N-glycosylation phenotypes. In the discovery cohort, our multi-phenotype method uncovers ten genome-wide significant loci, of which five are novel . We convincingly replicate all novel loci via multivariate tests. We show that IgG N-glycosylation loci are strongly enriched for genes expressed in the immune system, in particular antibody-producing cells and B lymphocytes. We empirically demonstrate the efficacy of multivariate methods to discover novel, reproducible pleiotropic effects.Joint modeling of a number of phenotypes using multivariate methods has often been neglected in genome-wide association studies and if used, replication has not been sought. Modern omics technologies allow characterization of functional phenomena using a large number of related phenotype measures, which can benefit from such joint analysis. Here, we report a multivariate genome-wide association studies of 23 immunoglobulin G (IgG)  N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes.Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG  Therefore, instead of conventional univariate GWAS, multivariate analysis approaches are needed in GWA analyses to incorporate the ubiquitous, partly genetically regulated, correlation among omics variables.A large number of genetic loci have been mapped for complex traits of clinical relevance via genome-wide association studies (GWAS). However, our understanding of the biology behind the association is rather limited for most of these loci. In order to move further towards the goal of unraveling the biological mechanisms underpinning complex disease, large-scale omics data have been generated as intermediate phenotypes to help fill in the gap between the genome and organismal level traits. Nonetheless, in contrast to the complex traits that are commonly analyzed, the large number of omics variables often have a strong correlation structure that cannot be neglected3 (see Supplementary Note 4). In GWAS context, multivariate omics analysis with explicit modelling of phenotypic covariance faces several challenges. In genetically structured samples, this structure needs to be taken into account. The multivariate methods allowing for covariates can handle population stratification by including principle components of the genomic kinship matrix in the model. However, the effects of small groups of close relatives—if present—are usually not reflected in the leading principal components because such kinship generates weaker LD than large-scale population structure does5. In such situations, the method of choice is mixed effect models; however, a fast mixed model analysis for multiple (more than tens of) phenotypes is still difficult. GEMMA6 and Limix7 were designed to overcome such difficulty, although the computational cost can be high for a large number of phenotypes such as that encountered in omics data. Another difficulty faced in multivariate analysis of omics phenotypes is finding a method for replication, which would allow for both tests of significance and consistency of the model being replicated.Many multivariate methods have been developed for genetic analyses9). These studies usually propose a new method, and investigate real data to demonstrate that one may expect increased mapping power when using these. However, few new loci have been convincingly identified using such methods for complex phenotypes, and no replication was attempted. Therefore—at least for the moment—the practical gain from multivariate GWAS of complex traits seems to be rather limited. The situation appears to be somewhat different for traits more proximal to the genotype. Stephens10 applied multivariate GWAS to summary level data from the Global Lipids Genetics Consortium  and identified 18 new loci on the top of 95 loci identified in single trait analyses. While no replication was provided in this work, importantly, we observe that 11 of the loci identified by multivariate analyses were later confirmed with genome-wide significance in the next, bigger, univariate meta-analysis by GLGC in 201312 . It appears the yield of new loci is even bigger for metabolomics traits. Inouye et al.13 performed multivariate GWAS of 130 NMR metabolites (grouped in 11 sets) in 6600 individuals. The study demonstrated that multivariate analysis doubles the number of loci detected in this sample. Again, no replication was performed, however, among seven novel loci discovered by Inouye et al. via multivariate analysis, three demonstrate genome-wide significant (P < 5×10−11) association with at least one NMR metabolite investigated recently in a sample of up to 24,925 individuals14.Despite the number of methodological studies, few empirical multivariate GWAS have been published for humans. This may be largely attributed to the statistical complexity and computational difficulty of such analyses as well as outlined above issues addressing population stratification, replication, and interpretation. Among multivariate GWAS conducted to date, most are dealing with very complex phenotypes , and require different approach to replication.These observations strongly suggest that at least some of the novel loci discovered via multivariate GWAS are true positives. The fact that “univariate” replication of novel loci from the lipid and metabolomic studies is not perfect (around 50%), even after substantially increased sample size, suggests that either multivariate analyses are more prone to the appearance of statistical artifacts and/or are subject to different genome-wide significance thresholds, or that the underlying pleiotropic effects captured in multivariate models are hard to capture using standard univariate analyses , which is first isolated from other plasma proteins, followed by quantification of enzymatically-released glycans23. These studies uncovered six loci for total plasma and nine loci for IgG glycosylation23; with only one locus (FUT8) overlapping between the two. The majority of plasma proteins are synthesized in the liver and pancreas24 while immunoglobulins are synthesized specifically in cells of the immune system25. The lack of overlap between results of these genetic studies suggests different mechanisms of biological control of glycosylation in these two tissues.Previous GWAS have analyzed either the N-glycosylation traits and empirically test whether novel loci can be convincingly replicated. To utilize data from populations with high kinship, we formulate and implement a multivariate GWAS workflow based on combination of a linear-mixed-model-based phenotypic transformation, MANOVA, and multiple regression.Here, we apply multivariate methods to the IgG 26). The N-linked glycans were assayed using ultra performance liquid chromatography (UPLC), resulting in 23 quantitative measurements.A single protein (IgG) was isolated from the plasma of 1960 individuals from the population of the Orkney Islands in Northern Scotland . However, in order to avoid the effect of confounding by population genetic structure and kinship, prior to MANOVA analysis each phenotype undergoes linear-mixed-model-based GRAMMAR+ transformation we have previously developedN-glycosylation phenotypes using MANOVA. The same procedure was also applied to eight subsets of the traits based on different chemical and structural properties of glycans, namely galactosylation, monogalactosylation, digalactosylation, sialylation, monosialylation, disialylation, fucosylation and bisecting GlcNAc (N-acetyl glucosamine). The definition of subgroups is given in Supplementary Table 29 /9 = 5.6×10−9, as genome-wide significant.We performed multivariate GWAS combining all the 23 IgG 23 IgG N-glycosylation loci .In total, nine GWA scans were performed. Our multivariate analysis replicated five out of nine previously establishedP-value, which, when significant, might be interpreted as replication. However, in regular single-trait GWAS, a stronger replication criterion is used. This criterion asks for both significance and consistency in the direction of effect, i.e., that the same allele should be associated with increased/decreased risk or trait value in both discovery and replication samples. To implement such a ‘significant and consistent’ replication criterion, we first suggest estimating a linear combination of the phenotypes, i.e., constructing a phenotypic score, S, that best fits the associated genotype in the discovery cohort, and then computing the score and testing its association with the genotype in a replication cohort. If the same allele is used as reference in both discovery and replication cohorts, this consistency criterion translates into finding a positive association between the phenotypic score and the genotype in the replication cohort. In Table βS and PS represent the estimation and replication of the genotypic effects on such phenotype scores. The coefficients to construct the scores were estimated using a linear regression of the SNP dosage on the phenotypes (Supplementary Table n = 850) and VIS (n = 840), and meta-analyzed for replication. Following the same protocol, we performed a second replication in a much larger cohort TWINSUK (n = 4479), which confirmed all novel loci at a high significance level . Such estimates also allow us to perform meta-analysis of estimates from individual cohorts, resulting in high significance for all the five newly discovered loci , we tested association between the top SNP and all 23 traits using conventional univariate model. We considered the results to be replicated by this approach if we observed an association, which was consistent between replication and discovery and with P-value passing Bonferroni correction for the number of SNPs and traits tested (5 SNPs and 23 traits). The results are presented in Supplementary Data ELL2 and FUT6 we can achieve replication of association between SNP and the “top” associated trait from univariate analysis and for HLA-B-C and AZI1 we achieve replication at the second-best trait. This provides additional and convincing evidence that the reported associations are true, and suggests the score replication procedure is valid.To be confident about our replication results and to verify the suggested replication procedure, we also considered “univariate” replication, in which for novel loci that were not detectable by univariate GWAS  in the discovery cohort to that in each replication cohort. In Table rρ and Pρ represent the estimates and significance of such correlations.Geometrically, the procedure for estimating the