The res feature –63. One  feature , 49, 50, feature , 46.In order to test different biological hypotheses, many genomic features can be constructed using different sources of prior information, such as prior QTL regions, chromosomes, sequence, biological pathways, and other types of external evidence. The gain in biological knowledge of complex traits relies highly on the genomic feature classification strategies. Since associated genomic markers are not evenly, or necessarily physically, clustered along the genome , 51, parE. coli mastitis may be limited to detect all the genes that are functionally relevant with mastitis. Thus, more RNA-Seq data from infections with other types of pathogens could help the detection of genomic features that are associated with mastitis and milk production. In addition, since gene expression patterns depend highly on time, cell types, and tissues, some trait-associated genes might not show differential expression in certain cell types and tissues at a certain physiological stage. Therefore, incorporating more molecular biological information from more tissues  and more physiological stages could be important to define the appropriate genomic features that are highly enriched in causal variants.Since mastitis can be caused by various pathogens, the current RNA-Seq data that originate only from Compared to GBLUP, GFBLUP models increased the accuracy of genomic prediction for mastitis and milk production traits in dairy cattle by incorporating biological information from gene expression data, and thus provide novel biological insights into the genetic basis of such complex traits. Compared to within-breed prediction, the increase in prediction accuracy seems to be more apparent in across-breed prediction. In addition, the SNP set test can be used as a computationally fast way to develop more predictive GFBLUP or similar models. The current genomic feature modelling approaches provide a general framework for incorporating biological knowledge from independent functional genomics studies to study the genetic architecture and to improve genomic prediction for complex traits. Approaches such as GFBLUP and SNP set test will be increasingly useful as the biological knowledge of functional genomic regions keep accumulating for a range of traits and species.Additional file 1: Table S1. The genomic features defined by RNA-Seq analysis. The data provided represent the number of genes in each of the 145 genomic features defined by using six different FDR cut-off values  and four log2(fold-change)s .Additional file 2: Table S2. Gene differential expression analysis of RNA-Seq data. The data provided represent the results of gene differential expression analysis of RNA-Seq data in five different comparisons, i.e. 3 versus −22 h, 6 versus −22 h, 9 versus −22 h, 12 versus −22 h and 48 versus −22 h.Additional file 3: Figure S1. Manhattan plots of single-marker genome-wide association analyses (GWAS) with imputed sequence SNPs. The figure provided represents the P values of all imputed sequence SNPs from GWAS for mastitis, protein, milk and fat yield in the HOL training population. Each point represents one SNP.Additional file 4: Tables S3, S4, S5 and S6. GFBLUP and SNP set test analyses in the Holstein (HOL) population. The data provided represent the detailed results of GFBLUP and SNP set test analyses for mastitis (Table S3), protein (Table S4), milk (Table S5) and fat (Table S6) yield in the HOL population.Additional file 5: Figure S2. Relationship between bias of genomic predictions and changes in prediction accuracy with GFBLUP for four traits in the Holstein population. Each point represents one of the 145 genomic features. The y axis is the absolute values of (1-bias (b)) for GFBLUP, and the x axis is the changes in prediction accuracy with GFBLUP relative to GBLUP.Additional file 6: Tables S7, S8, S9 and S10. GFBLUP analyses in the Jersey (JER) population. The data provided represent the detailed results of GFBLUP analyses for mastitis (Table S7), protein (Table S8), milk (Table S9) and fat (Table S10) yield in the JER population.Additional file 7: Figure S3. Relationship between bias of genomic predictions and changes in prediction accuracy for four traits in the Jersey (JER) population. Each point represents one of the 145 genomic features. The y axis is the absolute values of (1-bias (b)) for GFBLUP, and the x axis is the changes in prediction accuracy with GFBLUP relative to GBLUP.Additional file 8: Tables S11, S12, S13 and S14. Results of GFBLUP analyses across breeds. The data provided represent the detailed results of GFBLUP analyses for mastitis (Table S11), protein (Table S12), milk (Table S13) and fat (Table S14) yield across breeds.Additional file 9: Figure S4. Relationship between bias of genomic predictions and changes in prediction accuracy for four traits in across-breed prediction. Each point represents one of the 145 genomic features. The y axis is the absolute values of (1-bias (b)) for GFBLUP, and the x axis is the changes in prediction accuracy with GFBLUP relative to GBLUP.Additional file 10: Table S15. The 34 up-regulated genes associated with protein yield. The data represent the detailed information of the 34 highly up-regulated genes detected in the liver at 48h post intra-mammary infection (IMI).


Identifying variants that regulate gene expression and delineating their genetic architecture is a critical next step in our endeavors to better understand the genetic etiology of complex diseases. The appropriate genomic tools are in place, and preliminary analytic strategies have been developed.Here we used Genetic Analysis Workshop (GAW) 19 data to investigate the genetic complexity of expression quantitative trait loci (eQTL), chromosomal regions likely to harbor regulatory elements responsible for gene expression. For this investigation, we analyzed the lymphocyte expression profiles of 653 individuals in 20 pedigrees who were also genotyped by single nucleotide polymorphism (SNP) arrays, followed by sequencing and imputation. We used these data to examine the degree of allelic heterogeneity, a contributor to genetic complexity at eQTL, by sequentially conditioning on the most significantly associated SNPs.SOLAR -MGA (measured genotype approach) and FaST-LMM  software allowed us to analyze pedigree data. The power and Type 1 error rates for single SNP association testing and multiple SNP sequential association testing were consistent for these programs. Sequential conditioning of the real expression data revealed substantial levels of allelic heterogeneity at the 2 eQTL examined, illustrating this feature of genetic complexity.eQTL exhibit substantial genetic complexity among and within pedigrees. We examined these eQTL because we thought it would be easier to detect multiple association signals with small effects and/or single signals with large effects when the LOD scores are larger. However, such LOD scores would also be observed if each pedigree had its own transcript-driving highly penetrant allele for the expressed gene. Other, more complex scenarios, involving multiple independent transcript-driving alleles that are shared by the pedigrees would also lead to such strong evidence for linkage of transcript levels to gene regions. GAW19 expression and sequence data on the same individuals within pedigrees provide an excellent opportunity to explore the genetic architecture of eQTL, and we focused on 2 of the eQTL with the highest LOD scores, chosen for their different gene lengths [Genetic complexity derives from factors that contribute to the non-Mendelian inheritance of a trait. These contributors include polygenic inheritance, locus and allelic heterogeneity, mitochondrial inheritance, and gene–gene and gene–environment interactions. The data provided by Genetic Analysis Workshop (GAW) 19 , heritabWe were enthusiastic about the opportunity provided by GAW19 to assess the genetic architecture of eQTL using pedigrees, where the standard analytic approach of linear regression was not directly applicable. We compared 2 approaches appropriate for pedigrees, and used the GAW19 simulated data, where the models of inheritance employed to generate the data were known, in order to assess the power and Type 1 errors to detect associations for both single and multiple independent