Genetic polymorphisms and social factors  usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD).Simultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method.CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors , referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group  = 4.58–23.15) than in the OPMD group .We demonstrated that a combination of SNPs (Simulation-based “SNP-environment barcodes” may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based “SNP-environment barcodes” may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD. Oral malignant disorders include cancers of the oral cavity and pharynx and oral potentially malignant disorders (OPMD), such as leukoplakia, oral submucous fibrosis (OSF), verrucous hyperplasia, and erythroplakia . In the CYP) enzymes may be associated with the metabolic activation of environmental carcinogens D and vi ∈  D, respectively. The particle moves according to the individual best solution pBesti and global best solution gBest in each iteration. The particles of BPSO X and V were initialized by uniform random values and each particle is a candidate solution for a particular optimization problem. Each particle searches for optimized solutions according to the update formula in the topological space neighborhood in each generation.BPSO , 30 is axi has been redefined as encoding an integer, e.g. xi ∈ {0, 1}. The position update formula is described as follows:d = 1, 2, …, D. c1 and c2 are the constant for acceleration coefficients. r1, r2, and r3 are the unique random values between  at each iteration for each individual dimension. S  is a sigmoid function, which limits the transformation value from 0 to 1.Originally, BPSO was developed for the continuous optimization of problems; however, it has not been suitable for discrete optimization of problems. Therefore, the BPSO algorithm, which is an improved form of BPSO, was proposed for solving discrete optimization problems . In the This section introduces a BPSO algorithm to address the SNP-SNP interaction problem. We improve the fitness function to identify the interaction between OPMD vs. normal and oral cancer vs. normal. The description is written as follows and includes the encoding schemes, fitness function, and BPSO procedure.F combinations. The particle position is described as xi = {, , …, }, where M represents the number of factors and  shows the selection of the mth factor in the ith particle in which Fm1 and Fm2 denote the expression of the particle. These two expressions are based on 0 and 1 variables; thus, four different combinations: , ,  and  are included. Each particle as a candidate solution denotes the status of whole factors (selected or non-selected) and separates into three different statuses. The four different combinations are shown below:Each particle was designed to be binary-encoding and express a particular number of factor Fm1, Fm2) can display the selection states of a factor. For instance, a particle in position xi = {, , , , } indicates the selection state to be the 1st, 3rd, and 4th factor in status 1, 3 and 2, respectively. Consequently, this expression can be used to represent multiple order interactions.Based on formula , four diS  is the sigmoid function, which limits  each weight and establishes the weight equilibrium, ORCvN represents the OR value in oral cancer and normal cases, and ORPvN represents OR value in OPMD and normal cases. p indicates the Pearson chi-squared module to assess the significance in ORCvN and ORPvN, which is shown as formula  Initialize the population of particles with a uniform random position within {0, 1} and velocity within [Step 2) Calculate the fitness of each particle using the OR value, p-value, and the OR difference according to the Eq .Step 3) Update the individual and global best solution pBest and gBest according to the fitness estimation results.Step 4) Calculate the particle velocity and update the position of each particle according to Eqs –6)..6).Step 5) Repeat Steps 2–4 until the stop criterion has been met. The best combination of clinical diagnosis for risk factor of oral cancer and OPMD is consequently obtained.CYP26A1, CYP26B1, and CYP26C1 with minor allele frequency (MAF) were selected from a public reference Chinese HapMap database, CHB. Therefore, CYP26A1, CYP26B1, and CYP26C1 SNPs were selected with MAF > 10% variants from HapMap-CHB. In addition to MAF >10%, our previous study also indicated that the variants of CYP26A1 rs4411227 were significantly related to an increased risk of malignant oral disorders [CYP26A1 SNP analysis in this study.Peripheral blood (8 mL) was drawn from the subjects in ethylenediaminetetraacetic acid (EDTA) tubes. Using standard protocol, genomic DNA was extracted from peripheral blood lymphocytes and immediately stored at –20°C for further analysis. SNPs of isorders . As mentThe assay of TaqMan SNP genotyping  was used to test genotypes. DNA samples and negative controls were loaded and examined in 96-well plates using the real-time polymerase chain reaction (PCR) system of ViiA™ 7 Biosystems . Using the Sequence Detection System (SDS) 2.1 software (Applied Biosystems) fluorescence data were evaluated, and the fluorescence signals were plotted to determine the genotype of each sample.We conducted an OR-based BPSO  and chi-square test to examine the association between the habits of substance use  and disease groups . Chi-square statistical analyses were performed using the SAS statistical package . The application performance of SNP-environment barcodes was ascertained by receiver operating characteristic (ROC) curve and maximized Youden’s index (sensitivity + specificity– 1) was used to determine optimal cut-off points of OR scores for oral malignant disorders between low and high risk populations , 33.https://github.com/kuochuanwu/SNPbarcode) and my minimal data (filename: 190618 CYP26 SNPs-environment_risk.xlsx) set as the supplementary data, All variables in this study were available on a public repository website (CYP26 gene families are shown in Tables CYP26B1 (2p13.2), four candidate SNPs are indicated. SNP rs887844 is located in 500B downstream variant, followed by rs707718 in 3’ UTR, rs3768647 in 3’ UTR, and rs9309462 in intron. We selected only one SNP rs4411227 for CYP26A1 on chromosome 10q23.33 region and rs4411227 is located in 2KB upstream variant. In the region of CYP26C1 (10q23.33), SNP rs8211 is located in 2KB upstream and SNP rs12256889 is located in intron.The gene structure and SNP information of CYP26 polymorphism in individuals with oral/pharyngeal cancers (N = 242), OPMD (N = 70) and in control subjects (N = 264) with high prevalence of BQ chewing are described in CYP26A1 (rs4411227), and CYP26B1  were significantly different among the three groups (p < 0.05).The demographic characteristics of and CYP26B1 rs887844 (A/G), CYP26A1 rs4411227 (C/G), CYP26C1 rs12256889 (A/C), and CYP26B1 rs707718 (G/T)) and environmental factor (> 50 years old) combinations significantly increased the risks  for patients with OPMD compared to health controls with high prevalence of BQ chewing. Patients with oral and pharyngeal cancers and specific SNP (CYP26B1 rs887844 (A/G), CYP26A1 rs4411227 (C/G), CYP26C1 rs12256889 (A/C), and CYP26B1 rs707718 (G/T)) and environmental factor (> 50 old) combinations exhibited significantly increased risks  compared to health controls with high prevalence of BQ chewing. Elder subjects with rs887844 (A/G) or rs4411227 SNP (C/G) were more at risk of developing oral and pharyngeal cancers than OPMD. Inversely, elder subjects with rs12256889 (A/C), rs4411227 SNP (G/G) or rs707718 (G/T) SNPs were at lower risk of developing oral and pharyngeal cancers than OPMD. Thus, our results suggest the presence of a genome-wide cross-talk between polymorphisms of several genes and SNPs of CYP26, which may be involved in the occurrence of oral malignant disorders.After considering age, gender, race, education, alcohol consumption, BQ chewing, and cigarette smoking, the joint effect (adjusted OR and 95% CI) of specific SNP combinations and environmental factors on the occurrence of oral malignant disorders were obtained . SpecifiWe used the BPSO-generated barcodes to compute the relative strength of SNP-environment combination effects on the risk of developing oral malignant disorders and determined the risk effects of subjects with oral malignant disorders who exhibited specific SNP-environment combinations or other combinations . After up<0.001) of SNP-environment barcodes for oral malignant disorders was found by ROC curve. Maximized Youden’s index was used to determine optimal cut-off points of OR scores for predicting high risk population with oral malignant disorders. Optimal sensitivity (91%) for predicting high risk population was achieved with a OR cut-off of greater than or equal to 2.39. Indeed, we provided the best discrimination of OR < 2.39 and OR ≥ 2.39 for oral malignant disorders between low and high risk populations.In the risks assessment, the odds ratio (OR) was used to evaluate the impact of risk association of SNP-environment combinations for oral malignant disorders between high and low risk populations. We used ROC curve to predict the discriminatory power of OR risks of SNP-environment barcodes for population with oral malignant disorders . A signiCYP families SNPs and the risk associations in oral malignant disorders are less frequently mentioned. In terms of the computational biological challenge, potential and substantial associations are often hidden in the large number of possible combined effects on numerous SNPs genotypes. A number of studies have indicated computational approaches to ameliorate the effects of association of multiple SNPs analysis [The occurrence of cancers was implicated in the combined effects between genes and environment, but confirming joint effects was a computational and mathematical challenge. In terms of multiple SNPs, genome-wide association studies (GWAS) have been indicated commonly to discover the associative effects of diseases –39. A nuanalysis –49. Neveanalysis . Major iThe approaches of conventional statistics, machine learning, and data mining have been established to evaluate potential effects in GWAS association studies , 51–56. Epidemiological studies indicated that OPMD and oral/pharyngeal cancers have a close relationship with environmental exposure to BQ, alcohol, and cigarettes, particularly in heavy BQ users , 60–62. in vivo or in vitro tests can result in sister chromatographic exchange, chromosomal aberrations, and micronuclei formation [CYP26B1 expression [CYP26 has three isoforms—namely, CYP26A1, CYP26B1, and CYP26C1—which mainly metabolize retinoic acid (RA)-related compounds [BQ alkaloids, arecoline and arecaidine can cause bacterial mutagenicity, and in mammalian cells, ormation . In addiormation . We prevpression . CYP26 hompounds . RA is aompounds , 68.CYP26A1, CYP26B1, and CYP26C1 within and between different cancer pathways. Since interactions among multiple genes influenced the risk component of oral and pharyngeal cancers, our rationale for identifying interactions between genes was justified. We developed new and feasible analytical methods to systematically examine the interactions between genome-wide SNPs and various