PRDM16, PAX3, TP63, C5orf50, and COL17A1—in the determination of the human face. Three of them have been implicated previously in vertebrate craniofacial development and disease, and the remaining two genes potentially represent novel players in the molecular networks governing facial development. Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features. In addition to the reported GWA findings, we established links between common DNA variants previously associated with NSCL/P at 2p21, 8q24, 13q31, and 17q22 and normal facial-shape variations based on a candidate gene approach. Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology. This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology, as well as for potential applications of DNA prediction of facial shape such as in future forensic applications.Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans, and it is clearly under genetic regulation; however, almost nothing is known about the genetic basis of normal human facial morphology. We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts, considering almost ten thousand individuals of European descent from several countries. Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images (MRIs) and two-dimensional portrait images. We identified five independent genetic loci associated with different facial phenotypes, suggesting the involvement of five candidate genes— PRDM16, PAX3, TP63, C5orf50, and COL17A1. In addition, our data suggest that genetic variants associated with NSCL/P also influence normal facial shape variation. Overall, this study provides novel and confirmatory links between common DNA variants and normal variation in human facial morphology. Our results also suggest that the high heritability of facial phenotypes seems to be explained by a large number of DNA variants with relatively small individual effect size, a phenomenon well known for other complex human traits, such as adult body height.Monozygotic twins look more alike than dizygotic twins or other siblings, and siblings in turn look more alike than unrelated individuals, indicating that human facial morphology has a strong genetic component. We quantitatively assessed human facial shape phenotypes based on statistical shape analyses of facial landmarks obtained from three-dimensional magnetic resonance images of the head. These phenotypes turned out to be highly promising for studying the genetic basis of human facial variation in that they showed high heritability in our twin data. A subsequent genome-wide association study (GWAS) identified five candidate genes affecting facial shape in Europeans:  The morphogenesis and patterning of the face is one of the most complex events in mammalian embryogenesis. Signaling cascades initiated from both facial and neighboring tissues mediate transcriptional networks that act to direct fundamental cellular processes such as migration, proliferation, differentiation and controlled cell death. The complexity of human facial development is reflected in the high incidence of congenital craniofacial anomalies, and almost certainly underlies the vast spectrum of subtle variation that characterizes facial appearance in the human population.Facial appearance has a strong genetic component; monozygotic (MZ) twins look more similar than dizygotic (DZ) twins or unrelated individuals. The heritability of craniofacial morphology is as high as 0.8 in twins and families In a recent candidate gene study carried out in two independent European population samples, we investigated a potential association between risk alleles for non-syndromic cleft lip with or without cleft palate (NSCL/P) and nose width and facial width in the normal population In geometric morphometrics, there are different ways to deal with the confounders of position and orientation of the landmark configurations, such as (1) superimposition We extracted facial landmarks from 3D head MRI in 5,388 individuals of European origin from Netherlands, Australia, and Germany, and used partial Procrustes superimposition (PS) Characteristics of the study cohorts from The Netherlands , Australia , Germany , Canada (SYS) and the United Kingdom (TwinsUK) are provided in r = 0.96, r = 0.76; minimal r = 0.56 for Prn-Sbn; maximal r = 0.94 for ZygR-AlrL; r = 0.56; minimal r = 0.10 between EyeL-AlrL and ZygL-EyeL; maximal r = 0.96 between AlrR-Nsn and AlrL-Nsn; −300) and on average 5 mm larger inter-landmark distances  were on average much higher than those in DZ pairs , Zygion Left (ZygL), Eyeball Right (EyeR), Eyeball Left (EyeL), Alare Right (AlrR), Alare Left (AlrL), Nasion (Nsn), Pronasale (Prn), and Subnasale (Sbn) . The lowistances . These ves −141; . This sees −141; , showinges −141; . Increas−8 as the significance threshold in the discovery phase. The GWAS revealed five independent loci at P<5×10−8  block containing rs7559271, were associated with the distance between the eyeballs and nasion  from RS1, RS2, QTIMS, SHIP, and SHIP-TREND where facial shape phenotypes were all derived from comparable 3D head MRIs and using the same approach. We tested 2,558,979 autosomal SNPs for association with 48 facial phenotypes, including the centroid size, 36 inter-landmark Euclidean distances, and 11 shape PCs. Q-Q plots  and genoP<5×10−8 . All thePAX3PAX3 in adults here demonstrates that at least some genetic effects on facial features that manifest in adolescence remain detectable in adulthood. The association signal at PAX3, however, did not replicate in SYS, possibly due to the small sample size available in this replication cohort. Likewise, the signals at PRDM16 and TP63 were not replicated in SYS. Two other loci, C5orf50 and COL17A1, were successfully replicated for the same phenotypes . Unlike the other (adult) cohorts, the SYS cohort is an adolescent one, with a mean age of 15 and a minimum age of 12 years. This may potentially lead to false negative replications since the face continues to develop and ossify throughout adolescence in a different manner than in the adult, especially in male adolescents r = 0.42 in 218 MZ pairs, r = 0.16 in 533 DZ pairs; TwinsUK and BLTS combined sample), indicating that these phenotypes were more noisy than those derived from the 3D images. In spite of these limitations, we observed nominally significant associations for approximately the same phenotypes for 2 of the 5 loci identified from our GWAS, TP63 and C5orf50  or 2D photo  analyses. For PAX3 there was a significant association between rs974448 and the distance between the eyeballs in the 2D data . In our discovery phase GWAS a total of 102 SNPs at 29 distinct loci showed significant or suggestive evidence of association (P<5×10−7) with various facial phenotypes  and Australia  where we localized eight facial landmarks on 2D portrait photos , PAX3 (paired box 3), TP63 (tumor protein p63), C5orf50 (chromosome 5 open reading frame 50), and COL17A1 . In addition to our GWA findings, we confirmed links between NSCL/P cleft associated SNPs at 2p21, 8q24, 13q31, and 17q22 and normal human facial shape variation based on a candidate gene approach.We identified five independent loci at 1p36.23-p33, 2q35, 3q28, 5q35.1, and 10q24.3 consisting of common DNA variants associated with normal facial shape phenotypes in individuals of European ancestry. Candidate genes at these loci include PAX3 locus. We identified this gene in our discovery GWAS, a finding independent of, but consistent with, a recent GWAS PAX3 gene is indeed involved in facial morphology. The signal observed at C5orf50 at 5q35.1 in association with the nasion position was successfully replicated in both SYS using 3D MRI derived phenotypes under similar  methodology as well as in a combined sample of TwinsUK and BLTS using 2D photograph derived phenotypes. The association signal observed at COL17A1 was replicated only in SYS (3D MRI) and that at TP63 was replicated only in TwinsUK and BLTS (2D photos). The signal observed at PRDM16 was not replicated in our replication cohorts. The failure in replication for some of our GWAS findings may be explained by physical limitations in our replication cohorts as discussed in detail below.From a statistical perspective, the most robust result was the one at the PAX3 encodes a developmentally important transcription factor expressed in neural crest cells, a multipotent cell population contributing to most differentiated cell types in the vertebrate face. In humans, PAX3 is one of six genes mutated in Waardenburg syndrome, which is characterized by a range of neural crest related phenotypes including minor facial dysmorphism manifest as a broad nasal root and an increased distance between the medial canthi or corners of the eye (telecanthus) PAX3 during neural crest differentiation leads to cleft palate, due to inhibitory effects on osteogenesis PAX3 and position of the nasion Three of the five loci identified in this study have previously been shown to play an essential role in craniofacial development; in particular, they have been implicated in orofacial clefting phenotypes in mice or humans. PRDM16 was previously identified as a SMAD binding partner; it is thought to act in downstream mediation of TGFβ signaling in developing orofacial tissues PRDM16 is expressed in both the primary and secondary palate and the nasal septum in mouse embryos N-ethyl-N-nitrosourea-induced mutation in PRDM16 resulting in cleft palate and other craniofacial defects including mandibular hypoplasia PRDM16 locus have been implicated in NSCL/P TP63 encodes a transcription factor belonging to the p53 family, and plays important roles in orchestrating developmental signaling and epithelial morphogenesis. Heterozygous mutations in human TP63 are associated with a number of allelic syndromes characterized by orofacial defects, including Ectrodactyly-Ectodermal dysplasia-Cleft lip/palate and Ankyloblepharon-Ectodermal dysplasia-Clefting TP63 has been implicated in human NSCL/P C5orf50 at 5q35.1 is predicted to encode an uncharacterized transmembrane protein, which lies within a 1.24 Mb duplicated region in a patient with preaxial polydactyly and holoprosencephaly (HPE), a