Sudden death syndrome (SDS) is a serious threat to soybean production that can be managed with host plant resistance. To dissect the genetic architecture of quantitative resistance to the disease in soybean, two independent association panels of elite soybean cultivars, consisting of 392 and 300 unique accessions, respectively, were evaluated for SDS resistance in multiple environments and years. The two association panels were genotyped with 52,041 and 5,361 single nucleotide polymorphisms (SNPs), respectively. Genome-wide association mapping was carried out using a mixed linear model that accounted for population structure and cryptic relatedness.GmRLK18-1) in locus Rfs2. An additional variant independently associated with SDS resistance was also found in this genomic region. Other peaks were within, or close to, sequences annotated as homologous to genes previously shown to be involved in plant disease resistance. The identified loci explained an average of 54.5% of the phenotypic variance measured by different disease assessment criteria.A total of 20 loci underlying SDS resistance were identified in the two independent studies, including 7 loci localized in previously mapped QTL intervals and 13 novel loci. One strong peak of association on chromosome 18, associated with all disease assessment criteria across the two panels, spanned a physical region of 1.2 Mb around a previously cloned SDS resistance gene  contains supplementary material, which is available to authorized users. Glycine max (L.) Merr.], caused by the soil-borne fungal pathogen Fusarium virguliforme[Sudden death syndrome (SDS) of soybean [uliforme, is a couliforme. The funuliforme, 4. In ruliforme. In the uliforme.GmRLK18-1) has been tagged and cloned [Rfs2). Association mapping, which exploits historical recombination events at the population level, has become a powerful alternative to linkage mapping in the dissection of complex trait variation at the sequence level [Arabidopsis[Host plant resistance is believed to be the most effective control measure for SDS . Since nd cloned . This gece level . A more bidopsis, rice [1bidopsis and maizbidopsis, 16. Thebidopsis, 18. To bidopsis, 20. A pbidopsis. Therefobidopsis. TherefoThe goal of this study was to investigate the genetic architecture of soybean SDS resistance in released elite soybean cultivars. Here we present the first experimental results of GWA mapping for SDS, across two independent panels of elite soybean cultivars, using a high-density customized oligonucleotide genotyping array. We detected 20 QTLs including known candidate genes (or QTLs) as well as new candidate loci in the soybean genome. The identification of these loci will increase our understanding of mechanisms underlying SDS resistance, and provide valuable markers for breeding soybean lines with SDS resistance.G. max advanced breeding lines (association panel P2) developed by public breeders. The germplasm was chosen to represent a range of materials developed for the U.S. North Central soybean production area. Further information about the P1 and P2 panels is given in Additional file Two independent experiments were conducted in this study. Experiment 1 was done with a mapping population of 392 diverse soybean cultivars (association panel P1), consisting of 251 varieties released between 2010 and 2012 and 141 advanced breeding lines from Michigan State University. Experiment 2 used a set of 300 diverse Soybean genomic DNA was extracted from young leaf tissue following the previously described method . All theunpublished). The SDS disease index  was calculated as DI × DS/9. In panel P1, mean values of DI, DS and DX across replicates and years were used in association analysis throughout the study. The trait distribution for DX and DI was slightly skewed towards susceptible, thus a square root transformation was used to normalize the trait distribution prior to further analysis. The association panel P2 was phenotyped in multiple environments; best linear unbiased predictors (BLUPs) were used for the overall association analysis in panel P2. The BLUPs for each line were calculated with the R package, lme4, using the equation Yijk = Linek + Environmenti + Replicate (Environment)ij + (Line × Environment)ik + ϵijk, where Yijk is the observed phenotype for the kth line in the jth replicate of the ith environment; Linek is the random effect of the kth line; Environmenti is the random effect of the ith environment; Replicate (Environment)ij is the random effect of the jth replicate in the ith environment; (Environment × Line)ik is the random interaction effect of the ith environment and the kth line, and ϵijk is the error term. Analysis of variance (ANOVA) for the phenotypic data was performed with the R package, lm(stats) and anova.lm(stats). The heritability estimates were calculated using variance components obtained by ANOVA [The association panel P1 was evaluated for SDS resistance in a naturally infested SDS disease nursery at Decatur, Michigan during the growing season (May-October) in 2011 and 2012, where consistent, natural and heavy SDS disease symptoms was observed on susceptible checks. Four replications per year were grown in a lattice design with four-row plots 6 meters long. The association panel P2 was divided into four groups based on the maturity group I to IV, and were evaluated for SDS resistance during the summers of 2011 and 2012 in 14 locations including Michigan (Decatur), Iowa (Kanawha and Ames), Minnesota (Waseca and Rosemount), Illinois , Missouri (Sikeston) and Ontario, Canada (Harrow). Four replications per year were grown in a lattice design. SDS was evaluated by scoring disease incidence (DI) and disease severity (DS) at the R6 growth stage, the stage at which pods contain full-size green beans at one of the four uppermost nodes with a completely unrolled leaf . SDS leaby ANOVA .r2) for estimating the degree of LD between pair-wise SNPs  was calculated using the software TASSEL 4.0 [r2) dropped to half its maximum value.Principal components analysis and neighbor-joining (NJ) trees were applied to infer population stratification. A pairwise distance matrix derived from the Nei’s genetic distance for all polymorphic SNPs was calculated to construct Neighbor-joining trees under PowerMarker version 3.25 . Principy = Xα + e and y = Xα + Pβ + Kμ + e, respectively, where y is the vector of phenotypic observations, α is the vector of SNP effects, β is the vector of population structure effects, μ is the vector of kinship background effects, e is the vector of residual effects, P is the PCA matrix relating y to β, X and K are incidence matrices of 1 s and 0 s relating y to α and μ, respectively [P matrix for population structure correction in the two panels. Analyses were performed by the software TASSEL 4.0 which implemented the EMMA and P3D algorithms to reduce computing time [P-value threshold of 10-4, corresponding to an adjustment for 500 independent tests across the region examined, was used to declare statistical significance at secondary signals.Two different models were used to test associations between the SNPs (MAF >5%) and disease assessment criteria. The first model was a simple model where a general linear model (GLM), containing only the SNP tested as a fixed effect, was used to test the association between the SNP and the disease assessment criteria. The second model is a mixed linear model (MLM) where, in addition to the SNP being tested, PCA matrix and relative kinship matrix were included as fixed and random effects, respectively. The GLM and MLM can be expressed as ectively . Top sixing time . False dTwo independent association panels P1 and P2) were genotyped using Illumina BeadChip containing 52,041 and 5,361 SNPs, respectively. SNPs with MAF of <0.05 and call rate <80% were excluded from further analyses to avoid problems of spurious LD. Final sets of 30,345 and 4,297 high-performing SNPs were used for all analyses. Among these SNPs, samples had an average call rate of >96.5% and between technical replicates yielded >99% pairwise concordances. From these SNPs, we observed an average nucleotide diversity (polymorphism information content or PIC) of 0.281 and 0.284 in panels P1 and P2, respectively. Compared with a previous study , these e and P2 wIn both association panels, we observed abundant phenotypic variation in SDS resistance measured by disease incidence (DI), disease severity (DS) and calculated disease index . Given that our average inter-marker distance (density) is 35 kb and 241 kb for the panels P1 and the P2 respectively, we expect to have reasonable power to identify common large effect variants associated with SDS resistance in both association panels. Overall LD decay distance in panel P1 was smaller than that in the panel P2  between the two grouping factors. Thus the photoperiod response might have driven genetic differentiation among the tested accessions in both panels [see Additional file FST, was estimated at 0.168 among the four subgroups of panel P1, suggesting a moderate level of differentiation within panel P1 [see Additional file FST = 0.135) but still similar to that between different soybean landraces (FST = 0.130) [According to the NJ tree analysis as well as PCA, association panel P1 had 4 genetic subgroups Figure a and b, = 0.130) .Figure 2P-values from the simple model, which did not include population structure (Q) and familial relatedness (K), departed from the expected distribution under a model of no association with significant