environmental factors. In particular, we investigated the role of combinational SNPs in three metabolism-related genes  in oral malignant disorders. In association studies of disease predisposition, the interaction analyses of SNPs increased the performance [The risk of developing oral malignant disorders may be related to the functionally relevant combined effects of SNPs such as those in formance , 69–71.CYP26B1 rs887844 and CYP26C1 rs12256889) within CYP26 may significantly elevate genetic susceptibility to oral and pharyngeal cancers and OPMD. The association between the risk of developing oral/pharyngeal cancers and OPMD and CYP26 SNPs was detected by a robust BPSO algorithm combined with statistical analysis. The BPSO algorithm optimally evaluated the risk effects of CYP26 SNPs for oral and pharyngeal cancers and OPMD. Complex multifactor associations are difficult to analyze statistically [In oral and pharyngeal cancers and OPMD disorders, we also explored the risk factors for genetic variation of complex traits. We hypothesized that two important SNPs  and the risk of oral malignant disorder occurrence. Two significant CYP26 SNPs (CYP26B1 rs887844 (A/G) and CYP26C1 rs12256889 (A/C)) were selected using BPSO analysis, which was the best performance model for oral malignant disorder occurrence risk analysis. The analysis suggested that several combinations of environmental factors and candidate SNPs had the highest risk for susceptibility to oral malignant disorders. The risks were more prominent in the oral and pharyngeal cancers group  than in the OPMD group . This finding implies that older individuals with SNP rs887844 and rs12256889 of Minnan ethnicity who chewed BQ were more likely to suffer from oral and pharyngeal cancers than from OPMD.In this study, we revealed a strong correlation between In this study, we conducted a powerful binary particle swarm optimization (BPSO) of an odds ratio (OR)-based method to evaluate the joint effect of gene-gene-environment (SNP-SNP-environment) in oral malignant disorders. The BPSO-based SNP-SNP and SNP-environment interactions were not limited to SNPs on the same chromosome, and our algorithm could control potential confounders and diverse numbers of SNP. Moreover, the BPSO approach calculated the best performance of the SNP or SNP-environment model with the risk of maximum difference between cases and control groups. The ROC curve was used to discriminate OR risks of SNP-environment barcodes for oral malignant disorders as shown in Although we are do not create a barcode system for evaluating the risks of occurrence of oral malignant disorders, the term “barcodes” has been applied in a number of our published papers –28. OwinAfter calculations and a statistical analysis using BPSO approach, and we render combined associations of each high-risk group from SNP-environment data . We clarCYP26 SNPs and environmental factors on the risk of oral malignant disorder occurrence by BPSO method for the first time. Therefore, the BPSO-based SNP-SNP and SNP-environment approaches for assessing the combined effects of the novel SNP-environment approach may potentially assist in identifying complex biological relationships among cancer processes during the development of oral malignant disorders.We demonstrated the significant joint effects of CYP26 SNP-environment approach may predict the risk of occurrence of oral malignant disorders. Its application as a proxy of SNP-environment barcodes will provide insights into the importance of establishing screening tests for BQ chewers to promote the prevention of oral malignant disorders.In summary, the combined effects of the novel S1 Table(XLSX)Click here for additional data file.


The domestication of wild goats and subsequent intensive trait-driven crossing, inbreeding, and selection have led to dramatic phenotypic purification and intermediate breeds for the high-quality production of dairy, cashmere wool, and meat. Genomic resequencing provides a powerful means for the direct identification of trait-associated sequence variations that underlie molecular mechanisms of domestication.Capra aegagrus hircus; five each) selected for dairy, cashmere, and meat production in reference to their wild ancestors, the Sindh ibex  and the Markhor . Using ∼24 million high-quality single nucleotide polymorphisms (SNPs), ∼1.9 million insertions/deletions, and 2,317 copy number variations, we define SNP-desert-associated genes (SAGs), domestic-associated genes (DAGs), and trait-associated genes (TAGs) and attempt to associate them with quantitative trait loci (QTL), domestication, and agronomic traits. A greater majority of SAGs shared by all domestic breeds are classified into Gene Ontology categories of metabolism and cell cycle. DAGs, together with some SAGs, are most relevant to behavior, immunity, and trait specificity. Whereas, TAGs such as growth differentiation factor 5 and fibroblast growth factor 5 for bone and hair growth, respectively, appear to be directly involved in growth regulation.Here, we report our effort to define such variations based on data from domestic goat breeds (Capra populations, the sequence variations and candidate function-associated genes we have identified provide valuable molecular markers for trait-driven genetic mapping and breeding.When investigating the divergence of  Capra hircus, NCBI:txid9925) were domesticated ∼10,000 years ago [breed concept [As one of the most popular farm mammals, goats . They are one of the most adaptable livestock on all continents  and suppOur experimental design involves the resequencing (∼29 × in sequencing depth and 99% in genome coverage) of 15 domestic goats representing 3 breeds and 4 wild goats from 2 distinct species. The high-quality sequence data allow us to use high-quality genetic markers  to define artificial selection–related genes in the history of goat domestication. In particular, studies of trait-associated genes (TAGs) provide candidate loci for marker-assisted breeding of domestic goats.Capra aegagrus blythi and Markhor, Capra falconeri; two each) as controls (Capra hircus genome (GenBank Accession: GCA_003 17765.1), we identified 23,924,294 SNPs,  1,899,827 indels, and 2,317 CNVs.We sequenced three elite domestic goat breeds (five each), including dairy (Saanen), cashmere (Liaoning cashmere), and meat (Leizhou), and two wild goat species  than the two other breeds, suggesting that there may be strong or long-term selection during its breeding , intronic (23.06%), and protein-coding (0.74%) SNPs, and subsequently, the ratio of nonsynonymous to synonymous substitutions (NS/S) was calculated as 0.95 on average. However, the NS/S ratio shows variable distributions when correlated to minor allele frequency (MAF) in the low SNP rate region or the SNP desert . Second,breeding .We categorized  1,899,827 indels with nearly equal numbers of insertions and deletions, of which ∼0.13%  were found in protein-coding sequences and partitioned into 32.72% (792) in-frame (3-bp indels) and 1,628 out-of-frame indels that lead to an average of 499 pseudogenes per individual sample. Similar to the trend observed for SNPs, there are much rarer indels in the total ; we obseAADAC (arylacetamide deacetylase) appears to be selected in the dairy breeds, encoding an enzyme responsible for the hydrolysis of drugs [MYT1L (myelin transcription factor 1-like) marker associated with syndromic intellectual disability and early-onset obesity has shown a meat-specific high-frequency indel frame-shift polymorphism [In addition, based on our indel data, of drugs . The MYTmorphism   and 1,616 CNVs  in the domestic and wild goats, respectively. The wild goats and the meat breeds have relatively higher numbers of CNVs than their domestic counterparts and two other domestic breeds . The meaASIP  and AHCY (adenosylhomocysteinase) genes, which are related to skin pigmentation and coat color in sheep [ASIP and AHCY than those goats with colored hairs  for dairy and growth traits (APOL3 (apolipoprotein L3) is a lipid-transport and metabolism-associated gene that is also highly duplicated in beef breeds [NME6 (NME/NM23 nucleoside diphosphate kinase 6) is suggested to play a role in cell growth and the cell cycle [APOL3 locus conferred a selective advantage to dairy, while there is a negative correlation between the duplication of NME6 and growth, and need further investigation in dairy goat.Finally, to validate whether CNV loci were associated with dairy traits, 12 candidate dairy-specific CNV loci were detected in 130 Guanzhong dairy goats using the AccuCopy assay . Our CNV association study points to two CNV loci  and phylogeny reconstruction to evaluate the population structure of the domestic breeds. First, based on our genome-wide SNP data, we found that Sindh ibex is genetically closer to the domestic breeds compared to Markhor, which is consistent with previous reports . Second,SNP deserts are often linked to beneficial mutations as selective sweeps that are subjected to strong purifying selection . The SNPRSRC1; Fig. AR (androgen receptor) gene on chr X. AR is a hormone-inducible DNA-binding transcription factor that plays an essential role in male reproduction; its knock-out male mice display severely impaired reproductive tracts and sexual behavior [To further investigate, we also examined the large SNP deserts (>100 kb in length) as well as the top 10 larger deserts unique to each domestic breed. For the SNP deserts >100 kb in length, it is consistent that the dairy  and cashmere  breeds had more SAGs than the meat breed ; the function of the 231 breed-shared SAGs appears to be related to signal transduction  using autosomal SNPs from all individuals of the domestic breeds in a 100-kb sliding window. We also calculated the fixation index (Fst), which indicates population differentiation between domestic and wild populations in a 100-kb sliding window based on autosomal SNPs. We then transformed the Hp and Fst into Z (Hp) and Z (Fst), respectively, and the protocol defined 67 DAGs in a collective genomic length of 3.2 Mb Fig. . Our GO ADRA2A  and FXR2  [TNFSF13 (tumor necrosis factor (ligand) superfamily, member 13) and STIM1  are located in the region associated with cattle body weight gain [NR6A1 (nuclear receptor subfamily 6 group A member 1), which affects the number of vertebra, one of the most characteristic morphological changes in domestic pigs [STAT6 , which is associated with body weight as well as carcass and growth efficiency traits [This set of DAGs may contribute to behavioral, immune, and morphological differences between domestic and wild goats. First, genes that directly influence the nervous system and behavior include rhythms) , 26. Secght gain , 28 and ght gain , 30, restic pigs , and STAy traits   in a 100-kb window on SNPs from one breed against a pool of the two other breeds. Using the criterion of Fst >4 and a top 1% outlier of XP-EHH, we defined 54 TAGs .GDF5 (growth differentiation factor 5) T217C (amino acid changed: R73G) locus of meat breeds (Leizhou and Hainan) is dominant with the C allele, and the T allele is dominant in the dairy, cashmere, and wild goats. As R is conserved among all other known mammal sequences except goat, we suggest that T allele is ancestral whereas C is selected. In addition, we looked