phenotype score projects the SNP dosage vector onto the hyperplane defined by the phenotype vectors in the discovery cohort  when comparing ORCADES and TWINSUK, whereas when contrasting ORCADES and the Croatians, for HLA, FUT6 and some phenotype groups of IGH, such correlation estimates appear to be close to zero. As an example, for the IgG galactosylation phenotypes, we visualize the partial correlation contrast for the IGH and AZI1 loci  were run. Results are reported in Supplementary Data We used DEPICT softwarehttp://www.phenoscanner.medschl.cam.ac.uk/) for our reported top variants  and obtained 2011 association records. Filtered based on FDR < 5%, we identified associations between SNPs detected in this study and 17 complex diseases and disease-related traits  under different scenarios, where we see that, for unrelated individuals, the MANOVA statistic performs no worse than the other methods  and TATES groups of omics measurements reflecting specific biological processes, such as the glycosylation groups in this study.Certainly, increasing the number of tested phenotype groups would lead to more multiplicity of the statistical testing procedure. Here, we focus on nine particular groups and Bonferroni correct the number of GWA scans, in order to be conservative in reporting discoveries and follow the conventional rules in such genetic research. However, we would also emphasize that multiplicity is not simply a problem that reduces power, but also offers an opportunity to discover more.31. Given this, multivariate analysis can be applied, not only to omics data, but also to any correlated phenotypes in general, where power may be gained due to this statistical property.Another perspective of looking at power increase via multivariate analysis is that the power is gained by reducing noise in the measurements when combining multiple correlated phenotypes. For example, in our replication procedure, the effect at a single locus is interpreted by expressing the genotype dosage as a linear combination of multiple phenotypes, which is similar to our common knowledge that summing up multiple repeated measurements of a trait would lead to the reduction of relative residual variance and therefore improve statistical powerP-values with a weight for each cohort depending on its sample size. At the same time, pooled estimates of the coefficients for constructing the phenotype score could be obtained, though the meta-analysis based on such multiple coefficients will be less powerful. Meta-analysis in a replication procedure can be easily conducted based on the one degree-of-freedom test on the phenotype score estimated in the discovery cohort. However, our results suggest that pleiotropic models may differ between cohorts, which calls for extra caution  when performing multivariate replication.Methods for meta-analysis are critical in current statistical genomics: they allow for pooling of results of analyses from different studies thus achieving large sample sizes, statistical power and replication. As MANOVA was applied for the multivariate GWA analyses, there was no single effect size estimate for multiple phenotypes available. Meta-analysis based on the estimated effects size would therefore not be straightforward. In a large meta-analysis, we can combine the N-glycome; all of them were convincingly replicated. These loci include such positional candidates as the immunoglobulin heavy locus (IGH), IgH transcription elongation factor ELL2, human leukocyte antigen (HLA-B-C), and a locus including fucosyltransferases 6 and 3 (FUT6-FUT3); additionally, we have identified a locus on chromosome 17 flanked by AZI1 and TMEM105 . Immunoglobulin G consists of two biologically different regions—the antigen binding fragment (Fab) and crystallizable fragment (Fc). While the Fab region is responsible for binding with antigens, the Fc region is responsible for binding with effector molecules and cells35, guiding the immune response. Both regions can be glycosylated, with the majority of glycans coming from the Fc region and leading the immune response36. While a biological relationship between IGHG and IgG is obvious, it is not immediately clear what mechanism could link variation in IGHG region and IgG glycosylation. Similarly, it is interesting that in the novel associated interval on chromosome 5, the ELL2 gene is prioritized by DEPICT. ELL2 encodes the RNA polymerase II transcription elongation factor, which plays a role in immunoglobulin secretion. ELL2 regulates exon skipping of IGH and is necessary for processing mRNA transcribed from IGH37.For the 23 used single-trait analyses to detect nine loci, of which four contained genes encoding glycosyltransferases with obvious links to IgG N-glycosylation. Here, we find an additional five loci, of which only one contains a gene directly involved in protein glycosylation. Our results show that genetic control of IgG glycosylation is a complex process involving multiple biological pathways. Another interesting observation is that we observe clear biological links between some of the positional candidate genes, for example IGHG and ELL2, IKZF1 and IKZF3. With future larger studies, we should be able to further illuminate the complexity of the genetic control of glycosylation.In a previous GWAS, Lauc et al.26. Genetic diversity in this population is decreased compared to Mainland Scotland, consistent with the high levels of endogamy historically. Fasting blood samples were collected and over 300 health-related phenotypes and environmental exposures were measured in each individual. Genome-wide genotyping was performed using Illumina HumanHap300 and OmniExpress arrays.The Orkney Complex Disease Study (ORCADES) is a family-based study of 2078 individuals aged 16–100 years recruited between 2005 and 2011 in the isolated Scottish archipelago of Orkney38. The CROATIA-Korcula study includes 969 Croatians between the ages of 18 and 9839. The field work was performed in 2007 and 2008 in the eastern part of the island, targeting healthy volunteers from the town of Korčula and the villages of Lumbarda, Žrnovo and Račišće. Genome-wide genotyping was performed using Illumina HumanHap300 and OmniExpress arrays.The CROATIA-Vis study includes 1008 Croatians, aged 18–93 years, who were recruited from the villages of Vis and Komiža on the Dalmatian island of Vis during 2003 and 2004 within a larger genetic epidemiology programwww.twinsuk.ac.uk, also referred to as the UK Adult Twin Register) is an adult twin British registry shown to be representative of the United Kingdom female population41. From this registry, a total of 4479 subjects, had N-linked IgG glycans measurements and were included in the analysis. Genotyping was performed using the HumanHap300 and the HumanHap610Q array.The TwinsUK cohort (All research in this study that involved human participants has been approved by Research Ethics Committees—in Orkney and Aberdeen for the Orkney Complex Disease Study (ORCADES); in Croatia and Edinburgh for the VIS and KORCULA studies; and by St Thomas’ hospital Research Ethics Committee for TWINSUK. All ethics approvals were given in compliance with the Declaration of Helsinki . All human subjects included in this study have signed appropriate written informed consent.23. All samples were imputed to the 1000 Genomes using the b37 reference panel. Variants with minor allele frequencies (MAF) < 0.05, or imputation R-squared < 0.50, were excluded from the genome scan.Information about IgG glycosylation phenotypes can be found in Supplementary Table k phenotypes, where k is often much less than the sample size n, the association between the group of k phenotypes Yn × k and a biallelic marker g can be expressed as a multivariate regressionFor 27 so that the population structure is corrected using linear mixed models (see the next subsection).Although MANOVA has been a standard multivariate test method, here, we show how a Pillai trace statistic can be obtained from the data. Note that different MANOVA test statistics are equivalent for a single marker test  be the eigenvalues solvingAnalog of the univariate ANOVA F-test, let Pillai's trace can be calculated asF-statistic isand the corresponding n is large, k times the F-statistic is approximately χ2(k)-distributed.When 27 was implemented in the GenABEL-package42, part of the GenABEL-suite43. The genomic relationship matrix used in the analyses was generated by the ibs function (with weight = “freq” option), which uses SNP array data to estimate the realized pairwise kinship coefficient. The polygenic function was used to obtain the GRAMMAR+ transformed phenotypes  from linear mixed models. All univariate GWAS inflation factors  were close to 1, and the multivariate GWAS inflation factor was 1.005, showing that this method efficiently accounts for family structure.Prior to GWAS, each trait was adjusted for fixed effects of sex, age, and the other experimental factors. Glycans are quantified on 96-well plates, where the plate factor represents plate membership for each sample. The column factor represents the column on the plate (twelve columns per plate) for each sample and machine the UPLC machine on which the sample was ran. While the whole plate goes through the same procedure from the first step of the experiment, the UPLC instrument can quantify only one third of the plate at time, represented in the part factor. The residuals were inverse-Gaussian-transformed to standard normal distributions. The residuals expressed as Z-scores were used for all association analyses. In both the genotypes from SNP array and 1000 Genomes-imputed data, markers with minor allele frequency < 0.05 or imputation R-square < 0.30 were excluded. GRAMMAR+ transformation44 was used as the test statistic for the multivariate association. Univariate analysis of each phenotype at each locus was performed using linear regression via the lm function in R, with subsequent genomic control to correct the inflation factor. The resulted univariate P-values were passed onto TATES for P-value adjustment analysis.We implement the above multivariate analysis in the Multivariate function of the MultiABEL package. Implemented in this function is the MANOVA of multiple phenotypes against each single variant genotype dosage, and for the method option, in our analysis, Pillai’s traceF-test statistic of this regression model is equivalent to the MANOVA test statistic used in our GWAS. In each replication population, the same compound phenotype was constructed using the coefficients estimated in the discovery population, and thereafter, tested against the genotype dosage of the same variant using linear regression. The effect of the variant on the compound phenotype estimated from this regression model is denoted as βs. The R-squared from regressing the phenotype score on the genotype is equivalent to that from regressing the genotype on multiple phenotypes45  of the University of Edinburgh. The remaining data are contained within the paper and Supplementary Files or available from the corresponding authors upon request.The results of the nine genome-wide multivariate scans in the discovery analysis are available in the DataShare repository (Peer Review fileSupplementary InformationSupplementary Data 1Supplementary Data 2