defect in development of the forebrain and midface FBXW11, a gene with links to sonic hedgehog signaling, the main pathway affected in HPE C5orf50 locus influences craniofacial patterning through effects on FBXW11 expression, although it is also feasible that the protein encoded by C5orf50 has a novel, and more direct, effect on the face. Mutations in COL17A1 cause Junctional epidermolysis bullosa (JEB), a genetic blistering condition COL17A1 may play an as yet undefined role in patterning facial tissues. However, the association signal observed for COL17A1 at 10q24.3 spans a 300 kb region (105.7 Mb–106 Mb) and also harbors other genes including SLK, C10orf78 and C10orf79. Thus, we cannot exclude the possibility that these genes are responsible for the observed association.The remaining two loci identified in the discovery sample and replicated in both the SYS and TwinsUK & BLTS samples have no previously known direct involvement in craniofacial development to date. Many medical-genetic syndromes show a clear connection between genetic alteration and typical facial gestalt This study is not without limitations. The limited number of landmarks used in this study cannot capture the full range of the complex 3D shape variation in the face. This is partly due to the physical limitation of our MRI data that miss the lower part of the face and partly due to other factors such as partial incompatibility of 3D and 2D image sources for phenotype extraction. Consequently, some of the significant associations based on 3D distances could not be tested in the 2D photo analysis. For instance, the zygion landmarks available in 3D MRI could not be successfully derived in 2D photos. Further, the precision of phenotypes derived from 2D photos is expected to be lower than that from 3D MRIs. This is indicated by the lower twin correlations in 2D photos than in 3D MRI. Phenotypic noise in 2D photos may arise from slight differences in face orientation, an effect that cannot be removed by PS without measuring the 3rd coordinate. Furthermore, different image sizes and pixel resolutions in 2D photos may also influence the phenotyping results. Thus, we used the 2D photo analysis to provide supporting information but cannot be considered as an exact replication. Another concern is that the facial landmarks from the SYS cohort were derived in a previous study C5orf50 and COL17A1, neither of which have previously been implicated in facial development. The associated DNA variants may either affect neighboring genes, or alternatively identify C5orf50, and COL17A1 as potential new players in the molecular regulation of facial patterning. Overall, we have uncovered five genetic loci that contribute to normal differences in facial shape, representing a significant advance in our knowledge of the genetic determination of facial morphology. Our findings may serve as a starting point for future studies, which may test for allele specific expression of these candidate genes and re-sequence their coding regions to identify possible functional variants. Moreover, our data also highlight that the high heritability of facial shape phenotypes (as estimated here and elsewhere), similar to that of adult body height In spite of these limitations we have been able to demonstrate that a phenotype as complex as human facial morphology can be successfully investigated via the GWAS approach with a moderately large sample size. Three of the five loci highlighted here map to known developmental genes with a previously demonstrated role in craniofacial patterning, one of which has been unequivocally associated with nasion position in a recent independent GWAS 3 (up sampled from 0.6×0.7×0.8 mm3 using zero padding in the frequency domain), a repetition time (TR) of 13.8 ms, an echo time (TE) of 2.8 ms, an inversion time (TI) of 400 ms, and a flip angle of 20°. More details on image acquisition can be found elsewhere The RS is an ongoing population-based prospective study including a main cohort RS-I Adolescent twins and their siblings were recruited over a period of sixteen years into the BLTS at 12, 14 and 16 years, as detailed elsewhere The SHIP is a longitudinal population based cohort study assessing the prevalence and incidence of common, population relevant diseases and their risk factors with examinations at baseline , 5-year-follow-Up  and an ongoing 10-year-follow-Up  Adolescent sibpairs (12 to 18 years of age) were recruited from a French-Canadian population with a known founder effect living in the Saguenay-Lac-Saint-Jean region of Quebec, Canada in the context of the ongoing Saguenay Youth Study The TwinsUK cohort is unselected for any disease and is representative of the general UK population In our discovery cohorts, since the lower part of the face was not available from the MRIs, we focused on nine landmarks of the upper face . These iThe coordinates of these nine landmarks were derived with an automated technique as described previously In the SYS cohort, in total 56 facial landmarks were available from a previous quantitative analysis of craniofacial morphology using 3D MRI http://sourceforge.net/projects/opencvlibrary/). The automatically identified landmarks were then manually adjusted by 5 research assistants on a standard computer screen.We defined eight landmarks in 2D portrait photos that approximately correspond to the respective landmarks ascertained from our 3D MRIs. These include EyeL, EyeR, Prn, AlrL, AlrR, Nsn, earlobe left (EarL), and earlobe right (EarR) . Note thr = 0.95). We derived 11 principal components (PCs) from the superimposed landmarks, each explaining at least 1% of the total phenotypic variance. We also derived 36 Euclidean distances between all pairs of landmarks. Thus 48 phenotypes were included in our GWAS, including centroid size, 11 PCs, and 36 inter-landmark distances. All phenotypes were approximately normally distributed and outliers (>3sd) were removed. Deformation approaches including the use of transformational grids provide an alternate way to study shape difference. Thin plate splines (TPS) We used un-scaled PS, or partial PS For 3D MRI data in SYS, we used the 56 landmarks derived in a previous study and calculated 1,540 Euclidean distances between all pairs of landmarks. These distances were considered as phenotypes in our replication analysis of GWAS findings. We also chose a subset of nine landmarks most closely resembled those used for the current study for exact replication.Since the size of the face vary substantially between 2D portrait images, we used the full PS By clarifying which facial features are under strong genetic control, we should be better able to identify specific genes that influence facial variation. Heritability estimates are also important indicators of the phenotype quality. Using QTIMS  heritability analysis was carried out in Mx Details of SNP microarray genotyping, quality control and genotype imputation are described in prior GWAS conducted in RS −3 were not considered for report. Genomic inflation factors were estimated in range 1.0–1.03 for all studied phenotypes. The observed P-values were Q-Q plotted against the expected P-values at −log10 scale. We considered the traditional threshold of 5×10−8 as being genome-wide significant since many phenotypes were highly correlated. All SNPs in this study were annotated based on NCBI build 36.3.We conducted discovery phase GWAS in a combined set of all discovery cohorts  for 48 facial shape phenotypes. Imputed GWAS data in all discovery cohorts were merged according to the positive strand. We tested 2,558,979 autosomal SNPs with linear regression  was observed for SNPs with MAF>3% (results not shown).The linear modeling used here separately analyzes each facial phenotype. It is also possible to derive a global P-value for testing the shape difference between different genotype groups using other approaches, such as the Euclidean Distance Matrix Analysis (EDMA) −8 in our discovery phase GWAS were sought for replication in SYS, TwinsUK, and BLTS. Promising SNPs were tested for association with 1,054 inter-landmark distances in SYS and 28 inter-landmark distances in a combined sample of TwinsUK and BLTS assuming additive allelic effect adjusted for sex and age using MERLIN All SNPs with P values<5×10Figure S1Thin plate spline deformation illustrating facial shape differences in males compared to females in discovery cohorts (N = 5388). The pixel information obtained from the mean shape of males was mapped to that of females. The deformed images illustrate the difference between the mean shpae of males (the curved plates) compared to that of females (imaginary flat plates). A. a 3D view of the mean facial shape of all individuals in the discovery cohorts before deformation; B. side projection of the deformed grid; C. front projection of the deformed grid; D. up-down projection of the deformed grid.(TIF)Click here for additional data file.Table S1Correlation between PC and Euclidean distances in discovery cohorts. The color shade is independent between columns.(XLSX)Click here for additional data file.Table S2Correlation matrix between pair-wise Euclidean distances and size in discovery cohorts.(XLSX)Click here for additional data file.Table S3Effect of sex and age on facial shape in discovery cohorts. Distances are presented in millimeters. P values were adjusted for centroid size.(XLSX)Click here for additional data file.Table S4Heritability of facial shape phenotypes derived from 3D MRI in QTIMS. Twin correlations and proportions of variance due to additive genetic (A), common environmental (C), and unique environmental (E) influences, shown with 95% confidence intervals (age and sex adjusted).(XLSX)Click here for additional data file.Table S5SNPs (n = 102) associated (P<5e-7) with facial shape phenotypes in discovery phase GWAS. Trait, the phenotype for which the minimal P value was obtained. MinP, the minimal P value.(XLSX)Click here for additional data file.Table S6Raw genotype and respective phenotype data for all SNPs that revealed genome-wide significant and suggestive evidence.(XLSX)Click here for additional data file.


