Drosophila melanogaster is a relatively simple morphological trait, which serves as a model for mapping the genetic basis of variation in complex phenotypes. Here, we assessed natural variation in female abdominal pigmentation in 175 sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel, derived from the Raleigh, NC population. We quantified the proportion of melanization on the two most posterior abdominal segments, tergites 5 and 6 . We found significant genetic variation in the proportion of melanization and high broad-sense heritabilities for each tergite. Genome-wide association studies identified over 150 DNA variants associated with the proportion of melanization on T5 (84), T6 (34), and the difference between T5 and T6 (35). Several of the top variants associated with variation in pigmentation are in tan, ebony, and bric-a-brac1, genes known to affect D. melanogaster abdominal pigmentation. Mutational analyses and targeted RNAi-knockdown showed that 17 out of 28 (61%) novel candidate genes implicated by the genome-wide association study affected abdominal pigmentation. Several of these genes are involved in developmental and regulatory pathways, chitin production, cuticle structure, and vesicle formation and transport. These findings show that genetic variation may affect multiple steps in pathways involved in tergite development and melanization. Variation in these novel candidates may serve as targets for adaptive evolution and sexual selection in D. melanogaster.Pigmentation varies within and between species and is often adaptive. The amount of pigmentation on the abdomen of  Drosophila melanogaster Genetic Reference Panel, a wild derived population of fully sequenced inbred fly lines, to identify the contributions of both known and novel genetic variants to natural variation in abdominal pigmentation in female flies. Our results show that genetic variation within many biological pathways contributes to variation in D. melanogaster pigmentation.Body pigmentation contributes to the spectacular biodiversity present in nature and mediates mate choice, mimicry, and physiological functions such as thermoregulation and UV resistance. Thus, pigmentation is a significant contributor to fitness. In order to understand how complex traits such as pigmentation evolve, we must first identify the genetic variants underlying phenotypic variation. We used the  Body pigmentation is a conspicuous trait that is variable within species, giving rise to natural variation, polyphenism and sexual dimorphism –4. It alDrosophila it is a relatively simple and easily measured phenotype to study the genetic architecture of natural variation in complex traits  = GawB}pnrMD237/TM3, P{w[+mC] = UAS-y.C}MC2, Ser1(pnr-GAL4), has restricted expression in the midline [pnr-GAL4 driver adds a spatial component to the validation experiments and allows for more precise testing of the candidate genes : CG9134e00088, CG7852c04511, Exchange factor for arf6 (Efa6f03476), Fish-lips (Filif04573), and Glucose transporter 1 (Glut1d05758) showed increased melanization; and krotzkopf verkehrt (kkvc06225) showed decreased melanization . CG9134e0008, Efa6f03476, Filif04573, and Glut1d05758 showed increased melanization; and CG10625e01211, CG7852c04511, division abnormally delayed , kayak (kayf02002), kkvc06225, klarsicht (klard05910), locomotion defects (locod09879), and Sucbe01940 showed decreased melanization , and Kinesin-like protein at 61F (Klp61Ff02870) were not significantly different from the control. Efa6f03476 also has a light and somewhat elongated thoracic trident; this thoracic pigmentation is completely absent in the control flies . Efa6, klar, and Klp61F knockdowns increased the proportion of dark melanin on T6; buttonless (btn) and CG7852 decreased it. Knockdown of roughoid (ru) and sinuous (sinu) showed decreases in pigmentation for both T5 and T6.Of the 28 candidate genes, 26 were available as RNAi knockdown constructs  Tables. UAS-RNAi constructs to an ubiquitously expressed tub-GAL4 driver, and found that 11 (42%) were lethal in both sexes, suggesting pleiotropic effects on vital functions: Vesicular monoamine transporter (Vmat), Klp61F, CG7852, CG1887, klar, ru, sinu, Nedd2-like caspase (Nc), kkv, kay, and CG42340  effects on pigmentation. Knockdowns of btn, CG10625, and CG9134 had decreased proportions of dark melanin on T5 and T6; Efa6 and loco knockdowns showed increases in pigmentation on both tergites  effects on abdominal pigmentation , the dark melanin that is present is a light brown melanin that is only slightly darker than the adjacent sclerotin  and Glut1, which may facilitate the transport of glucose or glucose conjugates from the hemolymph to the epidermal cells. tfc is a C-type lectin with a carbohydrate binding domain and Glut1 is a membrane bound glucose transporter [Prior to molting and eclosion, insects accumulate tyrosine-derivatives conjugated with hydrophilic molecules such as glucose, phosophate, and sulfate in the hemolymph. This keeps the reactive pigment precursors in an inert state until the organism is ready to molt or eclose ,25–29. Wnsporter .kay, dally, Fili, and ru. kay is a transcription factor in the JNK signal transduction pathway [decapentaplegic (dpp) expression, wing and leg development, and the elongation of the cells in the epidermis [dpp expression in the tergite corresponds to the midline pigment stripe, and ectopic expression of dpp in pupae expands this stripe [dpp signal transduction is potentiated by dally [dpp and Epidermal growth factor receptor (Egfr) signaling work synergistically to specify the lateral tergite cell fate [32. ru, also known as Rhomboid-3 (Rho-3), activates Egfr signaling and thus may help determine epidermal cell fate in the developing tergites; however, there are several Rho proteins capable of this activation [Fili is a transmembrane protein that is involved with apoptosis in the wing imaginal disc and we speculate may facilitate proper tergite differentiation during metamorphosis [Several of our new pigmentation genes have roles in relatively well-described developmental pathways. These include  pathway . It is rpidermis . dpp exps stripe . Furtherby dally . Additiotivation . Fili isorphosis .pinstripe , Efa6, Klp61F, and klar. pns is predicted to have Rab guanyl-nucleotide exchange factor activity, which facilitates vesicle transport by activating Rab proteins [D. melanogaster wing [Efa6 is also involved with Rab signaling and vesicle mediated transport [Klp61F is a kinesin motor, and klarsicht (klar) regulates microtubule-dependent vesicle transport along microtubules. Both could be involved in transporting vesicles of enzymes and/or dopamine-derivatives to and from the cuticle. Together these genes may represent components of the little-known transport mechanism for cuticle tanning in D. melanogaster.Four validated candidate genes are involved with vesicle formation and transport: proteins ,37. Rab5ter wing . Efa6 isransport . Klp61F farmer , kkv, and sinu, and loco may affect cuticle development and structure. frm is a cuticle structural protein [kkv is one of two chitin synthase genes in D. melanogaster [sinu is a claudin required for septate junction organization, cell-cell adhesion, and proper localization of proteins involved in chitin filament assembly in D. melanogaster [loco regulates G protein signaling, and Gγ1 signaling is required for proper septate junction protein localization including sinu [ protein . kkv is nogaster . sinu isnogaster . loco reing sinu ,42. ThesSucb and ph may affect the organism more broadly. Sucb is a succinate-CoA synthetase in the Krebs cycle [ph is a CD36 homologue, and dipteran CD36 family members may have roles in transport of cholesterol and steroids during ecdysone synthesis [bs cycle . It is pynthesis . Since et and e, are pleiotropic as well [In summary, we provide evidence that genetic variation at a number of steps in regulatory, developmental, and transport may pathways contribute to natural variation in abdominal pigmentation. These findings exemplify the pleiotropic nature of these genes which may limit their potential as adaptive targets –46. Seve as well . The dif as well . Additiobab1 on pigmentation in female D. melanogaster, were generated from only two gravid wild caught females [et al. [e sequences among the 25 African lines eliminating any possibility of identifying additional loci [Drosophila pigmentation to date.Consistent with other DGRP studies, we identified many more genetic variants associated with pigmentation than previous studies –51. We s females . The stu [et al.  pooled inal loci . The DGRnal loci . Given bIt is important to acknowledge that gene expression knockdown and mutant analyses are only an approximate confirmation of causative SNPs. Genes implicated by the GWA analyses that do not confirm in these functional tests may be true positives and contribute to variation in pigmentation, but they do not change pigmentation when gene expression is reduced. Future studies should test effects of individual SNPs and further characterize the mechanisms though which the candidate genes affect variation in pigmentation; their potential interactions with variants in the candidate genes with major effects; and their allele frequency distributions in different populations. These studies will help elucidate the contribution of these novel variants to adaptive phenotypic evolution or whether they are population-specific deleterious variants that are maintained segregating by mutation-selection balance.Our results open the door for new hypotheses to be tested on the transport of dopamine derivatives and conjugates from the hemolymph to the cuticle, the formation and movement of vesicles within epidermal cells, the mechanisms of regulatory and developmental pathways during tergite differentiation, the interactions of chitin filaments with cell adhesion and cuticle proteins, and how metabolic and hormonal regulation could lead to variation in pigmentation. Genetic variants that affect these processes could potentially serve as targets of adaptive evolution or sexual selection in natural populations. This study is a start. However, much