The significant social and economic loss as a result of bovine tuberculosis (bTB) presents a continuous challenge to cattle industries in the UK and worldwide. However, host genetic variation in cattle susceptibility to bTB provides an opportunity to select for resistant animals and further understand the genetic mechanisms underlying disease dynamics.The present study identified genomic regions associated with susceptibility to bTB using genome-wide association (GWA), regional heritability mapping (RHM) and chromosome association approaches. Phenotypes comprised de-regressed estimated breeding values of 804 Holstein-Friesian sires and pertained to three bTB indicator traits: i) positive reactors to the skin test with positive post-mortem examination results (phenotype 1); ii) positive reactors to the skin test regardless of post-mortem examination results (phenotype 2) and iii) as in (ii) plus non-reactors and inconclusive reactors to the skin tests with positive post-mortem examination results (phenotype 3). Genotypes based on the 50 K SNP DNA array were available and a total of 34,874 SNPs remained per animal after quality control.Bos taurus autosomes (BTA) 2 associated with phenotype 1, and another on BTA 23 associated with phenotype 2. Genomic regions encompassing these SNPs were found to harbour potentially relevant annotated genes. RHM confirmed the effect of these genomic regions and identified new regions on BTA 18 for phenotype 1 and BTA 3 for phenotypes 2 and 3. Heritabilities of the genomic regions ranged between 0.05 and 0.08 across the three phenotypes. Chromosome association analysis indicated a major role of BTA 23 on susceptibility to bTB.The estimated polygenic heritability for susceptibility to bTB was 0.26, 0.37 and 0.34 for phenotypes 1, 2 and 3, respectively. GWA analysis identified a putative SNP on Genomic regions and candidate genes identified in the present study provide an opportunity to further understand pathways critical to cattle susceptibility to bTB and enhance genetic improvement programmes aiming at controlling and eradicating the disease.The online version of this article (doi:10.1186/s12863-017-0493-7) contains supplementary material, which is available to authorized users. Mycobacterium bovis (M. bovis) and usually manifests with tuberculous lesions predominantly in the respiratory tract, although lesions could also be found elsewhere [Bovine tuberculosis (bTB) is a chronic disease caused by lsewhere . Despitelsewhere , the inclsewhere  resultinlsewhere . During lsewhere . Scotlanlsewhere .M. bovis and M. avium tuberculins side-by-side into the skin of the neck, followed by examination for evidence of localised inflammation after 72 h. Interpretation of the test follows a standard procedure applied internationally [M. bovis tuberculin injection is estimated to be less than or equal to that to M. avium tuberculin injection then the skin test is deemed negative. A positive skin test result, also known as a ‘reactor’, is asserted when the reaction to M. bovis tuberculin exceeds that to M. avium tuberculin by more than 4 mm. In all other cases, the test is considered inconclusive and retesting is done at 60-day intervals to resolve their status. A breakdown (bTB incident) is declared once at least one reactor is discovered in a herd, prompting animal movement restrictions, suspension of the OTF status of the herd and testing of all animals in the herd at 60-day interval. Animals with a positive or two consecutive inconclusive skin tests are slaughtered and examined at the abbatoir for visible lesions of bTB in their organs. Samples of tissue from a representative number of infected animals from each breakdown are sent to the laboratory where M. bovis culture is performed. A positive post-mortem examination result, i.e. presence of lesions and/or positive M. bovis culture (confirmed case) elicits a change of the herd’s OTF status from ‘suspended’ to ‘withdrawn’. The breakdown remains ‘open’ and skin testing continues in the herd until two consecutive negative herd tests are obtained.In Great Britain, bTB control and eradication programme involves routine testing and compulsory slaughter of infected animals and cattle movement restrictions in the affected herds. Routine testing is based on the administration of the single intradermal comparative cervical tuberculin (SICCT) or ‘skin’ test to each animal, which entails simultaneous injection of both Bos taurus autosomes (BTA) 22 and 23, respectively, in Irish Holstein-Friesian dairy cattle. Bermingham et al. [Given the difficulties in eradicating bTB, breeding for resistance has been considered as an additional complementary control measure . Most ofm et al.  found rem et al.  applied m et al.  and Nortm et al. , and idem et al.  also idem et al. . In geneOur objective was to conduct a first study of the genomic architecture of susceptibility to bTB in the British Holstein-Friesian cattle population. We used GWA, RHM and chromosome association approaches to analyse alternative definitions of bTB susceptibility that have not been genomically addressed before.i)M. bovis culture. This phenotype represented the conservative definition of infected, which requires infection to be confirmed by post-mortem examination.Phenotype 1: positive reactors to the skin test with positive post-mortem examination results consisting of visible lesions of bTB and/or positive ii)Phenotype 2: positive reactors to the skin test regardless of post-mortem examination results, based on the very high specificity of the skin test (ca. 99%) and the trivial number of false positives expected . Phenotyiii)Phenotype 3: as in (ii) plus non-reactors and inconclusive reactors to the skin test who had been slaughtered and had positive post-mortem examination results, in order to include possible false negative skin tests in this definition . The majData for the present study were sire genetic evaluations that had been previously generated from the official genetic and genomic evaluation system for bTB resistance , 22. TheIn all cases, healthy animals were defined as live non-reactors to the skin test or slaughtered non-reactors with negative post-mortem examination results. Animals defined as healthy were all from the same breakdowns as the infected ones.Following the above trait definitions, a linear mixed model was used to calculate sire EBVs based on the phenotypes of their daughters. Each sire received three EBVs, one for each of the above trait definitions. More information about the genetic model used to derive these sire EBVs may be found in Banos et al. . In the −6). Quality control also removed animals with individual call rates below 0.90. A total of 34,874 autosomal SNPs and 803 individuals passed the quality control criteria and were retained for the subsequent analyses.Whole-genome genotypes based on the 50 K SNP Illumina BeadChip were available for 804 Holstein-Friesian sires with de-regressed EBVs for susceptibility to bTB. Genotype data were subjected to quality control using the software PLINK . QualityThe genomic data (sire genotypes) were explored for underlying population substructure using multi-dimensional scaling based on the genomic kinship matrix estimated from all SNPs in the analysis. The genomic kinship matrix was calculated as outlined by Amin et al. .i is the weighting factor of the deregressed EBV of the ith animal; h2 is the heritability of the trait ; ri2 i of 0.20  was consFurthermore, Pearson correlations between the three sets of sire EBVs were calculated.GWA analysis was performed by regressing the deregressed EBV on each individual SNP using the following model:P = 0.05) was defined at P = 1.43 × 10−6 which corresponds to a –log10(P) = 5.84, whereas the suggestive threshold  was defined at P = 2.87 × 10−5 corresponding to a –log10 (P) = 4.54. The P-values obtained from the GWA analysis were adjusted for inflation using the genomic inflation factor, λ, which accounts for any systematic deviation of observed from expected P-values. The estimated polygenic heritability was calculated as h2 = (σ2a/σ2p) in which the phenotypic variance (σ2p) was obtained by summing the additive genetic (σ2a) and residual variance (σ2e) from model 1.GWA analyses were conducted with the R-based statistical package GenABEL . After B2a is the total genetic variance of the trait calculated with model 1 excluding the SNP effect.SNPs found to be significant in the previous step were further tested by fitting the respective genotypes individually as a fixed effect in a mixed model similar to model 1. These analyses were conducted with the ASReml software package . The gen2 was calculated in the software PLINK [Bos_taurus_UMD_3.1.1 project of the National Centre for Biotechnology Information [Significant SNPs were also explored for linkage disequlibrium (LD) with other nearby SNPs. Pairwise LD, measured with rre PLINK  with LD re PLINK . The hapre PLINK . QTL regormation .The same data described above were analysed with the RHM approach, in which genomic regions of 100 SNPs were defined by sliding ‘windows’ shifting every 50 SNPs along each autosomal chromosome. A detailed description of RHM was given by .The following model was applied for the RHM:P = 1.40 × 10−4) and 8.93 (P = 2.80 × 10−3) for the genome-wide and suggestive significance thresholds, respectively. The phenotypic variance was calculated as σ2p = σ2a + σ2r + σ2e, while the regional (r) heritability was subsequently estimated as h2r = σ2r/σ2p.RHM analyses were performed using the DISSECT software . The sigP = 1.72 × 10−3) and 4.47 (P = 3.45 × 10−2), respectively. The phenotypic variance was calculated as σ2p = σ2a + σ2c + σ2e, where σ2c was the variance due to the chromosomal genetic effect. The chromosomal (c) heritability was subsequently estimated as h2c = σ2c/σ2p.In a separate set of analyses, the entire autosomal chromosome effect was fitted in model 2 instead of genomic region. After Bonferroni correction, the LRT significance thresholds for the genome-wide and suggestive levels were 8.55  and Hapmap38114-BTA-57971 (P = 1.48 × 10−5) were associated with phenotype 1 on BTA 2 and 24, respectively. The other SNP, BTA-56563-no-rs (P = 1.99 × 10−5) on BTA 23 was associated with phenotype 2. The SNP identified to affect phenotype 2 also reached but did not exceed the suggestive significance threshold for phenotype 3 , region 2 (33.9 - 41.6 Mb) and region 3 (38.5 - 44.8 Mb). The SNP identified on BTA 23 with the GWA analysis was located within regions 1 and 2. The regional heritability estimates ranged from 0.05 to 0.08 , where neither GWA nor RHM analyses had revealed any significant