Fine-mapping to identify causal variants in genome-wide association studies remains challenging. A recent study provides guidance for future research. Genome-wide association studies (GWAS) search for marker variants indirectly associated with certain diseases and/or traits. They assume that markers are in linkage disequilibrium (LD) with underlying causal variants. Compared to the initial discovery of associations, the fine-mapping effort required to identify causal variants—whether statistical or functional—remains challenging in this post-GWAS era.Reference panels such as those from the HapMap and 1000 Genome projects have improved, with better genome coverage including tens of millions of catalogued variants. Availability of these resources has led to methods for genotype imputation, in which genotypes for all variants in the reference are statistically inferred. Subsequent association analysis on imputed variants might allow refinement of the association hits originally discovered through array-based GWAS. However, fine-mapping through imputation is limited by the poor accuracy of imputed genotypes for rare variants, and the existence of underlying rare causal variants in reference panels cannot be guaranteed.Theoretically, with the application of whole-genome sequencing (WGS) in GWAS, all variants—including underlying causal variants—can be directly genotyped and tested to achieve the simultaneous goal of both discovery and fine-mapping. However, it is expensive to perform WGS on large numbers of samples, so it is unlikely to be adopted as a main approach for GWAS anytime soon. A key question is, what is the best strategy to increase mapping precision: to genotype and impute, sequence, or both?In a recent elegant paper, Wu et al.  attempteIn the “synthetic association” hypothesis, the association underlying a common variant is driven by many rare causal variants residing in a neighboring genomic region in LD with one particular allele of the common variant . HoweverThe authors measured the proportion of GWAS hits expected within a given physical distance from selected causal variants. They did this by simulating and comparing three typical study designs involving single nucleotide polymorphism (SNP) microarray genotyping, followed by imputation ), as well as the WGS-based approach. For the three imputation-based strategies, over 94% of GWAS hits fall within 100 kb of causal variants with a minor allele frequency >0.01. The proportion increased slightly to 98% with the WGS-based approach. The authors deduced that GWAS followed by imputation has comparable precision to WGS, and the latter is cost-ineffective for fine-mapping common variants.However, for rare variants, mapping precision for the best imputed dataset using 1KGP3 as a reference was substantially lower than that for WGS. Simulation studies showed that 98% of WGS-based GWAS hits fell within 100 kb of the causal variants with a minor allele frequency <0.01, whereas only 68% met the criteria for 1KGP3-based imputation. Underlying this finding is the fact that most of the rare variants in the 1000 UK Genomes study were not present in the imputation reference set. A limited number of LD surrogates also exist within a small genomic region harboring each rare causal variant.The authors noted that genome coverage is more important for fine-mapping precision than the sample size of the imputation reference set. However, the latter is important for imputation accuracy, and thus the statistical power, in detecting associations for rare variants. Particularly for rare variants, power loss caused by imputation is similar to sample size reduction and should therefore affect the fine-mapping precision. A possible explanation for the lack of observation of any remarkable effect of the sample size of the imputation reference set is that the simulated effect sizes were large. Thus, the power for detecting underlying associations was sufficiently high.Researchers are now shifting from imputation based on 1KGP3, which includes about 5000 haplotypes, to the new Haplotype Reference Panel, which includes about 65,000 haplotypes [The authors acknowledged that a weakness of their paper is their failure to consider loci with multiple causal variants, which may underlie some disease associations. For example, the best-known loci conferring germline cancer susceptibility are 8q24 and 5p15.3, which both include multiple independent signals and are associated with several cancers. A fine-mapping study of 5p15.33 revealed at least six independent associations with five different cancers [BRCA2 and CHEK2 genes have been associated with lung cancer risk [Decreasing costs will make WGS-based GWAS for large sample numbers more feasible. In the meantime, meta-analyses based on imputation are being put to good use to combine new and existing array-based GWAS studies, including fine-mapping efforts. For example, using this strategy, rare variants of moderately large effects in cer risk . To takecer risk ; this coWhether or not it is an advantage to employ WGS in GWAS depends on the allelic spectrum or genetic architecture of the disease/trait under investigation. For example, a recent WGS-based GWAS for type 2 diabetes  found va−9. Furthermore, functional annotations such as epigenetic footprints, transcriptional factor binding motifs, and expression quantitative trait loci could be used to improve power to detect associations. For example, a weighted Bonferroni adjustment based on the enrichment of sequence annotations among association signals might be used [An important consideration for the future is that rare variants, which are mostly in weak LD with neighboring variants, increase the number of independent tests, and thus the multiple-testing burden to control for false negative signals. In light of this, Wu et al. recommend applying a more stringent threshold of 5 × 10Rare variants, even if—in total—they contribute substantially to heritability, are likely to be distributed over many thousands of loci, each with small effects . Thus, u


Crossbreeding is a common strategy used in tropical and subtropical regions to enhance beef production, and having accurate knowledge of breed composition is essential for the success of a crossbreeding program. Although pedigree records have been traditionally used to obtain the breed composition of crossbred cattle, the accuracy of pedigree-based breed composition can be reduced by inaccurate and/or incomplete records and Mendelian sampling. Breed composition estimation from genomic data has multiple advantages including higher accuracy without being affected by missing, incomplete, or inaccurate records and the ability to be used as independent authentication of breed in breed-labeled beef products. The present study was conducted with 676 Angus–Brahman crossbred cattle with genotype and pedigree information to evaluate the feasibility and accuracy of using genomic data to determine breed composition. We used genomic data in parametric and non-parametric methods to detect population structure due to differences in breed composition while accounting for the confounding effect of close familial relationships. By applying principal component analysis (PCA) and the maximum likelihood method of ADMIXTURE to genomic data, it was possible to successfully characterize population structure resulting from heterogeneous breed ancestry, while accounting for close familial relationships. PCA results offered additional insight into the different hierarchies of genetic variation structuring. The first principal component was strongly correlated with Angus–Brahman proportions, and the second represented variation within animals that have a relatively more extended Brangus lineage—indicating the presence of a distinct pattern of genetic variation in these cattle. Although there was strong agreement between breed proportions estimated from pedigree and genetic information, there were significant discrepancies between these two methods for certain animals. This was most likely due to inaccuracies in the pedigree-based estimation of breed composition, which supported the case for using genomic information to complement and/or replace pedigree information when estimating breed composition. Comparison with a supervised analysis where purebreds are used as the training set suggest that accurate predictions can be achieved even in the absence of purebred population information. Bos taurus) beef cattle breeds of European origin (Bos indicus) breeds is a common strategy used to enhance beef production in tropical and subtropical areas  use genomic data to detect population structure due to differences in breed composition by means of parametric and non-parametric methods while accounting for the confounding effect of close familial relationships, and (2) compare breed composition inferred from genomic data with breed composition derived from pedigree.The study included 782 cattle from the multibreed Angus–Brahman herd at the University of Florida . The res® DNeasy® kit , and genotyping was carried out using the GeneSeek Genome Profiler F-250 . Several quality control (QC) measures were applied to genotype data at the animal and marker level . The extent of linkage disequilibrium (LD) was evaluated by observing LD decay as a function of genomic physical distance. Markers in high LD were then pruned with window size of 5,000 kb, step size of 10 bp, and LD threshold of 0.5 (PRJEB24746.Genomic DNA was extracted from blood samples using the QIAGENer level . At the d of 0.5 . All QC d of 0.5 . The genn = 2) and samples with low genotype completion rate , 676 samples were available for subsequent analysis. From an initial set of 221,077 