single-nucleotide polymorphisms (SNPs). These were the measured genotype approach as programmed in SOLAR  and the The variance component approach to gene mapping in pedigrees involves modeling a vector of pedigree member trait value deviations from the pedigree mean and a covariance matrix of kinship coefficients among the pedigree members. Given the appropriate constellation of relatives, the covariance matrix can be partitioned into additive genetic effects, along with the effects of genetic dominance and a common environment. In SOLAR-MGA , the genMAP4 gene on chromosome 3 were analyzed. For each replicate, taken separately, those on medication were removed from the analyses, and the diastolic blood pressure (DBP) and systolic blood pressure (SBP) quantitative traits were adjusted for age and sex. In the 200 simulated data sets, an average of 765 people, with a range of 742 − 783, remained in the study sample. For DBP, the age covariate had a mean effect of 0.07 with a range of 0.008 − 0.12 and the sex covariate had a mean effect of -3.15 with a range of -5.2 to -1.7 over the 200 replicates. For SBP, the age covariate had a mean effect of 0.45 with a range of 0.38 − 0.52, and the sex covariate had an effect mean of -4.6 with a range of -7.6 to -2.0. Power was assessed using the 0.05 and 5e-8 levels of significance in each simulated sample of 200. The Type 1 error was estimated using the 200 replicates of simulated trait Q1, which was not associated with the SNPs in MAP4. Power and Type 1 errors were also estimated for the sequential analysis approach that was subsequently applied to the real data. As with the single SNP assessments, simulated Q1, DBP, and SBP traits were used in each of the 200 replicates. To assess power, the analyses were conditioned sequentially on the MAP4 SNPs.To assess power and Type 1 errors for detecting the individual associated SNPs using both analytic approaches, SOLAR-MGA and FaST-LMM, the simulated GAW19 pedigree data [p value of 5e-8 and a p value of 0.05 to view the full range of possibilities, the conditioning options of SOLAR-MGA and FaST-LMM were used in an iterative fashion, conditioning on the single most statistically significant signal from the set, to generate the number of independent signals in each region, and this was done sequentially until no significant SNPs were observed in the analysis.Two genes were selected randomly according to length (one shorter than the mean of the genes and one longer than that mean) among those with the 10 highest eQTL LOD scores in this pedigree cohort  for assoTable MAP4 SNPs that have been modeled to be associated with SBP and DBP. Both programs are consistent, so we only provide the information once. The first row gives the number of MAP4 SNPs upon which the likelihood function has been conditioned sequentially. Rows 2 and 3 give the power to observe exactly that number of SNPs, sequentially, and the power to observe that number of SNPs and greater, sequentially, for p <0.05. The last 2 rows give the same for p <5e-8. The power for the 0.05 level of significance is appropriate, but the sample is substantially underpowered to provide adequate power for the simulated effect sizes. Estimated Type 1 errors for significance thresholds of 0.05, 0.01, and 0.001 are consistent with what is expected for both programs.Table p value among the significant SNPs at that step. Table Table cis-regulatory elements is supported by a functional analysis of the F7 gene in an independent study [Association testing of SNPs, conditioning sequentially with SOLAR-MGA and FaST-LMM, identified multiple independent SNP signals within each of 2 eQTL regions exhibiting high LOD scores in the GAW19 pedigrees. These analyses provide clear evidence that the genetic architecture of at least some eQTL exhibit allelic heterogeneity, with multiple independent signals, and, thus, are complex. Both software packages provide consistent power and Type 1 errors when testing for association with and without conditioning in the simulated data. The inference of complexity and multiple independent We investigated the presence of allelic association at eQTL in the GAW19 real expression data by conducting analyses that condition sequentially on associated SNPs in the full sample of pedigrees and within individual families. These analyses lead us to conclude that there are multiple independent eQTL for individual expression levels at the same locus. We are able to conclude that eQTL are genetically complex both across multiple pedigrees and within individual pedigrees. In addition, when comparing software that can be used for these analyses, statistical Type 1 error and power assessment on the simulated data GAW19 indicates that the results of FaST-LMM are consistent with those of SOLAR-MGA.


In apple tree, architectural traits were observed to be under genetic control. However, architectural traits also result from many organogenetic and morphological processes interacting with the environment. The present study aimed at combining a FSPM built for apple tree, MAppleT, with genetic determinisms of architectural traits, previously described in a bi-parental population. We focused on parameters related to organogenesis (phyllochron and immediate branching) and morphogenesis processes (internode length and leaf area) during the first year of tree growth. Two independent datasets collected in 2004 and 2007 on 116 genotypes, issued from a ‘Starkrimson’ × ‘Granny Smith’ cross, were used. The phyllochron was estimated as a function of thermal time and sylleptic branching was modeled subsequently depending on phyllochron. From a genetic map built with SNPs, marker effects were estimated on four MAppleT parameters with rrBLUP, using 2007 data. These effects were then considered in MAppleT to simulate tree development in the two climatic conditions. The genome wide prediction model gave consistent estimations of parameter values with correlation coefficients between observed values and estimated values from SNP markers ranging from 0.79 to 0.96. However, the accuracy of the prediction model following cross validation schemas was lower. Three integrative traits  were considered for validating MAppleT simulations. In 2007 climatic conditions, simulated values were close to observations, highlighting the correct simulation of genetic variability. However, in 2004 conditions which were not used for model calibration, the simulations differed from observations. This study demonstrates the possibility of integrating genome-based information in a FSPM for a perennial fruit tree. It also showed that further improvements are required for improving the prediction ability. Especially temperature effect should be extended and other factors taken into account for modeling GxE interactions. Improvements could also be expected by considering larger populations and by testing other genome wide prediction models. Despite these limitations, this study opens new possibilities for supporting plant breeding by  Tree architecture plays a key role in collecting light and assimilating carbon and thus affects plant growth and yield . MoreoveFunctional-structural plant models (FSPM) have been developed for analyzing the relationships between plant architecture, light interception, microclimate, and physiological processes such as carbon assimilation or water losses by transpiration . These mPrevious studies have included the genetic variability in FSPMs by estimating physiological or morphological parameters using QTL information . More prThe aim of the present study was to combine a FSPM previously built for apple tree MAppleT,  with genMAppleT simulates apple tree above-ground development over years , by combWe focused on the vegetative development during the first year of apple tree growth which is a critical period for architecture establishment. During this initial growth period, apple tree develops a main stem with immediate laterals  modeling the dynamic of phytomer emergence and the subsequent sylleptic branching depending on TT, (ii) estimating key MAppleT parameters based on SNP marker effects, (iii) integrating the environmental and the marker effects in MAppleT to simulate genotype development based on genetic and climatic information, (iv) validate the method by comparing simulation outputs with observed