inflation of nominal P-values. While the MLM method, which includes Q and K, allowed us to reduce the excess low P-values for DS, DI and DX , we successfully identified both known associations (candidate genes or QTLs previously reported in soybean), as well as new candidate loci in the soybean genome. The results of significant SNPs discovered in both association panels are summarized in Additional file X Figure d and h. Of the 52,041 SNPs evaluated in association panel P1, 30, 48 and 56 SNPs were significant, with FDR ≤ 0.05 for DS, DI and DX, respectively  was not only located outside of, but also belonged to a different LD block than the three Rhg1 genes  that increases the expression of a set of dissimilar genes. Alternatively, there exist other gene/s mediating SDS resistance that are closely linked with Rhg1. In fact, we found that the peak SNP (GM18-1709751) was located at approximately 2.2 kb upstream of GmRLK18-1, a gene that encodes a receptor-like kinase, and its resistance allele is sufficient to confer nearly complete resistance to both root and leaf symptoms of SDS [P-value of 1.2 × 10-8 for DX) located within an exon of GmRLK18-1. These results support previous studies with regard to the key role of GmRLK18-1. However, we cannot exclude the possibility that structural variation in the form of CNV may have functional importance and thus contribute to SDS resistance that is not captured by our SNPs.Notably, one of the overlaps is the QTL n by SCN . Previouiotropic . In thise Figure , 11, 12 e Figure , 11, 12.Rfs2/Rhg1 locus by conditioning on the peak SNP (GM18-1709751) at the Rfs2/Rhg1 loci. At 519 kb downstream of Rfs2/Rhg1, we found an independently associated SNP variant, Gm18-2228646 with P-value of 6.5 × 10-5 in the conditional model, as measured by DS. This SNP is located at 44 bp downstream of a gene encoding an aquaporin transporter  on chromosome 7 in panel P1. The Rzd locus contributed to SCN resistance and was strictly co-inherited in phase with Rfs2/Rhg1 in an earlier study [-5 vs 7.54 × 10-4) and explained phenotypic variance (8.14% vs 7.06%). However, some of them explain as much as, or even more phenotypic variance than that of known QTLs Additional file 1:SNPs Distribution of each chromosome on SoySNP 50 k (a) and SoySNP 6 k (b)BeadChip used in genotyping for panel P1 and P2, respectively. This figure is a color index showing the SNP distribution and density of 20 chromosomes on SoySNP 50 k (a) and SoySNP 6 k (b) BeadChip. (DOCX 79 KB)Additional file 2:Scale used for phenotyping sudden death syndrome disease severity (DS). Disease incidence (DI) is the percentage of plants in the plot showing leaf symptoms. Disease index (DX) = (DI × DS)/9. (DOCX 156 KB)Additional file 3:Kinship value between individual accessions among panels P1 (a) and P2 (b). Individuals are ordered according to their order listed in Additional file Additional file 4:The distributions of minor allele frequencies in P1 (a) and P2 (b) association panels. Two histograms, a for panel P1 and b for panel P2, showing the distributions of minor allele frequencies in two association panels. (DOCX 92 KB)Additional file 5:Phenotypic variation, heritability and correlation analysis in the two association panels. Descriptive statistics information, including mean, range, standard deviation, source of variation and correlation coefficient for DS, DI and DX. (DOCX 17 KB)Additional file 6:Distribution of accessions in each subgroup based on genetic distance in panel P1 and P2. Two-way classification of all accessions, with SNP-data-based clustering (NJ tree) at the top and the maturity-group-based grouping clusters at the left. (DOCX 15 KB)Additional file 7:The population differentiation statistics (FST) among subpopulation in panel P1 and P2. Pairwise population differentiation index (Fst) as well as corresponding significant levels are list in this table. (DOCX 16 KB)Additional file 8:Associations (FDR < 0.05) identified by GWA mapping for DS in association panel P1. Information of significantly associated SNPs, including name, physical position and phenotypic variation explained by the SNP, is reported in this table. (DOCX 17 KB)Additional file 9:Associations (FDR < 0.05) identified by GWAS for DI in association panel P1. Information of significantly associated SNPs, including name, physical position and phenotypic variation explained by the SNP, is reported in this table. (DOCX 19 KB)Additional file 10:Associations (FDR < 0.05) identified by GWAS for DX in association panel P1. Information of significantly associated SNPs, including name, physical position and phenotypic variation explained by the SNP, is reported in this table. (DOCX 20 KB)Additional file 11:Associations (FDR < 0.05) identified by GWAS in association panel P2. Information of significantly associated SNPs, including name, physical position and phenotypic variation explained by the SNP, are reported in this table. (DOCX 18 KB)Additional file 12:Genome-wide association study of DS in the two association panels. (a) Manhattan plots of the simple model for DS in association panel P1. The - log10 P values from a genome-wide scan are plotted against the position on each of the 20 chromosomes. The horizontal red line indicates the genome-wide significance threshold (FDR < 0.05). (b) Quantile-quantile plot of simple model for DS in the association panel P1. (c) Manhattan plots of MLM for DX in association panel P2, as in a. (d) Quantile-quantile plot of MLM for DS in the panel P1. (e) Manhattan plots of the simple model for DS in association panel P2, as in a. (f) Quantile-quantile plot of simple model for DS in the panel P2. (g) Manhattan plots of MLM for DS in the panel P1, as in a. (h) Quantile-quantile plot of MLM for DS in association panel P2. (DOCX 965 KB)Additional file 13:Genome-wide association study of DI in the two association panels. (a) Manhattan plots of the simple model for DI in association panel P1. The - log10 P values from a genome-wide scan are plotted against the position on each of the 20 chromosomes. The horizontal red line indicates the genome-wide significance threshold (FDR < 0.05). (b) Quantile-quantile plot of simple model for DI in the association panel P1. (c) Manhattan plots of MLM for DX in association panel P1, as in a. (d) Quantile-quantile plot of MLM for DI in association panel P1. (e) Manhattan plots of the simple model for DI in association panel P2, as in a. (f) Quantile-quantile plot of simple model for DI in the association panel P2. (g) Manhattan plots of MLM for DI in association panel P2, as in a. (h) Quantile-quantile plot of MLM for DI in association panel P2. (DOCX 1 MB)Additional file 14:Regional plots showing association mapping results for SNPs located aroundRfs2/Rhg1on chromosome 18. Negative log10-transformed P-values from the MLM are plotted on the left vertical axis for association panel P1; Negative log10-transformed P-values from the MLM are plotted on the right vertical axis for association panel P2. Blue horizontal dashed lines indicate the genome-wide significance threshold in association panel P1. Previously identified genes controlling the traits are labeled. (DOCX 94 KB)Additional file 15:


Diabetes mellitus (DM) is the most common chronic endocrine disorder, affecting an estimated 371 million people worldwide. It is associated with microvascular and macrovascular complications, including diabetic nephropathy (DN), a primary cause of end-stage renal disease (ESRD). Among the Gulf countries, Saudi Arabia has the highest number of people with diabetes and about 30-40% diabetic patients suffer from DN. Diabetes imposes a large economic burden on healthcare systems. Healthcare expenditures due to diabetes account for 11% of the total medical expenditures in the world in 2011. Mostly, individuals exposed to long durations of diabetes with relatively poor glycemic control develop progressive DN. However, some patients appear to be at increased risk while some will remain relatively protected. Differential disease risk in DN may be partly attributable to genetic susceptibility. A recent review identified genetic variants in the ACE and MTHFR genes to be significantly associated with type 2 diabetes in Arabs [A systematic meta-analysis was undertaken to collectively analyze all studies published till June, 2013 that investigated the association between genetic variants involved in inflammatory cytokines and angiogenesis and the development of DN. Genetic variants associated with DN were selected and analyzed using Comprehensive Meta Analysis software. Pathway analysis of the genes with variants showing significant positive association with DN was performed using Genomatix Genome Analyzer .After the inclusion and exclusion criteria for this analysis, 34 studies were included in this meta-analysis. 11 genetic variants showed significant positive association with DN in a random-effects meta-analysis. These included genetic variants within or near VEGFA, CCR5, CCL2, IL-1, MMP9, EPO, IL-8, ADIPOQ and IL-10. There are very few studies identifying genetic variants associated with DN in the Arab population. 819C/T genetic variant in IL10 showed protective effect for DN in Tunisian Arabs.We are in the process of studying the functional role of the genes showing significant positive association with DN in different pathways like signal transduction and molecular function. The functional relevance of the variants and their pathways can lead to novel biological insights and development of new therapeutic targets.


