O-glycosylation of serum immunoglobulin A1 (IgA1) represents a heritable pathogenic defect in IgA nephropathy, the most common form of glomerulonephritis worldwide, but specific genetic factors involved in its determination are not known. We performed a quantitative GWAS for serum levels of galactose-deficient IgA1 (Gd-IgA1) in 2,633 subjects of European and East Asian ancestry and discovered two genome-wide significant loci, in C1GALT1  and C1GALT1C1 . These genes encode molecular partners essential for enzymatic O-glycosylation of IgA1. We demonstrated that these two loci explain approximately 7% of variability in circulating Gd-IgA1 in Europeans, but only 2% in East Asians. Notably, the Gd-IgA1-increasing allele of rs13226913 is common in Europeans, but rare in East Asians. Moreover, rs13226913 represents a strong cis-eQTL for C1GALT1 that encodes the key enzyme responsible for the transfer of galactose to O-linked glycans on IgA1. By in vitro siRNA knock-down studies, we confirmed that mRNA levels of both C1GALT1 and C1GALT1C1 determine the rate of secretion of Gd-IgA1 in IgA1-producing cells. Our findings provide novel insights into the genetic regulation of O-glycosylation and are relevant not only to IgA nephropathy, but also to other complex traits associated with O-glycosylation defects, including inflammatory bowel disease, hematologic disease, and cancer.Aberrant  O-glycosylation is a common type of post-translational modification of proteins; specific abnormalities in the mechanism of O-glycosylation have been implicated in cancer, inflammatory and blood diseases. However, the molecular basis of abnormal O-glycosylation in these complex disorders is not known. We studied the genetic basis of defective O-glycosylation of serum immunoglobulin A1 (IgA1), that represents the key pathogenic defect in IgA nephropathy, the most common form of primary glomerulonephritis worldwide. We report our results of the first genome-wide association study for this trait using serum assays in 2,633 individuals of European and East-Asian ancestry. In our genome scan, we observed two significant signals with large effects, on chromosomes 7p21.3 and Xq24, jointly explaining about 7% of trait variability. These signals implicate two genes that encode molecular partners essential for enzymatic O-glycosylation of IgA1 and mucins, and represent potential new targets for therapy. N- and O-glycosylation are fundamental post-translational modifications of proteins in mammalian cells. Abnormalities in glycosylation have been linked to a broad range of human diseases, including neurologic disorders, immune-mediated and inflammatory diseases as well as cancer. Protein glycosylation is mediated by a large family of enzymes that have cell- and tissue-specific activity, and can generate highly diverse glycan structures that are important for signaling, cell-cell and cell-matrix interactions. The combinatorial possibilities of glycan structures imparted by the large number of glycosylation enzymes complicate a systematic analysis of protein glycosylation patterns and identification of critical steps involved in the activity, concentration, and regulation in any given cell or tissue. In such a setting, genetic studies of congenital defects of glycosylation in humans have provided significant insight into non-redundant regulatory nodes in this pathway[s pathway. The majO-glycans, resulting in a variety of IgA1 glycoforms in circulation. In healthy individuals, the prevailing O-glycans include the N-acetylgalactosamine -galactose disaccharide and its sialylated forms. In IgAN, galactose-deficient IgA1 (Gd-IgA1) glycoforms are significantly more abundant compared to those of healthy controls[O-glycosylated proteins is preserved, suggesting a specific defect within IgA1-producing cells[IgA nephropathy (IgAN), the most common cause of glomerulonephritis and a common cause of kidney failure worldwide, is a prototypical example of an immune-mediated disorder characterized by abnormal glycosylation. In huma controls. These uing cells. The pating cells, 5. Consing cells–9 and eling cells, 11.Helix aspersa (HAA), enables screening of sera to quantify the levels of circulating Gd-IgA1[th percentile for healthy controls. Examining family members of probands with familial and sporadic forms of IgAN, we also showed that elevated serum Gd-IgA1 levels segregate independently of serum total IgA levels and have high heritability (estimated at 50–70%)[The design of a simple lectin-based ELISA assay, using a GalNAc-specific lectin from g Gd-IgA1. Using tt 50–70%), 13. MorTo date, multiethnic genome-wide association studies involving over 20,000 individuals have identified 15 risk loci predisposing to IgAN, highlighting the importance of innate and adaptive immunity in this disorder. Power analyses indicated that discovery of additional risk loci using the case-control design will require significant expansion in sample size. However, a systematic analysis of quantitative endophenotypes that are linked to disease pathogenesis, such as Gd-IgA1, has not been conducted to date and may provide the opportunity to discover additional pathogenic pathways using a smaller sample size. In this study, we performed the first GWAS for serum Gd-IgA1 levels, and successfully mapped new loci with surprisingly large contributions to the heritability of the circulating level of Gd-IgA1 independently of IgA levels.Fig 1). While serum total IgG and IgA levels varied with time, Gd-IgA1 levels  remained remarkably stable over a 4-year period , demonstrating that O-glycosylation of IgA1 is minimally affected by random environmental factors.In order to test if serum levels of Gd-IgA1 remain stable over time, we first performed measurements of total serum immunoglobulin levels along with Gd-IgA1 levels at baseline and at four years of follow-up in 32 individuals of European ancestry followed longitudinally (Table 1). As previously reported, serum Gd-IgA1 levels were positively correlated with age  and total IgA levels , but were independent of gender (P > 0.05). In both cohorts, Gd-IgA1 levels were also significantly higher in IgAN cases compared to controls independently of age and total IgA levels , providing a large-scale replication of prior findings.We next used HAA lectin-based ELISA to analyze single time-point sera of 1,195 individuals in our discovery cohorts composed of 950 individuals of East-Asian ancestry (483 biopsy-documented IgAN cases and 467 controls) and 245 individuals of European ancestry , indicating negligible effect of population stratification.We next performed a GWAS for serum levels of Gd-IgA1 in these cohorts with and without adjustment for total IgA levels. For genome-wide analysis, we used a linear model with individual SNPs coded as additive genetic predictors, and the outcome defined as standardized residuals of serum Gd-IgA1 after normalization and additional adjustment for case/control status, age, ancestry and cohort membership see . Each et. In addition, we found no association between the global polygenetic risk score for IgAN, which captures the combined effect of all IgAN risk loci, and Gd-IgA1 levels. We also did not detect any associations of Gd-IgA1 levels with loci previously linked to variation in total IgA levels[N-glycosylation of IgG[ELL2 [We first examined potential associations with known IgAN susceptibility loci, but found no statistically significant or suggestive signals between Gd-IgA1 levels and known IgAN risk alleles , confirm-4) loci that we followed up in 1,438 additional individuals of East-Asian (N = 653) and European (N = 785) ancestry . Subsequently, we analyzed all cohorts  jointly to identify genome-wide significant loci (S2 Table). Our power calculations demonstrate that our design provides adequate power to detect variants explaining ≥1.5% of overall trait variance at a genome-wide significant alpha 5x10-8 (S3 Table).We next examined genome-wide distribution of P-values from the discovery stage to identify novel loci associated with Gd-IgA1 levels. Although no signal reached genome-wide significance in the discovery stage, we observed several suggestive . The strongest association was located within a 200-kb interval on chromosome 7p21.3 , explaining 4% of trait variance in Europeans and ~1% in East Asians (S4 Table). The only gene within this locus is C1GALT1, encoding core 1 synthase, glycoprotein-N-acetylgalactosamine 3-beta-galactosyltransferase 1. The top signal was represented by rs13226913 (P = 3.2x10-11), an intronic SNP within C1GALT1. This locus is further supported by rs1008897 (P = 9.1x10-10) in partial LD with rs13226913 . After mutual conditioning, both SNPs continue to be associated with the phenotype, suggesting a complex pattern of association at this locus (S5 Table).In the combined analysis, two distinct genomic loci, on chromosomes 7p21.3 and Xq24, reached genome-wide significance  from UDP-Gal to GalNAc-alpha-1-Ser/Thr. Core 1 O-glycans are the main glycans in the hinge region of circulating IgA1, as well as precursors of many extended mucin-type O-glycans on cell-surface and secreted glycoproteins. In humans, C1GALT1 is abundantly expressed in IgA1-secreting cells[S6 Table). Interrogation of eQTL databases revealed that rs13226913 has a highly significant cis-eQTL effect on C1GALT1 in peripheral blood cells (P = 3.9 x 10−23) with the T allele associated with lower mRNA levels (S7 Table). Consistent with this finding, rs13226913 imparts an additive effect with each T (derived) allele increasing Gd-IgA1 levels by 0.22 standard deviation units (95% CI: 0.10–0.30).The protein encoded by ing cells, as welling cells. The topFig 2C) and explains an additional 2.7% of the overall trait variance in Europeans and 1.2% in East Asians (S4 Table). The top signal at this locus is represented by rs5910940 (P = 2.7x10-8), a SNP 3’ downstream from C1GALT1C1. The T (derived) allele increases serum Gd-IgA1 levels by 0.14 standard deviation units per allele (95%CI: 0.11–0.17). Our post-hoc examination of genotypic effects suggests a dominant effect of the rs5910940-T allele in females , although skewed inactivation of chromosome X in IgA1-producing cells could also potentially explain this effect.The second genome-wide significant locus comprises a 100-kb interval on chromosome Xq24  lacks the galactosyltransferase activity, and instead acts as a molecular chaperone required for the folding, stability, and full activity of C1GALT1[C1GALT1C1 is also ubiquitously expressed in multiple tissues, including IgA1-secreting cells[C1GALT1C1 based on available datasets. However, rs5910940 tags a 2-bp insertion in the active promoter of C1GALT1C1 in B-lymphocytes and leukemia cell lines (S9 Table). Considering the known functional dependency of C1GALT1 and C1GALT1C1, we also tested for potential epistasis between these two loci, but did not detect any significant genetic interaction.f C1GALT1. C1GALT1ing cells, other bing cells. BecauseC1GALT1 and C1GALT1C1 expression, and leading to increased production of Gd-IgA1. We next performed siRNA knock-down studies in human cultured IgA1-secreting cell lines to confirm the effect of lower C1GALT1 and C1GALT1C1 transcript abundance on the production of Gd-IgA1 . Consistent with the observed genetic effect, in vitro