Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between ‘leaf senescence’ and ‘aspartic acid’, as well as between ‘days to maturity’ and ‘choline’. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes. A major issue in current biological research is to translate differences in phenotype to variation in genotype. Detailed knowledge of phenotypes and their underlying genetics is necessary, for example for plant breeding to meet its future challenges with respect to yield, quality and production in suboptimal environmental conditions . High-thWe here present a next level analysis of the information contained in QTL regions aiming at establishing such connections. The gene information contained in QTL regions is converted to Gene Ontology (GO) terms for gene function, notably the molecular function and biological process sub-ontologies . MoleculHere, we investigate and show how to compare and integrate macroscopic and molecular traits. To do so, we make use of semantic similarity , which iThe analysis requires QTL data for different types of traits . QTLs asOryza sativa) for macroscopic phenotype (phQTL) [To demonstrate the novelty and added value of the approach, we used public domain data for rice ( (phQTL) , 18, met (phQTL)  and expr (phQTL)  traits. For all genes, biological process (BP) terms and molecular function (MF) terms were predicted using BMRF –22 for BAs previously demonstrated for BP terms in the context of QTL candidate gene prioritization for macroscopic traits, overrepresentation analysis of gene function terms allows detailed characterization of traits . This prIn all three datasets, many of the links between gene function terms and traits confirm and/or strengthen prior understanding of the molecular basis of traits. For three traits in the phQTL dataset, ‘days to heading’, ‘days to maturity’ and ‘root volume’, examples of links with BP terms are shown in Links with the developmental trait ‘days to heading’ were presented previously : the BP Although fewer connections with MF terms are found for the traits from the phQTL dataset, also here known connections between macroscopic traits and MF terms are apparent. For example, the abiotic stress trait ‘relative shoot elongation under submergence’ is linked to two MF terms, both describing ‘DNA topoisomerase activity’. The role of topoisomerase in plant growth and development, including cell elongation, is well established .In the mQTL dataset, ‘succinate, malate and citrate transport’ terms are linked to the amino acid traits ‘glutamine’ and ‘leucine’. The connection between such transporters and amino acids agrees with the knowledge that amino acid biosynthesis uses intermediates from the citric acid cycle . The MF The BP terms overrepresented in the eQTL regions for the expression of the gene LOC_Os01g10504 (OsMADS3) in the eQTL dataset include ‘regulation of post-embryonic development’ and ‘regulation of multicellular organismal development’ . OsMADS3BP): fBP equals the number of links with BP terms obtained for a given trait, divided by the total number of links with both BP and MF terms obtained for that trait. Traits with mainly BP term links will have fBP close to 1, whereas traits with mainly MF links will have values close to zero; a value of fBP = 0.5 indicates that a trait has the same number of links with BP terms as with MF terms. Whereas the fBP for metabolite traits is distributed over the whole range of values  for two different traits is identical. In total, 69 such cases were obtained . For exaIf two traits have similar QTL regions, they are more likely to be associated with the same GO terms. However, 21 of the 69 trait pairs with identical GO terms have completely different QTL regions associated with the two traits in a pair. An additional 20 cases have only one QTL region which overlaps between the two traits in a pair. Because many pairs of traits have no or only limited similarity between their QTL regions, analyses of semantic similarity allow to integrate and compare traits at a higher level than just the similarity of their QTL regions. This also holds true for these five metabolite pairs with semantic similarity one mentioned above: for three of the five pairs of metabolite traits, there is no overlap or only a very limited overlap between the QTL regions. One example is the above mentioned pair of flavonoids, C-pentosyl-apigenin O-rutinoside and 3', 4', 5'-dihydrotricetin O-hexosyl-O-hexoside . Both arThe significance of the similarities obtained between traits was analyzed with a permutation test. As mentioned above, 69 pairs of traits were obtained with a similarity of 1; according to the permutation analysis this was significant with p = 0.002. Similarly, the number of trait pairs with a semantic similarity of at least 0.95 or 0.9 was also found to be significant (p = 0.001 in both cases).For any given trait, its semantic similarity with other traits displays a range of values. The semantic similarity values based on overrepresented BP terms for the trait ‘days to maturity’ relative to all macroscopic traits, metabolite traits and expression traits are plotted in This way, for each trait, the maximum semantic similarity with all other traits was obtainedseparately for macroscopic, metabolic and expression traits. The distribution of the maximum semantic similarity between each macroscopic trait and all other traits based on BP terms displays a range of values from ~ 0.25–1.0. This indicates that some macroscopic traits have a high semantic similarity with another macroscopic, metabolite or expression trait, whereas for other macroscopic traits, no such high semantic similarity with any macroscopic, metabolic or expression trait is observed . SimilarTo analyze the significance of the maximum semantic similarity of a trait with another trait, randomized trait-GO connections were used as input to generate expected values of maximum semantic similarity between traits. All trait-trait links were filtered based on significance using a stringent multiple testing correction (see http://itol.embl.de/tree/7716210613417711454683547 (BP terms) and http://itol.embl.de/tree/7716210613125231454685700 (MF terms). Three specific examples of small clusters  The macroscopic traits ‘leaf senescence’ and ‘chlorophyll content’ cluster with the metabolite trait ‘aspartic acid’ and three expression traits on the basis of semantic similarity in their BP terms . For som(ii) The macroscopic trait ‘days to maturity’ clusters with the two metabolite traits ‘tricin derivative’ and ‘choline’  on the b(iii) The macroscopic trait ‘root length’ clusters with the metabolite trait kaempferol and four other metabolite traits  on the bThese examples demonstrate that in addition to similarity relationships that confirm and/or validate biological knowledge, other similarity relationships are new or unexpected and may point to potentially new biological insights. Such new relationships illustrate how analyses on the basis of semantic similarity can provide potential molecular biomarkers for macroscopic traits and are likely to improve the understanding of the biological mechanism of the traits.The basis assumption underlying the overrepresentation approach is that in several of multiple QTL regions for a given trait, a causal gene is present, and that several causal genes have related or similar gene functions. We have demonstrated that macroscopic, metabolite and expression traits with multiple QTL regions in the crop rice can all be analyzed for enrichment of GO terms and all result in lists of significantly overrepresented GO terms.GO terms are an abstract representation of gene function, either in terms of a biological process (BP) or a molecular function (MF). The different trait types differ considerably in the number of overrepresented terms they link to. The number of links between metabolite traits and MF terms was larger than with BP terms, whereas the macroscopic traits linked more with BP terms. This difference may indicate that variation in macroscopic traits is predominantly influenced by biological processes , whereas variation in metabolite levels is largely influenced by well-defined biochemical (molecular) functions such as enzymatic reactions or transport. Such activities on, or resulting in, a given metabolite, may have a direct impact on metabolite level. Expression traits are more similar to macroscopic traits: more links were obtained with BP terms. This may seem a bit surprising at first, as expression traits are also molecular traits. However, it indicates that expression traits are much more complex than the term ‘molecular trait’ may suggests. Many more processes and factors establish the variation in expression than well-defined molecular functions can describe.The GO term overrepresentation analysis combines information from multiple QTL regions for a given trait to obtain links between trait and gene function terms. Traits with only one QTL region are not included in the analysis. It would however be straightforward to improve on the analysis by incorporating additional QTL data. With data from additional populations and/or different species, more traits can be included, and more accurate GO term lists will be obtained to connect and compare different traits to generate leads for future use or research.Overrepresented GO terms for the various traits create a common ground for comparison of heterogeneous trait types. For example, a link is predicted between the metabolite ‘choline’ and ‘days to maturity’. Such a relationship may result in a biomarker at the metabolite or gene expression level. To assess the predictive value of this potential biomarker, one could e.g. measure choline metabolite levels during growth and establish whether these are correlated to the trait of ‘days to maturity’. When validated, choline may be easier and cheaper to measure than ‘days to maturity’. The determination of a macroscopic trait is generally most time- and resources consuming. Therefore translation of such a trait into an easier-to-determine molecular trait is worthwhile even without full understanding of the underlying biology. This set-up is similar to the application of molecular biomarkers for disease traits in human health . In planThe metabolite QTLs were measured in flag leafs and in seeds. Any prediction about metabolite traits is therefore restricted to their role in these tissues. More generally, the experimental conditions that were applied during the experiments to obtain the QTL regions could influence our results. Given the lack of standardized descriptions of experimental conditions , it is cSemantic similarity based on