more work is needed to draw mechanistic inferences about these novel candidate genes and their contributions to the evolution of pigmentation.N = 10 flies per line). The flies were reared in vials at a controlled adult density (CAD) of 10 males and 10 females on cornmeal-molasses-agar medium at 25°C, 75% relative humidity, and a 12-h light-dark cycle. The parental generation was allowed to lay eggs for 3 days. Each fly was visually assessed by a single observer for the percentage of brown/black melanin covering each tergite; the scores ranged from 0 for no dark pigmentation to 4 for 100% dark pigmentation in increments of 0.5.The DGRP consists of 205 inbred lines with complete genome sequences. We scored female flies of 175 DGRP lines—aged 5 to 8 days—for the proportion of melanization on abdominal tergites 5 and 6. Two independent replicates for each DGRP line were reared and five individuals were scored from each replicate vial (Y = μ + L + T + L×T + R(L×T) + ε, where Y is phenotype, μ is the overall mean, L is the random effect of line, T is the fixed effect of tergite, R is the random effect of replicate vial, and ε is the residual. We also performed reduced ANOVAs separately for each tergite of form Y = μ + L + R (L) + ε. We estimated variance components for the random effects using REML. We computed the broad-sense heritability (H2) of pigmentation for each tergite separately as H2 = σ2L/ (σ2L + σ2ε), where σ2L is the among-line variance component and σ2ε is the error variance. We computed the genetic correlation between the tergites  as CovT5,T6/ σLT5σLT6, where CovT5,T6 is the covariance in pigmentation score between tergites 5 and 6. All analyses were performed with version 9.3 of the SAS System for Windows (2013 SAS Institute Inc.).We partitioned variation in pigmentation into genetic and environmental components using an ANOVA model of form Wolbachia infection and for the following common inversions: In(2L)t, In(2R)NS, In(3R)P, In(3R)K, and In(3R)Mo. We performed GWA studies in two stages. In the first stage, we adjusted the line means for the effects of Wolbachia infection and major inversions. We then used the adjusted line means to fit a linear mixed model in the form of Y =Xb +Zu + e, where Y is the adjusted phenotypic values, X is the design matrix for the fixed SNP effect b, Z is the incidence matrix for the random polygenic effect u, and e is the residual. The vector of polygenic effects u has a covariance matrix in the form of Aσ2, where σ2 is the polygenic variance component. We fitted this linear mixed model using the FastLMM program (version 1.09) [Wolbachia infection and major inversions but not corrected for the relationship matrix. Significant variants were annotated using the 5.49 Release of the Flybase annotations.To identify genomic regions harboring variants contributing to natural variation in the proportion of tergite melanization, we conducted a GWA study for each tergite. The DGRP lines are also segregating for on 1.09) . We perfon 1.09) . All segR2 of the model to represent the variance explained by it. Second, to calculate additional variance explained by a focal variant after accounting for variants in known pigmentation genes, we first used stepwise selection to select the top associations for each pigmentation gene , requiring P-values to be smaller than 10–5 if more than one variant entered the model, and no P-value requirement if there was only one variant. The R2 of this baseline model (different for each of the four traits) is the variance explained by the pigmentation genes. We added each focal variant to the baseline model and calculated the difference between the R2 of the new model and the R2 of the baseline model, which represented the additional variance explained by the variant after accounting for the pigmentation genes. To calculate total variance explained by all significant variants, we used a mixed model approach because of the large number of variants. We computed the variance/covariance matrix based on the genotype matrix and estimated the variance components using the rrBLUP R package.For each variant, we calculated two variance components. First, to calculate the variance explained by a variant without adjusting for variants in known pigmentation genes, we fitted a linear model for the adjusted line means for only the focal variant and used the D. melanogaster: CG33298, Fili, Vmat, mwh, Klp61F, CG9134, CG7852, CG1887, klar, Glut1, Efa6, and btn. From the T5 pigmentation GWA analysis, we selected candidate genes that (1) had an Exelixis mutant [bab1 and Dsx, and genes in the pigmentation biosynthesis pathway (such as t and e), a peak of expression at 24 hr after puparium formation and 2–4 days after puparium formation, respectively, according to the modENCODE tissue and temporal expression data [ru, CG10625, sinu, Sucb, dally, CG32052, Nc, Cerk, kkv, CG15803, loco, TwdlC, kay, CG1340, CG42594, and CG42340. For each candidate gene, we tested either an Exelixis transposon insertion line [We tested 12 of the 13 genes implicated by the T6 pigmentation GWA analysis, none of which were previously known to affect variation in pigmentation in s mutant  or VDRC s mutant  line avaion data ,52. Thision line , or whenCG33298d10678a, Filif04573, mwhd01620, Klp61Ff02870, CG9134e00088, CG7852c04511, klard05910, Glut1d05758, Efa6f03476, CG10625e01211, Sucbe01940, dallyf01097, kkvc06225, locod09879, and kayf02002. The Exelixis progenitor w1118 line was used as a control. The KK and GD library progenitor lines were used to make control crosses for the RNAi knockdown experiments. Males from the GAL4 driver line were crossed to virgin females of the VDRC UAS line for all crosses. Three GAL4 driver lines were used for the RNAi crosses. All VDRC UAS lines were crossed with the full-body tubulin-GAL4/Sb driver and a pannier-GAL4 driver . In instances of lethality with the tubulin-GAL4/Sb driver, the lines were crossed to another full-body ubiquitin-GAL4/Cy driver. All GAL4-driver lines were obtained from the Bloomington, Indiana Drosophila Stock Center. We tested in total 26 RNAi knockdown constructs: CG33298, Fili, Vmat, mwh, Klp61F, CG9134, CG7852, CG1887, klar, Efa6, btn, ru, CG10625, sinu, dally, CG32052, Nc, Cerk, kkv, CG15803, loco, TwdlC, kay, CG1340, CG42594, and CG42340.We evaluated 15 Exelixis transposon insertion lines: N = 150 flies per genotype) from each Exelixis line or RNAi cross. In a few instances where viability was low fewer than 50 individuals per replicate were scored: pnr-GAL4 x sinu (N = 23), ubi-GAL4 x CG1887 (N = 90), and ubi-GAL4 x klar (N = 95). Means of test lines were compared to those of the appropriate controls with a Dunnett's test, which corrects for multiple testing, using JMP Pro 10.0.0 (2012 SAS Institute Inc.)We reared three independent replicates for each Exelixis transposon insertion line, for each RNAi cross and for the appropriate controls under the same conditions as the DGRP lines, but in 8 oz. bottles with a controlled adult density of 20 males and 20 virgin females. We scored the proportion of melanization on T5 and T6 for 50 5–8 day old female progeny per replicate  T5. (B) T6. (C) T5—T6.((EPS)Click here for additional data file.S2 Figy-axis) is plotted against variance explained by the same variants without accounting for variants in known pigmentation genes. Each colored point represents an individual variant identified in the GWAS and classified according to the genes to which are mapped. Note that not all GWAS identified variants were in the known pigmentation genes.For each GWAS conducted , the additional variance explained by significant variants after accounting for variants in known pigmentation genes ((PDF)Click here for additional data file.S3 FigA) schematic of pnr expression (yellow) in the D. melanogaster abdomen. Positive control crosses of (B) pnr-GAL4 x UAS-ebony and (C) pnr-GAL4 x UAS-tan as examples.((EPS)Click here for additional data file.S4 FigA) Exelixis w1118 control and (B) Efa6f03476. (C) ubi-GAL4 x VDRC GD library control and (D) ubi-GAL4 x klar-UAS.((EPS)Click here for additional data file.S5 FigThe cuticle is semi-transparent and weak. The meconium (M) remains lodged at the end of the abdomen in this cross; all individuals die within 24 hours after eclosion. The third thoracic leg (T3) is malformed.(EPS)Click here for additional data file.S6 FigP-values were below 1 x 10–5 divided by 1,000 . We simulated phenotypes with underlying QTLs of effects ranging from 0 to 1.0 phenotypic standard deviation and minor allele frequency of the QTL ranging from 0.05 to 0.50. The range of the observed effect sizes in the present study is indicated by grey shading.Assuming a population size of 175 (the same as the present study), we estimated power to detect a QTL by simulating phenotypes based on the genetic parameters for 1,000 times and performing single marker regression. Power was calculated as the number of iterations when the (EPS)Click here for additional data file.S1 TableA) DGRP Tergite 5 and 6 line means and standard errors. (B) DGRP raw data.Sheet ((XLSX)Click here for additional data file.S2 Tableσ2: Variance component; H2: Broad sense heritability.(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(RTF)Click here for additional data file.S6 Table(DOC)Click here for additional data file.S7 Table(DOC)Click here for additional data file.S8 TableA) Exelixis mutants and control data. (B) tub-GAL4 x UAS lines from the VDRC KK library. (C) tub-GAL4 x UAS lines from the VDRC GD library. (D) pnr-GAL4 x UAS lines from the VDRC KK library. (E) pnr-GAL4 x UAS lines from the VDRC GD library. (F) ubi-GAL4 x UAS lines from the VDRC KK library. (G) ubi-GAL4 x UAS lines from the VDRC GD library.Sheet ((XLSX)Click here for additional data file.S9 TableA) Exelixis mutants and control data. (B) tub-GAL4 x UAS lines from the VDRC KK library. (C) tub-GAL4 x UAS lines from the VDRC GD library. (D) pnr-GAL4 x UAS lines from the VDRC KK library. (E) pnr-GAL4 x UAS lines from the VDRC GD library. (F) ubi-GAL4 x UAS lines from the VDRC KK library. (G) ubi-GAL4 x UAS lines from the VDRC GD library.Sheet ((XLSX)Click here for additional data file.


Genetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study’s analysis plan.We developed a Bayesian statistical model for the prior probability of phenotype–genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super–track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen–2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non–informative predictors and evaluated the model’s ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP’s presence in the GC. Further, using data from a genome–wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome–wide scale and improves power to detect associations.a priori odds of a variant’s association to a trait and how these signatures can be integrated into a standard genome–wide–scale association analysis, resulting in improved power to detect truly associated variants.We show how diverse functional annotations can be efficiently combined to create ‘functional signatures’ that predict the The online version of this article (doi:10.1186/1471-2164-15-398) contains supplementary material, which is available to authorized users. In the typical genome–wide association study (GWAS), the first two forms of data are combined to reconstruct genotypes to a desired density and these genotypes are then systematically tested for association with the phenotype. The functional annotation data are most frequently used in post hoc interpretation of evident associations raised by the analysis [The purpose of genetic association studies is to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype . Modern analysis .a priori in genetic association analyses exist, they are infrequently used. The prevailing approach to this is via a two–staged hierarchical model in which coefficients in the stage I generalized linear model for phenotype given genotype and exposure measurements are regressed, in stage II, on the annotation data [To date, functional annotation data have rarely played more than an indirect role in assessing evidence for association. For example, they may be used to suggest candidate genes and SNPs for study or to support links between candidate SNPs and genes. While methods to incorporate functional annotation data ion data –6. This ftp://stat.duke.edu/pub/Users/iversen/FunctionalSignatures/.It is becoming increasingly clear that a widening array of annotation data correlates with a variant’s having been associated with a human phenotype –14. In wIn association studies statistical analysis is utilized to measure the evidence in favor of association. The data that inform these analyses usually comprise phenotype labels, SNP genotype data and a set of non–genetic covariates in addition to functional annotations of the variants under study. The statistical analysis may take many forms, varying according to choice of modeling approach and inferential paradigm (Frequentist or Bayesian). The approach we develop here relies on Bayesian inference but can also be applied when the genetic association summaries are derived from a Frequentist analysis. In this paradigm, prior data on a quantity of interest (such as the binary association status of a genetic variant) are updated to reflect evidence in the current data set.a priori (to association data) models for functional annotation data we describe below into analyses of genetic association data. Details of the models and their assumptions are provided in Methods.A Bayesian analysis of genetic association data returns an estimate of the posterior odds of association of each marker given the available data. When the data take two distinct forms — here subject–level phenotype, genotype and covariate data and variant–level functional annotations — the posterior odds of association may be calculated in two stages, either by incorporating functional data prior to or following evaluation of the genetic data. The latter represents the heuristic typically followed in practice, whereby functional data is evaluated in an informal way (from the probabilistic point of view) conditional on evidence for association. Here we describe a model–based framework for combining functional and association data following the second factorization. We focus on the case–control study design for purposes of illustrating integration of the When the functional data are incorporated as prior information, the posterior odds of a SNP’s association given the functional and subject–level data can be written as the product of the Bayes factor (BF) in favor of association and the prior odds of association given the functional data. The BF is the ratio of the integrated likelihood of the phenotype data given the covariate and genotype data assuming the SNP is associated to the integrated likelihood of the phenotype data given the covariate data only (i.e. assuming the SNP is not associated). It is a commonly used Bayesian statistical measure of association and is calculated by the SNPTEST  and BIMBα + Fβ, where F denotes its functional data and α and β are parameters. We refer to the linear combination Fβ associated with a SNP as its ‘functional signature’.In short, the functional annotation data are incorporated into an analysis by formally updating the prior odds of association given the annotation data by a standard measure of genetic association. This process is depicted schematically in Figure βF, of the functional annotations, denoted F. We refer to this linear combination as a SNP’s ‘functional signature’ when it is evaluated using that SNP’s annotations, Fs. Figure We constructed the functional annotation signatures by estimating the multivariate relationship between a set of functional annotation variables and the binary association status of a set of SNPs. In this way, we identify a linear combination, of SNPs in which the cases were drawn from the GWAS Catalog [We began by constructing a matched case–control study  Catalog  and the  Catalog  for eachUsing on–line bioinformatics resources, we assembled a set of functional annotation variables representing a variety of contextual descriptions and empirical measurements with which we annotated each of the 48,889 case, control and LD partner SNPs. We included annotation variables shown to be correlated with presence in the GWAS Catalog or that we believed likely to be so. These were: dbSNP function designation; summaries of ENCODE Project , 20 dataWhile not a comprehensive set, the functional variables we chose to work with covered the major annotation classes available at the time of analysis and are readily available to individuals executing an association study. The infrastructure and methods described here are easily updated to accommodate new variables as they become generally available. Table at least one truly associated SNP and that each control block contains none; as in [The 48,889 SNPs included in the analysis were grouped into 2,093 case and an equal number of control blocks. We randomly selected 1,675 of these matched case–control pairs for development of the model (the ‘training set’) and left the remaining 418 pairs for model evaluation and comparison . We modeled the probability that a SNP is associated given its functional data using a logistic regression model in which the association status of the individual SNPs in the case blocks is assumed unknown. The model assumes, however, that each case block contains e; as in , the ideWhile the assembled list of functional predictors includes measures that have been demonstrated to correlate with the association status of SNPs in the GC, it also includes a number of measures whose utility in this regard was unclear. Hence, we expected that only a fraction of the 57 variables would contribute to predicting phenotype association. We used shrinkage priors , 38 to rversus GM12878 and K562; PC 5: GM12878, HMEC and NHEK versus HSMM, HUVEC and NHLF; PC 6: H1–hESC, HepG2 and HSMM versus GM12878 and HUVEC; PC 8: K562 versus H1-hESC; and PC 13: HepG2 versus H1–hESC). Additional file Table The sequence conservation PCs collectively make the next largest contribution, explaining 16% of variation in the functional scores; PCs 1 and 3 explain >97% of this. Each PC is a linear combination of the summary statistics of the 28 and 44 species PhyloP scores, each for all species and restricted to placental mammals. Additional file versus GM12878 and NHEK; PC 4: GM12878 and H1-hESC versus K562, NHEK and HepG2; PC8: HUVEC versus NHEK). Additional file The CalTech RNA–seq PCs collectively explain 10% of the signature, with PCs 1, 2, 4 and 8 contributing 87% of this. Additional file http://www.scandb.org; [RegulomeDB categorizes variants into a seven-level functional score based on a synthesis of regulatory data derived from ENCODE and other sources. Category 7 variants lack evidence of association; categories 4-6 show ‘minimal binding evidence’; categories 2 and 3 are ‘likely’ and ‘less likely to affect binding’, respectively; and category 1 variants are those ‘likely to affect binding and linked to expression of a gene target’. Its score explains the next largest fraction (8%) of variation. It is represented by six variables, each indicating a functional category; category 7 serves as the reference (‘baseline’). Categories 2, 4, 5 and 6 explain 99% of this variation suggesting that other annotation variables in the model better characterize the probability of phenotype association for variants in categories 1 and 3. This is especially notable for category 1 as it is the only variable included in the model explicitly tied to eQTL status. This failure of simple eQTL summaries to provide predictive power once the other annotations are included in the model is consistent with our findings from a preliminary analysis that included lymphoblastoid line–based eQTL summaries derived from the SCAN database , but noVirtually all (96%) of the 2.5% contribution to variation made by the DGV variables is due to the copy number and inversion variables. Finally, at 1.7%, the dbSNP functional class variables are the only remaining that contribute more than 1% of the variation in functional scores. Virtually all (99%) of this contribution is due to the non–synonymous designation and the PolyPhen–2 probability contributes to the model’s ability to predict within this class of variants. This suggests that more direct measures of a variant’s involvement in transcriptional regulation, such as location in or near a DNAse I hypersensitive site (DHS), are better predictors of association than proximity to a nearby transcription start site (TSS) or other gene–centric landmark. Indeed, it has been shown  that GWAWe estimated the concordance indexes  for each model using the 418 matched case–control block pairs in the validation set as a tool for comparing the accuracy of their out–of–sample predictions. Table no association, based on Jeffreys’ scale of evidence [We compared the ranks assigned to a group of variants in a GWAS analysis when those ranks are calculated with and without the functional annotation data. Each in the group of variants is assumed to have known association status (associated/unassociated) with epithelial ovarian cancer, where this determination is based on confirmatory studies subsequent to the GWAS. The group is constructed as follows. There are currently 11 published, genome–wide significant loci for epithelial ovarian cancer. Nine of the 11 have come to light through analysis of genome–wide SNP data. These are rs3814113 , rs8170 evidence  that we against association (both of their Bayes factors are less than 1.0). The GWAS SNPs are all ranked in the top 50,000 (of approximately 2.5 million) by the same measure and all have Bayes factors of at least 3 to 1 in favor of association.Table Only two of the truly associated SNPs (rs11782652 and rs9303542) are ranked