associations. The corresponding chromosomal heritability was 0.08 ± 0.04 and was probably due to an aggregation of moderate effects of different genomic regions along this chromosome. Similarly, neither BTA 18 nor BTA 24, where RHM had revealed genomic regions with suggestive effects, reached a significance level in the chromosomal association analysis of phenotype 1  manifested by a small effective population size, which may result in false associations . HoweverThe present study identified two additive SNPs in moderate LD with neighbouring SNPs on BTA 2 and 23 that were significantly associated with different traits of susceptibility to bTB. In both cases, the allele with the minor frequency had the favourable additive effect, conferring increased resistance to bTB in the studied population. A similar result reported by Bermingham et al.  indicatePARD3B, which has been implicated in protection against disease progression in patients affected by the human immune deficiency virus and acquired immune deficiency syndrome (HIV/AIDS) [RNF144B. This protein coding gene has been found to play a role in the regulation of NF-κB in human macrophages. NF-κB regulates the expression of various genes involved in diverse cellular processes including inflammation and immunity [RNF144B gene include roles in regulation of apoptosis and cell proliferation, making the gene a possible candidate for therapeutic treatment of endometrial cancer [The length of the putative QTL regions defined in the present study (1.20-1.29 Mb) was similar to those reported by Kim and Kirkpatrick  where thIV/AIDS) . Similarimmunity  and has immunity . Other fl cancer . FurtherThe present study did not confirm QTL identified in previous association studies on bTB susceptibility –21, whicThe RHM analysis overcame some of the limitations of GWA due to the former’s capacity to consolidate a proportion of genomic variation based on multiple neighbouring marker effects . In the Furthermore, the present study has highlighted a major overall chromosomal influence of BTA 23 on susceptibility to bTB, when the definition of the latter is not restricted to post-mortem confirmed cases but includes all positive skin test reactors and all animals with a positive post-mortem result. Actually, chromosome 23 was the only chromosome that featured in the significant results of all our analyses . Notably, BTA 23 harbours the major histocompatibility complex (MHC), which plays a central role in immune response to infection , 45. OurPrevious genomic studies on cattle susceptibility to bTB have not resulted in consistent outcomes to support a common genomic mechanism underlying the trait. Some of our results might have added to the wealth of diverse findings. As discussed, reasons for such discrepancies include the complexity of the phenotype, the largely polygenic inheritance mode of the trait, genetic differences between populations and differences in methodologies used across studies. Additional reasons may be different allele frequencies of either the marker or causative mutation even when the same QTL is segregating in various populations, and possible mutation linkage phases that may not be the same between populations , 47. MorOur results suggest that bTB susceptibility in the British Holstein cattle population is a moderately heritable polygenic trait, potentially amenable to improvement with selective breeding. Our findings may inform genomic predictions  within a genomic selection programme, where differential emphasis can be placed on specific genomic regions identified to have significant effects on the trait. At the same time, it would be useful to quantify the impact of such a selection process on the disease dynamics as well as other traits of the breeding goal. Our results may also provide target areas for possible future gene editing applications within a genetic improvement programme.Additional file 1:Multi-dimensional scaling  analysis of an identity by state matrix of 804 bulls. A single cluster was formed which reflect homogeneity of the population. (DOCX 407 kb)Additional file 2:Genetic parameters of three bovine tuberculosis traits. (DOCX 14 kb)Additional file 3:P-values from genome-wide association analyses: (a) phenotype 1, positive reactors to the skin test with positive post-mortem results; (b) phenotype 2, positive reactors to the skin test regardless of post-mortem results; (c) phenotype 3, as phenotype 2 plus non-reactors and inconclusive reactors with positive post-mortem examination results. (DOCX 1200 kb)Quantile-quantile plots of observed against expected Additional file 4:Additive and dominance effects for significant SNPs identified by genome-wide association analysis. (DOCX 13 kb)Additional file 5:2) map of a QTL region on BTA 2 affecting bTB (phenotype 1). The region ranges from SNP ARS-BFGL-NGS-40833 (bp = 93065483) to SNP ARS-BFGL-NGS-109114 (bp = 94352603); white for r2 = 0, shades of grey for 0 < r2 < 1 and black for r2 = 1. (DOCX 54 kb)Linkage disequilibrium  map of a QTL region on BTA 23 affecting bTB (phenotype 2). The region ranges from SNP ARS-BFGL-NGS-88425 (bp = 38206814) to SNP BTA-01409-rs29012374 (bp = 39411428); white for r2 = 0, shades of grey for 0 < r2 < 1 and black for r2 = 1. (DOCX 57 kb)Linkage disequilibrium  phenotype 1, positive reactors to the skin test with positive post-mortem results; (b) phenotype 2, positive reactors to the skin test regardless of post-mortem results; (c) phenotype 3, as phenotype 2 plus non-reactors and inconclusive reactors with positive post-mortem examination results. Dashed and solid lines represent suggestive and genome-wide thresholds, respectively. (DOCX 2254 kb)


FTO SNP rs55872725 , and using a significance level of p = 0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP , located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.The Nurses’ Health Study (NHS), Nurses’ Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)). We observed the strongest BMI association for the  Large, well-phenotyped cohort studies have constituted the backbone of epidemiology for several decades. Prospectively collected longitudinal information on exposures and outcomes enables a broad spectrum of analyses and has led to novel insights into disease etiology, such as the link between smoking and lung cancer ,2 as welWithin the Nurses’ Health Study (NHS) , Nurses’Here, we describe our pipeline for merging and imputing the individual GWAS datasets within NHS, NHSII, HPFS and PHS. Datasets were merged based on genotype platform family and all data were subsequently imputed to a common reference panel . We preIn 1976, the Nurses’ Health Study (NHS) was launched with the goal of studying women’s health . Since tEach GWAS study was approved by the Brigham and Women’s Hospital Institutional Review Board. Return of the mailed self-administered questionnaires was voluntary. Thus, receipt of a completed questionnaire was considered as evidence of a desire to participate in the study and was taken as a formal indication of consent.Table 1). The primary traits are breast cancer [Table 1) at four different genotyping centers . Standard quality control filters for call rate, Hardy-Weinberg equilibrium, and other measures were applied to the genotyped SNPs and/or samples. In total, these GWAS data sets comprise 20,769 participants including 11,522 from NHS, 934 subjects from NHSII, 7,018 subjects from HPFS and 1,305 subjects from PHS.Since 2007, twelve separate GWAS have been conducted within these four cohorts . To achieve the largest GWAS datasets as possible without losing SNP information, we created three datasets–HumanHap comprising six GWAS datasets, OmniExpress comprising four GWAS datasets and Affymetrix 6.0 comprising two GWAS datasets. In the merging process, we removed any SNPs that were not in all studies for a specific platform or had a missing call rate>5%. We flipped strands where appropriate and removed A/T and C/G SNPs to create the final compiled datasets.Successfully merging genotype data for different individuals requires complete overlap in SNPs. SNPs that are missing by design (due to different genotyping platforms) from some studies will be correlated with the primary phenotype for that dataset. This might cause spurious results in any secondary analysis on related traits. Although a missing SNP can be imputed, it will have a higher degree of inaccuracy in imputed compared with genotyped SNPs, potentially creating differential measurement error that could also lead to bias ,46,47. T0, Z1 and Z2 . If 0≤Z0≤0.1 and 0≤Z1≤0.1 and 0.9≤Z2≤1.1 then a pair was flagged as being identical twins or duplicates. Pairs were considered full siblings if 0.17≤Z0≤0.33 and 0.4≤Z1≤0.6 and 0.17≤Z2≤0.33. Half siblings or avunculars were defined as having 0.4≤Z1≤0.6 and 0≤Z2≤0.1. Some of the duplicates flagged were expected, having been genotyped in multiple datasets and hence having the same cohort identifiers. In this case, one of each pair was randomly chosen for removal from the dataset. In instances where pairs showed pairwise genotype concordance rate>0.999 but were not expected duplicates, both individuals were removed. Related individuals  were not removed from the final datasets. In the HumanHap dataset, 107 individuals were removed because they were duplicates or flagged for removal in the genotyping step, leaving 6,787 subjects. In addition, 8 pairs of individuals were flagged as related. In the OmniExpress dataset, we removed 39 subjects leaving 5,917 IDs and 5 pairs of related subjects. In the Affymetrix dataset, 167 individuals were removed because they were duplicates or were flagged for removal from secondary genotype data cleaning, leaving a total of 8,065 individuals. Across all three datasets, we identified 444 duplicate pairs (406 expected) and thus removed additional 482 individuals from analysis across all three platform families.We ran a pairwise identity by descent (IBD) analysis within and across the combined dataset to detect duplicate and related individuals based on resulting IBD probabilities ZAfter removing duplicate and related pairs of IDs, we used EIGENSTRAT  to run p−8) and examined QQ plots. In the Affymetrix dataset, 100 SNPs were flagged and removed. In the HumanHap dataset, 8 SNPs had p<10−8 in at least one of the QC regressions and were removed. No SNPs in the OmniExpress dataset had p<10−8 and hence, no SNP was removed.To identify any SNPs that created spurious associations, we ran several logistic regression analyses among subjects that were selected as controls in the initial GWAS . For each regression, we used cohort-specific controls from one original GWAS as cases