overrepresented GO terms as here presented provides a new way of obtaining connections between different types of traits that may define novel markers for traits. The very many trait-trait relationships that do not yet translate easily in current biological models or knowledge could help direct or focus future academic or applied research. Similarity with other traits may generate hypotheses about their identity that can be experimentally investigated. For example, given that only 272 out of the 623 traits in the mQTL dataset have a known identity (characterized metabolites), the possibility to characterize unknown metabolites in terms of their similarity to known metabolites, is one of the attractive applications of the GO-term based method of linking traits here presented.This approach is complementary to the approach of finding direct correlation between macroscopic traits measurements and metabolite levels. Such approaches have been demonstrated for various plant systems , 40. DirOverlap between QTL regions associated with gene expression and QTL regions associated with trait values has been analyzed previously as an alternative approach to assess similarity between traits , 41. TheSemantic similarity of traits does not necessarily imply causation between the molecular and macroscopic traits involved, nor does it give information about the direction of possible causality. The direction of causality and whether there is a direct or an indirect influence of one trait on another are all issues for further study. For better understanding of the underlying biology, the direction of causality is relevant. Experiments are conceivable in which metabolite levels are altered to study the effect on the macroscopic trait, or conversely, in which the macroscopic trait is perturbed for example via mutation of a relevant gene, and the effect on metabolite levels is assessed. No such detailed biological insight is necessary for the use of molecular traits as easy biomarker for macroscopic traits.By connecting gene functions to different types of traits, different biological levels can be integrated and compared on a level that was not yet possible. This approach helps to disentangle the underlying mechanisms of traits and compare different traits with each other. It will help to direct future research aimed at better understanding and use of plant phenotypes in the context of plant genotypes, and ultimately enable more efficient marker-assisted breeding programs in next-generation ‘breeding by design’ , 43.The phQTL dataset contains QTL regions for macroscopic phenotypes from various studies assembled by Gramene , 18. TheQTL intervals reported as significant in the respective publications , 17, 19 To predict the biological process (BP) terms for each gene, BMRF was applied as described –22. PredSemantic similarity between traits was calculated trait-by-trait with the Gossto tool . With thmax-sim where sim is the semantic similarity value and simmax the maximum observed value over all pairs of traits. Clustering based on distance between traits was performed using the R-function hclust using complete linkage [To cluster traits based on semantic similarity values, similarity values were converted to distances by applying a linear transformation: distance = sim linkage . Kernel  linkage . Trait n linkage . Traits  linkage .The significance of the similarities obtained between traits was analyzed with two different types of permutation tests. (i) Use of randomly chosen genome regions as input for each trait, instead of QTL regions. The number of randomized regions and their length was identical to that of the QTL regions, and 1,000 of such randomized input sets were used. The exact same procedure and settings as described above were applied to these regions to obtain trait-trait similarities: overrepresentation analysis, filtering on the minimum fraction of QTL regions in which a GO term should at least occur (0.5), filtering on the number of genes to which a GO term was annotated (not more than 1%), and semantic similarity calculation. The p-value was calculated as the number of times  for which the permutated dataset resulted in at least a similar number of trait-trait pairs with a given value of semantic similarity. (ii) To analyze the significance of the trait-trait links obtained based on maximum semantic similarity of a trait with other traits, randomized trait-GO connections were used as input to generate expected values of maximum semantic similarity between traits. The p-value was calculated as the number of times  for which the permutated dataset resulted in at least the same value of semantic similarity for the trait-trait connection. This was followed by Benjamini-Hochberg multiple testing correction (FDR 0.05).S1 TableRice gene function terms were predicted using BMRF for biological process (BP) terms and Argot2 for molecular function (MF) terms. The total number of annotations, the number of genes with at least one annotation, and the unique number of gene function terms involved is shown.(DOCX)Click here for additional data file.S2 TableThe following sheets are provided:phQTL–BPphQTL–MFmQTL–BPmQTL–MFeQTL–BPeQTL–MFIn each sheet, links between traits and biological process (BP) terms or molecular function (MF) terms are presented. phQTL indicates macroscopic traits; mQTL indicates metabolite traits; eQTL indicates expression traits. For macroscopic traits, the corresponding Trait Ontology (TO) identifier is given. For metabolite traits, the identifier is given, and for metabolites with known identity, also the metabolite name is given. For expression traits, gene names are given.(XLSX)Click here for additional data file.S3 TableThis file contains one sheet for the semantic similarity based on BP terms, and one sheet for semantic similarity based on MF terms. Pairs of traits are ordered based on the level of semantic similarity.(XLSX)Click here for additional data file.S4 TableTraits associated with exactly the same set of overrepresented BP or MF terms. Each row indicates a pair of such traits. For macroscopic traits, the Trait Ontology identifier and the trait name is given; for metabolites, the metabolite identifier is given, and for characterized metabolites, the metabolite name.(DOCX)Click here for additional data file.S5 TableThis file contains the following sheets, presenting the most similar trait for each trait in the other set of traits:ph–ph (BP): for each macroscopic trait, the most similar macroscopic trait, based on BP termsph–m (BP): for each macroscopic trait, the most similar metabolite trait, based on BP termsph–e (BP): for each macroscopic trait, the most similar expression trait, based on BP termsm–ph (BP): for each metabolite trait, the most similar macroscopic trait, based on BP termsm–m (BP): for each metabolite trait, the most similar metabolite trait, based on BP termsm–e (BP): for each metabolite trait, the most similar expression trait, based on BP termse–ph (BP): for each expression trait, the most similar macroscopic trait, based on BP termse–m (BP): for each expression trait, the most similar metabolite trait, based on BP termse–e (BP): for each expression trait, the most similar expression trait, based on BP termsph–ph (MF): for each macroscopic trait, the most similar macroscopic trait, based on MF termsph–m (MF): for each macroscopic trait, the most similar metabolite trait, based on MF termsph–e (MF): for each macroscopic trait, the most similar expression trait, based on MF termsm–ph (MF): for each metabolite trait, the most similar macroscopic trait, based on MF termsm–m (MF): for each metabolite trait, the most similar metabolite trait, based on MF termsm–e (MF): for each metabolite trait, the most similar expression trait, based on MF termse–ph (MF): for each expression trait, the most similar macroscopic trait, based on MF termse–m (MF): for each expression trait, the most similar metabolite trait, based on MF termse–e (MF): for each expression trait, the most similar expression trait, based on MF termsIn each of these sheets, for a given trait, the most similar trait is listed, followed by the p-value (after multiple testing correction).(XLSX)Click here for additional data file.S1 Fig(A) Macroscopic traits, based on BP. (B) Metabolic traits, based on BP. (C) Expression traits, based on BP. (D) Macroscopic traits, based on MF. (E) Metabolic traits, based on MF. (F) Expression traits, based on MF. In all panels, red indicates maximum similarity to macroscopic traits, blue maximum similarity to metabolic traits, and green maximum similarity to expression traits.Histograms of the maximum semantic similarity for each trait with any other trait of either the same type or of different type, based on either BP or MF terms. (PNG)Click here for additional data file.S1 DatasetIn these networks, each node represents a trait, and a connection between nodes indicates a significant similarity between the two traits. The following files are provided:NetworkBP.sif, networkMF.sif NodeattributesBP.out, nodeattributesMF.out .These files can be used to visualize the network in Cytoscape. Two Cytoscape session files are also provided which directly provide a view of the network using a spring-embedded layout, and coloring the nodes based on the type of trait: networkBP_colorednodes.cys and networkMF_colorednodes.cys.(ZIP)Click here for additional data file.S2 Datasethttp://tree.bio.ed.ac.uk/software/figtree/.Two files are provided: hclustBP_exported_tree.newick and hclustMF_exported_tree.newick. These can be visualized in various tree/phylogeny software tools, e.g. in figtree http://itol.embl.de/tree/7716210613417711454683547 (BP) and http://itol.embl.de/tree/7716210613125231454685700 (MF).A visualization of these trees is also provided via To briefly explain the use of iTOL, we will focus on reproducing http://itol.embl.de/tree/7716210613417711454683547 in a web-browserOpen the BP based trait clustering tree by pasting Change the display mode from Circular to Normal (via the top right hand control)Note that trait names are now visible; green indicate expression traits, blue metabolite traits and red macroscopic traits. For macroscopic traits, hovering over the trait ontology identifier displays the trait name. Similarly, for metabolite traits with known identity, the metabolite name, the way in which the metabolite was identified, and the compound class, are indicated.Use the Tree search tool (available at the top left hand side): type 0000227, the TO identifier for root length.The result displays the part of the tree around the trait root length. Similar to (ZIP)Click here for additional data file.