Empirical studies and evolutionary theory support a role for rare variants in the etiology of complex traits. Given this motivation and increasing affordability of whole-exome and whole-genome sequencing, methods for rare variant association have been an active area of research for the past decade. Here, we provide a survey of the current literature and developments from the Genetics Analysis Workshop 19 (GAW19) Collapsing Rare Variants working group. In particular, we present the generalized linear regression framework and associated score statistic for the 2 major types of methods: burden and variance components methods. We further show that by simply modifying weights within these frameworks we arrive at many of the popular existing methods, for example, the cohort allelic sums test and sequence kernel association test. Meta-analysis techniques are also described. Next, we describe the 6 contributions from the GAW19 Collapsing Rare Variants working group. These included development of new methods, such as a retrospective likelihood for family data, a method using genomic structure to compare cases and controls, a haplotype-based meta-analysis, and a permutation-based method for combining different statistical tests. In addition, one contribution compared a mega-analysis of family-based and population-based data to meta-analysis. Finally, the power of existing family-based methods for binary traits was compared. We conclude with suggestions for open research questions. Rare variants have increasingly become a focus in studies of complex traits. There are many reasons for this increasing interest. Accessibility, both in cost and technology, of next-generation sequencing has led to the discovery of a plethora of rare variants. Nelson et al.  estimateA more contentious argument for focusing genetic research on rare variants pertains to the so-called phenomenon of “missing heritability”. Genome-wide association studies (GWAS) have successfully identified numerous common variants associated with complex traits; however, the common variants tend to have relatively small effects and explain only a fraction of the overall heritability . Human hThere is emerging evidence that rare variants are involved in complex disease, including Alzheimer disease , lipids Here, we provide a summary of the current literature with respect to the association of rare variants and ways to increase the power of these tests. We then provide results from the Collapsing Rare Variants Working Group of Genetic Analysis Workshop 19 (GAW19) and conclude with recommendations and open problems.Although there is no formal definition for a rare variant, variants with a MAF between 5 % and 50 % are generally considered common. Variants with a MAF in the range of 1 % to 5 %  or 0.5 %The 2 major types of methods for collapsing rare variants within a meaningful genetic region, such as a gene, consist of burden tests and variance component tests. Burden tests measure the burden of variants within a genetic region and range from a simple indicator of whether a genetic region contains at least 1 rare variant , to a sA general formula for the burden of rare variants within a region is shown in Eq. .1\documeGi,m is the genotype coding based on a genetic model  for individual i and variant m, wm is the weight for variant m, and M is the total number of variants in the region. This model is applicable to nearly all burden tests mentioned above. For example, the CAST is often written in terms of an indicator function such as Gi,m is either 1 or 0, depending on whether the subject has or does not have variant m, respectively, and wm = 1. However, we can use Equation (1) directly by making Bi is 1 for subjects who have at least 1 variant in the gene and 0 otherwise. For a simple count of the number of variants within a region, a weight of 1 for each variant is used. Others have proposed more informative weights. Madsen and Browning ) with meta-analysis resulting in discernibly higher power compared to mega-analysis for only one of the top genes (TNN with both SBP and diastolic blood pressure [DBP]). However, when the power to detect association to a gene-region was considerably lower within the family-based sample versus the population-based sample, the power of the mega-analysis was much lower than the analysis based on the population-based sample alone, while the meta-analysis had a less-severe power loss. This suggests that the mega-analysis may be better when there is sufficient power to detect an association in both samples, but a meta-analysis might be more suited to situations where one study is underpowered and/or there is heterogeneity in the genetic associations between study samples. Both meta-analysis and mega-analysis indicated elevated type 1 error with estimates based on the 200 simulated data sets of the null trait Q1 ranging from 0.055 to 0.130 and 0.050 to 0.135, respectively, for the 23 genes.Katsumata and Fardo  applied Wang et al.  also conFinally, Wang et al.  comparedOver the last 10 years, there has been considerable methods development for association tests of rare variants. Tests have been proposed that are ideal for unidirectional and bidirectional effects, as well as an optimized combination of the 2 types of effects. Methods have been proposed for binary as well as normally distributed traits, for population-based and family studies. Most tests allow for the use of different weighting schemes , and meta-analysis procedures have also been developed.Contributions to the GAW19 Collapsing Rare Variants group expanded upon the literature in several ways. Green et al.  providedNew statistical tests were developed to allow for a retrospective likelihood based on optimized variant weights  and to iKatsumata and Fardo  providedFinally, Wang et al.  comparedAlthough much work has been done, there are still many open research areas pertaining to the analysis of rare variants. We mention a number of these areas; however, this list is by no means exhaustive. For example, great care has been taken in studies of common variants to control for population substructure. These have included the use of genetic principal components, genomic matching and linear mixed models; see, eg, the review by Price et al. . Given rIt often makes sense to focus on the gene region as the unit for collapsing methods, especially given analyses within the coding regions of the genome. However, GWAS associations are often in intergenic regions, and there is building evidence that much of the noncoding region of the genome is indeed functional , 90. ThuAs we have detailed, methods exist for incorporating genomic annotation as weights in region based methods. The choice of the best weight and, in fact, which information to consider at all, remain somewhat open questions. Currently, MAF, functionality, consequence, evolutionary conservation and many other metrics can be used as weights and the list continues to grow, especially in non-coding regions as functional research continues at a rapid pace. Thus, there is a need to further develop efficient methods of deriving the most appropriate weight. This can be done to some extent through the adaptive methods discussed previously. However, the adaptive methods, which often rely on permutation, may become computationally infeasible given the increasing amount of information on which to weight, increasing sample size, and analysis on the entire genome. Thus, there will continue to be a need for computationally efficient methods of determining the weights while retaining the appropriate type 1 error.To date, most collapsing of rare variants is done on a contiguous region of the genome, whether it is a gene or a genomic window. Alternative approaches include the use of pathways or gene sets developed, for example, from expression studies or protein-protein interaction studies. Recent studies have found some success with this approach , but morFinally, given the continued struggle to adequately power studies of rare variants, more work is needed on ways to improve power. One approach is to continually increase the sample size of the studies, perhaps through including publically available population controls. Another, perhaps more feasible approach may require refocusing the phenotype through use of multidimensional phenotypes or homogeneous subphenotypes.Given this relatively brief discussion of remaining areas of research for the association of rare variants, there is little doubt that this will continue to be an active area of research for several more years.


Hordeum vulgare L.). It requires elaborate, expensive phenotyping, which involves micro-malting experiments. Although there is abundant historical information available for different cultivars in different years and trials, that historical information is not often used in genetic analyses. This study aimed to exploit historical records to assist in identifying genomic regions that affect malting and kernel quality traits in barley. This genome-wide association study utilized information on grain yield and 18 quality traits accumulated over 25 years on 174 European spring and winter barley cultivars combined with diversity array technology markers. Marker-trait associations were tested with a mixed linear model. This model took into account the genetic relatedness between cultivars based on principal components scores obtained from marker information. We detected 140 marker-trait associations. Some of these associations confirmed previously known quantitative trait loci for malting quality . Other associations were reported for the first time in this study. The genetic correlations between traits are discussed in relation to the chromosomal regions associated with the different traits. This approach is expected to be particularly useful when designing strategies for multiple trait improvements.Malting quality is an important trait in breeding barley ( Hordeum vulgare L.) is a major cereal crop in Europe. It ranks fourth in worldwide production, after wheat, rice, and maize. It is grown for feed, food, and malting. Most of the malt produced is used for brewing beer and, to a lesser extent, for distilling . In Europe, two-rowed spring cultivars are used mainly for malting and brewing; six-rowed winter barleys are predominantly used for food. However, six-rowed barley has been increasingly used for malting in Europe, following the trend started in the US. Therefore, depending on the end-use, there are two primary aims in breeding barley: 1) superior food and feed quality with high protein content, and 2) high malting quality with high starch and low protein contents. Improving the malting quality is a central goal in breeding, in addition to improving the yield of barley. Malting quality is a complex trait, because it consists of several components, and all are polygenic. Moreover, the definition of high malting quality is not straightforward; it depends on the processing and brewing methods. In general, the main breeding goals for malting barley are high malting extract, low protein content, good solubility properties, good kernel formation, and low glume content.Barley , which is often not feasible in the early generations of a breeding cycle. In addition, some malting quality parameters can only be determined in time-consuming, wet lab analyses. These limitations may be overcome with the use of historical phenotypic data recorded in statistical year books, like those from the Deutsche Braugerstengemeinschaft or the European Brewery Association. These resources may provide a cost-effective approach. The complex dataset considered in the present study may serve as a valid resource for breeding barley varieties with high malting quality.In addition, the identification of marker-trait associations (MTAs) may represent a cost effective strategy for selecting traits that are typically expensive to identify in MAS schemes Association mapping, also known as linkage disequilibrium (LD) mapping, represents an interesting alternative to traditional linkage analysis. It provides the advantages of (i) wider genomic diversity than provided by biparental segregating populations, (ii) high mapping resolution, by exploiting historical recombinations in the population, and (iii) rapid results, because it is not necessary to create a segregating population Two association mapping methodologies are widely used in plants. The first is a candidate gene approach, which relates polymorphisms in candidate genes to phenotypic variations in traits. The second approach is a genome-wide association study (GWAS), which relates polymorphisms of anonymous markers to trait variations Hordeum included a GWAS conducted to detect yield-associated genes DArT markers are bi-allelic, dominant markers. A single DArT assay can genotype thousands of SNPs and insertions/deletions across the genome simultaneously. Barley was one of the first plant