The aim of this study was to evaluate the level of introgression of breeds in the Canchim (CA: 62.5% Charolais—37.5% Zebu) and MA genetic group (MA: 65.6% Charolais—34.4% Zebu) cattle using genomic information on Charolais (CH), Nelore (NE), and Indubrasil (IB) breeds. The number of animals used was 395 (CA and MA), 763 (NE), 338 (CH), and 37 (IB). The Bovine50SNP BeadChip from Illumina panel was used to estimate the levels of introgression of breeds considering the Maximum likelihood, Bayesian, and Single Regression method. After genotype quality control, 32,308 SNPs were considered in the analysis. Furthermore, three thresholds to prune out SNPs in linkage disequilibrium higher than 0.10, 0.05, and 0.01 were considered, resulting in 15,286, 7,652, and 1,582 SNPs, respectively. For k = 2, the proportion of taurine and indicine varied from the expected proportion based on pedigree for all methods studied. For k = 3, the Regression method was able to differentiate the animals in three main clusters assigned to each purebred breed, showing more reasonable according to its biological viewpoint. Analyzing the data considering k = 2 seems to be more appropriate for Canchim-MA animals due to its biological interpretation. The usage of 32,308 SNPs in the analyses resulted in similar findings between the estimated and expected breed proportions. Using the Regression approach, a contribution of Indubrasil was observed in Canchim-MA when k = 3 was considered. Genetic parameter estimation could account for this breed composition information as a source of variation in order to improve the accuracy of genetic models. Our findings may help assemble appropriate reference populations for genomic prediction for Canchim-MA in order to improve prediction accuracy. Using the information on the level of introgression in each individual could also be useful in breeding or crossing design to improve individual heterosis in crossbred cattle. Bos primigenius indicus) and the higher productive and carcass traits presented by European cattle (Bos primigenius taurus) [Breeding methods using different genetic groups or breeds are very common in animal production, which aims to explore the maximum heterosis on the crossbred individuals as well as for producing composite breeds . In Braz taurus) . As an e taurus) –4.Canchim cattle has a small inventory of around 30 thousand registered animals, however, this breed has excellent meat yield and quality and performs well when raised on natural pastures of Brazil. The genetic makeup of this composite breed was a 62.5% Charolais and 37.5% Zebu proportion. The genetic group called MA (resulting from mating between Charolais bulls and ½ Canchim + ½ Zebu dams) has an expected genetic makeup of 65.6% Charolais and 34.4% Zebu and is widely used by breeders in order to expand the genetic basis of Canchim , and genBeef cattle crossbred populations present animals with different breed compositions. According to Sölkner et al. , the intThe complexity of studying composite breeds is due to the occurrence of population stratification, which has a major impact on genomic analyses (i.e. genomic prediction and genome-wide association studies) . The chaThis study was approved by the Embrapa Southeast Livestock Ethical Committee for Animal Use (CEUA-CPPSE) under protocol 02/2009 . All Charolais animals were managed according to the guidelines established by the Canadian Council of Animal Care in 1993.All animals except steers from Charolais breed were genotyped with the BovineHD Genotyping BeadChip from Illumina . Charolais steers were genotyped with the Bovine50SNP BeadChip from Illumina . Thus, this study was carried out using the following datasets:285 animals from the Canchim breed (CA) and 114 animals from the MA genetic group (MA) provided by Embrapa Southeast Livestock, located in the municipality of São Carlos, SP, Brazil. Canchim and MA animals were the progeny of 49 bulls and 355 dams, born in seven farms in the states of São Paulo and Goiás between 2003 and 2005.814 animals from the Nelore breed (NE) which are the progeny of 34 bulls, born in the facilities of Embrapa Southeast Livestock and Embrapa Beef Cattle  between 2007 and 2009.405 steers from the Charolais breed (CH) provided by “Agriculture and Agri-Food Canada Research Centre (AAFC)”, from Canada. Animals were the progeny of 44 bulls, born in the facilities of Onefour Research Ranch, Lethbridge, AB, Canada, and in the Kinsella Research Station, Kinsella, AB, Canada. Animals were born between 2004 and 2009 and a full description was presented by Mao et al. .38 animals from the Indubrasil breed (IB) provided by Embrapa Dairy Cattle, located in Juiz de Fora, MG, Brazil. This breed was developed in Brazil from crossings between Gir, Guzerat, and Nelore, all from indicine origin .We highlight that the Charolais, Nelore, and Indubrasil animals herein considered had no direct genetic relationship with Canchim-MA individuals. In B. p. taurus and B. p. indicus proportions and the contribution from the purebreds were evaluated. By means of pedigree verification, the average expected proportion of B. p. taurus in C1, C2, C3, CA, and MA were 0.57, 0.67, 0.64, 0.62, and 0.65, respectively.The target animals in our study were Canchim and MA. All other breeds were used for the identification of population stratification and breed composition estimation. Thus, Data of animals genotyped with the high-density panel  were extracted to a 50K SNP panel considering the Bovine50SNP map (UMD_3.1 bovine genome assembly). Only autosomal chromosomes and SNPs with known position were used.The following steps were conducted within PLINK software ,19 for gPrincipal component analyses (PCA) were carried out for each breed separately  and considering all breeds together. The function—pca, from PLINK software, followed by the number of principal components desired was used. The PCA were calculated from the genomic relationship matrix and aided in determining clusters of individuals that may or not differ within the breeds. If animals were observed out of the breed main cluster, they were excluded. This exclusion was carried out for CH, NE, and IB breeds. Thus, the final number of animals used for introgression analyses were 395 (CA and MA), 763 (NE), 338 (CH), and 37 (IB).As the introgression analyses assumes that loci are independent within populations (further explanation in the next section) and that the heterozygosity estimates are sensitive to various ascertainment biases when loci discovered in one breed are used to genotype other breeds , we cons2) higher than 0.10, 0.05, and 0.01 were used to remove SNPs, resulting in three datasets with the number of SNPs of 15,286 (15K), 7,652 (7K), and 1,582 (1K), respectively. The observed and expected heterozygosities for each breed were obtained by the function—hardy of PLINK. The number of SNPs per autosome is presented in The function—indep-pairwise was used to prune the genotypes, which considered 50 SNPs sliding window and each sliding window moved forward by 10 SNP each time. Three thresholds of linkage disequilibrium (r2) was measured as proposed by Hill and Robertson [pij is the frequency of the two-marker haplotype, and pi and pj are the marginal allelic frequencies in the ith and jth SNP, respectively [Ne is the effective population size, c the marker distance in Morgans . The age of Ne for any distance was estimated by Ne estimation.Linkage disequilibrium analyses were carried out for Canchim-MA, Nelore, and Charolais . The SNP1101 software, provided by Dr. Mehdi Sargolzaei (University of Guelph), was used for these analyses. The quality control considered the exclusion of minor allele frequency below 0.05 while all the other items described in PLINK were maintained. The linkage disequilibrium  was obtained using the ADMIXTURE software in pairwise analyses. The number of populations (k) varied from one to five. The 5-fold cross-validation procedure present in ADMIXTURE was used to define the best k, which should present low cross-validation error when compared to other k values. For the comparison between estimated proportions obtained in these analyses as well as the expected proportions based on pedigree, we calculated the—log of the differences of squared values between each analysis.To detect the introgression of breeds present in CA and MA, the ADMIXTURE  softwarej is the number of SNP genotypes in i unrelated subjects resulting from a composite population with k ancestry populations. Population k contributes a fraction qik of the ith individual genome. The allele 1 at the jth SNP has fkj frequency in the kth population.In the ADMIXTURE software , the likgij, which represents the observed number of copies of the allele 1 on marker j of the individual i. Assuming that the individuals are not related, the log likelihood for the full sample id given by:Q = {qik} and the population allele frequencies F = {fkj} have I x K and K x J dimensions for the total K(I + J) parameters. The convergence criteria used for all analyses was 10−6, by means of the block relaxation algorithm.The method accounts for linkage equilibrium between the markers and the data is recorded as The STRUCTURE software  considerX. The priors considered in the model are: populations of origin of the individuals (unknown) are denoted by Pr(Z), allele frequencies in all populations are denoted as Pr(P), and admixture proportions for each individual are denoted as Pr(Q). The elements of Q are qk(i), which represents the proportion of individual i’s genome that originated from population k. The elements of X are represented by xl, which is the observed allele in the lth locus for the ith individual. The elements of Z are represented by zl, which is equal to the population of origin of allele xl.In this method, genotypes of the sampled individuals are represented by P and Q can be represented by:j is the allele frequency at locus l in population k. Markov chain Monte Carlo were used for summarizing the information and to obtain the posterior mean for the estimates. The recombination rate was treated as uniform.Thus, the probability model to estimate y is the transformed genotypes  of dimension 32,308 by 1,534, X is a matrix of dimension 32,308 by 1 containing the allele frequencies for each breed, β is a vector of regression coefficients representing the percentage contribution of each breed to the animal in y, and e is a random residual vector. Allele frequencies were obtained using the function—freq of PLINK software. Analyses were carried out using the lm function present in R software [To predict the breed composition using the regression method , genotypes were transformed into copies of allele 1 and divided by 2, resulting in 0, 0.5, or 1. The following single regression model was used:software .The regression coefficients were mostly within the range from 0 to 1, however, negative and low values or values higher than 1 were observed. Negative values were coded as 0 and values higher than 1 were coded as 1. The coefficients were adjusted for each individual calculating the product of each regression coefficient with the sum of the regression coefficients. For k = 2 and k = 3, the allele frequencies of Nelore and Charolais; and Nelore, Charolais, and Indubrasil were used, respectively.In 2 values for Canchim, Nelore, and Charolais animals, respectively. The effective population sizes for Charolais and Nelore animals for generations 1, 5, 10, 20, 50, 100, and 200, based on the results of linkage disequilibrium analyses, are presented in The number of SNP considered from linkage disequilibrium analyses were 37,266 (Canchim-MA), 29,460 (Nelore), and 39,667 (Charolais). In To characterize the different breeds herein evaluated, principal component analyses were conducted. B. p. indicus and B. p. taurus, respectively. For k = 3, we assumed that the first, second, and third clusters are from indicine (Nelore), taurine (Charolais), and indicine (Indubrasil) contribution, respectively.In the B. p. taurus and B. p. indicus, when using k = 2, is presented in B. p. taurus