and the rest of the controls in that dataset as controls. For example, in the OmniExpress dataset, we considered NHS controls from the gout GWAS as “cases” while treating controls from the gout (HPFS), endometrial cancer (NHS), colon cancer , and mammographic density (NHS) as “controls”. We repeated this, treating each cohort-specific “controls set” as “cases” and all other controls as “controls”. For each GWAS, we extracted genome-wide significant SNPs  as the reference panel. SNP and indel genotypes were imputed in three steps. First, genotypes on each chromosome were split into chunks to facilitate windowed imputation in parallel using ChunkChromosome (v.2011-08-05). Then each chunk of chromosome was phased using MACH ,51 (v.1.2 (cm) and obtained it by extracting information on weight from the accompanying questionnaire collected at time of blood draw. If weight information was missing, we extracted it from the questionnaire closest in time to time of blood draw. Height was extracted from the baseline questionnaire. We obtained data on BMI for 20,283 participants. VTE is a spectrum of disease that includes pulmonary embolism (PE) and deep vein thromboembolism (DVT). Physician-diagnosed PE has been asked on every biennial NHS questionnaire since 1982, and every NHSII and HPFS questionnaire since cohort inception. In the NHS, DVT without PE is captured when a nurse answers that she has had phlebitis or thrombophlebitis (ICD-9 = 453.x). In NHS, NHSII and HPFS cohorts through 2010 (we did not have VTE data for PHS), we identified 6,041 individuals who reported VTE. Self-reported PE was verified through medical records review by a trained physician (CK). DVT cases are based on self-report, though a validation study of 100 DVT cases found self-reports to be highly consistent (>96%) with medical record review. In total, we identified 1,364 VTE cases with GWAS data. We treated all non-VTE cases with GWAS data as controls . Since we did not have data on VTE in PHS, we excluded PHS from this analysis.To validate our merged GWAS datasets, we conducted two proof-of principle GWAS of one quantitative trait (BMI) and one binary trait (VTE). We defined BMI as weight (kg)/heightSNPs and indels with an imputation quality score <0.3  or a minor allele frequency (MAF) <0.01 were excluded. Primary association analysis was performed separately within each platform family . For imputed SNPs, the estimated number of effect alleles (ranging from 0 to 2) was used as a covariate. For BMI, we conducted linear regression adjusting for study (indicator variables including cohort as well as primary GWAS outcome), age at blood draw and the top four principal components. For VTE, we conducted logistic regression adjusting for study as above and the top four principal components. For both BMI and VTE, we combined platform family-specific results with fixed-effects meta-analysis using the METAL  softwareFig 2 and the distribution of imputation quality score for rare (MAF≤0.01) variants is shown in ) with 49–51% of markers having an imputation quality score ≥0.3. When restricting to markers with MAF>0.01 (~10 million), 92–94% of the markers had a quality score ≥0.3, compared to 29–32% of markers with MAF≤0.01. After filtering markers based on MAF (>0.01) and imputation r-sq (≥0.3), approximately 9.8 million markers were available for analysis.We imputed a total of 31,326,389 markers  and the majority (69%) of these had a MAF≤0.01. The average imputation quality score by minor frequency for each platform family is shown in S4A–S4C Fig) showed no indication of systematic bias (genomic inflation factor λ = 1.00–1.02). The results from the meta-analysis are shown in Figs 4. We observed a tail of strongly associated SNPs with the top SNPs located in the known BMI FTO locus . Given that the FTO locus has also been associated with Type 2 Diabetes, we reran the analysis excluding all Type 2 Diabetes cases , The association for the FTO SNP rs55872725 remained strongly significant . We also observed genome-wide significant associations for the previously identified TMEM18  and FANCL loci . None of the SNPs that were originally reported were the top SNP in our data. However, for these three regions . Using a significance level of p = 0.05, 59% (19/32) known BMI SNPs [.We had BMI and GWAS data for 20,283 individuals  within NHS, NHSII, HPFS and PHS. Platform-specific QQ-plots  showed no indication of systematic bias (genomic inflation factor λ = 1.00–1.01). The results from the meta-analysis are shown in Figs 7 (genomic inflation factor λ = 1.00). We observed a strong association located downstream of the F5 gene . We also observed genome-wide significant associations for the ABO locus  and a nominal association (P = 0.007) with the previously VTE-associated F11 locus. For both the F5 and ABO regions , our top SNPs showed moderate correlation with previously reported top SNP . Using a significance level of p = 0.05, three of nine known VTE SNPs [.We had information on VTE status and GWAS data for 1,364 cases and 17,628 controls within NHS, NHSII and HPFS. The median number of case subjects by dataset was 87.5 and ranged from 16 in the NHSII breast cancer GWAS dataset  to 417 in the type 2 diabetes GWAS dataset . The small number of cases in many individual GWAS data sets led to unstable study-specific association statistics. Restricting to studies with an expected case minor allele count >10 for SNPs with a MAF of 0.05 (i.e. studies with at least 200 cases) reduced the sample size to 417 cases and 5,356 controls. However, within each compiled imputed GWAS dataset, VTE case numbers ranged from 406 (OmniExpress) to 532 (Affymetrix). Thus, combining the individual GWAS datasets into three main datasets enabled association analysis of hundreds of cases rather than tens, leading to more stable estimates in the regression analysis. Platform-specific QQ-plots  since the SNP overlap across platforms was low on a genome-wide scale . An attempt to impute a genome-wide dataset comprising only 75,000 SNPs as starting point would have resulted in decreased imputation accuracy in regions of the genome with sparse genotype data. Moreover, it has been shown that different platforms might call SNPs differently and that SNP-specific allele frequencies can differ between platforms (see  for furtFTO and TMEM18 that both reached genome-wide significance (P<5x10-8). In addition, out of 32 known BMI SNPs we observed nominal significance (P<0.05) for 19 of them, all in the same direction as expected from previous reports. Of note, our sample size  is less than 10% of the original GWAS that had a total sample size of 249,766 individuals. Therefore, we would not expect to observe significant associations for all BMI SNPs due to limited power. For VTE, we observed genome-wide significant associations for the F5 and ABO loci that are both known to be associated with VTE. In addition, we also observed a nominal association (P = 0.007) with the F11 region. Our BMI and VTE results confirm that GWAS analysis of secondary traits in this data is valid and provides a platform for future studies of secondary traits. We ran the BMI and VTE analyses twice, the first time without removing duplicates between the datasets , and the second time with the duplicates removed. Although the 444 pairs constitute less than 5% of our total sample size, including them had an impact on the genomic inflation factor . These results are especially interesting as it is often difficult to identify duplicates across studies when raw data from all participating studies are not available. Care should be taken to remove overlapping subjects across GWAS contributing to a meta-analysis, but any remaining cryptic overlap may inflate association statistics. In that case, statistical adjustment procedures like LD score regression [To assess the validity of our data, we conducted two proof-of-principle GWAS. The first trait we studied was BMI, and in line with what expected, we observed strong evidence of associations with known BMI loci including gression  can be uOne of the main benefits with collecting comprehensive genetic information on cohort subjects is the opportunity to assess interactions between genetic factors and prospectively collected environmental data. To date, few gene-environment interactions have been identified and although their extent and clinical impact remain an open empirical question, the current lack of homogenous large datasets with both genetic and environmental data has precluded comprehensive investigation. Capitalizing on this GWAS resource, we will be able to explore gene-environment interactions for a plethora of outcomes including complex traits such as height and BMI, but also disease outcomes. It will also allow us to study the impact of environmental factors within genetic strata to identify individuals for whom a particular intervention might be especially important –59.Accumulation of these GWAS data is ongoing and we expect to generate new GWAS data for an additional 15,000 participants within the next two years, almost doubling our total GWAS sample size. This growing resource will be a core component of future studies aiming to elucidate how genes and the environment impact public health.S1 FigDifferent colors correspond to different imputation quality score r-sq thresholds. Data is categorized by minor allele frequency.(PDF)Click here for additional data file.S2 FigDifferent colors correspond to different imputation quality score r-sq thresholds. Data is categorized by minor allele frequency.(PDF)Click here for additional data file.S3 FigDifferent colors correspond to different imputation quality score r-sq thresholds. Data is categorized by minor allele frequency.(PDF)Click here for additional data file.S4 FigA: QQ-plot for GWAS analysis of body mass index on the Illumina Omniexpress platform . B: QQ-plot for GWAS analysis of body mass index on the Affymetrix platform . C: QQ-plot for GWAS analysis of body mass index on the Illumina HumanHap platform .(PDF)Click here for additional data file.S5 FigFTO locus. B: LocusZoom plot for the BMI TMEM18 locus. C: LocusZoom plot for the BMI FANCL locus.A: LocusZoom plot for the BMI (PDF)Click here for additional data file.S6 FigA: QQ-plot for GWAS analysis of venous on the Illumina Omniexpress platform . B: QQ-plot for GWAS analysis of venous on the Illumina Omniexpress platform . C: QQ-plot for GWAS analysis of venous on the Affymetrix platform .(PDF)Click here for additional data file.S7 FigF5 locus. B: LocusZoom plot for the VTE ABO locus.A: LocusZoom plot for the VTE (PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.


Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information across the markers spanned by the CNV. However, CNV boundaries are not known in advance, and the best way to proceed with this pooling is unclear. In this article, we propose a kernel-based method for aggregation of marker-level tests and explore several aspects of its implementation. In addition, we explore some of the theoretical aspects of marker-level test aggregation, proposing a permutation-based approach that preserves the family-wise error rate of the testing procedure, while demonstrating that several simpler alternatives fail to do so. The empirical power of the approach is studied in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine and compares favorably with several competing approaches. CNV, or copy-number variation, at that position). Understanding the contribution of CNVs to human variation is one of the most compelling current challenges in genetics [The classical genetic model states that humans have two copies of each gene, one on each chromosome. The sequencing of individual human genomes, however, has revealed that there is an unexpected amount of structural variation present in our genetic makeup. Sections of DNA can be deleted or duplicated, leaving individuals with fewer or more copies of portions of their genome  arrays has increased, it is increasingly possible to use this data to infer the CNV status of individuals. Indeed, recent generations of such chips include probes specifically designed to enable measurement of copy number ,3. AlthoTwo general strategies have been proposed for conducting genetic association studies of copy-number variation. The majority of analytic techniques attempt to (1) identify or “call” CNVs for each individual, then (2) carry out association tests of whether individuals with a CNV differ from individuals without a CNV with regard to disease or some other phenotype. An alternative approach is to reverse the order of those two steps: (1) carry out association testing at the single marker level, then (2) aggregate information from neighboring markers to determine CNVs associated with disease/phenotype. The key idea of both approaches is that, because the data is noisy, it is virtually impossible to identify CNV associations from a single marker. Because copy number variants extend over multiple markers in a sufficiently high-density array, however, we are able to carry out inferences regarding CNVs by pooling information across neighboring markers.variant-level testing and marker-level testing. In Breheny et al. [We refer to these two approaches, respectively, as y et al. , the autOne serious complication with variant-level testing is that the estimated CNV boundaries from different individuals do not, in general, coincide. This presents a number of difficulties. Whether or not two individuals with partially overlapping CNVs should be in the same risk group for the purposes of association testing is ambiguous and complicates both the association test itself, as well as attempts to correct for multiple comparisons. With a large sample size, the complexity of partially overlapping CNV patterns quickly becomes daunting. Marker-level testing is an attractive alternative, as the aggregation is carried out on the test results, thereby avoiding the complications posed by partially overlapping CNV call boundaries during the analysis process.p-values of the marker-level tests vs. position along the chromosome. The details of the hypothesis tests for this example are described in p-values may arise from any test for association between copy number intensity [p-values between 102.5 and 102.7 Mb. The presence of so many low p-values in close proximity to one another suggests an association between the phenotype and copy-number variation in that region.We illustrate the idea behind marker-level testing and aggregation in ntensity  and phenet al. [p-values from marker-level tests. We present two main findings. First, we develop a computationally efficient kernel-based approach for p-value aggregation. Second, we analyze the multiple comparison properties of this approach and of p-value aggregation in general. In particular, we demonstrate that naïve aggregation approaches assuming exchangeability of test statistics do not preserve the family-wise error rate (FWER). To solve this problem, we present a permutation-based approach and show that it preserves family-wise error rates while maintaining competitive power.To locate these clusters on a genome-wide scale, Breheny et al.  used a met al. ,15. Herei to index subjects and j to index markers. Let i at marker j, let i and let p-value arising from a test of association between phenotype and intensity at marker j. Finally, let j along the chromosome and J denote the total number of markers; in this article, we use physical distance, but genetic distance could be used instead.Throughout, we will use j and j is along the chromosome from the target location, h defines the bandwidth of the kernel and, thereby, controls the bias-variance tradeoff—a larger bandwidth pools test results over a larger region and, thereby decreases variance, but potentially introduces bias by mixing test results beyond the boundary of a CNV with those inside the boundary.Consider the aggregationp-values lead to large values of Although, in principle, one could apply Equation  at any aIn this section, we describe the choice of kernel We consider two primary choices with regard to the kernel: shape and definition of bandwidth. First, we may consider varying the shape of the kernel. Two common choices are the flat (“boxcar”) kernel and the Epanechnikov kernel: Intuitively, the Epanechnikov kernel would seem more attractive, as it gives higher weight to markers near the target location and diminished weight to distant markers where bias is a larger concern.constant width and constant marker . In the constant width approach, as illustrated in Equations ,,constantk markers in the positive support of the kernel. Specifically, the constant marker approach replaces the scalar h in Equations   k markerThe general tradeoff between the two approaches is that as we vary the target location, As a point of reference, the flat, constant marker kernel is similar to the simple moving average, although not exactly the same. For example, consider the following illustration.Suppose p-values is not necessarily optimal. Various transformations of p may be able to better discriminate true associations from noise. Specifically, we consider the following transformations: p-values produce high values of Directly pooling p-values. Forming a combination test statistic based on the sum of p-values) was first proposed by Fisher [et al. [p-values. Finally, Equation  have a direction associated with them, while others (F-tests) do not. As we will see in Some association tests . This method aggregates neighboring p-values by calculating the t-test statistic comparing the intensity of a given region with that of the surrounding region. The significance of this test statistic is quantified by comparing it to the distribution of maximum test statistics obtained by permuting the One approach, suggested in Breheny et al. , is to u} values ,15. Crucp-values are independent.Alternatively, we may use the kernel methods described in Uniform0,. Thus, fAn alternative to the Monte Carlo approach for quantifying the significance of DNAcopy does not calculate exact p-values, only whether or not they fall below a cutoff. This is sufficient for Consider a genomic region in which individuals may have a CNV. The goal of the analysis is to detect CNVs associated with a particular phenotype. Thus, the null hypothesis may hold in one of two ways: (“No CNV”) no individuals with CNVs are present in the sample or (“No association”) individuals with CNVs are present in the sample, but the CNV does not change the probability of developing the phenotype. i.e., its type I error rate is not as badly violated. (2) The data simulated here for the “no association” setting are somewhat exaggerated: the CNV was present in 50% of the population, and the signal to noise ratio was about twice as high as that typically observed in real data. In more realistic settings, the violation of type I error rate is not nearly as severe. The results in We make the following additional observations: (1) the CBS approach is somewhat more robust to the exchangeability issue than the Monte Carlo approach; We now formally define the kernel permutation method introduced in If the tests are directional, with results n is the sample size. The procedure is as follows. At any given iteration, draw a random vector of phenotypes, To obtain the null distribution of We may then use the empirical CDF of these draws from the permutation distribution of Theorem 1. Let  denote the hypothesis that the phenotype, , is independent of the vector of CNV intensities, . Then, using the permutation approach described above with any of the kernel aggregation approaches in , for any B, the number of permutations evaluated. For the numerical results in It is worth pointing out that the above theorem is proven for the case in which all permutations of The global test above is of limited practical benefit in the sense that it does not indicate the location of the associated CNV. Thus, we also consider the following equivalent procedure: declare significant evidence for the presence of a CNV-phenotype association at any marker for which Corollary 1. Let  denote the hypothesis that the phenotype, , is independent of the CNV intensity at marker j, . Then, under the global null hypothesis that  is jointly independent of , for any ,using the permutation approach described above and  as the test function for . In other words, the testing procedure described above controls the FWER in the weak sense at level α.It is worth noting that the procedure controls the FWER only in the weak sense—in other words, that it limits the probability of a false declaration of a CNV only under the global null hypothesis that there are no CNVs associated with the outcome. Typically in multiple testing scenarios, strong control is desirable. However, in the case of CNV-phenotype association, strong control is impractical, as it would imply that a method not only identifies CNV-phenotype associations, but can perfectly detect the genomic boundary of any associated CNV. This is an unrealistic requirement; in practice, there is no way to prevent the possibility that a detected CNV-phenotype association may spill over beyond the boundary of the CNV.In this section, we describe a pharmacogenomic study of gemcitabine, a commonly used treatment for pancreatic cancer. We begin by describing the design of the study (this description is similar to that provided in ), then aet al. [The gemcitabine study was carried out on the Human Variation Panel, a model system consisting of cell lines derived from Caucasian, African-American and Han Chinese-American subjects . Gemcitabine cytotoxicity assays were performed at eight drug dosages  . Estimatic model . Marker ic model ,27. Raw et al. [172 cell lines  had both gemcitabine cytotoxicity measurements and genome-wide marker intensity data. To illustrate the application of the kernel-based aggregation approach, we selected one chromosome (chromosome 3) from the genome-wide data. To control for the possibility of population stratification, which can lead to spurious associations, we used the method developed by