Helianthus annuus L.) association mapping population that captures nearly 90% of the allelic diversity present within the cultivated sunflower germplasm collection. We used these data to characterize overall patterns of genomic diversity and to perform association analyses on plant architecture  and flowering time, successfully identifying numerous associations underlying these agronomically and evolutionarily important traits. Overall, we found variable levels of linkage disequilibrium (LD) across the genome. In general, islands of elevated LD correspond to genomic regions underlying traits that are known to have been targeted by selection during the evolution of cultivated sunflower. In many cases, these regions also showed significantly elevated levels of differentiation between the two major sunflower breeding groups, consistent with the occurrence of divergence due to strong selection. One of these regions, which harbors a major branching locus, spans a surprisingly long genetic interval (ca. 25 cM), indicating the occurrence of an extended selective sweep in an otherwise recombinogenic interval.The combination of large-scale population genomic analyses and trait-based mapping approaches has the potential to provide novel insights into the evolutionary history and genome organization of crop plants. Here, we describe the detailed genotypic and phenotypic analysis of a sunflower  and flowering time. We found substantial variation in levels of linkage disequilibrium (LD) across the genome, with islands of elevated LD generally corresponding to genomic regions underlying traits that have been targeted by selection during the evolution of cultivated sunflower. A number of these same regions also exhibited strong population genetic differentiation across the sunflower gene pool, suggesting that they may harbor genes underlying adaptation following domestication. Our analyses also identified numerous genomic regions underlying variation in both plant architecture and flowering time, many of which fall in genomic regions that have not previously been shown to influence these traits using more traditional quantitative genetic approaches. Undoubtedly, these same selective pressures will also have produced significant genomic consequences. Indeed, genomic regions that have been targeted by selection during crop evolution are expected to exhibit characteristic changes in their levels and/or patterns of nucleotide diversity. For example, under strong directional selection, one would expect a marked decrease in genetic variation in and near the targeted loci  mapping analyses have provided considerable insight into the genetic basis of the phenotypic changes that have occurred during crop evolution e.g., . This geHelianthus annuus L.) and perform association analyses of plant architecture and flowering time in this valuable crop species.Ultimately, detailed insights into standing levels of nucleotide diversity, background patterns of LD, and relatedness amongst individuals within the focal population are critically important for the successful application of association mapping approaches. The use of high-density single nucleotide polymorphism (SNP) data derived from known genomic locations can facilitate the development of these insights and thus has the potential to enable the genetic dissection of important phenotypes in crop plants. Moreover, the outcomes of such analyses have potential downstream applications in marker-assisted breeding programs H. annuus) approximately 4,000 years ago by Native Americans. Following its domestication, sunflower was originally used as a source of edible seeds and for a variety non-food applications  th century in Eastern Europe where breeding efforts increasingly focused on improving oil yield in a subset of the available germplasm. Commercial production commenced in North America in the mid-20th century, along with a focus on the development of sunflower as a hybrid crop. Modern sunflower is maintained in two primary breeding pools: the unbranched female (A) lines (differing only in cytoplasm from paired maintainer or B lines), and the typically recessively-branching, multi-headed male restorer (R) lines that are crossed to generate the unbranched, fertile hybrids grown by producers. In this study, we used an Illumina Infinium 10 k SNP array Cultivated sunflower is a globally important oilseed crop that was domesticated from the wild, common sunflower , breeding history , and agronomic use (oil vs. non-oil). Note that an oilseed vs. confectionery designation was not available for the INRA accessions; therefore, these were divided into INRA-derived B and R lines . For the USDA accessions, the following categories were defined: HA non-oil, HA oil, RHA non-oil, RHA oil, introgressed, OPVs, other non-oil, and other oil. Accessions designated ‘non-oil’ were either confectionery types, or could not be clearly defined as being oil types. The ‘introgressed’ category included accessions with a recent history of introgression from wild on e.g., . The OPVhttp://www.designcomputing.net/).In the spring of 2010, we planted our association mapping population in replicate at three locations: the Plant Sciences Farm in Watkinsville, GA, USA, the North Central Regional Plant Introduction Station in Ames, IA, USA, and the University of British Columbia Campus in Vancouver, BC, Canada  . ReplicaTotal DNA was extracted from bulked tissue collected from four individuals of each line using a CTAB extraction protocol Population-wide estimates of genetic diversity, including allele frequencies, observed heterozygosity, and unbiased gene diversity K population genetic clusters based on their multi-locus genotypes. Clusters were assembled so as to minimize intra-cluster Hardy-Weinberg and linkage disequilibrium and, for each individual, the proportion of membership in each cluster is estimated. We employed the admixture model without the use of prior population information . For each analysis, we evaluated K = 1–12 population genetic clusters with 5 runs per K value and averaged the probability values across runs for each cluster. For each run, the initial burn-in period was set to 50,000 with 100,000 MCMC iterations. The most likely number of clusters was then determined using the DeltaK method of Evanno et al. Population structure was investigated using the Bayesian, model-based clustering algorithm implemented in the software package STRUCTURE Genetic relationships amongst the cultivated sunflower accessions were also investigated graphically via principal coordinates analysis (PCoA) using GenAlEx and the same set of polymorphic SNPs (MAF ≥10%) that were used for the STRUCTURE analyses. A standard genetic distance matrix A relative kinship matrix was then estimated from this set of SNPs using the program SPAGeDi r2 values, the squared allele frequency correlations, was constructed between all possible pairs of polymorphic loci with MAF ≥10%. Following the methods of Macdonald et al. r2 values using the k-smooth function in the statistical programming language R (http://www.R-project.org/). We also visualized the extent of LD and genetic variation across the genome by averaging the r2 and UHe values, respectively, in a 5 cM sliding window across each linkage group (LG).To investigate the extent of linkage disequilibrium (LD) across the genome, a correlation matrix of ST for all polymorphic SNPs between the two major heterotic groups (including 125 RHA lines and 100 HA lines) and plotted the results as a function of map position. We also performed outlier analyses on these data using the software BayeScan ST estimates. Ten pilot runs of 5,000 iterations and an additional burn-in of 50,000 iterations were first performed. We then used 100,000 iterations to identify loci under selection based on locus-specific Bayes factors. Strong evidence for selection is indicated by a Bayes factor above 10, or log10 = 1.0 We calculated FIn order to visualize genome-wide haplotypic structure in the association population, graphical genotypes were constructed by defining haplotypic blocks of 25 or more consecutive SNPs  was recorded at the R-5.1 reproductive stage. The R-5 stage commences at the onset of flowering, and is divided into substages according to the percentage of disc florets that have opened; R-5.1 corresponds to the stage at which 10% of the disc florets have opened. The total number of branches per plant (hereafter referred to as “branching”) was measured in the field at the R-9 reproductive stage. This stage is regarded as physiological maturity and is characterized by the presence of yellow/brown bracts on the back of the sunflower head. Four plants per accession were scored for each replicate at each of three locations .P<0.001) genotype x environment (G×E) interactions (data not shown), all subsequent analyses were performed separately by location. At each location, the entry  was treated as a fixed effect and blocks and reps were treated as random effects. For branching, the block and rep effects were not significant in the model and thus raw means were used for association testing (below). Because there were significant block and rep effects for DTF, the least-squares means (LS means) were used for the association mapping of this trait. Finally, variance components using the VARCOMP function in SAS were calculated for branching and DTF and were used to estimate broad-sense heritabilities (2H) as the total genotypic variance divided by the total phenotypic variance.Data were analyzed using SAS software version 9.3 . We calculated Pearson pairwise correlation coefficients for the branching data and the average DTF across