higher when the functional data are ignored than when they are used, however their respective changes in rank are small. The median  rank of the truly associated SNPs was 5,272  without and 3,532  with the functional data included. If design constraints allowed only for followup of the top 5,000 variants, a larger fraction (7/11) would be discovered with addition of the functional data than without (5/11); with followup of 10,000 variants, these fractions become 8/11 and 7/11. In contrast, when the function data were included, the median  rank among a set of ‘true negative’ SNPs increased from 181,116  to 244,393 , while the number selected for followup fell from 244 to 204 under the 5K scenario and from 443 to 373 under the 10K scenario.While a few of the functional variables, such as the function class designation ‘nonsynonymous’, incorporated in the signature are base pair specific, most map to contiguous regions of 100’s or 1000’s of base pairs. Hence, the functional signatures associated with nearby SNPs are correlated. Figure Using the GWAS Catalog as a sampling frame, we developed a model for the probability that a given polymorphism is associated with an observable human phenotype given a set of functional annotation variables and demonstrated that this model has the ability to predict a set of phenotype associated variants not used in the model building exercise. We demonstrate several methods for incorporating functional annotation signatures defined by this model and evaluated for a SNP’s annotation data as prior data and show through example that by doing so we improve the efficiency of GWAS scale analysis to identify true positive associations for follow–up study.The approach we describe is computationally tractable and scalable to modern genome–wide analysis. Our use of penalized regression techniques to model the functional data and construct the function signatures allows us to consider a relatively large number of individual annotation variables while controlling for over–fitting. We evaluated sensitivity of the model’s out–of–sample predictions to choice of shrinkage prior and found that the most aggressive choice we examined, the model whose results are summarized herein, resulted in the best out–of–sample concordance estimates. Our approach can be expanded and adapted to incorporate more detailed annotation data such as was recently released by the ENCODE consortium  or generversus 3,705) and true negative SNPs . This suggests that the added value of Bayesian updating to the functional signatures will typically be limited.In principle, estimates of the parameters in the model for SNP association status given the functional data can be refined via Bayesian updating as part of an association analysis. This requires an additional layer of analysis that is feasible, but computationally demanding to implement on a genome–wide scale. However, the value of this will be limited in settings where there are few truly associated SNPs and/or the case–control data supporting associations are weak, i.e. the vast majority of applications. Here, Bayesian updating will yield estimates equivalent to those using the approach we describe above up to Monte Carlo simulation error. Indeed, we formally compared the two approaches using the ovarian cancer GWAS data and found little change in the median ranks of the true positive . This may explain, at least in part, the relatively modest nature of the improvements observed in our GWAS analysis and the failure of general summaries of eQTL status to contribute meaningfully to the functional signatures. Indeed, many regulatory processes are cell–type–specific , 12 and Finally, our analyses have been carried out entirely at the HapMap III density. Our approach succeeds at this density because the functional signatures of SNPs nearby, at distances typical of HapMap III, are highly correlated and hence the functional signatures of HapMap III polymorphisms essentially tag function of nearby polymorphisms not in the database. As coverage (genotype/imputation density) of the typical association study becomes more complete, the need to rely on correlations between functional signatures will diminish and their power to assist in identifying and localizing associations is expected to increase. Association analyses at the density of the 1000 Genomes Project database  are now G be an n by p matrix of SNP genotypes, D be an n by 1 vector of disease indicators where Di=1 if individual i has the disease and Di=0 otherwise, X be an n by r matrix of covariates used in the association model and F be a p by m matrix of SNP–level functional annotation data where n is the number of individuals, p is the number of SNPs, r is the number of covariates and m is the number of annotation variables. Finally, let A be a p by 1 vector of 0-1 indicators of the (unknown) association status of the variants, where As=1 if SNP s is associated with the phenotype of interest.Let X, D, G) and function (F) data. To this end, we let As’s are conditionally independent given  and (2) that the As’s are conditionally independent of other variants  given . The notation Gs− indicates the matrix obtained by removing column s from G.In what follows, we specify the likelihood for the association indicator given the association  not included in F. Given this, the posterior odds of association of SNP s given its association and functional data can be written as the product of the (prior) odds of its association given its functional data times the (integrated) likelihood ratio or Bayes Factor (BF) of the phenotype given the SNP genotype and other covariate data, i.e.Further, we assume that the disease phenotype data are conditionally independent of the functional data for SNP We describe estimation of the association summary Bayes factor below.α and β, odds(As|Fs) = exp(α + Fsβ) and hence, given α and βGiven the binary, logistic link model developed below for association status given the functional data and the parameters Fsβ is the ‘functional signature’ of SNP s. Provided that estimates of α and β are available from an external analysis such as described in the next section, one can estimate Pr bywhere αi and βi are samples from the posterior distribution from an analysis such as described in Section “Functional signatures of known associations”.where the The above procedure depends on estimates of the marginal likelihoods,o (As=0) and under Ha (As=1). Pr is a logistic regression of the disease status indicator, D, on the covariates, X, and SNP genotype, Gs, and with coefficient vector θ1 under Ha and is a logistic regression D on X with coefficient vector θ0 under Ho. We place independent normal mean 0, standard deviation 10 prior distributions on all components of θ0 and θ1, with exception of the coefficient of Gs, which is accorded a normal mean 0, standard deviation 0.25 prior distribution, as the majority of log–odds estimates cited in the GWAS catalog are smaller than 0.5 in absolute value. We estimate the SNP–specific marginal likelihoods under each hypothesis of association using the Laplace approximation [of the association data for each SNP under Hximation  implemenximation  and avaiximation , 18, allIn what follows, we detail the steps we took to assemble the case–control study of SNPs used to build and evaluate the models for a variant’s association status given the functional data. These comprised identification of a representative set of phenotype–associated SNPs to serve as ‘cases’ in the analysis and a matched set of ‘controls’ and the collection of a set of measurements related to function to serve as annotations for the variants. The process is depicted in Additional file Many genomewide genotyping arrays were designed to over–sample variants with characteristics related to their ability to explain phenotypic variation, such as proximity to coding regions, type of variant (e.g. missense) and minor allele frequency (MAF). Hence, a comparison of case SNPs identified using such assays to control SNPs drawn randomly from the HapMap or dbSNP, for example, may lead to spurious associations between assay design variables and the SNP’s association with a human phenotype. In order to avoid confounding due to the selection method employed in the design of the genomewide genotyping platform, we constructed a sampling frame of SNPs by combining SNPs on the Affymetrix GeneChip Human Mapping 500K Array Set and the Illumina HumanHap550 Genotyping BeadChip, as this generation of arrays and their predecessors cover most of the reported findings in the GWAS Catalog attributed to an Affymetrix or Illumina product and these products were the most commonly used. We labeled SNPs in the sampling frame according to whether they appeared only on the Affymetrix list, only on the Illumina list or on both and confined attention to those variants appearing in both the Genome Browser’s dbSNP 130 and HapMap Release 27 tables  with one or more case SNPs as reported in the HapMap Release #27 LD files  [SNPs in the GWAS Catalog are arguably more likely to tag the variant that is directly associated with the phenotype than to be that variant . For thiAll data drawn from the UCSC Genome Browser  used then=5) of nonsense variants, we created a ‘coding–nonsynonymous’ designation by combining the ‘missense’ and ‘nonsense’ categories; similarly, we combined the ‘untranslated–3’, and ‘untranslated–5’ designations into the category ‘untranslated’.Variants described in dbSNP  release We investigated the PhyloP evolutionary conservation scores  applied The Database of Genomic Variants (DGV) , 34 is aa priori prediction of functional, disease-associated variants. In particular, we include variables (see below) summarizing: transcription levels assayed in six cell lines by RNA–seq [The ENCODE Project , 20 is a RNA–seq , 22 and  RNA–seq , 24; evi RNA–seq , 26; and RNA–seq –31.The Broad ChIP–seq, Caltech RNA–seq, and PhyloP signal tracks are summarized at the level of genomic bins. The ChIP–seq signals are measured within 118,084 contiguous bins of 25,600 bases apiece. The RNA–seq and PhyloP signals are measured in sets of non–overlapping, non–uniform bins. Bins are indexed according to the hierarchical scheme described in . The ENCThe three Broad types  comprise data on eight, eight and nine cell lines, respectively. We found significant pairwise correlations among the 75 variables (25 each of log(mean), log(maximum), and log(z)), ranging as high as 0.949, and therefore conducted a principal components analysis to identify the linear combinations, i.e. principal components (PCs), that explain most of the variability in the data. The top 18, 27 and 44 PCs explain 90%, 95% and 99% of variability, respectively, in the 75 measures. Finally, we mapped each SNP to the appropriate Broad bin and annotated each with the top 18 PCs for purposes of the analysis. Additional file The Caltech tables comprise RNA–seq raw signal enrichment data on six cell lines. Pairwise correlations among the 24 variables ranged as high as 0.970. The top 11, 12, and 16 PCs explain 92%, 95%, and 99% of the variability. The top 11 PCs were used in the analysis. Additional file The transcription factor ChIP–seq data are summarized by scores, ranging from 6 to 1000, measuring strength of evidence for binding within specified, sometimes overlapping, chromosomal bins (‘clusters’). We summarize these data as they apply to each SNP using two variables: the number of clusters it intersects with (‘TFBSfreq’) and the average loge(score) (‘logTFBS’) assigned to those clusters (coded as 0 if the SNP does not intersect with a cluster). Similarly, the DNaseI hypersensitivity data are summarized by scores, ranging from 16 to 1000, within specified chromosomal bins (‘clusters’). We