SNP, a subset of 89,728 SNP was kept after removing 64,496 SNP with low MAF (<1%); 13,386 SNP for failing to meet minimum call rate (<0.9); 8,088 SNP for Hardy–Weinberg equilibrium deviation and 45,379 SNP due to LD pruning.After removing duplicates , efforts to identify population structure due to differences in breed composition can be biased by the presence of close familial relationships and shared recent ancestry among the sample set being analyzed . These tA subset of mutually unrelated animals that is representative of overall population structure in the entire sample set was identified using an algorithm described by K) specified beforehand. Subgroup memberships were considered as breed membership, and pedigree information was used to identify the breed associated with a subgroup. Estimates of genomic breed composition and marker allele frequency were obtained from the Q and F matrices. The Q matrix contains membership coefficients of samples to each subgroup/breed. Since fractional subgroup membership is allowed, membership coefficients can also be conveniently interpreted as the proportion of an animal’s genome with a particular breed ancestry. The F matrix contains allele frequency estimates for each marker in each subgroup/breed.Individual breed composition was estimated from genomic data using a maximum likelihood model implemented in the software ADMIXTURE v1.3 . ADMIXTUThe accuracy of the genomic breed-composition estimation using the maximum likelihood model of ADMIXTURE depends on the extent to which several underlying assumptions are met. These include a relative level of linkage equilibrium between markers, a sample set with adequate representation of all parental/ancestral breeds and a sample set composed of only unrelated animals . The samTo account for the confounding effect of known and/or cryptic familial relationships, population structure identified in the unrelated subset was used as a reference when inferring genomic breed composition in the related set . ADMIXTUK = 2 as inputs, and individual breed membership coefficient (Q) and breed allele frequency (F) estimates were obtained. Genotype data for the remaining samples was then projected onto the population structure inferred for the unrelated samples (F) estimated for the unrelated set, along with genotype data for the rest of the samples and a K = 2 as inputs when estimating breed membership coefficients (Q) for the rest of the samples. A K = 2 was used based on our knowledge of two parental breeds used for all animals in the study  estimates. Genotype data for the remaining, non-purebred samples was then projected onto the population structure inferred for the purebred samples.An additional projection was used where ADMIXTURE was run in the unsupervised mode on a subset of 44 purebred Angus and 44 purebred Brahman animals to obtain breed allele frequency  and the pedigree-based breed composition. The agreement between the different methods was evaluated by computing pairwise Pearson’s correlation coefficients.n = 228,150) is shown in Figure 1.The R function “pcairPartition” identified a subset of 74 samples as unrelated and representative of the population structure in the entire sample set. The selection was based on pairwise kinship and divergence estimates by the KING-robust method. The distribution of KING-robust estimated for all unique pairwise comparisons among the 676 study animals  or the 588 samples in the non-purebred set. As expected  between breed composition estimates for either breeds from ADMIXTURE and the estimates obtained using pedigree records. As indicated by the less than perfect correlation, there were discrepancies between the estimates from the two methods for certain samples . The mean and standard deviation of the absolute difference between breed composition estimates from the two methods were 0.056 and 0.060, respectively. For 72% of the animals, the difference was within one standard deviation, and 5% had a difference of more than two standard deviations. The correlation between the breed composition estimates from the pure-bred and unrelated trained ADMIXTURE analyses was very strong (R = 0.994).There was a very strong correlation . The PC1 had a very strong correlation (R = 0.966) with the Angus proportion derived from pedigree data. The relationship between PC1 and pedigree breed composition is also illustrated in Figure 4B. Animals with approximately 2/3 Angus ancestry  had more variation in PC2, indicating the presence of a source of population structure other than Angus–Brahman differentiation. This variation could be attributed to the distance of Brangus animals from the initial crossbreeding forming a Brangus animal, with first generation Brangus animals being closest to the average, and the distance from the average increasing with the number of generations from this initial Brangus formation . In contrast, F1 and first generation Brangus cattle were located along the line connecting the two clusters formed by the purebreds  and showed much less variation across PC2 as would be expected in the case of a recent two-way admixture (The first and second PCs (PC1 and PC2) explained 27 and 5.6% of the variation in the entire genetic data, respectively. Both unrelated and related subsets show a similar pattern of variation across these two PCs (dmixture .R = 0.999) with the Angus proportion estimated from ADMIXTURE. However, the relationship between the two values appeared to be different for the related and unrelated subset of samples . For the related set, there was a linear relationship between the two values, whereas for the unrelated set, ADMIXTURE estimates had more extreme values at both ends.The PC1 had also a very strong correlation . In addition to not requiring allele frequency estimates from external reference populations, such methods allow inference on the correct number of ancestral populations, an important feature when the full range of parental breeds involved is not certain.Figure 1. Based on these estimates, pcairPartition identified 74 samples as unrelated and ancestrally representative of the entire sample set. This is expected to minimize the confounding effect of close familial relationships on breed membership inference for the related set of samples.Unsupervised model-based clustering used to infer genomic breed composition runs a higher risk of confounding by sources of population structure other that heterogeneous breed ancestry. This is because, unlike the regression methods mentioned earlier, breed allele frequencies are not explicitly specified, and familial relationships among the sample can bias cluster  allele frequency estimates. Assuming the sample population is large enough, one way to control for such confounding is to base genomic breed composition inference on a subset of mutually unrelated and ancestrally related samples. This involves estimating cluster  allele frequencies using the unrelated subset in an unsupervised run, and using allele frequency estimates from this run as an input in a supervised run involving the rest of the samples. A useful feature of the KING-robust estimator used in the present study is that it gives out negative estimates  for unrelated pairs with different breed ancestry, and this relationship is inversely proportional such that the more divergent the ancestry between the pairs, the more negative the divergence estimate. The extent of both familial relationships and ancestral heterogeneity in the sample is shown in R = 0.965) between breed composition estimates for either breeds from ADMIXTURE and the estimates obtained using pedigree records, which is in agreement with results from other studies. There was a very strong correlation . For crossbred animals, breed composition derived from genomic data should be more accurate than pedigree-based estimates since pedigrees can be incomplete or incorrect (R = 0.994) between breed composition estimates obtained from ADMIXTURE when either an unrelated subset or a set of purebred parental populations was used, suggests that the model-based analyses provide highly accurate estimates of breed composition. In addition, a very high correlation of estimates from both of these methods and estimates from a completely unsupervised ADMIXTURE analysis of the entire dataset  suggest the model-based analysis is robust and should be expected to generate highly accurate estimates even in the absence of a training unrelated or purebred dataset.However, similar to the other studies, there were discrepancies between breed composition estimates from genome-wide data and pedigree for certain samples  estimated between the two parental populations. Consistent with Fst average (0.25) calculated based on the Weir and Cockerham method implemented in PLINK (R = 0.966) with breed composition derived from pedigree, with some discrepancies likely due to inaccuracies in pedigree records and/or the effect of Mendelian sampling . First generation Brangus cattle showed less variation across PC2, and were located along the line connecting the two clusters formed by the purebreds , as would be expected in the case of a recent two-way admixture . This result is in agreement with The PC1 in our PCA explained 27% of the variation in the genetic data, consistent with two major parental breeds . In a twin PLINK  was clossampling . An intedmixture . The disdmixture . Close fR = 0.999) with Angus–Brahman proportion from ADMIXTURE, similar to The PC1 had a very strong correlation , ADMIXTURE estimates membership coefficients to all K breeds using a constrained optimization process (via quadratic programming) which forces the coefficients to be non-negative and to sum to 1  can be confounded by other sources of population structure and highlights the importance of accounting for such sources by using an unrelated, breed-representative reference population. Although there was strong agreement between breed proportions estimated from pedigree and genetic information, there were significant discrepancies between these two methods for certain animals. This was likely due to inaccuracies in the pedigree-based composition of these animals, which supports the case for using genomic information to complement and/or replace pedigree information when estimating breed composition.MG conducted the analysis and drafted the manuscript. RM conceived and assisted with the analysis and the manuscript. ME assisted with the analysis and the 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.