species for which DArT markers became available An important issue in GWAS is that the population structure which arises from heterogeneous genetic relatedness between entries in the association panel can cause high LDs between unlinked loci i) genotyping the germplasm with DArT markerDArT markers, (ii) investigating the degree of intrachromosomal LD decay within this barley collection, and (iii) performing a GWAS with a mixed linear model approach.This study aimed to identify chromosomal regions that influenced kernel and malting quality parameters in barley, based on a diverse set of cultivars and historical phenotypic data. The approach included  for grain yield, eight kernel traits, and ten malting quality traits for each cultivar . InspectThe correlations among all considered traits are shown in The original set of 1088 DArT markers was reduced to 839, because we discarded monomorphic markers, markers with rare alleles , and markers missing more than 10% of the values. The marker map showed a high genomic coverage, with a density of about one marker in every 5 cM for most genomic regions, except for chromosome 4H, which showed some inter-marker distances larger than 15 cM .a priori classifications would suggest. These included the 2-row spring varieties “Fergie” and “Phantom”, which were located in the 2- and 6-row winter pools, respectively; the 2-row winter variety “Cordoba”, which was grouped with the 6-row winter types; the 6-row winter variety “Tilia”, which was located in the 2-row spring pool; and the 2-row spring variety “Stella”, which appeared isolated between the 2-row spring and the 6-row winter pools. These results were consistent with results observed in other barley studies The first three principal components were found to be significant factors by the eigen analysis, and they cumulatively explained 28% of the total variation . The plot of the first two principal components showed a clear division of the germplasm into three subpools, which largely coincided with the row number and seasonal habit  and their map distances, in cM , the markers were, on average, in LD up to a distance of 10 cM.After correcting for population structure, the intrachromosomal LD was studied in all seven barley chromosomes by inspecting the plot of the associations between linked markers  of MTAs. We used significance thresholds of -log10(P) >3.65 and –log10(P) >3.35, based on the strict (r2 = 0.20) and more liberal (r2 = 0.10) thresholds for LD, respectively. Cumulative p-values obtained by the naïve model and the MLM with correction for population structure and kinship by PCA were compared  >3.35, we identified 140 MTAs. With a more strict threshold of −log10(P) >3.65, we found 101 significant MTAs. These numbers are remarkably large, considering the relatively low sample size of this study (2) of the traits and the number of detected MTAs. More MTAs were found for high-heritability traits than for low-heritability traits. For example, kernel formation (KF) and glume fineness (GF) had nearly the highest h2 values , and color on chromosomal regions of 6H that had not been identified before as QTLs. Furthermore, new MTAs were detected for K_RP, extract, KF, and GF, on chromosome 2H; for TGW, friability, and MQI on chromosome 5H; and for viscosity and MQI on chromosome 7H. Overall, the DArT markers located in previously described QTLs and the MTAs that we found were generally associated with the same or similar characteristics . For exavs. European material). A similar explanation can account for differences in GWAS that mapped GY in landraces cultivated in the high- and low-yield environments of Spain and Syria, respectively It was not always straightforward to make comparisons with other known QTLs reported in the literature, because different studies used different reference maps, marker types, germplasms, experimental sites, and trait measuring protocols Genetic correlations among traits typically result from either pleiotropic or tightly-linked QTLs. In the present study, we found many MTAs for different traits that co-localized to a single chromosomal region. This co-localization may drive genetic correlations among barley quality traits. In particular, we found clusters of MTAs for malting quality traits on chromosomes 1H, 2H, and 5H, which pointed to hot spots for barley quality. This was consistent with findings from Szücs et al. 2009 In general, our findings were consistent with the literature and corresponded well with the observed correlations among traits Some traits, like MQI, friability, and extract are genetically correlated with each other. This correlation was reflected in our results by the finding that these traits were significantly associated with the same markers. Traits such as viscosity and friability or final attenuation, VZ45, and extract interact to define malting properties, which contribute to important phenotypic effects. It is crucial to be aware of these interactions to understand the trade-offs implied in the optimization of cultivars.For example, breeders should be aware that high grain protein concentration is associated with low levels of malt extract. High grain protein increases the likelihood of a chill haze in beer, and barleys with low grain protein concentrations are more economically efficient in the malting process The current work contributed to the understanding of the genetic basis of kernel and malting qualities in barley. The use of a broad phenotypic data collection that spanned a long time range and several locations provided a means to de-emphasize environmental effects on barley trait expression. We found that combining this historical phenotypic dataset with high-density, low-cost markers such as DArTs facilitated the discovery of new MTAs for barley. As shown previously, we found that association mapping was a powerful, promising approach for dissecting the complexities of malting and brewing qualities in barley. In addition to confirming various known QTLs, we identified some new MTAs; e.g., markers for MQI and viscosity. The MTAs identified in this study will be useful for selecting favorable genotypes in this germplasm that can be used to develop improved barley varieties. The findings of this study should be validated in future field experiments. Our research demonstrated the advantage of combining more than 20 years of expensive phenotyping information with high-density, low-cost marker technology.http://www.braugerstengemeinschaft.de). The historical data were collected from 1985 to 2007 and stored in a database called “MetaBrew” A set of 174 European barley cultivars that included 85 two-rowed spring and 89 winter types (57 two-rowed and 32 six-rowed) were included in this study. Historical phenotypic data were available for all 174 cultivars in the annual statistical reports from the German Brewery Association , as described previously thi cultivar, in the thj year, and the thk replicate (location) nested within year j; µ is an intercept; iG is the fixed cultivar effect; Our objective was to concentrate on major, stable MTAs. Therefore, we calculated cultivar-adjusted means over locations and years for each of the traits. Prior to the analysis, traits that were expressed in percentages were log-transformed, such as sieve fraction (SF) and raw protein in kernel or malt . The following mixed model was used to estimate adjusted cultivar means (random terms underlined):ent GWAS .m, n) in the same linkage group was assessed with the following statistical model:in are the scores of markers, m and n, of genotype i ; ipS denotes the scores of the first p principal components from an eigenanalysis , as described in 2r associated with the 2r values and the other was more liberal, based on the upper 80% quantile of the observed 2r values. To assess how far the LD extended on a particular chromosome, we used the intersection of the threshold 2r with a 95% quantile non-linear regression line fitted to the observed 2r values on the particular chromosome. The non-linear quantile regression fitting was based on the method of Koenker & D’Orey According to previous studies cl is the length in cM of chromosome c, and dc is the extension of LD for chromosome c, and calculated the P-value significance threshold, as follows  where; *P is the genome-wide threshold level (set as 0.05 in our study):In turn, the LD-decay information for each chromosome was used to define a multiple testing correction threshold for the GWAS, as described previously GWAS was performed with models that accounted for the genetic relatedness between varieties. Genetic relatedness was expressed in several alternate ways, including the realized kinship (model K), a group factor, based on population structure (fixed or random group models), or a set of individual principal components scores that served as fixed or random covariables in the model . We also used a model that did not include a correction for genetic relatedness (naïve model).µ is a constant (intercept); ix is a marker covariate with values −1 or 1 to denote one of the two homozygous marker genotypes; α is the marker effect; psi denotes the scores of the first p principal components, and In the models above, P-values on a –log10 scale. The performances of the different models were compared by their inflation factors. We focused the discussion of significant MTAs on results from the model that performed best (fixed PCA score model).The significance of each MTA was assessed with the Wald test, and results are expressed with the associated All models were fitted with GenStat version 16 http://www.graingenes.org) and from Barley World (http://www.barleyworld.org/) were compared to the reference DArT map created previously For comparisons between significantly-associated DArT markers and known QTLs, the marker and chromosome position information from GrainGenes Click here for additional data file.Figure S2Chromosomal distribution of the 839 DArT markerDArT markers used for the genome-wide association analysis. Distances are given in cM.(TIF)Click here for additional data file.Figure S3Intrachromosomal LD-decay between all pairs of DArT markers shown for each barley chromosome, 1H to 7H, after correcting for population structure.(TIF)Click here for additional data file.Figure S4Comparison of P-values obtained by applying the naïve model (blue line) and the mixed linear model (MLM). The MLM incorporates corrections for population structure and kinship, based on PCA scores (red line). The comparison permits a check of the quality of the association results depicted for four traits (a) Grain yield (GY), (b) hectoliter weight (HLW), (c) kernel formation (KF), and (d) thousand grain weight (TGW).(ZIP)Click here for additional data file.Figure S5Manhattan plots show GWAS results from the PCA-corrected model for all kernel and malting parameters. GY = grain yield, MY = marketable yield, TGW = thousand grain weight, HLW = hectoliter weight, KF = kernel formation, GF = glume fineness, SF = sieve fraction, K_RP = raw kernel protein content, M_RP = raw malt protein content, solN = soluble nitrogen, solP = soluble protein, Visc = viscosity, Col = color, Fria = friability, VZ45 = saccharification number VZ45°C, Extr = malt extract, FiAt = final attenuation, MQI = malting quality index.(ZIP)Click here for additional data file.Table S1Summary of phenotypic parameters for all 174 cultivars. BLUES and means are shown for the breeder, origin, seasonal habit (SH), including spring (S) or winter (W), row number (RN), and phenotypic traits. Abbreviations are: BLUES = best linear unbiased estimators, GY = grain yield, MY = marketable yield, TGW = thousand grain weight, HLW = hectoliter weight, KF = kernel formation, GF = glume fineness, SF = sieve fraction, K_RP = raw kernel protein content, M_RP = raw malt protein content, solN = soluble nitrogen, solP = soluble protein, Visc = viscosity, Col = color, Fria = friability, VZ45 = saccharification number VZ45°C, Extr = malt extract, FiAt = final attenuation, MQI = malting quality index; Sheet 1: The average BLUES (best linear unbiased estimators) for the nine kernel traits and ten malting quality traits considered in this genome-wide association study. Each cultivar represents the average of several accessions of the same variety. Sheet 2: Individual accessions for each cultivar variety; the location of each accession is given with the BLUES (best linear unbiased estimators) for each trait. Sheet 3: Phenotypic means for each cultivar across all accessions. Group assignments indicate the population structures.(XLSX)Click here for additional data file.Table S2Allele frequencies for DArT markers and marker effects on 19 phenotypic parameters based on MTAs identified in the GWAS in barley. Results are shown for MTAs identified with strict (-log10(P) >3.65) or liberal (-log10(P) >3.35) significance thresholds.(XLSX)Click here for additional data file.