summarize these data as they apply to each SNP using (1) an indicator for the variant falling within a clusters and (2) the loge(score) assigned to that cluster.The Open REGulatory ANNOtation database (ORegAnno) , 59 is aPolyPhen–2 (PPh2)  assigns RegulomeDB  annotateF; when F is indexed we are referring to the functional data for a given SNP (Fs) or, as just below, to a given SNP, s, in a given LD block, b (Fsb).We collected the annotation variables described above for each SNP in the HapMap Release 27 tables into an annotations data base that is available on our web site. In what follows, we refer to the collection of these variables as s in block b was an associated SNP, πsb, given the functional data for that SNP, Fsb, using the logistic regression model logit(πsb)=α+Fsbβ. We assumed that there was at least one associated SNP in each case block and that there were no associated SNPs in control blocks. Hence, each case block contributed the factor at least 1,675 of the 48,888 SNPs in the training set to be phenotype associated. This corresponds to α=−3.34 (columns of F are centered); if 10%  of case blocks contain two phenotype associated SNPs, α=−3.24 . Hence the normal mean -3.24, standard deviation 0.1 prior distribution we placed on α is consistent with our expectation that there were fewer than 2,178  true phenotype associated variants among the case blocks.For purposes of the analysis, we assumed that blocks and the SNPs within the blocks were independent conditional on the functional data. We modeled the probability that a SNP β was guided by the observation that, in the normal model with the normal–exponential–gamma (NEG) distribution as prior on the mean and the variance known, the posterior mode is identically zero when the maximum likelihood estimator (MLE) is in a neighborhood around zero, but rapidly converges to the MLE as the MLE diverges from zero . The NEG distribution is specified by its shape and scale parameters and the width of the threshold neighborhood is a function of these parameters. For purposes of our analysis, we chose parameter values for which no more than 10% of the coefficients are outside of the threshold region with probability 0.90, a priori. We placed independent NEG prior distributions on the components of β; in addition, we also considered the model with independent standard normal distributions on the components of β. Inference for each of these models was carried out using the training set and were evaluated using the evaluation set.Our specification of the prior distribution on We used random–walk Markov chain Monte Carlo (MCMC) algorithms , 61 to en1 SNPs in the case block (‘ b1’) was larger than the corresponding average over the n0 SNPs in the matched control block (‘ b0’); i.e. ifWe used the concordance index (CI) to measure the out–of–sample predictive accuracy of the model. We calculated the CI as the fraction of matched pairs in the ‘evaluation set’ in which the average probability of association given the functional data over the As,bn | Fs,bn) bywhere we estimated Pr to servWe compared the rankings of these two sets of SNPs in the original GWAS analysis when association was measured using genotype data only to those obtained with incorporation of the functional signatures. We compared the procedures based on their power to identify the truly associated variants for follow–up assuming budgets allowing for evaluation of the top 5,000 or 10,000 SNPs.Fsβ), for adjacent pairs of SNPs included in the ovarian cancer GWAS analysis. We identified the quantile of each adjacent variant pair in the overall distribution of distances measured in base pairs (BPs). For purposes of this analysis, we defined quantiles in increments of 0.025, i.e. with each containing 2.5% of the mass of the distance distribution. We estimated the Pearson correlation between the functional signatures of the adjacent SNP pairs within each quantile and plotted these estimates against BP distance, locating the estimates at the midpoints of the quantile bins.In most association studies, genotypes are determined, through a combination of genotyping and imputation, for only a subset of the universe of variants. In this setting, it is standard to rely on correlations between genotyped variants (‘tags’) and those that are ‘tagged’ (not genotyped) to identify and localize associations. Likewise, the utility of functional signatures in a typical study will depend on the degree to which they reflect the likelihood of function of both the tag for which it is calculated and for the set of variants it tags. We evaluated correlations between functional signatures, defined as Additional file 1:


AVPR1A on chromosome 12q has also been implicated in music perception, music memory, and music listening, whereas SLC6A4 on chromosome 17q has been associated with music memory and choir participation. Replication of these results in alternate populations and with larger samples is warranted to confirm the findings. Through increased research efforts, a clearer picture of the genetic mechanisms underpinning music ability will hopefully emerge.Music is an integral part of the cultural heritage of all known human societies, with the capacity for music perception and production present in most people. Researchers generally agree that both genetic and environmental factors contribute to the broader realization of music ability, with the degree of music aptitude varying, not only from individual to individual, but across various components of music ability within the same individual. While environmental factors influencing music development and expertise have been well investigated in the psychological and music literature, the interrogation of possible genetic influences has not progressed at the same rate. Recent advances in genetic research offer fertile ground for exploring the genetic basis of music ability. This paper begins with a brief overview of behavioral and molecular genetic approaches commonly used in human genetic analyses, and then critically reviews the key findings of genetic investigations of the components of music ability. Some promising and converging findings have emerged, with several loci on chromosome 4 implicated in singing and music perception, and certain loci on chromosome 8q implicated in absolute pitch and music perception. The gene  Music is ubiquitous in all known human cultures. The general capacity for human beings to perceive, produce, and enjoy music even in the absence of formal music training suggest that music may be “hardwired” in our genetic makeup. However, the diversity of music competency across individuals adds impetus to the long-standing debate of whether musicians are born or made.Studies on the genetic basis of music ability have been relatively scarce, compared with more extensive investigation conducted in the language domain  is also available as Supplementary Material online.Familial aggregation can address this question by comparing whether the prevalence of a trait is higher within the family of a proband than that in the general population sibling recurrence-risk ratio  indicate that a genetic hypothesis is worth pursuing , which is the ratio of the proband λs to the control λs.One of the first questions asked in human genetic analysis is whether a trait clusters in families above chance level. s may be inflated by ascertainment bias  twins share 100% of their genes whereas dizygotic (DZ) twins share on average 50% of their genes. By comparing the similarity of the MZ twin pairs on the trait of interest with that of the DZ twin pairs, greater similarity exhibited by MZ twin pairs indicates a possible genetic influence. In other words, if the concordance for a trait is much higher in the MZ twins, the trait is likely to have significant heritability.Twin studies take a step further by disentangling the relative contributions of genetic and environmental factors on trait variation , and unique environment on a trait can be estimated through structural equation modeling using statistical programs such as There are some criticisms surrounding the validity of twin studies given that the twin design is built on a number of assumptions Heritability in the narrow sense  can be identified. An important measure from segregation analysis is the segregation ratio, which is the proportion of offspring who inherit the trait of interest from a parent  arrays. If a genetic locus is thought to predispose a trait, family members who share the same markers near this locus should exhibit greater trait resemblance compared to those who do not share the markers. Therefore, linkage analysis aims to identify the markers on the SNP array that are commonly present in participants within or across pedigrees who exhibit the trait of interest. It should be noted that the identified markers are not the actual genes predisposing a trait; they are merely known locations on the genome that are near the gene of interest  can be conducted without prior knowledge of potential candidate genes. It involves an “agnostic” or non-candidate driven search of the entire human genome, typically using SNP arrays with a large number of common SNP markers found throughout the genome  technologies, exome sequencing has emerged as a rapid and cost-effective method for human genetic analysis  whose copy numbers deviate from those found in the human reference genome. Such variation in the genome (known as copy number variation) includes insertions, deletions, duplications, inversions and translocations  platforms to detect copy number gains or losses in the test sample compared with a reference sample  will improve and the cost of massive parallel sequencing will become less expensive. Nevertheless, this approach has not yet reached maturity and standard protocols and measures for sequencing-based CNV analysis have not been established. Moreover, the bioinformatics infrastructure, support, and cost required for managing, analyzing, and storing large amounts of sequencing data need to be carefully considered  in the keyword field. Terms such as music therapy, algorithm, dystonia, musicola, animal, bird, and songbird were excluded. Subject areas such as computer science, engineering, health professions, physics and astronomy, mathematics, nursing and business, management and accounting were also excluded. Only papers published in English between 1988 and 2014 were considered since earlier genetic studies were often fraught with methodological limitations, such as small sample sizes and lack of scientific rigor. Based on these criteria the search identified 97 articles that were then manually canvassed to select those directly addressing the genetic bases of various aspects of music ability using behavioral genetic or molecular genetic methods. In addition, the reference section of each article was examined to identify additional studies not captured in the initial search. In total, this process identified 21 papers included in this review.Indexed searches were performed in Scopus by searching for the following keywords: musicTable Absolute pitch (AP) or “perfect pitch” is the rare music ability of being able to identify or produce pitches without relying on an external reference. It has an estimated prevalence of less than 1 in 10,000 Bachem, . While Avertical transmission was commonly observed. The segregation ratio was estimated to lie between 0.24 and 0.37, suggesting a possible autosomal dominant gene with incomplete penetrance. Recurrence risk ratios could not be determined because the study did not recruit any control participants.Several studies have explored the genetic basis of AP through family studies. One of the earliest was a segregation study conducted by Profita and Bidder , who reps) estimate