Price et al. , which up = 0.16) after controlling the chromosome-wide FWER. Other choices of bandwidth and transformation produce qualitatively similar, although somewhat less significant, results.We analyzed these data using the kernel-based approach described in et al. [et al. [Copy number variation in the region of chromosome 3 at 102.6 Mb, which is in close proximity to the gene, ZPLD1, has been found by Glessner et al.  to be as [et al.  indicateet al. [In this section, we study the ability of the proposed approach to detect CNV-phenotype associations using simulated CNVs and corresponding intensity measurements. The validity of our conclusions depends on how realistic the simulated data is, so we have given careful thought to simulating this data in as realistic a manner as possible. The spike-in design that we describe here is also described in Breheny et al. .The basic design of our simulations is to use real data from the gemcitabine study described in i denote subjects and j denote markers, the following variables are generated: i; j for individual i; and We then used these residuals to simulate noise over short genomic regions in which a single simulated CNV is either present or absent. Letting i was then drawn from the observed measurement errors at those markers for a randomly chosen row of For each simulated data set, 200 markers were independently selected at random from the columns of jth marker; otherwise, the added signal is equal to the standard deviation of the measurement error times the signal-to-noise ratio. Our simulations employed a signal-to-noise ratio of 0.8, which corresponded roughly to a medium-sized detectable signal based on our inspection of the gemcitabine data. Note that the phenotype and intensity measurement are conditionally independent given the latent copy-number status The intensity measurements, For the Illumina Human1M-Duo BeadChip, which has a median spacing of 1.5 kb between markers, 200 markers corresponds to simulating a 300 kb genomic region. We varied the length of the CNV from 10 to 50 markers, corresponding to a size range of 15 to 75 kb. For the simulations presented in the remainder of the article, we used a sample size of We begin by examining the impact on power of the various transformations proposed in The relationship between power and transformation choice is illustrated in vs. constant-marker) and kernel shape (flat vs. Epanechnikov), defined in In this section, we examine two aspects of kernel choice: bandwidth implementation  on the basis of whether a CNV was detected by CBS. A two-sample For rare CNVs (5% population frequency), the power of the variant-level approach and the aggregated marker-level approach are comparable. However, for more common CNVs, the marker-level approach offers a substantial increase in power. For the most part, this increase in power persists even when the bandwidth is misspecified. Only when the bandwidth was much too small (selecting a 10-marker bandwidth for a 50-marker CNV) did the variant-level approach surpass marker-level aggregation.et al. [Generally speaking, these results are consistent with the findings reported in Breheny et al. , who fouet al. [p-values, as described in et al. found that the change-point approach had very low power at 5% frequency—much lower than the variant-level approach. On the other hand, in the same setting, we find that the kernel approach is comparable to, and even slightly more powerful than, the variant-level approach. Furthermore, as discussed in et al. [Comparing the results in et al. , who aggn et al. .a priori. Indeed, it is possible that multiple CNVs associated with the outcome are present on the same chromosome and have different lengths. A method that is not robust to bandwidth will be incapable of detecting both CNVs. Generally speaking, a bandwidth of roughly 30 markers seems to provide good power over the range of CNV sizes that we investigate here.A potential drawback of the kernel approach is the need to specify a bandwidth. This makes the robustness of the method to bandwidth misspecification, as illustrated in We have explored the use of a kernel-based method for aggregating tests that possess a spatial aspect, whereby underlying latent features cause nearby tests to be correlated, demonstrated some of the analytical challenges and developed an approach that properly accounts for multiple comparisons in a challenging setting. Our motivation for this work is the problem of association testing involving copy-number variants, but our findings may also be applied to other problems in genome-wide association studies, such as testing for haplotype associations.The computational burden of the method is worth further discussion, due to the permutation testing requirement. For simple tests, such as the linear regression tests we used in the gemcitabine study, the burden is quite manageable. On our machine (using an Intel Xeon 3.6 GHz processor), it took under a second to perform the 70,542 marker-level tests on chromosome 3 and under 0.1 s to perform the kernel aggregation. Carrying out 1,000 permutation tests took 1,000 times longer: 15 min to carry out all the permutation tests and 21 s to perform all the kernel aggregation. Extrapolating, a genome-wide analysis would take 3.5 h.i.e., doubling the subjects should double the computing time), but more complicated marker-level tests based on nonlinear, mixed-effects or mixture models would require substantially more time. Fortunately, the procedure is easy to run in parallel on multiple cores or machines, with each processor carrying out a fraction of the permutation tests. It is also worth pointing out that kernel aggregation may be used as an exploratory tool without the need for permutation testing (the black dots of These calculations, however, are for simple marker-level tests and a fairly small sample size (The simulation studies of


Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort. Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models. The accumulation of genetic data from existing association studies has led to a growing interest in predicting traits and diseases using genetic markers 9. In animals or plants, accurate phenotype prediction with genetic markers can assist the selection of individuals with desirable breeding values and can improve the effectiveness of breeding programs10. In humans, accurate phenotype prediction with genetic markers can facilitate disease prevention and intervention at early stages and can aid in the development of personalized medicine by using genotype information to customize the treatment and predict the outcome11. Phenotype prediction has also been proposed recently as a key step for integrating functional genomic sequencing studies with GWASs: we can construct more powerful and interpretable gene-set tests in GWASs by setting variant weights to be the coefficients inferred from predictive models in expression quantitative trait locus mapping studies12.Genome-wide association studies (GWASs) have identified thousands of genetic loci harboring associated single-nucleotide polymorphisms (SNPs) for many complex traits and diseases, providing unprecedented insights into the genetic basis of phenotypic variation13. Similarly, many animal or plant traits are contributed by hundreds of causal variants 15; and cattle-related traits, such as backfat thickness, milk yield (MY) and hot carcass weight)17. Because most complex traits and common diseases have a polygenic architecture, a handful of identified associated SNPs often only capture a small proportion of the phenotypic variation and thus cannot be used to yield accurate phenotype and risk prediction. Instead, accurate phenotype prediction requires polygenic models that can make use of all genome-wide SNPs20. In the past decade, successful development and application of many polygenic models in the context of genomic selection has revolutionized many animal breeding programs23. More recently, applications of polygenic models to human GWASs have also yielded fruitful results27.Progress toward accurate phenotype prediction requires the development of statistical methods that can model all SNPs jointly. Previous association studies have demonstrated that most complex traits and common diseases have a polygenic background and are each influenced by many genetic variants with small effects. For instance, it is estimated that thousands of causal variants influence human height28. The Bayes alphabetic  methods assume that the variant effect sizes follow a t-distribution or its variation29. The Bayesian lasso assumes a double exponential/Laplace distribution31. NEG generalizes the Bayesian lasso by assuming a normal exponential gamma distribution32. BVSR and BayesCπ assume a point-normal distribution33. BSLMM assumes a mixture of two normal distributions34 and is closely related to the early reversible jump Markov Chain Monte Carlo (rjMCMC) method20. BayesR35 assumes a three-component normal mixture together with a point mass at zero. Given the large number of modeling choices, one naturally wonders which method to use for any given trait. Previous studies have suggested that accurate prediction requires choosing a prior effect size distribution that can closely match the shape of the true effect size distribution, such that the inferred posterior can approximate well the polygenic architecture of the given trait36. However, the effect size distribution for any given trait or disease is unknown a priori and varies for different diseases in terms of the number of causal variants, their minor allele frequencies (MAFs), and their individual effect sizes11. Therefore, to achieve robust performance, it is important to design prior distributions that are flexible enough to resemble the true effect distribution in many traits as close as possible35.Most existing polygenic models for prediction make an assumption on the effect size distribution and different methods differ mainly in such modeling assumption. For example, the commonly used linear mixed model (LMM), also known as the best linear unbiased predictor (BLUP), assumes that the effect sizes from all variants follow a normal distribution37, the number of parameters in a parametric model determines model complexity and bounds the amount of information in data that can be captured by the model40. Therefore, using only a few parameters to characterize the effect size distribution can limit the flexibility of the model38 and impede its robust performance across a range of genetic architectures. As an example, the commonly applied LMM uses a normal distribution with one variance component parameter to characterize the effect size distribution. For highly polygenic traits, the assumed normal distribution can approximate the true effect size distribution well, and as a result, LMM can achieve good predictive performance35. However, for traits with large effect variants, the assumed normal distribution can no longer capture the true effect size distribution well and the performance of LMM decays35.Up to now, almost all existing polygenic models are parametric in nature and use a prior effect size distribution that is characterized by a few parameters. From the information channel perspectiveTo allow for greater flexibility on the a priori effect size distribution and to enable robust phenotype prediction performance across a range of phenotypes, we develop a Bayesian non-parametric model, which we refer to as the latent