present in Canchim was approximately 0.75 when k = 2. The average contributions obtained by the Regression method were similar to the pedigree expected proportions for k = 2 and k = 3 in the analysis considering 32,308, 15,286, and 7,652 SNPs (The average level of contribution (or ancestry contribution) of 652 SNPs  in CanchFor k = 2 , the SNPB. p. taurus contribution  and for Charolais contribution . We considered an empirical threshold of 2 to explore how the methods behave among each other and with the expected pedigree proportions of B. p. taurus or Charolais.Pearson correlations were not adequate to compare the methodologies and SNP densities to estimate the introgression of breeds present in Canchim animals, thus for the comparison between estimated proportions obtained in these analyses as well as the expected proportions based on pedigree were calculated using the—log of the differences of squared values between each analysis. The results of this approach are presented in Fst) in pairwise analyses are presented in Fst considering k = 2. For 32,308, 15,286, 7,652, and 1,582 SNPs the Fst estimates were equal to 0.09, 0.08, 0.06, and 0.04, respectively.Similar results between the analyses conducted using 32,308, 15,286, and 7,652 SNP were observed for ADMIXTURE and Structure software. The levels of ancestry divergence  up to physical distances of 40 Kb and 30 Kb, respectively. Lu et al. [2 for Angus, Charolais, and crossbred individuals (Angus-Charolais) using the Bovine50SNP BeadChip and obtained r2 above 0.20 up to 70 Kb, for Angus, and up to 30 Kb, for Charolais and crossbred individuals. In our study, we observed r2 values at a longer physical distance only for Charolais (40 Kb), while the other breeds presented r2 above 0.20 up to 20 Kb , which produce animals with expected higher 286 SNPs . Further286 SNPs , while B286 SNPs  and over286 SNPs  the anceAs observed by O’Brien et al. , the levFor k = 3 , the ADMAs observed by Ardlie et al. , recombiFst values are related to low differentiation between breeds, which can be observed between Canchim and Charolais  up to linkage disequilibrium pruning of 0.05  up to linkage disequilibrium pruning of 0.10. Higher differentiation was observed between Canchim and Indubrasil  than those observed with Nelore because Indubrasil had a former contribution to Canchim. Thus, the level of differentiation between B. p. taurus and B. p. indicus was higher when considering the analysis of 32,308 SNPs.Low  of 0.05 . The FstThis is the first work using genomic information to characterize the levels on introgression of beef breeds in Canchim cattle. The methods evaluated could also be suitable for inferring and discriminating animals without pedigree information by using genotype information to assign animals to correct the clusters based on the level of breed introgression. The results herein obtained could provide great information to future studies, such as genomic selection and design of breeding schemes in order to increase or maintain variability. As observed by Ventura et al. , genotypPrincipal component analyses helped to determine clusters of breeds as well as the proximity of Canchim and MA to Charolais. In MA genetic group, individuals were assigned to different clusters due to sire country of origin. Assign our analyses into two clusters (k = 2) seems to be more appropriate for analyzing Canchim-MA animals due to its biological interpretation (regarding the genetic makeup of the breed). Moreover, the usage of 30K density in the analyses presented similar results between the estimated and expected genetic proportions of introgressed breeds in Canchim-MA. In the Regression approach, a remote contribution of Indubrasil was observed in Canchim-MA when three clusters were considered (k = 3).Genetic parameter estimation in Canchim-MA could account for the level of each introgressed breed as a source of variation in order to improve the accuracy of genetic models. The results of this study may assist to assemble appropriate genomic prediction training population for Canchim and MA animals in order to improve prediction accuracy in genomic selection. Using the information on the level of introgression in each individual could help in breeding or crossing design to improve individual heterosis in Canchim cattle and other Brazilian composite breeds.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.S1 File(RAR)Click here for additional data file.


A genome-wide association study using 609,361 SNP markers and 1,811 animals was performed to detect genomic regions affecting fatty acid levels. These regions were further refined using sequencing data to impute millions of additional genetic variants. Results suggest associations of PAEP with the content of C4:0, AACS with the content of fatty acids C4:0-C6:0, NCOA6 or ACSS2 with the longer chain fatty acids C6:0-C14:0, and FASN mainly associated with content of C14:0. None of the top-ranking markers caused amino acid shifts but were mostly situated in putatively regulating regions and suggested a regulatory role of the QTLs. Sequencing mRNA from bovine milk confirmed the expression of all candidate genes which, combined with knowledge of their roles in fat biosynthesis, supports their potential role in de novo synthesis of bovine milk fatty acids.The relative abundance of specific fatty acids in milk can be important for consumer health and manufacturing properties of dairy products. Understanding of genes controlling milk fat synthesis may contribute to the development of dairy products with high quality and nutritional value. This study aims to identify key genes and genetic variants affecting  The difference in melting point between saturated and unsaturated acids also affects the softness, flavour and colour of dairy products such as butter and cheese6.Bovine milk is an important source of many nutrients including proteins, fat, minerals, vitamins and bioactive lipid components. The relative abundance and concentration of individual fatty acids (FAs) in milk affect both human health and the manufacturing properties of dairy products. Myristic (C14:0) and palmitic acid (C16:0) are associated with cardiovascular disease through increased level of blood cholesterol7 to optimally balance individual FAs allowing industry to satisfy consumer demands for healthy food of high quality. The short- and medium-chain length acids C4:0 to C14:0 are potential targets for this purpose. In contrast to the bulk of long-chained milk FAs and around half of C16:0 which are largely derived from the cow’s diet, C6:0 to C14:0 and a fraction of C4:0 are synthesized de novo in the bovine mammary gland8. These acids occur in milk in relatively high concentrations and show moderately high heritabilities 12 and are therefore well suited for genetic analyses such as a genome-wide association study (GWAS).By improving our understanding of the pathways in bovine milk FA synthesis and identifying the genes and genetic polymorphisms associated with variation in milk FA content, it may be achievable through genome-based selection methodsde novo-synthesis. The process of carbon chain elongation from C2:0 or C4:0 to C16:0 involves a cyclic reaction13 which also generates intermediate products, C4:0 to C14:0, via a chain termination mechanism14. Newly synthesised FAs are transported from the cytosol to the endoplasmic reticulum where they are linked to a glycerol 3-phosphate backbone to form triacylglycerols, a final series of steps sees them secreted into the milk in the form of milk fat globules.The synthesis of short- and medium-chained FAs is founded upon C2 and C4 precursors absorbed from the diet. After being transported to the mammary gland, acetate and acetoacetate are converted to acetyl-CoA and then to malonyl-CoA which, along with butyryl-CoA (from plasma β-hydroxybutyrate and C2), are used as precursors for cytosolic de novo-synthesis of short- and medium-chained FAs (C4:0 to C14:0). Milk fatty acid composition was predicted from Fourier transform infrared spectroscopy (FTIR) using prediction equations derived from GC/FTIR calibration sets. This method has been shown to provide fast and cheap large-scale phenotyping of the breeding population, especially for acids with relatively high concentration and heritability such as the de novo-synthesized FAs17. These phenotypes were combined with array-based single nucleotide polymorphism (SNP) genotypes in a genome-wide association study to identify chromosomal regions (quantitative trait loci - QTLs) with substantial effects on the traits under investigation. QTL regions identified on bovine chromosomes (BTA) 11, 13, 17 and 19 were re-analysed using a higher density of sequence variants (SNPs and indels) imputed from re-sequencing data in an attempt to identify putative functional polymorphisms. Moreover, mRNA sequence analysis of mammary epithelial cells from 36 milk samples was used to verify that the candidate genes indicated by GWAS were expressed in the mammary gland during milk production.The current study explores genetic variation associated with the de novo synthesis of these acids.Our analysis began with combining daughter yield deviations (DYDs) for C4:0 to C14:0 from 1,811 bulls with genotypes from 609,361 autosomal SNPs to perform a GWAS and identify chromosomal regions with a major impact on As shown in Fig. associated endometrial protein (PAEP) gene at 103.3 Mb. The QTL region on BTA13 was rather broad and covered at least two potential candidate genes; nuclear receptor coactivator 6 (NCOA6) at 64.6 Mb and acyl-CoA synthetase short-chain family member 2 (ACSS2) gene at 64.8 Mb. BTA17 displayed a QTL close to the acetoacetyl-CoA synthetase (AACS) gene at 53 Mb. Closer examinations of BTA19 revealed that the associations were located in two distinct regions; one at 37.4 Mb which is around 500 kb from acyl-CoA synthetase family member 2, mitochondrial precursor (ACSF2) at 36.9 Mb. The second QTL region was close to fatty acid synthase (FASN) at 51.4 Mb. However, analysis of sequence variants revealed that the significant markers detected around 36.9 Mb were not situated within or very close to ACSF2. Since no other convincing candidate gene was detected in this region, we chose not to follow up this QTL in further analyses.All major QTL regions spanned genes with an established function in milk fat biosynthesis. The QTL on BTA11 was detected close to the 18 reports an internally calculated parameter, allelic r-squared (AR2), that is the estimated squared correlation between the most likely allele and the true allele for each marker19. The mean value of this parameter ranged from 0.84 for variants with minor allele frequency (MAF) below 0.05 to 0.94 for variants with MAF above 0.05. Imputed SNPs and indels, with AR2 above 0.7, were included in a reanalysis of the QTLs for significant phenotype associations. Detailed information of the top significant variants on each chromosome for each FA tested, is shown in Table To characterize as much genetic variation as possible in and around the candidate genes we imputed SNPs and indels identified from whole genome sequence data resulting in a more than 20-fold increase in marker density after quality filtering in the regions 90–110 Mb on BTA11, 60–70 Mb on BTA13, 20–60 Mb on BTA17, and 45–55 Mb on BTA19. The quality of imputation relates most to marker allele frequencies. The Beagle softwarePAEP  was associated with a slight, but noteworthy increase in C4:0 levels (0.02 g/100 g milk fat). Closer examination of the haplotype containing the top ranked markers  revealed that the minor alleles for all markers were included in a single haplotype (frequency ≈ 0.3) that covered a region beginning 11 kb upstream from PAEP and extending into the neighbouring gene glycosyltransferase 6 domain containing 1 (GLT6D1). The high level of linkage disequilibrium (LD) among these markers , one splice region variant , and three SNPs in the 5′UTR of PAEP . The four top-ranked markers were grouped in a region ≈1,000 bp upstream of PAEP which can suggest a regulatory role of the