Brassica napus) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.Oilseed rape ( Brassica napus L.; AACC, 2n = 38) is one of the most important sources of vegetable oil in the world. Owing largely to the development of high yielding varieties of rapeseed in breeding programs, now hybrid rape has the largest market share due to its superior performance for multiple agronomic traits  interaction  are now becoming one of the standard methodology for revealing the genetic variation of complex traits for human, plants, and animals Clark, . SeveralSclerotinia sclerotiorum in rapeseed, and the additive by additive interactions were the predominant type of epistasis , main inflorescence length (IL), branch number (BN), number of seeds per silique (SS), effective silique number on main inflorescence (ISN), thousand seed weight (TSW), biomass yield per plant (BY), and seed yield per plant (SY). In the present study, the eight yield-related traits of 367 experimental materials were detected in two environments. GWAS performed with high-throughout SNPs was firstly used to find SNPs with additive, dominance, epistasis and environmental interactions for these traits. Additionally, prediction of genetic effects was conducted based on the detected quantitative trait SNPs (QTSs) of eight agronomic traits to predict high-yield genotype combination. The study will provide a better understanding of the genetic mechanism of yield-related traits, and the QTSs are beneficial for efficiently selecting ideal parental lines for breeding new hybrid rapeseed.1 hybrids including 97 hybrids taken L155 as female and 119 hybrids taken L157 as female. All the lines came from China and adapted to the middle reaches of the Yangtze River agro-climatic conditions, China. The 367 experimental materials, including 151 inbred lines and 216 F1 hybrids were grown in farm fields in Wuhan  and Xiangyang  in Hubei province, China, under normal conditions for crop production during the rapeseed growing season of 2012–2013. The annual mean temperature of Wuhan was 1~2° higher than Xiangyang, and the average rain days of Wuhan was 3 days more than Xiangyang during the rapeseed growing season of 2012–2013.To generate genetic population used for the association studies, we adopted Yu5 (L155) and Zheyou18 (L157) as female parents to pollinate with another 149 male parents (L1-L149) and finally harvested 216 FIn 2012, all trials were designed as randomized complete blocks with three replications in each environment. Each plot consisted of three rows of 3.5 m length with 0.25 m distance between rows. During the last 5 days of September and the first 5 days of October, seeds were sown with a distance of 0.15 cm between plants in each row. The management of the field experiments was performed in accordance with local standard production practices. At maturity, 12 plants in the middle row were harvested (randomly within this row) from each plot for evaluation of the following eight quantitative traits: plant height (PH), main inflorescence length (IL), branch number (BN), number of seeds per silique (SS), effective silique number on the main inflorescence (ISN), thousand seed weight (TSW), biomass yield per plant (BY), and seed yield per plant (SY). Measurements for PH, IL, BN, SS, ISN, TSW, BY, and SY were completed as described by Shi et al. .http://www.illumina.com/technology/infinium_hd_assay.ilmn) according to the manufacturer's protocol. The SNP data were clustered and called automatically using Illumina BeadStudio genotyping software. SNPs with QQ or qq frequencies equal to zero, call frequency <0.9, or minor frequency <0.05 were excluded. The remaining SNPs were scrutinized visually, and those SNPs not showing three clearly defined clusters representing the three possible genotypes  were also excluded. A total of 33,689 SNP markers among the 367 lines and hybrids that met the QC criteria were used for the association analyses.Genotyping of the entire inbred line population was performed commercially by Emei Tongde Co. (Beijing) using the Brassica 60 K Illumina® Infinium SNP array (ε|/ σε > 3) by QQ plot (Quantile-Quantile plot) for each trait  the frequency of missing data for each subject <0.1; (2) outliers were discarded according to a distribution-based outlier detection of residuals  can be expressed by the following linear mixed model:The genetic model for the phenotypic value of the ai is the additive effect of the i-th locus with coefficient uAik ; di is the dominance effect of the i-th locus with coefficient uDik ; aaij, adij, daij, and ddij are the digenic epistasis effects with coefficients of random variables uAAijk , uADijk , uDAijk , and uDDijk (1 for Qq × Qq); eh is the random effect of the h-th environment; aeih is the additive × environment interaction effect of the i-th locus in the h-th environment with coefficient uAEihk; deih is the dominance × environment interaction effect of the i-th locus in the h-th environment with coefficient uDEihk; aaeijh, adeijh, daeijh, and ddeijh are the digenic epistasis × race interaction effects in the h-th ethnic population with coefficients of random variables ; and εhk is the residual effect of the k-th line or hybrid in the h-th environment.where μ is the population mean; VP) is the sum of genetic variance (VG), genetic by environment interaction variance (VGE), and residual variance (Vε),Heritability of individual genetic effects were estimated by The total heritability was estimated byQTXNetwork software (http://ibi.zju.edu.cn/software/QTXNetwork/) to scan 33,689 SNP markers in 367 subjects for 1D~3D significant candidate SNP markers, and obtained 539 candidate SNPs . The QTS mapping module in the QTXNetwork was then used to dissect the genetic architecture of the eight agronomic traits of oilseed rape  between predicted breeding values and phenotypic values was estimated for each trait. Based on the predicted genetics effects of SNP loci for eight traits, we predicted total genotypic effects for the best lines and the best hybrids of the mapping population, and also predicted genotypic effects of superior lines and superior hybrids to inform further selection decisions  to other 149 inbred lines as male parents. Eight yield-related traits were investigated in our study by using a full genetic model with genetic effects of additive, dominance, epistasis, and their environment interactions. A total of 17 QTSs controlling eight yield traits were detected: 4 QTSs for PH, 2 QTSs for BN, 3 QTSs for IL, SS, ISN, TSW, BY, and SY, respectively  predicted genetic effects of 16 QTSs are presented in Table Highly significant  had very strong environment-specific additive and dominance effects (Effective silique number on main inflorescence (ISN) had high heritability  had relatively large additive and epistasis variances  Figure .For biomass yield per plant (BY), the largest contributions of genetic variance were environment-specific dominance and epistasis  Figure .QQ, qq), and heterozygote (Qq) for eight traits in two environments, respectively , the predicted genotypic values were much larger than minor-allele homozygote (qq) for eight traits but not for TSW in one environment. It was implied that for this rapeseed population, hybrid breeding could potentially increase breeding values of seven yield traits but not for shortening plant height.The Genotype-Phenotype (G-P) maps of epistasis SNPs for each trait, in both environments were presented in Figure ly Table . All theE2. For another trait ISN, the breeding value of the best line  was larger than the superior line , because the best lines (L41 in E1 and L45 in E2) still had heterozygote locus (C/A of C04_M31883).There was no difference between the best lines of mapping population and the predicted superior lines for four traits . It was suggested that pure-line variety breeding might have only limited potential for improving these traits based on the QTSs detected for this rapeseed population. For trait IL, breeding value of the best lines was smaller than the predicted superior lines, which was due to one locus in the best line L155 . The superior line could be obtained by replacing minor-alleles to major-alleles (A/A) of A01_M15289 for increasing E1 based on the best hybrids  and maintained homozygote A/A for other two loci . There could have dramatic decrease for plant height of hybrid by just selecting all detected four QTSs of PH as major-allele homozygotes .Among the eight