Dirichlet process regression (DPR). DPR does not use any fixed parametric distribution as the prior choice for the effect size distribution. Instead, DPR relies on the Dirichlet process to assign a prior on the effect size distribution itself and is thus capable of inferring an effect size distribution from the data at hand. Effectively, DPR uses infinitely many parameters a priori to character the effect size distribution, and with such a flexible modeling assumption, DPR is capable of adapting to a broad spectrum of genetic architectures and achieves robust predictive performance across a wide range of complex traits. We illustrate the benefits of DPR with simulations and real data applications for gene expression prediction, gene-based test via PrediXcan, genomic selection for four traits in two species, as well as genetic prediction of eight complex traits in a human cohort.36, we expect our non-parametric model to perform robustly well across a range of polygenic architectures. Our method is implemented in the DPR software, freely available at http://www.xzlab.org/software.html.An overview of our method is provided in the Methods section with details provided in the 29; (2) BayesR35; (3) LMM28; (4) MultiBLUP41; (5) rjMCMC20; (6) DPR.VB, the variational Bayesian (VB) version of DPR; and (7) DPR.MCMC, the Markov chain Monte Carlo (MCMC) version of DPR. Note that both BayesR and MultiBLUP have been recently demonstrated to outperform a range of existing prediction methods; thus, we do not include other prediction methods into comparison here.We first compare the performance of DPR with several other commonly used prediction methods using simulations. A total of seven different methods are included for comparison: (1) BVSR17 with 5024 individuals and 42,551 SNPs and simulated phenotypes. To cover a range of possible genetic architectures, we consider eight simulation settings from four different simulation scenarios with the phenotypic variance explained (PVE) by all SNPs being either 0.2, 0.5, or 0.8 (details in Methods). In each setting for each PVE value, we performed 20 simulation replicates. In each replicate, we randomly split the data into a training data with 80% individuals and a test data with the remaining 20% individuals. We then fitted different methods on the training data and evaluated their prediction performance on the test data . We evaluated prediction performance using either the squared correlation coefficient (R2) or mean squared error (MSE). We contrasted the prediction performance of all other methods with that of DPR.MCMC by taking the difference of R2 or MSE between the other methods and DPR.MCMC. Therefore, an R2 difference below zero or an MSE difference above zero suggests worse performance than DPR.MCMC. Figure R2 differences for different methods across 20 replicates in each of the eight simulation settings for PVE = 0.5. Because Fig. R2 values across cross variation replicates; various methods display similar prediction variability. Supplementary Figs. R2 differences for PVE = 0.2 and PVE = 0.8, respectively. The corresponding results for MSE differences are shown in Supplementary Figs. R2 and MSE values of the baseline method, DPR.MCMC, are shown in the corresponding figure legend.To make our simulations as real as possible, we used genotypes from an existing cattle GWAS data setc = 10, 100, and 1000 in scenario III, and normal, t and Laplace distributions in scenario IV; where “among the best” refers to the case when the difference between the given method and the best method is within ± 0.001) and is ranked as the second best in the rest one simulation setting . Similarly, when the total PVE is high  but can perform poorly in sparse settings . The performance of LMM, MultiBLUP, and rjMCMC in polygenic vs. sparse settings presumably stems from their polygenic assumptions on the effect size distribution. In contrast, because of the sparse assumption on the effect size distribution, both BayesR and BVSR have an advantage in sparse settings  but suffers in polygenic settings . The performance of BVSR is also generally worse than BayesR in the challenging setting when PVE is either small or moderate, presumably because of the much simpler prior assumption employed in BVSR for the non-zero effects. Finally, the VB version of DPR  performs considerably less well compared with the MCMC version of DPR , especially when PVE is high  predict gene expression levels using cis-SNPs; (2) conduct subsequent PrediXcan based gene set test; (3) perform genomic selection in animal studies; and (4) predict complex traits in humans.42. The GEUVADIS data contains gene expression measurements on 15,810 genes and 465 individuals after quality control (Methods). These individuals have their genotypes measured in the 1000 Genomes project43. In the data, we first identified cis-SNPs that are within 100 kb of each gene and obtained an average of 175 cis-SNPs per gene. Then, for each gene in turn, we applied different methods to predict gene expression levels using these cis-SNPs. To measure prediction performance, we carried out 20 Monte Carlo cross validation data splits as in simulations. In each data split, we fitted methods in a training set with 80% of randomly selected individuals and evaluated method performance using R2 in the test set with the remaining 20% of individuals. In addition to the seven methods used in the simulations , we also applied Elastic Net (ENET)44, which is the default method used in the original PrediXcan paper12. Table R2 above different thresholds for different methods. The predictive R2 obtained from DPR.MCMC vs. various other methods across all genes is shown as scatter plots in Supplementary Fig. Our first application is predicting gene expression levels using cis-SNPs in the GEUVADIS dataR2 > 0.10 in ~1300 genes, which is ~100 more than that by the second best method at this threshold 17, where we focus on three phenotypes: milk fat percentage (MFP), MY, as well as somatic cell score (SCS); (2) a maize study15, where we use GDD as the phenotype; (3) the Framingham heart study (FHS) data45, where we focus on five plasma traits that include low-density lipoprotein (LDL) cholesterol, glucose (GLU), high-density lipoprotein (HDL) cholesterol, total cholesterol (TC) and triglycerides (TGs), and three anthropometric traits that include height, weight and body mass index (BMI). As in simulations, for each phenotype, we performed 20 Monte Carlo cross validation data splits. In each data split, we fitted methods in a training set with 80% of randomly selected individuals and evaluated method performance using R2 or MSE in a test set with the remaining 20% of individuals. We again contrasted the performance of the other methods with that of DPR.MCMC by taking the R2 difference or MSE difference with respect to DPR.MCMC. The results are shown in Fig. R2 difference) and Supplementary Fig. R2 and MSE of DPR.MCMC presented in the corresponding figure legend. Supplementary Table R2 values across cross variation replicates. Supplementary Fig. Finally, we compare the performance of DPR with the other methods in predicting phenotypes in three GWAS data sets: 1) a cattle studyenotype;  the Fram a cattle46: while MFP and MY are affected by a few large or moderate effect SNPs together with many small effect SNPs, SCS is a highly polygenic trait influenced by many SNPs with small effects. BVSR performs poorly for these three traits in the cattle data. In the maize data  with equal sample size but different levels of relatedness (details in Methods): individuals in D1 are more closely related to each other than those in D2. We then compared method performance by performing cross validation in each of the two data sets separately. While the difference between methods becomes smaller due to smaller sample size in the two sub data sets, the relative performance of most methods for the eight traits are largely unchanged in these two sub data sets as compared to that in the complete data . PIP computed in this way measures SNP marginal association strength in the presence of polygenic effects, and may represent a more powerful association indicator than standard single SNP association test statistics50. An important feature of using PIP in the context of Bayesian models is that PIP quantifies the uncertainty of association strength50, which is a desirable feature that is not easily achieved by penalized frequentist counterparts51.We have focused on one application of DPR—genetic prediction of phenotypes. Like some other polygenic methods41. However, it would be desirable to extend DPR to accommodate case control data or other discrete data types in a principled way, by, for example, extending DPR into the generalized linear model framework. In particular, we could use a probit or a logistic link to extend DPR to directly model case control data. We could use a Poisson or an over-dispersed Poisson distribution to extend DPR to model count data. Extending DPR to various discrete data types would likely lead to wider applications of DPR beyond GWASs. For instance, by modeling count data, DPR could be used to perform differential expression analysis52 or expression QTL mapping in RNA sequencing studies54. Similarly, by modeling proportional data, DPR could be used to perform differential methylation analysis or methyl-QTL mapping in bisulfite sequencing studies55. Extending DPR to modeling discrete data types using the generalized linear model framework is thus an important avenue for future research.Here, we have restricted ourselves to applying DPR to continuous phenotypes. For case control studies, we could follow previous approaches of treating binary phenotypes as continuous traits and apply DPR directly56. In addition, untyped causal SNPs in related individuals can be more easily tagged by neighboring typed SNPs than that in unrelated individuals, thanks to the relatively high linkage disequilibrium (LD) in related data. Because both the shared environmental factors and easy tagging of causal SNPs can facilitate prediction, cross validation using related individuals often results in better prediction performance than using unrelated individuals49. However, we caution that the prediction accuracy measured in the test data obtained with cross-validation in related individuals are likely inflated if our ultimate goal is to perform prediction in unrelated individuals instead of related ones. In addition, the predictive model inferred from related individuals may not generalize well to unrelated individuals who are not necessarily influenced by the same set of environmental factors and who do not share the same LD pattern near the causal SNPs. We have attempted to tease apart the influence of relatedness on prediction performance by splitting the FHS data into two parts with different levels of relatedness. Our results indeed show that, while the relative performance of various methods remains largely the same, the absolute performance of all methods do increase with individual relatedness. Additionally, while our method performs well relative to the other methods, we caution that DPR’s prediction accuracy is still unlikely of practical use in human clinical setting. Studies on unrelated individuals or studies using a fully independent validation data are likely required to establish the practical utility of prediction methods, which often have unsatisfactory performance there57. Despite the practical importance of using completely