data.The study was carried out on 116 genotypes corresponding to an apple tree  F1 progeny issued from a “Starkrimson” × “Granny Smith” cross. The parents were selected for their contrasted architecture and bearing behavior. Indeed, the ‘Starkrimson’ maternal parent displays an erect growth habit with many short shoots and a tendency to irregular bearing . ConversTwo field experiments were planted in 2007 (Experiment 1) and 2004 (Experiment 2) at the Melgueil INRA Montpellier experimental station . In both experiments, two replicates of each genotype were grafted on Pajam 1 rootstock and planted 5 m × 1.5 m apart in a north-south orientation. Throughout the experiments, the 232 trees were well-watered and not pruned.In each experiment, the topology of each tree was described as explained in ® 20K SNP array  were compared to analyze the effect of temperature on the daily rate of leaf emergence (RLE) and its genetic variability depending on temperature. Since we did not want to increase the number of parameters in the MAppleT, the BIC was chosen for model selection because it is known to be more adequate for selecting models with a few number of parameters compared to the AIC (Mean) or a single sine method (TSSM) using 7 and 35°C as lower and upper threshold as in GDD) and computed as the slope of the relationship between TT using TSSM and the number of emitted leaves.Nine linear mixed models  below the terminal apex, in order to represent the inhibition of axillary bud outgrowth by the apical meristem . The best combination of parameters (20 LD and 7 daysn) was defined as the combination which minimizes the root mean squared error (RMSE) between observed and simulated distributions of sylleptic laterals along the trunk. kr and daysn were estimated on the mean distribution of sylleptic laterals considering all genotypes together. Indeed, since sylleptic branching is a rare event, the number of replicates (only two) was not enough to get a consistent representation of the distribution of sylleptic laterals along the trunk for each genotype of the population. Values of kr and daysn were thus considered as identical for each genotype.The values of GDDRLE, (ii) the length of individual internode (IN_length), (iii) the area of individual leaf (Leaf_area), and (iv) sylla. In MAppleT, these parameters are the main parameters driving primary growth and branching. The other parameters that can affect primary tree growth in MAppleT are the parameters related to the dynamics of organ expansion but we did not consider them in this study because they had no impact on the final architecture  and because they are difficult to estimate experimentally on a large range on genotypes.We focused on four parameters used in MAppleT, (i) IN_length was the mean internode length recorded at the end of the experiment. Leaf_area was estimated as the mean leaf area of the four leaves measured at the end of Experiment 1. These dimensions were then considered in MAppleT as the final dimensions of internodes and leaves for all the metamers along the trunk and branches expect for the preformed metamers (eight first leaves). In MAppleT, organ expansion is modeled as a logistic growth functions and the value of this function reaches Leaf_area or IN_Length at the end of the organ growth period , the RLEGDD and the sum of the daily thermal time (dTT) during the growing season , as follows:The value of bd is the day when the node of rank kr appears on tree, ed is the day of the end of growing season.where GDDRLE for each individual was estimated from the linear model between RLE and TT that minimized the BIC (see Supplementary Table 1).The value of Trunk_length), the total number of leaves (Nb_Leaves), and the number of sylleptic laterals (Nb_syll). Moreover, as sylla distribution was not Gaussian, its logarithm, called syllloga, which was normally distributed, was used for the genetic analyses.Analyses of genetic variability and heritability were performed on the four MAppleT parameters and on three architectural traits directly measured on trees: the length of trunk , μ is the overall mean of phenotypic value, X (232 × 2160) is the matrix of markers for each individual, g (2160 × 1) is the marker effects which are assumed to be normally distributed e (232 × 1) is the residual effects where software .G, (116×1)) for all genotypes in the population using the estimated marker effects (ĝ):We estimated the genetic values of each parameter (W the matrix of markers for each genotype (116 × 2160). In order to predict the phenotypic values of MAppleT parameters, markers effects were multiplied by the ratio between the standard deviation of observed phenotypes and the standard deviation of the genotypic predictions.with P, (116 × 1)) was calculed for each parameters and for all the genotypes as follows:The phenotypic values of MAppleT parameters  for all the genotypes were calculated using the genome wide prediction model based on the matrices of marker effects and genotypes. Simulations were then performed with MAppleT with the environmental conditions of both experiments.The values of the four parameters  were compared with observed values. Since the sylleptic branching model is stochastic, different values were obtained for the different simulations with the same set of genotypic parameters. The observed number of sylleptic laterals per genotype was thus compared with the mean number of sylleptic obtained after running five simulations.For validating the approach, the values of the three integrated architectural traits , and the bias (BIAS) were used to compare simulations (is) and observations (io):The correlation coefficient, the TT (see Supplementary Table 2) had the lowest BIC. Although BIC values between the different models were close, this sustains the hypothesis of a significant impact of temperature variations on RLE. Moreover, model 4 includes a genetic effect (βι) on the slope between RLE and TT (RLEGDD) which also suggests that the slope values were genetically dependent.The analysis of temperature effect on the RLE showed that the mixed linear model 4 which includes the effect of TT computed with the single sine method, TT) during the growing season (15 April–31 September) was almost similar between the two experiments, even though slightly higher in Experiment 1 than in Experiment 2 . However, the annual dynamics were different. Indeed, daily thermal time was strongly higher during the first 30 days and was slightly lower during summer  in Experiment 1 compared to Experiment 2 . For the two experiments, the highest GDD was observed at the end of July.The cumulate daily thermal time (d) and the probability of sylleptic lateral emergence directly depends on TT in our model, the evolution of these two variables followed the daily thermal time. The mean value of the leaf emergence rate expressed in growing degree-day (GDDRLE) and estimated on data from Experiment 1 was 0.033 Leaf.GDD-1 which corresponds to a duration between the emergence of two leaves of 30 GDD (∼3 days during the first growing month and 2 days during summer). The mean value of the estimated parameter syllloga was -1.25. The mean value of logsylla corresponded to a probability of emergence of a sylleptic lateral on each metamer of 0.095 during first growing months and of 0.143 during summer.As the leaf emergence rate (RLENb_Leaves), 12% lower for the trunk length (Trunk_length), and 48% higher for the number of sylleptic laterals (Nb_syll) and these differences were significant (Table 1). It must be noticed that the higher number of leaves in Experiment 2 compared to Experiment 1 is contradictory with the corresponding value of cumulated daily thermal time which was lower in Experiment 2.Compared to Experiment 1, phenotypic values observed in Experiment 2 were on average 5% higher for the number of leaves  and MAppleT parameters  showed intermediate (0.38) to high (0.86) heritabilities in both experiments (Table 1). The highest heritability values were found for IN_length in Experiment 1. Heritabilities for Trunk_length were similar whether computed considering the two experiments together or for each year separately. Conversely, heritability values were reduced by 26 and 32% for Nb_Leaves and Nb_syll, when considering either both years together or each year separately.The architectural