of 20, meaning that siblings of AP possessors are approximately 20 times more likely to possess AP relative to the general population , which is expressed almost exclusively in the brain and is implicated in learning and memory processes . EPHA7 has been implicated in brain development, particularly establishing neural connectivity between auditory cortex and other cortical regions with the thalamus  and 36 multiplex families with synaesthesia  due to a significantly smaller right mean PT volume compared with non-AP possessors  is a fine-grained pitch perception deficit characterized by the inability to detect “wrong” notes in melodies, despite normal intellect, language and hearing abilities  perception may also be genetically influenced, as inferred from an ethnicity study by Hove et al. . These rFoster and Zatorre  observedIn a large twin study conducted in 2001, 136 MZ twin pairs and 148 DZ twin pairs undertook the Distorted Tunes Test (DTT), in which they judged whether simple well-known melodies contained incorrect pitches that rendered them “out-of-tune”  haplotype RS1+RS3 on chromosome 12q has significant associations with performance on the Karma Music Test and the combined score on the Karma and Seashore music tasks  plays a key role in social cognition and behavior , which is implicated in neural migration, differentiation, and synaptogenesis, as well as learning and memory  gene, which has been implicated in the development of the inner ear  gene. Notably, two SNPs (rs13146789 and rs13109270) of PCDH7 also showed strong associations with the combined test score. PCDH7 is expressed in the developing cochlea of the chicken and the amygdala of the mouse  gene on chromosome 7q31 impairs rhythm perception and production, while leaving pitch perception and production abilities intact  gene . The rationale for targeting these two genes included a previously reported relationship between arginine vasopressin (AVP) and spatial and social memory  and the SLC6A4 (HTTLPR) polymorphisms using population-based and family-based association analyses. The phonological and music memory performance of the participants were assessed using an extensive battery of tests. Results revealed significant gene by gene epistatic interactions between the AVPR1A and SLC6A4 polymorphisms for two melodic memory tasks, one rhythmic memory task, and one phonological memory task, even after applying conservative Bonferroni corrections for multiple testing. This provides initial evidence for an epistatic relationship between AVPR1A and SLC6A4 polymorphisms that may be linked to short-term memory for music, or more generally, to phonological memory.In the only genetic study of music memory to date, Granot et al.  investigAVPR1A and SLC6A4 polymorphisms, Ukkola-Vuoti et al.  was shown by Ukkola et al. , whose encoded protein is highly expressed in the brain, especially the substantia nigra  platform showed that a copy number loss at 5.6 kb (5600 base pairs) upstream of UGT8 may be negatively associated with singing accuracy. Although environmental factors such as education and music training were not considered in this study, the authors argued that because the participants resided in an isolated region with homogeneous culture and most were educated in the same school without additional music training, environmental factors were unlikely to impact greatly on the results. In other words, this study yields evidence that singing accuracy may be heritable in this population and possibly associated with a region on chromosome 4q.Park et al.  investigCoon and Carey  analyzedAVPR1A and SLC6A4. An overall association with choral singer status was observed at the STin2 (intron 2) polymorphism in the SLC6A4 gene, with the STin2 9-repeat and 12-repeat alleles being more common in choral singers, and the 10-repeat alleles more common in non-musically active controls. No significant differences in allele frequencies were observed between the two groups for other SLC6A4 and AVPR1A polymorphisms. Previous studies have reported possible involvement of STin2 in personality traits and reward behavior  have also been linked to participation in creative dance  rather than music ability per se.Similar to the association study on music memory by Granot et al. , a candiTPH1, COMT, and AVPR1A were found.The genetic basis of music creativity was investigated in 19 Finnish musical families using a web-based questionnaire. Participants were asked about their music background and participation in creative music activities, such as music composition, improvisation or arrangement  and music-related functions such as choir participation  and exceptional talent. In particular, the participants were asked to rate their level of competence in various domains such as music, arts, language and sports  structure of different populations may help to refine a gene locus of interest  (McCarroll, epistasis, gene-environment interactions, and epigenetic influences on music ability. These factors may explain why many of the genetic variants and loci implicated in complex traits could only account for a small percentage of the heritability estimated from family studies (Stranger et al., Another important, but as yet under-researched avenue of music genetics research involves exploration of the potential contributions of FOXP2 may play a role in music rhythm processing as well as language and speech (Alcock et al., While the results garnered to date are promising, a more comprehensive investigation of music ability is warranted, including more precise characterization of music phenotypes to identify their genetic basis. As evident from Table In conclusion, although currently there is only a handful of research studies in this area, music genetics research has yielded promising preliminary results, highlighting the need for increased research effort in this emerging field. Elucidating the genetic basis of music ability may be challenging due to its multifaceted nature, necessitating careful identification, characterization, and genetic investigation of its many different facets. Coupled with the propitious rate at which molecular genetics and statistical designs are advancing, an increasingly clearer picture of the genetic mechanisms underpinning the etiology of music traits will begin to emerge. These mechanisms may then be linked to established neuroscientific findings of the neurobiological basis of specific music functions and behaviors. Ultimately, this will allow us to gain a deeper understanding of the way in which interactions between nature and nurture shape the development of human music ability over the lifespan.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.


Oryza sativa L.) yield is a crucial challenge for modern agriculture. The ideal plant architecture is considered to be critical to enhance rice yield. Elite plant morphological traits should include compact plant type, short stature, few unproductive tillers, thick and sturdy stems and erect leaves. To reveal the genetic variations of important morphological traits, 523 germplasm accessions were genotyped using the Illumina custom-designed array containing 5,291 single nucleotide polymorphisms (SNPs) and phenotyped in two independent environments. Genome-wide association studies were performed to uncover the genotypic and phenotypic variations using a mixed linear model.Increasing rice (NAL1 and Rc) and new significant loci showed pleiotropy and gene linkage. In addition, a long genome region covering ~1.6 Mb on chromosome 11 was identified, which may be critical for rice leaf architecture because of a high association with flag leaf length and the ratio of flag leaf length and width. The pyramid effect of the elite alleles indicated that these significant loci could be beneficial for rice plant architecture improvements in the future. Finally, 37 elite varieties were chosen as breeding donors for further rice plant architectural modifications.In total, 126 and 172 significant loci were identified and these loci explained an average of 34.45 % and 39.09 % of the phenotypic variance in two environments, respectively, and 16 of 298 (~5.37 %) loci were detected across the two environments. For the 16 loci, 423 candidate genes were predicted in a 200-kb region (±100 kb of the peak SNP). Expression-level analyses identified four candidate genes as the most promising regulators of tiller angle. Known  contains supplementary material, which is available to authorized users. MOC1, a gene that is important in the control of rice tillering, was mapped on chromosome 6  . The array consisted of 5,291 single nucleotide polymorphisms (SNPs) that were chosen from the Rice Haplotype Map Project Database (http://202.127.18.221/RiceHap2/index.php) [Rice genomic DNA was extracted from the young leaf tissue of each of the 523 accessions, which had been grown in the experimental field of the China National Rice Research Institute in Hangzhou in 2014. All of the accessions were genotyped using Illumina custom-designed arrays by following the Infinium HD Assay Ultra Protocol [FST) among different subpopulations , were inferred using STRUCTURE version 2.2 [ad hoc statistic ΔK [Major allele frequency, gene diversity, polymorphic information content (PIC)  were calculated using Microsoft Office Excel 2007. The broad-sense heritability was computed through an analysis of variance using the following formula:A total of 12 morphological traits were evaluated in two environments. The statistical analyses of mean, standard error (SE) and broad-sense heritability (σg2 is genetic variance and σe2 is error variance. Correlation coefficients among the 12 agronomic traits were computed by R (http://www.r-project.org/). An ANOVA was carried out to evaluate the effects of genotype (G), environment (E) and genotype × environment (G × E) using SAS system 9.0 , and the percentage of phenotypic variation explained by population structure was also carried out in SAS 9.0.where An association analysis was performed using TASSEL version 4.0 , and they was the vector of phenotypic observation, α was the vector of SNP effects, β was the vector of population effects, μ was the vector of kinship effects, e was the vector of residual effects, Q was the matrix from STRUCTURE relating y to β, and X and K were incidence matrices of 1 s and 0 s relating y to α and μ, respectively [e × p × ln(p)) [P = a/n) [a = 1, 1/4,136 = 2.4 × 10−4) as the cutoff for all traits except for pericarp color .respectively, where ectively , 50. For× ln(p)) , 53 and P = a/n) , 55. In For the significant marker-trait associations, the elite and null alleles were used to determine the choice of elite accessions for further breeding by design. The formula for calculating phenotypic effect of alleles can be described asxi was the phenotypic value of accession carrying the i allele, yj was the phenotypic value accession carrying the j allele, and n and m represented the number of accessions. If a was positive, then the i allele was defined as an elite allele with an increasing effect and the j allele was defined as the null allele with a decreasing effect, and vice versa. In our study, we chose parental accessions with elite or null alleles based on the characteristics of different traits.where actin was used to standardize the data for each run. The expression experiment was performed at three replications for each sample. The qPCR primers and materials are summarized in Additional file The expression levels of 14 TA candidate genes were measured by quantitative real-time PCR (qPCR) using eight accessions with large TAs at the seedling, early and later tillering stages, respectively. Total RNA was extracted from the base of tiller tissue using a MiniBEST Plant RNA Extraction