locations using PROC CORR and corrected the resulting significance levels for multiple tests using a sequential Bonferroni correction (Holm 1979). We also analyzed our data using the GLM procedure of SAS. Because our initial analyses revealed highly significant  probability distribution. These quantile-quantile (q-q) plots indicate the extent to which the analysis produced more significant results than expected by chance. Models that follow the expected line more closely are assumed to have produced fewer false positives. Given that non-independence of linked makers in the dataset could lead to overly conservative significance thresholds Linear model testing was performed by plotting the observed Where possible, to enable the identification of novel genetic effects, we also compared the genetic map positions of significant associations to those of previously mapped branching and flowering time QTL. This was done by projecting QTL onto the sunflower consensus map  based on shared markers. Note that some previous QTL results could not be included in this comparison due to a lack of shared markers and/or differences in linkage group nomenclature. The linkage group names were standardized by Tang et al. The total number of readily scorable, bi-allelic SNPs in the focal population was 5,788. The number of SNPs with a MAF ≥10% was 5,359. Expected heterozygosity, or Nei's unbiased gene diversity, averaged 0.404±0.005 (mean ± standard error), and ranged from 0.007 to 0.5. Observed heterozygosity averaged 0.034±0.0044, and ranged from 0 to 0.38. Gene diversity and observed heterozygosity for each line classification grouping are found in K = 3 (hereafter referred to as Q = 3 corresponding to the Q matrix for the association testing results below), providing support for the existence of three genetically distinct clusters in our association panel. STRUCTURE results are grouped and graphed according to the line classifications (see Methods) in K and the mean likelihood values are plotted in Our STRUCTURE results using the full set of SNPs with MAF ≥10% indicated that Relative kinship was also estimated using the full set of markers (MAF ≥10%). Approximately 60% of the pairwise kinship estimates were near zero , indicating the lines were essentially unrelated . The remr2) plotted for the ordered markers (MAF ≥10%); the x- and y-axes correspond to the 17 LGs in sunflower. Regions of the genome where LD extends for considerable genetic map distance are visible as yellow to red squares on the figure . r2) for 10,000 random pairs of SNPs within 50 cM as a function of genetic map distance between SNPs for the 17 LGs. Looking across chromosomes, different overall patterns of LD are apparent. For example, LG 10 exhibits a relatively slow overall decay in LD, largely due to strong haplotypic structure across a portion of the chromosome . On a per chromosome basis, the average genetic distance at which r2 dropped below 0.1 ranged from 6.95 cM to 12.6 cM with the medians ranging from 3.93 cM to 10.1 cM. The sliding window analysis of r2 further illustrates the variability in LD across the genome  are summarized in see also , wherease genome . In somee genome .ST between the two primary breeding pools  for the full set of markers (MAF ≥10%), plotted the results against genetic map position  on several chromosomes, including portions of LGs 8, 10, and 13, and a single marker on LG 14.We also estimated Fposition , and tesP<0.01) for both traits studied. An important consequence of such G×E interactions is that different associations may be detected across environments; thus, we performed the association analyses separately for each location. The estimates of broad-sense heritability for branching and DTF were 0.861 and 0.124, respectively. The number of plants that showed a complete lack of branching was 79, 110, and 70 in Georgia, Iowa, and British Columbia respectively. The number of branches per genotype averaged 7.3, 7.2, and 7.6 and ranged from 1–29, 1–19, and 1–30 in Georgia, Iowa, and British Columbia respectively. DTF averaged 57.1 (range 41–77), 68.7 (45–95), and 80.4 (63–104) in Georgia, Iowa, and British Columbia respectively.Our association mapping population exhibited substantial phenotypic diversity for both plant architecture and flowering time, expressed here in terms of branching and DTF. Significant positive correlations were found for both branching and flowering time across locations , whereas there was an overall significant negative correlation between branching and DTF  provided fewer significant results than expected by chance, suggesting that these models may be overly conservative  and the SSRs (FST = 0.047). The much larger number of markers in the present study has, however, allowed us to refine our earlier findings. Notably, we found evidence for the presence of three genetically distinct groups within the germplasm collection. One of these groups was primarily composed of the RHA lines, while a second group consisted of a large number of HA lines, and the third included a more diverse assemblage of lines. The PCoA likewise demonstrated a split between the RHA and HA lines, with an even clearer division between the RHA-oil and HA lines. This genetic distinction between B and R lines is expected given the breeding history of sunflower, which involves the maintenance of distinct gene pools to maximize heterosis in hybrid crosses In terms of overall population structure, our results are largely in agreement with our prior analyses based on a much smaller set of simple-sequence repeat markers (SSRs) 2r≤0.10) within 3 cM. In some instances, however, LD remained elevated (on average) over greater distances  and the plots of population differentiation (FST) vs. genetic map position. In both cases, spikes are clearly visible in that same region along LG 10 , 12, 13 (a and b) and 17  [9], [59r2 and FST , it seems likely that the driving force behind the pattern observed on LG 10 was selection on branching. The situation on LGs 8 and 13 is less clear; the observed patterns may have been driven be selection on branching, flowering time, disease resistance, or some combination thereof. It must be kept in mind, however, that the branching associations that we detected on LG 8 were most apparent in the kinship-only model, and disappeared almost entirely when we controlled for population structure. As such, this result in particular may have been a byproduct of population structure as opposed to a true functional association.In terms of the relationship between marker-trait associations and both LD and population differentiation, the branching and DTF associations co-localized with spikes in r2<0.1 and over 85% had an average r2<0.2. As such, future analyses aimed at assaying genetic variation at a much higher density , United States; Iowa (IA), United States; and British Columbia (BC), Canada. Map from d-maps.com.(PDF)Click here for additional data file.Figure S2K and log-likelihood values for the STRUCTURE analyses.Summary of STRUCTURE results. The plots of the Delta(PDF)Click here for additional data file.Figure S3Principal coordinates analysis (PCoA) using all polymorphic SNP markers. Line classifications were simplified to HA, Other, RHA, and RHA-Oil in order to improve viewing of the figure. See text for details.(PDF)Click here for additional data file.Figure S4Plot of the frequency and distribution of kinship, or pairwise relatedness, amongst accessions in the association population. Values of 0.5 or greater were grouped into a single category.(PDF)Click here for additional data file.Figure S52r). Plots of the squared allele frequency correlations (2r) as a function of genetic map distance between 10,000 randomly selected pairs of SNPs separated by 50 cM or less. The red line on each graph summarizes the observed r2 values as a function of map distance using the ksmooth function in the statistical programming language R (see text for details).Linkage group plots of the squared allele frequency correlations ((PDF)Click here for additional data file.Figure S6E) across the sunflower genome.Sliding window analysis of genetic diversity. Sliding window analysis of the unbiased expected heterozygosity (UH(PDF)Click here for additional data file.Figure S7Correlation matrix for the branching and flowering time (DTF) data across the three locations. The cells are color coded to indicate positive (red) or negative (blue) correlations. Significant correlations after correcting for multiple tests are starred (see text for details).(PDF)Click here for additional data file.Figure S8Graphical genotypes for the 17 LGs across the full population of 271 accessions. Accessions were grouped according to the previously defined accession categories as in (PDF)Click here for additional data file.Figure S9Co-localization of branching associations and QTL. Comparison of genetic map positions for significant associations and previously identified branching QTL.(PDF)Click here for additional data file.Figure S10Co-localization of flowering time associations and QTL. Comparison of genetic map positions for significant associations and previously identified flowering time QTL.(PDF)Click here for additional data file.Table S1Association population with class and USDA designation.(XLSX)Click here for additional data file.Table S2Association mapping results for branching in the three locations. Note that, because the sign of the effect estimates is a byproduct of arbitrary allele assignments, these data are presented as absolute values.(XLSX)Click here for additional data file.Table S3Reference individuals for graphical genotypes.(XLS)Click here for additional data file.


Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs.We predicted GEBVs for one quantitative trait in a dataset simulated for the QTLMAS 2010 workshop. Predictive accuracy was measured as the Pearson correlation between GEBVs and observed values using 5-fold cross-validation and between predicted and true breeding values. The importance of each marker was ranked using RF and plotted against the position of the marker and associated QTLs on one of five simulated chromosomes.The correlations between the predicted and true breeding values were 0.547 for boosting, 0.497 for SVMs, and 0.483 for RF, indicating better performance for boosting than for SVMs and RF.Accuracy was highest for boosting, intermediate for SVMs and lowest for RF but differed little among the three methods and relative to ridge regression BLUP (RR-BLUP). Genomic selection is a method for estimating GEBVs using dense molecular markers spanning the entire genome . Given ti) the number of markers can far exceed that of observations, (ii) all markers, including those with weak effects, highly correlated and interacting markers have a chance to contribute to the model fit, (iii) complex interactions between markers can be easily accommodated, (iv) they can perform both simple and complex classification and regression accurately, (v) they often require modest fine-tuning of parameters and the default parameterization often performs well [vi) they make no distributional assumptions about the predictor variables. Boosting is a stagewise additive model fitting procedure that can enhance the predictive performance of weak learning algorithms [RF has several appealing properties that make it potentially attractive for GS ,4: (i) trms well ,3, and b characterizes the bth RF tree in terms of split variables, cutpoints at each node, and terminal node values.where ΨrandomForest with decision trees as base learners [ntree = {500, 1000, 2000}, the number of SNPs randomly selected at each tree node, mtry = {0.5, 1, 2} × the default value of mtry of sample size/3 for regression, and the minimum size of terminal nodes of trees, below which no split is attempted, nodesize = 1. The parameter configuration with the highest prediction accuracy was ntree =1000, mtry = 3000 and nodesize =1. We ranked SNPs by the relative importance of their contributions to predictive accuracy, quantified by how much prediction error increased when the observations left out of the bootstrap samples, the out-of-bag data for a SNP, were randomly permuted while data for all the other SNPs were left unchanged [We implemented RF in the R package learners . Followilearners ,3, we evBoosting is an ensemble learning method for improving the predictive performance of classification or regression procedures, such as decision trees . Gradienmβ, m =1,2,…, M are the basis expansion coefficients, and b are simple functions of the multivariate argument x, with a set of parameters γ=.where gbm[We used regression trees as basis functions. Boosting regression trees involves generating a sequence of trees, each grown on the residuals of the previous tree ,9. PrediSVMs perform robustified regression for quantitative responses by exploiting the relationships between observations by arraying predictors in observation space using a set of inner products. For regression with a quantitative response, SVM uses the modelh(x)T, which can be linear (or nonlinear) transformations of one (or more) predictors (x), are additively combined with the vector of weights (β). We used the “ε-insensitive” SVM regression that uses only residuals smaller in absolute value than some constant (ε) and a linear loss function for larger residuals. This is a robustified regression for which the minimization exercise can be written in regularized sum of squares form [where the basis functions, res form ,6 as:(4)whereε-insensitive” error measure, ignoring errors less than ε, λ is a positive constant that controls the trade-off between the approximation error and the amount up to which deviations larger than ε are tolerated to get solutions for the SVM regression problem, y is a quantitative response and is an “the form ,6:(6)K is a N × N symmetric and positive definite matrix [K, a flexible fitting is achieved by transforming the cross-products using the kernel function ) that alters how two observations are related to each other.where e matrix . Typicalε-insensitive SVM regression with a linear kernel to predict GEBVs in the R package e1071[ε = 10 and a regularization (cost) parameter (λ > 0) of λ = 0.001 determined by grid search.We used the age e1071 with an We used 5-fold cross-validation and the Pearson correlation between the simulated values and predicted GEBVs from the validation set and between the predicted and true breeding values (TBVs) for the non-phenotyped individuals constituting the fifth generation to quantify the predictive accuracy of each method. The training and validation sets respectively contained 60 and 15 crosses and encompassed all phenotyped individuals except the 20 founders.The correlations between the simulated values and predicted GEBVs indicated better performance for boosting and SVMs than for RF Table . The cor-Table RF produced reasonable importance rankings of the SNPs Figure , which cThe two ensemble methods can accommodate complex relationships and interactions (epistasis), which is a potential advantage, but the simulated data did not display many such interactions. A few simulated interacting SNPs with large effects were ranked highly but not top-ranked by RF possibly because RF and boosting had to randomly subsample the 10031 predictors. Thus, it may happen that the SNPs closest to a QTL are not sufficiently frequently sampled, so that the signal of the QTL is captured by other more distant SNPs. Consequently, the signal of a QTL gets blurred relative to classical QTL mapping approaches, which always scan all the markers. This may be one reason that these methods may not perform as well as some other much simpler competitors . Nevertheless, for data with complex traits controlled by many genes that show epistatic interactions, the machine learning methods hold much promise and perhaps may even outperform BLUP. Not surprisingly, Moser et al. [Predictive accuracies of all three methods were remarkably similar, but boosting and SVMs performed somewhat better than RF. Although boosting was only slightly better than the other methods, it holds perhaps the greatest promise for GS because of its wide versatility, allowing it to assume simpler, faster and more interpretable forms, such as componentwise boosting, able to incorporate automatic predictor selection.The authors declare that they have no competing interests.JOO conceived the study, conducted the statistical analysis and drafted the manuscript. HPP participated in discussions, helped refine the manuscript and oversaw the project. TSS participated in discussions, data preparation and analysis, and writing of the manuscript. All the authors read and approved the manuscript.


The proteins, tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1), act in concert to balance thrombus formation and degradation, thereby modulating the development of arterial thrombosis and excessive bleeding. PAI-1 is upregulated by the renin-angiotensin system (RAS), specifically by angiotensin II, the product of angiotensin converting enzyme (ACE) cleavage of angiotensin I, which is produced by the cleavage of angiotensinogen (AGT) by renin (REN). ACE indirectly stimulates the release of t-PA which, in turn, activates the corresponding fibrinolytic system. Single polymorphisms in these pathways have been shown to significantly impact plasma levels of t-PA and PAI-1 differently in Ghanaian males and females. Here we explore the involvement of epistatic interactions between the same polymorphisms in central genes of the RAS and fibrinolytic systems on plasma t-PA and PAI-1 levels within the same population (n = 992). Statistical modeling of pairwise interactions was done using two-way ANOVA between polymorphisms in the ETNK2, RENIN, ACE, PAI-1, t-PA, and AGT genes. The most significant interactions that associated with t-PA levels were between the ETNK2 A6135G and the REN T9435C polymorphisms in females (p = 0.006) and the REN T9435C and the TPA I/D polymorphisms (p = 0.005) in males. The most significant interactions for PAI-1 levels were with REN T9435C and the TPA I/D polymorphisms (p = 0.001) in females, and the association of REN G6567T with the TPA I/D polymorphisms (p = 0.032) in males. Our results provide evidence for multiple genetic effects that may not be detected using single SNP analysis. Because t-PA and PAI-1 have been implicated in cardiovascular disease these results support the idea that the genetic architecture of cardiovascular disease is complex. Therefore, it is necessary to consider the relationship between interacting polymorphisms of pathway specific genes that predict t-PA and PAI-1 levels. Preventing excessive blood loss following vessel injury is controlled through a process known as hemostasis. The tight regulatory mechanisms of hemostasis manage this response to prevent thrombus formation. One mechanism involved in this balance is fibrinolysis, the physiological breakdown of fibrin, an essential component of blood clots. Upon release of the serine protease tissue plasminogen activator (t-PA) from vascular endothelial cells, circulating plasminogen is cleaved to produce plasmin, a proteolytic enzyme responsible for the degradation of fibrin In the present study, polymorphisms have been selected from genes in the renin-angiotensin and fibrinolytic pathways to assess the genetic contribution to the protein concentrations of t-PA and PAI-1 in a large-scale population-based cohort; this study supplements the available literature that is primarily based on high-risk populations. To this end, we have designed complementary population based studies in the Netherlands and Ghana The population-based sample from urban West Africa analyzed in this study has been described previously Blood samples were collected for measurement of fasting glucose, lipids, t-PA and PAI-1 levels by commercially available assays, protocols have been described elsewhere DNA isolation was carried out on-site using PureGene DNA purification kits (Gentra Systems). Polymorphisms were detected using TaqMan probes and PCR primers designed through the Assays-by-Design service of Applied Biosystems. TaqMan SNP Genotyping Assays were processed and analyzed according to the manufacturer's recommendations with an ABI 7900 HT Fast Real Time PCR system and the SDS 2.0 genotype calling software (Applied Biosystems), respectively. The nine polymorphisms genotyped are listed in The variants selected for this study included two polymorphisms in the renin gene (REN), two polymorphisms in the ethanolamine kinase gene (ETNK2), and one in each of the angiotensinogen (AGT), ACE, t-PA and PAI-1 genes. Note that although ETNK2 is not expected to affect fibrinolytic balance per se, the SNPs in this gene were chosen because ETNK2 is less than 5 KB from REN, and these may represent or tag variants in REN or variants that affect REN expression. All of these genes have been associated with cardiovascular endpoints or as functional variants associated with plasma PAI-1 and t-PA concentrations 2 test. The baseline characteristics of the population-based sample are provided in Baseline characteristics for continuous and categorical data have been reported as mean ± standard deviation and as group percentages, respectively. Comparisons of continuous characteristics between males and females were done using the Student's t test. Comparisons of categorical characteristics between males and females were done with the χ2 values of the interaction term in each statistically significant model as an estimate of the amount of variability explained by the SNP pairs. All tests were carried out in males and females separately as the distribution of PAI-1 have been