QTL. Results for all tested markers and trait combinations are shown in Supplementary Fig. Results for BTA11 showed the strongest associations between C4:0 and a group of markers that were situated within and immediately outside of AEP Fig. . The mosNCOA6 and ACSS2. The most significant marker for C8:0, C10:0, C12:0 and C14:0 was rs381037433 at 64,523,817 bp , which is an intronic insertion (G/GA) in phosphatidylinositol glycan anchor biosynthesis class U (PIGU). The insertion had a frequency of 0.21 and was associated with a reduction of C8:0 level of 0.02 g/100 g milk fat. C6:0 was most significantly associated to rs41700742 at 64,648,620 bp , which is a synonymous SNP in NCOA6. LD (r2) between these two markers is 0.3.On BTA13, the most significant results were found for C8:0, with decreasing significance levels for acids with shorter and longer chain lengths. For all traits, we detected similar p-values for a large number of markers in a region spanning from approximately 63.5 to 65.4 Mb  among the most significant markers were generally low.Many other markers in the 63.5 to 65.4 Mb region displayed p-values and allele substitution effects similar to those of rs381037433. However, MAFs varied from 0.07 to 0.47. Haplotype analyses revealed that the least frequent allele of all these markers was present in one specific haplotype with a frequency of approximately 0.08 that spanned the entire 63.5 to 65.4 Mb region. For markers where the MAF was higher than 0.08, the least frequent allele was also found in other haplotypes. Hence, the LD  and rs446495267 in PIGU. Also, there were two 3′ UTR variants in zinc finger protein 341 (ZNF341) and ENSBTA00000000308. However, neither of these genes have a function that can easily be related to milk fat synthesis. All other significant markers were either synonymous (i.e. not causing an amino acid shift) or positioned in non-coding regions such as introns and intergenic regions. This suggests a regulatory role also for this QTL. Results for all tested markers and trait combinations are shown in Supplementary Fig. Only two non-synonymous SNPs were found among these top-ranking markers; rs383480158 in In agreement with the GWAS analysis, imputed sequence variants on BTA17 were found to have a main effect on short C4:0 and C6:0 fatty acids. The most significant association was found for C4:0 and rs477658921 at 53,078,216 bp , which does not appear to be an especially good functional candidate gene, but it is also in close proximity to AACS at 52.97–53.03 Mb which may be involved in utilizing ketone body for fatty acid-synthesis. LD among the top-ranking markers was high (r2 higher than 0.84), indicating that the significance of the rs47765892 polymorphism on C4:0 could be a result of polymorphisms in or near AACS. As with BTA11, we found that the least frequent alleles of the most significant markers were contained within a haplotype with a frequency of 0.083 that spanned AACS and BRI3BP. All the top-ranking markers are either situated in introns of or outside these two genes and suggest a regulatory role of the QTL.The rs477658921 indel is located within intron 1 of zinc finger protein 280B (ZNF280B). The LD between significant SNPs within this QTL region and the QTL embracing AACS and BRI3BP at 53.07 Mb is low which suggests that these are two different QTLs. The p-values of this second peak were approximately 1e-9 for all traits. Results for all tested markers and trait combinations are shown in Supplementary Fig. A second peak was detected at 51.49 Mb within the FASN and annotated both as an upstream gene variant of FASN and as a 3′ untranslated region (3′ UTR) variant and a non-coding exon variant in various predicted transcript variants of coiled-coil domain containing 57 (CCDC57). Similarly high significance levels were found for 24 variants situated either in introns of CCDC57 and FASN or in the region between these two genes . This G/C SNP has a MAF of 0.04 where the minor C allele was associated with a reduction of C14:0 level of 0.14 g/100 g milk fat. It is situated ≈3,7 kb upstream of the transcription start site of PAEP was the most abundantly expressed gene of those found to be significant in the association analyses, with a mean count of approximately 444,000 reads. No reads were found for its neighbour GLT6D1. The QTL region on BTA13 contains at least 39 characterised genes  of which 28 genes were found to be expressed in somatic milk cells. Mean expression levels varied from two to 5,407 normalised counts for these genes, with the highest expression found for ACSS2. NCOA6 showed the eight highest expression level of these genes. On BTA17, both AACS and BRI3BP were expressed in the SMC, but expression level of AACS was much higher than for BRI3BP. FASN on BTA19 showed the second highest expression level of the studied genes with a normalised read count of ~29,000, which was approximately 233 times higher than the expression level of the neighbour CCDC57.To verify that the candidate genes we detected from GWAS are present in the udder during lactation we isolated mRNA from somatic milk cells and measured their level of expression. Specifically, all genes in the region between 63.5 to 65.4 Mb on BTA13 were tested, along with candidates and, where appropriate, neighbour candidates on BTA11, 17 and 19. Expression levels in the form of mean normalised gene count can be found as Supplementary Table Understanding the role of genetic variation on fatty acid composition in bovine milk may reveal opportunities to produce superior raw product, and at the very least will improve our understanding of the genetics of fatty acid synthesis. Uniquely, our study combined data from 4.6 million FTIR recordings (FA composition phenotypes) representing 640,000 cows, with combined high-density genotyping and whole genome sequencing representing 1,811 bulls. This analysis allowed us to reveal a number of genetic variants associated with the synthesis of short- and medium-chained FAs C4:0 to C14:0.PAEP, which is a novel candidate gene in the context of fatty acid content, and several previously known candidate genes. Our results revealed that concentration of C4:0 was most strongly affected by PAEP on BTA11 and AACS on BTA17. The QTL on BTA13, which is most likely caused by NCOA6 or alternatively ACSS2, seems to be related to the generation of longer chain length acids, while the de novo-synthesis of the longest chain length acid, C14:0, is most strongly affected by a polymorphism in or around FASN on BTA19.We identified one gene, 24. Inaccurate predictions and correlations among acids or between acids and other milk components may reduce the ability to identify true QTLs and determine exactly which fatty acids that are affected. We have previously reported that FAs with a concentration of 1% or higher are predicted with acceptable accuracies12. This finding was also reflected in the current study, where all the tested de novo-synthesized acids  were present in concentrations above 1% and had prediction accuracies  ranging from 0.73 (C4:0) to 0.90 (C6:0 - C14:0) and hence were considered well predictable. An argument against using FTIR to predict FA composition is that the acids are correlated to total fat and the prediction merely reflects total fat rather than individual acids20. This correlation was accounted for in our two studies by presenting the fatty acid concentrations as percentages of total fat instead of as gram acid per unit of milk23. Soyeurt21 suggested that predictions were due to real absorbance of the acids if the calibration correlations were higher than the correlations between the acids and total fat. As reported in our previous study12, the squared correlation to total fat ranged between 0.001 and 0.012, indicating that the predicted concentrations are due to real absorbance values of these acids. A consequence of this normalization is that the prediction accuracies are expected to be lower than when FA concentrations are expressed as a quantity per unit of milk23, however with the exception of C4:0, the accuracies were found to be comparable to those obtained by milk-based models23. Although C4:0 was predicted with lower accuracy than the other FAs included in our study, our analysis detected two candidate genes with functions judged relevant for C4:0 content. Separating FAs with similar chain lengths such as C4:0 and C6:0 using an FTIR approach can be challenging since their chemical structure is relatively similar, however, the technology allowed us to identify two clearly different QTL profiles for C4:0 and C6:0 which, if the phenotype measurements were severely confounded together, would not be as distinct as they appear to be.A key condition for using phenotype data (FA composition) predicted from FTIR profiles in an association study is that individual acids can be predicted with a high degree of confidence. The effectiveness of mid-infrared spectroscopy to predict bovine milk fatty acid composition have been thoroughly discussed in a number of papersde novo-synthesis have, essentially, defined roles in bovine milk fat synthesis, and operate across the core pathways responsible for de novo-synthesis and triacylglycerol metabolism  is synthesised from acetyl-CoA and malonyl-CoA in a repeated, cyclic reaction. Importantly, intermediate length acids (C4:0 to C14:0) can leave the elongation cycle before the chain reaches full length. The entire lipid synthesis machine is regulated by a network of genes encoding transcription factors and nuclear receptors. One of these, peroxisome proliferator-activated receptor gamma (PPARG), is a well described transcriptional regulator affecting lipid storage27, while NCOA6 (which is a ligand for PPARG and PPARA29) is a transcriptional coactivator enhancing the activity of, among other things, PPARG.The genes highlighted as candidates for ism Fig. . Early iPAEP encodes the milk protein beta-lactoglobulin (β-LG) which is the major whey protein in bovine milk. Although the effect of PAEP on milk production traits including total fat yield and fat percentage has been well documented31, its influence on individual fatty acids is poorly understood. β-LG is found to bind both saturated and unsaturated FA in vitro32, which might suggest a function as an intracellular transporter of FAs. The B variant of the β-LG protein is commonly known to be less abundant than the A variant33, and it is unclear if the effect of PAEP on C4:0 found in our study is due to the polymorphisms causing the A and B β-LG variants or to regulatory sites affecting PAEP expression. Although the promoter region has been extensively studied, the causal polymorphism has not been identified due to an extensive level of LD between the two polymorphisms that produce the A and B protein variants and polymorphisms situtated within putative transcription factor binding sites37. The effect of this QTL on C4:0 could possibly be due to the combined influence of alterations in several sites simultaneously rather than to one specific SNP in one single site.ACSS240 and NCOA612 as positional and functional candidates. Due to high levels of LD among SNPs in the 2 Mb QTL region embracing these genes, our association analyses have expanded the BTA13 candidate list to include 39 characterised genes of which several have functions related to milk fat biosynthesis. This list also includes E2F transcription factor 1 (E2F1) which is shown to regulate important genes involved in FA synthesis such as ACSL1, FASN and PPARG41, and agouti signalling protein (ASIP) which might regulate lipid metabolism in adipocytes42. However, the most significant markers detected by the association analyses were either found in non-coding regions or genes without known relevant functions in fat synthesis such as PIGU. Expression analyses revealed that 28 of the 39 genes were expressed in the bovine mammary gland during lactation. While ACSS2 was distinctly more expressed than NCOA6 in all samples  or the use of different breeds.Our finding of an association between C4:0 and markers near FASN and the neighbouring gene