into the breeding history of Leizhou and learned that the body size is smaller than that of Sannen and Liaoning cashmere goats. GDF5 is a member of the TGF-beta superfamily, which is involved in height [LRP4 (low-density lipoprotein receptor-related protein 4) is detected with a conserved amino acid change (TT at the 266th nucleotide) in goats, and it also showed opposite selection in the meat and other goat breeds  located in chr 1 was under high selection. Polymorphism of RSRC1 is associated with altered brain function in schizophrenia [SHOX2 is involved in height and chondrogenesis [RPL3  is reported to be highly expressed in the breast milk fat globule, involved in regulation of energy balance, suggesting that translational pressure is at work during lactation [VPS13C (vacuolar protein sorting 13 homolog C) is suggested to act on glucose homeostasis for high milk production in dairy cows [VPS13A, has also been reported in pigs undergoing directional selection for heat adaptation [VPS13B was detected within a QTL associated with leg morphology, related with fertility and milk production in cattle and buffalo [There is no gene shared by TAGs, SAGs, XP-EHH, and QTL . Many TAophrenia  and heigophrenia . SHOX2 iogenesis , 39 , belonging to the PRDM family of transcriptional repressors, and is reported to be highly expressed in NOTCH1-deficient mice embryos. We expected PRDM6 to be a candidate gene for the cashmere trait because NOTCH1 is considered to control follicular proliferation rates and melanocyte populations [FGF5 (fibroblast growth factor 5) stands out because it is an inhibitor of hair elongation and is associated with hair growth and length in mammals [FGF5, we performed an association analysis between exonic mutations and cashmere-related traits in 224 Inner Mongolian cashmere goats by Sanger sequence. Cashmere production and body weight appear to be associated with one synonymous mutation in exon 3 of FGF5  altered residual sulfate transporter activity, associated with short stature and skeletal dysplasias [SASS6, HIAT1, and SLC35A3 genes; the HIAT1 gene may encode a novel sugar transporter and disruption causes globozoospermia and infertility in mice [SLC35A3 gene were associated with vertebral and multiple organ malformations in cattle and human [The most important TAGs to the meat breed are four genes:  and QTL . Mutatiosplasias . Among o in mice . The mutnd human , 50. Funcasting a larger net (such as SNP desert and QTL data) and investigating discrete lists in other cases, trying to provide an overview of the genetic landscape of selection-centric genetic heterogeneity in the up-to-date molecular terminology. Third, the candidate genes we described as DAGs and TAGs are complex in function but were clearly biased toward certain functional categories. It is essential to validate them in a specialized breed with a larger population before any mechanistic studies. Finally, NGS technology provides an efficient tool for systematically deciphering the genetic background of domestication and trait selection in a thorough way for goats and other farmed animals. We should heckle GO and expression information at the same time while we are expecting thousands of gene sequences to become available in the years to come.In this study, we interrogated whole genome sequences from three trait-driven goat breeds and assessed three categories of sequence variation SNPs, indels, and CNVs to search for the functional relevance of three categories of candidate genes SAGs, DAGs, and TAGs. First, we used several methods, including SNP desert, fixation index, pooled heterozygosity, and XP-EHH, to define these candidate genes based on the allelic frequencies of the different sequence variations. Although the sampling itself is rather limited for each breed, a number of follow-up studies with increased population sampling showed consistent results. Second, we grouped the breeds and the data in various ways for detailed analyses, sometimes We sequenced DNA samples from 19 goats: 4 from wild female goats (2 Markhor and 2 Sindh ibex) and 15 from domestic male goats (5 Saanen collected in 2008), 5 Liaoning cashmere goats (collected in 2006), and 5 Leizhou goats (collected in 2007). To validate the sequence variation at the population level, we genotyped using sequencing polymerase chain reaction amplicons in 7 domestic breeds, including 99 Saanen, 85 Liaoning cashmere, 23 Leizhou, 16 Dera Din Panah, 30 Guanzhong, 26 Inner Mongolian cashmere, and 24 Hainan goats. The samples from Markhor and Sind ibex were collected from skin biopsies, Quetta, Pakistan. Blood samples from the domestic goats were collected in China. DNA sequences were obtained using paired-end sequencing (2 × 150 bp) technology on the Illumina HiSeq X10 platform. The institutional review board of the Xi'an Jiaotong University Health Science Center approved the study protocol (project identification code 2011–054).The procedure to remove low-quality reads included meeting one or more of the following criteria: N content more than 10%; >60% read length below Q7; reads overlapping >10 bp with the adapter sequence and a maximum of 2 bp mismatches to the adaptor sequence; paired-end reads overlapped by >10 bp with others; and duplicated reads. We also trimmed up to 10 bp at the 5’ end or 30 bp at the 3’ end of a read if the local N content was >20%.RRID:SCR_010910) to map the clean reads onto the reference genome of Capra hircus genome V1. The command “aln -t 4 -e 10” was used to find the suffix array coordinates of the good hits of each read. Then, we used the command “sample -a 500” to convert suffix array coordinates into chromosomal coordinates and paired reads. Other parameters were set to the defaults. We filtered the alignments as follows: a mapping quality score lower than 20; nonunique alignments; and duplicated alignments.We used BWA 0.5.9  [RRID:SCR_006244) [We performed principal component analysis (PCA) with all population-scale autosomal SNPs using the Eigensoft package  . The phy_006244) .Based on the SNP data, we computed the SNP rate in 10-kb sliding windows. We normalized the SNP rates over the length of the ≥ 8-fold aligned sequence in each bin rather than the bin size, and bins with less than 1 kb of the aligned sequence were rejected. We then selected the windows with the lowest 10% SNP rate of the genome data and joined these windows as a longer region if the gap between them was ≤10 kb. We defined these low SNP-rate windows or regions as “SNP deserts.”2. With the same 100-kb window, the Fst was calculated between 15 domestic and 4 wild goats. We then transformed Hp into ZHp: ZHp = (Hp-μHp)/σHp and Fst into ZFst: ZFst = (Fst-μFst)/σFst. For DAG analysis, we applied a threshold of ZHp = -3 OR ZFst = 3 for detecting putative selective sweeps. For TAG analysis, we measured the pairwise Fst and tested one domestic breed and a pool of the other two breeds. The windows pass the threshold of ZFst = 4, and the top 1% XP-EHH [To find a selective sweep in the domestic lines, Hp and Fst were used to extract outliers . For eac% XP-EHH  scores wP > 0.05. We aligned the genome sequences of sheep and goat with lastz (version 1.02.00) and mapped the QTL to goat chromosomes based on the axt file produced by lastz.We downloaded known sheep and cow QTL data from Animal QTLdb  and qualGigaScience repository GigaDB. [Data are available via the National Center for Biotechnology Information database, BioProject ID: PRJNA399234, SRA accession number: SRP124668. Supporting data, including the reference assembly and annotations, SNPs, indels, and phylogenetic tree data, are also available via the  GigaDB. .Supplementary Figure S1. The distribution of the wild and domestic goats used in this study.Supplementary Figure S2. Introduction of wild and domestic goats sequenced in this study.Supplementary Figure S3. Sequencing depth distribution of domestic and wild goats.Supplementary Figure S4. Minor allele frequencies (MAFs) of SNPs among the domestic breeds.Supplementary Figure S5. Ratio of nonsynonymous and synonymous SNPs at different MAFs.Supplementary Figure S6. Ratio of SNP-desert associated nonsynonymous and synonymous SNPs at different MAFs shared among the domestic breeds.Supplementary Figure S7. A Venn diagram of SNPs among the domestic breeds.Supplementary Figure S8. Distribution of total and breed-specific SNPs.Supplementary Figure S9. Distribution of indels found among the domestic breeds.Supplementary Figure S10. Length distribution of indels in domestic population.Supplementary Figure S11. A Venn diagram of domestication-specific short indels.Supplementary Figure S12. A Venn diagram of genes related to domestication-specific CNVs.Supplementary Figure S13. Validation of ASIP copy number in a larger population size from 4 domestic breeds.Supplementary Figure S14. SNP rate distribution with variable sliding windows.Supplementary Figure S15. A Venn diagram of SNP-desert-associated genes (SAGs) in the domestic breeds.Supplementary Figure S16. SNP rate distribution of RSRC1 gene.Supplementary Figure S17. A Venn diagram of SAGs and domestication-associated genes (DAGs).Supplementary Figure S18. Gene Ontology (GO) enrichment analysis of DAGs.Supplementary Figure S19. Opposite selection for LRP4 in the cashmere and meat breeds.Supplementary Figure S20. A Venn diagram of SAGs, QTL-associated, and trait-associated genes (TAGs).Supplementary Figure S21. GO categories analysis of genes shared by SAGs and QTLSupplementary Figure S22. Association between phenotype and genotype of the 699th nucleotide of FGF5.Supplementary Table S1. Summary of samples and sequencing.Supplementary Table S2. Non-synonymous/synonymous SNP ratios across groups.Supplementary Table S3. Heterozygosity in each individual.Supplementary Table S4. Distribution of breed-specific indels.Supplementary Table S5. Genes with frame-shift of the domestic breeds (specific≥60% mutation rate).Supplementary Table S6. CNV distribution in each individual.Supplementary Table S7. CNV associated genes of domestic breeds (≥60% frequency).Supplementary Table S8. CNV association with economic trait in the dairy breeds.Supplementary Table S9. The distribution of genes in SNP deserts.Supplementary Table S10. GO enrichment of the breed-shared SAGs.Supplementary Table S11. SAGs in the top 10 larger SNP deserts of the cashmere breed.Supplementary Table S12. SAGs in the top 10 larger SNP deserts of the dairy breed.Supplementary Table S13. SAGs in the top 10 larger SNP deserts of the meat breed.Supplementary Table S14. A list of genes overlapping between DAGs and SAGs.Supplementary Table S15. GO enrichment of DAGs.Supplementary Table S16. TAGs in chromosomal regions.AADAC: arylacetamide deacetylase; ADRA2A: alpha-2-adrenergic receptors; AHCY: adenosylhomocysteinase; APOL3: apolipoprotein L3; AR: androgen receptor; ASIP: agouti signaling protein; CNV: copy number variation; DAG: domestic-associated gene; FDR: false discovery rate; FGF5: fibroblast growth factor 5; Fst: fixation index; FXR2: fragile X mental retardation; GDF5: growth differentiation factor 5; GO: gene ontology; Hp: heterozygosity; indel: insertion/deletion; MAFs: minor allele frequencies; MYT1L: myelin transcription factor 1-like; NGS: next-generation sequencing; NME6: NME/NM23 nucleoside diphosphate kinase 