independent or cross-population studies for prediction performance validation, however, we also want to point out its potential caveat: using completely independent data for cross-validation may fail to correctly characterize the relative performance of different methods. In particular, a good method that properly captures the signal in the training data may suffer in the validation data due to different LD patterns between the two data sets. Similarly, a poor method that fails to capture the signal in the training data may perform well in the validation data where such signal is no longer relevant. Therefore, using training and validating data that are both representative of the study population is important to not only ensure a proper comparison among methods but also to ensure the clinical relevance and wide applicability of the prediction methods. Exploring the use of such data is an important direction for future research.In the present study, while we used unrelated individuals for GEUVADIS gene expression prediction and PrediXcan tests, we used related individuals for the other two real data applications. Related individuals not only share similar genetic background but also are likely influenced by a common set of environmental factors58, our Gibbs algorithm for fitting DPR is computationally slow and can only be applied to moderate-sized GWAS studies. To make DPR widely applicable, we have explored the use of VB approximation for fitting DPR. VB approximation obtains an approximate posterior distribution through optimization59 and represents a much faster alternative to MCMC sampling. Indeed, DPR.VB is orders of magnitude faster than DPR.MCMC. However, despite its faster computational speed, the VB algorithm is less accurate than MCMC when SNP heritability is large, sometimes by quite a large margin . The loss of accuracy in VB is not unexpected because our VB assumes that the posterior distributions of the SNP effect sizes are independent from each other. Posterior independence is an unrealistic assumption given that SNP genotypes are correlated through LD. Therefore, it is important to explore alternative VB algorithms to incorporate the posterior correlation among effect sizes, by, for example, adapting algorithms developed elsewhere61. It would be ideal if we could develop algorithms that can achieve a high predictive performance as DPR.MCMC but incurs a small computational cost as DPR.VB. Certainly, besides developing alternative algorithms to MCMC, there is still room for improvement on our MCMC algorithm. For example, we could use all individuals to compute some quantities while use only a subset of individuals to compute other quantities, as in our previous MQS method62, in order to reduce the computational burden while maintaining the accuracy of the algorithm. In any case, developing efficient and accurate algorithms likely represents a key step to adapt existing polygenic methods to association studies that are orders of magnitude larger.DPR is not without its limitations. Perhaps the biggest limitation is its computational cost. Like any other MCMC-based approachesy is an n-vector of phenotypes measured on n individuals; W is an n by c matrix of covariates including a column of 1 s for the intercept term; α is a c-vector of coefficients; X is an n by p matrix of genotypes; β is the corresponding p-vector of effect sizes; ε is an n-vector of residual errors where each element is assumed to be independently and identically distributed from a normal distribution with variance We provide a brief overview of DPR here. Detailed methods and algorithms are provided in the 41, we assume that the effect size of ith SNP, βi, follows a normal distribution with variance σ2, i.e., βi ~ N. Unlike previous methods, however, we specify a non-parameter prior on the hyper-parameter σ2 to induce a non-parametric prior on βi. To motivate our prior choice for σ2, it helps to provide a brief review of the previous polygenic prediction methods. Among the many polygenic prediction methods developed recently, a surprisingly large number of them assume a priori that the effect sizes follow a particular class of prior distribution—the scale mixture of normal distributions63. Specifically, these methods assume that each effect size βi follows a normal distribution βi ~ N, with the variance parameter  σ2 following another distribution p(σ2). The prior distribution on σ2, p(σ2), thus differentiates many different predictive methods. For example, LMM assumes a flat prior p(σ2) that is proportional to a constant28. The Bayes alphabetic methods assume that σ2 follows an inverse gamma distribution to induce a t-prior on βi64. The Bayesian lasso assumes that σ2 follows a Rayleigh distribution to induce a double exponential distribution (a.k.a. Laplace distribution) on βi58. NEG assumes an exponential gamma distribution on σ2 to induce an NEG prior on βi32. BVSR and BayesCπ assume a mixture of a point mass at zero with another flat prior to induce a point-normal distribution on βi33. BSLMM assumes a mixture of two point masses to induce a normal mixture distribution on βi34. While BayesR assumes a three point masses together with another point mass at zero on σ2 to also induce a normal mixture distribution on βi35.Like many previous methodsσ2 can be used to induce many smooth unimodal distributions on βi. However, existing predictive methods explicitly make a parametric prior assumption on σ2, which necessarily relies on a limited number of parameters to characterize the distribution on σ2. Consequently, the induced effect size distribution on βi from a parametric prior on σ2 can be restrictive and may sometimes fail to resemble closely the unknown truth effect size distribution underlying complex traits. Motivated by the potential drawback of parametric priors on σ2, we instead develop a non-parametric prior distribution on σ2 to induce a more flexible distribution on βi. Because a non-parametric distribution is characterized by effectively infinitely many parameters, our induced effect size distribution on βi has the potential to resemble a wide range of genetic architectures and achieve robust predictive performance across a variety of traits.The scale mixture of normal distributions is flexible because different distributions on the scale parameter σ2 follows a Dirichlet process (DP) prior40H is the base distribution, and λ is the concentration parameter that describes how the distribution on σ2, G, deviates from the base distribution. Here, we use an inverse gamma distribution as the base distribution and set the two parameters in the inverse gamma distribution to small values to keep the prior relatively uninformative. We treat the concentration parameter λ as an unknown hyper-parameter and intend to infer it from the data at hand. Because we use the Dirichlet process as a prior for the latent variance parameter σ2 we refer to our regression model based on Eqs. (βi (after integrating out σ2) is also non-parametric and can robustly resemble a large classes of unimodal distributions. Indeed, the distribution on βi can be used to adaptively and accurately approximate a t-distribution, a point-t mixture distribution, a mixture of step functions, as well as the marginal effect sizes estimated from a real data set; whereas a normal distribution cannot  to be either 0.2, 0.5, or 0.8, representing low, moderate, and high heritability, respectively. This simulation scenario consists of one simulation setting for each PVE.Scenario II satisfies the BayesR modeling assumption, where a small proportion of SNPs are causal. These causal SNPs come from three effect-size groups. The simulations were similar to scenario I with the only exception that the group-four SNPs have zero effects. Here, the proportion of PVE by the three groups are 0.1, 0.2, and 0.7, respectively. Again, we set the total PVE to be either 0.2, 0.5, or 0.8. This simulation scenario consists of one simulation setting for each PVE.c = 10, 100, or 1000 SNPs as group-one SNPs and set the remaining SNPs as group-two SNPs. We simulated their effect sizes from a standard normal distribution and scaled their effects in each group separately so that the proportion of PVE by the two groups are 0.2 and 0.8, respectively. Again, we set the total PVE to be either 0.2, 0.5, or 0.8. This simulation scenario consists of three simulation settings for each PVE .Scenario III is similar to Scenario I except that SNPs come from two effect-size groups, thus representing a simpler scenario than I. In particular, we selected either Scenario IV is related to the assumption made in LMM and MultiBLUP. Here, all SNPs have non-zero effects and their effect sizes come from either a normal distribution, a t-distribution with four degrees of freedom, or a Laplace distribution. We scaled their effect sizes further so that the total PVE equals 0.2, 0.5, or 0.8. This simulation scenario consists of three simulation settings for each PVE .In each setting, we performed 20 simulation replicates. In each replicate, we randomly split the data into a training data with 80% individuals and a test data with the remaining 20% individuals. We then fitted different methods on the training data and evaluated their prediction performance on the test data .42 contains gene expression measurements for 465 individuals from five different populations: CEPH (CEU), Finns (FIN), British (GBR), Toscani (TSI), and Yoruba (YRI). Following previous studies65, we focused only on protein coding genes and lincRNAs that are annotated from GENCODE66 (release 12). We removed lowly expressed genes that have zero counts in at least half of the individuals and obtained a final set of 15,810 genes. Afterwards, following previous studies67, we performed PEER normalization to remove confounding effects and unwanted variations. In order to remove potential population stratification, we quantile normalized the gene expression measurements across individuals in each population to a standard normal distribution, and then quantile normalized the gene expression measurements to a standard normal distribution across individuals from all five populations. In addition to the gene expression data, all individuals in GEUVADIS also have their genotypes sequenced in the 1000 Genomes Project. Among the sequenced genotypes, we filtered out SNPs that have a Hardy-Weinberg equilibrium (HWE) p-value < 10−4, a genotype call rate < 95%, or an MAF < 0.01. We retained a total of 7,072,917 SNPs for analysis. We intersected these SNPs with imputed SNPs from WTCCC data4  and kept a final set of 2,793,818 overlapping SNPs for analysis. Then, for each gene in turn, we obtained its cis-SNPs that are located within either 100 kb upstream of the transcription start site or 100 kb downstream of the transcription end site, resulting in an average of 175 cis-SNPs per gene.The GEUVADIS data4 1 data consists of about 14,000 cases from seven common diseases and 2938 shared controls. The cases include 1963 individuals with type 1 diabetes (T1D), 1748 individuals with Crohn’s disease (CD), 1860 individuals with rheumatoid arthritis (RA), 1868 individuals with bipolar disorder (BD), 1924 individuals with type 2 diabetes (T2D), 1926 individuals with coronary artery disease (CAD), and 1952 individuals with hypertension (HT). We obtained quality controlled genotypes from WTCCC and imputed missing genotypes using BIMBAM68. We obtained a total of 458,868 SNPs shared across all individuals. We then