traits (Table 2). Correlations between MAppleT parameters were low. Only the correlations between RLEGDD and the mean internode length and between the leaf area and the mean internode length were significant . As expected, Nb_leaves, Trunk_length, and Nb_syll were strongly correlated to GDDRLE, IN_length, and syllloga, respectively. The correlations between the two experiments for the architectural traits Nb_Leaves, Trunk_length, and Nb_syll were significant but the correlation coefficients remained low (from 0.25 to 0.45).The analyses of correlations gave similar results if genetic or phenotypic correlations were considered . This QTL was located on LG 12 and explained 16.9% of the genetic variability. It was associated with the SNP FB_0152374_L12_PA with a LOD score of 4.67. For the three other variables , no significant QTL could be detected. Because these variables were under genetic control (moderate to high heritability), the absence of detected QTL led us to assume that they were under the control of a high number of markers with low effect. We therefore tested a genome wide prediction model.QTL detections on BLUPs of GDDRLE, 0.98 for IN_length, 0.87 for syllloga, and 0.80 for Leaf_area, respectively . All these correlations were significant according to Pearson’s test (P < 0.05) showing the ability of the genome wide model to estimate parameter values from SNP markers. However, the prediction accuracies were notably lower using the cross validation methods . The mean accuracy from the 10-fold cross validation was 0.26 for GDDRLE, 0.30 for IN_length, 0.15 for syllloga, and 0.31 for Leaf_area, respectively. The standard deviation of the accuracy from the 10-fold cross validation was very high for the four variables: 0.23 for GDDRLE, 0.30 for IN_length, 0.35 for syllloga, and 0.24 for Leaf_area. The accuracy obtained using the “leave-one-out” cross validation were 0.20 for GDDRLE, 0.30 for IN_length, 0.25 for syllloga, and 0.30 for Leaf_area.Correlations between mean observed phenotypes and predicted phenotypes with the genome wide estimation model and using all genotypes to estimate marker effects was 0.82 for Nb_Leaves, 0.83 for Trunk_Length, and 0.80 for Nb_syll . These correlations were significant according to Pearson’s test (P < 0.05). The normalized RMSE (nRMSE) was low for the Nb_leaves (8%) and Trunk_length (10%) but high for Nb_syll (63%). The Nb_syll was correctly predicted for the genotypes with low number of sylleptic laterals but the difference between observation and simulation increased for genotypes with more than 10 sylleptic laterals. On average, the number of sylleptic was slightly overestimated (by 0.86). Conversely, Nb_leaves and Trunk_length were on average underestimated by 2.29 and 8.42 mm, respectively. The distribution of sylleptic laterals along the trunk was more homogeneous in the simulated population than in the observed one . Indeed, a large variation in this frequency that was not simulated by the model was observed along trunk between 10 and 30 nodes. However, the highest frequency of sylleptic laterals was obtained within the same internode rank zone (∼ at rank 25) and the decrease after node rank 40 was correctly simulated.Simulations with MAppleT were performed under environmental conditions of Experiments 1 and 2. Under the environmental conditions of Experiment 1 (experiment used to parameterize MAppleT), correlations between observed and simulated values were equal to 0.65 for Figure 5). Correlations were low but significant for Trunk_length (0.30) and Nb_syll (0.29). The correlation was not significant for Nb_Leaves (0.10). The nRMSE obtained for Nb_leaves, Trunk_length, and Nb_syll were 20, 21, and 83%, respectively.The performance of the model was also evaluated by comparing observed and predicted values obtained from simulations under the environmental condition of Experiment 2 which was not used to estimate marker effects. In Experiment 2, correlations between observed and simulated values were lower than in Experiment 1 . This discrepancy resulted from the average number of leaves observed in Experiments 1 and 2  which were not in accordance with the lower cumulate thermal time observed in Experiment 2 than in Experiment 1. Despite of the strong underestimation of the number of internodes (corresponding to the number of leaves), the trunk length was overestimated in Experiment 2, unlike Experiment 1. This probably results from an overestimation of the mean individual internode length in Experiment 2. Indeed, mean internode length in MAppleT was identical in the two simulations whereas their observed values were higher in Experiment 1 than in Experiment 2 . The number of sylleptic laterals was highly underestimated in Experiment 2 (bias was -5.01). This is probably due to the underestimation of the leaf emergence rate which in turn affects the probability of sylleptic emergence (Equation 1).Figure 6). This suggests low correlations between the simulated traits. Indeed, the simulated architectural traits were slightly correlated (Table 3) with correlation coefficients similar to those observed (Table 2). The correlations between the architectural traits obtained from simulations in the two climatic conditions were close to 1 (Table 3) probably showing that the model did not generate large GxE interaction.The 1-year-old apple trees simulated in the environmental condition of Experiment 1 exhibited a large range of phenotypes. Genotypes with high number of leaves and high trunk length or with low number of leaves and high trunk length were simulated  were estimated with two independent datasets which provided similar results. However, heritability values were lower than those obtained by Previous studies aiming at linking FSPM with genetic information have been based on QTL effects only . Here, tTo overcome this, our strategy was to perform a genome wide estimation of marker effects for the four studied parameters. Correlations between estimated and observed values from the genome wide prediction model were high, showing that the model was able to estimate genotypic differences from SNP polymorphisms. However, during the cross validation, the accuracy was much lower even though similar to previous studies on apple  or on otIN_length, GDDLeaf_area, RLE, and syllloga) were used to simulate tree development with MAppleT and three integrative traits  were considered for validating our approach. The RMSE was low for the three variables when the genotypes were simulated in climatic conditions of Experiment 1, used to parameterize the genome wide model. This demonstrates the feasibility and relevance of the approach, i.e., including genetic information into a FSPM. As expected, the number of sylleptic laterals was correctly simulated for Experiment 1, consistently with the fact that this variable was used to estimate the probability of sylleptic lateral emergence. Moreover, the node ranks along the trunks at which the frequency of sylleptic laterals was the highest was similar to observations. The decrease in sylleptic frequency after node rank 40 was also consistent with observations. This supports the assumption we made of a close relationship between the RLE and the sylleptic branching probability. However, the sylleptic laterals were more homogeneously distributed along the trunks in the simulated population than in observations. This suggests that the link between the RLE and the sylleptic branching could be stronger or more complex than modeled in this study. Further refinements in the link between the dynamics of primary growth and immediate branching, considering non-linear relationships between RLE and branching probability (The predicted values of four MAppleT parameters (bability , could bConsistently with the discrepancy between the number of leaves and the GDD in the two observation years, the comparison of the integrated variables simulated and observed with the climatic conditions of Experiment 2 generated high RMSE. As previously stated for peach tree by Despite these limitations and the necessity of further improvements of the predictions based on genome wide information, the MAppleT model including the genotypic marker effects simulate genotypes with contrasting architectures for which correlations among simulated architectural traits were similar to observations. This suggests that the different hybrid genomes corresponding to different trait recombination were correctly taken into account. The existence of numerous recombination of architectural traits in F1 hybrids and the difficulties that this generates for the qualitative assessment of hybrid architecture was previously underlined by The integration of other environmental factors in the present modeling approach would be necessary to extent the domain of validity and to further analyze GxE interactions. This could be achieved by integrating other environmental response curves and adding the effects of each individual environmental factor on plant growth processes as done for instance by Finally, the extension of such models coupling FSPM and genetic information to larger germplasms and to the generation of a new simulated mapping population, as recently proposed in rice , will liEC and BP contributed to design the work. VM and BP performed the modeling tasks and analyzed the data. VM, BP, and EC wrote the manuscript. EC contributed to data acquisition.