6; NR6A1: nuclear receptor subfamily 6 group A member 1; NS/S: the ratio of nonsynonymous to synonymous substitutions; PCA: principal component analysis; PRDM6: PR domain containing 6; QTL: quantitative trait loci; RPL3: ribosomal protein L3; RSRC1: arginine and serine rich coiled-coil 1; SNP: single-nucleotide polymorphism; SAG: SNP desert-associated gene; SHOX2: short stature homeobox 2; SLC26A2: solute carrier family 26 sulfate transporter, member 2 gene; STAT6: signal transducer and activator of transcription 6; STIM1: stromal interaction molecule 1; TAG: trait-associated gene; TNFSF13: tumor necrosis factor (ligand) superfamily, member 13; VPS13C: vacuolar protein sorting 13 homolog C; XP-EHH: cross-population extended haplotype homozygosity; SN: Saanen dairy goats; LN: Liaoning cashmere goats; LZ: Leizhou goats; GZ: Guanzhong goat; IM: Inner Mongolian cashmere goat; HN: Hainan goat; MA: Markhor; SI: Sindh ibex.The authors declare that they have no competing financial interests.This work was supported by the National Natural Science Foundation of China , the National Science Foundation for Post-doctoral Scientists of China (grant 2013M532056), and the Research Fund for the Doctor Program of Higher Education of China (grant 20120204110007).L.S.B., Y.J., Z.H., L.B., and Z.B. designed the experiments and managed the project. C.L., Z.B., F.D.K., L.X.Y., and Y.J. performed the data analysis. A.N., C.H., Y.C.X., Z.X.Y., and H.Y.Z. performed the phenotyping and prepared DNA samples. C.L., F.D.K., G.F.L., and Z.H.B. performed the sequencing, genotyping, and validation. Z.B., C.L., and Y.J. wrote the manuscript.GIGA-D-17-00226_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00226_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments.pdfClick here for additional data file.Reviewer_1_Report_ -- Judit Salces Ortiz10/24/2017 ReviewedClick here for additional data file.Reviewer_1_Report_Revision_1 -- Judit Salces Ortiz04/30/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Alessandra Stella11/16/2017 ReviewedClick here for additional data file.Reviewer_2_Report_Revision_1 -- Alessandra Stella06/15/2018 ReviewedClick here for additional data file.Supplement FileClick here for additional data file.


Genome wide association studies (GWAS) were conducted on 7,853,211 imputed whole genome sequence variants in a population of 3354 to 3984 animals from multiple beef cattle breeds for five carcass merit traits including hot carcass weight (HCW), average backfat thickness (AFAT), rib eye area (REA), lean meat yield (LMY) and carcass marbling score (CMAR). Based on the GWAS results, genetic architectures of the carcass merit traits in beef cattle were elucidated.P-value < 10–5, 51, 33, 46, 40, and 38 lead DNA variants on multiple chromosomes were significantly associated with HCW, AFAT, REA, LMY, and CMAR, respectively. In addition, lead DNA variants with potentially large pleiotropic effects on HCW, AFAT, REA, and LMY were found on chromosome 6. On average, missense variants, 3’UTR variants, 5’UTR variants, and other regulatory region variants exhibited larger allele substitution effects on the traits in comparison to other functional classes. The amounts of additive genetic variance explained per DNA variant were smaller for intergenic and intron variants on all the traits whereas synonymous variants, missense variants, 3’UTR variants, 5’UTR variants, downstream and upstream gene variants, and other regulatory region variants captured a greater amount of additive genetic variance per sequence variant for one or more carcass merit traits investigated. In total, 26 enriched cellular and molecular functions were identified with lipid metabolisms, small molecular biochemistry, and carbohydrate metabolism being the most significant for the carcass merit traits.The distributions of DNA variant allele substitution effects approximated a bell-shaped distribution for all the traits while the distribution of additive genetic variances explained by single DNA variants conformed to a scaled inverse chi-squared distribution to a greater extent. At a threshold of The GWAS results have shown that the carcass merit traits are controlled by a few DNA variants with large effects and many DNA variants with small effects. Nucleotide polymorphisms in regulatory, synonymous, and missense functional classes have relatively larger impacts per sequence variant on the variation of carcass merit traits. The genetic architecture as revealed by the GWAS will improve our understanding on genetic controls of carcass merit traits in beef cattle. Carcass merit traits are important to beef production as they directly determine carcass yield, grade, and consumer preferences for meat consumption, and therefore profitability. Genetic improvement of carcass merit traits has been made possible by recording pedigree and/or performance data to predict genetic merit of breeding candidates. However, carcass merit traits are expressed at later stages of animal production and are mostly assessed at slaughter, which sacrifices potential breeding stock although real-time ultrasound imaging technologies can be used to measure some carcass traits such as backfat thickness, longissimus dorsi muscle area, and marbling score on live animals . With thEarly attempts to understanding the genetic control of quantitative traits in beef cattle were made with the detection of chromosomal regions or quantitative trait loci (QTL) , 11. HowThe recent discovery and functional annotation of tens of millions of DNA variants in cattle has offered new opportunities to investigate whole genome wide sequence variants associated with complex traits in beef cattle . The whoMeans and standard deviations of raw phenotypic values for the five carcass merit traits in this study Table  are in lP-value < 0.005 as proposed by Benjamin et al. [P-value < 0.005. Further investigation showed that all of these suggestive significant SNPs detected by the 50 K SNP panel GWAS were also significant by the 7.8 M SNP GWAS if the significance threshold was relaxed to P-value < 0.01, indicating that the imputed 7.8 M SNP panel GWAS was able to detect all the significant SNPs of the 50 K SNP panel. The small discrepancy in P-values of each SNP between the two DNA variant panels is likely due to the different genomic relationship matrices used. This result is expected as the 7.8 M DNA variant panel included all SNPs in the 50 K panel and this study used a single marker based model for GWAS. These additional or novel significant SNPs detected by the 7.8 M DNA marker panel corresponded to the increased amount of additive genetic variance captured by the 7.8 M DNA variants in comparison to the 50 K SNP panel, indicating that the imputed 7.8 M DNA variants improved the power of GWAS for the traits. Therefore, we will focus on the GWAS results of the 7.8 M DNA variants in subsequent result sections.At the suggestive threshold of n et al. , the GWAPlots of the allele substitution effects of imputed 7,853,211 WGS variants showed a bell-shaped distribution for all the traits  that were downloaded from the Ensembl BioMart database   of the 31 genes involved in lipid metabolism for AFAT have roles in lipid concentration.With a window of 70kbp extending upstream and downstream of each of the lead SNPs at FDR < 0.10, 319 candidate genes for HCW, 189 for AFAT, 575 for REA, 329 for LMY, and 198 for CMAR were identified based on annotated Additionally, our results also revealed small molecular biochemistry and carbohydrate metabolism as other important molecular and cellular processes for AFAT, CMAR, HCW, and LMY  as the base genotypes, a reference population of 4059 animals of multi-breeds genotyped with the Affymetrix HD panel, and a panel of 1570 animals with WGS variants from run 5 of the 1000 Bull Genomes Project, we achieved an average imputation accuracy of 96.41% on 381,318,974 whole genotype sequence variants using FImpute 2.2 [P-value < 10− 5, leaving 7,853,211 DNA variants for GWAS. With this WGS DNA panel, we demonstrated that the additive genetic variance and corresponding heritability estimates increased by 6.1 to 22.5% for all the five carcass traits in comparison to the 50 K SNP panel  . For conTo provide further insight into relative importance of each functional class, we fitted the GRM of the functional class and GRM constructed from DNA variants of all other functional classes simultaneously to estimate the additive genetic variance captured by each functional class. For each functional class, the sum of the additive genetic variances captured by the two GRMs Table  was almoP-value. For instance, a missense variant rs42661323 at 4,916,731 bp on BTA20 had an allele substitution effect of 10.73 on HCW, which was larger than that (b = 10.14) of its nearby lead SNP rs41574252 located at 4863507 bp. However, the P-value of the missense variant rs42661323 was 8.10 × 10− 8 and was slightly larger than that  of its lead SNP  at 24,332,917 bp on BTA16 for AFAT. The missense variant rs379314731 had an allele substitution effect of − 0.64 on AFAT with a P-value of 8.76 × 10− 7. However, its nearby downstream gene SNP rs381910687 was selected as the lead SNP due to its lower P-value of 5.89 × 10− 7 although its allele substitution effect was slightly smaller (i.e. b = − 0.63). Therefore, support SNPs that are located in more important functional classes such as missense and regulatory variants are also worth further investigation. Additionally, as the imputed 7.8 M DNA variants represent a proportion of whole genome DNA polymorphisms, the intergenic or intronic SNPs with larger effects may also be in LD with the causative DNA variant(s) that are not present in the 7.8 M DNA variant panel. In this case, fine mapping of QTL in the region of lead SNPs is needed to identify the causative DNA variants for the trait.It was observed that most top lead SNPs with larger effects are located between genes or located in intronic regions, although their average SNP effects or variances captured by individual DNA variants were relatively smaller than missense and regulatory DNA variants including 3’UTR and 5’UTR variants Table . Howeverhttps://www.animalgenome.org/cgi-bin/QTLdb/index, accessed on 22 August 2018) [Mapping QTLs via linkage or association analyses are subject to a false positive rate. Therefore, validation of QTL or DNA variants associated with a trait in independent studies provides confidence on the identified candidate QTLs or DNA variants. We compared our lead significant SNPs with the QTL regions reported in the Cattle QTL database (st 2018) . With a rs109696064”, which was a downstream gene variant that is 3164 bps away from the nearest gene LCORL on chromosome 6  that is within gene ENSBTAG00000005932 (FAM184B), one intronic variant (rs110995268) of gene LCORL, one downstream gene variant (rs109843602) that was in proximity to genes NCAPG and DCAF16, and one downstream gene variant (rs109696064) located within 70 kb of genes LCORL and NCAPG. The region that harbors genes NCAPG-LCORL on BTA6 is likely to be a candidate QTL region with pleiotropic effects for carcass merit traits including HCW, AFAT, REA, and LMY. The lead significant SNPs located on BTA 6 in the region of 37.9 Mb to 39.9 Mb were also found to have relatively larger effects on