New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. Several factors can complicate the process of selection and recommendation of cultivars in a maize breeding program. One of the most severe factors is the potentially large number of hybrids that can be obtained from a relatively small set of lines. This number of crosses complicates the choice of the formation of combination pairs, even when well-established heterotic groups are available. Should the breeder perform all or most of these combinations, the high financial costs and operational difficulties under testing conditions with several replicates and installed at a high number of sites would render the evaluation of these hybrids financially unfeasible. For example, the combination between 100 lines of one heterotic group and 100 lines of another heterotic group allows 10,000 combinations of simple hybrids that must be evaluated at several sites and with replicates. Accordingly, the availability of tailor-made methods of prediction of the best combinations between candidate lines may be a key point in maximizing the use of financial resources in breeding programs.In recent years, the development of large-scale genotyping methods—such as Genotyping-by-sequencing (GBS), Dart Array Technology (DArT) and Restriction site Associated DNA (RAD) markers –3—the stThe progress of these molecular methods occurred concurrently with the development of statistical and computational methods capable of handling the large amount of data generated by these markers with genome-wide coverage. New regression models have recently been proposed for the analysis of phenotypic data and molecular markers in breeding programs. These models are integrally applied to selection and prediction in a process commonly known as genomic selection (GS) . The maiMultiple regression models, mixed models, ridge regression, semi-parametric methods and Bayesian methods are generally used frequently in GS . After sIn these models, GS is usually applied directly to the target trait; however, direct genomic selection may be ineffective depending on the heritability and complexity of the genetic architecture ,16. AccoIn addition to indirect selection, which may be included in multi-trait models, the inclusion of dominance effects in the model to take advantage of the heterosis phenomenon is another factor that may be used by breeders to increase predictive ability. In the context of the GBLUP model, the inclusion of dominance effects may be easily performed using the kinship matrix including dominance estimated using molecular markers without drastically changing the model size, as would occur in a regression model using markers .The proposal of a model using dominance data and multiple traits in the context of GS has not been reported in the literature thus far. Accordingly, this study aims to (i) construct single cross maize hybrids using actual phenotypic and genetic data based on maize lines while preserving the genetic architecture of the traits; (ii) propose the inclusion of dominance effects in the multivariate GBLUP (GBLUP-MV-AD) model; and (iii) compare the GBLUP-MV-AD model with the multivariate additive GBLUP (GBLUP-MV-A), univariate additive GBLUP (GBLUP-UV-A), and univariate GBLUP models with the inclusion of dominance effects (GBLUP-UV-AD) regarding the predictive ability and estimation of genetic parameters.http://www.panzea.org/). The genotypes were obtained from http://www.panzea.org/#!genotypes/cctl using the file ZeaGBSv1.0 and the NAN phenotypes were obtained from http://www.panzea.org/#!phenotypes/c1m50. The NAM population was prepared from crosses between the line B73 and 25 other inbred lines of high genetic diversity. Those crosses were planned to represent the gene pool of those lines.This study used single-nucleotide polymorphisms/genotyping by sequencing (SNPs/GBS) data and phenotyping of lines derived from the Nested Association Mapping population (NAM) of the germplasm bank of the United States Department of Agriculture-Agricultural Research Service (USDA-ARS), genetically characterized by . This isst and 25th marks, and the others were selected linearly every 25 marks until fully covering all linkage groups . Lost marks were allocated using the A.mat function, mean method of the rrBLUP package ..8]. All orted in . The mixThe one hundred largest allelic substitution effects plus 100 largest dominant effects—among the 54,000 estimated effects by Bayes B—was ordered totaling 200 potential QTNs. These effects were reparametrized using the equation in After to obtain and to reparametrize the 200 genes effects the genomic architecture was constructed based on this QTLs plus null effects distributed over 27,000 markers. Thus, this approach allowed obtaining pleiotropic and linked SNPs along the traits and producing two distinct sources of genetic correlation. The resume of all QTLs are found in the In short, each one of five traits presented the following number of QTLs (additive plus dominant effects): 186 (PH), 179 (EH); 200 (EL); 196 (ERN) and 197 KW.A) related to 4,065 lines and the two PCs were used in the biplot in order to identify the lines more related to B73 and MO17. These two lines present a well-known distinct heterotic pattern.The artificial crosses were performed using a code developed in the R program in order to obtain 400 single-cross hybrids by using of a 20x20 partial diallel crosses design. The two heterotic groups were defined by graphical analysis based on principal components . For thiE(mij)is the expectation of the genotypic value at loci j of the hybrid i from the lines λ and Ω; pλj is the favorable allelic frequency in the line λ for the loci j; pΩj is the favorable allelic frequency in the line Ω for the loci j.The genotypic state for each locus in the hybrid was defined by the expectation of the allelic contribution of each parental line. This expectation was given by:For missing markers, intermediated imputed values were observed in line’s SNP matrix, ranging from 0 to 2. Numeric interpolations were applied for missing information through the estimation of allelic frequency for missing markers.ysc is the vector (400×1) of phenotypic value related to single cross hybrids; μ is the general mean related to a specific trait; a* is the vector of allelic substitution effects (27.000×1); d* is the vector of dominance effects (27.000×1); e is the residual vector adjusted to different levels of heritability; WAe WD are the additive and dominant matrices respectively, built from the marker hybrids matrix M.The phenotypic values of hybrids for each of the five traits were built by:The phenotypic values were adjusted for different levels of heritability . In addition, we used the historic values for each trait. The historical heritability was outlined by  and was h2 is the requested broad-sense heritability and The residual vector corrected by a specific heritability was sampled from a normal  the traits PH, EH, EL, ERN, KW, in h2 of 0.3, 0.5, 0.7, 0.7, and 0.3, respectively, randomly sorted by draw; and (iii) the five traits considering the historical h2  the five traits in rical h2 . The GBLyi is the vector of phenotypic observations for trait i  of size ni×1, wherein ni is the total number of observations for trait i; Xi is the ni×1 incidence matrix of fixed effects (sample mean) for trait i; Zi is the ni×ni incidence matrix of random effects  for trait i, wherein βi is the mean of the i-th trait, and αi and δi are vectors of allele substitution effects and dominance deviations from Xi, Zi, respectively; and ei are ni×1 residual effects. Because this is an similar to individual animal model, matrix Zi is an identity matrix and is thus equal for αi and δi.The multivariate GBLUP model  includinThe GBLUP mixed y ~ N where Σ = I ⊗ V and V is a 5x5 unstructured matrix and I it as n×n matrix. The prior for random effects α and δi was assumed as normal distribution given respectively by α~N where Gα = A⊗Vα and δ~N where Gδ = D⊗Vδ and where Vα and Vδ are unstructured 5x5matrices.Thus, it was assumed that A) and dominance (D) matrices, estimated using molecular markers [pk is the frequency of the favorable allele at locus k;The additive kinship  [The matrices obtained by the Kronecker product (⊗) -s (MMEs)  accordins (MMEs) .i j for additive and dominant effect related to the global mixed models inverse C-1. The residual variance estimator is as follows:W is a resultant of columns concatenation related to{X,Z,Z}; the trace depends on the submatrices for i and j; and n is the length of the vector {j,i}.Using the EM algorithm described by , the REMZiδi was discarded when using the additive models for uni and multitrait approaches. In addition, each character i was analyzed separately in the case of the univariate model.The component A and D can both be non-positive definite; therefore, a spectral decomposition procedure was performed [-4) of decreasing magnitude throughout each diagonal element of the matrix. A positive definite matrix was obtained after the reconstruction, which characterizes the Gaussian process required for the mixed model adopted in this study.Kinship matrices erformed ,34. The g and the genetic values estimated The efficiency of the three models was assessed by three genetic statistics. One was Pearson’s correlation coefficient αi (additive model) or αi+δi (additive-dominant model).The other statistic was the heritability coefficient, estimated by:PRESS):αi and δi (computed separately for each variable) and θi the parametric value. The same measure was used for the additive and dominance variance components. All data set and codes used in this work could be accessed in https://github.com/Jhonathan-Pedroso/PlosOne_MVGBLUP_paperThe last statistic used was the predicted residual error sum of squares  between traits PH and EH was 0.5501. This high value is actually expected because tall plants usually have high EH.There was no clear difference between the models when estimating the correlations and covariance between traits. Despite the small difference between the models, the GBLUP-UV-AD model produced correlation estimates closer to the parametric values considered in the hybrid construction process. For example, the total genetic correlation between traits PH and EH was estimated at 0.5731 using the GBLUP-UV-AD model, while the parametric value was 0.5477.The variance component and PRESS values estimated using the univariate and multivariate models can be found in Marginal differences in the efficiency of the GBLUP-UV-A and GBLUP-MV-A models were detected in any heritability settings when estimating the additive genetic variance or between the GBLUP-UV-AD and GBLUP-MV-AD models when estimating the additive  enabled the exploration of a gene pool rarely found in a genetic breeding program restricted to one company or university. The high genetic variability of that population helped to estimate the genetic parameters representative of several germplasm sources. Genetic parameter scenarios based on background results were also adopted by , who anaThe main factors already reported that may affect the predictive performance during the use of multivariate models with kinship data are (i) the availability of phenotypic traits well correlated with the target trait; (ii) good genome coverage using molecular markers; (iii) low/moderate heritability of the target trait; (iv) high heritability of the traits correlated with the target; and (v) moderate/high correlation between traits ,21.Our results diverged partly from those reports. There were no improvements in predictive ability between PH and KW in the analyses using multivariate models for traits PH, EH, EL, ERN and KW fitted to 0.3, 0.5, 0.7, 0.7 and 0.3 heritability, respectively. This result was not expected because both traits have low heritability and are moderately correlated with the others.The results of the analysis of data generated in historical heritability scenarios also show no clear differences between the multivariate and univariate GBLUP models. This fact corroborates the findings reported in other studies, wherein combined analyses of traits using multivariate models tend to have a predictive performance similar to univariate models under intermediate conditions of heritability and correlation ,21,22.No advantage resulted from adopting the multivariate model under the historical scenario given the greater computational demand required by the multivariate method and the marginal difference with the univariate methods. The same result was observed by  in GS. HSituations in which all or most traits have low