knock-down of C1GALT1 resulted in 30–50% increased production of Gd-IgA1 by the cells derived from IgAN patients (P = 0.025) as well as from healthy controls (P = 0.011). Similar to C1GALT1, in vitro siRNA knock-down of C1GALT1C1 in IgA1-producing cell lines significantly increased the production of Gd-IgA1 in healthy individuals (P = 0.032) and a similar trend was observed in IgAN patients . Consistent with the genetic data, there was no multiplicative effect on Gd-IgA1 production with combined siRNA knock-down in IgA1-secreting lines.Taken together, these data predict an additive regulatory effect of rs13226913 and rs5910940, resulting in lower C1GALT1 and C1GALTC1 loci explain up to 7% of variance in serum Gd-IgA1 levels in Europeans and 2% in East Asians (S4 Table). Further examination of effect estimates by ethnicity confirms that the European cohorts predominantly drive these associations (S10 Table). Notably, the derived (T) allele of rs13226913 at C1GALT1 locus is considerably more frequent in Europeans (freq. 47%) compared to East Asians (freq. 10%), additionally contributing to the difference in variance explained between ethnicities. Subsequent examination of allelic frequencies in the Human Genome Diversity Panel  confirms that the derived allele of rs13226913 is rare or absent in some Asian populations, while being the predominant (major) allele in Europeans (freq. ≥50%). In contrast, the T (derived) allele of rs5910940 at C1GALT1C1 locus is equally frequent in Asian and European populations (freq. ~50%), but nearly fixed in selected African populations. These findings suggest potential involvement of geographically confined selective pressures acting on the loci controlling the O-glycosylation process.Jointly, the newly discovered . This locus is represented by rs978056 (P = 3.3x10-5), an intronic SNP in HECW1 (encoding E3 ubiquitin ligase) previously studied in the context of colon and breast cancer . Based on the analysis of known protein-protein interactions, HECW1 is a second-degree neighbor of C1GALT1 and COSMC, with ubiquitin C as a common interacting protein .Lastly, we detected additional suggestive signals, including a locus on chromosome 7p13 that warrants further follow-up in larger cohorts N-glycosylation and autoimmunity[O-glycosylation of IgA1.Genetic studies of immune endophenotypes have provided novel insights into the genetic architecture of complex traits and enhanced sub-classification of several autoimmune and inflammatory disorders. The power of immune endophenotypes is best exemplified by recent genetic studies of ANCA titers in vasculitis, IgE levoimmunity. Taking O-glycan synthesis have been linked to several human diseases, including IgAN, inflammatory bowel disease (IBD), hematologic diseases, and cancer. Dense O-glycosylation of various mucins produced by epithelial cells is critical for the formation of a protective viscous barrier with anti-microbial properties at the mucosal surfaces of the gastrointestinal, urogenital and respiratory systems. Recent studies indicate that proper O-glycosylation of mucins is required for intestinal integrity in mice[O-glycosylation can affect the structure and immunogenicity of the modified proteins. For example, defective O-glycosylation represents the key pathogenic feature of Tn syndrome[O-glycans leads to exposed terminal GalNAc residue (Tn antigen) on the surface of red blood cells, triggering polyagglutination by naturally occurring anti-Tn antibodies[O-glycosylation defects in tumor cells are presently not known[Abnormalities in the y in mice, 26 and y in mice, 28. In  syndrome, where antibodies. Moreovenot known.O-glycosylation of IgA1 in the Golgi apparatus of IgA1-producing cells[Similar to Tn syndrome, the pathogenesis of IgAN involves autoimmune response to Tn antigens. In this case, the Tn antigen is exposed at the hinge region of IgA1 molecules as a result of aberrant ing cells. In patiing cells, leadinging cells–9. Severing cells, 13, 31.C1GALT1 gene, that encodes human core 1 β1–3-galactosyltransferase (C1GALT1), the key enzyme responsible for the addition of galactose to the Tn antigen. Mice deficient in C1GALT1 protein develop thrombocytopenia and kidney disease attributed to defective O-glycosylation of cell-surface proteins[C1GALT1C1 residing within our second genome-wide significant locus on chromosome Xq24. We also localized a suggestive locus on chromosome 7p13 that encodes an E3 ubiquitin ligase, but it is presently not known if this protein participates in the proteosomal degradation of C1GALT1. This signal will require further follow up. Importantly, our study demonstrates that there are several common genetic variants with relatively large effects on IgA1 O-glycosylation. These effects are conveyed by different genes, but converge on a single enzymatic step in the O-glycosylation pathway.In this study, we quantified the levels of Gd-IgA1 in sera of 2,633 subjects of European and East-Asian ancestry using a simple lectin-based ELISA assay. Using GWAS approach, we discovered two genome-wide significant loci, on chromosomes 7p21.3 and Xq24, both with large effects on circulating levels of Gd-IgA1. The 7p21.3 locus contains  proteins. Moreove proteins, 26. C1G proteins. InteresC1GALT1 locus. The lead allele at this locus has a direction-consistent effect in both ethnicities, but because of the Gd-IgA1-increasing allele is relatively rare or even fixed in some Asian populations, this signal would have been missed if the discovery were performed entirely in East Asians. The second limitation relates to genomic resolution of our discovery study. Although genome-wide imputation was not performed at the time the study was conducted, post-hoc imputation using the latest 1000 Genomes reference revealed no additional loci outside of the regions that were originally selected for follow-up. Moreover, our conditional analyses revealed no additional signals among the imputed SNPs after controlling for the lead alleles at each locus, suggesting that our top SNPs captured most of the signal at the newly reported loci .While we successfully identified two novel loci for serum Gd-IgA1 levels, several important limitations of our study design need to be acknowledged. First, our GWAS has a two-stage design and involves bi-ethnic cohorts. Although to date this is the largest study of individuals worldwide with measured serum Gd-IgA1 levels, this sample size is still not adequate to detect ethnicity-specific loci. Thus, our design is presently limited to the discovery of alleles that have similar effects in both Europeans and East Asians. At the same time, the bi-ethnic composition of our cohorts clearly enabled identification of the S5 Fig), we estimate that we would require a sample size of at least 24,000 cases and 24,000 controls to detect the effect of C1GALT1 and C1GALT1C1 loci in a bi-ethnic GWAS for IgAN . In Stage 1 (the discovery phase) we performed a genome-wide meta-analysis of two discovery cohorts: the Chinese cohort of 950 individuals , and the US cohort of 245 individuals . Genome-wide scan was performed in both cohorts and fixed-effects meta-analysis was applied to prioritize signals for follow-up studies. In Stage 2 we performed targeted genotyping of the top signals from Stage 1 in five cohorts of European and East-Asian ancestry . We estimated the power of our study design for a range of effect sizes under the following assumptions: standard normal trait distribution, additive risk model, no heterogeneity in association, marker allelic frequency of 0.25 (average MAF for the microarrays used), a follow-up significance threshold of P<5×10−4, and a combined significance level of P<5×10−8. These calculations demonstrate that we have adequate power to detect variants explaining ≥1.5% of overall trait variance (S3 Table). Our study was conducted according to the principles expressed in the Declaration of Helsinki; all subjects provided informed consent to participate in genetic studies, and the Institutional Review Board of Columbia University as well as local ethics review committees for each of the individual cohorts approved our study protocol.This study has a two-stage design 2 fragment of goat IgG anti-human IgA at 3 μg/ml. Plates were blocked with 1% BSA in PBS containing 0.05% Tween 20, and serial two-fold dilutions of samples and standards in blocking solution were incubated overnight at room temperature. To remove terminal sialic acid, the samples were treated with 100 μL (1 mU) per well of neuraminidase (Roche) in 10 mM sodium acetate buffer (pH = 5) for 3 h at 37°C. Next, the samples were incubated with GalNAc-specific biotinylated HAA lectin (Sigma-Aldrich) for 3 h at 37°C. The bound lectin was detected with avidin-horseradish peroxidase conjugate, followed by the peroxidase substrate, o-phenylenediamine-H2O2 (Sigma); the reaction was stopped with 1 M sulfuric acid. The concentration of Gd-IgA1 was calculated by interpolating the optical densities at 490 nm on calibration curves constructed using a myeloma Gd-IgA1 standard. The intra-assay coefficients of variation (CVs) for calibration curves, plotted by a 4-parameter model, ranged from 2–10% for the extremes of the curves and 1–5% in the middle region. If higher values were noted, the samples were re-analyzed. The inter-assay CV was also consistently under 5% and our prior studies demonstrated excellent reproducibility of this assay[The serum level of total IgA was determined using standard ELISA. The serhis assay. In the P-values using QQ-plots for each individual cohort, as well as for the joint analysis of both cohorts. We estimated the genomic inflation factors[P-values .The discovery cohorts have been published, including details of the genotyping, genotype quality control, and ancestry analyses, 37. Brin factors, that we-4 from the GWAS analyses. First, we clustered the top signals into distinct loci based on their genomic coordinates and metrics of LD. Conditional regression analysis was carried out to detect independent association between signals within the same genomic regions. For genotyping in replication cohorts, we prioritized the independent SNPs that had the lowest P-value at each genomic locus. In addition, we required that each SNP be successfully genotyped in both discovery cohorts. We excluded 'singleton signals' defined as loci supported by only a single SNP in the absence of supporting signals with P<0.01 within the same LD block. If the genotyping assay failed for the top SNP, a back-up SNP was selected on the basis of its strength of phenotypic association, LD with the top SNP, genotyping quality, and ability to successfully design working primers. Moreover, we added SNPs for which the signals became more significant (P<5x10-4) after adjustment for serum total IgA levels. In all, we successfully genotyped 50 carefully selected SNPs in 1,438 independent replication samples across five cohorts. Similar to our prior studies, the genotyping of replication cohorts was performed using KASP . In our prior studies, this technology had >99.8% accuracy rates[Table 1 summarizes the ethnic composition of our replication cohorts along with the genotyping method and average genotype call rates. We first carried out association analyses individually within each of the cohorts