The human oral microbiome is formed early in development. Its composition is influenced by environmental factors including diet, substance use, oral health, and overall health and disease. The influence of human genes on the composition and stability of the oral microbiome is still poorly understood. We studied both environmental and genetic characteristics on the oral microbiome in a large twin sample as well as in a large cohort of unrelated individuals. We identify several significantly heritable features of the oral microbiome. The heritability persists in twins even when their cohabitation changes. The heritability of these traits correlates with the cumulative genetic contributions of over half a million single nucleotide sequence variants measured in a different population of unrelated individuals. Comparison of same-sex and opposite sex cotwins showed no significant differences. We show that two new loci on chromosomes 7 and 12 are associated with the most heritable traits.An analysis of 752 twin pairs from the Colorado Twin Registry, shows that the beta-diversity of monozygotic twins is significantly lower than for dizygotic or unrelated individuals. This is independent of cohabitation status. Intraclass correlation coefficients of nearly all taxa examined were higher for MZ than DZ twin pairs. A comparison of individuals sampled over 2-7 years confirmed previous reports that the oral microbiome remains relatively more stable in individuals over that time than to unrelated people. Twin modeling shows that a number of microbiome phenotypes were more than 50% heritable consistent with the hypothesis that human genes influence microbial populations. To identify loci that could influence microbiome phenotypes, we carried out an unbiased GWAS analysis which identified one locus on chromosome 7 near the gene IMMPL2 that reached genome-wide significance after correcting for multiple testing. Another locus on chromosome 12 near the non-coding RNA gene INHBA-AS1 achieved genome-wide significance when analyzed using KGG4 that sums SNP significance across coding genes.Using multiple methods, we have demonstrated that some aspects of the human oral microbiome are heritable and that with a relatively small sample we were able to identify two previously unidentified loci that may be involved.The online version of this article (doi:10.1186/s12864-017-4008-8) contains supplementary material, which is available to authorized users. Humans support the growth and maintenance of diverse sets of microbes in niches in contact with the environment including skin, lungs, mouth and gut . StudiesCandidate gene studies have been most effective at identifying human genetic influences on the microbiome. By this approach, informed hypotheses about human genes that may conceivably influence a particular microbiological phenotype (i.e. susceptibility to infection) are tested with family or population-based studies to identify human variants that are statistically consistent with the hypothesis. Examples include MHC genes , SLC11A1The oral microbiome is one of the most diverse microbial niches in the human body, including over 600 different microorganisms . It is in continual contact with the environment, and has been shown to be susceptible to many environmental effects. These environmental factors include tobacco use –22, romaIn this paper, we describe an unbiased approach to studying the effects of human genes on the oral microbiome with a two-step strategy. The first step utilizes twin information to establish heritable phenotypes related to the microbiome; and the second identifies DNA sequence variation associated with the identified highly heritable traits. From 16S rRNA sequence information, a large number of potential phenotypes can be explored with the twin studies to allow identification of the most heritable and therefore the phenotypes most likely to be mapped in the association study. A key strength of this approach lies in the independence of the data underlying the two steps (i.e. MZ/DZ status in the twin study and SNP association data in the second) reducing multiple testing and type 1 effects on the power to carry out the test for association. The ability to refine a phenotype prior to carrying out an association study can lead to greater likelihood of detecting specific SNPs that influence it , 4, 31. Twin samples were obtained from the Colorado Twin Registry . The twin sample included 366 monozygotic pairs (MZ), 263 same sex, and 123 opposite sex dizygotic pairs (DZ). Unrelated individuals were ascertained from community and clinical samples participating in the Colorado Center for Antisocial Drug Dependence and isolation of DNA from saliva and characterization of their genotypes was as previously described .Pooled DNA from triplicate PCR with the 16S V4 hypervariable primers 515F/806R was done according to the Earth Microbiome Project 16S rRNA amplicon Protocol, with unique barcode indices for multiplex sequencing on the forward primer –35. Conchttp://hannonlab.cshl.edu/fastx_toolkit/commandline.html) and ea.-utils fastq-mcf package were used to trim and quality filter the forward and reverse reads (https://wiki.rc.ufl.edu/doc/EA-Utils) [The fastx-toolkit (A-Utils) . The comA-Utils)  implemenA-Utils) , DADA2 [A-Utils)  roth, orA-Utils)  was usedSamples from 1504 twins of whom 111 within-twin longitudinal samples with at least 3500 reads and DNA samples from 1481 unrelated individuals with at least 3000 reads produced 2664 and 2679 OTUs respectively. All samples were rarefied to 2500 sequences to retain as many samples as possible to improve power with little effect to results , 42.. ToP values were calculated similarly to as previously described [P value was then calculated by dividing the number of W test statistics greater than the observed W test statistic plus 1 by the number of permutations plus 1. Biplot analyses were used as implemented in QIIME . In experiments where cohabitation was required, only cotwins 18 and under and those over 18 who identified themselves as cohabitating were included, which removed 328 subjects from the total twin sample who were living separate from their cotwin. This population of 588 twins pairs is referred to as the “cohabitation sample.” Cohen’s D effect size for β-diversity measurements was calculated using the R package ‘effectsize’ (command ‘cohen.d’) [β-diversity was analyzed via Bray Curtis and UniFrac (Unweighted and Weighted) using QIIME  and R . Analyseescribed . In shorohen.d’) .α -diversity measurements, and principal coordinates from β-diversity measurements  or categorical . Shapiro Wilk test was performed use the R packaged ‘stats’ (command ‘shapiro.test’) [Microbial traits included taxonomic groups, OTUs, o.test’) . CategorP value was calculated as similarly to as previously described in which the zygosity labels of the twin pairs were randomized 10,000 times and the ICC values then calculated [The MZ and DZ ICC values were calculated using the R package ‘irr’ (icc command)  and werelculated . This anThe ACE/ADE univariate twin modeling used the OpenMx package as implemented in R , 45, 46 n = 344) and 6,862,363 SNPs in the EUR sample (n = 823).Genotypes were obtained as previously described . Ancestrn = 818). The following covariates were included in the model: age; sex; sequencing run (1–5); year DNA was collected; saliva collection method for 16S sequencing; DNA collection method for host genotyping; and the first 10 PCs to control for population stratification. GCTA estimates for the Admixture American sample were not reported due to the small sample size after the threshold of IBS estimates less than 0.025 were applied.Genome Complex Trait Analysis (GCTA) was performed on all traits categorized as continuous in both the twin and unrelated populations using the GCTA software . The GCTGenome wide association study analyses were performed using the software EPACTS . The Q.EP value and direction of effect for each variant is weighted by sample size correcting the test statistics for population stratification with the “genomiccontrol” option. The results of the METAL analysis were then re-run through the program to confirm that population stratification was properly controlled for as suggested by the METAL guidelines. QQ-plots were created in R using the package “qq-man” [ADM and EUR GWAS analyses were combined in a meta-study using the METAL package. METAL analyses were performed on overlapping SNPs with the “samplesize” scheme in which the “qq-man” , 51.The 16S rRNA gene sequencing data from this study has been submitted to the EMBL-EBI under study numbers ERP023086, ERP023087, ERP023088, ERP023089, ERP023090, and ERP023091. The host genome sequencing data used in this study was made publically available by Derringer et al. 2015.We performed an analysis of 752 twin pairs from the Colorado Twin Registry  to estimMeasurements comparing mean β-diversity among MZ, DZ and unrelated individuals allows for assessment of microbial population differences between groups. With either Bray-Curtis  or WeighThe Colorado Twin Registry , 53 inclTo quantify the differences between groups the Cohen’s D effect size was calculated for all β-diversity measurements  are useful for estimating heritability of individual observations within a group of related observations ; the higher the ICC values for MZ pairs compared to DZ pairs, the greater the heritability . As showTwin modeling approaches are used to estimate the amount of variance attributable to additive genetics (A), common environment (C) or dominance (D), and unique environment (E) . An ACE alpha diversity, β-diversity PCos, OTUs, taxa) a trend that PCos of measurements have the highest mean heritability estimates emerged for either the full sample or to just twin pairs that are cohabitating  and PCo 2 for Bray-Curtis   for traits within each phenotype category described above  with phenotypes seen to be heritable in 5 studies of gut [We compared phenotypes deemed to be heritable in our study  and traits. The power to detect this is a function of the number of individuals, the number of tests and the number and types of SNPs available. The greatest power to uncover association given a fixed sample size is obtained by analyzing a limited number of phenotypes (hypotheses) based on prior information rather than repeatedly testing multiple hypotheses on the same data . To limihttps://imputationserver.sph.umich.edu/index.html#!run/) for phasing and imputation (see Methods). After quality filtering this produced 6,862,363 European (EUR) and 8,172,048 American Admixed (ADM) imputed variants respectively that were used in all subsequent analyses. Imputed SNPs from two different randomly selected chromosomal areas in 68 individuals were resequenced with Sanger sequencing to validate imputation. We found that 65/68 imputed calls validated completely with 3 apparently incorrectly imputed (data not shown). We conclude that imputation provides significantly greater resolution to SNP-based maps at little cost to accuracy.DNA was previously prepared from saliva and blood of 1480 individuals unrelated to the twins and to each other . Human Dn = 55 traits). OTU4446902 (unnamed species of the family Gemellaceae) and its corresponding taxa levels  showed suggestive significant GCTA heritability estimates after controlling for multiple testing (OTU4446902 V(G)/Vp = 0.944 SE = 0.357 P value-BH corrected = 0.053, see Additional file P values although it has been noted that the meaning of such P values is limited but even in small samples observable trends can be meaningful [The salivary microbiome of the 1480 individuals was characterized by 16S RNA sequencing identifying 2679 OTUs, where again as in the twin study, the most prevalent phyla were Firmicutes (55%), Proteobacteria (14%), Bacteriodetes (14%), Actinobacteria (11%), and Fusobacteria (6%). Filtering by prevalence and abundance as described above produced a total of 931 OTUs used for our studies. The SNP-based heritability of microbiome phenotypes in the unrelated population was assessed using Genome Complex Trait Analysis (GCTA)  that estaningful . NeverthP value of 0.0405 shown in Additional file Efficient and Parallelizable Association Container Toolbox (EPACTS) [Granulicatella (R2 = 1) and the latter has a more refined taxonomic resolution. It is well established that continuous traits afford greater power in both twin studies and in GWAS [n = 823) and Admixture (n = 344) [We ranked the continuous traits based on their heritability (the top trait showing a significant Benjamin-Hochberg corrected (EPACTS) . This wo in GWAS , 72. Thella R2 =  and the n = 344) . Due to P value = 2.251e-09, Fig. 2 values around the peak SNP lending greater confidence to the association. Without a replication sample this result is provisional but potentially interesting. Using PLINK 1.9 [To control for population stratification a kinship matrix created from all the chromosomes and the first ten principal components from the LD-pruned SNPs were included as covariates (see methods). To control for the fact that 6 traits were tested, the genome wide significance level was lowered to 8.33e-09 (5e-08/6traits) . After the initial analyses of the 6 most heritable traits, a GWAS was completed in the remaining 64 continuous traits in the European sample. No variant was found to be significant after controlling for multiple testing for these additional tests ((5e-8)/70 = 7.142857e-10) (data not shown).A comparison of the 100 SNPs with lowest We have used a relatively conservative approach to controlling for population stratification (kinship matrix + first 10 PCs). To evaluate if this may have produced false negatives, we repeated the GWAS with EPACTS kinship only, PLINK 10 PCs, and GCTA LOCO (leave one chromosome out) (see Methods). Each consistently identified the same SNP at chr7:110,659,581 significantly associated with the trait along with nearby SNPs in high LD associated as well . The METAL analysis identified the same suggestive significant SNP on chromosome 7 that was associated with Granulicatella abundance in the EUR GWAS  involved in protein processing associated with mitochondrial import and a non-coding antisense RNA INHBA- AS1   and PCo2 of Bray Curtis  and the cohabitating sample (n = 588 twin pairs) . This SNP is located in an intron of the IMMP2L gene. The GWAS meta-analyses combining the EUR and ADM samples using METAL with the same 6 traits showed no new information about the chr7 SNP due to its low frequency in the ADM population but did produce an additional association with suggestive significance, chr12:82,166,911  for the phenotype Unweighted UniFrac PCo3, though it was not robust to correction for multiple testing. This SNP is located in the gene LIN7A that is widely expressed in endothelial cells. Markers in LD with the top SNPs (i.e. high r2) were also highly associated with the phenotype, but in addition, markers of somewhat lower LD (i.e. low r2) that were nearby also displayed elevated significance for both hits. This provides an argument that these loci may not be due purely to chance  populations (defined above). The GWAS of the abundance of the genus ce Figs.  and 5c.To be adequately powered one must have a large sample size or the single SNP effect must be very large. However, most complex traits are polygenetic and so many loci with small effects account for the variation of the trait. Therefore, where sample size is limited, it may be difficult to observe significant SNP associations. To address this, it is possible to use biological information to inform analyses and increase statistical power. This may be done by aggregating the association of multiple SNPs known to be present within a known gene. By this approach, the possibly small effects of all SNPs in the gene are combined and then the association of the entire gene may be determined. Even if no single SNP is found to be genome-wide significant the combined SNP contributions across the gene may be. One widely used gene-based GWAS analysis method is the Knowledge-based mining system for Genome-wide Genetic Studies (KGG4) , 92–95.P value = 0.0176) and additionally identified a non-coding RNA INHBA- AS1  (see methods) as significant hits. IMMP2L functions in the mitochondrion where it is involved with processing of signal peptides as a peptidase directing transport to the interior mitochondrial space [An analysis by KGG4 confirmed the gene IMMP2L Additional file 2:Supplemental_Information. (PDF 28601 kb)


As seed oil content (OC) is a key measure of rapeseed quality, better understanding the genetic basis of OC would greatly facilitate the breeding of high-oil cultivars. Here, we investigated the components of genetic effects and genotype × environment interactions (GE) that govern OC using a full diallel set of nine parents, which represented a wide range of the Chinese rapeseed cultivars and pure lines with various OCs.G) and GE (VGE), which together accounted for 86.19% of the phenotypic variance (VP). GE (VGE) alone accounted for 51.68% of the total genetic variance, indicating the importance of GE interaction for OC. Furthermore, maternal variance explained 75.03% of the total genetic variance, embryo and cytoplasmic effects accounted for 21.02% and 3.95%, respectively. We also found that the OC of F1 seeds was mainly determined by maternal effect and slightly affected by xenia. Thus, the OC of rapeseed was simultaneously affected by various genetic components, including maternal, embryo, cytoplasm, xenia and GE effects. In addition, general combining ability (GCA), specific combining ability (SCA), and maternal variance had significant influence on OC. The lines H2 and H1 were good general combiners, suggesting that they would be the best parental candidates for OC improvement. Crosses H3 × M2 and H1 × M3 exhibited significant SCA, suggesting their potentials in hybrid development.Our results from an embryo-cytoplasm-maternal (GoCGm) model for diploid seeds showed that OC was primarily determined by genetic effects (VOur study thoroughly investigated and reliably quantified various genetic factors associated with OC of rapeseed by using a full diallel and backcross and reciprocal backcross. This findings lay a foundation for future genetic studies of OC and provide guidance for breeding of high-oil rapeseed cultivars. The seed oil content (OC) is a key measure of rapeseed quality and is also a complicated quantitative trait easily affected by the environment and difficult to investigate –3. Previ1 seeds is usually accompanied by weak xenia [A previous study on summer rapeseed has suggested that OC might be primarily controlled by maternal factors or embryo genotype or xenia . The strak xenia –13. For ak xenia , 15. Seeak xenia , but maiak xenia , 12. Phoak xenia , 18. In ak xenia . Therefoak xenia , 21.2 seeds [1 hybrids that does not exist between reciprocal F2 hybrids would indicate that OC is determined by maternal effect without cytoplasmic effect [Xenia, which represents the effect of pollen on the development and characters of seed, can be demonstrated by analyzing the differences between seeds fertilized using different pollen sources from the same plant , 23. The2 seeds . In contc effect .1 generation [Diallel mating designs have been frequently used to investigate the genetic effects of parents or to determine which cultivars are the best combiners for favorable alleles in hybrids. Diallel analysis provides information on the genetic behaviors of these attributes in the Fneration . The founeration ; howeverneration . The varneration , 32. Theneration . Additivneration .In the present study, we investigated whether OC is determined by embryo, maternal, cytoplasmic, xenia or GE effects, or a combination of these factors. To this end, we estimated the components of different genetic systems and their corresponding GE. In addition, we investigated the roles of GCA, SCA, and reciprocal effect in the inheritance of OC. An improved understanding of the genetic components of OC and the combining abilities of inbred lines will help breeders to develop high-oil rapeseed cultivars and hybrids for particular geographic locations.1 hybrids , including 36 crosses and 36 of their reciprocals in March of 2010. These 72 crosses were performed again in March of 2012, and these F1 seeds were only analyzed for maternal effect and xenia on the OC of rapeseed.The experiments were carried out from September of 2009 to May of 2012. Nine semi-winter rapeseed lines with differences in seed oil content (OC) were selected as parental lines from the Rapeseed Laboratory of Huazhong Agricultural University  obtained from the complete diallel cross of the nine parents in May of 2010, were directly sown in the field at Huazhong Agricultural University, Wuhan, China around September 27th of both 2010 and 2011. The parental and F1 seeds were planted in a randomized block design with three replications over two consecutive growing seasons, and each block contained two rows with 10 plants in each row at a space of 30 cm × 15 cm. The OC of self-pollinated parents and F2 generation (F2 seeds from F1 plants) plants was calculated as the mean value of 20–56 individually harvested plants in both 2011and 2012, and that of the open-pollinated F2 generation was calculated as the mean value of 13–50 individually harvested plants in 2011.A total of 81 genetic entries , eight RBC1s, and eight RBC2s , and each of the combinations was repeated three times and used to study the maternal, xenia and cytoplasmic effects on OC.Environmental factors, such as temperature, light, and weather during seed development and difference in flowering time, could influence OC and the results of the study. Therefore, since Fproblems . HO  [All plants were isolated by paper bags at the beginning of flowering to ensure self-pollination of parents and FMD, USA) , with thMD, USA) .1 seeds was calculated as F1 = MP1 + XP2, where M was the value of maternal effect and X was the value of xenia (X = 1–M).Data were analyzed using a general linear model (GLM) . Based ohttp://ibi.zju.edu.cn/software/qga/index.htm). The genetic variance components were estimated using the minimum norm quadratic unbiased estimation (MINQUE) (0/1) method [The partitions of embryo, maternal, cytoplasmic effects as well as the corresponding GE were estimated using the embryo-cytoplasm-maternal (GoCGm) model , 41 for ) method , and the) method . Standar) method , and t-t1 hybrids was strongly influenced by the maternal parent. In 2012, the mean OC of the eight F1 lines with H1 as the female parent was 42.01%, which was comparable to that of H1 (42.79%), whereas that of the eight F1 lines with H1 as the male