HCW, AFAT, REA, and LMY as shown in the Manhattan plots  and LCORL (ligand-dependent nuclear receptor co-repressor like) were found to have significant associations with feed intake and body weight gain in beef cattle [NCAPG including 4 synonymous variants, 177 intronic variants, 2 missense variants, and 2 other regulatory region variants. Also a total of 409 WGS variants were within gene LCORL, including 404 intronic variants, 1 missense variant, and 4 3’UTR variants. At P-value less than 10− 5, 17 SNPs (including 15 intronic variants and 2 missense variants) within gene NCAPG were found to be in significant association with HCW but none of them were identified to be a lead SNP. The intronic SNP rs110175987 of NCAPG was significantly associated with HCW , accounting for 4.18% of the phenotypic variance, and it was the largest proportion of phenotypic variance explained by a single DNA marker among the 17 within-gene variants , REA  and LMY , explaining 2.72, 3.19, and 2.41% of the phenotypic variance, respectively  and REA  accounting for 1.45 and 1.18% of phenotypic variance, respectively. Previous studies reported that this missense variant had strong association with fetal growth and birth weight in Holstein and Charolais crossbreed [NCAPG was also found to be associated with ADG, DMI, and MWT, respectively.To investigate potential pleotropic effects of SNPs or QTL regions on the carcass merit traits, we also compared lead significant SNPs among the five carcass merit traits. It was found that CMAR did not share any lead significant SNPs with HCW, AFAT, REA, or LMY. HCW, AFAT, REA, and LMY, however, shared a common significant lead SNP “f cattle . In our nt rs1098602 that ossbreed , the SNPLCORL, the intronic SNP rs110995268 at 38,914,196 bp was significantly associated with AFAT , explaining 2.87% of the phenotypic variance , REA , and LMY , explaining 4.33, 3.19, and 2.50% of the phenotypic variance, respectively  at 93,244,933 bp on chromosome 7 was a lead SNP in significant association  with REA, explaining 1.11% of phenotypic variance  in association with HCW on chromosome 14 at 25015,640 bp was mapped to the intergenic region (6344 bp upstream) of PLAG1  accounted for 3.41% of phenotypic variance of HCW in this study , and 6th for HCW, followed by carbohydrate metabolisms and small molecule biochemistry. In studies using RNAseq on bovine liver samples, lipid metabolism, and small molecule biochemistry were also among the top enriched molecular processes for marbling score in Charolais steers , 52. In The imputed 7,853,211 DNA variants explained more genetic variance than the 50 K SNP panel and led to identification of additional QTL regions in associations with carcass merit traits in Canadian multi-breed beef cattle. The DNA marker allele substitution effects on the carcass traits based on the imputed 7,853,211 DNA variants approximated a bell-shaped distribution, and the additive genetic variances explained by single DNA variants followed a scaled inverse chi-squared distribution to a greater extent. On average, missense variants, 3’UTR variants, 5’UTR variants, and other regulatory region variants exhibited larger allele substitution effects in comparison to DNA variants that are located between genes and in intronic regions. Intergenic and intronic variants also accounted for a smaller amount of additive genetic variance per DNA variant for the carcass traits whereas single regulatory, synonymous, and missense variants had relatively larger impacts on the variation of carcass merit traits. The five carcass merit traits appear to be controlled by a few DNA variants with relatively larger or modest effects complementary by DNA variants with small effects. Lipid metabolism, small molecular biochemistry, and carbohydrate metabolism were the top biological processes for the carcass merit traits. The genetic architecture as revealed by the 7.8 M DNA variant GWAS will improve our understanding on the genetic control of carcass merit traits in beef cattle.The populations used in this study, i.e., Angus, Charolais, Kinsella Composite, Elora crossbred, PG1, and TXX, were described previously , 53–56. 2) −  −  as described by Basarab et al. [The animals were initially measured for feed intake using the GrowSafe system  at their respective feedlot test station under multiple projects, which were described previously , 57–59. P-value < 10− 3), or on sex chromosomes were filtered out. SNPs removed from one data source were also excluded from all other data sources. In addition, animals with more than a 5% missing rate of total SNP genotypes were deleted. After SNP data editing, 33,321 SNPs were retained for further analyses. Sporadic missing SNP genotypes in the SNP data set (< 0.065%) were then imputed via the population-based algorithm implemented in Beagle 3.3.2 [All animals entering the feedlot tests were genotyped with bovine 50 K SNP panels under multiple projects. SNP data consolidation and imputation was described in the companion paper . Brieflyle 3.3.2 . Populatle 3.3.2 .P-value < 10− 5 in either population/breed were excluded from further analyses. The post-imputation quality control resulted in 7,853,211 DNA variant genotypes that contain 30,155 SNPs from the 50 K SNP genotypes on all the animals. The 7,853,211 DNA variants included 7,497,128 SNPs and 356,083 INDELs (termed 7.8 M DNA variants or 7.8 M DNA variant panel or 7.8 M SNP panel in the text). The imputed 30,155 SNPs in the 7.8 M DNA variant panel were replaced by their actual genotypes to facilitate comparison of the 50 K SNP panel and the 7.8 M DNA variants panel.SNP imputation was conducted using FImpute 2.2  in a twoFunctional annotation of SNPs or DNA variants on the 30,155 SNPs and on the 7,853,211 DNA variants was provided in the companion paper . The WGSn = 3354 for AFAT to n = 3984 for HCW  was constructed using GCTA-GRM as implemented in GCTA software and defined in Yang et al. [G matrix calculated by the second method of VanRaden [Animals with carcass data were merged with their imputed genotype data in the 7.8 M DNA variant panel, resulting in a sample size of CW Table . For theg et al. , 69, whiVanRaden :\documeAjk is off-diagonal element for animal j and animal k or represents the diagonal element if j = k, with genotype codes of xij = 0, 1, 2 for A1A1, A1A2, and A2A2, respectively. pj is the allele frequency of A2 at locus j calculated based on SNP genotype data of the population and M is the number of SNPs in the panel. The G matrix was constructed using all DNA variants in the 7.8 M DNA variant panel, i.e. mixed linear model with candidate marker included (MLMi) so that the G matrix was constructed based on all 30,155 SNPs for the 50 K SNP GWAS and on all the 7,853,211 DNA variants for the 7.8 M SNP panel GWAS.Where P-value were estimated using the GCTA package [p and q denote the minor frequency and major frequency for the SNP, respectively, ß is the SNP allele substitution effect, and 2pqβ2 is the additive genetic variance, and S2 is phenotypic variance. DNA variants (or SNPs) that have a nominal P-value < 0.005 were considered as suggestive QTLs as proposed by Benjamin et al. [P-value < 10− 5 were classified as significant QTLs based on the recommendation of the Wellcome Trust Case Control Consortium [P-value < 10− 5 were further examined for the genome-wise false discovery rate (FDR), which was calculated following the Benjamini-Hochberg procedure for each SNP [P-value was identified as the lead SNP whereas the remaining SNPs were classified as support SNPs. A 70 kb window was chosen for this study as this was the chromosomal length within which a high LD phase correlation (> 0.77) was maintained in a Canadian multibreed population [For each SNP or DNA variant, the allele substitution effect and its  package , 68. Then et al. , while Snsortium . SNPs theach SNP . At eachpulation .Heritability of a trait was estimated using GREML-LDMS , 73 for Distribution of SNP effects of each carcass trait was generated by plotting squared allele substitution effects of all DNA variants in the 7.8 M DNA variant panel, and by plotting the amount of additive genetic variances explained by single DNA variants in the panel. The average of squared allele substitution effects was obtained for each of the 9 broad functional classes Table  by summihttps://www.ensembl.org/). Ingenuity Pathway Analysis (IPA)  (IPA Spring 2019 release) was used for the functional enrichment analyses of the candidate genes identified via the GWAS. Briefly, for the genes with known human orthologues from Ensembl, their gene IDs were replaced with their human orthologous gene IDs, whereas those without human orthologues their bovine gene IDs were maintained in the gene list. These Ensembl gene IDs were then used as input gene identifiers in IPA and a core analysis was performed on the genes that were mapped to the IPA knowledge base database. With the list of candidate genes and genes mapped to the human orthologues, enhanced molecular processes and gene network were inferred using IPA. Molecular, cellular, and biological processes or functions were significantly enriched if the P-value for the overlap comparison test between the input gene list and the IPA knowledge base database for a given biological function was less than 0.05. Additionally, genes and biological processes or sub-functions’ interaction networks within the most significant molecular and cellular function were produced to show possible biological networks for the trait.Lead SNPs with a FDR < 0.10 were selected to search for candidate genes. Subsequently, genes located within 70 kb upstream and downstream of the lead SNP were considered candidate genes associated with the trait based on SNP annotation information from the UMD3.1 bovine genome assembly from the Ensembl genome browser  and additive genetic variances explained by individual SNP (right) based on GWAS of imputed 7.8 M whole genome sequence (WGS) variants for HCW, AFAT, REA, LMY, and AFAT; Figure S2. Cellular and molecular processes for HCW, AFAT, REA, LMY, and CMAR; Figure S3. Gene network for major gene expression, Carbohydrate/lipid metabolisms and Cell Morphology for HCW, AFAT, REA, LMY, and CMAR; Figure S4. Venn diagram showing the overlapped lead significant SNPs (a) and candidate genes (b) among five carcass merit traits based on the imputed 7.8 M DNA variant GWAS.Additional file 2: This file contains information of all suggestive significant SNPs/INDELs at P-values < 0.005 based on the 7.8 M WGS variant GWAS for HCW, AFAT, REA, LMY, and CMAR.Additional file 3: This file contains additional Tables including Table S1. Functional annotation of 7.8 M WGS variants along with the number of variants in each class, classification of SNP functions, percentage of WGS and 9 functional class assignments; Table S2. Functional annotation of 50 K SNP genotypes after quality control along with the number of variants in each class, classification of SNP functions, percentage of WGS and 9 functional class assignments; Table S3. SNP functional annotation of all DNA variants  based on DNA variants of the 1000 bulls genome project; Table S4. List of lead SNPs that were overlapped with QTLs published in the Cattle QTL database within 1 M bp up or 1 M bp downstream for HCW, AFAT, REA, LMY, and CMAR.