CCDC5744. CCDC57 is poorly characterised, and its putative role in milk fat synthesis is unknown. Medrano et al.45 reported that CCDC57 was expressed in mammary tissues of a lactating cow with expression levels higher than that of FASN. This is in contrast with the results of the present paper, where FASN was expressed more than 200 times higher than CCDC57. FASN is an obvious candidate gene because of its known role in fat synthesis. FASN has been extensively studied in candidate gene studies for fat content in milk and adipose tissue51, but similarly to the present study, this has not yet resulted in a clear identification of a causal polymorphism.For BTA19, several authors have reported significant associations within or near diacylglycerol acyltransferase 1 (DGAT1) on BTA14 and stearoyl-coenzyme A desaturase 1 (SCD) on BTA26. DGAT1 encodes an enzyme that catalyses the final stage of triacylglycerol synthesis52, while SCD on BTA26 is involved in the synthesis of monounsaturated FAs by introducing a double bond in the delta-9 position of C14:0, C16:0 and C18:0, primarily, thus producing the cis-9 variant of these acids53. No significant associations have been detected near these genes in any of our studies of Norwegian Red cattle. Resequencing of 147 widely used NR bulls revealed that all individuals were homozygous for the A variant of the DGAT1 K232A polymorphism (data not shown), suggesting that this variant is almost fixed in Norwegian Red cattle. The SCD polymorphism does segregate in our breed but was not significantly associated with any fatty acid neither in the present study nor in the previous study where a larger number of acids also including C14:1cis-9 and C16:1cis-9 were analysed12. However, these acids were poorly predicted by the FTIR approach12, which hampers the possibility to detect significant associations for these traits.Most genome scans performed in other cattle breeds than Norwegian Red cattle have reported strong associations between milk fatty acids and the genes 54. In this study, 153 animals were whole genome sequenced and used as the imputation reference. When performed within breed, imputation for high-density genotypes to sequence has previously been shown to work acceptably with reference dataset of about 13055. We did not perform a cross validation procedure to test the expected accuracy in our dataset, but Beagle outputs a measure (AR2), defined as estimated squared correlation between most probable and true genotype, depending on the internally calculated uncertainty in the imputation model for each marker56. All markers with AR2 below 0.7 was filtered from our marker list before association analysis, as values above this threshold has shown to be a good indicator for reliable imputation accuracies58. Overall, mean AR2 was 0.92 for all sequence-level imputed variants, and 0.84 for variants with MAF below 0.05. AR2 was close to 1 for all variants found significant by the association analyses of sequence-level variants.Imputation from HD-density to sequence level is heavily dependent upon MAFs and number of animals in the reference datasetde novo synthesis of milk fatty acids. These genes were PAEP (on BTA11), AACS (BTA17), NCOA6 or ACSS2 (BTA13) and FASN (BTA19). Their roles in fatty acid synthesis were further supported by their expression levels in milk.Understanding of genes and polymorphisms controlling milk fat synthesis may reveal opportunities to tailor the fatty acid content and thereby improve the nutritional value and quality of dairy products. In this study we identified a set of positional candidate genes within milk fat synthesis pathways by combining dense genotyping and whole genome sequencing with high-throughput phenotypes for All animals included in the study were Norwegian Red cattle, and experiments were conducted in accordance with the rules and guidelines outlined in the Norwegian Animal Welfare Act 2009, issued by the Norwegian Ministry of Agriculture and Food. Most data were generated as part of routine commercial activities outside the scope of that requiring formal committee assessment and ethical approval (as defined by the above guidelines).et al.12, with some adjustments to number of spectra and animals used. In brief, 224 milk samples obtained from a feeding experiment and 659 samples from field sampling were analysed in parallel by FTIR and gas chromatography with flame ionization detector (GC-FID) reference analysis. FTIR spectra (regressors) were subsequently calibrated against GC-FID reference values (regressands) by using powered partial least squares regression (PPLSR59). Regressands were presented as percentages of GC-FID fatty acid values to total fat to reduce to a minimum value the correlation between the FA and total fat in milk samples. The calibration model was applied to a total of 4,619,737 infrared spectra from 640,304 cows sampled in the periods February to November 2007 and July 2008 to June 2014. The traits that were calibrated for in this study were C4:0, C6:0, C8:0, C10:0, C12:0 and C14:0.Milk fat composition was estimated from FTIR spectroscopy data as described in Olsen et al.12. In short, the estimation of heritabilities were performed on a reduced dataset of 2,209,486 profiles from 426,505 cattle with a pedigree of 716,753 animals using the DMU software version 6 release 5.160. The data were analysed with the following mixed linear animal repeatability model:A detailed description of the estimation of heritabilities and DYDs is given in in Olsen 12.DYDs for the GWAS were then estimated using the 4,619,737 spectra for the full dataset of 640,304 cows with a pedigree of 999,470 animals as the sire averages of daughters’ predicted FA compositions, which were each corrected for her fixed effects, non-genetic random effects and half of her dam’s genetic effect2CV) and heritabilities were as reported in Olsen et al.12. Mean concentration ranged from 1.48% of total fat for C8:0 to 11.21% of total fat for C14:0. R2 ranged from 0.73 for C4:0 to 0.91 for C8:0, C10:0 and C12:0, while heritabilities ranged from 0.11 for C14:0 to 0.35 for C4:0.The concentration of each fatty acid together with the accuracy of prediction , a custom Affymetrix 50 K SNP array, the Illumina 54 K BovineSNP50 BeadChip  and the 777 K Illumina BovineHD Genotyping BeadChip .18, with effective population size (Ne) set to 200 and number of phasing iterations (niterations) set to 20. Remaining parameters were set to default. Map positions were based on the UMD 3.1 reference assembly61.Imputation was done in a step-wise manner, were the 25 K Affymetrix dataset was imputed to the custom 50 K Affymetrix density, and then the combined Illumina 54 K and Affymetrix 50 K dataset were imputed to 777 K. The Affymetrix 50 K reference counted 5,009 NR animals and the Illumina 777 K reference consisted of 750 widely used AI bulls. Imputation was done using Beagle version 4.162. SNPs on unplaced scaffolds and sex chromosomes were also discarded from the dataset.For each imputation step, the following quality control of the markers was applied: Variants with MAF less than 0.01 and Hardy-Weinberg Equilibrium p-values less than 1e-7 were filtered. Animals with more than 10% Mendelian errors were removed from the dataset, and all remaining genotypes with Mendelian errors were set to missing and later imputed. Markers and animals with a call rate below 95% were removed. Markers on sex chromosomes were discarded. For each step, the imputation quality was tested using 5-fold cross validation. Markers with discordance between true and imputed genotypes above 10% were removed, as these markers are likely to be misplaced in the reference assemblyA total of 2,434 genotyped AI bulls were considered for the initial 777 k GWAS analysis. After filtering bulls with less than 20 daughters, the dataset contained 1,811 bulls with imputed genotypes for the 777 K Illumina BovineHD BeadChip. Of the 1,811 bulls, 57 bulls had genotypes imputed from the Affymetrix 25 K array, 237 were imputed from the custom Affymetrix 50 K SNP array, 1,113 animals from the Illumina 54 K BeadChip and 404 were already genotyped on the 777 k Illumina BovineHD BeadChip. The resulting dataset consisted of 1,811 bulls with trait data in the form of DYDs based on 20 or more daughters for the relevant FAs and with genotypes for 609,361 SNPs distributed on all 29 autosomes.et al.63. The AI bulls were selected based on maximum number of daughters in production and by ensuring an even contribution to the population structure of Norwegian Red cattle, by manually examining the recorded pedigree. Animals were sequenced to an average coverage of 9 × using Illumina sequencing . All reads were aligned against UMD 3.1 using BWA-mem version 0.7.1064. Variant calling was done with FreeBayes version 1.0.265. Missing genotypes in the resulting Variant Call Format (VCF) file were imputed and phased using Beagle version 4.118. This phased dataset was used as a reference panel for imputing the 1,811 animal high-density panel to full sequence with the Beagle software using the same imputation parameters as described before except that expected allele miscall rate (err) were set to 0.01. In a final filtering step, variants with minor allele frequency above 0.02 were kept. Also, variants with Beagle’s reported allelic R2 (AR2) below 0.7 were filtered, as this has been shown to be a robust and reliable threshold for filtering of imputed sequence variants58. The raw, unfiltered VCF-file were kept for future reference.Whole-genome sequencing data were obtained from 153 animals (132 AI bulls and 31 cows) as described in Olsen The 36 cows used for the RNA sequencing were also genotyped on the Illumina BovineSNP50 BeadChip . Blood samples were collected by certified personnel, and DNA extraction and genotyping on the Illumina BovineSNP50 BeadChip  were performed according to the manufacturer’s protocol. Genotypes were quality checked and imputed to sequence density as described above.66 for computational feasibility. A mixed linear model association analysis was performed with the –mlma-loco option of GCTA. The model fitted to the performance information for each trait and each SNP was:− is the random polygenic effect, i.e. the accumulated effect of all SNPs except those on the chromosome where the candidate SNP is located, and e is the residual. The var(g−) will be re-estimated each time when a chromosome is excluded from calculating the genomic relationship matrix. The chromosome-wide significance level was set at p = 1e-5 which is a default value from qqman67. The genome-wide significance level was set at  = 1.37e-8, corresponding to a nominal type I error rate of 0.05 and Bonferroni correction for 609,361 markers and 6 traits.A single-marker genome-wide association analysis was performed for the fatty acids C4:0 to C14:0, and 609,361 genome-wide distributed SNPs. This analysis was conducted with the GCTA software68. ASReml were selected for this step since it allowed us to weight the DYDs by number of daughters as well as to use genotype dosage data as input.All sequence-level polymorphisms that passed quality control and were situated in the QTL regions detected by the GWAS were analysed using the ASReml package version 2.0Analysed regions and traits were 100 to 107 Mb on BTA11 (C4:0), 60 to 70 Mb on BTA13 (C6:0 to C14:0), 20 to 60 Mb on BTA17 (C4:0 and C6:0) and 45 to 55 Mb on BTA19 (C10:0 to C14:0).DYD is the vector of bull performances weighed by the number of daughters, 1 is a vector of ones, μ is the overall mean, X is a vector of marker genotypes coded as a decimal number between 0 and 2 depending on the estimated dosage of the alternate allele (as reported by Beagle 4.1), b is the fixed effect of the marker, Z is an incidence matrix relating phenotypes to the corresponding random polygenic effects, a is a vector of random polygenic effects, and e is a vector of residual effects. Genetic and residual variances were estimated from the data. a was assumed to follow a normal distribution A is the relationship matrix derived from the pedigree, and e was assumed to follow a normal distribution The model that was fitted to the information on performance for each trait – marker combination was:2) were estimated and haplotypes were identified for the top ranking markers within the relevant QTL regions using the Haploview 4.2 software69 on phased genotypes. Haplotypes were defined by Haploview according to the confidence intervals strategy70 or the four gamete rule71.Pairwise LD measurements  of 36 cows from the research facilities at the Norwegian University of Life Sciences, Aas, Norway. Pedigree information was used to avoid selection of close relatives. The cows were part of a research herd at our University. All milk samples were collected approximately 50 days (range 47 to 55) after calving. This sampling period was chosen since it coincides approximately with peak expression of several relevant genes involved in bovine milk fat synthesis including 73. Cánovas et al.74 found that compared to other sources , the quality of the total RNA extracted from the SMC was high. Moreover, the expression of genes investigated in SMC derived material was highly correlated with the expression observed in laser-dissected MEC. Several studies have confirmed the usefulness of this method76.Milk is excreted by the mammary epithelial cells (MEC) lining the inside of the udder, which are subject to turnover and shed into the milk and therefore represent a proportion of the somatic cells found in milkwww.sequencing.uio.no) using an Illumina HiSeq. 