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Meta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this study, we performed CPASSOC analysis on the summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium using a novel method recently developed by our group. Sex-specific meta-analysis data for height, body mass index (BMI), and waist-to-hip ratio adjusted for BMI (WHRadjBMI) from discovery phase of the GIANT consortium study were combined using CPASSOC for each trait as well as 3 traits together. The conventional meta-analysis results from the discovery phase data of GIANT consortium studies were used to compare with that from CPASSOC analysis. The CPASSOC analysis was able to identify 17 loci associated with anthropometric traits that were missed by conventional meta-analysis. Among these loci, 16 have been reported in literature by including additional samples and 1 is novel. We also demonstrated that CPASSOC is able to detect pleiotropic effects when analyzing multiple traits. For over a decade, genome-wide association studies (GWASs) have been a major tool for detecting genetic variants underlying complex traits , includiSeveral multivariate analysis approaches for analyzing multiple phenotypes have been developed to further improve statistical power and provide biological interpretation of results, and their pros and cons were well reviewed . StatistIn this paper, we hypothesized that genetic variants commonly underlying the three anthropometric traits can be identified by combining all three traits when single trait analyses may not have enough power to detect them. Since the three traits are correlated, conventional meta-analysis assuming independence will result in an inflated type I error. In addition, it is unclear whether there are any overlapped or related samples among GIANT consortium studies, which would lead to correlated summary statistics among cohorts. Findings from meta-analysis accounting for correlation among cohorts will result in a more accurate estimation of genetic effects on traits. In this study, we compared the results of CPASSOC with that from conventional meta-analysis of GIANT consortium studies when using the same discovery phase data set. We performed meta-analysis with summary statistics of height, BMI, and WHRadjBMI obtained from the GIANT consortium  using thhttps://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files). The correlations among the six cohorts were calculated using the summary statistics of common SNPs after linkage disequilibrium pruning . SHom makes an assumption that genetic effect is homogenous across traits and cohorts while SHet assumes genetic heterogeneity. SHom and SHet. The genomic control values (λ) for statistics SHom, are 1.078, 1.079, 1.025 and 1.092 for height, BMI, WHRadjBMI and combining the three traits, respectively. For SHet, the λ values are 0.998, 1.006, 0.994 and 0.992 for height, BMI, WHRadjBMI and combining three traits, respectively. We did not observe any inflation for SHet but slight inflation was observed for height and BMI for SHom. Both height and BMI have many causal variants, which can inflate the corresponding λ values. In the original and the latest GIANT reports ..SHom andSHom and SHet but were missed by the conventional meta-analysis (SHom and SHet and were located more than 500 kb apart. These two variants were in weak linkage disequilibrium (r2 = 0.4). Thus, we consider them as two separate signals. Variant rs17391694 was reported to be associated with height by the GIANT study using conventional meta-analysis when combining discovery and follow-up phase data [SHet. SNP rs6441170 as well as rs13107325 were reported in later GIANT studies for height [2 = 0.84) with reported SNP rs4640244 in later GIANT studies for height [TOX  that were missed by conventional meta-analysis for a single trait analysis when using the same discovery phase data. All 10 loci  resides within TOX . The downloaded data include sex-specific [The summary statistics of height, BMI and WHRadjBMI were downloaded from the GIANT consortium website T represents a vector of test statistics for testing the association of a SNP with K traits. We used a Wald test statistic kth trait in the jth cohort. SHom is then defined asχ2 distribution with one degree of freedom, where eT =  has length J × K and W is a diagonal matrix of weights for the individual test statistics. We used the sample sizes for the weights, i.e., nj of the jth cohort.We applied the CPASSOC package developed by Zhu et al.  to combis method  but accoSHet, we first letT(τ) is the sub-vector of T satisfying |Tjk| > τ for a given τ>0, and R(τ) is a sub-matrix of R representing the correlation matrix, and W(τ) be the diagonal submatrix of W, corresponding to T(τ). Here we let SHet = maxτ>0S(τ).To define SHet does not follow a standard distribution but can be evaluated using simulation. SHet is an extension of SHom but power can be improved when the genetic effect sizes vary for different traits. The distribution of SHet under the null hypothesis can be obtained through simulations or approximated by an estimated beta distribution. We first applied both SHom and SHet to combine sex-specific summary statistics for each of the three traits and compared the results with those from conventional meta-analysis of the same discovery phase data in GIANT consortium studies [SHom and SHet for combining all the sex-specific summary statistics of the three traits: height, BMI and WHRadjBMI. We hypothesized that meta-analyzing multiple traits would allow us to identify additional variants that are likely to be missed by the conventional meta-analyses for a single trait.The asymptotic distribution of  studies –8. We nehttp://www.ncbi.nlm.nih.gov/gap). In brief, the ARIC EA cohort includes 9,707 individuals with approximately 840,000 SNPs genotyped on the Affymetrix Array 6.0 [2 threshold of 0.2 using the software PLINK (http://pngu.mgh.harvard.edu/purcell/plink/). SNPs with large effect sizes may represent true association, and consequently may inflate correlation among summary statistics. Therefore, we removed SNPs whose summary statistics Z scores were greater than 1.96 or less than -1.96. The final SNP sets for correlation estimation include 81,322 SNPs for height, 82,012 SNPs for BMI, and 81,130 SNPs for WHRadjBMI. We chose the common sets of SNPs for both sexes and three traits that can be mapped to dbSNP human Build 142 to perform the CPASSOC analyses. The numbers of SNPs used in this study are presented in To perform CPASSOC analysis, a correlation matrix is required to account for the correlation among phenotypes or induced by overlapped or related samples from different cohorts. Zhu et al.  suggesterray 6.0 , 27. We P < 5 × 10−8) by CPASSOC from sex-specific data [P = 5 × 10−8 as in GWAS because CPASSOC performs the same number of tests although multiple traits are analyzed. To do this, for a SNP reaching P < 5 × 10−8 by either SHom or SHet, we examined the region within 500 kb of each side of the SNP. The SNP was considered to be identified only by CPASSOC if no SNPs that are genome-wide significant with conventional meta-analysis from the discovery phase data were found in the 1.0 Mb region, and it is not in LD with the index SNPs of the GIANT studies. We performed meta-analysis by combining male and female data for each trait separately, as well as by combining all the three traits and both sexes.We reported loci that reached genome-wide significance Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency  or rare (MAF<0.005) variant, including Arg123His in LCAT. Gene-based associations (P<10−10) support a role for coding variants in LIPC and LIPG with lipoprotein subclass traits. 