Fusarium verticillioides infects maize, causing ear rot, yield loss and contamination by fumonisin mycotoxins. The fungus can be transmitted via kernels and cause systemic infection in maize. Maize resistance to the fungus may occur at different developmental stages, from seedling to maturity. Resistance during kernel germination is part of the plant-pathogen interaction and so far this aspect has not been investigated. In the present study, a genome wide association study (GWAS) of resistance to Fusarium during the seedling developmental stage was conducted in a maize diversity panel using 226,446 SNP markers. Seedling germination and disease phenotypes were scored on artificially inoculated kernels using the rolled towel assay. GWAS identified 164 SNPs significantly associated with the traits examined. Four SNPs were associated with disease severity score after inoculation, 153 were associated with severity in asymptomatic kernels and 7 with the difference between the severity ratings in inoculated and non-inoculated seeds. A set of genes containing or physically near the significant SNPs were identified as candidates for Fusarium resistance at the seedling stage. Functional analysis revealed that many of these genes are directly involved in plant defense against pathogens and stress responses, including transcription factors, chitinase, cytochrome P450, and ubiquitination proteins. In addition, 25 genes were found in high linkage disequilibrium with the associated SNPs identified by GWAS and four of them directly involved in disease resistance. These findings contribute to understanding the complex system of maize-F. verticillioides and may improve genomic selection for Fusarium resistance at the seedling stage. Fusarium verticillioides (Sacc.) Nirenberg   in maize. Phenotypic evaluation of FER disease is usually performed in field trials using natural and artificial inoculation techniques (Most studies concerning host resistance to Fusarium spp. (Fusarium infection of seedlings (FIS) has not yet been investigated using the RTA.As an alternative to field tests of FER, the rolled towel assay (RTA) is a bioassay used for testing the ability of different pathogens to infect and colonize seedlings. RTA has been previously and successfully applied in both soybean and maize genotypes to evaluate the resistance to A number of distinct population structures are currently available for genetic analysis in maize, including biparental recombinant inbred line (RIL) populations obtained by crossing two parental inbreds (Genome wide association study (GWAS) is a genomics-based strategy for crop improvement and can be applied to a population of unrelated genotypes, capturing a considerable portion of species variation. Examining the genome-wide associations between single nucleotide polymorphisms (SNPs) and the phenotypes of a desired trait allows the identification of loci for quantitative traits and is especially useful when combined with QTL mapping . HoweverF. verticillioides can be transmitted from seed to seedling  from that of artificial infection (SEV_I). Indeed, a high natural infection severity could indicate the presence of the fungus inside the seed of some lines, masking the effect of the artificial inoculation. As second objective of the work, the three traits  were considered to perform GWAS and linkage disequilibrium (LD) analyses to identify SNPs associated with resistance to F. verticillioides infection in seedlings.The first objective of this study was to evaluate phenotypically the maize “Goodman” association population for resistance to FIS using the RTA bioassay and considering the effects of the natural and artificial infection , and the differences between them (Fv_ADD). SEV_C may contribute to seed infection because seedling . In factF. verticillioides resistance. Seeds were retrieved from USDA-ARS-NCRPIS . Due to seed availability, only 265 out of 302 inbred lines of the population were screened (Table S1), and after excluding lines with <50% germination percentage, 230 lines were used for GWAS.The “Goodman” maize association population (6 ml-1 spore suspension of F. verticillioides ITEM10027 (MPVP 294). The strain was isolated from maize in South Tuscany, Italy, by the Department of Sustainable Crop Production, Piacenza, Italy and deposited in their fungal collection and also in the Institute of Science and Food Production, National Research Council of Bari, Italy (http://server.ispa.cnr.it/ITEM/Collection). Another moistened towel was placed over the inoculated seeds, the towels were rolled up and placed vertically in a 25-L bucket covered with a black plastic bag and incubated for 7 days at 25° in the dark. For each inbred, a control RTA was prepared as previously described, replacing the inoculation step with F. verticillioides by 100 μl of sterilized distilled water. Inoculated and non-inoculated towels were incubated in common buckets, but kept in separate, open plastic bags to avoid cross-contamination.For each inbred line, seeds with similar size and shape and without visible damage were selected for the experiment. To reduce as much as possible the presence of contaminating fungi, seeds were surface-sterilized by shaking them in 50-ml tubes at room temperature with 70% ethanol for 5 min, followed by sterile distilled water for 1 min, then with commercial bleach solution for 10 min, and finally rinsed three times with sterile distilled water for 5 min each time. Two towels of germination paper  were moistened with sterilized distilled water. For each inbred, 10 seeds were placed on the germination paper about 10 cm from the top of the towel with the embryo side facing out. Kernels were inoculated on the embryo side near the pedicel with 100 μl of a 1x10). The percent of germinated seeds was recorded for each towel. Thirty-five inbred lines with germination percent below 50% in the control condition were removed from further analysis, since their phenotypes could be conditioned by infections from other pathogens during seed development. SEV_I and SEV_C were recorded for each line as well as the difference between SEV_I and SEV_C (Fv_ADD) was calculated for each line. For each trait, means and standard deviations were calculated. The ANOVA analysis was performed using R software  approaches have been used to genotype the complete maize association panel . After sr = 0.5 were pruned. This step yielded a subset of ∼100 kb SNPs used to compute the realized additive relationship matrix (K matrix) for the set of 230 lines by using TASSEL v5.2.25  = KGenome Wide Association Analysis (GWAS) was performed in TASSEL (version 5.2.25). The mixed linear model (MLM) fitted was:P < 1×10−4 within at least 30% of data subsamples, i.e., a resample model inclusion probability (RMIP) threshold of 0.30, were considered as significant , restricting the search to Zea mays (taxid:4577) protein function; conserved domains identified in this way were recorded.Candidate genes containing, or adjacent to associated SNPs were identified using the Maize GDB genome browser. The B73 RefGen_V3 was the reference genome used to localize SNPs and genes. Transposable elements and low-confidence genes were not considered. Where possible, the predicted function of the gene was recorded. All proteins encoded by the genes were submitted to NCBI-protein BLAST (http://www.ebi.ac.uk). Coding sequence translation was performed using the translate tool available from ExPASy (http://web.expasy.org/translate), and amino-acid variations caused by the SNPs were detected by alignment with the corresponding reference protein available from Maize GBD .SNP positions inside genes were identified and the possible effect of each trait-associated SNP on protein sequence was investigated. To detect introns and translated regions, genic and protein sequences were submitted to GeneWise online tool (r2) were estimated between each SNP significantly associated with resistance traits and all other SNPs within a surrounding window of 60 adjacent SNPs using TASSEL  measures (https://doi.org/10.6084/m9.figshare.c.4162766. Table S1. (DOCX file) List of germplasm and phenotypic values. Figure S1. (PDF file) Disease severity scale. Table S2. (DOCX file) Table of SNPs resulting significantly associated to the traits from GWAS analysis. Table S3. (DOCX file) Table of genes associated to significant SNPs from GWAS analysis. Table S4. (DOCX file) Table of LD analysis. Figure S2. (PDF file) Localization of genes and QTL for resistance on Chromosomes 1, 2 and 3. Figure S3. (PDF file) Localization of genes and QTL for resistance on Chromosomes 4, 5 and 6. Figure S4. (PDF file) Localization of genes and QTL for resistance on Chromosomes 7, 8, 9 and 10. All supporting data are included as supplemental files and are available at Figshare: −16), but not between replications , demonstrating that the assay is repeatable. Indeed, the correlation between replicates was high (r = 0.91).Different types of response to FIS were found in the association maize panel . The pheF. verticillioides was observed in some non-inoculated kernels, indicating natural symptomless infection inside the kernels . Extreme values, less than 2 (low infection) or greater than 4.5 (high infection), were observed for 4 inbreds in each tail of the distribution . One hundred and eleven SNPs were located in 75 genes, while the remaining 53 SNPs were intergenic, but within 61 kb from known genes. A total of 101 genes, containing or close to SNPs significantly associated with at least one of the measured traits were identified. Eleven SNPs occurred inside two different genes each, as two genes were annotated in the same region on complementary strands. A summary of SNPs and gene numbers, associated with SEV_C, SEV_I or Fv_ADD, is reported in For the trait SEV_C, 153 SNPs were found significantly associated and localized on all the chromosomes . The majFor SEV_I, 4 SNPs were found significantly associated and localized on chromosomes 3, 4 and 7 . Two SNPSNPs associated with SEV_C were found in 95 genes. Among these, 30 are reported to have a function that can be connected to disease resistance mechanisms (Table S3). Four of these candidate genes are discussed as follows.in planta . Four of the SNPs are predicted to change amino acids in the protein encoded by