using the same methods as in the discovery study. Next, we combined the results using a fixed-effects model (S2 Table). For each of the genotyped SNPs, we derived pooled effect estimates and their 95% confidence intervals. To declare genome-wide significance, we used the generally accepted threshold of P<5x10-8, initially proposed for Europeans genotyped with high-density platforms based on extrapolation to infinite marker density[We next prioritized the top 50 SNPs for replication among the top suggestive SNPs with P<5x10r density.. In secondary analyses, we assumed complete X-inactivation in females and a similar effect size between males and females. In this test, females are considered to have 0, 1, or 2 copies of an allele as in an autosomal analysis while males are considered to have 0 or 2 copies of the same allele . The main limitation of this approach relates the assumption of complete X inactivation. Because approximately 15–25% of X-linked genes escape inactivation in female-derived fibroblasts[We performed two types of association tests for X-linked markers. Our primary association test involved sex-stratified meta-analysis of chromosome X markers: each male and female sub-cohort was analyzed separately and the association statistics were combined across all sub-cohorts using fixed effects meta-analysis. This approach is not affected by the type of allele coding in males and allows for different effect size estimates between males and femalesbroblasts and chroS8 Table). We also tested for all pairwise genetic interactions between the suggestive and significant loci using two different tests. First, we used a 1-degree of freedom likelihood ratio test to compare two nested linear regression models: the model with main effects only versus the model with main effects plus additive interaction terms. Second, a more general 4-degree of freedom genotypic interaction test was performed. In this test, we compared a model with allelic effects, dominant effects, and their interaction terms with a reduced model without any of the interaction terms. All models were stratified by sex and cohort. The analyses were performed in R 3.0 software package (CRAN).For the genome-wide significant loci, we explored two alternative genetic models (dominant and recessive) and compared these models using Bayesian Information Criterion  and imputed our discovery cohorts using ethnicity-specific reference panels. The haplotypes were phased using Markov Chain Haplotyping software (MACH) and the imputations were carried out with Minimac3. For downstream analyses, we applied strict quality control filters post-imputation, including only markers that were either genotyped or imputed with high confidence (R2 ≥ 0.8). Association testing of imputed SNPs was performed assuming an underlying additive linear model and including cohort-specific significant principal components as covariates. Primary analysis was performed using a dosage association method in PLINK, that accounts for uncertainty in prediction of the imputed data by weighting genotypes by their posterior probabilities. We used a similar approach to perform conditional analyses across the top loci, with conditioning SNPs added as additional covariates in linear models.C1GALT1, C1GALT1C1, and HECW1 regions, we examined all of the top most associated variants as well as all SNPs in LD with the lead SNP (r2>0.5) at each locus. We annotated these variants using ANNOVAR[Using the imputed results for the g ANNOVAR, Seattleg ANNOVAR, SNPNexug ANNOVAR, FunciSNg ANNOVAR, HaploReg ANNOVAR, and Chrg ANNOVAR. The trag ANNOVAR, (2) prig ANNOVAR, and (3)g ANNOVAR, 54. We g ANNOVAR, e-LiSe[g ANNOVAR, and FACg ANNOVAR. We alsog ANNOVAR and Protg ANNOVAR. We usedC1GALT1, COSMC, or both. The ON-TARGETplus Non-targeting Pool siRNAs was used as a control. We followed our previously published protocol for Amaxa nucleofector II (Lonza)[Helix aspersa specific for terminal GalNAc, as described[IgA1-secreting cell lines from five patients with IgAN and five healthy controls were transfected using ON-TARGETplus SMARTpool siRNAs (Thermo Fisher Scientific) specific for human I (Lonza). Twenty-I (Lonza), 60. Thedescribed, 60.S1 Fig(a) Study flowchart summarizing the discovery cohorts (stage 1) and the replication cohorts (stage 2) with final numbers of individuals after phenotype and genotype quality control analyses; (b) QQ-plot for the genome-wide discovery meta-analysis  of serum Gd-IgA1 levels without adjustment for serum total IgA levels and (c) after adjustment for serum total IgA levels. All signals with P<5x10-4  from both analyses were prioritized for follow-up in replication cohorts (stage 2). Lambda: genomic inflation factor.(PDF)Click here for additional data file.S2 Figa) Mean trait values (+/- standard errors) by rs978056 genotype. (b) Regional plot of the HECW1 locus and the top signal represented by rs978056 (P = 3.3x10-5); the x-axis presents physical distance in kilobases (hg18 coordinates), and the y-axis presents −log P values for association statistics. (c) The network of known protein-protein interactions between HECW1, C1GALT1, and C1GALT1C1-encoded proteins. Each node represents a protein and each edge represents a high confidence physical interaction. The seed terms are highlighted in green and their common interactors in yellow. The protein interactions were analyzed and visualized using the Protein Interaction Network Analysis (PINA2) platform.((PDF)Click here for additional data file.S3 Figth, 2015 and the gene-phenotype network was visualized in Cytoscape v.2.8. IgAN: IgA nephropathy; HSPN: Henoch-Schoenlein purpura nephritis; ALS: amyotrophic lateral sclerosis.The co-citation network was constructed based on all PubMed abstracts for the query terms C1GALT1 (61 abstracts), C1GALT1C1 (39 abstracts), and HECW1 (5 abstracts). Both human and mouse disease phenotypes (circles) were analyzed for co-citation (edges) with the three query terms (green diamonds). Common interactors are highlighted in yellow. The PubMed query was performed on December 15(PDF)Click here for additional data file.S4 Fig(a)C1GALT1, (b)C1GALT1C1, and (c)HECW1 loci. The bottom row depicts the discovery meta-analysis results after conditioning individual cohort results for the lead SNP(s) at each locus: (d) rs13226913 and rs1008897 at the C1GALT1 locus, (e) rs5910940 and rs2196262 at the C1GALT1C1 locus, and (f) rs978056 at the HECW1 locus. The red dotted line corresponds to P = 1 x 10−3 and is provided for reference.The top row depicts unconditioned discovery meta-analysis results for all the imputed markers at the (PDF)Click here for additional data file.S5 Fig(a) all study cohorts, (b) European cohorts, and (c) East Asian cohorts. The Gd-IgA1 trait is expressed as standardized residuals of natural log-transformed serum Gd-IgA1 levels after adjustment for age, sex, total IgA levels, and cohort membership; each standard deviation increase in the Gd-IgA1 endophenotype is associated with disease OR (95% CI) of 1.53 (1.40–1.68), 1.49 (1.31–1.72), and 1.56 (1.37–1.78) for All, European, and East Asian cohorts, respectively.The distributional differences in Gd-IgA1 levels between cases and controls for (PDF)Click here for additional data file.S1 TableThe association results were adjusted for age, total IgA, case-control status, ancestry, and cohort membership.(PDF)Click here for additional data file.S2 TableSerum Gd-IgA1 levels before and after adjustment for serum total IgA levels.(PDF)Click here for additional data file.S3 Table−4 (top row) and a joint significance level of P<5×10−8 (bottom row). Shaded in red is the study detection limit corresponding to alleles explaining 1.5% of total variance.The power was estimated for a range of effect sizes expressed as fraction of total variance of the quantitative trait explained by a genetic variant (columns). The assumptions include: standard normal trait distribution, additive risk model, no heterogeneity, marker allelic frequency of 0.25, perfect LD between a marker and a causal allele, a follow-up significance threshold of P<5×10(PDF)Click here for additional data file.S4 Table2 for the regression model. The total variance explained across multiple cohorts was calculated as an average fraction of explained variance for individual cohorts weighted by cohort size. The variance explained by the C1GALT1 locus was calculated by including both rs13226913 and rs1008897 in the regression model. For C1GALT1C1 locus, both rs5910940 and rs2196262 were included under additive coding. The total variance explained jointly by C1GALT1 and C1GALT1C1 loci was calculated by including all four SNP predictors from these loci in a single regression model.The fraction of total variance explained was estimated by regressing individual genetic predictors (additive coding) against the outcome of standardized residuals for the trait  and deriving R(PDF)Click here for additional data file.S5 TableEach SNP that reached genome-wide significance in our study was conditioned on all other SNPs that reached genome-wide significance, one at a time. Highlighted in red are independent effects for markers located within the same locus after conditioning on the other significant marker within the same locus. Notably, conditioning within each locus demonstrates residual effects, while mutual conditioning across loci strengthens the association signal at each locus. Because chromosome X markers are included in these analyses, all models were sub-stratified based on sex; the conditioning was first performed within each sub-cohort, then the results were combined using fixed effects meta-analysis. In all analyses, markers were coded under an additive model and the Gd-IgA1-increasing allele was used as a test allele. StdErr. Standard error.(PDF)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 TableC1GALT1C1 locus) is best explained by a T-allele dominant model. All analyses were stratified based on sex, explaining slight differences in effect estimates and p-values compared to We explored two alternative genetic models (dominant and recessive) and compared these models using Bayesian Information Criterion (BIC). The best model is highlighted in red. While this analysis suggests an additive model for 4 out of 5 top markers, the effect of rs5910940 ((PDF)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableThe East Asians include the Chinese Discovery, the Chinese Replication, and the Japanese Replication cohorts . The Europeans include the US discovery cohort (100% self-identified Whites), German, French, and US Replication cohorts . The results for all ethnicity-defined cohorts were combined using fixed effects meta-analysis. Allelic frequencies were averaged within the ethnicity-defined cohorts.(PDF)Click here for additional data file.S11 Table−8, and power (1-β) of 80%, 90% and 99.9%. The variance explained by each locus was derived as in Int J Epidemiol 42,1497–501 (2013) and implemented in the online calculator at https://cnsgenomics.shinyapps.io/mRnd/Minimum sample sizes (cases + controls) required to detect associations of the newly detected Gd-IgA1 loci with the risk of IgAN in East Asian, European, and bi-ethnic GWAS assuming observed effect sizes, 50% case proportion, α = 5 x 10(PDF)Click here for additional data file.