3000 platform.Milk samples were collected manually 2–3 hours after milking to maximise the amount of viable cells present in the milk. Teats were cleaned with water followed by 70% ethanol before milking by hand, and 2 × 50 ml milk from each animal was collected in Falcon tubes. Samples were stored on ice immediately after collection and centrifuged at 4 °C for 10 min at 2,300 g within 1.5 hours to collect cells in the bottom of tubes. After centrifugation, most of the fat layer was removed with a clean pipette tip and supernatant decanted. Each pellet was dissolved in 4 ml 1xPBS by pipetting up and down. The liquid was transferred to a new 50 ml Falcon tube. Samples were centrifuged at 4 °C for 10 min at 2,300 g and supernatant decanted. Cell pellets were dissolved in 1 ml Trizol (Qiagen), and cells were lysed by pipetting up and down. Samples were stored in −80 °C until RNA extraction with Qiagen RNeasy Plus Universal Tissue Mini Kit (Qiagen) according to the manufacturer’s protocol. RNA concentrations and quality were measured with a NanoDrop8000 spectrophotometer (Thermo Fisher Scientific) and Agilent RNA 6000 assay on Agilent BioAnalyzer 2100 (Agilent Technologies), respectively. All samples had an RNA integrity number (RIN) between 6.6 and 9.2. Samples were prepared for paired-end sequencing (2 × 150 bp) using the Illumina® TruSeq® stranded mRNA library preparation kits and sequenced by the Norwegian Sequencing Centre (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), Illumina adaptors were removed, and the sequences were quality-trimmed using cutadapt77. Cutadapt was set to cut adaptors with a minimum overlap length of 8 and low-quality 3′ ends were removed by setting a quality threshold of 20 . An index of the UMD 3.1 reference genome was built, and reads were aligned to the reference using STAR version 2.3.178. Sorting, indexing and conversion to the BAM file format (the compressed binary version of a SAM file) of the resulting SAM files were completed using SAMtools version 1.379. The code for the described RNAseq mapping method is available as part of a bash-script pipeline (version 1.1.0) found at https://gitlab.com/fabian.grammes/RNAseq-analysis/.Before mapping, raw read quality were assessed using fastQC version 0.11.5 80 based on the Ensembl Bos taurus annotation release 86.All variants were annotated using the web version of Ensembl Variant Effect Predictorhttp://www.ebi.ac.uk/ena. Phenotype and genotype data are available only upon agreement with Geno Breeding and AI Organization (http://www.geno.no).DNA and RNA sequence data will be submitted to the European Nucleotide Archive, Supplementary InformationSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S10


In the offspring cohort, the spouse correlations were lower but still highly significant for PC1  and for PC2 . We observed significant Hardy-Weinberg disequilibrium for single nucleotide polymorphisms (SNPs) loading heavily on PC1 and PC2 across 3 generations, and also significant linkage disequilibrium between unlinked SNPs; both decreased with time, consistent with reduced ancestral endogamy over generations and congruent with theoretical calculations. Ignoring ancestry, estimates of spouse kinship have a mean significantly greater than 0, and more so in the earlier generations. Adjusting kinship estimates for genetic ancestry through the use of PCs led to a mean spouse kinship not different from 0, demonstrating that spouse genetic similarity could be fully attributed to ancestral assortative mating. These findings also have significance for studies of heritability that are based on distantly related individuals (kinship less than 0.05), as we also demonstrate the poor correlation of kinship estimates in that range when ancestry is or is not taken into account.Genetic similarity of spouses can reflect factors influencing mate choice, such as physical/behavioral characteristics, and patterns of social endogamy. Spouse correlations for both genetic ancestry and measured traits may impact genotype distributions (Hardy Weinberg and linkage equilibrium), and therefore genetic association studies. Here we evaluate white spouse-pairs from the Framingham Heart Study (FHS) original and offspring cohorts  to explore spousal genetic similarity and its consequences. Two principal components (PCs) of the genome-wide association (GWA) data were identified, with the first (PC1) delineating clines of Northern/Western to Southern European ancestry and the second (PC2) delineating clines of Ashkenazi Jewish ancestry. In the original (older) cohort, there was a striking positive correlation between the spouses in PC1  using genome-wide genotype data to characterize their genetic ancestry. By examination of spouse-pairs, we observed that individuals of Northern/Western European, Southern European and Ashkenazi ancestry preferentially chose spouses of the same ancestry, however, the degree of endogamy decreased in each successive generation, especially between Northern/Western and Southern Europeans. We then showed that the mating pattern results in Hardy-Weinberg disequilibrium (HWD) at ancestrally-informative SNPs, and also results in linkage disequilibrium (LD) between unlinked loci. The HWD and LD decrease as theoretically expected with the decrease in endogamy noted in each generation. In the FHS sample, spouse genetic similarity can be explained by ancestry-related assortative mating. The mating pattern determines the genetic structure of a population , 2, 3. TPopulation substructure can arise in a study population from geographic stratification and/or non-random mating. Geographic stratification occurs when the study participants are recruited from different geographic areas, and the genotype data is aggregated for analysis. In this setting, although there may be random mating within each geographic subgroup, there is non-random mating within the aggregated study population. Population substructure can also occur when the study participants are recruited from the same geographic area but individuals are more likely to choose mates with similar genotypes to themselves–which can be a reflection of trait-based or ancestry-related assortative mating , 10.Assortative mating occurs when spouse choice is influenced by some observable phenotype or social characteristic . PhenotyBy contrast, when assortative mating is related to ancestry (ancestry-related assortative mating), the same phenomena are seen, but in this case, at all loci that differ in allele frequency between ancestral populations, whether they have a phenotypic impact or not , 10, 25.While observed Hardy-Weinberg deviations and linkage disequilibrium can be due to a number of factors , there is a very specific pattern of Hardy-Weinberg deviation and linkage disequilibrium caused by assortative mating. As opposed to inbreeding, which affects all loci equally and creates no linkage disequilibrium, and genotyping error, which presumably is based on technical aspects of genotyping and also has no population pattern, assortative mating only influences the univariate and joint genotype distributions of the loci that are related to the trait for which assortment occurs, and not others. And, there is also a direct relationship between the strength of the association of particular SNPs with the trait undergoing assortment and the degree of HWD and LD expected for them. This relationship was first described for a general, observable trait . We prevThe Framingham Heart Study (FHS) is an epidemiologic longitudinal study of several cohorts of participants who reside in Framingham, Massachusetts . Three gHere, we analyze all three cohorts of the FHS, to investigate evidence of phenotypic assortative mating between spouses for the physical traits of height, weight, body mass index (BMI) and blood pressure. We also assess the FHS original and offspring cohorts for evidence of ancestry-related assortative mating, evaluate the key genetic factors affecting mate selection and assess how the change in mating patterns over time has affected HWE and LD in subsequent generations.For comparison, we also examine the genetic consequences of assortative mating for height, a highly heritable trait. However, height is correlated with European genetic ancestry. We investigate the degree to which the genotypic consequences of spouse assortment for height is primarily due to the effect of the contributing SNPs on height versus their correlation with genetic ancestry in the FHS original, offspring and third generation cohorts.Estimates of kinship between individuals using genome-scale genotype data has become a topic of recent interest. For example, reliably estimating the proportion of complex trait variation that is explained by genome-wide association study (GWAS) SNPs has been a challenging problem in the genetics community. The discrepancy between the heritability of the trait estimated from family-based studies and the proportion of the trait variation explained by common SNPs is termed the “missing heritability” . Recent All participants provided written informed consent for genetic analysis, and the study was approved by the Institutional Review Board of Boston University Medical Center. Third party data were used and therefore no participant consent was required for this study. Original ethical approval is available for review from previously published articles ,28, 34.The participants are from the Framingham Heart Study (FHS) original, offspring, and third generation cohorts. The original cohort was ascertained from the population of Framingham, Massachusetts , and subThis study includes 8,507 participants who had good quality genotypes (less than 3% missing data) from the Affymetrix arrays (described below), with 962 (124 white spouse-pairs) from the original cohort (Gen1); 3,576 (755 white spouse-pairs) from the offspring cohort (Gen2); and 3,872 from the third generation cohort (Gen3), plus an additional 97 spouses of the FHS offspring (Gen2) participants who provided DNA but did not have trait values available and hence did not contribute to the correlation analyses. Genotyping was conducted by the FHS SHARe  project using the Affymetrix 500K mapping array plus Affymetrix 50K supplemental array. A total of 549,781 SNPs were originally available for analysis, but several quality control (QC) exclusion measures were implemented to insure the highest quality data were used. The SNPs that were excluded were: SNPs that were duplicates, not available in HapMap or not reliably mapped to the HapMap SNPs ; SNPs on the sex chromosomes ; SNPs with ambiguous