transcript 2 (T2) of the gene: N159K (Asparagine-Lysine), N159T (Asparagine-Threonine), A160T (Alanine-Threonine) and Y161H (Tyrosine-Histidine). These allelic variations are in the catalytic residues of the Glycoside hydrolase family 19 (chitinase) domain and they could have a significant impact on the protein function. Among the sequences currently available online, amino acid variability is reported for positions 159 and 160, while the Tyrosine 161 appears to be highly conserved . The N- and C-ends of the proteins in this family are necessary to create the catalytic pocket required to split xylanases. Phytopathogens produce xylanases that destroy the hemicellulose in the cell walls, whereas the ability of host plants to proteolyze xylanases is vital for plant-survival. Two significant SNPs (28-29 in Tables S2 and S3) were found inside this gene; one is responsible for the change G225S in the catalytic residue of the enzyme . TAXI proteins occur in several species where they are induced by pathogens and wounding. Moreover, TAXI proteins are active against xylanases produced by F. graminearum (A significant SNP on chromosome 3 is in the gene GRMZM2G132509 coding for a xylanase inhibitor TAXI-IV precursor (Tables S2 and S3). This gene encodes for a glycine rich protein (GRP) A3. These proteins are structural component of the cell walls of many higher plants  of GRMZM2G160027 on chromosome 5 . In plants, cytochrome P450s are involved in hormone metabolism, the oxygenation of fatty acids for the synthesis of cutins, lignification, and the synthesis of secondary metabolites  occurring inside the conserved domain of the protein.SNPs in three genes were associated with SEV_I: GRMZM2G087628, encoding the plastid S1-binding domain protein; GRMZM2G029153, encoding for a protein with domains common to sugar carriers and transporters . The latter is on chromosome 6 and encodes for a putative WRKY125 transcription factor. WRKY proteins have a regulatory function in several plant metabolisms and in transcriptional reprogramming as a response against pathogens and abiotic stresses . Seven genes identified by LD analysis have an annotation which can be connected with disease resistance mechanisms (Table S4).Linkage disequilibrium analysis was performed to identify correlations between markers due to cosegregation or population structure. In diverse maize, LD generally decays to very low levels on average between 5 and 10 Kb, with some variation observed among chromosomes  and measurement of different disease phenomena (FER vs. FIS resistance). Moreover, resistance to ear rot in the field is also expected to be influenced by different characteristics, such as ear architecture traits, including husk tightness, ear coverage, and differences in flowering and maturing times  in a RIL population, which was evaluated with a different phenotyping method. Nine genes associated with SEV_C identified in the present work were located inside three of these QTL.In a recent work, F. verticillioides inoculation. In the case of a non-germinating seed the fungus can grow as a saprophyte, conversely, in the RTA bioassay the living seedlings were able to counteract fungal attack.On chromosome 2, GRMZM2G062471, AC218093.3_FG005, GRMZM2G376731 and GRMZM2G357972 were located in the QTL qFR2-1 F. verticillioides-maize pathosystem. Nonetheless, these new markers may be useful to improve genomic selection for identifying new maize genotypes adequate to counteract F. verticillioides infection at the seedling stage.A RTA bioassay was applied in this study for the first time to screen the “Goodman” maize association population for FIS resistance. GWAS analysis revealed 164 SNPs associated with 3 phenotypic traits  that are the direct measure of fungal growth on the kernel. Globally, 101 genes were found associated to the SNPs and many of these genes have a role in disease response. LD analysis among markers revealed 23 additional genes, 3 of those are directly involved in mechanism of pathogen defense. Co-localization between genes described in GWAS analysis and QTL reported for FER or fumonisin accumulation resistance revealed that the majority of the genes associated with the SNPs, localized inside QTL for FER and/or fumonisin accumulation resistance traits. The high number of markers identified and the small effect of each marker on disease severity is consistent with the quantitative nature of the


Selection signature regions, haplotype blocks and SNP data from a previous whole genome sequencing study in the founders of this chicken F2 population were used to refine the list of PCGs and to identify potential causative SNPs. We obtained high genomic heritabilities (0.43–0.56) and identified 22 unique QTLs for abdominal fat and carcass fat content traits. These QTLs harbored 26 PCGs involved in biological processes such as fat cell differentiation, insulin and triglyceride levels, and lipid biosynthetic process. Three of these 26 PCGs were located within haplotype blocks there were associated with fat traits, five overlapped with selection signature regions, and 12 contained predicted deleterious variants. The identified QTLs, PCGs and potentially causative SNPs provide new insights into the genetic control of fat deposition and can lead to improved accuracy of selection to reduce excessive fat deposition in chickens.Excessive fat deposition is a negative factor for poultry production because it reduces feed efficiency, increases the cost of meat production and is a health concern for consumers. We genotyped 497 birds from a Brazilian F However, chickens selected for higher body weight might exhibit increased appetite and excessive energy consumption, which may lead to excessive fat accumulation5.In the past, slow growth rates were a challenge in poultry production systems; consequently, intensive selection of this trait in elite great-grand parent lines has dramatically increased poultry productivity. Modern commercial broiler chickens are produced from crosses that have been simultaneously selected for rapid growth, increased meat-production, and improved carcass yield6. Therefore, understanding the genetic architecture, uncovering genomic regions, and finding positional candidate genes (PCGs) associated with fat deposition related traits could be helpful in breeding programs.Excessive fat deposition in chickens is a negative factor for meat production because it reduces feed efficiency and the value of the carcass7. Previous QTL studies, performed in the same population evaluated in this study (Embrapa F2 Chicken Resource Population) using 128 microsatellite markers for 22 autosomal chromosomes, mapped QTLs for abdominal fat traits10 and carcass fat traits9, but the genomic locations of these QTL had large confidence intervals. Genotyping of animals using high-density marker arrays can help the identification of genomic regions with smaller intervals11, which, in turn, facilitates the identification of candidate genes.A total of 200 quantitative trait loci (QTL) have been reported for abdominal fat weight, 139 for abdominal fat percentage and 11 for carcass fat content15, but the discovery of a causal mutation is still a challenge. In a study with chicken, candidate genes for egg production and feed efficiency were identified by genome-wide association study (GWAS), and this information was integrated with selection signature results highlighting important biological processes and causal variants16. However, to the best of our knowledge, no integrative studies were performed to identify potential causal genes and mutations for fat deposition regulation.Many studies have been performed to identify candidate genes for fat deposition traits in chicken2 population from a cross between a broiler line and an egg-layer line. We also integrated these results to refine our list of candidate genes using selection signature regions and SNP data information previously obtained from whole genome sequence of the parental generation evaluated in this study.Thus, the aims of this study were to estimate genomic heritability for different fat deposition related traits and identify genomic regions and PCGs in an F2 chickens were used for the association analysis.From the 529 genotyped chickens, 40 were removed before GWAS analysis. Of these, 12 were removed because their genotypes did not pass quality control , and 28 did not have complete phenotypic data. As a result, 489 F2 population were kept for further analysis. A total of 4,304 were removed due to low minor allele frequency (MAF ≤0.02), and 23,603 SNPs located in sex chromosomes and unmapped linkage groups were also removed. After these filtering criteria, 371,786 SNPs from the autosomal chromosomes (GGA1-28) remained for the GWAS analysis. The average genotype density per chromosome was 541 SNPs/Mbp, with the lowest density being observed on GGA2 (297 SNPs/Mbp), and the highest density on GGA21 (816 SNPs/Mbp).From the 580,961 SNPs originally available on the chicken SNP array, 399,693 SNPs segregating in the FThe number of animals, mean and standard deviations, variance components and estimated genomic heritabilities are presented in Table et al.17, ranged from 0.41 to 0.85 for these regions, and the percentage of genetic variance explained by the windows ranged from 0.53 to 1.71.The genomic windows associated with fat traits are detailed in Table 18. In order to identify the SNPs within the windows most probably linked to the QTL, we selected those with highest model frequency  is available in Supplementary Spreadsheet Many SNPs were fitted simultaneously in the model (see Methods), and due to high linkage disequilibrium, the QTL effect will be distributed across these markers, shrinking the SNPs effects at individual locicy Table , as adopCarcass fat content exhibited the highest number of significant QTLs Fig. , followeAdjacent significant windows were considered as likely representing the same QTL, and their respective percentages of genetic variance explained were summed. For abdominal fat traits, three adjacent windows on GGA1 52–54 Mb) were associated and cumulatively accounted for 3.3% of the genetic variance for ABF, and two adjacent windows on GGA2 (61–62 Mb) cumulatively accounted for 2.17% and 1.37% of the genetic variance for ABF and ABFP, respectively  were