Fst statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics  With ever-increasing sizes of GWAMA, there is a need for additional QC that goes beyond the cohort-by-cohort genotype-level analysis performed to date.To elucidate genetic architecture, which requires maximized statistical power for discovery of risk alleles of small effect, large genome-wide association meta-analyses (GWAMAs) are tending towards ever-larger scale that may contain data from hundreds of cohorts. At the individual cohort level, genome-wide association study (GWAS) analysis is often based on various genotyping chips and conducted with different protocols, such as different software tools and reference populations for imputation, inclusion of study-specific covariates and association analyses using different methods and software. Although solid quality control (QC) analysis pipelines of GWAMA exist,In this study, we propose a new set of QC metrics for GWAMA. All these applications assume that there is a central analysis hub, where summary statistic data from GWAS are uploaded for each cohort. All methods proposed are implemented in freely available software GEAR.2 Metabochip summary statistics for body mass index (BMI) were from 43 cohorts (120 files), representing a total of 103 047 samples from multiple ethnicities with about 200 000 SNPs genotyped on customised chips.4The height GWAS summary statistics were provided by the GIANT Consortium and were from 82 cohorts (174 separate files) representing a total of 253 288 individuals, and ~2.5 million autosomal SNPs imputed to the HapMap2 reference.5 were used as the reference samples for estimating Fst and meta-PC. When assessing the global-level Fst measures, Yoruba represent African samples , Han Chinese in Beijing represent East Asian samples , and Utah Residents with Northern and Western European Ancestry represent European samples  were employed as the reference panels. For calculating within-Europe Fst, CEU, Finnish , and Tuscani  were employed to represent northwest, northeast, and southern Europeans, respectively. For analyses using a whole European panel, CEU, FIN, TSI, GBR , and IBS  were pooled together as an ‘averaged’ European reference.1000 Genomes Project (1KG) reference samples6 Individual GWAS was conducted for each disease using PLINK7 and their summary statistics used to estimate λmeta.WTCCC GWAS data has 2934 shared controls for seven diseases with a total of 14 000 cases.The four proposed metrics include:Fpc: a genome-wide comparison of allele frequency differences across cohorts or against a common reference population.Meta-PC: principal component analysis of reported allele frequencies.λmeta: a pairwise cohort statistic that uses allele frequency or effect size concordance to detect the proportion of sample overlap or heterogeneity.Pseudo profile score regression: an easy to implement analysis to pinpoint each between-cohort overlapping sample that does not require the sharing of individual-level genotype data.The technical details of these four methods summarized here can be found in the 5 are part of standard QC protocols,1 but checking for more differentiation than expected across the entire genome is not usually part of the QC pipeline. We propose that a genetic distance inferred from Fst, which reflects genetic distance between pairwise populations, is a useful additional QC statistic to detect cohorts that are population outliers. Using the relationship between Fst and principal components,8 our Fst cartographer algorithm can be used to estimate the relative genetic distance between cohorts  cohorts .Fst metric to the GIANT Consortium BMI Metabochip cohorts , which were recruited from multiple ethnicities,3 such as Europeans, African Americans in the Atherosclerosis Risk in Communities Study (ARIC) and cohorts from Jamaica (SPT), Pakistan (PROMISE), Philippines (CLHNS), and Seychelles (SEY). For each Metabochip cohort, we sampled 30 000 independent markers to calculate Fst values with each of three 1KG samples . For validation of the method, we also calculated Fst values against the 1KG Japanese , Indian , Kenyan , and European samples , to see whether the known genetic origins of those cohorts can be recovered.We applied the Fst cartographer algorithm  space, constructed by YRI, CHB, and CEU as the reference populations , and is similar to those observed in PCA using individual-level GWAS data for populations of various ethnicities such as in 1KG samples.5 Therefore, our method to place cohorts in geographical regions from GWAS summary statistics works well at a global-population scale.According to the origins of the samples, each Metabochip cohort showed a different genetic distance spectrum to the three reference populations . The JPTlgorithm , the cohulations . The all9 Here we analyzed the 103 GIANT European-ancestry Metabochip cohorts  for fine-scale Fst genetic distance measure using the CEU, FIN, and TSI reference populations, which represent northwest, northeast, and southern European populations, respectively. For each of the GIANT European-ancestry Metabochip cohorts, Fst was calculated relative to each of these three reference populations and showed concordance with the known origin of the samples , which were all of European ancestry imputed to the HapMap reference panel.2 Given the three Fst values to CEU, FIN, and TSI  cohorts, which are recruited from Finland, Sweden, Spain, and the United States, the same allele frequencies were reported for all their sub-cohorts, and all cohorts were allocated to southern Europe , most cohorts in GIANT had Fst<0.005 with this average, which agrees with previously reported results using individual-level data from European nations.9 A few cohorts showed large Fst, such as the AMISH cohort with Fst=0.018, and the North Swedish Population Health Study12 with Fst=0.014. Both populations are known to have been genetically isolated . In meta-PCA, each cohort was analogously considered as an ‘individual’. For example, 120 Metabochip cohorts were considered as a sample of 120 ‘individuals’. Although the inferred ancestral information was for each cohort rather than any individuals, implementation of meta-PCA was the same as the conventional PCA  as the reference cohorts. Consistent with demographic information, the inferred ancestral information of each cohort agreed well with demographic information. For example, PROMISE (Pakistan) located very close to GIH, CLHNS (Philippines) close to CHB and JPT, ARIC (African American) and SPT (Jamaican) close to YRI and LWK, and the European cohorts close to CEU and FIN .9 For example, of the four cohorts from Italy, the MICROS cohort was from South Tyrol, northern Italy. MICROS had its meta-PC coordinates much closer to CEU than another three Italian cohorts, reflecting its geographic location; the InCHIANTI cohort had its coordinates almost identical to TSI; the cohort SardiNIA located more southward than TSI, reflecting its relative geographic and genetic isolation as recently confirmed.13 Similarly, in the sub-plots for Finland and Sweden, the cohorts from the MIGEN consortium, which all had reported allele frequencies of south Europe origin, were located near 1KG TSI and IBS.We also applied meta-PCA to 174 GIANT height GWAS cohorts for nearly 1M SNPs, with the inclusion of 10 1KG reference cohorts. At the global-population level, the 174 cohorts were all allocated close to CEU and FIN, consistent with their reported demographic information . For fine-scale inference, we conducted meta-PCA again but with the inclusion of the five 1KG European samples. As demonstrated , the resolution of the inferred relative location between European cohorts reflected their real geographical locations, as previously observed using individual-level data.Fpc as described in the last section, and also agreed well with demographic information. Therefore, based on the reported allele frequencies, the demographic information could be verified by the meta-PCA method.These results were consistent to what was observed from λmeta, a metric that examines heterogeneity from the concordance of reported effect sizes and sampling variance. For a SNP marker (i), given its reported estimated effect size (bi) and sampling variance (σi2) in a pair of cohorts 1 and 2, we can calculate a test statistic T statistics using markers in linkage equilibrium along the genome for a pair of cohorts. Under the null hypothesis of no overlapping samples/heterogeneity, T follows a χ2 distribution with 1 degree of freedom  has an expected value of 1 for two independent GWAS summary statistics sets for the same trait. When there is heterogeneity between estimated genetic effects, the expectation is λmeta>1, and in contrast λmeta<1 if there are overlapping samples. In general, not only overlapping samples but also close relatives present in different cohorts can lead to correlated summary statistics generating λmeta<1. However, unless the proportion of overlapping relatives is substantial and their phenotypic correlation is high, the correlation of the summary statistics due to the effective number of overlapping samples (no) is expected to be dominated by the same individuals contributing phenotypic and genetic information to different cohorts  was observed between GWAS summary statistics of rheumatoid arthritis from European and Asian studies,16 for which λmeta=1.09  genetic architecture and analysis protocols. However, the mean is close to 1.0 and based upon this QC metric, the results are consistent with stringent QC and data cleaning. The minimum λmeta value was ~0.88 , and the maximum was 1.245   . When thλmeta is to estimate it from differences in allele frequencies between a pair of cohorts instead of differences between estimated effect sizes , which involves sharing of weighted linear combinations of SNP genotypes with the central meta-analysis hub. In essence, multiple random profile scores are generated for each individual in each cohort, using SNP weights supplied by the analysis hub, and the resulting scores are provided back to the analysis hub. PPSR works through three steps  for detecting overlapping individuals, we needed to generated at least 57 PPSs. We generated scores S=, where each s is a vector of M elements, sampled from a standard normal distribution. S is shared across seven cohorts for generating PPSs for each individual. In total, 57 PPSs were