previously shown to be sex specific The purpose of the statistical analysis in the current study was to assess the role combinations of genetic polymorphisms, within the renin-angiotensin and fibrinolytic pathways, play in the prediction of plasma t-PA and PAI-1 levels. The null hypothesis being tested is that the effect of any single polymorphism on t-PA and PAI-1 plasma levels is independent of the effects of any other polymorphism. To test the null hypothesis we used two-way analysis of variance (ANOVA) to calculate an interaction term. The dependent variable (t-PA or PAI-1) has been natural log transformed for all analyses for normalization as previously reported More specifically, all genotypes were coded to allow for assessment of additive effects , dominant effects  and recessive effects . Using ANOVA, all 28 possible pairs of genotypes  were evaluated for nine possible interaction effects including additive by additive, additive by dominant, additive by recessive, dominant by additive, dominant by dominant, dominant by recessive, recessive by additive, recessive by dominant and recessive by recessive. In total, 970 tests were performed. After adjusting for missing genotypes following the conversion of the genotype coding to account for the various interaction effect models, 243 tests were carried out in females per phenotype and 242 tests were carried out in males per phenotype. We set three thresholds of significance for making inferences about the epistatic effects of the polymorphisms. First, we use an uncorrected significance level of α = 0.10 to indicate suggestive evidence for an interaction. This liberal threshold was set as an exploratory cut-off with consideration for the known role that the pathways and genes included in this study play in t-PA and PAI-1 biochemistry and the likelihood that the chosen polymorphisms are strong candidates. Second, we use an uncorrected significance level of α = 0.05 to indicate evidence for an interaction. Our most stringent condition is a significance level of α = 0.025. This significance threshold represents a correction for the different genetic models tested and was derived from 1000 Monte Carlo simulations under the null hypothesis and has been described elsewhere http://cran.rproject.org/web/packages/extRemes/index.html).As the final step in this study we selected a subset of the results containing the 112 most significant interaction pairs (i.e. 28 possible genotype pairs by sex and phenotype) to create a new data set of minimum p-values from the set of different genotype codings. Because we selected for rare or extreme events we would not expect these data to follow a normal distribution. As such, this subset of the results was modeled by fitting the generalized extreme value distribution. The parameters of this distribution are location, scale, and shape. Using the maximum likelihood estimates of these parameters from the model we calculated the probabilities of getting the observed p-values for the most significant interacting pairs of SNPs. This analysis was carried out in R using the package extremes (A summary of the results are presented as network diagrams in 2 = 1.82%), but the largest r2 was for an interaction between markers REN T9435C and ETNK2 A6135G  (2 = 1.98%) . We also= 2.27%) . The mos = 1.98%). Interac = 1.98%) and 2. Ill model .2 = 1.80%) (2 = 1.08%)  . The lar = 2.62%). We also = 2.62%). The mos= 1.08%) . InteracFocusing on the results displayed in the network plots , 2, 3, 4While some of the interactions are shared between the sexes, there are also many differences. For example, we have strong evidence supporting an interaction effect of ETNK2 A6135G and REN T9435C on plasma t-PA levels in females (p = 0.006) while there is no supporting evidence in males (p = 0.613). Similarly, we see strong evidence for an interaction effect of REN G6567T and AGT M235T on plasma t-PA levels in males (p = 0.008) but not in females (p = 0.496). The same is true when considering the strong evidence in support of an interaction effect between TPA I/D and REN T9435C on plasma levels of PAI-1 in females (p = 0.001) compared to that in males (p = 0.971).It is also of interest to examine the structure of these interactions. Interaction plots of the most significant findings for each sex and phenotype pair are presented in Here we have described and illustrated the 31 most significant gene-gene interactions. However, a total of 86 of the 970 tests were significant or marginally significant (p<0.10). Of the most significant models three were characterized as dominant-dominant interactions, five were characterized as dominant-additive interactions, eleven were characterized as dominant-recessive interactions, one was characterized as an additive-additive interaction, seven were characterized as recessive-additive interactions, and the remaining four were characterized as recessive-recessive interactions.The fibrinolytic system maintains balance through a series of enzymatic reactions, regulating the dissolution of thrombi. The principal regulators of the system are t-PA and its primary inhibitor PAI-1. Furthermore, crosstalk exists between the fibrinolytic pathway and the renin-angiotensin pathway providing an additional layer of regulation. In this study we investigated the effects of epistatic interactions, between polymorphisms within genes of t-PA, PAI-1 and the renin-angiotensin system, on the plasma concentration of the t-PA and PAI-1 proteins. Our results support the idea that the genetic architecture underlying cardiovascular disease is complex and requires consideration of multiple interacting polymorphisms in concert to reliably predict the levels of associated proteins, t-PA and PAI-1.In a previously published study we reported that anthropometric, metabolic, and genetic cardiac risk factors, in this population, are sex-dependent We chose not to correct for multiple testing but instead to validate our findings by using extreme value analysis. We found that 15 of the 31 observed p-values representing pairwise interactions had a probability of less than 10% while 5 of the observed p-values had a probability of less than 5%. Based on these results we believe that at a minimum these 15 pairwise interactions represent true statistical epistasis. These validated interactions coincide with the most statistically significant results illustrated in Evaluation of all pairwise combinations of the eight measured polymorphisms revealed many statistical interactions involving polymorphisms within the renin-angiotensin system, as well as interactions involving polymorphisms between the renin-angiotensin system and polymorphisms in the t-PA or PAI-1 genes. Because this was an exploratory analysis we tested the 28 possible pairwise interactions per sex and phenotype combination across each of the 9 interaction models but selected only the single best model per interacting pair. 2 values for all statistically significant interacting partners. These values serve as an estimate of the amount of variability explained by each interaction in each model. Our two-locus interaction terms explain as much as 2.6% of the variability in protein levels, but even these modest effect sizes are larger than those of the single SNP analyses  and REN T9435C  do show individual associations, but, notably, when combined in a two-way model the correlation becomes stronger . If these two SNPs had independent main effects on t-PA levels we would expect the r2 value of the two-way model to equal the sum of the individual r2 values. It is important to note that the complete genetic model will likely include main effects, numerous gene-gene and gene-environment interactions Nonetheless, in some cases we are able to explain more than 2% of the variability in protein levels just by focusing on two-locus interaction terms. This underscores the necessity of considering gene-gene interactions when studying common human phenotypes/diseases.In addition to reporting p-values we have also reported rThe variants ETNK2 A6135G, REN G6567T, TPA I/D, and PAI 4G/5G, were the most frequently observed in our models, appearing in all of the network diagrams. Of these variants ETNK2 A6135G is involved in the most statistically significant interactions. The ETNK2 gene codes for a protein that catalyzes the first reaction the CDP-ethanolamine pathway, resulting in production of the major membrane lipid phosphatidylethanolamine (PtdEtn), this gene was chosen not for its function but because it is located on chromosome 1q32 near the renin gene and may include alternative promoter regions for renin. It is known that ETNK2 −/− mice often have failed fetal development at the late stages of pregnancy due to extensive placental thrombosis, suggesting a critical role for ETNK2 sequences in hemostasis By evaluating all pair wise combinations of these polymorphisms for association with t-PA and PAI-1 protein levels we have begun to assess the contribution statistical epistasis plays in defining genetic risk factors of cardiovascular disease. And because these genes have good prior evidence for biological interaction Table S1p-values for epistatic effects between polymorphisms in association with plasma t-PA levels for females. All nine interaction models are presented. Each table displays the results of one interaction pairing as indicated in the upper left-hand corner. The notation is column by row, for example, a DxA interaction indicates that the SNPs across the columns are encoded as dominant while the SNPs down the rows are endoded as additive. p-values <0.10 are displayed in boldface. D  =  dominant, A  =  additive, R  =  recessive.(DOC)Click here for additional data file.Table S22) of t-PA and PAI-1 levels explained by models combining two SNPs without an interaction term (SNP1 + SNP2), models combining two SNPs with an interaction term (SNP1*SNP2) and the variability obtained by extracting only the interaction term from the full model in females.A. Variability Click here for additional data file.Table S3p-values for epistatic effects between polymorphisms in association with plasma PAI-1 levels for females. All nine interaction models are presented. Each table displays the results of one interaction pairing as indicated in the upper left-hand corner. The notation is column by row, for example, a DxA interaction indicates that the SNPs across the columns are encoded as dominant while the SNPs down the rows are endoded as additive. p-values <0.10 are displayed in boldface. D  =  dominant, A  =  additive, R  =  recessive.(DOC)Click here for additional data file.Table S4p-values for epistatic effects between polymorphisms in association with plasma t-PA levels for males. All nine interaction models are presented. Each table displays the results of one interaction pairing as indicated in the upper left-hand corner. The notation is column by row, for example, a DxA interaction indicates that the SNPs across the columns are encoded as dominant while the SNPs down the rows are endoded as additive. p-values <0.10 are displayed in boldface. D  =  dominant, A  =  additive, R  =  recessive.(DOC)Click here for additional data file.Table S5p-values for epistatic effects between polymorphisms in association with plasma PAI-1 levels for males. All nine interaction models are presented. Each table displays the results of one interaction pairing as indicated in the upper left-hand corner. The notation is column by row, for example, a DxA interaction indicates that the SNPs across the columns are encoded as dominant while the SNPs down the rows are endoded as additive. p-values <0.10 are displayed in boldface. D  =  dominant, A  =  additive, R  =  recessive.(DOC)Click here for additional data file.Table S6Description of the data underlying the points in the interaction plots displayed in . The mean protein level, 95% confidence interval and number of individuals for each genotype combination (i.e. each data point of interaction plot) is presented.(DOC)Click here for additional data file.