30 established lipid-associated loci had a stronger association for a subclass trait than any conventional trait. These novel association signals provide further insight into the molecular basis of dyslipidemia and the etiology of metabolic disorders.Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at  LIPC and LIPG with HDL subclasses. Investigating the genetic association of lipoprotein subclass traits may help lead to a better understanding of the etiology of cardio-metabolic diseases, and provide novel therapeutic targets.Lipid and lipoproteins are heritable traits that differ in content and size and are correlated with coronary heart disease and mortality. To identify genetic variants associated with different subclasses of lipoproteins, we conducted a genome-wide association study of 8,372 Finnish men. We curated a dataset of all genetic variants known to be associated with lipid or lipoprotein subclasses and used these data to conduct rigorous analyses to identify new associations in the same gene region or new ones. We identified five new signals at established lipid-associated loci revealing possible complex regulatory mechanisms underlying the signals. Using the contribution of rare coding variants predicted to be protein truncating or missense, we uncovered novel associations for a set of variants at  Genome-wide association studies (GWAS) have identified hundreds of common MAF>0.05) variants associated with conventional lipid and lipoprotein traits: high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TG)–4. While variantsr2<0.8); non; nonHIF3datasets . Hyper-m children,22. HIF3 subunit), which hALB and SYS1  located in intron 1 of ANKRD17 was associated with increased (β = 0.60) concentration of small LDL particles  and 26 additional traits, including increased TC, LDL-C, esterified cholesterol, free cholesterol, and IDL/LDL/VLDL subclasses  with this variant span >1.2 Mb (r2<0.01) and moderate (r2 = 0.39) pairwise LD with rs187918276 (Psingle= 5×10−15), suggesting the signals are distinct. An additional variant at this locus, rs115136538, was reported previously to be associated with albumin levels. The. TheLIPG.5×10−5) . rs53850analyses , Fig 1C.the liver. SeveralLIPC (Pgene = 7.1×10−11) and LIPG (Pgene= 3.8×10−17) were associated with lipid and lipoprotein subclasses using the gene-based method; these results remained significant after adjusting for nearby noncoding signals (LIPC P<1.3×10−10 and LIPG P<1.2×10−17)  based on the predicted function of the coding variants. Sets of variants in 2×10−17) .LIPC, the set of five rare missense variants, R138C, A145T, R208H, R281Q, and R329H, showed the strongest association using the protein truncating variant (PTV)+missense mask with triglycerides in very large HDL (Pgene = 7.1×10−11). Of the five single-variant tests of association with triglycerides in very large HDL, A145T was individually the most significant (Pdiscovery= 5.3×10−8). Four of the variants  showed higher trait levels (β = 0.72 to 1.8) and were predicted to be deleterious by Variant Effect Predictor (VEP), while R329H, observed in one individual, showed a modestly lower trait level (β = –0.24) and was predicted to be benign[LIPC expression can result in increased large HDL levels[At be benign. Three obe benign. Among tDL levels, the rarDL levels.LIPG, the PTV+missense mask showed five variants with the strongest association with phospholipids in medium-size HDL (Pgene= 3.8×10−17). Of the five single-variant tests, a rare missense (A172V) variant rs201922257 was the only one significantly associated (Pdiscovery= 8.6×10−9) with the subclass trait, and in three of four transcripts the amino acid substitution is predicted by VEP to be ‘deleterious’ and ‘probably damaging’ in most of the transcripts (r2 = 0.98) with the non-coding index variant rs538509310 for phospholipids in medium HDL  is located at the 3’ end of intron 1; this variant has only been observed once  in non-Finnish ExAC samples. Based on position, this splice variant is predicted to cause skipping of exon 2, which would lead to four aberrantly coded amino acids and a stop codon in exon 3. The remaining missense variants are predicted by VEP to be deleterious except for E391K. N396S and E391K have been reported previously to be associated with increased HDL-C levels[LIPG function and HDL-C subclass levels.At nscripts . This vadium HDL , Fig 1A.-C levels. HoweverP<5×10−8) here with at least one subclass trait, 30 showed stronger association with a subclass trait than any conventional trait (PLTP (phospholipid transfer protein), rs4812975 was much more strongly associated with HDL diameter (Psubclass = 1.4×10−15) than with HDL-C , consistent with PLTP mediating the net transfer of phospholipids between lipoproteins and uptake of phospholipids into the HDL-C core[ANGPTL3 (angiopoietin-like 3), ANGPTL4 (angiopoietin-like 4), and LPL (lipoprotein lipase), the variants were all more strongly associated with VLDL subclass traits than with the conventional traits  than any conventional trait  loci . Variantese genes.r2>0.95) to 885 variants to account for multiple known signals in a genome-wide conditional analysis. With this approach, we identified five novel signals at established lipid loci. We confirmed that signals were independent by reciprocal conditional analyses.In this study we conducted GWAS for 72 lipid and lipoprotein subclass traits in 8,372 Finnish men participating the METSIM study, and focused on identifying association signals that had not been identified previously with any lipid or lipoprotein trait. From the literature of existing lipid and lipoprotein association studies, we identified 1,714 cholesterol, TG, lipid, and lipoprotein-associated variants. We trimmed this list based on LD . All study participants gave their written informed consent.2)[Among the 10,197 participants in the METSIM study, we analyzed 8,372 non-diabetic individuals (mean age 57±7 SD years and BMI 26.8±3.8 kg/m2).https://cran.r-project.org/web/packages/ellipse/) and lattice packages (http://lattice.r-forge.r-project.org/) within R . Based on principal component analysis, eighteen exome array sample duplicates, one individual each from seven monozygotic twin pairs, and twelve population outliers were removed from analysis. Due to sex chromosome inconsistencies, fourteen OmniExpress samples were removed. Samples with low genotype call rate (<95%) for either array were removed. Variants with low-mapping quality to build hg19, low genotype completeness (<95% for OmniExpress and <98% for exome array), or multi-allelic variants were removed. The remaining high quality variants were phased using Shape-It v2[We genotyped the study samples using the HumanOmniExpress-12v1_C BeadChip and Infinium HumanExome-12 v1.0 BeadChip, resulting in 631,879 and 236,849 variants, respectively. Imputation was performed using the GoT2D reference panel of >19M variants  based on whole-genome sequence of 2,657 Europeans consisting of German, Swedish, Finnish, and British participants; with the majority of the cohort comprised of Finns. The resape-It v2.http://genome.sph.umich.edu/wiki/epacts). Traits were adjusted for age, age2, smoking status, and lipid lowering medication. Residuals were inverse normalized. To assess the level of genomic inflation, we calculated the genomic control statistic (λGC) for all of the trait-variant associations using R[We tested for association using imputed dosages for all variants with summed minor allele count dosage >1 with each of the 72 lipid and lipoprotein traits assuming an additive model and accounting for cryptic relatedness using the EMMAX linear mixed model approach as implemented in EPACTS http://gehttps://www.ncbi.nlm.nih.gov/pubmed/) and Google Scholar (https://scholar.google.com/), screened a GWAS Catalog (http://www.ebi.ac.uk/gwas/), and used SNIPPER (https://csg.sph.umich.edu/boehnke/snipper/) to query publicly accessible databases. The resulting curated list contained 1,714 variants, at >150 loci from 33 studies  the compiled list of 1,714 variants  after genome-wide conditional analysis were considered novel and further tested by single-variant conditional analyses to determine independence. At each of the five loci (Psingle<5×10−8) after single-variant conditional analysis were considered independent. Signals that only achieved a significance threshold of Psingle<5×10−6 after single-variant conditional analysis were considered distinct. The single-variant conditional analyses considered variants within 1 Mb of the signal, which accounts for <1% of the genome. Therefore, the significance thresholds for the distinct and independent additional signals are conservative. At each locus, we validated that signals were distinct/independent by reciprocal conditional analysis with the putative novel lead associated variant for the trait. Additionally, the association data for the novel signals was adjusted for each of the four conventional traits , and the effect of the association is reported in We LD-pruned : no MAF limit; variants are nonsense, frameshift, or essential splice variants. PTV+missense: MAF<1%; all PTVs and missense variants. PTV+Nonsynonymous strict (NSstrict): no MAF limit; all PTVs and missense variants predicted as deleterious by five variant annotation algorithms: LRT, Mutation Taster, PolyPhen2-HumDiv, PolyPhen2-HumVar, and SIFT. PTV+NSstrict+NSbroad: MAF<1%; all variants in PTV+NSstrict and variants predicted to be deleterious by any of the five algorithms above. Only genes containing two or more variants in a given mask were tested. We conducted gene-based conditional analyses to determine whether a single variant or a net-effect of multiple variants could explain the observed association signal.To determine the contribution of rare coding variants, we used the Optimal Sequence Kernel Association Test (SKAT-O) with EMMAX, as implemented in EPACTS, to test for gene-based associations with the 72 lipoprotein traits. Only cor2>0.7 in METSIM) at each signal were within ChIP-seq peaks of epigenomic transcriptional regulatory elements Click here for additional data file.S2 FigCorrelogram showing Pearson correlations between lipoprotein traits. Rows and columns are ordered according to a complete linkage hierarchical clustering of traits based on the Pearson correlation matrix. Percent correlation is shown for each trait-trait comparison. The color scale is from red (negative correlation) to blue (positive correlation). The shapes are representations of the correlation with a circle representing ‘no correlation’, ellipse representing ‘moderate correlation’, and a diagonal line representing ‘high correlation’.(PDF)Click here for additional data file.S3 Fig10 for the variant-trait association. The horizontal line is at the cutoff P = 5×10−8.Manhattan plots for the five traits with signals identified from this study. X-axis shows the chromosomes, and the y-axis is the–log(PDF)Click here for additional data file.S4 Fig2) between each variant and the reference variant (purple diamond); X-axis, genomic (GRCh37/hg19) position in Mb; Left y-axis, p- value of variant-trait association in–log10; Right y-axis, local estimates of genomic recombination rate in cM/Mb, represented by blue vertical lines.Each circle represents a single variant. The color is based on LD (r(PDF)Click here for additional data file.S5 Fig2) between each variant and the reference variant (purple diamond); X-axis, genomic (GRCh37/hg19) position in Mb; Left y-axis, p-value of variant-trait association in–log10; Right y-axis, local estimates of genomic recombination rate in cM/Mb, represented by blue vertical lines.Each circle represents a single variant. The color is based on LD (r(PDF)Click here for additional data file.S6 FigPvalue is shown in -log10 and in the direction (+ or −) of the effect (Beta). The red line denotes the significance cutoff of P≤5E-8. The red asterisk indicates the most significantly associated trait. CAD, coronary artery disease.The (PDF)Click here for additional data file.S7 FigPvalue is shown in -log10 and in the direction (+ or −) of the effect (Beta). The red line denotes the significance cutoff of P≤5E-8. The red asterisk indicates the most significantly associated trait. CAD, coronary artery disease.The (PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table−8) 32,524 trait-variant associations; Chr:position, hg19 chromosome and position; Genotype counts for homozygous reference allele/heterozygous/homozygous alternate allele; MAC, minor allele count; MAF, minor allele frequency; P-value, best unconditional p-value for any of the 72 traits; Beta, effect size of the alternate allele; Trait, trait with strongest association.3,784 unique variants that comprise the significant (P<5×10(XLSX)Click here for additional data file.S3 TableVariants and association data in this table were collected from 33 published GWAS, fine-mapping, and exome-sequencing studies. The reported trait, Beta, and P-value are taken from the study with the lowest p-value. N.R., data not reported in the study; Chr, chromosome; Position, hg19; Trait, strongest associated trait from study shown; EA, effect allele; NEA, non-effect allele; N, sample size of the study; Reference, the first study in the list is the study with the lowest p-value for the given trait.(XLSX)Click here for additional data file.S4 Table2>0.95 cutoff. The reported trait, Beta, and P-value are taken from the study with the lowest p-value. N.R., data not reported in the study; Chr, chromosome; Position, hg19; Trait, strongest associated trait from study shown; EA, effect allele; NEA, non-effect allele; N, sample size of the study; Reference, the first study in the list is the study with the lowest p-value for the given trait.Variants on this table were trimmed from the 1,714 variants on (XLSX)Click here for additional data file.S5 TableTrait, subclass trait (defined in (XLSX)Click here for additional data file.S6 TablePdiscovery, unconditional p-value for the lead variant and trait; Pconditional, p-value for the lead variant after conditioning on 885 known lipid GWAS variants Click here for additional data file.S7 TablePdiscovery, unconditional p-value for the lead variant and trait. § PHDL, PLDL, PTC, and PTG, p-value for the lead variant and trait after adjusting for each of the four traits.Lead variant, the most associated variant for the Lead Trait at a given locus; Lead trait, the lipoprotein subclass or triglyceride measure with lowest p-value across the 72 traits; Locus, biologically relevant gene within 1 Mb of lead variant; Chr, chromosome; MAF, minor allele frequency; (XLSX)Click here for additional data file.S8 Table2>0.7) with the lead trait variant (in bold) from this study and overlapped a regulatory element in relevant tissue datasets. Abbreviations of the tissues tested for overlapping of regulatory elements are: A, adipose; B, blood; L, liver. Chr, chromosome; Nearest coding TSS, distance from nearest GENCODEv12 basic annotation transcription start site. Negative distance indicates the variant is upstream of the TSS relative to the direction of transcription; N, total number of overlapping datasets across experiments and cell types; Open chromatin, variants overlapping FAIRE/DNase hypersensitivity elements; ChIP-seq Peak, Variant overlaps ChIP-seq peaks. Transcription factor name:Tissue.Variants listed were in LD (r(XLSX)Click here for additional data file.S9 TablePgene, p-value for the gene and trait; Conditional variant, known lipid or lipoprotein associated variants used for single-marker conditional analyses; Chr:Position, the hg19 chromosome and base-pair position of the Conditional variant; P, p-value for the gene-based association after conditioning on the Conditional variant.Gene, gene tested in gene-based association tests; Lead trait, the lipoprotein subclass or triglyceride measure with lowest p-value across the 72 traits; (XLSX)Click here for additional data file.S10 TableP <5×10−5) for a subclass trait was stronger than for any of four conventional lipid traits . Log difference, log10. Chr, chromosome.Variants at established CAD loci for which the METSIM association ((XLSX)Click here for additional data file.