generated for each individual in each cohort. For a pair of cohorts, PPSR was conducted for each possible pair of individuals for any two cohorts over the generated PPSs. Once the regression coefficient (b) was greater than the threshold, here b=0.95, the pair of individuals was inferred to be having highly similar genotypes, implying that the individual was included in both cohorts  overlapping samples were detected, only slightly lower than PPSR. For example, for BP and CAD, Gencrypt detected 2912 shared controls, but was unable to identify ~20 overlapping controls, due to missing data (on average 1% missing rate).When using 200 and 500 random SNPs, all the known 2934 shared controls were detected from 21 cohort-pairwise comparison; when using 100 randomly SNPs, on average 2931 shared samples were identified, which is more accurate than using quencies . In addiquencies . For comFurthermore, PPSR is able to detect pairs of relatives. For example, between the BD and CAD cohorts, two pairs of apparent first-degree relatives were detected . To findS matrix (the weights for generating PPS), is determined by the ratio between the number of markers (M) that generated PPS and the number of PPS (K). Therefore, inferred information on individual genotypes can be minimized and tailored to any specific ethics requirements. We suggest PPSR for each individual uses very little personal information and can be minimized so that there is very low probability of decoding it. One way to attempt to decode the genotypes from PPS is to reverse the PPSR, so that the individual genotypes can be predicted in the regression . The indaccuracy .Fst-derived genetic distance measure, we can place all cohorts on an inferred geographic map and can easily identify cohorts that are genetic outliers or that have unexpected ancestry. In application, we should note that the Fst measure can identify unusual summary information, such as detected in the MIGEN cohorts from GIANT Consortium GWAMAs, in which the same allele frequencies were reported for all cohorts. Meta-PCA can also be used to infer the genetic background of cohorts. The high concordance between Fpc and meta-PCA indicates the both methods are robust.In this study, we provide four metrics for monitoring and improving the quality of large-scale GWAMA based on summary statistics. Using the FPC that has close-form analytical results provides a theoretical ground for meta-PCA. There are limitations for both FPC and meta-PCA. First, FPC depends on the choice of reference cohorts, such as 1KG reference cohorts, and the projection may be slightly different when other reference cohorts are adopted. Resembling any PCA, the projection from meta-PCA depends on the context of all cohorts, and the inclusion or exclusion of other cohorts will change the projection slightly. However, we believe the impact will not influence the inference of the genetic background of cohorts in a meta-analysis. Second, various mechanisms can give an identical projection in PCA. The purpose of both methods is to find the discordance between demographic information and genetic information, or outliers, in GWAMA.In practice, meta-PCA is much easier to implement when there are many cohorts, but λmeta provides information on sample overlap and heterogeneity between cohorts by utilizing the estimated allelic effect sizes and their standard errors. In most meta-analyses, the overall λmeta is likely to be slightly >1 solely due to unknown heterogeneity, slight as observed, in generating the phenotype and genotype data that cannot be accounted for by QC. The observed mean of λmeta for the GIANT height GWAMA was 1.03 but with more variation than expected by chance. The strong correlation between λGC and λmeta indicated the reported sampling of the reported data were systematically driven by analysis protocols, such as single-marker regression and linear mixed model methods. For cohorts with λGC<1 and λmeta<1, it is likely that the GWAS modeling strategy employed for GWAS in the cohort was too conservative, eg, MIGEN cohorts might have on average too small sample size for each cohort. Conversely, for cohorts with λGC>1 and λmeta>1, results are too heterogeneous, perhaps reflecting systematically smaller sampling variances of the reported genetic effects. As GWAMA often uses inverse-variance-weighted meta-analysis,21 such cohorts may lead to incorrect weights to the different cohorts in the meta-analysis, suggesting that the statistical analysis in meta-analyses can be improved by applying better weighting factors.Our third metric post hoc correction of the test statistic is possible,21 stringent QC ruling out overlapping samples makes the whole analysis easier and lowers the risk of false positives. A better solution would be to rule out shared samples at the start, for pairs of cohorts that show deflated λmeta, and we propose PPSR to accomplish this.It is well recognised that overlapping samples may inflate the type-I error rate of GWAMA and therefore lead to false positives. Although


Genomic imprinting is one of the well-known epigenetic factors causing the association between traits and genes, and has generally been examined by detecting parent-of-origin effects of alleles. A lot of methods have been proposed to test for parent-of-origin effects on autosomes based on nuclear families and general pedigrees. Although these parent-of-origin effects tests on autosomes have been available for more than 15 years, there has been no statistical test developed to test for parent-of-origin effects on X chromosome, until the parental-asymmetry test on X chromosome (XPAT) and its extensions were recently proposed. However, these methods on X chromosome are only applicable to nuclear families and thus are not suitable for general pedigrees.In this article, we propose the pedigree parental-asymmetry test on X chromosome (XPPAT) statistic to test for parent-of-origin effects in the presence of association, which can accommodate general pedigrees. When there are missing genotypes in some pedigrees, we further develop the Monte Carlo pedigree parental-asymmetry test on X chromosome (XMCPPAT) to test for parent-of-origin effects, by inferring the missing genotypes given the observed genotypes based on a Monte Carlo estimation. An extensive simulation study has been carried out to investigate the type I error rates and the powers of the proposed tests. Our simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects. Moreover, XMCPPAT substantially outperforms the existing tests and has a much higher power than XPPAT which only uses complete nuclear families (with both parents) from pedigrees. We also apply the proposed methods to analyze rheumatoid arthritis data for their practical use.The proposed XPPAT and XMCPPAT test statistics are valid and powerful in detecting parent-of-origin effects on X chromosome for qualitative traits based on general pedigrees and thus are recommended.The online version of this article (10.1186/s12859-017-2001-5) contains supplementary material, which is available to authorized users. Genomic imprinting is one of the well-known epigenetic factors causing the association between traits and genes, where the expression level of a gene depends on its parental origin. Imprints are laid down in the parental germ cells, which affect embryonic growth in the womb and behavior after birth . AberranTherefore, taking information on imprinting effects into account when conducting association analysis could improve the test power . On the In this article, inspired by the need to utilize all available family trios in a general pedigree like PPAT and to consider X chromosome as well, we propose the pedigree parental-asymmetry test on X chromosome (XPPAT) statistic to test for parent-of-origin effects in the presence of association for qualitative traits. When there are missing genotypes in some pedigrees, we further develop the Monte Carlo pedigree parental-asymmetry test on X chromosome (XMCPPAT) by inferring the missing genotypes given the observed genotypes based on a Monte Carlo estimation , 16, to D and the normal allele d, with frequencies p and 1−p, respectively, where we assume that the frequencies of the same allele in males and females are equal. Next, the females are typed into four possible genotypes D/D, D/d, d/D and d/d, where the left allele of the slash is paternal and the right one is maternal. Let ρ be the inbreeding coefficient in females. Then, the frequencies of genotypes D/D, D/d, d/D and d/d in females are Pr(D/D)=p2+ρpq, Pr(D/d)=Pr(d/D)=pq(1−ρ), and Pr(d/d)=q2+ρpq, respectively. When ρ=0, the Hardy-Weinberg equilibrium (HWE) holds in females. Also, let f11, f10, f01 and f00 be the four penetrances corresponding to genotypes D/D, D/d, d/D and d/d, respectively. Suppose that I=(f10−f01)/2, which is used to measure the degree of parent-of-origin effects. I=0 indicates no parent-of-origin effects. Note that males have only one X chromosome. So, they are not informative when we calculate the test statistics for testing parent-of-origin effects. Therefore, we define an informative family, which has at least one affected heterozygous daughter together with her parents. Further, in this article, we assume that there is no maternally-mediated effect.For a candidate diallelic SNP locus on X chromosome, suppose that there are two alleles, the deleterious allele F, M and C denote the count of allele D in the father, the mother and the affected daughter, respectively. Note that there are only four genetically possible types of informative family trios FMC: 101, 111, 011 and 021.A general pedigree consists of multiple two-generation nuclear families. For each nuclear family, we divide it into multiple parents-child trios, each with a child and his/her parents. However, only the trios with an affected heterozygous daughter and her parents are informative for parent-of-origin effects. For convenience, in each informative trio, let N independent pedigrees, and there are ni family trios in pedigree i,i=1,...,N. For trio j in pedigree i, let i=1,…,N;j=1,…,ni, where I{comparison statement} is 1 when the “comparison statement” is true and 0 otherwise; Fij,Mij and Cij are the counts of allele D of the father, the mother and the affected daughter in trio j of pedigree i, respectively. Note that D in father are more than or equal to those in mother and their daughter is