heritability  are amonThe correlations of total genetic effects between traits ranged from -0.014 between traits PH and EL to 0.5477 between traits PH and EH in the analysis of data from the 4,091 inbred lines from the Panzea database. A simulation study  showed tThe adoption of indirect selection in maize could be not always advantageous compared to direct selection for grain yield. Previously published studies have shown that despite the existence of traits well correlated with grain yield, these secondary traits usually do not exhibit high heritability encouraging the adoption of indirect selection to increase the selection gains, and when there is high heritability, high correlation may not occur ,37. We bThe use of multivariate models adopting mixed models was first proposed by . The proPinus taeda pedigrees alone with univariate models. The main conclusion was that molecular markers are more informative for estimating the kinship matrices of additive and non-additive effects and that they enable the orthogonal decomposition of the variance components. Those authors also argue that kinship matrices of non-additive effects designed by pedigree have serious restrictions that may compromise inference processes. The explanation is that all elements of these matrices are functions of the elements of the additive kinship matrix and, therefore, are highly correlated with the elements of other kinship matrices, thereby precluding the effective decomposition of variance components. Because of this assertion, we did not compare data on pedigrees versus molecular markers. Using only molecular markers, we concluded that both the GBLUP-UV-AD and GBLUP-MV-AD models effectively decompose the total genetic variance components into additive and dominance components, especially in low-to-moderate heritability scenarios. A previous study [p ≠ 0.05).Several applied prediction studies including non-additive effects have been conducted. Among these studies,  examinedus study  reportedAmong the available kinship matrices, we chose to use the one proposed by . This maA considerable increase in the values of correlations between parametric and simulated genetic effects was observed when including the dominance effects in the GBLUP-UV and GBLUP-MV models. This impact has already been reported in other studies using the GBLUP-UV model ,39,40,43Also, in cross-validation results, the inclusion of the dominance improved the correlation between missing and predicted values about 20% indicating its importance to predict missing values. On the other hand, there was no advantage of the multivariate over the univariate model for cross-validation data. It is worth to highlight that in cross-validation results, the accuracy threshold can be given by square root of heritability. In this sense, the accuracies from the cross-validation process were slightly lower than those obtained by true vs. predicted values. It can be questioned whether cross-validation is necessary given we know the true values. In a real situation, we do not know the true values and cross-validation might be a useful tool to evaluate the predictive model. Therefore, using the dominance effect, it was observed that the cross-validation predictions reached the heritability limits (approximately 0.75) exploring all genetic variation. Because of the greater computational demand required by the multivariate method and the marginal difference with the univariate methods, there was no advantage in adopting the multivariate model under a simulated historical scenario and using balanced data. The same result was observed by  in GS whOur strategy of constructing single-cross hybrids using real data on maize lines was effective at preserving the genetic architecture of the traits tested. There were no benefits related to predictive ability or to the quality of the decomposition of variance components in the analysis of the traits plant height, ear height, ear length, ear row number and kernel weight of maize when adopting the multivariate GBLUP compared to the univariate GBLUP. There were considerable gains in predictive ability and quality of the decomposition of variance components when dominance effects were included in both the univariate and multivariate analyses using the GBLUP model for the traits studied.S1 FigModels used: GBLUP-UV-A, GBLUP-UV-AD, GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) correlations between the parametric and estimated total genetic effects; (B) correlations between the parametric and estimated additive effects; (C) correlations between the parametric and estimated dominance effects.(TIF)Click here for additional data file.S2 FigModels used: GBLUP-UV-A, GBLUP-UV-AD, GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) correlations between the parametric and estimated total genetic effects; (B) correlations between the parametric and estimated additive effects; (C) correlations between the parametric and estimated dominance effects.(TIF)Click here for additional data file.S3 FigModels used: GBLUP-UV-A, GBLUP-UV-AD, GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) correlations between the parametric and estimated total genetic effects; (B) correlations between the parametric and estimated additive effects; (C) correlations between the parametric and estimated dominance effects.(TIF)Click here for additional data file.S4 FigModels used: GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) correlations between the parametric and estimated total genetic effects; (B) correlations between the parametric and estimated additive effects; (C) correlations between the parametric and estimated dominance effects.(TIF)Click here for additional data file.S5 FigModels used: GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) Broad-sense heritability coefficients; (B) Additive heritability coefficients; (C) Dominance heritability coefficients.(TIF)Click here for additional data file.S6 FigModels used: GBLUP-UV-A, GBLUP-UV-AD, GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) Broad-sense heritability coefficients; (B) Additive heritability coefficients; (C) Dominance heritability coefficients.(TIF)Click here for additional data file.S7 FigModels used: GBLUP-UV-A, GBLUP-UV-AD, GBLUP-MV-A and GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) Broad-sense heritability coefficients; (B) Additive heritability coefficients; (C) Dominance heritability coefficients.(TIF)Click here for additional data file.S8 FigModels used: GBLUP-MV-A e GBLUP-MV-AD. Traits analyzed: Plant height (PH), ear height (EH), ear length (EL), ear row number (ERN) and kernel weight (KW). (A) Broad-sense heritability coefficients; (B) Additive heritability coefficients; (C) Dominance heritability coefficients.(TIF)Click here for additional data file.S1 TableTraits: plant height (PH), ear height (EH), ear length (EL), ear row number (ERN), kernel weight (KW).(DOCX)Click here for additional data file.S2 TableTraits: plant height (PH), ear height (EH), ear length (EL), ear row number (ERN), kernel weight (KW). The adjusted heritability values used during the data construction are in parentheses.(DOCX)Click here for additional data file.S3 TableTraits: plant height (PH), ear height (EH), ear length (EL), ear row number (ERN), kernel weight (KW). The adjusted heritability values used during the data construction are in parentheses.(DOCX)Click here for additional data file.S4 TablePRESSα), dominance (PRESSδ) and total genetic (PRESSg) predicted residual errors for the traits plant height (PH), ear height (EH), ear length (EL), ear row number (ERN), and kernel weight (KW). The adjusted heritability values used during the data construction are in parentheses.Estimated additive ((DOCX)Click here for additional data file.S5 TablePRESSα), dominance (PRESSδ) and total genetic (PRESSg) predicted residual errors for the traits plant height (PH), ear height (EH), ear length (EL), ear row number (ERN), and kernel weight (KW). The adjusted heritability values used during the data construction are in parentheses.Estimated additive ((DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Understanding the genetic basis of airflow obstruction and smoking behaviour is key to determining the pathophysiology of chronic obstructive pulmonary disease (COPD). We used UK Biobank data to study the genetic causes of smoking behaviour and lung health.1) distribution among heavy smokers (mean 35 pack-years) and never smokers. We developed a custom array for UK Biobank to provide optimum genome-wide coverage of common and low-frequency variants, dense coverage of genomic regions already implicated in lung health and disease, and to assay rare coding variants relevant to the UK population. We investigated whether there were shared genetic causes between different phenotypes defined by extremes of FEV1. We also looked for novel variants associated with extremes of FEV1 and smoking behaviour and assessed regions of the genome that had already shown evidence for a role in lung health and disease. We set genome-wide significance at p<5 × 10−8.We sampled individuals of European ancestry from UK Biobank, from the middle and extremes of the forced expiratory volume in 1 s  and between individuals with and without doctor-diagnosed asthma (p=6·06 × 10−11). We discovered six novel genome-wide significant signals of association with extremes of FEV1, including signals at four novel loci  and independent signals at two previously reported loci (NPNT and HLA-DQB1/HLA-DQA2). These variants also showed association with COPD, including in individuals with no history of smoking. The number of copies of a 150 kb region containing the 5′ end of KANSL1, a gene that is important for epigenetic gene regulation, was associated with extremes of FEV1. We also discovered five new genome-wide significant signals for smoking behaviour, including a variant in NCAM1 (chromosome 11) and a variant on chromosome 2 (between TEX41 and PABPC1P2) that has a trans effect on expression of NCAM1 in brain tissue.UK Biobank participants were recruited from March 15, 2006, to July 7, 2010. Sample selection for the UK BiLEVE study started on Nov 22, 2012, and was completed on Dec 20, 2012. We selected 50 008 unique samples: 10 002 individuals with low FEVBy sampling from the extremes of the lung function distribution in UK Biobank, we identified novel genetic causes of lung function and smoking behaviour. These results provide new insight into the specific mechanisms underlying airflow obstruction, COPD, and tobacco addiction, and show substantial shared genetic architecture underlying airflow obstruction across individuals, irrespective of smoking behaviour and other airway disease.Medical Research Council. Previous large genome-wide association studies (GWAS) of general population cohorts have identified 32 common genetic variants  associated with lung function,1).Chronic obstructive pulmonary disease (COPD) is a global public health concern and is currently the third leading cause of death worldwide.Evidence before this studyP3G Catalogue. We did not identify any other biobank with spirometry data and DNA as large as UK Biobank. Evidence regarding the global burden of disease due to smoking or chronic obstructive pulmonary disease (COPD) was obtained from the WHO Global Health Risks Report and a systematic analysis of the Global Burden of Disease Study 2010. Tobacco smoking accounted for about 5·1 million deaths globally in 2004; because of recent increases in smoking prevalence in developing countries, the full global effect of smoking is yet to occur. COPD is the third leading cause of death globally. For previous evidence of genetic associations, we gave the highest ranking to associations reaching genome-wide significance in genome-wide association studies, a lower ranking to associations not reaching genome-wide significance in genome-wide association studies, and the lowest ranking to associations reported in candidate gene studies. To assess evidence of loci associated with lung function, COPD, and smoking behaviour, we queried the Catalog of Published Genome-Wide Association Studies. We used this evidence to report our known findings for genetic variants shown to be associated with forced expiratory volume in 1 s  and smoking behaviour (seven loci). We report candidate gene associations only for variants for which we found genome-wide evidence of association.UK Biobank had completed its recruitment, including its baseline phenotyping and biobanking of samples, before our study began. The DNA had not yet been extracted from the biobanked samples, and the spirometry data quality