strand information (A/T or C/G) ; SNPs with minor allele frequency (MAF) < 1% ; SNPs with HW p-values < 0.0001 ; and SNPs with genotype call-rates < 99% . The 246,190 SNPs that passed these quality-control measures were used for further analyses, including the initial principal components (PC) analysis used to detect global ancestry. To interpret the principal components (PCs), we used SNP data from the HapMap  and fromAs mentioned above, the majority of the FHS participants are white. Because our analyses focused on the white participants, we first identified participants with evidence of other (e.g. African or Asian) continental genetic ancestry.We selected the maximum number of unrelated FHS participants for this analysis (521) to ensure the PCs were not confounded or exaggerated by the correlation between relatives. We performed an initial PC analysis of these 521 unrelated FHS participants along with the unrelated individuals from the HapMap , Yoruba from Ibadan, Nigeria (YRI), Han Chinese from Beijing (CHB) and Japanese from Tokyo (JPT)). The PC analysis was performed using the smartpca program from the EIGENSOFT package , 40. TheThe results revealed that the first PC clearly separated the HapMap participants with European ancestry (CEU) from the participants with African ancestry (YRI), and the second PC clearly separated the participants with Asian ancestry (CHB+JPT) from the participants with European ancestry (CEU). The large majority of FHS participants clustered with the CEU individuals suggesting that these individuals had European ancestry. However, there were a few FHS participants seen to cluster between the CEU and the YRI, consistent with African/European admixture as would be seen in African-Americans. There were also a few participants identified who clustered between the HapMap CHB + JPT and CEU individuals, likely representing admixed participants with European and Asian (or Native American) ancestries. A total of 27 admixed and/or non-white participants were identified and excluded from the subsequent analyses.A second PC analysis was used to delineate European/Middle Eastern ancestry in the FHS. To help provide geographic orientation to the results, HGDP individuals were projected onto the results of these PC analyses. Because we were investigating the genetic ancestry and phenotypic correlation between spouse-pairs and to eliminate any bias from inclusion of relatives and both members of each spouse-pair in the PC analysis, we selected the maximum number of unrelated individuals 339) including 333 FHS participants  and 6 spouses of the FHS participants who provided DNA but were not part of the FHS. When we selected unrelated individuals from the 97 spouses of the FHS participants who provided DNA but were not part of the FHS, only 6 of the spouses were selected. For the other 91 spouses, a relative (child) was already selected for inclusion so these 91 spouses were omitted. Again, the PC analysis was performed using smartpca in the software package EIGENSOFT  includin. There w2>.30 so that among the remaining 50,606 markers, no two had an r2 greater than .30. The PC scores for the two top PCs derived with the reduced set of markers  gave virtually identical results to the analysis with the full marker set  .Price et al.  recommenFHS spouse-pair demographics were collected at the time of participant recruitment into the FHS (baseline exam) including age, weight, height, BMI, SBP and DBP.The total number of white spouse-pairs in this analysis was 879, with 124 from the original cohort (Gen1) and 755 from the offspring cohort (Gen2). No information on spouse-pairs was available from the third generation (Gen3) cohort. Spouse correlations for anthropometric measures , age, SBP and DBP were calculated. The eigenvalues/scores from the first and second PCs from the PC analysis were used as measures of genetic ancestry. Quantitative evidence of ancestry-related assortative mating was obtained by examining spouse correlations for PC scores in the original and offspring cohorts. In addition, scatterplots of PC scores for husbands versus wives for PC1 and PC2, in both the original cohort and the offspring cohort were assessed to determine trends in the mating pattern.AA, Naa and NAa are the individual counts of genotypes AA, aa and Aa, respectively [To assess the impact of ancestry-related assortative mating on the genetic structure of the FHS, we calculated the standardized homozygote excess parameter F  and the ectively .2 is the variance of genetic ancestry (in this case the proportion of ancestry from one of the progenitor populations), ρ is the correlation in ancestry between spouse-pairs, δ2 is the squared marker allele frequency difference between the two ancestral populations and p* and q* = 1-p* are the allele frequencies in the admixed population. This formula shows directly the relationship between F and the squared marker allele frequency difference, which in this case is the relevant measure of strength of association of the marker with ancestry. In fact, defining X as genetic ancestry of an individual and S as the presence of an allele at a marker locus, it can be shown that the correlation of X and S is given by:F is positive when there is ancestry-related assortative mating and F increases with the ancestry information of the marker. Previously , we consFor the case we consider here, genetic ancestry is continuous and not assumed to represent admixture from ancestral populations. Rather, it is represented by principal components of the genetic marker data. However, there is a direct analogy based on formula  above. STherefore, to demonstrate the impact of ancestry-related assortative mating, we performed a linear regression analysis, using the F value for each SNP as the dependent variable and the square of the PC loading for that SNP as the independent variable. The F values for the 246,190 SNPs were calculated, and the regression of F on the square of the PC loading for each SNP was performed using a random sample of 100 unrelated participants from each generational cohort. These 100 unrelated participants were a subset of the 333 unrelated participants in the original and offspring cohorts used for deriving the PCs from the principal component analysis. The 100 unrelated participants in the third generation cohort were a random unrelated sample from that cohort.We also previously showed  that und formula  above weAs we described above, in the case where genetic ancestry X is represented by a principal component, Correl reflects the PC loading for that marker; hence, we also expect a linear relationship between D and the product of the PC loadings for the pair of markers.Therefore, to demonstrate the effect of assortative mating on LD, we performed a linear regression analysis with D as the dependent variable and the product of PC loadings as the independent variable. For computational efficiency in the regression analysis, a subset of 1000 SNPs was randomly selected, and D values were computed between SNPs on different chromosomes  for the same individuals from each generation. We calculated D values between SNPs on different chromosomes, because these SNPs are unlinked. The D values were calculated and regressed on the product of the loading on PC1 for the first SNP and the loading on PC1 for the second SNP in the random sample of 100 unrelated participants from each generation. The same subset of participants used in the F regression described above was utilized for this analysis. This analysis was repeated regressing D values on the product of the loading on PC2 for the first SNP and the loading on PC2 for the second SNP.2>.80) from HapMap release 22. For the HWD and LD analyses, for the imputed SNPs we assigned the most likely genotype for each SNP/individual. For regression analyses of F and D, instead of PC-loadings for the independent variables, we used the SNP regression coefficients (betas) as reported in [Height is a highly heritable trait for which we also observed a significant spouse correlation. Large scale GWA studies of height have identified a substantial number of height quantitative trait locus (QTL) SNPs . For comorted in  as the mWe evaluated spouse-pair kinship estimates adjusting for genetic ancestry using PC-Relate software  and compPC analysis based on 339 FHS participants was performed and the remaining FHS and the HGDP individuals were projected onto the first two principal components . From thSeparating the PC results by generation demonstrates a highly clustered distribution for the first generation . In addiIn Interclass correlations for spouse-pairs calculated separately for the first two generations  were generated for age, the various anthropometric, and blood pressure measures along with the PC scores . As expe−21) and r = 0.80 (P<10−27) for PC1 and PC2, respectively. The pattern is similar in the offspring generation, although the correlations are attenuated compared to the prior generation, r = 0.38 (P = 10−27) for PC1 and r = 0.45 (P = 10−38) for PC2. In both generations of the FHS, the ancestry correlation of spouses far exceeds the correlations observed for the anthropometric and clinical traits, indicating that ancestry has been the most significant factor related to spouse choice apart from age. The reduced spouse ancestry correlations in the offspring generation reflect decreased endogamy over time.On the other hand, the spouse correlations for ancestry, as represented by PC1 and PC2, are strikingly positive and significant, especially in the first FHS generation, with values of r = 0.73  and between-locus (LD) observable effects. We examined HW deviations in each of the three generations of the FHS by calculating the standardized homozygote excess measured by F for each SNP, and performed regression analysis with F as the dependent variable and squared PC loading as the independent variable . In all Similarly, we examined the LD parameter D for pairs of unlinked SNPs as a function of the product of PC SNP loadings via regression analysis . Again wHeight is a trait with high heritability and also showed significant spouse correlations in our sample ; however there is no significant effect of the beta products . Thus, the impact of assortative mating for height on population structure (i.e. LD) appears to be dramatically less than assortative mating for ancestry.For the regression model on F, neither the beta-squared nor quasi PC1-loading-squared terms are statistically significant. This is not surprising given the limited number of SNPs for this analysis and the fact that assortative mating has less impact on HWD than on LD. By contrast, there is a highly significant effect of the quasi PC1-loading products on D  to those using PC-Relate (with ancestry adjustment). We categorized individuals into three groups based on scores for PC1 and PC2 –those representing an Ashkenazi cluster, those representing a Northwestern European cluster, and those representing a Southern European cluster. It is important to note that there are individuals who are mixed between these groups, so to some degree the clustering is imprecise; however it is still useful to demonstrate the impact of ancestry adjustment.A clear pattern of difference between ancestry adjusted and ancestry unadjusted kinship estimates is observed , both in−4 and P<10−7, respectively), while the ancestry adjusted means are not significantly greater than 0.These results are also reflected in histograms of kinship coefficients for the original and offspring cohorts . The unaWe have conducted the first multi-generational analysis of mating patterns for a U.S. population, based on FHS participants from Framingham, Massachusetts. Our findings have significance for a variety of reasons: they reflect demographic patterns that have occurred over the past 60 years and reveal changes in those patterns over time; document the genetic implications of mating patterns and changes in mating patterns over time; and provide a warning about potentially simplistic assumptions in the genetic modeling of human populations.Soon