traits studied, there were six traits  with no difference of breeding values between the best hybrids and the superior hybrids in at least one environment. It was indicated that no selection advantage could be expected based on this mapping population for improving these six traits of hybrids. Expected gain of hybrid breeding could be obtained for two traits based on the best hybrids of this mapping population. For trait IL, the superior hybrid genotypes could be selected as A/G of A03_M4640 in two environments, and A/G of A07_M11103 in The genotype of receptor lines and donor lines was listed in Table crtRB1) was associated with β-carotene concentration in maize kernels  in the 151 cultivars of this study was 11.07% (4.67~16.67%) for the detected 17 QTSs , and minor alleles had impacts on various genetic effects  according to the genetic model. These minor alleles (qq) increased breeding values for all the eight traits and made contributions to total heritability increasing by different genetic effects.There are evidences that rare variants have large impacts on common human diseases . Dominance had much larger impacts on eight traits studied due to 21 main dominance loci with 19 having positive effects   . However, the heritability of each additive locus was quite low (Qq), and minor-allele homozygotes epistasis  was contributed for seven yield traits. It was concluded that epistasis together with heterozygotes loci play an important role in yield heterosis in B. napus.Fully characterizing the genetic mechanism mediating heterosis is helpful for increasing crop yield. While none of the current genetic models can completely explain the heterosis phenomenon. Previous study indicated that epistasis together with all levels of dominance from partial to overdominance is responsible for the expression of heterosis in rapeseed  and heterozygotes (Qq) for additive, dominance and epistasis effects. All superior hybrids for these six traits could be obtained via introducing heterozygote into L155 or L157, suggesting that L155 and L157 already offered suitable genetic background. Only limited amount (3~13) of donor lines could be crossed to acceptor line to gain desirable properties, except for trait ISN. It is indicated that donor lines should be chosen with caution, and that marker-assisted selection could help to reduce blindness in hybrid breeding based on the predicted breeding values.In the study, there were six traits  having no difference of predicted breeding values between the best hybrids and the superior hybrids in at least one environment Table . It was CM conceived and designed the experiments. XL, YY, and CM performed the experiments. JZ, YD and XL analyzed the data. CM and JZ contributed materials/analysis tools. JZ, CM, LZ and XL wrote the paper. JS polished the paper.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.


Analysis of a large volume of meat inspection data suggests availability of genetic variation for most common indicators of poor animal welfare. This genetic variation can be used to select pigs that have the potential to resist common infections and other unfavorable welfare conditions. Genetic selection can be a tool in addition to farm management in reducing the risk of diseases, thereby reducing pain and suffering of animals. In general, the slaughter remarks have small but favorable genetic relationships with finishing and carcass quality traits. Therefore, it is possible to enhance animal welfare along with the genetic selection for economically important production traits.Animal health and welfare are monitored during meat inspection in many slaughter plants around the world. Carcasses are examined by meat inspectors and remarks are made with respect to different diseases, injuries, and other abnormalities. This is a valuable data resource for disease prevention and enhancing animal welfare, but it is rarely used for this purpose. Records on carcass remarks on 140,375 finisher pigs were analyzed to investigate the possibility of genetic selection to reduce the risk of the most prevalent diseases and indicators of suboptimal animal welfare. As part of this, effects of some non-genetic factors such as differences between farms, sexes, and growth rates were also examined. The most frequent remarks were pneumonia (15.4%), joint disorders (9.8%), pleuritis (4.7%), pericarditis (2.3%), and liver lesions (2.2%). Joint disorders were more frequent in boars than in gilts. There were also significant differences between farms. Pedigree records were available for 142,324 pigs from 14 farms and were used for genetic analysis. Heritability estimates for pneumonia, pleuritis, pericarditis, liver lesions, and joint disorders were 0.10, 0.09, 0.14, 0.24, and 0.17 on the liability scale, respectively, suggesting the existence of substantial genetic variation. This was further confirmed though genome wide associations using deregressed breeding values as phenotypes. The genetic correlations between these remarks and finishing traits were small but mostly negative, suggesting the possibility of enhancing pig health and welfare simultaneously with genetic improvement in finishing traits. A selection index based on the breeding values for these traits and their economic values was developed. This index is used to enhance animal welfare in pig farms. Meat inspection is routinely carried out in many slaughter plants around the world. In many countries, it is regulated by local government agencies such as the Canadian Food Inspection Agency (CFIA) in Canada, the Food Safety and Inspection Service (FSIS) in USA, and the European Food Safety Agency (EFSA) for European countries. The primary purpose is to check carcasses for safety of meat as a food for human consumption. Carcasses are checked by trained inspectors and remarks are made for different diseases and abnormalities. Many of the slaughter plants are very large and process thousands of carcasses every day. This generates large volumes of data on various aspects of animal health and welfare. Some slaughter plants use these data to provide feedback to the farms that supply the pigs. Apart from this, the data could be very useful for taking preventive measures for diseases at the farms and enhancing animal welfare ,2. HowevIn this investigation, data were obtained from slaughter plants in Germany. These pigs had radio-frequency identification (RFID) tags and the plants had devices to read them during the fast moving slaughter pig chains. The data could also be linked to individual performance and pedigree records. The main objective of this study was to investigate the possibility of reducing the risk of most prevalent unfavorable health and welfare indicators through genetic selection. As part of this main objective, effects of various non-genetic factors were also examined to help in farm management.The data for this study consisted of slaughter remarks on 140,375 pigs from slaughter plants in Germany from July 2011 to July 2016. The data consisted of 69 slaughter remarks. The most common remarks were pneumonia (15.4%), joint disorders (9.8%), pleuritis (4.7%), pericarditis (2.3%), and liver lesions (2.2%). These traits were analyzed in detail. A total of 27.1 (%) pigs had either one or more such remark. Therefore, an additional trait was considered as the presence or absence of any of these remarks (yes = 1 or no = 0) and denoted as ‘remark’ in this manuscript. These pigs originated from 13 farms in Germany and the Netherlands. Pedigree records for five generations and records on finishing and carcass quality trait were available on 142,324 pigs from these farms and were used for genetic analysis. All these pigs were progeny of Pietrain sires and Large White x Landrace (F1) dams.A brief description of the slaughter remarks analyzed is given below.Pneumonia: Is an inflammation of the tissues of the lungs. It mainly results from the response of the animal to an infectious agent, either a virus or bacteria, or in most cases both. Pneumonia was recorded at three different levels according to the severity of the lesions. For the purpose of this analysis, three classes of pneumonia were merged and pneumonia was scored as 1 present when observed regardless of intensity and 0 when absent.Pleuritis: Also known as pleurisy, it is the inflammation of the tissue layers (pleura) lining the lungs and inner chest wall. It causes discomfort to pigs while breathing. Carcasses from affected pigs