had not yet been analysed across all UK Biobank participants. We searched for evidence of other large biobanks with spirometry data, including the Added value of this study1 in individuals without asthma are also informative in individuals with asthma. We report new loci associated with extremes of FEV1 and COPD, including evidence that a genomic region of complex structural variation has an effect on lung function and airflow obstruction in the general population. Our novel signals implicate epigenetic mechanisms as contributors to lung health. These findings, taken together with previous findings, will help define pathways underlying predisposition to development of COPD and smoking behaviours. A full understanding of the biological mechanisms underlying these genetic associations will improve our understanding of the pathophysiology of COPD and smoking behaviour, and potentially give rise to novel therapeutic strategies for the management of airway disease and prevention of nicotine addiction.We describe, to our knowledge, the first genetic study using the UK Biobank resource and show the quality of the phenotype and genotype data. Additionally, we describe an advance in imputation quality afforded by the use of a newly designed genotyping array used in conjunction with the largest reference panel available so far. A slightly modified version of this array is being used to genotype the remaining samples in UK Biobank. As evidence of the usefulness of these data, we describe novel insights into the genetic architecture of airflow obstruction and smoking. Specifically, we show that there are shared genetic causes of airflow obstruction between smokers and non-smokers, consistent with the limited evidence for gene–smoking interactions described so far. We show that the genetic determinants of low FEVImplications of all the available evidenceThis study has improved our understanding of the genetic and molecular basis of smoking behaviour and lung function and provided potential targets for therapeutic intervention. It has also shown the value of sampling from the extremes using a large biobank such as UK Biobank. A similar approach could be adopted for genetic studies of other health-related traits in UK Biobank, using either new genetic assays or the extensive genome-wide data that we and UK Biobank have generated.Tobacco smoking accounted for about 5·1 million deaths globally in 2004 and for 18% of deaths in high-income countries.UK Biobank is the largest European biobank available at present and represents an extensive resource from which to sample phenotypic extremes in the UK population.1 and high FEV1, and a shared genetic cause of low FEV1 between never smokers and heavy smokers and between individuals with and without a doctor diagnosis of asthma; (2) identify novel variants associated with extremes of FEV1 and smoking behaviour; and (3) provide further insight into regions of the genome that had already shown evidence for a role in lung health and disease.In the UK Biobank Lung Exome Variant Evaluation (UK BiLEVE) study, we undertook nested case-control studies in individuals of European ancestry from UK Biobank to: (1) identify whether there are shared genetic causes underlying low FEV1 distribution among both heavy smokers (mean 35 pack-years) and never smokers. We developed a custom array to provide optimum genome-wide coverage of common and low frequency (MAF 1–5%) coding variants and rare (MAF <1%) coding variants relevant to the UK population; this platform also provided dense coverage of genomic regions implicated in lung health and disease. Spirometry data in UK Biobank were obtained using a Vitalograph Pneumotrac 6800  on at least two occasions. Sampling was undertaken such that equal numbers of males and females were selected in total and the numbers of individuals selected from each age–sex band were proportional to the number of individuals in the band being sampled  Stage 2+ COPD (FEV1:FVC ratio <0·7 and percent predicted FEV1<80%) as COPD cases and we defined individuals with FEV1:FVC ratio >0·7 and percent predicted FEV1 in excess of 80% from the high FEV1 strata as controls. Post-bronchodilator spirometry was not available, although drug treatment was not withheld before spirometry.To assess whether the novel regions that we identified as associated with FEV1 between individuals with and without reported or doctor-diagnosed asthma, we identified individuals within our study selection who were also asthma cases as participants who either (1) answered “asthma” to the touch-screen question “Has a doctor ever told you that you have had any of the following conditions?” or (2) reported asthma in a verbal interview at the time of recruitment to UK Biobank.To assess the extent of the shared genetic causes of low FEVUK Biobank has received ethics approval from the National Health Service National Research Ethics Service (Ref 11/NW/0382).UK10K ProjectWe undertook genome-wide genotyping of variants using a new custom Affymetrix Axiom array . This finding suggests a polygenic component to low FEV1, in which many variants of individually small effect size contribute to the risk of low FEV1. We found substantial sharing of genetic causes across thousands of genetic variants between low FEV1 in heavy smokers and low FEV1 in never smokers  with low FEV1 versus high FEV1  and showed a stronger association with low FEV1 in never smokers than heavy smokers. The sentinel SNP at an intergenic signal between RBM19 and TBX5 was a rare variant (MAF=0·13%) that showed strongest association with low FEV1 in heavy smokers. The lead SNPs at each of these loci showed association with COPD 1 in our data; adding in the SNPs representing our six novel signals of association with FEV1 extremes, we explained 3·63% of the variance of FEV1  with another SNP in NPNT—rs6856422—which has also been identified as a novel secondary signal of association at 4q24 by an independent concurrent study of lung function in the general population.−16) was only slightly attenuated (p=4·66 × 10−11). The novel signal at TET2 (rs2047409) also showed strongest association in never smokers (p=1·31 × 10−8). rs2047409 is separated from the previously reported association of rs10516526 with FEV1 (GSTCD)−9). TET2 encodes tet methylcytosine dioxygenase 2, which has a role in myelopoiesis, and SNPs in TET2 have shown association with height.TET2 was differentially expressed during fetal lung development , confirming that rs9274600 and rs9273359 are correlated. After exclusion of individuals with doctor-diagnosed asthma, the odds ratio for rs9274600 decreased from 1·18 (95% CI 1·11–1·25) to 1·14 (1·08–1·20), but remained significant .We detected a signal of association with FEV1 extremes in heavy smokers only, after adjusting for pack-years of smoking , but was also detected in heavy smokers (p=1·47 × 10−5). Genes in this locus, which include MAPT and CRHR1, have previously been associated with pulmonary fibrosisr2>0·8]) was associated with mRNA expression levels of at least 15 genes in lung and blood ; the allele associated with low FEV1 was positively correlated with the inverted haplotype.KANSL1  was associated with extremes of FEV1  in an inversion locus at 17q21.31  was within TSEN54 and occurred only in never smokers  evidence of association with FEV1 extremes , and rs10193706, an intergenic SNP on chromosome 2 downstream of TEX41 and upstream of PABPC1P2 (r2<0·0001 with rs4466874) SNPs in TTC12 and ANKK1, near to NCAM1, have also previously shown association with nicotine dependence (r2=0·48) is a trans eQTL for NCAM1 on chromosome 11 in brain tissue (r2=0·86) is a trans eQTL in the substantia nigra for WDR61 on chromosome 15, 300 kb from the established 15q25 smoking locus  evidence of association with smoking behaviour (CHRNA4 at 20q13.33 (p=1·01 × 10−7).We also noted novel genome-wide significant signals of association with smoking behaviour in nd DNAH8 . A SNP iehaviour , includi−7; −8). Three of the six variants associated with FEV1 extremes —ie, it is associated with the level of expression of NCAM1. This second SNP is also a trans eQTL in substantia nigra tissue for another gene called WDR61, which is close to the genes CHRNA3 and CHRNA5 at 15q25—a locus strongly associated with smoking behaviour.WDR61 other than that expression can be induced by mechanical strain in mesenchymal stem cells.Two of our novel signals of association with smoking behaviour implicate 1 extremes, all of which were also associated with COPD using our definition based on spirometry. Five of these signals were most strongly associated with extremes of FEV1 (low vs high) in never smokers. The signal at 17q21.31 for extremes of FEV1 suggests a role for structural variation and epigenetic regulation in lung health. We found that the number of copies of the 5′ end of KANSL1—a gene disrupted by duplication events—is associated with FEV1 extremes. KANSL1 encodes a protein that is a key component of the NSL1 (histone acetyltransferase) complex.KANSL1 causes a rare multisystem disorder,KANSL1 in epigenetic regulation. In a genome-wide pathway analysis, we identified the histone gene set, further implicating a role for epigenetic regulation in lung health.We describe six new signals of association with FEV1 extremes in NPNT and KANSL1 in never smokers were also significantly associated in the independent set of heavy smokers. Furthermore, to corroborate the new signals we identified in TET2 and TSEN54, we present evidence of association with FEV1 in a previously reported large study of FEV1 in the general population.1 was exclusive to the recently released UK10K Project component of the imputation panel and has not yet been measured in suitably large studies. Our comparison of smokers and never smokers represents a powerful approach because of the restriction to heavy smokers rather than ever smokers. For novel signals for smoking behaviour, we present additional evidence of association with smoking behaviour for NCAM1, TEX41/PABPC1P2, and NOL4L in independent populations.We maximised the power of our study by sampling from the extremes of a large biobank. No other similar resources of a comparable size were available for replication studies. Nevertheless, the novel genome-wide significant signals of association with FEVNPNT that was independent of the previously reported signal of association at this locus . The independent NPNT signal captured by the genotyped variant rs34712979 was not detected in previous or concurrent studies because it was neither directly genotyped nor imputed with sufficient quality; this finding highlights a further advantage of the UK BiLEVE and UK Biobank array design.One of the strengths of our study design was that the genotyping platform we used allowed for fine mapping of regions already known to contain genetic variants that affect lung function. For example, we were able to identify a novel signal in The design of this genotyping array combined the best features of existing genome-wide platforms targeting common SNPs (MAF ≥5%) and putative functional exome chip content, plus additional content to improve imputation of low-frequency variants (MAF 1–5%). In combination with a new large UK-specific imputation reference panel (UK10K Project), these features increase the potential to discover novel signals. In our study, more than 28·5 million variants were imputed; current large meta-analyses combining data from several studies with older arrays and using equivalent quality control filters after imputing to 1000 Genomes Project Phase 1 alone typically measure about 10·6 million variants.Despite the strengths of using a large resource such as UK Biobank, this study has some limitations. In particular, there is a trade-off between obtaining the large sample sizes generally needed for genetic studies and the depth of phenotyping that is practicable in such large populations. Our spirometric definition of COPD was not based on bronchodilator reversibility testing, although we have shown previously that by limiting inclusion to individuals with GOLD stage 2+ spirometry, most of these individuals are likely to have COPD according to more rigorous criteria.In summary, we show the usefulness in sampling from the extremes of UK Biobank data to identify novel genetic signatures underlying phenotypes important in the development of airway disease and smoking behaviour. The ongoing genotyping, and further phenotyping, of the rest of the UK Biobank resource will facilitate further GWAS, which will undoubtedly improve our understanding of the genetic and molecular basis of common disease.