heterozygous, which means that the allele D in the daughter is paternal (FijMijCij=101 or 111), and vice versa for FijMijCij=011 or 021). Therefore, Si)=0 and Suppose that we collect u et al. , we haveTherefore, Then we construct the following XPPAT test statistic for general pedigrees to test for parent-of-origin effects on X chromosome: When the number of pedigrees is large enough, XPPAT follows a standard normal distribution approximately.Gm given the observed genotypes Go in each pedigree. Let S be the contribution of a pedigree to the statistic XPPAT in Eq.  sampling procedure is used to infer the missing genotypes T in Eq. , and SMCS depends on the missing genotypes (Gm), the observed genotypes (Go) and the collection of the observed phenotypes of all the individuals in the pedigree (A). Note that to calculate SMC, it is computationally intensive and time consuming due to the huge amounts over all possible missing genotypes Gm given Go. So, we follow Zhou et al. [SMC. Firstly, we generate K independent samples Gmk,k=1,…,K from Pr(Gm|Go) by using the SLINK software based on the peeling algorithm of Weeks et al. [S’s as the estimate of SMC, where u et al.  and Dingu et al.  by takins et al. . Then, tS by SMC and obtain the following XMCPPAT test To this end, we calculate the statistic XPPAT in Eq.  by replaSMC)=0 [see Appendix A of Additional file Gm|Go) may be different from Pr. So, we treat A as random and the minimal ascertainment criterion used is that only pedigrees with at least one affected daughter can be included, just like Zhou et al. [Under the null hypothesis of no parent-of-origin effects, we have E= with f10=f01 being assigned for simulating the type I error rates of the proposed tests, while S1: = , S2: = , S3: =  and S4: =  for the power investigation.The frequency α=5% and 1% for the type I error rate assessment and set α=5% for the power investigation. The simulated type I error rates and powers are based on 10,000 replications. For each replication, 50 Monte Carlo samples of missing genotypes are generated by using the SLINK software [t, XMCPPATf, XMCPPATm and XMCPPATfm, which are based on the true allele frequencies, those estimated from female founders, male founders and both female and male founders, respectively. Further, XPPATfull denotes the test for complete data without any missing data , which can be considered as the gold standard. XPPAT deals with pedigrees after removing missing data without using the MC procedure. That is, XPPAT only uses individuals 4, 6, 9, 10, 11 and 12 in four-generation pedigrees. As for XPAT, we use the youngest two-generation nuclear families in four-generation pedigrees having individuals 9, 10, 11 and 12.We use the nominal significance levels software . We consα (0.05 and 0.01), N (150 and 300), p (0.1 and 0.3) and ρ  values under the null hypothesis of no parent-of-origin effects. It can be seen from the table that XPPATfull, XMCPPATt and XMCPPATfm control the type I error rate well. Most of the size results of XMCPPATf are quite good, except for some appearing little conservative. On the other hand, some of the type I error rates of XMCPPATm based on the estimated allele frequencies from male founders are inflated. So, we only conduct power comparison based on the true allele frequencies and those estimated from both female and male founders later. The size results of XPPAT and XPAT are also generally close to the nominal level 5% when N=300. However, other empirical type I error rates of XPPAT and XPAT are smaller than the respective nominal significance levels, especially for α=1%. This may be because the number of the informative families for XPPAT and XPAT is small. In addition, it appears that there is little impact of ρ on the validity of the proposed tests.Table ρ is 0, with N=150 and 300, respectively. The corresponding power results for ρ=−0.05 and 0.05 are given in Figs. A–D in Additional file t and XMCPPATfm are very close to each other, which are merely a little less than the gold standard XPPATfull. This indicates that XMCPPATt and XMCPPATfm can recapture much of missing information. Further, XMCPPATt and XMCPPATfm are much more powerful than the proposed XPPAT test and the existing XPAT test. Since the missing data are omitted, XPPAT, which only uses individuals 4, 6, 9, 10, 11 and 12 in four-generation pedigrees, suffers from substantial power loss under all the situations. However, XPPAT still has better power than XPAT, which only uses individuals 9, 10, 11 and 12 in four-generation pedigrees. The powers of all the tests under the complete parent-of-origin effect models (S1 and S2) are much higher than those under the incomplete models (S3 and S4). When the frequency p of allele D increases from 0.1 to 0.3 and ρ is fixed, the powers of the proposed tests are higher as the bars in the second row of both figures are taller than those in the first row. This is mainly because the number of affected heterozygous daughters will be larger as the frequency p increasing, which means that the number of the collected informative trios under p=0.3 is bigger than that under p=0.1. By comparing Fig. N=300 are much larger than those with N=150. Finally, by comparing Fig. ρ has little effect on the parent-of-origin effects testing when N=150, similar to N=300 by comparing Fig. Figures We apply the proposed methods to a rheumatoid arthritis (RA) data set, which is made available from North American Rheumatoid Arthritis Consortium of Genetic Analysis Workshop 15 . There aBefore using this data set, we have the following quality control (QC) rules. All the included pedigrees at least have one affected daughter. If the genotypes of all the individuals in a pedigree are unavailable, then we delete this pedigree. The pedigrees with stepfamilies are also excluded. Further, it should be noted that too many individuals’ genotypes are missing in this data set and thus, for too large pedigrees, it may take much time to calculate the value of XMCPPAT by the Monte Carlo sampling and estimation scheme. Therefore, we exclude the pedigrees with the number of members being bigger than 27. However, after filtering the original data set by the above QC rules, there are still lots of missing genotypes in the pedigrees. Note that the pedigrees with the genotypes of more than 50% individuals missing will give large variability to the analysis. So, we delete these pedigrees. After that, we ultimately obtain 246 pedigrees with 1109 individuals, including 407 males and 702 females for analysis. On the other hand, due to the large proportion of missingness, to obtain the stable allele frequency estimates, we use all the female and male founders in the original data set to estimate the allele frequency. We conduct the XMCPDT approach  to test p-values of XMCPDT and XMCPPAT at 13 SNPs with p-values of XMCPDT being less than the 5% level. It is noticed that two SNPs have p-values of XMCPPAT smaller than 5%. However, after taking into account multiple testing based on Bonferroni correction for XMCPDT (α′=0.05/293=0.00017), none of the p-values of XMCPDT is smaller than 0.00017, and thus there is no statistically significant SNP on X chromosome for the association test XMCPDT. Note that the parent-of-origin effects test XMCPPAT is valid only in the presence of association. So, XMCPPAT could not find any statistically significant SNP.Table fm has nearly the same performance as XPPATfull based on complete data without any missing genotypes and XMCPPATt on the basis of the true allele frequencies. This indicates that XMCPPATt and XMCPPATfm can recapture much of missing information. As such, XMCPPAT will be practicable for real data application. However, the traits we consider in this article are restricted to be qualitative. So, our future work may be conducted for quantitative traits.In this article, we propose the novel and powerful methods, XPPAT and XMCPPAT, for testing parent-of-origin effects on X chromosome in general pedigrees for qualitative traits. Our proposed methods not only can take advantage of nuclear family data, but also can use general pedigree data. Simulation study is conducted under various simulation settings, including two sample sizes, two groups of allele frequencies, three different values of inbreeding coefficient, and five different parent-of-origin effect models. The simulation results show that the type I error rates of the proposed tests are controlled well. Moreover, the powers of the proposed tests are much higher than the existing XPAT. With the MC procedure, XMCPPAT also performs well when there are missing genotypes. Further, in the simulation study, we find that the proposed XPPAT and XMCPPAT do not depend on the assumption of HWE in females as the inbreeding coefficient almost has no effect on XPPAT and XMCPPAT. Note that, for XMCPPAT, which is suitable for missing data, we have raised four different ways to evaluate the allele frequencies: true allele frequencies, those estimated from female founders and male founders, and those estimated from both female and male founders, respectively. It appears that using the estimated allele frequencies from both female and male founders, XMCPPATOn the other hand, our current manuscript only focuses on the parent-of-origin effects test based on SNP data. However, it should be noted that RNA sequencing (RNA-seq) data convey more epigenetic information than SNP data and RNA-seq data will be more commonly available with constantly decreasing cost. Thus, the most direct way to identify imprinted genes is to directly use RNA-seq data and score the differential allelic expression depending on the parent-of-origin . So, we Besides imprinting effects, X chromosome inactivation (XCI) is another important biological mechanism on X chromosome . It happD/d offspring is different from that in d/D offspring, which is a broader concept than an imprinting effect and can be caused by genomic imprinting or other factors. Imprinting effect is the most important form of parent-of-origin effects [Finally, it should be emphasized that it is important to make a distinction among the terms “imprinting effect”, “maternal effect” and “parent-of-origin effect” , 27. Par effects . TherefoThe proposed XPPAT and XMCPPAT test statistics are valid and powerful in detecting parent-of-origin effects on X chromosome for qualitative traits based on general pedigrees and thus are recommended.