We investigated the ability of several principal components analysis (PCA)-based strategies to detect and control for population stratification using data from a multi-center study of epithelial ovarian cancer among women of European-American ethnicity. These include a correction based on an ancestry informative markers (AIMs) panel designed to capture European ancestral variation and corrections utilizing un-thinned genome-wide SNP data; case-control samples were drawn from four geographically distinct North-American sites. The AIMs-only and genome-wide first principal components (PC1) both corresponded to the previously described North or Northwest-Southeast axis of European variation. We found that the genome-wide PCA captured this primary dimension of variation more precisely and identified additional axes of genome-wide variation of relevance to epithelial ovarian cancer. Associations evident between the genome-wide PCs and study site corroborate North American immigration history and suggest that undiscovered dimensions of variation lie within Northern Europe. The structure captured by the genome-wide PCA was also found within control individuals and did not reflect the case-control variation present in the data. The genome-wide PCA highlighted three regions of local LD, corresponding to the lactase (LCT) gene on chromosome 2, the human leukocyte antigen system (HLA) on chromosome 6 and to a common inversion polymorphism on chromosome 8. These features did not compromise the efficacy of PCs from this analysis for ancestry control. This study concludes that although AIMs panels are a cost-effective way of capturing population structure, genome-wide data should preferably be used when available. Genome-wide association studies (GWAS) have become an essential tool for discovering genetic predisposition to complex disease Although the confounding effect of population stratification has been acknowledged, it has been considered to be of practical concern primarily in admixed or mixed populations with ancestry from different continents Panels of SNPs have been designed to specifically detect and control for population stratification in European Americans These European AIM panels were designed with the objective of providing a cost effective way of controlling for stratification through the reduction of genotyping costs in candidate gene studies and validation studies This study compares the performance of controlling for PS through PCA using the Paschou et al. AIMs panel BRCA1 and BRCA2 mutation carriers and women with a prior history of ovarian, breast, endometrial, or early-onset colorectal cancer. All controls had at least one ovary intact at the reference date and were frequency-matched to cases on age-group. The study protocol was approved by the institutional review board at each center  and all study participants provided written informed consent.Details of the ovarian cancer GWAS are published Blood served as the source of genomic DNA. All samples were genotyped using the Illumina Infinium 610K Array and Illumina’s Genome Studio™ software was used to perform automated genotype clustering and calling. After the quality control described in Permuth Wey et al PCA was performed on 4 sets of markers: (1) The Paschou European AIMs panel (Paschou PCA), (2) all available GWAS markers from the Illumina 610K array genotyped in this study (GWAS PCA), (3) all available markers using controls only (GWAS control PCA) and (4) all available markers with removal of markers in high LD regions (GWAS LD PCA), using the snpMatrix package in R software T. The eigenvectors correspond to the PC scores (S) which can then be used to calculate the loadings (B) of the SNPs for each PC through multiplication with the diagonal matrix of the eigenvalues (V):Given a data matrix X with N individuals in the rows and P SNPs in the columns, we calculated the eigenvalues and eigenvectors of the N by N matrix, XXTB). Only the first 10 eigenvalues are retained throughout these calculations.For GWAS control PCA, the controls only were used to obtain B and then the PC scores are obtained through multiplying the entire data set to these loadings  genotypic effect. The inflation factors were estimated by the ratio of the observed trimmed mean to its expected value under the chi-squared assumption. Association tests of the PCs to site and disease were conducted via multiple linear regression implemented in R. Each PC was regressed on disease status and site.In additon, maximum likelihood estimation was used to determine estimates for Northwestern European, Southeastern European and Ashkenazi Jewish ancestry based on an additonal European AIM panel by Price et al We compared the GWAS and Paschou PCs on the basis of their correlations to one another, their associations with disease controlling for site and their impact on inflation factor, where we relied on their association to site as proxy for their relevance to ancestry. The correlation between the first PCs (i.e PC1) of the Paschou PCA and the GWAS PCA was 0.79. This first PC corresponded to the Northwest-Southeast axis of variation that the Paschou et al panel was solely designed to capture. A separate analysis using Price et al’s panel confirmed this see  [16]. Alth PC. This additonal structure was corroborated by exploring the first 100 PCs and their association to site. Including all pairwise site comparisons, the greatest significance was restricted to the first 20 PCs  in which values of the case PCs were obtained as described in Although PCs 1 and 2 of GWAS control PCA were very highly correlated to their counterparts in GWAS PCA (ρ>0.9), PCs 3 and 4 were also correlated, albeit to a lesser degree  in which only the SNP with the highest loading was retained to represent each of the LD regions see  to deterThe GWAS PC1, PC3 and a fraction of the GWAS PC4 axes of variation were largely retained by the GWAS LD PCA, while the PC2 axis of variation is lost see . GWAS LDThinning GWAS PCA LD regions resulted in less effective control of the inflation factor see . Only thGWAS PCs 1, 3 and 4 are each highly significantly associated with study site after adjustment for case-control status see . Each PCA plot of PC1 against PC3 shows that variation represented by PC 3 was within individuals of Northwestern European ancestry see . It alsoEven though the information provided by all the SNPs genotyped on a genome-wide panel can be used to control for population structure via PCA, using a smaller predesigned AIMs panel may be thought to confer certain advantages. First, controlling for stratification using the GWAS data may undesirably reduce the case-control variation that the study seeks to identify, while the chance that an AIMs panel will include disease associated SNPs is remote. Secondly, corrections based on un-thinned GWAS data may highlight local structure in place of genome-wide, ancestral variation and, hence, compromise the effectiveness of control for PS. AIMs panels deliberately exclude redundancies between SNPs and therefore avoid this problem. These potential disadvantages of GWAS-based corrections may be compounded in populations with more subtle genome-wide structure and stronger patterns of local LD such as the European American population.We found that these drawbacks were not realized in our analysis of the ovarian cancer GWAS data. In particular, we found that a full GWAS PCA recapitulated structure present within the control individuals and was therefore not capturing a significant amount of case-control variation. This is not surprising since case-control variation, both genome-wide and local, will seldom be large enough to overtake genome-wide sources of population variation in a PCA. This and the significantly reduced inflation factors compared with those achieved using the Paschou panel suggests that the association to ovarian cancer found for GWAS PCs 1, 3 and 4 represent a real correction for PS even after accounting for site, one that is likely due to the greater precision afforded by using the entire GWAS data set. Note that only 460 of the Paschou panel’s 500 markers were available to us in the ovarian cancer GWAS data set, thus reducing its power somewhat. However, this will often be the case when using a pre-designed AIMs panel for population structure control in a GWAS analysis.Potential pitfalls of not taking into account the effect of regions of high local LD on controlling for PS using PCA can be classified into two case scenarios:(1) the functional variant lies outside of these regions; in this case PCs that only represent variation in these regions will not effectively control for PS, i.e. the inflation factor is not sufficiently lowered, and (2) the functional variant lies within such a region; in this case the PCs that strictly represent the local structure of that region may control away the association, i.e. the inflation factor is lowered too much. Although in this study the regions of high local LD changed the results of the GWAS PCA, the practical implications of this on testing SNP association to ovarian cancer were questionable.Only GWAS PC2 qualified as an example of this first phenomenon. Its disappearance in GWAS LD PCA and its lack of impact on the inflation factor and association to disease show that it is primarily representing local structure in the HLA region and suggests that functional variants are unlikely to lie within that region. Even though the HLA LD region contained enough variation to fully account for a high ranking PC, the effect of including this PC when controlling for stratification is not very different from that of including any number of non-informative PCs when routinely taking the first 10 PCs as covariates see . Which