Traditionally, exploration of genetic variance in humans, plants, and livestock species has been limited mostly to the use of additive effects estimated using pedigree data. However, with the development of dense panels of single-nucleotide polymorphisms (SNPs), the exploration of genetic variation of complex traits is moving from quantifying the resemblance between family members to the dissection of genetic variation at individual loci. With SNPs, we were able to quantify the contribution of additive, dominance, and imprinting variance to the total genetic variance by using a SNP regression method. The method was validated in simulated data and applied to three traits  in three purebred pig populations. In simulated data, the estimates of additive, dominance, and imprinting variance were very close to the simulated values. In real data, dominance effects account for a substantial proportion of the total genetic variance (up to 44%) for these traits in these populations. The contribution of imprinting to the total phenotypic variance of the evaluated traits was relatively small (1–3%). Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been described previously for other traits in other species. We also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits and shows promise to apply the SNP regression method to other traits and species, including human diseases. However, the estimation of heritability via the use of additive models does not only capture additive gene action but can potentially also capture part of the dominance effects and epistatic interactions  have been identified in mammals (http://geneimprint.com/site/genes-by-species); however, the fraction of the total genetic variance due to imprinting effects has not yet been investigated using genomic data.Dominance effects are of great interest for both plant and livestock breeding because dominance has been suggested as one of the genetic mechanisms explaining heterosis , GenCall < 0.15, unmapped SNPs, and SNPs located on sex chromosomes, according to the Sscrofa10.2 assembly of the reference genome  where the G allele was inherited, with certainty, from its father , the probabilities would be P1= 1 and P2= 0. To obtain the regressors that allow the estimation of additive (regA), dominance (regD), and imprinting (regI) genetic variance, the following transformation of these probabilities was applied: regA = [(P1 + P2) – 1], regD = (|P1 – P2|), and regI = (P1 – P2). Thus, the genotypes  were recoded as ,  and  to evaluate additive, dominance and imprinting variances, respectively.For each SNP of each individual, AlphaImpute  of a single QTL was defined as described by a, Vd, and Vi are, respectively, the additive, dominance, and imprinting variances and a, d, and i are, respectively, the additive, dominance, and imprinting effects of a given QTL with allele frequencies p and q. Because each QTL was simulated to either have an additive, dominance, or imprinting effect, a of each additive QTL, d of each dominant QTL and i of each imprinted QTL could be defined as: jth animal then becomes:n is the number of QTL (SNPs) affecting the trait, e is a random environmental component sampled from a random distribution with variance equal to 0.70, and regA, regD, and regI are defined as described in the section Genomic data. We generated 10 replicates of this simulation, and SNPs that met the selection criteria were allowed to have an effect in only one of the replicates.To verify whether our data structure and statistical model allow disentangling additive, dominance, and imprinting effects, we simulated for the Landrace population a trait with additive, dominance, and imprinting effects, with mean 0, total genetic variance equal to 0.30, and total phenotypic variance equal to 1. Real genotypes of this population were used in the simulation procedures. A total of 15 SNPs with minor allele frequency between 0.45 and 0.50 were selected randomly to have an effect on the trait [quantitative trait loci (QTL)]: five with only additive effects, five with only dominance effects, and five with only imprinting effects. These SNPs were located on different chromosomes that also were selected randomly. Each QTL accounted for 2% of the total phenotypic variance. Therefore, the additive, dominance, and imprinting heritabilities were 10% each. The genetic variance , the same for both real and simulated data:y is a vector of preadjusted phenotypic observations; µ is the mean of the populations and 1 a vector of ones; L is the design matrix for the common litter effects (only used for backfat and lifetime daily gain); b is a unknown vector of common litter effects; A, D, and I are design matrices with regressors for additive, dominance, and imprinting effects respectively; a, d, and i are unknown vectors of additive, dominance, and imprinting effects, respectively;, and e is a vector of residuals. The entries of the design matrices A, D, and I are regressors calculated from the observed phased probabilities of the marker genotypes , as described in the genomic data section above.Parameters were estimated using models with random regression on SNP genotypes. Single trait within-line analyses were performed with three different models implemented in the program BayZ , d ∼ N, i ∼ N, b ∼ N, and e ∼ N, with http://www.bayz.biz/) as described by Assumed distributions were: Aa is to write var(Aa) = AA′AA′ to provide the constant to scale the per-SNP explained variance to total explained variance. In this way total variance in the models can be expressed as AA’ , DD’ , and II’ . Only for backfat and lifetime daily gain, Aa, Dd, and Ii at each iteration of the Gibbs sampler (Aa) per Markov chain Monte Carlo cycle only for the part of the covariates in A and matching regression parameters in a that belong to a particular chromosome. The narrow-sense heritability was defined as Because the evaluated models were SNP-based models, they do not readily provide estimates of total explained variance. One way to obtain the total explained variance from a model term like i.e., they lead to the same probability model). However, the parameters obtained with these models have different interpretations. In the genotypic model, the additive variance is the variance of additive effects (average difference between homozygotes), whereas in the breeding model, additive effects are functions of allele substitution effects. To make the variance estimates from the genotypic model comparable with the estimates of the breeding model, a transformation of these results was proposed by File S1).The portioning of the genetic variance as described previously has been defined as the “genotypic model” (2) from the regression of variance explained on physical length. Variance explained by each chromosome individually was obtained based on the effects of SNPs on that chromosome. SNP effects were from the analyses where all SNPs from all chromosomes were evaluated simultaneously. Variance per chromosome was estimated for all three models evaluated .Finally, we evaluated whether the proportion of variance explained by a single chromosome was related to its physical length. The length of a given chromosome was defined as the distance between the first and the last SNP on this chromosome according to the Sscrofa 10.2 assembly  were observed between the residual variance and the variance of the three components of genetic variance evaluated. The lowest correlations (approximately zero) were observed between imprinting and additive variance and between imprinting and dominance variance.The pairwise sampling correlation between additive, dominance, imprinting and error variance are shown in For the trait number of teats, the narrow-sense heritability estimated using MA was 0.319 in the Landrace and 0.343 in the Large White population . When boFor the trait backfat, the narrow-sense heritability estimated using MA was 0.520, 0.390, and 0.419 in the Landrace, Large White, and Pietrain populations, respectively. Additive heritabilities decreased to 0.469, 0.353, and 0.394 in the Landrace, Large White, and Pietrain populations, respectively, when the MA model was replaced by MADI . MAD andFinally, for the trait lifetime daily gain, the narrow-sense heritability estimated using MA was 0.267 in the Landrace, 0.241 in the Large White, and 0.314 in the Pietrain population . Again, Based on the estimated DIC , MAD andFile S2. We report results obtained using MADI. Very similar results were observed with all three models .After estimation of SNP effects using all SNPs simultaneously, we estimated the additive, dominance, and imprinting variance by using the complete set of SNPs and also the variances per individual chromosomes. Estimates obtained from genome-wide SNPs were close to results from adding up the contributions of individual chromosomes. The largest difference was observed for number of teats in the Landrace population. The additive variance estimated using all SNPs was 0.33 for number of teats, while adding up the contributions of individual chromosomes resulted in an estimate of 0.39 for number of teats. The variance explained per chromosome for all traits in all populations using MADI is shown in 2 ranging from 0.84 to 0.94).For all traits, the proportion of additive, dominance, and imprinting variance explained per chromosome showed a strong linear relationship with chromosome length (rTable S1). On the basis of these simulation results, we therefore concluded that data structure and the methodology will allow us to disentangle additive, dominance and imprinting variance, although this simulated scenario may not be representative of the genetic architecture of a real complex trait.Analysis of simulated data with the MADI model resulted in estimates of additive, dominance, and imprinting variance that were very close to the simulated values . Howevere.g., using MAD instead of MA). The smallest decrease in narrow-sense heritability was observed for number of teats (4.2%) and the highest for lifetime daily gain (21.3%), both in the Landrace population , the parameterization proposed by In all populations, we observed a reduction in the narrow-sense heritability of all evaluated traits when dominance effects were accounted for , with a ratio between dominance and additive variance of 0.82. In other lines, the ratios were also high. Using the same model, we found that the ratio between dominance and additive variance was 0.71 in the Landrace and 0.66 in the Large White population. Moreover, our results also show that additive variance accounts for the largest fraction of the genetic variance. This is in agreement with a previous study that described that additive variance is expected to account for >50% (often about 100%) of the total genetic variance . Besides chromosome 8, the number of SNPs per chromosome was similar in all three populations. This difference in number of SNPs on chromosome 8 is observed because in the Pietrain population, compared to the Landrace and the Large White populations, more SNPs presented low minor allele frequency or were completely fixed and therefore were excluded from the estimation of the variance components. This large number of SNPs with low minor allele frequency (or completely fixed) could be due to an ascertainment bias due to the selection of SNPs for the SNP chip used in this study. However, this could be also an indication that genes that influence traits included in the selection index of Pietrain are located on this chromosome. The breeding objectives in Pietrain (sire line) are distinct from those in the Landrace and Large White (dam lines) which are more similar. Given this difference, some alleles could have moved to fixation in Pietrain but not in Landrace and Large White.The relationship between variance explained and chromosome length for number of teats in the Large White population, backfat in the Landrace population, and lifetime daily gain in the Pietrain population using MADI is illustrated in variance  was loweVRTN gene, explaining 2.5% of genetic variance  for number of teats, backfat and lifetime daily gain in the pig populations evaluated. Although the contribution of imprinting effects to the total phenotypic variance of the evaluated traits was relatively small (1–3%), the SNP regression method allowed estimation of the additive, dominance and imprinting effects and resulting variances. Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been previously described for other traits in other species. In addition, we also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits. The model can now be applied to other traits and species. Our results also open new perspectives for the inclusion of dominance and imprinting effects in prediction of phenotypes, especially regarding mate allocation techniques in animal and plant breeding, and for assessment of the risk of disease in humans.