after World War II, when the FHS was initiated, the demography of Framingham was largely white and middle class, with recent growth from the baby boom. It largely reflects the ancestral make-up of Massachusetts, which is both Northern and Southern European. According to the U.S. Census Bureau 2010 American Community Survey, among whites, the predominant ancestries in Framingham are: Irish (23%), English (13%), French (10%), other Northern European (27%), Italian (18%), and other Southern European/Mediterranean (8%). Similarly, in the FHS, the ancestries are predominantly Irish (15%) or other British (20%), Italian (19%), and other Western European (32%) . There iMate choice reflects a large number of factors including local geodemographics, social class, nationality, ethnicity, religion, anthropometric traits such as height and weight, as well as behavioral characteristics. Our findings clearly document the strong endogamy that existed in Framingham prior to World War II. These patterns may also have reflected neighborhood characteristics, and the tendency for unions to occur locally. Intermixing between participants with Northwestern and Southern European ancestries was relatively uncommon in the original cohort, but increased in subsequent generations. Similarly, there was a strong tendency towards endogamy in the participants that clustered along PC2, implicitly those with Ashkenazi ancestry.The strong endogamy was also clearly evident in the large deviations from HW of SNPs correlated with either of the first two PCs, as well as the dramatic evidence of LD between unlinked markers correlated with those same PCs. It is the strength of the regressions of F and D on these PC loadings that demonstrates directly the impact of the ancestry-related assortative mating, as other factors creating HW deviations and LD (e.g. inbreeding and genotyping error) would not produce such a pattern .The spouse genetic ancestry correlations observed in the original cohort were remarkably high, even greater than spouse correlation in age. This assortment was also evident in the strong HW deviations and LD observed in the subsequent generation (offspring cohort). In fact, the regression analyses on HW and LD parameters revealed evidence of even stronger spouse ancestry correlations in the generation that gave rise to the original Framingham cohort.We also observed a consistent decay in endogamy in Framingham over 6 decades, reflected both in the spouse correlations for genetic ancestry and in the HW and LD deviations. While strong clustering was observed in the original FHS cohort, one can see a clear decay in such clustering in subsequent generations, and also an increase in the number of unions between the clusters in the offspring cohort compared to the original cohort.Although examination of genetic ancestry correlation in spouse-pairs provides direct evidence of assortative mating, indirect evidence can also be obtained by examination of HW and LD patterns of SNPs as a function of their correlation with genetic ancestry, as we have also demonstrated here and previously , 10. IndST) among the ancestral groups contributing to the current population. Because the large majority of white ethnic groups have only modest FST values on average, it is expected that most SNPs typed in a sample similar to the FHS would not demonstrate significant homozygote excess, especially in a sample that is not large.Although we have provided clear evidence of HW deviations and LD in the FHS, it is still important to note that the strength of such deviations depends on the degree of allelic differentiation . By contrast, a similar analysis of SNPs underlying a phenotypic trait undergoing assortative mating would not typically show such evidence of HWD and LD because the individual SNP effects on the trait are too modest. For example, we also examined height, a highly heritable trait with a significant spouse correlation, in the same fashion, but found no evidence of correlation of the LD measure D among the pairs of 154 height-associated SNPs with the product of their regression coefficients on height, but did find a significant correlation of LD for the same pairs of SNPs with their PC1-loading products.On the other hand, genotyping error can also have a strong influence on HWD (but not LD). For example, in our original SNP QC analysis, we examined the proportion of SNPs with a significant HW test (P < .0001) as a function of call rate. For SNPs with call rate of at least 99%, 0.2% had a significant HW test. For SNPs with call rate of 98–99%, 1.2% had a significant HW test. For SNPs with call rate <97%, 5.9% had a significant HW test. However, the HWD of these SNPs is unlikely to be correlated with PC1 or PC2.We also demonstrated that ancestry adjustment can influence kinship estimates, particularly those for pairs of individuals less than close (e.g. third degree) relatives. Because modern heritability estimation approaches depend on kinship estimates in this range, caution needs to be exerted in interpreting those heritability estimates if ancestry is not considered. In other words, heritability estimates obtained using kinship estimates without ancestry adjustment may be biased, depending on the degree to which genetic ancestry is related to the trait being examined.A remaining and important question is the degree to which our observations in Framingham generalize to other populations, both within the U.S. and elsewhere. The issue becomes an even greater concern as populations from different geographic locations are combined in meta-analyses. The 2010 US census collected ancestry information, and maps created from that information reveal patterns of national diversity as well as changes over time due to migrations. While unions historically have been preferentially local, increased movements of the population over past decades are contributing to the decay of local endogamy, as seen in Framingham. In addition to geography, which itself is correlated with ancestry, other social factors may have influenced mate selection such as race/ethnicity, nationality, education, socioeconomic status and religion. While the exact patterns we have seen in the FHS may not replicate elsewhere, it is likely that similarly complex mating patterns, including spouse correlations related to genetic ancestry, are a common feature, and need to be addressed in any population study.


Trans-ancestry meta-analysis and fine-mapping with European-ancestry individuals suggest the presence of distinct HLA class II variants driving associations in Uganda. In addition, we identify four putative, novel, very rare African-specific loci with preliminary evidence for association with anti-viral capsid antigen IgG responses which will require replication for validation. These findings reinforce the need for the expansion of such studies in African populations with relevant datasets to capture genetic diversity.Epstein Barr virus (EBV) infects 95% of the global population and is associated with up to 2% of cancers globally. Immunoglobulin G (IgG) antibody levels to EBV have been shown to be heritable and associated with developing malignancies. We, therefore, performed a pilot genome-wide association analysis of anti-EBV IgG traits in an African population, using a combined approach including array genotyping, whole-genome sequencing and imputation to a panel with African sequence data. In 1562 Ugandans, we identify a variant in  Epstein Barr virus (EBV) is a common human herpesvirus infecting 95% of the global adult population. Following primary infection, often in childhood, EBV establishes a latent infection in B cells, allowing virus persistence in the face of an active immune system. EBV can reactivate from latency and enter the lytic cycle allowing viral replication and transmission. The majority of people live with EBV infection with the absence of clinical symptoms. However, EBV causes the self-limiting condition, Infectious Mononucleosis and up to 2% of cancers, including Burkitt's lymphoma, Hodgkin's lymphoma, nasopharyngeal carcinoma and some gastric cancers , 2. It iEBV infection induces a strong cell-mediated and humoral immune response which actively contains EBV replication in healthy individuals. Antibodies against EBV nuclear antigen-1 (EBNA-1) reflect infection history, whilst those against viral capsid antigen (VCA) reflect viral reactivation and together are widely used as markers to study the latent and lytic stages of infection, respectively. Early life infection and high antibody titres have been strongly linked to the development of certain cancers , 5–7. InHLA) class II region of the major histocompatibility complex on chromosome 6p21.3, contributing to variability in responses to EBNA-1 [While EBV has been extensively studied, the host genetics underpinning potential disease outcome are still unclear , particuo EBNA-1 , 12. No o EBNA-1 , 14, theHLA class II region contributing to anti-EBNA IgG response levels and also perform trans-ethnic meta-analysis and fine-mapping of EBNA-1 IgG traits with an additional cohort of European ancestry, revealing distinct variants driving associations in the two populations. Finally, we identify four potentially novel, rare loci that are African-specific with preliminary evidence of association with anti-VCA IgG serostatus, warranting replication in larger sample sizes.Here, we present a pilot study describing the first genome-wide association analysis performed for anti-EBV IgG traits in an African population. We highlight the combination of whole-genome sequencing and imputing genotypes to a panel with additional African sequence data to aid discovery of low-frequency and population-specific variants. We replicate variants in the s.d. = 34 ± 19.6 years, 54% female) to the genetic data generated from samples collected from the GPC in 2011. Informed consent was obtained for genetic testing from participants either with a signature or a thumbprint if the individual was unable to write. The study was approved by the Uganda Virus Research Institutes, Research Ethics committee (UVRI-REC) (Ref. GC/127/10/10/25), the Uganda National Council for Science and Technology (UNCST), and the UK National Research Ethics Service, Research Ethics Committee (UK NRES REC) (Ref. 11/H0305/5).The general population cohort (GPC) is a community-based open cohort study originally established in 1989, by the UK Medical Research Council and the Uganda Virus Research Institute, in the Kalungu District, south-western Uganda, to examine prevalence, incidence, risk factors and trends of infection with the human immunodeficiency virus (HIV) in a rural African population . Data arWe quantified mean fluorescence Intensities (MFI) of IgG antibodies to EBNA-1 and VCA using multiplex serology on the Luminex platform based on glutathione-S-transferase (GST) fusion capture immunosorbent assays combined with fluorescent bead technology . In thiset al. in submission) reference panel in IMPUTE2 [et al. in submission).5000 GPC samples were densely genotyped on the Illumina HumanOmni 2.5 M BeadChip array and we then imputed additional variants into the genotype chip dataset using a merged 1000 Genomes phase 3 , African IMPUTE2 . Whole gs.d. from the mean, discordant genetic sex and reported sex, and sites deviating from Hardy Weinberg equilibrium (p < 10−8). Following imputation, we only included high-quality sites (info score >0.3 and r2 > 0.6) with minor allele frequency (MAF) ⩾0.5%. We also removed samples without matching phenotype and genotype or sequence data. Of the merged datasets, 343 samples had overlapping genotype and sequence variant calls for which a final concordance of 93.1% was achieved for all SNPs. The merged datasets post QC filtering resulted in 1562 samples with EBV phenotypes and ~17 M SNPs across the autosomes and X-chromosome for analyses.Stringent variant and sample quality control (QC) filtering were performed. Low-quality variants that mapped to multiple regions within the human genome or did not map to any region, and duplicate variants genotyped