typically require stripping and removal of the inflamed pleural membrane from the thoracic cavity. In more severe cases, extensive trimming of the chest wall becomes necessary. This involves additional costs to the processor in terms of lost carcass weight, compromised carcass grading, and requirement of extra time in trimming affected areas. Severity of pleuritis was not recorded, but a remark was made as 1 when it was observed. Pericarditis: Pericarditis is inflammation of the pericardium, the fibrous sac surrounding the heart. It holds the heart in place and helps it work. There is a small amount of fluid between two layers of the pericardium that keeps the layers separate and avoids friction between them. Pericarditis causes chest pain to the pig. The affected carcasses require trimming that causes economic losses to the processor. This was also recorded as 0 or 1, like other remarks.Liver lesions: Liver lesions are mainly caused by parasitic infections. White spots, also called milk spots, are observed in the liver of affected pigs. A liver was scored as 1 when more than five white spots were observed. Joint disorders: This included bursitis and other disorders leading to inflammation of joints. The bursa is a sac filled with lubricating fluid, located between bone, muscle, tendons, and skin to decrease rubbing, friction, and irritation. Bursitis is the inflammation or irritation of the bursa. Bursitis causes pain and difficulty in walking for the animal. Therefore, it indicates poor animal welfare. If a joint disorder was observed in a carcass, it was recorded as 1. Remark: In addition to remarks on the individual traits above, a general variable whether any of the remarks was recorded (yes/no) was also analyzed. It was considered as 1, when any remarks were observed. The data were analyzed in two steps. In the first step, effects of non-genetic factors were studied using a generalized linear model in PROC GLM of SAS. The model for each trait included fixed effects of the farm of origin, sex of the pig, and date of slaughter.In the second step, data were analyzed with a sire model using ASReml 4.0 . In addi2 > 600). Unmapped SNPs and SNPs located on the sex chromosomes were also excluded. Positions of the SNPs were based on the Sscrofa10.2 assembly of the reference genome. All genotyped animals that had a frequency of missing genotypes below the threshold of 0.05 were excluded. After quality control and imputation procedures, 446,117 SNPs were used in GWAS. An association between SNPs and phenotypes was considered significant when the probability was less than 0.000001 (−log10 (p) > 6) [A genome wide association study (GWAS) was conducted using the GCTA software developed by . In the (p) > 6) .p < 0.001) for each of the traits. There was very high incidence of pneumonia in farms B, C, and J at 59.5, 59.1, and 57.3 percent, respectively. The pigs from these farms were slaughtered between January 2014 and June 2016. The incidence was higher than average during winter but also during summer months, indicating some unfavorable management conditions at these farms.In the initial analysis, it was observed that there were differences between farms with respect to the incidences of the slaughter remarks observed at the slaughter plant. The remarks could be affected by the sex of the animal and by day to day differences due to handling of pigs at the farm plant and scoring by different inspectors. Therefore, least-squares means of the slaughter remarks were estimated using the generalized linear model accounting for these differences. The least-squares means for the different farms are given in p < 0.001) for all traits except pneumonia. Least-squares means suggest that the incidence of pneumonia was quite similar among the three sexes  than in Farm M (507 g/day). The farm L with slower growth rate higher average incidence for joint disorder (13.1%) than farm M (6.1%) which had a faster growth rate. This is the reverse order of the trend in The relationship between growth and joint disorders differed among the farms. In some farms, there was a favorable trend as the pigs with higher growth rate had lower incidences while, in some other farms, the relationship was reversed. Examples of the patterns in two largest farms are given in The estimates of variances due to genetic and common litter effects are given in p < 0.001). Pericarditis could lead to pain and in death of adult sows. In a study of the most important causes of sow mortality, heart failure was considered to be most important cause [Results of the genome wide association study (GWAS) to further verify the existence of genetic variation for these traits are given in nt cause . AlthougIn general, there were very low phenotypic correlations among the slaughter remarks . Especiap > 0.05), except for the correlation with liver lesions. The genetic correlations between the slaughter remarks were of similar magnitude or even higher. However, they were associated with larger standard errors. The correlations among slaughter remarks were close to zero and non-significant for most of the traits. It was highest between pleuritis and pericarditis and even larger than the phenotypic correlation, suggesting similar genetic predisposition for both the traits. However, the genetic correlation between pneumonia and pleuritis was lower than phenotypic correlation and not significantly different from zero. This suggests that the phenotypic association was mainly due to the transmission of infection between the lungs and pleura. The genetic correlation between pericarditis and liver lesions was higher than the respective phenotypic correlation but still lower than the correlation between pericarditis and pleuritis. The genetic correlations of joint disorders with pneumonia, pleuritis, and pericarditis were low and non-significant  were 0.67 for pleuritis and 0.40 for lung lesions and 0.16 for pericarditis. Similarly, ref. [All these factors were also carefully evaluated during his study by visiting the slaughter plants, observing the inspection process and through discussion with the staff. Another issue is the relationship of the observations at the slaughter plants and on the actual farm level. In Danish finisher pigs, ref.  observedly, ref.  observedTherefore, efforts were made to record the identity of the inspector to account for the differences among their remarks in the statistical model. However, it was not possible due to several factors including the regulations relating to the data recording by government officials. Therefore, an attempt was made to partly account for these differences by including the effects of slaughter dates. It is expected that these inaccuracies will have minimal effect on estimates of genetic parameters due to the large data size and the genetic evaluation models used. In a similarly large study of the Food Standards Agency (FSA), data from over 19 million pigs from three abattoirs in Great Britain were gathered and compared with data from a targeted surveillance system of slaughtered pigs (BHPS) and laboratory diagnostic scanning surveillance (FarmFile), [Management plays a crucial role in the occurrence of meat inspection remarks. Management is the first and major factor to be addressed. That is one of the main reasons for differences between farms. At the same time, there is meaningful genetic variation. In fact, some genetic variation should be expected as some full sib or half sib families have better joints, lungs, hearts, livers, etc. than others that make them less liable to diseases related to these organs. Genetic selection for the better families or genetic lines can reduce the incidence of infections and, with that, reduce the chance that other animals are infected. However, genetic selection for more robust animals should not reduce the focus on management improvements.To our knowledge, this is the first example of using meat inspection data for genetic selection to enhance animal welfare in pigs. A system has been put in place to obtain these data by direct links with the organizations responsible. This is allowing routine genetic evaluations and selection of pigs using the welfare index to reduce the risk of unfavorable slaughter remarks. In this way, this is also an example of cooperation along the pork value chain, from processing back to production and genetics, as a concerted effort of the value chain partners is required to enhance animal welfare.