parent was 38.04% , and that of the eight F1 lines with L3 as the male parent was 37.10%, which was much higher than that of L3. Similar trends were observed in the other sets of crosses in both 2010 and 2012. Thus, the OC of F1 hybrids was similar to that of maternal parent, even with different male parents.We found that the self-pollinated seeds from nine parents, including HO H1, H2, and H3), MO , and LO , exhibited significant differences in OC, and that the differences between the parents with the highest and lowest OC in each year was about 12.00% , the various backcrosses exhibited different OCs, indicating the presence of a slight xenia  and H1 (2.87**) had the highest GCA for OC, whereas L3 (−3.06**) and L2 (−2.06**) were negative combiners with reduced OC in F1. The SCA in several crosses was significant, and reciprocal effect was only significant in two crosses. Lines H3 and M2 showed significantly positive GCA, whereas M1, M3 and L1 exhibited significantly negative GCA. Crosses H1 × M3, H1 × L1, H1 × L3, H2 × H3, H2 × M2 and H3 × M2 exhibited significantly positive SCA and were the best specific combiners for improvement of OC  harvested in 2010 and 2012, the mean estimated value of maternal effect was 0.84 in 2010 and 0.89 in 2012, and that of xenia was 0.16 in 2010 and 0.11 in 2012 (Table 1 seeds and a weak effect of xenia. Some crosses (H1 × MO and M3 × LO) exhibited values of maternal effect around 1 consistently across the two years, suggesting 100% maternal influence. Some crosses (H3 × LO and M1 × LO) showed maternal effect and xenia varying markedly between two years and also had large standard errors (SE), suggesting that these crosses had relatively weak maternal effects.Based on the 54 FAnalyses of backcrosses revealed the minor or negligible influence of cytoplasmic effect, although the backcrosses also displayed some variations between 2011 and 2012 Fig. . The aveP) in OC could be attributed to environmental variations and experimental errors, whereas the rest (86.19%) was attributable to genetic (VG) and GE (VGE) components (Table G + VGE). Embryo additive (VA), embryo additive interaction (VAE), maternal additive (VAm) and maternal dominant interaction (VDmE) variance components were significant, whereas embryo dominant interaction (VDE), cytoplasm interaction (VCE), maternal additive (VAmE), and maternal dominant (VDm) variance components were not. Maternal variances (VAm + VDm + VAmE + VDmE) explained 75.03% of the total genetic variance, whereas embryo and cytoplasmic effects accounted for only 21.02% and 3.95% of it, respectively. Overall, these results indicated that OC was predominantly influenced by maternal effect, followed by embryo and cytoplasmic effects.The results from GoCGm model showed that 13.81% of the phenotypic variance  than identical hybrids constructed in the cytoplasm (nap) common in rapeseed [Our analysis of combining ability indicated the presence of both additive and non-additive gene actions in the parental lines Table . Additivrapeseed , 54. LinBased on analyses of a 9 × 9 full diallel scheme and selected backcross and reciprocal backcross, we concluded that the OC is simultaneously controlled by genetic components of maternal, embryo, xenia, cytoplasmic, and GE effects in rapeseed. Maternal effect is the most important factor, accounting for 75% of the genetic variance, followed by embryo effect, which accounts for 21% of the genetic variance. Therefore, selection of maternal parents is paramount in the genetic improvement of OC in rapeseed. Together with previous studies, additional information regarding the role of genetic components in determining OC could help breeder to better manipulate the OC of rapeseed.


However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at  Recently, there has been accumulating evidence suggesting pleiotropy, i.e., genetic components shared across multiple phenotypes. Incorporation of pleiotropy in genetic analysis might improve statistical power to identify risk associated genetic variants. Several statistical approaches have been proposed to utilize pleiotropy for association mapping but they are currently still limited to a relatively small number of phenotypes, e.g., a pair of phenotypes. This restricts potential gain in statistical power in association mapping and investigation of pleiotropic structure among a large number of phenotypes. In order to address this challenge, in this paper, we propose graph-GPA, a novel statistical framework to integrate a large number of phenotypes using a hidden Markov random field architecture. Application of the proposed statistical method to GWAS datasets for 12 phenotypes showed that graph-GPA does not only provide a parsimonious representation of genetic relationship among these phenotypes, but also identify significantly larger number of novel genetic variants that are potentially functional. We believe that this novel approach might help investigation of common etiology and improvement of diagnosis and therapeutics. Despite these great achievements, researchers have had concerns about the fact that these significantly associated SNPs could explain only a small portion of genetic contributions to complex traits/diseases, which is referred to as “missing heritability” [p-value < 5 × 10−8 after Bonferroni correction) together can only explain 16% of variation in height [Genome-wide association studies (GWAS) have identified many single-nucleotide polymorphisms (SNPs) associated with various phenotypes, including cancer, diabetes, and autoimmune diseases, among others. As of July 2016, more than 20,000 SNPs have been reported to be significantly associated with at least one complex trait in the NHGRI-EBI catalog of published GWAS [ability” . For exaability” , genome-n height  reportedAlthough polygenicity provides attractive explanation for the missing heritability phenomenon, the polygenic architecture imposes a great practical challenge in GWAS. While identification of genetic variants with small effect sizes requires a larger sample, it is often not a practical option to recruit a larger sample because recruitment may be expensive and time-consuming. Hence, it would be desirable if we could increase statistical power to detect risk associated genetic variants with smaller effect sizes, without extensive additional subject recruitment. Integrative analysis of genetic and genomic data has recently been considered as a promising direction, including combining GWAS data of multiple genetically related phenotypes. In the last few years, researchers have provided convincing evidence of “pleiotropy”, i.e. the sharing of genetic factors, especially between human complex traits. For example, a systematic analysis of the NHGRI GWAS Catalog demonstrated that 16.9% of the reported genes and 4.6% of the reported SNPs are associated with multiple traits . As a spIn order to address these challenges and opportunities, Chung and others proposed a statistical framework, namely GPA , that integrates genetic data for multiple phenotypes . Using eThis paper is motivated by the joint analysis of GWAS data for 12 phenotypes, including psychiatric disorders, autoimmune diseases, lipid-related phenotypes, and cardiovascular phenotypes. The extensive integrative analysis is of interest because it is expected that genetic basis is not shared only within a phenotype group  but also between phenotype groups , as suggested in recent literature . HoweverIn order to address the challenge, in this paper, we propose a novel statistical framework, namely graph-GPA. Specifically, we utilize the Markov random field (MRF) architecture to model hidden binary indicators for association of a SNP with each phenotype. The MRF architecture does not only provide a parsimonious representation of genetic relationship among phenotypes, but also improves statistical power to identify risk associated genetic variants by sharing information across GWAS data for these phenotypes. This paper is structured as follows. We first propose our novel Bayesian hierarchical model for the joint analysis of GWAS data for multiple phenotypes. Then, we evaluate the proposed method with simulation studies and conduct the joint analysis of GWAS datasets for 12 phenotypes, including psychiatric disorders, autoimmune diseases, lipid-related phenotypes, and cardiovascular phenotypes. Our real data analysis reveals genetic mechanisms shared among the phenotypes. Furthermore, graph-GPA improves statistical power to identify both risk variants associated with each phenotype (bipolar disorders) and those with pleiotropic effects (psychiatric disorders and inflammatory bowel diseases).p-values obtained from a logistic regression of phenotype on genotype or a contingency table for genotype-phenotype association. This approach allows broader application of the proposed method because summary statistics are more readily available. Let pit denote the p-value of association testing of SNP t = 1, 2, ⋯, T with phenotype i = 1, 2, ⋯, n. To facilitate data visualization and modeling, we transformed pit as yit = Φ−1(1 − pit), where Φ(⋅) is the cumulative distribution of the standard normal variable [eit denote the latent (unobservable) association indicator for t-th SNP and i-th phenotype, where eit = 1 if SNP t is associated with phenotype i and eit = 0 otherwise. We model the density of yit given the latent association status eit by a normal mixturey; μ, σ2) and N denote the log-normal density with mean eit = 0) is equivalent to the theoretical null distribution assumption  [yit corresponding to associated SNPs (eit = 1) because it is likely that associated SNPs will only account for a negligible proportion of the SNPs with p-values larger than 0.5, i.e., yit smaller than zero. We evaluated the appropriateness of these assumptions using real GWAS datasets and confirmed that no significant violation of these assumptions is detected  . We assuG =  denote an MRF graph, where V =  are the vertices and E represents the edges such that E = 1 if there is an edge between vi and vj and E = 0 otherwise. Here, vi corresponds to phenotype i and E = 1 implies that phenotypes i and j are genetically correlated in the sense of conditional dependence. Assuming an auto-logistic spatial scheme, the conditional distribution of et =  is written byβij is the MRF coefficient for the pair of phenotypes i and j, and i ∼ j denotes that vi is adjacent to vj, i.e., the sum of the second term is over all pairs of phenotypes i and j such that E = 1.For effective integration of multiple GWAS datasets for genetically related phenotypes, we suggest a graphical model based on an MRF  that repa, b) denotes the gamma distribution with mean a/b and δ0 denotes the Dirac delta function at zero. This prior setting for βij reflects the graph structure G by putting βij = 0 if there is no edge between phenotypes i and j in the graph, i.e., E = 0. We put p(G) = 1/|E|, where |E| denotes the number of edges in the current graph G, which leads the model to favor simple graphical representation with a small number of edges, i.e., the small number of βij having non-zero values. As informed by a referee, our estimation of the graphical structure G is conceptually similar to “learning the structure” of MRF in the machine learning literature. Specifically, in the “learning the structure” approach, the L1 (Lasso) regularization is directly imposed instead of using the prior distribution of G in our current model. This approach tends to lead to sparse models where many βij have zero values while not much sacrificing the model fit  = 1 a priori distinct from zero, having mean 2 and variance 1, while allowing a handful of values be close to zero. Note that we defer the discussion about model robustness to the hyperparameters to the Results Section.For Bayesian inference, we introduce conjugate prior distributions for t e.g., –22). In . In μi∼Np-values from GWAS datasets, we estimate the parameters of the graph-GPA model and other related interesting quantities based on posterior samples from a Markov Chain Monte Carlo (MCMC) sampler. Specifically, we implement a Metropolis-Hastings within Gibbs algorithm whose full details are provided in Section 1 in βij given G, we adopt the reversible jump MCMC [G during the MCMC run.Given the ump MCMC  to add oG representing genetic relationship among phenotypes can be summarized by p|Y), interpreted as the posterior probability that two phenotypes i and j are genetically correlated with each other. In addition, the posterior summary of βij can be interpreted as a relative metric to gauge the degree of correlation between phenotypes i and j. Using these posterior summaries, we can come up with several strategies to make an inference about the pairwise relationship between phenotypes. A convenient and widely used approach is to set an arbitrary threshold for p|Y) to declare phenotypes i and j to be correlated, for example, p|Y) > 0.5 as in [E = 1) but their corresponding MRF coefficient  is close to zero. Hence, here we adopt an alternative approach to improving specificity in identifying correlated pairs of phenotypes using both p|Y) and the posterior summary of βi, j. Specifically, we declare phenotypes i and j to be correlated when p|Y) > 0.5 and βij is significantly different from zero, e.g., p(ijβ > 0|Y) > 0.95.The graph structure of .5 as in , 25. Howp(eit = 1|Y), i.e., the posterior probability that SNP t is associated with phenotype i. SNPs associated with both i-th and j-th phenotypes can be inferred based on p and SNPs associated with more than two phenotypes can also be identified in similar ways. We use the direct posterior probability approach [i, denoted as fit ≡ 1 − p(eit = 1|Y). Then, we increase the threshold for local false discovery rates, κi, from zero to one untilτ is the pre-determined bound of global false discovery rates, and 1{⋅} is the indicator function with value one if the statement is true and zero otherwise. Finally, we declare SNPs with corresponding fit < κi to be associated with phenotype i. Similarly, SNPs shared between phenotypes i and j can be identified by usingfijt ≡ 1 − p.Association mapping of a single SNP on a specific phenotype is inferred from approach  to contrG) as depicted in α1 = −4.7, α2 = −3.0, α3 = −5.5, α4 = −4.8, α5 = −3.6, α6 = −2.5, and α7 = −3.5, while setting β12 = 4.0, β13 = 1.8, β23 = 2.3, β34 = 2.5, and β45 = 5.0 . These MRF coefficients were determined based on the estimated coefficients obtained using real GWAS datasets. Then, given the MRF coefficients, we generated association status of 20,000 common SNPs (eit) from the model in the yit from eit = 1, and N if eit = 0, where μ =  and σ = . Note that we set the signal strengths relatively weak in order to mimic the distribution of p-values in real GWAS datasets. We applied the graph-GPA model with 10,000 burn-in and 40,000 main MCMC iterations to the simulation dataset. We also analyzed the simulation dataset with GPA as a representative method that can utilize a smaller number of phenotypes because GPA outperformed other currently available methods such as conditional FDR [We conducted simulation studies to evaluate the performance of the proposed graph-GPA model. In the simulation studies, we mainly investigate the proposed model from the perspectives of 1) parameter estimation and false discovery rate control; 2) estimation of genetic relationship among diseases (graph structure); and 3) statistical power to identify risk-associated SNPs. We generated our simulation data as follows. First, we assumed the true phenotype graph  as follows:βij, i.e., the extremely strong prior belief that the phenotypes are independent. Note that these models correspond to separate analyses of GWAS datasets because they do not share information between phenotypes. Finally, we evaluated the SNP prioritization performance of graph-GPA. As a baseline, we ran a null model without interaction term (http://www.med.unc.edu/pgc); 2) three autoimmune diseases, including Crohn’s disease (CD) and ulcerative colitis (UC) from the International Inflammatory Bowel Disease Genetics Consortium [http://www.ibdgenetics.org/) and rheumatoid arthritis (RA) [http://www.broadinstitute.org/ftp/pub/rheumatoid_arthritis/Stahl_etal_2010NG/); 3) two lipid-related phenotypes, including high-density lipoprotein (HDL) from the Global Lipids Consortium [http://csg.sph.umich.edu//abecasis/public/lipids2010/) and type 2 diabetes (T2D) from the DIAbetes Genetics Replication And Meta-analysis Consortium [http://diagram-consortium.org); and 4) two cardiovascular phenotypes, including coronary artery disease (CAD) from the CARDIoGRAM Consortium [http://www.cardiogramplusc4d.org/data-downloads/) and systolic blood pressure (SBP) from the International Consortium for Blood Pressure [http://www.georgehretlab.org/icbp_088023401234-9812599.html). We used the intersection of SNPs among these datasets, which consists of 228,944 SNPs. For the graph-GPA model, we collected the posterior sample of 40,000 MCMC iterations, removing the first 10,000 iterations as burn-in. The MCMC converged quickly to a stationary distribution  five psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and schizophrenia (SCZ) from the Psychiatric Genomics Consortium , 10   |Y) > 0.5 and the 95% credible interval of βij does not include zero. Tables G and H in P(Gij|Y) and βij, which demonstrates the effectiveness of our approach in constructing a phenotype graph using both βij and Gij, discussed in the Materials and Methods Section. Specifically, by additionally considering credible interval of βij, we could exclude the case to have nonzero E with βij close to zero. In In order to further demonstrate stability of our findings, we introduced slight perturbation to the dataset. Specifically, we partitioned the cohorts for RA into two groups . a priori. In contrast, graph-GPA integrates a much larger number of GWAS datasets and this might result in more robust improvement in statistical power in the sense of both sensitivity and specificity, regardless of its signal strength. These results indicate that graph-GPA improves statistical power to identify associated SNPs in a more robust way and the novel genetic variants identified by graph-GPA might also be potentially important ones associated with disease risks.We next considered novel SNPs identified by graph-GPA by evaluating and comparing functional importance between the SNPs identified in separate analyses and by graph-GPA. Specifically, we used the GenoCanyon score  that meahttp://genocanyon.med.yale.edu/GenoSkyline/). We next evaluated overall and tissue-specific functional importance of novel SNPs identified by graph-GPA. Here, in addition to the GenoCanyon scores, we also utilized GenoSkyline scores  that meahttps://david.ncifcrf.gov/). As expected, for each of CD, UC, and RA, multiple pathways related to autoimmune diseases  are enriched for both the SNPs identified in separate analyses and those identified by graph-GPA. In addition, these pathways are also enriched for the SNPs associated with both RA and CD, and for the SNP associated with both RA and UC . Moreover, cardiomyopathy pathways are enriched for both the SNPs identified in separate analyses and the SNPs identified only by graph-GPA for IBD (CD and UC). Various signaling pathways  are also enriched for the SNPs identified in separate analyses for CD and the SNPs identified only by graph-GPA for UC. Finally, we evaluated overall and tissue-specific functional importance of novel SNPs using the GenoCanyon and GenoSkyline scores for these SNPs. We first investigated the genes that were previously reported to be associated with both CD and UC, which are collectively called as inflammatory bowel disease (IBD) . Separatp-values for background SNPs, which is equivalent to the uniformity assumption of p-values for background SNPs . While this assumption is mathematically justified [p-values used as an input for graph-GPA reasonably satisfy this assumption. For example, the distribution assumption for background SNPs can be violated and type I errors of graph-GPA can be inflated if population stratification and cryptic relatedness are not properly taken into account. Hence, these confounding effects should be checked carefully and addressed before applying graph-GPA to the p-values. Second, in the proposed model, genetic variants were assumed to be independent. This independence assumption greatly simplifies our model structure and results in efficient computation, which allows practical use of the graph-GPA framework. However, in real GWAS data, genetic variants are often correlated due to linkage disequilibrium (LD) [Here we first briefly discuss some key assumptions made in the graph-GPA framework. First, in the current paper, we assumed the standard normal distribution to model probit transformed ustified  and alsoium (LD) . In practe (Step S1), which is the most computationally expensive part, can be easily updated in parallel across SNPs because the conditional distribution of te given  is independent of that of t ≠ t′ in our model. In addition, more computationally efficient algorithms beyond standard MCMC can also be considered, including advanced Monte Carlo techniques such as Hamiltonian MCMC [In spite of great successes of the proposed graph-GPA framework, it can be further improved as follows. First, in this paper, we assumed a uniform distribution over all possible subgraphs as a prior distribution for the phenotype graph. While this approach already provided a parsimonious graph that is well supported by the literature, it can be further improved by utilizing other related information useful to construct a phenotype graph. Second, in our real data analysis with the GWAS data for 12 phenotypes, we conservatively generated 50,000 MCMC iterations to guarantee the chain’s mixing and convergence. In this setting, our MCMC algorithm ran in a reasonable time, i.e., about three computation days on a standard single-core desktop machine. However, in practice, a significantly smaller number of iterations can be used after checking the chain’s convergence. For example, we re-analyzed our real data by generating 10,000 MCMC iterations, which took about 16 computation hours, and obtained inference results that were very similar to those generated from the five-time longer run of the MCMC chain. However, we note that as the number of phenotypes increases, the computation time will also increase dramatically. In order to improve the scalability of the proposed method, we plan to implement parallel computing algorithms for cluster and GPU computing. Specifically, in our MCMC steps for posterior inference Click here for additional data file.