on the chip were removed. We excluded samples with a call rate <97% and heterozygosity >3 r2 = 0.5) using a sliding window approach with a window size of 200 Kb, sliding 5 SNPs sequentially.PCA was performed using SMARTPCA in Eigensoft v4.2 with 1000 Genomes Project phase 3 and African Genome Variation Project populations as a reference panel. PCA was done including markers with MAF ⩾0.05 after linkage disequilibrium (LD) pruning (h2) for anti-EBNA-1 IgG and anti-VCA IgG traits were estimated using a linear mixed model (LMM) in FaST-LMM with two random effects, one based on genetic effects and the other on environmental effects using spatial location [Narrow-sense heritability (location  recordedk = 1 option in GEMMA. The data were LD pruned (r2 = 0.2) using dosages and a MAF threshold of 1% was applied. Genotyping or sequencing method was also adjusted for as additional covariates during analysis in GEMMA. To identify distinct SNPs, conditional analysis was performed in GEMMA. Each SNP within 1 MB of the lead association SNP was conditioned. If any SNP was statistically significant it was added stepwise onto the mixed model and analysed jointly, this was done until no SNPs with p < 5 × 10−9 remained. All SNPs remaining statistically significant were considered distinct association signals. For conditional analysis where genotype data was unavailable, association summary statistics were obtained and conditional analysis as described above was performed using genome-wide complex trait analysis (GCTA).We conducted analyses for both quantitative antibody traits and discrete serostatus (i.e. presence/absence of antibody response) based on MFI cutoffs applying a linear or logistic regression model, respectively, in R. For anti-EBNA-1 IgG analysis age, sampling round, Hepatitis B virus and Hepatitis C virus status were adjusted for as significant covariates (online Supplementary Table S1). For anti-VCA IgG analysis Kaposi's sarcoma-associated virus and HIV statuses were also adjusted for in addition as significant covariates (online Supplementary Table S1). Residuals of MFI values used for analyses were transformed using inverse, rank-based normalization in R to ensure a standard normal distribution for the phenotypes and ascertained by visualization and Shapiro–Wilk test in R . To control for cryptic relatedness and population structure within the GPC, the GWAS was performed using the standard mixed model approach in genome-wide efficient mixed-model association (GEMMA) . A kinshTo functionally annotate our most significant associations we used the Ensembl Variant Effect Predictor (VEP) and the gene/tissue expression database (GTEx) to access data on expression quantitative trait loci (eQTLs) from tissues.10 Bayes Factor (BF) >6, which is comparable with a p < 5 × 10−8, previously determined by Wang et al. [Q-test for heterogeneity in METAL [MANTRA was used to perform a genome-wide trans-ethnic meta-analysis for anti-EBNA IgG responses with association summary statistics of 1473 EBV seropositive individuals from our Ugandan GWAS combined with publically available data of 1000 Genomes imputed European ancestry GWAS from 2162 seropositive individuals, giving a total of 3635 individuals with ~4.6 million shared SNPs for analysis. The MANTRA approach leverages differences in LD structures across populations to account for differences in genetic architecture and accommodates heterogeneity of allelic effects between distantly related populations within a Bayesian partition framework . To deteg et al. . Heterogin METAL . Using Min METAL , 26. Allet al. in submission) reference panel. In this cohort, 94% (1473/1570) of individuals were categorized as EBV seropositive (i.e. presence of detectable IgG response to EBNA-1 or VCA) based on mean fluorescence intensity (MFI) cutoffs [To assess the contribution of human genetic variation on antibody responses to EBV we investigated 1570 individuals from a rural GPC  and UG2  cutoffs . Stringe cutoffs  with AGVλ) ~1.0 for all traits . Correcting for multiple testing and accounting for the lower LD in African populations the genome-wide significance threshold was adjusted to p < 5 × 10−9 .As previous studies have reported heritability of IgG responses to EBNA-1 at 37–43% and to VCA at 32–48% , we alsoHLA class II region  was associated with lower antibody response levels (p = 0.139) [C4A, HLA-DQA1, HLA-DQB1-AS1, HLA-DQB1, HLA-DQB2, HLA-DRB1, HLA-DRB5, XXbac-BPG254F23.6, NOTCH4, HLA-DMA) in 34 tissues were found to be affected by rs9272371 in the GTEx database. All of these genes are known to mediate immune function. rs9272371-C was significantly associated with a downregulation of expression of HLA-DQA1 in all tissues including whole blood  and EBV transformed lymphocytes , which is consistent with the direction of our associations    previously identified as the lead association SNP in the European ancestry study, whilst our Ugandan lead SNP rs9272371 (log10BF = 15.8) displayed heterogeneity in effect sizes in the two studies (PQ = 3.56 × 10−8) (p = 2.01 × 10−9) and in moderate LD with our lead SNP rs9272371 (r2 = 0.32) , the association was markedly attenuated when conditioned on rs9272371 (pcond = 0.0065) (online Supplementary Table S2). To further investigate whether the signals are distinct or partially tagging an un-typed functional variant contributing to both underlying association signals, we performed reciprocal conditional analysis of rs6927022 on our Uganda GWAS in GEMMA and also conditioned on our lead SNP rs9272371 in the European GWAS with association summary statistics using GCTA [HLA class II region, with a single signal in Europeans (rs6927022) and a signal mostly driven by rs9272371 in Uganda (online Supplementary Table S2). No other locus was found to be in association with anti-EBNA-1 IgG response.Next, we used MANTRA  to perfo10 BF >6  we found × 10−8) , Table 2ing GCTA . Both leThe availability of whole-genome sequence data and smaller LD blocks in African populations are advantageous for the refinement of genetic association signals. In line with this, using MANTRA results we generated 99% credible sets most likely to drive association signals and contain causal variants  and compared fine mapping intervals for each associated lead SNP by analysing the variants 500 kb up and downstream of the lead SNP in the Ugandan and combined Ugandan + European datasets as described previously , 26. Thiv. seronegative), we identified four potentially novel genome-wide significant associations  an intronic variant in THADA on chromosome 2p21  an intergenic variant on chromosome 7p21.3 with the nearest gene a non-coding RNA U3, 17 kb upstream  an intronic variant in GALC on chromosome 14q31.3  an intronic variant in CACGN5 on chromosome 17q24.2 (N = 343) with overlapping genotype and sequence data, giving confidence in the accuracy of genotypes (online Supplementary Table S3). All SNPs were associated with seronegativity to VCA, potentially reflecting a lack of EBV lytic replication, and were low-frequency variants (r2 = 0.3) did not yield any additional genome-wide significant results . No secondary associations were identified following conditional analyses on the lead SNPs for all traits.For anti-VCA IgG response, 1344 individuals were categorized as seropositive and 218 individuals as seronegative based on VCA MFI cutoffs . Using aome 2p21 a, rs1901upstream b, rs1152 14q31.3 c and rs1 17q24.2 d. All levariants . rs18381In this study, we assessed the host genetic contribution on anti-EBV IgG responses in a rural African population cohort and highlight the utility of dense genotyping combined with whole-genome sequencing and imputation of genotypes to a combined reference panel with African sequence data to aid locus discovery and refinement of causal variants. As we are limited by sample size, particularly in African populations, to conduct well-powered GWASs for EBV-associated diseases such as Burkitt's Lymphoma, IgG response traits provide a good intermediate phenotype, indicating the strength of the humoral immune response and control of infection. EBV infection is nearly ubiquitous in Africa, with infection occurring early in childhood , 29, 30 It is interesting that despite the fact that both anti-EBV IgG traits display low heritability in this population after accounting for a shared environment, we still identify strong associations with SNPs contributing to variability in immune responses in African populations. This suggests that while genetic factors play a role in inter-individual variability in immune responses, environmental effects may have not been well accounted for by other studies, or differences in gene–environment interactions between populations result in different estimates. It is also a possibility that differences in assay/study design could contribute to differences in heritability estimates, however, in this setting, exposure to other pathogens are potentially strong cofactors influencing these traits and thus have been adjusted for in the study.HLA class II region. As a result of the complex LD structure in the HLA region, it is possible that both SNPs are tagging an underlying HLA allele, which we would have to explore further. Disentangling signals in the HLA region to pinpoint causal alleles is nontrivial owing to the poor representation of African ancestry data on HLA imputation reference panels that are heavily skewed towards European populations. In the European GWAS, Hammer and colleagues were able to achieve resolution of 4 digit classical HLA alleles and amino acids in HLA-DRB1 through imputation using the Type 1 diabetes genetics consortium (T1DGC) Immunochip/HLA reference panel, which is predominantly European [HLA class II molecules present peptides to CD4+ T cells (T helper cells) eliciting both cell-mediated and antibody responses to control viral infection. EBV has also been found to use HLA class II molecules as a co-factor mediating entry into B cell lymphocytes [HLA haplotypes are highly polymorphic and display geographic variability, conducting host genetic studies in diverse populations will allow us to capture variation and understand its’ contribution to EBV immune control and disease.We successfully replicated association signals for anti-EBNA-1 IgG responses identified in individuals of Mexican American and European descent, and through trans-ethnic meta-analysis of European and African individuals in addition to fine-mapping identify distinct association signals in the European , 33. HLAphocytes , 35. GivPreviously, no GWASs had been done for anti-VCA IgG responses and one linkage analysis had been performed without success in identifying statistically significant associations. For the first time, we have identified putative novel, African-specific genetic loci with evidence of association with anti-VCA IgG serostatus . While tIn summary, the results of our pilot study substantiate the contribution of host genetic variation to EBV immune response and viral control. Our study reinforces the importance of studying diverse populations to uncover population-specific variants, differences in effect sizes and gene–environment interactions, which are known to vary significantly between European and non-European populations. A limitation at this stage is that with a small sample size we are underpowered to reliably identify rare genetic variants. Expanding these studies in African populations to include replication of the putative novel loci in larger sample sizes is key to validating our findings. In addition, the development of African resources such as an HLA imputation reference panel based on African genetic data and gene expression data will be crucial to be able to leverage approaches such as GWAS and refine our findings. While GWAS still remains a leading tool to identify variants, to follow up significant findings and gain biological insights, the development of pathway analysis tools with African populations also well represented would be necessary to reliably identify gene enrichments in pathways and protein interaction networks.