kit . Complementary DNA (cDNA) was synthesized by PrimeScript RT Master Mix . The reaction was performed on an ABI Step-One Plus Real Time PCR System . The expression level of O. sativa collected from 25 countries . The results showed that the LnP(D) value for each given K, from 1 to 12, increased but did not show a maximum value Fig. . The valups Fig. , and theups Fig. . In addiindica and japonica subspecies, only 469 indica accessions, most of which were distributed in southern China  and four groups under two subpopulations  + 0.9733 (R2 = 0.9422)   Fig. . TherefoHB2 was 71.43 %, ranging from 36.49 % for flag leaf length (FLL) to 99.61 % for pericarp color (PC). The average percentage of phenotypic variation explained by population structure was 21.76 %, indicating that phenotypic variation was influenced by population structure. Hence, the Q-matrix was necessary as a covariate for the GWAS analysis to control false positive associations.A wide range of phenotypic variations were observed for the 12 morphologic traits in LS and HZ (Table P < 0.001) except for the effect of E on panicle number  or significantly (P < 0.05) related to each other in LS  is shown in Fig. To control false positive trait-marker associations, four models, GLM, Q, K and Q + K, were compared with each other using the quantile-quantile (Q-Q) plot shown in Fig. P-values were maintained within a 200-kb window according to the average LD decay rate in the panel. Finally, in LS and HZ, 126 and 172 significant SNPs, including known and new candidate loci, respectively, were identified for the 12 traits and all of the significant loci explained ~34.45 % (10.29 to 72.19 %) and 39.09 % (20.10 to 71.76 %) of the phenotypic variation on average, respectively  was detected on chromosome 2, with the SNP explaining ~5.2 % of the total phenotypic variation, and 32 candidate genes were predicted within 200 kb (±100 kb of the peak SNP). For TA, a significant SNP (seq-rs3945) was identified on chromosome 8 both in LS (P = 4.52E-04) and HZ (P = 1.10E-06), explaining ~3.5 % and ~5.9 % of the total phenotypic variation, respectively. Moreover, 22 candidate genes were contained within 200 kb. For FLL in HZ, a significant locus of seq-rs5454 (P = 1.25E-05) was mapped on chromosome 11, explaining ~5.0 % of the total phenotypic variation and 34 candidate genes were obtained. For FLW in HZ, seq-rs4707 (P = 3.53E-06) was detected on chromosome 10, explaining ~5.6 % of the total phenotypic variation and 34 candidate genes were predicted. For panicle type (PT) in LS, a peak SNP of seq-rs1802 (P = 4.67E-06) was observed on chromosome 3, explaining ~5.4 % of the total phenotypic variation and 38 candidate genes were included. For PC, two significant SNPs, seq-rs1190 and gwseq-rs2, explained ~25.59 % of the phenotypic variation in LS and ~16.74 % in HZ were mapped on chromosomes 2 and 8, respectively. In addition, 29 and 27 candidate genes were predicted  containing 43 candidate genes  detected in two environments was chosen to test the association Fig. . FourteeNAL1 locus at ~31.8 Mb on chromosome 4 was closely related to FLW, FLLW and PN in LS. Moreover, the correlation coefficients of FLW-FLLW , FLW-PN  and FLLW-PN  were highly significant in LS  in LS, and the correlation coefficient was 0.41 (P < 0.001)  values except for markers seq-rs5397 and seq-rs5399  Fig. . In addi399 Fig. . The resThe effects of elite alleles for 10 traits that are directly related to rice plant architecture are summarized in Additional file Based on the characterization of the new plant type (NPT)  and the Rice is a staple food and provides energy for more than half of the world’s population. In recent decades, rice production has only increased slightly and improving rice yields has become a pressing problem. Plant type modification is a potential way to solve these challenges. The NPT was characterized by Khush et al. in the 1990s to change rice plant architecture to enhance rice production , 28. Theindica subspecies to control false positive associations. In addition, the program STRUCTURE was used to uncover population structures in the indica panel for a further GWAS analysis [Although GWAS is a feasible tool to reveal complicated genetic variations in rice, it may be highly affected by population structure, resulting in false positive associations . Structuanalysis , 41. Fouanalysis   and relative kinship (K matrix) play key roles in controlling type I error. Therefore, combining the two factors has been suggested to reduce the false positive loci in association mapping , 62. In Arabidopsis [LD plays an extremely important role in association mapping and determines the resolution of an association study . In our bidopsis , than inbidopsis . Marker bidopsis , 28, 31,sd1 [MOC1 [D53 [LC1 [indica panel. The third possible reason was false negative associations because of the strict threshold value.To date, many morphological genes (or QTLs) have been identified in rice using the bi-parental mapping strategy , 15–22. sd1 , MOC1 [2OC1 [D53  and LC1 Rc gene at ~6.1 Mb on chromosome 7, which was highly associated with both PC and HC traits in LS   Fig. . Previouin yield –14. Our  LS Fig. , and the LS Fig. . In addi LS Fig. . This reThe results of the allelic analysis demonstrated that with the continual pyramid of elite alleles, the average phenotypic value increases significantly (Additional file indica accessions. Moreover, 16 significant loci, including 423 candidate genes, were obtained across two environments. Relative expression analyses identified four candidate genes as the most promising regulators of tiller angle. In addition, many significant loci showed pleiotropy and gene linkage. A long genomic region covering ~1.6 Mb may be critical for rice leaf architecture. The genetic variants and elite alleles will be useful for rice improvement. Furthermore, the biological validation of the candidate genes will be focused on in future research. Genome selection and breeding by design using the elite varieties as parents will also be of interest in future.In this work, we identified a number of significant marker-trait associations and candidate genes, and also uncovered the genetic variations of rice morphological traits using the GWAS strategy in 469 The 5291 SNPs genotype data for the 523 accessions was deposited in the Dryad digital repository: http://dx.doi.org/10.5061/dryad.cp25h.


We performed a GWAS to analyze 22 traits related to growth and fatness on two pig populations: a White Duroc × Erhualian FSus scrofa) and backfat thickness at the first rib. This region had pleiotropic effects on both fatness and growth traits in F2 animals and contained a promising candidate gene HMGA1 (high mobility group AT-hook 1). Unexpectedly, population genetic analysis revealed that the allele at this locus that reduces fatness and increases growth is derived from Chinese indigenous pigs and segregates in multiple Chinese breeds. The second strongest association was between the region around 82.85 Mb on SSC4 and average backfat thickness. PLAG1 (pleiomorphic adenoma gene 1), a gene under strong selection in European domestic pigs, is proximal to the top SNP and stands out as a strong candidate gene. On SSC2, a locus that significantly affects fatness traits mapped to the region around the IGF2 (insulin-like growth factor 2) gene but its non-imprinting inheritance excluded IGF2 as a candidate gene. A significant locus was also detected within a recombination cold spot that spans more than 30 Mb on SSCX, which hampered the identification of plausible candidate genes. Notably, no genome-wide significant locus was shared by the two experimental populations; different loci were observed that had both constant and time-specific effects on growth traits at different stages, which illustrates the complex genetic architecture of these traits.We identified 14 and 39 loci that displayed significant associations with growth and fatness traits at the genome-wide level and chromosome-wide level, respectively. The strongest association was between a 750 kb region on SSC7  contains supplementary material, which is available to authorized users. Domestic pigs display great phenotypic diversity, that is attributable to approximately 10 000 years of natural and artificial selection .2 intercross populations using Chinese and Western breeds as founder animals [http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). Notably, QTL that are significantly associated with growth and fatness have been consistently identified on pig chromosomes (SSC for Sus scrofa chromosome) 1, 2, 4, 6, 7 and X using F2 intercross populations of Chinese and Western origin [2 crosses; confidence intervals are generally on the order of ~20 cM. Such large regions can contain an abundance of genes, which hampers the prioritization of plausible candidate genes. Thus, the causal variants that underlie the identified QTL remain poorly understood. To date, only one nucleotide, in intron 3 of the IGF2 (insulin-like growth factor 2) gene on SSC2, has been convincingly shown to cause a major QTL effect on muscle mass and heart weight [To dissect the molecular basis of the divergent phenotypes seen between Chinese and Western pigs, researchers have established multiple F animals -6. Genomn origin -6. These2/F3 intercross population and a Chinese Sutai half-sib population. The findings confirm our previous QTL mapping results and, more importantly, reveal a list of novel loci, refine the confidence intervals, and identify strong candidate genes for the major and multifaceted QTL on SSC4 and SSC7.Recently, genomic tools have revolutionized genetic studies of complex traits. Large human or animal populations can be affordably genotyped using high-density SNP (single nucleotide polymorphism) arrays. The resulting data, together with advanced statistical tools, allow researchers to perform genome-wide mapping of loci for phenotypic traits with greater power and higher accuracy than with traditional QTL mapping. The so-called genome-wide association study (GWAS) mapping approach has been successfully implemented in a growing list of species, including pigs -12. HereAll procedures involving animals were in compliance with the guidelines for the care and use of experimental animals established by the Ministry of Agriculture of China. The ethics committee of Jiangxi Agricultural University specifically approved this study.2/F3 intercross population and a Chinese Sutai half-sib population. The White Duroc × Erhualian intercross population was established as previously described [1 boars and 59 F1 sows were then intercrossed to produce a total of 1912 F2 animals in six batches and 560 F3 animals in two batches. Sutai pigs are a Chinese synthetic breed that is derived from Chinese Taihu (50%) and Western Duroc (50%) following more than 18 generations of artificial selection. All piglets were weaned at day 46 and males were castrated at day 90. All fattening pigs were raised under controlled indoor conditions in the experimental farm of Jiangxi Agricultural University (China) and were fed ad libitum on a diet containing 16% crude protein, 3100 kJ digestible energy and 0.78% lysine. Pigs were slaughtered for phenotype recording at the age of 240 ± 3 days. Body weight was measured at birth and at days 21, 46, 120, 210 and 240 for each F2 and F3 individual. For Sutai pigs, body weight was also recorded at these growth stages except at day 46. Average daily weight gain at multiple time intervals was calculated. After slaughter, seven fatness-related traits, including backfat thickness at the shoulder, the first rib, the last rib and the hip, and weight of leaf fat, veil fat and abdominal fat, were recorded for all individuals from both populations. A