P value < 2.2e−16) and rare SNP set . Our results suggested that low-frequency and rare variants may play more significant roles in the genetics of MDD.Major depressive disorder (MDD) is a common but serious psychiatric disorder with significant levels of morbidity and mortality. Recent genome-wide association studies (GWAS) on common variants increase our understanding of MDD; however, the underlying genetic basis remains largely unknown. Many studies have been proposed to explore the genetics of complex diseases from a viewpoint of the “missing heritability” by considering low-frequency and rare variants, copy-number variations, and other types of genetic variants. Here we developed a novel computational and statistical strategy to investigate the “missing heritability” of MDD. We applied Hamming distance on common, low-frequency, and rare single-nucleotide polymorphism (SNP) sets to measure genetic distance between two individuals, and then built the multi-dimensional scaling (MDS) pictures. Whole-exome genotyping data from a Los Angeles Mexican-American cohort (203 MDD and 196 controls) and a European-ancestry cohort (473 MDD and 497 controls) were examined using our proposed methodology. MDS plots showed very significant separations between MDD cases and healthy controls for low-frequency SNP set ( One significant avenue for preventing and treating depression lies in uncovering the genetics of this condition7. Despite rapid advances on genome-wide association studies (GWAS)10, little is understood about its fundamental biological basis and much further research needs to be carried out to fully unravel the genetic elements that confer susceptibility to this disorder12.Major depressive disorder (MDD) is a common mental illness with tremendous medical, economic, and social impact. MDD, as a principal contributor to disease load worldwide, leads to high levels of morbidity and mortality16. For example, some genetic effects are not owing to the common single-nucleotide polymorphisms (SNPs) examined in the candidate-gene studies or GWAS, but due to low-frequency and rare variants, copy-number variations, and other types of genetic mutations17. Actually, GWAS focus on the identification of significant common  ≥ 5%) variants, thus analyses of low-frequency (0.5% ≤ MAF < 5%) and rare (MAF < 0.5%) variants would be promising to elucidate additional disease risk or trait variability18. Furthermore, using next-generation sequencing, family-based linkage analysis has also provided an important way to understand the role of rare variants in disease etiology20. For example, some family-control studies applied Hamming distance to identify disease genes based on sequencing data22. However, high-priced sequencing expenses are currently a concern that restricts acquiring large datasets.Many studies have been proposed to explore the genetic causes of complex diseases from a point view of the “missing heritability”23. Our results suggested that the “missing heritability” in MDD may be partly explained by rare variants, because most of the functional variations detected in the cohorts were rare. In this study, we designed a novel computational and statistical strategy to further investigate this conclusion. In our methodology, we used Hamming distance on common, low-frequency, and rare SNP sets to measure the genetic distance between two individuals. Then we built the multi-dimensional scaling pictures, in which separation between MDD cases and healthy controls revealed valuable information for hidden genetic factors of major depression. The corresponding statistical results in the pictures were reported.Recently, we have applied GWAS and rare-variant analysis to investigate the genetics of MDD based on a whole-exome genotyping data from a Mexican-American cohort in Los Angeles and a replication European-ancestry cohort23, we have investigated a cohort of MDD cases (n = 203) and controls (n = 196) of Los Angeles Mexican-Americans. They were mostly recent immigrants born in Mexico and experienced high levels of hyperactivation of the hypothalamic-pituitary-adrenal axis related to distress, challenges, and acculturation issues caused by immigration. MDD were diagnosed using the Structured Clinical Interview for DSM-IV  . Subjects met the diagnostic criteria for current, unipolar major depressive episode, attended a pharmacogenetic study on antidepressant treatment, and had an initial 21-Item Hamilton Depression Rating Scale (HAM-D21 for abbreviation) score of ≥18 with item number 1 (depressed mood) rated ≥2. Controls responded that they were in good health and replied questionnaires about acculturation. But they were not screened for medical illnesses and did not respond to structured psychiatric interviews. The controls were also Mexican-American and recruited from the same community in Los Angeles. The control group have similar sex ratio and age distribution (mean and standard error) to the MDD group . The research was approved by the Institutional Review Boards of the University of California Los Angeles and University of Miami, USA, and by the Human Research Ethics Committees of the Australian National University and Bellbery Ltd, Australia.In our previous workn = 473) and controls (n = 497), which was used for replication in our previous study23. In this cohort, the MDD group also have similar sex ratio and age distribution (mean and standard error) to the control group  was used to ascertain that healthy controls were free from lifetime history of psychiatric disorders; for this cohort, we also used DSM-IV criteria and HAM-D21 for the main diagnostic of MDD and mood assessment. The study on this cohort was approved by Human Research Ethics Committee protocols at the University of Münster, Germany, and University of Adelaide and Flinders University, South Australia, Australia.We also included the European-ancestry cohort of MDD cases  and >99% . Cohen’s h suggests 0.2, 0.5, and 0.8 represent small, medium, and large effect sizes, respectively; thus, it indicates that our study had enough power to detect medium to large effect sizes for current association tests.Our previous power analysis on the same cohortswww.agrf.org.au) using the Illumina HumanExome BeadChip-12v1_A, in which exonic content consists of >250,000 markers representing diverse populations and a range of common conditions. All the human samples passed the Illumina expected SNP calling rate (>99%). Then we filtered the raw whole-exome SNPs by a pipeline considering variant call rate, allele numbers, and Hardy–Weinberg equilibrium deviations. For this follow-up study, we analyzed 83,898 SNPs for the Mexican-American cohort and 121,174 SNPs for the European-ancestry cohort, which remained after quality control (QC) and filtering out criteria. Detailed QC and filtering analyses have been well reported in our previous work23.The two cohorts were genotyped by the Australian Genome Research Facility  in the HumanExome BeadChip, because this chip has been designed to concentrate on rare variants rather than common ones30, which provides a powerful tool for population stratification based on pairwise identity-by-state (IBS) distance and multi-dimensional scaling (MDS) plots, was used here.We then used the four classes of SNPs  to check population stratifications of the two cohorts. PLINK software31, a natural distance without assuming any model mutation/substitution rate, to investigate the genetic distance between two individuals based on a set of SNPs.In this study, we use Hamming distanceS be an SNP set which contains n SNPs. We use k . Thus, S = {X and Y are two individuals who have their own genotypes on this SNP set S, namely and respectively, SX and SY . Let SX be {SY be {X and Y is defined asS. Considering the size of the SNP set, we can also get the normalized Hamming distance asLet X, Y, and Z show their genotypes on an SNP set of eight SNPs. The Hamming distance between X and Y is 7 . Similarly, the Hamming distance between Y and Z is 3, and the Hamming distance between X and Z is 5. Our hypothesis was that if two individuals have closer Hamming distance in this way, then those two individuals would have closer genetic distance and more similar phenotypes such as diseases or traits. We assume that Y and Z have more similar phenotypes in the above example.Take Table 32 can be used to observe the distance relationships among those individuals in a two-dimensional graph. The display of scatters representing individuals which shows separating variability between MDD cases and healthy controls can reveal interesting genetic information hidden in the SNP sets. Then for statistical analysis, Hotelling’s T2 test for two independent samples is used to examine whether the means of the two groups (case and control) are equal.Given a group of individuals, we can compute their Hamming distance matrix based on a specific SNP set such as common, low-frequency, or rare-variant set. After obtaining the distance matrix between all pairs of individuals, MDS approachwww.r-project.org) of data analysis for this study can be accessed from the authors.The codes , it is now possible to collect most or even all low-frequency and rare genetic variants in large samples and test their roles in human disease risk37. Thus, future work would be needed to further investigate our methodology on much larger SNP sets.Genetic factors play important roles in the susceptibility to major depression, as indicated by family, twin, and adoption studies38 considers mutation rates and is designed as a measure of genetic divergence between populations within a species. Therefore, it is not appropriate to examine the genetic variation associated with a complex disorder within a human population, namely Mexican-American. In this work, Hamming distance was used to investigate the genetic distance between two individuals based on their SNP sets. As sequencing costs are currently dropping further, we may examine single-nucleotide variants (SNVs) which involve much more individual genetic information. For example, we recently proposed a new concept of SNV proportion in genes and employed it to develop a predictive approach for major depression39. Using similar classification and cluster analysis methods, a potential tool for MDD diagnosis could also be constructed based on Hamming distance and low-frequency/rare SNPs.The traditional genetic distanceUsing our methodology with a case–control study, we could examine the effect of a group of variants within a specific range of MAF. The design and methodology we have developed can also be extended to other complex disorders. Our approach based on exome genotyping array or sequencing data may reveal the “missing heritability” resulted from allele frequency for many complex diseases.34. Our Mexican-American cohort is comprised of first-generation individuals (60%) who have experienced significant levels of stress and hyperactivation of the hypothalamic-pituitary-adrenal axis related to acculturation issues41. In contrast to the European-ancestry cohort, the significant stressful life events for Mexican-Americans could cause much higher levels of depression. Therefore, the depression effect differences between case and control in two cohorts may be large. Further studies using our methodology could be tested on a larger size of European-ancestry sample. Second, the European-ancestry cohort that we studied have much lower levels of genetic variants. In our previous work23, whole-genome sequencing analyses of a subset of the two cohorts revealed that European-ancestry subjects have a significantly reduced (around 50%) number of SNVs compared with Mexican-American subjects. For this reason, the roles of low-frequency and rare variants may vary across populations.Our European-ancestry cohort failed to show significant separations of cases and controls in the MDS planes on Hamming distance. The reasons could be as follows. First, MDD is clearly a gene–environment interaction disorderSupplementary Information(PDF 263 kb)