oWe did not observe an example of the second phenomenon noted above in this data set. Instead, the axes of variation described by the PCs that were found to be associated with disease  were retained to a considerable extent when the regions of high local LD were thinned. This suggests that although these PCs show high correlation to local LD regions and these regions can potentially harbor functional variants, the PCs represent real, ancestral, genome-wide structure and not just variation within the LD region.Using schizophrenia GWAS data on European Americans, Zou et al. found the same LD regions as the current study, and an additional peak on chromosome 17. Using a shrinkage method to control for LD effects in PCA, they found that all peaks disappear with the exception of the LCT region peak. They conclude that it is important to account for LD when using PCA to control for PS less likely to overshadow ancestral population variation when using un-thinned GWAS data for PCA, in studies of less homogenous populations, such as those that bring together subjects from different continental ancestries and/or that focus on admixed populations. In conclusion, we recommend that a careful analysis using PCA of the full data set be performed prior to deciding how to control for PS. Use of PCs from a full GWAS PCA may provide better control for PS and result in a lower inflation factor. An additional benefit is that such an analysis may aid discovery and removal of outliers and or related individuals that may be missed through other quality control/quality assessment procedures. In this study, the outliers we removed significantly influenced the PCs from the original GWAS control analysis and proved to contain related individuals missed by earlier QC filters.Population stratification will vary from study to study depending on the characteristics of the study population and the disease and it may therefore be argued that the results presented here are specific to this study. However, populations of European ancestry such as the one studied here are particularly homogeneous and case-control or local LD variation will be It should be pointed out that the Paschou panel did remarkably well in capturing a great proportion of the PS for such a small number of SNPs. In fact, in a more recent paper the investigators behind the Paschou panel show that it is possible to predict individual ancestry within Europe down to a few hundred kilometers from the origin, using panels of 500 or 1,000 SNPs It is interesting to note that the association between GWAS PCs 1,3 and 4 and disease persists even after taking into account site differences see . Taking Our findings confirm what is known about immigration history of North America th century. It wasn’t until after WWII that Toronto became the extremely diverse city it is with immigrants from all over the world Toronto is similar to the South coastal United States in that its population was dominated by British citizens at the start of the 20Genome-wide data PCA allows for a detailed assessment of the population structure present in the geographic region(s) being studied as it is relevant to the disease phenotype in a way that a specific AIMs panel based PCA cannot. Utilizing genome-wide PCA data can also inform investigators about genomic regions in LD that correlate to ancestry and disease and that are interesting population features in themselves. Although AIMs panels are an efficient cost-effective way of capturing population structure, genome-wide data should preferably be used whenever it is available.Figure S1Screeplots for Paschou et al. AIMs PCA and GWAS PCA.(TIF)Click here for additional data file.Figure S2Association to site of first 100 PCs in GWAS PCA. P-values for each pair-wise comparison among the four sites are given.(TIF)Click here for additional data file.Figure S3Scores for PCs 1 and 3 of GWAS PCA across the 4 sites. TOR, TBO, NCO and MAYO individuals are highlighted in blue in the respective panel.(TIF)Click here for additional data file.


Parameters are estimated under maximum likelihood framework implemented with the EM algorithm. Extensive simulations show good performance in a variety of scenarios. The utility of the method is demonstrated by analyzing a published data set in an F2 family derived from C3H/HeJBir and C57BL/6 J mouse strains. Important cyto-nuclear interactions were identified. Our approach provides a quantitative framework for identifying and estimating cyto-nuclear interactions subject to genomic imprinting involved in the genetic control of complex traits.Cytoplasm contains important metabolism reaction organelles such as mitochondria and chloroplast (in plant). In particular, mitochondria contains special DNA information which can be passed to offsprings through maternal gametes, and has been confirmed to play a pivotal role in nuclear activities. Experimental evidences have documented the importance of cyto-nuclear interactions in affecting important biological traits. While studies have also pointed out the role of interaction between imprinting nuclear DNA and cytoplasm, no statistical method has been developed to efficiently model such effect and further quantify its effect size. In this work, we developed an efficient statistical model for genome-wide estimating and testing the cytoplasmic effect, nuclear DNA imprinting effect as well as the interaction between them under reciprocal backcross and F One of the central foci in biological study is to unravel the genetic secrets of complex traits of agricultural, evolutional and biomedical importance. Quantitative trait locus (QTL) mapping has been the major tool for this purpose over decades Genomic imprinting, one of the major epigenetic phenomena, plays key roles in controlling embryonic growth and development Genomic imprinting effect is considered as one type of parent-of-origin effect due to allelic effect with specific parental origin. In contrast to this, maternal effect or cytoplasmic effect is also considered as one type of parent-of-origin effect in which the offspring's expression is influenced by maternal parent. For example, a mother's genotype, even if not transmitted to her offspring, may influence in utero conditions and increase risk and/or interact with genetic predisposition for particular diseases among those offspring On the other hand, the existence of such parent-of-origin effects may lead to incorrect interpretation of the  effects of particular genes when performing genetic mapping studies, unless such effects are appropriately accounted for in the analysis 2 design. When an F2 design is considered, sex-specific difference in recombination fractions, which is initially discovered in 2 family derived from two inbred mouse strains.In this work, we discuss potential scenarios of parent-of-origin effects, and present a statistical method to dissect the cyto-nuclear interaction effects subject to genomic imprinting. The developed framework is based on experimental crosses which can be realized through two different designs, reciprocal backcross and F2 design is proposed so that variations and interactions can be fully introduced.Consider a design initiated with two inbreed lines with two segregating alleles 2 design. Denote the left one as design 1's generates four possible allele-specific F2 genotypes. Assuming there is a cytoplasmic effect, F2 offsprings may show different phenotypes depending on the genotype of the maternal parental lines. For example, if maternal cytoplasmic effect exists, the offspring phenotypic value for 2 design, the two reciprocal heterozygotes For a particular cross, let 2 population initiated with cross 1 parents can be expressed as,For the Similarly for the For the reciprocal backcross design which consists of 2 design. For the purpose of illustration, the following estimation and inference is demonstrated through the F2 design. The same procedure applies to BC design too.For simplicity, we will use matrix form to rewrite the models. Let us denoteM and unknown parameters Statistical methods for QTL interval mapping based on a mixture model traced back to the work by Lander and Botstein 2 design we described above, there are four genotypes at each locus  can be achieved. Nevertheless, such imprinting estimation problem can be solved under the reciprocal backcross design illustrated in In this paper, we developed a statistical model to evaluate the cytoplasmic environment and nuclear gene interaction subject to imprinting effect within the framework of QTL interval mapping. The model that considers eight genetic factors which measure the degree of imprinting, cytoplasmic, additive, dominance effects as well as the interaction effects among them, provides a complete dissection of cyto-nuclear epistasis subject to imprinting effect. A number of hypothesis tests can be performed not only to assess major genetic effect(s) responsible for phenotypic variation, but also to find the statistical evidence for the existence of imprinting, cytoplasmic effect as well as the cyto-nuclear interactions. Simulation study showed relative good performance of the model under the FIn the real data analysis, one QTL located on chromosome 1 were found to have significant cytoplasmic, imprinting effect and cyto-nuclear interaction effects for spleen/body weight ratio. Other than that, no imprinting effect was found for all other QTLs, and only one on chromosome 3 that shows cytoplasmic effect for the MLN/body weigh ratio trait. It is worth mentioning that several QTL on chromosome 3 are detected by our model, but they are located relatively close to each other, as shown in Appendix S1Detailed derivation of the EM algorithm.(PDF)Click here for additional data file.