For dichotomous traits, the generalized disequilibrium test with the moment estimate of the variance (GDT-ME) is a powerful family-based association method. Genomic imprinting is an important epigenetic phenomenon and currently, there has been increasing interest of incorporating imprinting to improve the test power of association analysis. However, GDT-ME does not take imprinting effects into account, and it has not been investigated whether it can be used for association analysis when the effects indeed exist.In this article, based on a novel decomposition of the genotype score according to the paternal or maternal source of the allele, we propose the generalized disequilibrium test with imprinting (GDTI) for complete pedigrees without any missing genotypes. Then, we extend GDTI and GDT-ME to accommodate incomplete pedigrees with some pedigrees having missing genotypes, by using a Monte Carlo (MC) sampling and estimation scheme to infer missing genotypes given available genotypes in each pedigree, denoted by MCGDTI and MCGDT-ME, respectively. The proposed GDTI and MCGDTI methods evaluate the differences of the paternal as well as maternal allele scores for all discordant relative pairs in a pedigree, including beyond first-degree relative pairs. Advantages of the proposed GDTI and MCGDTI test statistics over existing methods are demonstrated by simulation studies under various simulation settings and by application to the rheumatoid arthritis dataset. Simulation results show that the proposed tests control the size well under the null hypothesis of no association, and outperform the existing methods under various imprinting effect models. The existing GDT-ME and the proposed MCGDT-ME can be used to test for association even when imprinting effects exist. For the application to the rheumatoid arthritis data, compared to the existing methods, MCGDTI identifies more loci statistically significantly associated with the disease.Under complete and incomplete imprinting effect models, our proposed GDTI and MCGDTI methods, by considering the information on imprinting effects and all discordant relative pairs within each pedigree, outperform all the existing test statistics and MCGDTI can recapture much of the missing information. Therefore, MCGDTI is recommended in practice.The online version of this article (10.1186/s12863-017-0560-0) contains supplementary material, which is available to authorized users. Genomic imprinting is an important epigenetic phenomenon in studying complex traits, where the expression levels of certain genes rely on their parental origin –3. MorisFor a diallelic marker locus, there have been many family-based methods to test for the association between genotype scores and dichotomous traits –15. AmonTo incorporate imprinting effects into association analysis, in this article, we develop a novel decomposition of the genotype score of each individual according to the paternal or maternal source of the allele. Based on these paternal and maternal allele scores, we propose the generalized disequilibrium test with imprinting (GDTI) for association for complete pedigrees without any missing genotypes. Then, borrowing the idea of Zhou et al.  and DingM1 and M2, and three possible genotypes are respectively M2M2, M1M2 and M1M1. We consider a disease susceptibility locus with the disease allele D and the normal one d, and the corresponding ordered genotypes are D/D, D/d, d/D and d/d with penetrances f2, f10, f01 and f0, respectively. f10 = f01 indicates no imprinting effects at the disease susceptibility locus. Further, the coefficient of linkage disequilibrium (LD) between alleles M1 and D is taken as P(DM1) is the frequency of haplotype DM1, and PD and D and M1, respectively. Suppose that we collect n independent pedigrees. Within the ith pedigree which contains Ni family members , without loss of generality, we assume that the first Aiindividuals are affected and the other Ui = Ni − Ai members are unaffected. Let Yij be the disease status of the jth individual in the ith pedigree , i.e. Yij= 1 (0) denotes that the individual is affected (unaffected).Suppose a diallelic marker locus with alleles Xij by the number of allele M1 in the genotype of the jth individual in the ith pedigree, i.e. Xij=0, 1 and 2 for the genotypes M2M2, M1M2 and M1M1, respectively. As such, the logistic regression model isβ0 is the intercept, and β1 is the regression coefficient; Yij is the disease status of the jth individual in the ith pedigree. Then, the GDT-ME test statistic can be expressed as follows, which is used to model the association between the disease status and Xij:ith pedigree and We begin by describing the existing GDT-ME test . For con unknown . For conXij is coded as the counts of allele M1 for the jth individual in the ith pedigree, i.e.Although GDT-ME is a powerful association test and is robust to population stratification (PS) , it doesXij into two parts, To incorporate the information on imprinting effects into analysis, we divide the Yij and the allele scores β0 is the intercept, and βp and βm are the regression coefficients; βp is used to describe the effect of allele M1 coming from his (her) father, and βm measures the effect of allele M1 whose parental origin is his (her) mother. The null hypothesis H0 : βp = βm = 0 denotes no association and no imprinting; βp = βm ≠ 0 indicates that the association exists while there are no imprinting effects, and the logistic regression model can be reduced to the model of GDT-ME , the score test statistic for testing for association incorporating imprinting effects is formulated as follows , where extended pedigrees are considered as multiple nuclear families, and so information is not fully utilized.GDTI asymptotically follows a chi-square distribution with the degrees of freedom being 2, under the null hypothesis of no association and no imprinting. It is noted from the above that the scores Di1, Di2, Ii11, Ii12, Ii21 and Ii22 in GDTI by their conditional expectations, Di1MC, Di2MC, Ii11MC, Ii12MC, Ii21MC and Ii22MC, given the observed genotypes, Go, where TMC = E| Go) for some statistic T, Gm is the set of missing genotypes; A is the collection of the observed phenotypes (disease affection statuses); T is the expanded notation of T to explicitly show its dependences on the missing genotypes Gm, the observed genotypes Go and the observed phenotype collection A. Following Zhou et al. [Di1MC, Di2MC, Ii11MC, Ii12MC, Ii21MC and Ii22MC based on a MC simulation scheme. Specifically, if we set the MC size to be K, then we draw independent sample Gmk, k = 1, 2, …, K, from P(Gm| Go), which can be accomplished efficiently based on the peeling algorithm using the SLINK software [Di1MC can be estimated byDi2MC, Ii11MC, Ii12MC, Ii21MC and Ii22MC can be similarly estimated by Di1, Di2, Ii11, Ii12, Ii21 and Ii22 in Equation = 0.15×0.25 = 0.037,5.In this section, to explore the performance of the proposed GDTI, MCGDTI and MCGDT-ME statistics and compare the powers of GDTI, MCGDTI and MCGDT-ME with the existing MCPDTI, GDT-ME and GDT, we conduct the following simulation studies. We consider a homogeneous population. The marker locus and the disease susceptibility locus are in complete linkage. Three groups of haplotype frequencies for haplotypes f2 and f0 for genotypes D/D and d/d, {0.390, 0.260}, {0.440, 0.240} and {0.480, 0.220}, are investigated with the corresponding relative risk (RR=f2/f0) being 1.500, 1.833 and 2.182, respectively, which are similar to those in Ding et al. [f10 and f01 are considered: no, incomplete and complete imprinting effect models. For no imprinting effect model, we set f1 = f10 = f01 = (f2 + f0)/2. Note that no association implies no imprinting effects. So, we simulate the type I error rates of the proposed test statistics only under no association and no imprinting. Tables Three sets of two homozygote penetrances g et al. . For eacIn addition, three types of pedigree structure are considered in our simulation study. The pedigree structures are shown in Fig. T, MCGDT-MET and MCPDTIT are on the basis of the true marker allele frequencies, while MCGDTIE, MCGDT-MEE and MCPDTIE are based on the estimated marker allele frequencies. GDT-ME and GDT are also considered for incomplete data. Under each simulation setting, 10,000 replicates are simulated and the significance level is set at 1%. All the simulations are implemented by using the R software (version 3.4.1) [For assessing the performance of the proposed tests  and for comparing with the existing GDT-ME and GDT without considering imprinting effects , and MCPn 3.4.1) .T, MCGDTIE, MCGDT-MET, MCGDT-MEE, GDT-ME, GDT, MCPDTIT and MCPDTIE are demonstrated in Table T, MCGDTIE, MCGDT-MET and MCGDT-MEE test statistics are valid for testing association.Under 9 simulation settings given in Table T, MCGDTIE, MCGDT-MET, MCGDT-MEE, GDT-ME, GDT, MCPDTIT and MCPDTIE based on 150 pedigrees at the 1% significance level under complete, incomplete and no imprinting effect models for different LD and RR values, respectively. The first 5 statistics are proposed tests, while the remaining four are existing tests. Additional file T vs MCGDTIE, MCGDT-MET vs MCGDT-MEE, and MCPDTIT vs MCPDTIE), respectively. MCGDTIT and MCGDTIE can recapture much of the missing information, which are a little less powerful than GDTI for complete pedigree data. The existing MCPDTI test performs the worst even though it is constructed for testing association when imprinting effects are taken into consideration. On the other hand, MCGDT-ME, GDT-ME and GDT, though without accounting for imprinting, can be used for testing association even when imprinting effects exist. Moreover, they outperform MCPDTI substantially. It is probably due to the fact that MCGDT-ME, GDT-ME and GDT consider genotype differences between all discordant relative pairs, thus utilizing much more information than first-degree relative pairs used by MCPDTI. In Fig. Figures We apply our proposed methods to the RA dataset from North American Rheumatoid Arthritis Consortium , which iα= 5%, and Bonferroni correction would test each individual hypothesis at the significance level of α′= 0.05/5407 = 9.247,3 × 10−6, based on 5407 SNPs. The MC size for MCGDTI, MCGDT-ME, MCPDTI and MCPPAT is set to be 50.We use the following quality control rules to filter the data. First, a pedigree to be included has at least one affected nonfounder. Second, we delete pedigrees with stepfamilies. Finally, if the proportion of the individuals with missing genotypes among all the members in a pedigree is more than 50% based on the first SNP on Chromosome 1, then we exclude this pedigree. This can avoid the large variability on estimation created by pedigrees with high proportions of missingness. To this end, we get 246 pedigrees with 1109 individuals. Among them, there are 11 individuals with the affection statuses being unavailable and we treat them as unaffected. We use all the available individuals  in this dataset to estimate the marker allele frequencies, not just using the available founders, due to the large proportion of the individuals with missing genotypes in this dataset. Then, we calculate the values and the corresponding p-values of all the test statistics based on the estimated allele frequencies and 246 selected pedigrees. The significance level is fixed at α=5%, with Bonferroni correction based on the p-values of these methods are shown in Table The corresponding results of MCGDTI and MCGDT-ME at the significance level of In this article, based on a novel decomposition of the genotype score of an individual according to the paternal or maternal source of an allele, we develop the GDTI test to test for association incorporating imprinting for complete pedigrees without missing genotypes. Then, using a MC sampling and estimation scheme, we extend GDTI and GDT-ME, and respectively develop MCGDTI and MCGDT-ME to deal with incomplete pedigrees, in which some individuals’ genotypes are unavailable. Compared to PDTI and MCPDTI, GDTI and MCGDTI make use of the genotype differences between all discordant relative pairs, including beyond first-degree relatives. Simulation results indicate that GDTI, MCGDTI and MCGDT-ME control the size well under the null hypothesis of no association and no imprinting. As for the simulated powers, under complete and incomplete imprinting effect models, our proposed GDTI and MCGDTI methods by considering the information on imprinting effects and all discordant relative pairs outperform all the existing test statistics and MCGDTI can recapture much of the missing information. The application to the RA dataset also demonstrates the advantage of MCGDTI over other methods. Further, in this article, we demonstrate that, the existing GDT-ME and the proposed MCGDT-ME, although not constructed under imprinting, can be used for testing association even when the effects exist. Moreover, we propose the MCGDT-ME test to handle incomplete pedigree data with missing genotypes, and the test is found to perform better than GDT-ME in simulation studies.PD and f2, f10, f01 and f0 of genotypes D/D, D/d, d/D and d/d are assumed to be 0.45, 0.30, 0.30 and 0.20 in both subpopulations, respectively. In MCGDTI, MCGDT-ME and MCPDTI, the allele frequency PD and One of the major reasons for using within-family tests (e.g. GDT-ME and GDT) for association is their robustness to PS. On the other hand, note that MCGDTI, MCGDT-ME and MCPDTI need the MC sampling and estimation scheme to infer missing genotypes in pedigrees, which requires these pedigrees from a homogenous population. To investigate the performance of the proposed test statistics in the presence of PS, we consider a population consisting of two subpopulations and conduct the following simulation study. The parameters are set to be the same as those in Chen et al. . Specifimentclass2pt{minimmentclass2pt{minimmentclass2pt{minimbe 0.1 0. in the fJust like the genotypes of some members in the collected pedigrees may be missing, it is also common in practice that the affection statuses of some individuals in the pedigrees may be unavailable. As mentioned in the real data application subsection, one way to deal with these individuals is to treat them as unaffected. To investigate if this influences the validity of the proposed test statistics, we conduct a few simulation studies. The simulation results show that the proposed methods are still valid to test for association by handling the missing affection status in this way (data not shown). However, this may impact their test powers under alternative hypotheses and we will carry out some simulation studies to check it in our future work.jth and kth individuals in the ith pedigree is obtained, two allele scores in this pair may be different from each other for GDTI and MCGDTI and thus we cannot estimate the corresponding variance based on the IBD information, which is different from GDT . For exWe should mention that, because of utilizing the genotype differences between all discordant relative pairs, the requirement for a pedigree to be included is that this pedigree should have at least one affected and one unaffected individuals. In addition, GDTI and MCGDTI do not take account of the covariates in analysis, which may cause the dependences between individuals within a family, even though under the null hypothesis of no association. This may be handled from the quasi-likelihood for a conditional logistic regression model , 24, 25.Under complete and incomplete imprinting effect models, our proposed GDTI and MCGDTI methods, by considering the information on imprinting effects and all discordant relative pairs within each pedigree, outperform all the existing test statistics and MCGDTI can recapture much of the missing information. Therefore, MCGDTI is recommended in practice.Additional file 1: Appendix.Table S1. P-values of the test statistics applied to RA data at 3 SNPs with PMCGDTI< 9.247,3 × 10−6. Figures S1 - S3. Simulated powers of all the test statistics. The test statistics are T1: GDTI, T2: MCGDTIT, T3: MCGDTIE, T4: MCGDT-MET, T5: MCGDT-MEE, T6: GDT-ME, T7: GDT, T8: MCPDTIT and T9: MCPDTIE. The simulations are conducted under complete, incomplete and no imprinting effect models at 1% significance level based on 10,000 replicates for 90 pedigrees when LD = 0.092,5, 0.142,5, and 0.157,5, and RR = 1.500, 1.833 and 2.182, respectively. The first 5 statistics are proposed tests, while the remaining 4 are existing tests. (PDF 76 kb)Construction of the GDTI test statistic.