Solanum lycopersicum) fruit weight (FW), soluble solid content (SSC), fruit shape and fruit color are crucial for yield, quality and consumer acceptability. In this study, a 192 accessions tomato association panel comprising a mixture of wild species, cherry tomato, landraces, and modern varieties collected worldwide was genotyped with 547 InDel markers evenly distributed on 12 chromosomes and scored for FW, SSC, fruit shape index (FSI), and color parameters over 2 years with three replications each year. The association panel was sorted into two subpopulations. Linkage disequilibrium ranged from 3.0 to 47.2 Mb across 12 chromosomes. A set of 102 markers significantly (p < 1.19–1.30 × 10−4) associated with SSC, FW, fruit shape, and fruit color was identified on 11 of the 12 chromosomes using a mixed linear model. The associations were compared with the known gene/QTLs for the same traits. Genetic analysis using F2 populations detected 14 and 4 markers significantly (p < 0.05) associated with SSC and FW, respectively. Some loci were commonly detected by both association and linkage analysis. Particularly, one novel locus for FW on chromosome 4 detected by association analysis was also identified in F2 populations. The results demonstrated that association mapping using limited number of InDel markers and a relatively small population could not only complement and enhance previous QTL information, but also identify novel loci for marker-assisted selection of fruit traits in tomato.Tomato ( Solanum lycopersicum) is one of the most consumed vegetables and ranks second in production among the solanaceous crops worldwide (http://www.fao.org/statistics/en/). It is also an excellent plant genetic analysis system, particularly for investigating the mechanisms of fruit development, color formation, and nutrient accumulation. Furthermore, clinical nutrition studies have suggested that increased consumption of tomato products can improve human health and reduce the risk of developing certain cancers  derived from crosses between cultivated varieties and wild species. This approach maximizes the marker polymorphisms and has led to the discovery of new genes. To date, many loci for weight (FW), shape, color, and SSC of tomato fruits have been reported from various species using this classical genetic analysis approach, and some of them have been validated using different mapping populations , four loci for fruit shape or size , three loci for SSC , and several loci for color  have been well-characterized  in the whole genome and the availability of automatic large-scale genotyping platform, SNPs have been popularly used in association analysis in tomato  with relatively low SSC (average 4.1% for OH88119 and 4.6% for OH9242 in 2013 and 2014), while Black cherry is a small-fruited tomato line (average FW 18.6 g in 2013 and 2014) with a relatively high SSC (average 6.3% in 2013 and 2014). A sub-population of 503 individuals from the F2 population of OH88119 × Black cherry were grown in the fall season of 2012, and another sub-population consisting of 752 individuals from the same cross as well as 276 individuals from the F2 population of OH 9242 × Black cherry were grown in the spring season of 2013.To identify loci for SSC and FW using the structural population mapping approach, two FTomato seeds for all studies were sown in 288 cell flats filled with a mixture of peat and vermiculite (3:1) in a protected greenhouse. Seedlings were transplanted ~50 days after germination to field. All experiments were conducted at Shangzhuang Research Station of China Agricultural University in Beijing, China. Production practices, plant spacing, and row spacing were as recommended for commercial growers . Plot means for FSI, values of color parameters and SSC were calculated based on measurements of all fruits in each plot. Pearson correlation coefficients for each trait between 2 years and among traits were obtained using SAS v9.4 . Analysis of variance was conducted using PROC GLM in SAS with the model show to best fit the data: Xijb = μ + Gi + Yj + Rb/j + Mij+ εijb, where Xijb is the trait value of the bth replication of the ith genotype in the jth year, Gi is the random effect of the ith genotype, Yj represents the fixed effect of the jth year, Rb/j is the fixed effect of the bth replication in the jth year, Mij the random effect of the genotype by year interaction and εijb is the residual. Broad sense heritability (H2) for each trait was calculated based on the plot level using the equation H2 = σG2/(σG2 +σGY2 +σε2) according to the description in Nyquist and Baker . The fruit shape index (FSI) was calculated as the ratio of maximum height to maximum width  within the 164 accessions of r2-values onto genetic distance in base pairs on the same WGS chromosome (SL2.40) of tomato variety Heinz1706  was determined following the instruction in Pritchard et al.  was estimated using Structure 2.3.4 software  were calculated using the software SpAGeDi  cluster analysis was performed to develop a phylogenetic tree using the software PowerMarker V3.25 and the tree was viewed in MEGA5 , and a general linear model (GLM) using population structure (Q matrix) as a fixed factor were used for association identification of loci conferring fruit traits. Significance of marker-trait association was determined based on p-value at a level of 5% after Bonferroni  taking into account both population structure (Q matrix) and the kinship matrix  associated with a trait, the marker was used to genotype the whole F2 population of OH88119 × Black cherry and the F2 population of OH9242 × Black cherry for validation.Since most markers significantly associated with loci for FW or SSC were not polymorphic between Black cherry and OH88119 or OH9242, we selected InDel markers that showing polymorphisms between the parents in Yang et al. . Our goaS. pimpinellifolium to S. lycopersicum var. cerasiforme and then to S. lycopersicum, the cultivated tomato. The proportion of polymorphic markers were 97.4% in 10 accessions of S. pimpinellifolium, 85.2% in 18 accessions of S. lycopersicum var. cerasiforme, 64.7% in 23 vintage accessions, 78.4% in 20 Latin American Cultivars, 67.3% in 54 fresh-market lines, and 73.3% in 59 processing lines  for individual markers Table . Among tes Table . Subsequum Table .r) of 0.85 for SSC, 0.91 for FW, 0.96 for FSI, 0.86 for L*, 0.92 for Hue, and 0.83 for Chroma. Thus, the mean of the 2-year data for each trait was used for the following analysis.Significant difference for each trait was observed among genotypes, and FSI was the only trait that showed no significant difference between 2 years or three replications Table . However*, Hue, and Chroma in the 192 tomato accessions . No differences of FSI were observed among three species. The means for fruit color parameters L*, Hue, and Chroma were lower in S. pimpinellifolium than in S. lycopersicum var. cerasiforme and S. lycopersicum. Broad-sense heritability for each trait was obviously different. Chorma was the least heritable trait with heritability of 0.69, while FSI was the most heritable trait with heritability of 0.91  and ΔK analysis . The larger subpopulation was composed of 134 accessions including all fresh-market, vintage, and unknown type accessions. One accession of S. pimpinellifolium LA2183, two-thirds of S. lycopersicum var. cerasiforme or cherry tomato, one-third of processing accessions, and four-fifths of Latin American Cultivars were also assigned into the larger subpopulation. Although, there was no certain trend of clustering in the larger subpopulation, the fresh-market accessions from Florida, USA formed one independent cluster. The smaller subpopulation consisted of 58 accessions including nine accessions of S. pimpinellifolium, two-thirds of processing, one-third of S. lycopersicum var. cerasiforme or cherry tomato, and four Latin American Cultivars. Within the smaller subpopulation, 38 processing accessions were clustered together, while the seven accessions of S. pimpinellifolium formed one cluster.Although, the 192 tomato accessions were from three species, model without prior population information was used to assign individual accession to a subpopulation using the software package of STRUCTURE2.3.4. In order to define the number of subpopulations within the 192 accessions, a series of independent runs of the data were run at a range of r2 of 0.393 was observed between all pairs of linked loci throughout the genome, with a mean maximum r2-value of 0.571 on chromosome 5 and a mean minimum r2-value of 0.086 on chromosome 10  to 0.381 (chromosome 10) estimated by the 95th percentile method. Based on LOESS curves, the baseline r2-values corresponded to physical distances varying from 3.0 to 47.2 Mb. LD decayed within 6.5 Mb on chromosomes 1, 2, 3, 7, 8, 10, and 12, while chromosome 5 showed the lowest decay with a baseline r2-value of 0.335 reached ~47.2 Mb.A mean K + Q model and Bonferroni correction for p-values, a total of 102 genotype/phenotype associations with phenotypic variation explained >2.0% were detected under the threshold p-values of 1.30 × 10−4 for SSC, 1.19 × 10−4 for FW and FSI, 1.22 × 10−4 for L* and Chroma, and 1.24 × 10−4 for Hue. The numbers of associations per trait ranged from three for Hue to 27 for Chroma. Regions carrying the presumed genes/QTLs were identified on 11 of the 12 chromosomes, but none were detected on chromosome 10  were common for L* and Chroma, and one marker Sli2792 on chromosome 2 was common for Hue and Chroma.A total of 46 genotype/phenotype associations were detected for three parameters of fruit color, of which 16, 3, and 27 were for L* also had impact to Chroma, which was consisted with that the increase of chromaticity makes a color becomes more intense  associated with FW  associated with SSC in the F2 population of OH88119 × Black cherry grown in the spring season 2013  or /QTL (fw2.2) can be validated through association analysis approach , the numbers of predicted genes within 2 Mb regions corresponding to the 22 markers varied from 19 to 286, of which 615 for SSC, 102 for FW, 41 for FSI, 430 for L*, 1,000 for Chroma, and 342 for Hue  associated with FW in the F2 population of OH9242 × Black cherry. The physical distance between Sli2377 and Sli2388, a marker detected significant association with FW in the association mapping panel but not polymorphic among OH88119, OH9242 and Black cherry, was 2.14 Mb that was smaller than the LD on chromosome 4. Thus, this region could be one unit conferring FW. Comparing the physical positions of markers (Grandillo et al., Among the 102 phenotype/genotype associations for FW, SSC, fruit shape, and fruit color, 22 with the ue Table . A lot oue Table . The useue Table . To iden2 populations in this study. The results demonstrated that the use of limited number of InDel markers and a relatively small number of accessions was effective in validating known genes/QTLs and identifying novel genotype/phenotype associations for marker-assisted selection of fruit traits in tomato.In conclusion, association mapping using InDel marker data was applied to uncover the genomic regions harboring genes underlying FW, SSC, shape, and color in tomato fruits followed by confirmation with FConceived and designed the experiments: HS, WY. Performed the experiments: XL, XG, HZ. Analyzed the data: XL, XG, WY. Contributed reagents/materials/analysis tools: HS, WY. Wrote the paper: XL, XG, WY.