APOC3 gene in a founder population from the island of Crete in Greece (n = 1239) and find significant evidence for association with blood triglyceride levels with the previously reported R19X cardioprotective null mutation  and a second loss of function mutation, rs138326449 . The signal cannot be recapitulated by imputing genome-wide genotype data on a large reference panel of 5122 individuals including 249 with 4x WGS data from the same population. Gene-level meta-analysis with other studies reporting burden signals at APOC3 provides robust evidence for a replicable cardioprotective rare variant aggregation .Cohort-wide very low-depth whole-genome sequencing (WGS) can comprehensively capture low-frequency sequence variation for the cost of a dense genome-wide genotyping array. Here, we analyse 1x sequence data across the  APOC3 gene, coding for apolipoprotein CIII, have been associated with plasma TG levels at genome-wide significance in studies of ∼100 000 individuals  was found to have a cardioprotective effect in the Amish founder population (n ∼ 1800) (n ∼ 1000)  ,2. Apoliividuals . Power tividuals . In 2008 ∼ 1800) , and theAPOC3 was found to be associated with coronary heart disease and TG levels in the Exome Sequencing Project study across ∼110 000 individuals from cosmopolitan populations  variants in ulations . RecentlAPOC3 gene , using a threshold of 1 × 10−8 to define genome-wide significance. Two variants exceeded this threshold, the null mutation R19X , which is a C/T substitution in exon 2 that changes codon 19 into a premature stop codon, and the splice donor variant rs138326449 , located 1 base pair downstream, which disrupts the donor splice site in intron 2. These two variants are in very low linkage disequilibrium (LD) (r2 < 0.0001)  study were sequenced at 1x depth and 249 at 4x depth using Illumina HiSeq . Following variant calling and imputation-based genotype refinement, we identified 57 single nucleotide variants (SNVs) in the  0.0001) .Figure n = 1087), and found both variants to remain significantly associated with TG levels , further confirming the validity of this signal.To confirm genotype calling and imputation accuracy, we genotyped both R19X and rs138326449 in a subset of 1087 individuals using Sequenom massARRAY technology. In total, 98.9% of all genotypes were concordant for R19X and 99.1% for rs138326449. Minor allele concordance reached 72.2 and 80%, respectively. The fraction of true positives among non-reference calls, or positive predictive value (PPV) was high for both variants (96.3 and 100%), indicating that most mismatches were caused by false negatives rather than overconfidence in calling the alternate allele. We repeated the association analysis using the directly genotyped samples (APOC3 (APOC3-001) . The resulting amino acid substitution (R19L) is predicted to be deleterious by SIFT , Cambridge, MA. accessed April 2015). Lastly, rs187628630 was also included in the burden, as it resides in the 3’ untranslated region (UTR) of APOC3-001. The burden test was performed using SKAT (P = 3.0 × 10−18) (P = 6.15 × 10−10). R19X is not in LD with rs138326449, 11:116701489 or rs187628630, therefore this burden constitutes an independent association not driven by R19X. When rs187628630 and 11:116701489 are removed from the model, the significance of the association with TGs stays unchanged (P = 4.3 × 10−18), but when both R19X and rs138326449 are removed, the association is fully attenuated (P = 0.49).For burden testing, we restricted our focus on the four potentially functional rare or low-frequency [minor allele frequency (MAF) < 5%] variants that reside in exons or the essential splice sites in the consensus splice variant of OC3-001) . These i by SIFT  and is n by SIFT , Exome S by SIFT  or Exomeing SKAT  on the s× 10−18) . EvidencAPOC3 by two recent large-scale exome sequencing studies. The first, by Crosby et al. . Inhibition of apolipoprotein C-III in pre-clinical and clinical studies has been shown to reduce plasma TGs, a major risk factor for cardiovascular disease  overlapped with the sequenced samples, were merged and imputed up to an in-house reference panel constructed with IMPUTE2 , which is directly typed on the ExomeChip array, but absent from the combined reference panel. Three of the four rare variants included in the sequence-based burden test are present in the imputed data, with the exception of 11:116701489. Imputation quality scores for rs138326449 and rs187628630 are 0.49 and 0.70, respectively, and their association P-values for TGs are 0.045 and 0.19 (2.23 × 10−3 and 0.24 for HDL). The lipid-associated burden of these three variants (P = 6 × 10−13) is fully attenuated when R19X is removed .Despite being imputed up to a large reference panel including WGS from the same founder population, the only signal above genome-wide significance in the imputed GWAS dataset is R19X  on the island of Crete. Blood lipids were assessed using enzymatic colorimetric assays including total cholesterol (cholesterol oxidase—phenol aminophenazone method), HDL-cholesterol and TGs (glycerol-3-phosphate oxidase—phenol aminophenazone). The study was approved by the Harokopio University Bioethics Committee, and informed consent was obtained from all subjects. The Mylopotamos villages, which include Anogia, Zoniana, Livadia and Gonies , have remained geographically isolated for an estimated 1000 years. The genetic isolatedness and demographic history of this population has been established previously .Nine hundred and ninety-five samples were sequenced at 1x depth and 249 samples at 4x depth using Illumina HiSeq 2000 and Illumina HiSeq 2500 sequencers. Both datasets were then converted to the BAM format and aligned separately using BWA . The 4x N = 1239) using Beagle v.4 . Analysis was restricted to the APOC3 gene region given by Ensembl, and examined suggestive association signals as well as existing loci associated with lipid traits.After phenotype QC, 1192 TG values remained in the sample. TG levels in mmol.lng GEMMA  v.0.94 uP = 4.37 × 10−6). A common synonymous variant in exon 3, rs4520, has previously been implicated in hyperlipidaemia in a small-scale study (P = 0.55) and is in low LD with all predicted loss-of-function (LoF) variants in APOC3  in MANOLIS. We identified one further variant in the 3’ UTR in exon 4, rs5128, which has previously been associated with lipid levels in a candidate gene study in the Hutterite founder population (P = 4.17 × 10−4) but is in very low LD with functional variants . These low LD figures suggest that these early common-frequency associations reported in the literature were not driven by linkage between the reported variants and one or other loss-of-function variants within APOC3.We noted the presence of the common intronic variant rs5130 (MAF 15.2%), which is not mentioned in the literature and is absent from the Global Lipids Genetics Consortium  dataset le study , but is pulation . rs5128,Supplementary Material is available at HMG online.