further imputed SNPs using the 1000 Genomes as the reference panel with SHAPEIT and IMPUTE269. We filtered out SNPs that have a HWE p-value < 10−4, a genotype call rate < 95%, or an MAF < 0.01 to obtain a total of 2,793,818 imputed SNPs. For PrediXcan analysis12, as in the GEUVADIS data (see above), we focused on the same 15,810 genes. As in ref. 12, we further restricted our association analysis on a set of 4343 genes that have a predictive R2 above 0.01 by all predictive methods.The WTCCC17 consists of 5024 samples and 42,551 SNPs after removing SNPs that have a HWE p-value < 10−4, a genotype call rate <95%, or an MAF < 0.01. For the remaining SNPs, we imputed missing genotypes with the estimated mean genotype of that SNP. We analyzed three traits: MFP, MY, and SCS. All phenotypes were quantile normalized to a standard normal distribution before analysis.The cattle data15 consists of 2267 inbred accessions and 98,385 SNPs after removing SNPs that have a HWE p-value < 10−4, a genotype call rate <95%, or an MAF < 0.01. For the remaining SNPs, we imputed missing genotypes with the estimated mean genotype of that SNP. We used the GDDs to silking as the phenotype in genomic selection. GDD was calculated using climate data from weather stations located near the farms15, and was quantile normalized to a standard normal distribution before analysis.The maize datap-value < 10−4, a genotype call rate <95%, or an MAF < 0.01 to obtain a final set of 387,741 SNPs. For these SNPs, we imputed missing genotypes with the estimated mean genotype of that SNP. We performed analysis on eight traits: five commonly used plasma traits that include LDL cholesterol, GLU, HDL cholesterol, TC, and TGs; and three anthropometric traits that include height, weight, and BMI. Each trait was quantile normalized to a standard normal distribution before analysis. Note that the FHS data is a family-based study where individuals are genetically related. To tease apart the influence of individual relatedness on prediction performance among methods, we also divided the samples in FHS into two separate data sets with different levels of relatedness. Specifically, we first used genotypes to compute the genome-wide genetic relatedness matrix. We then ordered individual pairs based on their genetic relatedness values. From top to bottom of the ordered individual pair list, we selected individuals from individual pairs with high levels of relatedness into a data set D1, and continued this process until the sample size of D1 was half of the full data. We then kept the remaining individuals from individual pairs with low levels of relatedness into a data set D2. The relatedness threshold for separating individual pairs between the two data sets was 0.151. Nevertheless, the majority pairs in D1 and D2 have genetic relatedness values close to zero: 99.6% of pairs in D1 and 99.9% of pairs in D2 have a genetic relatedness value between +/−0.01. As another way of measuring relatedness, we also computed the effective number of chromosome segments (Me)49 in the two data. Me is a crucial parameter that measures the effective number of independent SNPs and is also closely related to the effective number of independent individuals: Me is small in a data with related individuals and is large in a data with unrelated individuals. A small value of Me often correlates to high prediction accuracy70. With 20 cross-validation replicates, we estimated Me in D1 and D2 sub data to be 34541.39 (sd = 140.87) and 81786.01 (sd = 651.52), respectively.The FHS data contains genotype data on 6950 individuals and 394,174 SNPs. We filtered out SNPs that have a HWE 28 as implemented in the GEMMA software ; (2) BVSR29 as implemented in the GEMMA software ; (3) MultiBLUP41 as implemented in the LDAK software (version 4.9); (4) BayesR35 as implemented in the bayesR software; (5) rjMCMC20 as implemented in the gwas_rjmc1.163 software. We used default settings to fit all these methods. For rjMCMC, because it requires us to provide a variance component parameter, we used LMM to estimate the variance component first in all analyses. In addition, rjMCMC does not output parameter estimates. Therefore, for the PrediXcan analysis, we first merged the GEUVADIS and WTCCC files for every gene, labeled WTCCC individuals as having missing phenotypes, and then ran rjMCMC on these files to obtained predicted gene expression values using the WTCCC genotype data. The same strategy was also applied to perform cross-validation prediction between D1 and D2 sub data sets in FHS. For gene expression prediction and PrediXcan analysis, following the original PrediXcan paper12, we also used ENET44, which is implemented in the R package glmnet (version 1.9-5). For ENET, following12, we set one penalty parameter  to be 0.5 and selected the other one using 100-fold cross validation in the training data.We compared the performance of DPR.MCMC and DPR.VB mainly with five existing methods: (1) LMMhttp://www.xzlab.org/software.html.Our method is implemented in the DPR software, freely available at http://www.geuvadis.org. The genotype data from the 1000 genomes project is publicly available at http://www.internationalgenome.org. The WTCCC genotype and phenotype data is publicly available at https://www.wtccc.org.uk. The genotype and phenotype data from the cattle and maize studies are available from the authors upon reasonable request and with permission of Prof. Xiaolei Liu at the HuaZhong Agriculture University (xiaoleiliu@mail.hzau.edu.cn). The FHS genotype and phenotype data is available in dbGaP (https://www.ncbi.nlm.nih.gov/gap) with accession number phs000007.No data were generated in the present study. The GEUVADIS gene expression data is publicly available at PDF SI


In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture.Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories.We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.The online version of this article (10.1186/s40168-018-0479-3) contains supplementary material, which is available to authorized users. Our understanding of the microbial communities populating the human body (human microbiota) has progressed tremendously in recent years, catalyzed by the use of next-generation sequencing techniques that overcome the limitations of anaerobic cultivation . Much efMore knowledge of the microbial ecosystem and the role of different factors in its structure is an essential path leading to more understanding of human biology . Cross-sLCT [Given the complex interplay between the microbiome and host physiology, a certain percentage of host genetics, as well as genetic interactions with environmental factors, is expected to shape the composition of the microbial community . Proof-oLCT ., and Wang et al. then simultaneously reported GWAS results from three independent cohorts, each revealing glimpses into the genetic landscape underlying the gut microbiota structure [SLIT3 was the only gene to pass a standard genome-wide significance threshold of 5 × 10−8 in the TwinsUK and Bonder et al. studies [LCT locus, associations to various C-type lectin genes were observed by both Bonder et al. and Wang et al. [Bonder et al., Turpin et al.tructure –14. Toge studies , 12, butg et al. , 12, 14.g et al. .Fig. 1OvWe have therefore established the MiBioGen consortium to study the influence of human genetics on gut microbiota. This collaborative effort currently comprises 18 cohorts worldwide and new members will join us after completing their data collection. We aim to develop a uniform pipeline to allow maximum harmonization across the microbiome data and to use GWAS meta-analyses to provide a fuller picture of human gene-microbiome associations. Furthermore, since all the cohorts have been well phenotyped, their data will aid future investigations into other research questions.Most of the 18 studies participating in the consortium are prospective cohort studies in countries in Europe, Asia, and North America  , or FoCuTo provide a platform for robust and reliable results and also to simplify study participation in MiBioGen, we have standardized all the procedures and protocols that participating cohorts need to follow. The MiBioGen data processing pipeline comprises four steps: (1) microbiome data processing, (2) genotype data processing, (3) genome-wide association analyses, and (4) meta-analyses.n = 8472), although five cohorts sequenced the V3-V4 region (n = 5719), and another four sequenced the V1-V2 region (n = 4774). We assessed the compatibility of the datasets obtained from sequencing different regions by comparing technical replicates of ten samples (three replicates each) generated from different hyper-variable regions. This analysis showed that the influence of technical differences in microbiome profiles is less than the inter-individual differences . The most frequently sequenced hyper-variable region of the 16S rRNA gene was V4 . This filtering effectively reduces the total number of tests and also makes cross-validation and meta-analysis feasible among all the participating cohorts. 16S data processing is currently being performed in all the cohorts and shows a high level of congruence: the core-measurable microbiome (CMM) , definedIndividual genome-wide genotype data was generated by the different cohort studies using different genotyping platforms and arrays Table . In ordehttps://imputationserver.sph.umich.edu/index.html) that uses a two-step approach: phasing with the Eagle v2.3 algorithm, followed by imputation with Minimac [Quality control performed prior to imputation was carried out by each cohort independently according to our general recommendations. Imputation was performed on a freely available Michigan server , as well as our large number of 18 cohorts, we expect to be able to identify individual bacteria and new genomic loci that affect microbiome composition in general. Based on the effect size  in some 1800 individuals [The MiBioGen consortium’s large-scale meta-analysis of 18 cohorts drawn from different populations will permit us to explore the genetic architecture of the gut microbiome. In addition to classic association studies, we will adopt more sophisticated approaches to gain a better understanding of the role of the gut microbiome as a mediator between genetic predisposition and human health/disease. For example, we will explore the association of individual risk scores [LCT non-functional variant and for dairy intake in relation to the abundance of Bifidobacteria [We will also explore human gene-environment interactions with respect to gut microbiome composition. Such interactions have been observed for the bacteria , 19. Combacteria , 12–14, Finally, the additional phenotypes available in each cohort will provide a unique opportunity for quantifying the contribution of the gut microbiome to different phenotypes. For example, GWAS analyses have already been focused on metabolic traits and diseases in different cohorts, and much more cross-checking can be carried out using the EBI GWAS Catalog. The overlap in significant loci will reveal intrinsic relationships between the microbiome, genetics, and diseases, thereby adding to our knowledge of the molecular basis of these pathologies. Recently developed strategies, such as linkage disequilibrium score regression  and polyAdditional file 1:Supplementary nformation. (DOCX 361 kb)Additional file 2:Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative Acknowledgement and funding information. (DOCX 37 kb)