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In this study, we developed a double-weight local polynomial model for estimating homozygosity intensity. This new estimation method enables considering the local property and genetic information of homozygosity in the human genome when detecting regions of HD. By using this new method, we estimated whole-genome homozygosity intensities by analyzing real whole-genome sequencing data of 959 related individuals from 20 large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). Through the analysis, we derived the distribution of HD in the human genome and provided evidence for the genetic component of natural variation in HD. Generalized estimating equation analysis for 855 related individuals was performed to identify regions of HD associated with diastolic blood pressure (DBP), systolic blood pressure, and hypertension (HTN), with concomitant adjustment for age and sex. We identified one DBP-associated and 2 HTN-associated regions of HD. We also studied the gene regulation of HD by analyzing the real whole-genome transcription data of 647 individuals. A set of gene expressions regulated by the DBP- and HTN-associated regions of HD was identified. Finally, we conducted simulation studies to evaluate the performance of our homozygosity association test. The results showed that the association test had a high power and that type 1 error was controlled. The methods have been integrated into our developed Loss-of-Heterozygosity Analysis Suite software, which can be downloaded at  Homozygosity disequilibrium (HD) is defined as a nonrandom pattern of sizable runs of homozygosity (ROH) that deviated from a random distribution of homozygotes and heterozygotes in the genome . The 2 tWe developed statistical methods and software  to dissect the whole-genome patterns of HD through whole-genome single nucleotide polymorphism (SNP) analysis . Our metIn this study, we analyzed real WGS and whole-genome transcription (WGT) data and analyzed simulated data from Genetic Analysis Workshop 19 (GAW19) and our previous study [1 from 849 individuals (filename: SIMPHEN.I.csv). Q1 was generated from a normal distribution and was independent of genetic variants in this study. Q1 was used to evaluate the type 1 error of our homozygosity association test. To assess statistical power, we reanalyzed 200 additional simulation data sets of Q1-associated regions of HD generated in our previous study [p denote the probability that all genotypes in the region were replaced by homozygotes. The 3 regions were created as Q1-associated regions of HD by using the following logistic regression model:b0 ranged from −25 to −10 with an increment of 5, and b1 ranged from 0.3 to 1 with an increment of 0.1.GAW19 provided a combined imputation WGS data set, including deep sequencing data for the whole genomes of 464 individuals and genome-wide SNP genotyping data for 495 individuals, derived from 20 large independent pedigrees enrolled in the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Project 2 (filename: chrN-geno.csv.gz). Constituting the 8,348,663 single nucleotide variants (SNVs) were 2,769,837 SNPs and 5,578,826 RVs for odd-numbered autosomes. The clinical data (DBP and SBP), covariates (age and sex), and antihypertensive medication of 855 individuals were available (filename: PHEN.csv). Patients were considered hypertensive if they had ever taken antihypertensive medication or their DBP exceeded 90 mm Hg or SBP exceeded 140 mm Hg at the most recent examination. Among patients who received hypertensive medication, the values of DBP and SBP were increased by 5 mm Hg and 10 mm Hg, respectively, to adjust for the effects of the medication . After dh(b), which corresponded to the b% of SNVs on a chromosome that were contained in each window . Second, for each individual and for each window, we estimated homozygosity intensity by using a double-weight local polynomial model. Let {xi, i = 1, ⋯, mc} denote the physical position of the i th SNV, where mc denotes the number of SNVs on chromosome c. Let {Yi, i = 1, ⋯, mc} denote an indicator of the homozygous status of the i th SNV, taking value 1 (0) for a homozygous (heterozygous) SNV. The estimator of homozygosity intensity at the physical position of E as follows:We estimated homozygosity intensities on a chromosome as follows. First, we constructed sliding windows on a chromosome by using the nearest neighbor method with a bandwidth of K(u) was designed to consider a local correlation of SNVs. A higher weight was assigned to an SNV closer to the physical position of x. Locus weight L(xi) was designed to reduce the weights of the common homozygotes of RVs when defining ROH, because common homozygotes of RVs that had a lower MAF carried less homozygosity information. In this study, we considered cubic kernel weight and locus weight as follows:In Eq. , kernel The estimates of the homozygosity intensity ranged from 0 to 1. A higher value indicates a higher homozygosity. Heritability of homozygosity intensity in a region of HD was calculated by using a variance component linkage analysis in MERLIN.Generalized estimation equation (GEE) analysis was used to examine 855 related individuals and their WGS data, blood pressure (SBP and DBP), HTN, and covariates (age and sex). In the GEE analysis, DBP values, SBP values, and HTN status were modeled as 3 separate response variables. Subsequently, within each sliding window, the association between each response variable and homozygosity intensity was examined separately, with concomitant adjustment for age and sex. An identity link was used in the analysis of SBP and DBP and a logit link was used in the analysis of HTN. Furthermore, we identified gene expression regulated by the significant region(s) of HD. In GEE analysis, each gene expression was modeled as a response variable and its association with homozygosity intensity in the DBP-, SBP-, and HTN-associated regions of HD was examined individually, with concomitant adjustment for age and sex.1 and homozygosity intensities, with covariate adjustments for age and sex, by using GEE analysis. The test power was evaluated by examining the association between Q1 and homozygosity intensities in the 3 Q1-associated regions of HD, with covariate adjustments for age and sex, by using GEE analysis. Throughout the study, the nominal significance level of statistical tests was 0.05 after adjusting for the false discovery rate (FDR) for the multiple-testing problem.We performed simulation studies to evaluate the type 1 error and power of homozygosity association tests. The type 1 error was analyzed by examining the association between QOur analyses were performed without knowledge of the underlying simulation model. We estimated the whole-genome homozygosity intensities of 959 individuals. For example, the profiles of homozygosity intensity of 8 individuals on chromosome 13 are shown . HTN-associated regions of HD were located between 112.60 and 113.43 Mb on chromosome 11  and between 35.43 and 36.13 Mb on chromosome 15 . No SBP-associated regions of HD were identified.We performed whole-genome homozygosity association tests to identify DBP-, SBP-, and HTN-associated regions of HD. To examine the multiplicative effect of homozygosity intensity and reduce the influence of outliers, SBP and DBP values were transformed through log transformation and winsorization with a threshold of 0.01. After FDR correction, several DBP- or HTN-associated regions of HD were identified through GEE analysis Fig. . DBP-asshttp://geneticassociationdb.nih.gov/). Moreover, the expression of 6 and 2 genes was regulated by the HTN-associated regions on chromosomes 11 and 15, respectively  = , , and . The power for the 3 Q1-associated regions of HD that represented the 10th, 50th, and 90th percentiles of proportions of RVs on chromosome 21 was: 0.920, 0.985, and 0.775 for  = , respectively; 0.630, 0.820, 0.43 for  = , respectively; and 0.075, 0.070, and 0.125 for  = , respectively. The power for  =  was very low because the probability p was lower than 0.03 for every individual.We evaluated the type 1 error of the homozygosity association tests by analyzing 200 simulation data sets of quantitative trait QOur previous study examined the genomic patterns of HD in 270 samples from populations with African, Asian, and European ancestry in the International HapMap II Project [The contributions of this study are summarized as follows: First, we developed a new method for considering the local property and genetic information of homozygosity in homozygosity intensity estimation. In this method, the assumption that RVs have equal importance when defining ROH is relaxed and, the method has a higher power than our previous homozygosity association test . Moreove