total of 930 F2 individuals and 432 Sutai pigs from five sires and 60 dams were used for GWAS mapping in this study.Two experimental populations were used in this study: a White Duroc × Erhualian Fescribed . Briefly2 pigs, 87 parental pigs in the F2 cross and 432 Sutai pigs, were genotyped for 62 163 SNPs using the Illumina PorcineSNP60 BeadChip according to the manufacturer’s protocol. Quality control procedures were carried out using Plink v 1.07 [P value greater than 10−6 for the Hardy-Weinberg equilibrium test and mendelian error rate smaller than 0.1 were included. A final set of 39 788 informative SNPs from 1017 animals from the F2 cross and the 432 Sutai pigs were used for subsequent analyses.Genomic DNA of each animal was extracted from ear or tail tissue using a standard phenol/chloroform method. A total of 1017 animals, including 930 Fk v 1.07 , and theY = Xb + Sa + Zu + e, where Y is the vector of phenotypes; b is the estimator of fixed effects including sex, batch and carcass weight; a is the SNP substitution effect; and u is the vector of random additive genetic effects following the multinormal distribution u ~ N, in which G is the genomic relationship matrix that was constructed based on SNPs, as described in [α2 is the polygenetic additive variance. X and Z are the incidence matrices for b and u, S is the incidence vector for a, and e is a vector of residual errors with a distribution of N. All single-marker GWAS analyses were conducted using the GenABEL package [The allelic effect of each SNP on phenotypic traits was tested using a general linear mixed model -17. The ribed in , and σα2 package ,21. Base2 population using the 60 K SNP data, pedigree information and a Hidden Markov model [Y = Xb + Zu + e [Y is the vector of phenotypes; b is the estimator of fixed effects including sex, batch and carcass weight; X and Z are the incidence matrices for b and u; u is the random additive genetic effect following the multinormal distribution u ~ N, in which G is the individual-individual similarities matrix which was calculated from whole-genome information and σα2 is the polygenetic additive variance; and e is a vector of residual errors with a distribution of N. The ancestral haplotype-based LDLA analysis can use both within-family linkage information and across-family linkage disequilibrium information resulting from historical recombination events in ancestors of founder animals in the F2 population. We conducted the LDLA analysis using R scripts that were written for this purpose. The 95% confidence interval (CI) was determined by a LOD score drop-off of 2 from the value of the most significant loci.First, the haplotype of each chromosome was reconstructed for each animal in the Fov model . The mod+ Zu + e , where YFor the major locus on SSC7, we constructed haplotypes for all 17 founder sows and two founder boars using the DualPHASE software . The eff2 population and Chinese Sutai pigs. Both populations showed comparable body weights at early stages, while the Sutai pigs had obviously lower body weights at adult ages (days 210 and 240). For fatness traits, Sutai pigs exhibited leaner phenotypes than the F2 animals; indeed, all fatness-related measurements were greater (P < 0.05) in F2 animals.Table S1  and 36 329 680 bp (ss478941605) was shared by 30 Erhualian founder chromosomes  and leaf fat weight (LFW) in the F2 animals. The shared haplotype appeared to be a haplotype that was significantly associated with decreased fat deposition (referred to hereafter as Q haplotype)  and six F3 animals that had inherited the q? haplotype from individuals 142 and 146  and LFW (P = 0.28) between qq and qq? individuals. Additionally, there was no significant difference in ABFT (P = 0.29) and LFW (P = 0.29) between Qq and Qq? individuals. In comparison, QQ animals had significantly lower ABFT (P = 0.05) and LFW (P = 0.06) than Qq? animals. Although we did not observe significant differences in ABFT (P = 0.15) and LFW (P = 0.17) between Qq and qq? animals, Qq individuals tended to have lower ABFT (2.95 ± 0.77 vs. 3.20 ± 0.41 cm) and LFW (2086.6 ± 1076.2 vs. 2416.3 ± 864. g) than qq? individuals. Altogether, our findings indicate that the q? haplotype is most likely a q haplotype that increases fat deposition, rather than a Q haplotype that decreases fat deposition. All the q-type haplotypes were distinct from the Q-type haplotypes in the 1.7-Mb Q-sharing segment  were derived from both White Duroc founder boars  that was contained within both the 2.1-Mb 95% CI and the 1.7-Mb Q-sharing segment , HMGA1 (high mobility group AT-hook 1), C6ORF1 (chromosome 6 open reading frame 1), NUDT3 (nudix (nucleoside diphosphate linked moiety X)-type motif 3), RSP10 , SPDEF (SAM pointed domain containing ETS transcription factor), PACSIN1 (protein kinase C and casein kinase substrate in neurons 1) and C6ORF106 (chromosome 6 open reading frame 106)  activity and consequently regulates glucose uptake [HMGA1 variant (g.3135C > T) in all individuals of the F2 population [HMGA1 SNP in the single-marker GWAS, it was the most significant SNP for backfat thickness on SSC7 , which has been identified as the gene responsible for ear size in the F2 population [PPARD falls outside the 95% CI of the SSC7 locus, which indicates that the growth and fatness traits associated with this locus are not affected by polymorphisms in the PPARD gene. When we included PPARD G32E SNP, the causal mutation for ear size, in the GWAS analysis of fatness traits, we found that this SNP had a much lower association significance than the top SNP ss107837325  genes, such as FABP4, have been proposed as candidate genes for this locus [FABP4 gene is located 9 Mb away from the top SNP in this study. When we corrected for the effect of the top SNP, the GWAS signal was entirely absent from SSC4. Thus, FABP genes are probably not the genes of interest here. Instead, just ~240 kb downstream from the top SNP lies the PLAG1 (pleiomorphic adenoma gene 1) gene, which has been reported to be associated with bovine stature [PLAG1 appears to be one of the top genes that was under selection during the domestication of European pigs, and PLAG1 variants have been associated with growth and fatness traits in a European Wild boar × Large White F2 intercross population [PLAG1 region harbors multiple SNPs that show strong signals of selection and marked differences in allele frequency between Asian pigs/wild boars and European domestic pigs [PLAG1 SNP by Sanger sequencing for all animals of the F2 population. The PLAG1 SNP data were then incorporated into the 60 K SNP dataset for the single-marker GWAS, after correction for the effect of the SSC7 locus. Our results showed that the association between the PLAG1 SNP and growth and fatness traits was as strong as that with the top SNP from the 60 K panel (ss131269801), which was attributable to complete linkage disequilibrium between the two SNPs (data not shown). Based on this, we propose that PLAG1 is a strong candidate gene for the SSC4 locus. It should also be noted that another locus with significant effects on body weight at day 21 in Sutai pigs was identified in a different region on this chromosome, with the top SNP located at 134 935 929 bp.The second strongest effect on the measured traits was observed within an interval of 7 Mb (between 82 and 89 Mb) on SSC4. After fitting the effect of the SSC7 locus, the strength of the association between this locus and fatness traits in F pig QTL  and subs pig QTL -41. Multis locus ,43. Howe stature  and huma stature . Moreovepulation . The PLAtic pigs . To inveIGF2 gene, which affects muscle mass and fatness in pigs [IGF2 is a paternally expressed gene. However, the effect at the SSC2 locus disappeared when we applied a paternally-expressed-imprinting statistical model to the data (data not shown). Therefore, IGF2 is likely not the gene responsible for this locus. Another region, around 101.69 Mb on SSC2 had a significant effect on body weight at day 21 in Sutai pigs.After correcting for the effects of the SSC7 locus, the effect of SSC2 on fatness traits surpassed the genome-wide significance threshold  locus was associated with a region on SSC3, but no obvious candidate gene was identified. A locus on SSC2, with its top SNP (ss107909052) at 101 694 375 bp (P = 2.66 × 10−8), corresponded to a QTL with chromosome-wide significance for body weight, which had been previously identified in a Chinese Meishan × Large White population [ARRDC3 (arrestin domain containing 3), ~125 kb away from this top SNP, appears to be a functionally related candidate gene at this locus. This gene has been reported to play a role in mechanisms that control the regulation of glucose and lipid metabolism and insulin secretion [P = 8.89 × 10−7), overlaps with a QTL of genome-wide significance that affects body weight at birth in Western hybrid pigs [A total of seven loci of genome-wide significance were identified in Sutai pigs, with one each on SSC2, 10 and 14, and two on SSC3 and two on SSC4  in Sutai pigs.In addition to the significant loci mentioned above, we identified 39 QTL for which a chromosome-wide, but not a genome-wide, association was detected with 20 growth and fatness traits  to identify a majority of potential causal variants. The GWAS top SNPs are usually in the vicinity of causal mutations, allowing us to more accurately pinpoint the most likely candidate genes for the loci of interest. In F2 intercross population [We note that fewer significant loci were detected by GWAS in this study than in a previous study in which we used QTL mapping in the same White Duroc × Erhualian Fpulation . This in2 intercross and Sutai populations, a finding that highlights the complex genetic architecture of growth and fatness traits. Sutai pigs were originally developed from a cross between Chinese Taihu pigs  and Duroc pigs and have been artificially selected for at least 18 generations for lean meat and litter size [2 intercross pigs and the Sutai pigs due to their similar genomic backgrounds. In fact, we have detected both shared and distinct GWAS results for multiple phenotypic traits in the two populations [In this study, we did not observe any locus of genome-wide significance that was shared between the White Duroc × Erhualian Fulations -57. To t2 intercross population and a Chinese Sutai half-sib population. Fourteen QTL of genome-wide significance and 39 suggestive loci of chromosome-wide significance were identified on 16 chromosomes. The discovery of strongly associated loci on SSC2, 4, 7 and X confirms our previous QTL mapping results. The critical region of the locus on SSC7 was refined to a ~750 kb segment, and the Q allele that promotes fast growth and decreases fat deposition was confirmed to be of Chinese origin and probably arose after the domestication of Chinese wild boars. Population genetic analysis revealed that geographically diverse Chinese breeds segregate for the Q allele, which illustrates the importance of this allele in genetic improvement of Chinese indigenous pigs. Several promising candidate genes were identified, including HMGA1 at the SSC7 locus and PLAG1 at the SSC4 locus. No significant loci were shared between the Sutai and F2 populations, and both time-constant and time-specific loci were detected in association with growth traits at different stages, which illustrates the complexity of the molecular mechanisms that underlie growth and fatness in pigs. Our findings provide novel insights into the genetic basis of growth and fatness in pigs and may contribute to the identification of the causal variants for the identified loci, especially for the major loci on SSC4 and SSC7.We performed a GWAS of 14 growth and eight fatness traits in a White Duroc × Erhualian F