Improvements in eating satisfaction will benefit consumers and should increase beef demand which is of interest to the beef industry. Tenderness, juiciness, and flavor are major determinants of the palatability of beef and are often used to reflect eating satisfaction. Carcass qualities are used as indicator traits for meat quality, with higher quality grade carcasses expected to relate to more tender and palatable meat. However, meat quality is a complex concept determined by many component traits making interpretation of genome-wide association studies (GWAS) on any one component challenging to interpret. Recent approaches combining traditional GWAS with gene network interactions theory could be more efficient in dissecting the genetic architecture of complex traits. Phenotypic measures of 23 traits reflecting carcass characteristics, components of meat quality, along with mineral and peptide concentrations were used along with Illumina 54k bovine SNP genotypes to derive an annotated gene network associated with meat quality in 2,110 Angus beef cattle. The efficient mixed model association (EMMAX) approach in combination with a genomic relationship matrix was used to directly estimate the associations between 54k SNP genotypes and each of the 23 component traits. Genomic correlated regions were identified by partial correlations which were further used along with an information theory algorithm to derive gene network clusters. Correlated SNP across 23 component traits were subjected to network scoring and visualization software to identify significant SNP. Significant pathways implicated in the meat quality complex through GO term enrichment analysis included angiogenesis, inflammation, transmembrane transporter activity, and receptor activity. These results suggest that network analysis using partial correlations and annotation of significant SNP can reveal the genetic architecture of complex traits and provide novel information regarding biological mechanisms and genes that lead to complex phenotypes, like meat quality, and the nutritional and healthfulness value of beef. Improvements in genome annotation and knowledge of gene function will contribute to more comprehensive analyses that will advance our ability to dissect the complex architecture of complex traits. Increasing consumer demand for beef is an important strategic objective for the beef industry and recent studies suggest consumers have a strong focus on beef quality  and the intramuscular fat content (IMFC) were identified from an extensive set of carcass and meat composition traits to be the best predictors of eating quality  have been performed in different The first objective of this study was to carry out GWAS to identify chromosomal regions associated with each of the different components of meat quality. The second objective was to use the Association Weight Matrices (AWM) and Partial Correlation and Information Theory (PCIT) to explore the functional mechanisms underlying GWAS associations for meat quality traits in Angus cattle to explore the biological mechanism by which GWAS-identified genomic variants give rise to phenotypic differences in eating quality.n = 500), steers , and heifers (n = 400) representing 155 sires were used in this study. All cattle were finished on concentrate diets in Iowa (n = 994), California (n = 345), Colorado (n = 352), or Texas (n = 419). Animals with an average age of 457 ± 46 days were harvested at commercial facilities. Details on production characteristics, meat sample collection, and preparation have been previously reported  and call rate for sample and SNP (95%). After quality control, 40,875 SNP were left and included in subsequent analyses. All GWAS were performed using the single-locus mixed linear model procedure implemented in Golden Helix SVS v8.4.4 software . The efficient mixed model association (EMMAX) approach in combination with a genomic relationship matrix was used to directly estimate the genetic and residual variance components Genomic DNA extracted from the meat sample was genotyped with the Bovine SNP50 Infinium II BeadChip . Those SNP with significant deviations from Hardy–Weinberg equilibrium at a significance level Where Y is a vector of phenotypes for each of the meat quality traits measured on all the animals, β is the effect size of fixed effects (contemporary groups), g ~ N, finishing location , and harvest date, which resulted in a total of 33 groups. Contemporary groups were fit as fixed class effects in all genomic analyses. Pseudo-heritability was estimated as hP < 0.05. A less stringent level at this stage is recommended to allow for a proper integration of potentially important regulators across multiple traits. One advantage of the AWM/PCIT methodology is the ability to include SNP with relatively small effects which do not reach genome-wide statistical significance but are potentially linked to elements controlling the trait of interest. It is well-recognized that many elements with minor effects are usually not able to reach significance at the genome level, but they will be uncovered through a gene network when multiple correlated traits are used in the analysis  associated with WBSF is in Table P < 0.05, from 331 to 428 at P < 0.01, and 25 to 112 at P < 0.001. There were 68 SNP significantly associated with 10 or more traits at P < 0.05 and 7 SNP significantly associated with 15 or more traits (Table CAST (on BTA7) and CAPN1 (on BTA29) have been consistently identified and have a role in muscle proteolysis during meat aging  and four of the 56 SNP were located around CAST.Summary statistics for carcass quality, meat quality, mineral content, fatty acid composition, and peptide content phenotypes are presented in Table P < 0.05) for WBSF, there were 839 SNP associated with at least two other phenotypes. Some 772 SNP were found to be located within a gene (n = 712) or within 2.5 kbp from a gene (n = 60) and therefore were used to form the AWM. A total of 688 annotated genes were found associated with at least one other gene and had significant direct and partial correlations. This correlation matrix generated a gene network consisting of 688 genes (nodes) and 99,568 gene relationships (edges). The Cytoscape MCODE plugin colocalized these SNP into 17 separate networks and detailed information on the top five networks is in Table Among the 1,842 SNP significant , “Protein Binding” (P = 8.48 × 10−8), “Catalytic Activity” (P = 4.44 × 10−5), “Metal Ion Transmembrane Transporter Activity” (P = 5.89 × 10−5), “Enzyme Binding” (P = 8.14 × 10−5), “Transmembrane Transporter Activity” (P = 3.4 × 10−4), and “Gated Channel Activity” (P = 6.73 × 10−3). We found 21 over-represented pathways including “Angiogenesis,” “Inflammation mediated by chemokine and cytokine signaling pathway,” “Ionotropic glutamate receptor pathway,” “TGF-beta signaling pathway,” “Apoptosis signaling pathway.” Many of these pathways have been previously reported as important biological pathways involved in meat quality or tenderness in beef cattle (Guillemin et al., Overrepresented terms for GO-Slim Molecular Function in the network included “Transmembrane Transporter Activity,” “Ligand-Gated Ion Channel,” “Extracellular Matrix Protein,” “Transporter,” “Nucleic Acid Binding,” and “RNA binding protein” for PANTHER Protein Class; “Ion Binding,” “G-Protein Coupled Receptor Activity,” and “Receptor Activity” for GO Molecular Function. Functional annotation analyses revealed an enrichment for “Ion Binding” (A functionally grouped annotation network Figure  was deveThe gene network technique employed in this work advances the genomic analysis of complex traits beyond the simple marker association analysis by allowing the inclusion of markers which initially are not able to reach a very stringent genome-wide significance status. These markers and the genomic regions they represent could be legitimate markers and regions explaining a small portion of the variation in these complex traits, but they do not have large enough effects in order to reach significance. The danger of a false positive is overcome through the gene/network enrichment analysis where a true false positive gene would most likely be eliminated while genes with a real but small effect on the trait will be validate through their biological role in a specific pathway contributing to trait of interest. However, it is important that these results are validated through additional functional analyses at the gene expression or proteomics level.Traits including four carcass measures, five meat quality phenotypes, seven mineral concentrations, and four peptide concentrations were used in GWAS to populate a gene network analysis using the methodology of AWM/PCIT. An analysis of genomic regions that affect different aspects of meat quality highlighted genes overrepresented in molecular functions related to calcium and other ion binding and regulation, catalytic and transporter activity, and nucleic acid and RNA binding. Several genes were found in a majority of enriched pathways suggesting possible key regulatory roles for these genes. This also provides evidence for the interconnections between the individual pathways and sheds some light on how these different pathways control the meat quality phenotype. The combination of GWAS results with PCIT and network visualization represents a powerful methodology for identifying novel candidate genes of interest for complex traits influenced by multiple component phenotypes. This methodology allows for a dissection of the biological mechanisms and gene networks that lead to these complex phenotypes.RM conceived and conducted the analysis and drafted the manuscript. JR assisted with the analysis and manuscript. DG assisted with the analysis and manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This research was supported by Pfizer Animal Genetics. The funder was not involved in the study design or collection, analysis, or interpretation of the data.