associated and cumulatively accounted for 3.27% of the genetic variance for CFC; two adjacent windows on GGA7 (35–36 Mb) cumulatively accounted for 2.18% and 1.31% of the genetic variance for CFC and CFCDM, respectively; two adjacent windows on GGA15 (9-10 Mb) cumulatively accounted for 1.53% of the genetic variance for CFC; and two adjacent windows on GGA28 (3-4 Mb) cumulatively accounted for 2.47% of the genetic variance for CFCDM  were identified on GGA1 at 53 Mb, GGA1 at 179 Mb, GGA7 at 35 Mb and on GGA28 (3-4 Mb).The 22 QTL regions identified in our study overlapped with 56 previously published QTLs for fatness traits mapped in different populations. The QTL on GGA28 at 0 Mb is novel Table .Table 3O2 population10 used herein 21. One PCG (CRY1) was located 1.5 kb from a selection signature region  using the records of 300 chickens from a commercial female grandparent stock26, and could be explained by the fact that female broiler chickens generally deposit more abdominal fat than male broiler chickens27.For the evaluated traits, pedigree-based heritability estimates were found in the literature. In a study with 1,069 purebred full-sib male chickens, Chen 2 Chicken Resource Population), pedigree-based heritability for ABF was estimated at 0.33 ± 0.19 in the F2-CTCT generation  and 0.82 ± 0.3 in the F2-TCTC generation 28. Considering the carcass fat content traits, heritability estimates for CFC expressed in percentage of wet carcass was 0.53 ± 0.10, and the heritability for fat percentage in dry-matter basis (CFCDM) was 0.55 ± 0.10, using records of 3,422 chickens3.In the same population  and ABFP 0.31) were reported in one recent study performed with 1,408 chickens from a Brazilian broiler population under multiple trait selection were rep10, corroborating our findings. It is important to highlight that most of the genomic windows explained <0.53% of the genetic variance for the traits analyzed in our study  line and a meat-type  line29. The TT Reference population originated after 17 generations of multiple trait selection on birds from the broiler line (TT) used in the cross to obtain the Embrapa F2 Chicken Resource Population32. Thus, our findings indicate that even after many generations of multiple trait selection, the QTL located on GGA27 still has an effect on fat deposition whereas the other QTLs detected herein were fixed in the TT Reference population.Almost all the QTLs detected overlapped with previously reported QTLs associated with fat deposition traits in this or in other populations, confirming and refining previous results. The QTL located in the first bases of GGA28 was novel and might be a population specific QTL. Additionally, the QTL located on GGA27 at 3 Mb associated with ABF overlapped with a QTL previously mapped in the TT Reference population for chicken skin weight and percentage, used as indicators of subcutaneous fat10. For example, the QTL for ABF on GGA27 at 3 Mb overlapped with two known QTLs mapped to a region of 1.9 Mb (#11817 and #11809). The high-density SNP panel narrowed this region down to a 1 Mb interval, thus allowing a more focused search for candidate genes.We compared the size of the QTL regions obtained in this study with a previous one that used 128 microsatellite markers in the same populationThe QTLs mapped on GGA1 (at 53 and 179 Mb), GGA7 (35 Mb), and GGA28 (3-4 Mb) were associated with both abdominal fat and carcass fat content traits, indicating that they might be pleiotropic or reflect multiple causal mutations in the same QTLs.35. From the 22 QTLs detected, 17 did not overlap with QTLs for body weight traits as expected since we included BW42 as a covariate in our analysis. The inclusion of BW42 as a covariate allowed the detection of 17 QTLs associated with fat deposition traits but with no phenotypic association with body weight traits. The five QTLs that overlapped with BW traits may have pleiotropic effects.We checked the overlap of our QTLs associated with fat deposition related traits and previously mapped QTLs for body weight at 35 (BW35) and also 41 (BW41) days-of-age mapped in the same F2 populationPositional candidate genes were selected based on their associated Gene Ontology (GO) terms and literature information. From the 26 PCGs selected, 17 showed GO terms for fat cell differentiation, insulin and triglycerides levels among other processes involved in fat deposition (Table IL6 gene that exhibited a GO term for positive regulation of pancreatic β cell activation (responsible for synthesizing and secreting insulin36). Increases in insulin levels in chicken may affect expression of genes related to glucose and lipid metabolism37, consequently affecting fat accumulation. Based on these facts, we considered the genes annotated with GO terms related to insulin as PCGs for lipid metabolism and fat deposition regulation in chicken.From the genes related to lipid metabolic processes described in Table PLA2G1B, SELM, DOK5, HTR2A, and GDF3 have been previously associated with obesity42. The PLA2G1B gene harbored a SNP associated with fat accumulation and distribution in humans38. In mice, the knock-out of SELM gene resulted in elevated white adipose tissue deposition39. In the DOK5 gene, genetic variants were associated with obesity in North Indian patients40. In the HTR2A gene, polymorphisms were associated with central adiposity in a study with humans41. Mice that were GDF3 deficient exhibited a modest reduction in adiposity42. These studies corroborate the selection of these genes as PCGs for fat deposition.Several genes, namely 21 in founders of the F2 population used in this study: CHST11, NR4A2, GPD2 and INSR  were not annotated with GO terms related to fat deposition but were considered PCGs for fat deposition for the reasons explained below. Myoglobin (MB) expression was associated with fatty acid metabolism in a study with mice53. Members of the solute carrier (SLC) family encodes membrane-bound transporters54, and one of these has been associated with obesity in humans56. Additionally, the SLC1A6 gene belongs to the SLC1 family that regulates glutamate transport, and in liver cells, this amino acid is a precursor of fatty acid biosynthesis57. In a study with rats, mice and rabbits, Collin et al.58 demonstrated that glutamate transporter activity might regulate energy balance. Energy balance is directly related with fatty acid biosynthesis and consequently, fat storage.Seven other genes , and splice sites , we predicted the SIFT score as described in the Methods section.Variants in coding regions can be related to phenotypic variation, and, more specifically, non-synonymous variants imply amino acid changesNR4A2, involved in the regulation of hepatic glucose affecting lipid metabolism, and TM6SF2, involved in the regulation of triglyceride levels in the liver. Changes in the function of these genes may affect fat deposition in chicken. All deleterious mutations are important candidates for further association and functional studies.Twelve potentially deleterious SNPs were identified in eight PCGs Table , and mosTM6SF2 gene was present in the Affymetrix genotyping array. However, that SNP was removed after genotyping quality control due to low MAF. The search for potential candidate SNPs identified previously from whole genome sequence data in similar chicken populations allowed us to identify a great number of SNPs, which are not present in the Affymetrix genotyping array, improving our chances to identify potentially causative mutations.From the 12 potentially deleterious SNPs, only rs315426765 SNP on the NR4A2 gene is a strong candidate for fat deposition regulation in chicken, since it is within a QTL for carcass fat content traits, is in linkage disequilibrium with the SNP with the highest model frequency, is under selection in the founder lines, and contains three potential causal SNPs.In summary, our study identified 22 unique QTLs for abdominal fat and carcass fat content, and approximately 40% of the QTLs detected were considered novel QTLs for the traits analyzed. The 22 QTLs detected harbored 26 PCGs that were involved in biological processes of fat deposition. Three of these 26 PCGs were located within haplotype blocks that were associated with fat traits and five of these 26 genes overlapped with selection signature regions. From the total number of SNPs annotated in PCGs, approximately 35% were in functional regions, and from those 12 were predicted to be deleterious variants. The GWAS using a high density of SNPs allowed us to map QTLs with better resolution than previously done with microsatellite markers, thus facilitating the search for PCGs. The integration of haplotype blocks detection, selection signature regions and potentially deleterious SNPs allowed us to refine the list of PCGs for fat deposition traits. The PCGs identified, especially those within the haplotype blocks, overlapped with selection signature regions and harboring genetic variants located on potentially functional gene regions, are strong candidates for selection in poultry breeding programs aiming to improve the accuracy of selection and reduce excessive fat deposition. Further functional validation studies could be helpful to understand the role of the candidate genes and genetic variants associated with fat deposition regulation.All experimental protocols related to animal experimentation in this study were performed in agreement with resolution number 010/2012 approved by Embrapa Swine and Poultry Ethics Committee on Animal Utilization to ensure compliance with international guidelines for animal welfare.2 Chicken Resource Population were genotyped . This F2 population is the result of crosses between two closed parental lines: a broiler line  and a layer line . The TT line had been selected for higher body weight, feed conversion, carcass and breast yield, viability, fertility, hatchability, reduction of abdominal fat weight and metabolic syndromes. The CC line had been selected for egg production, egg weight, feed conversion, viability, sexual maturity, fertility, hatchability, egg quality and low body weight. More details about the Embrapa F2 Chicken Resource Population were described by Rosário et al.30. All the birds from the Embrapa F2 Chicken Resource Population were reared as broilers with free access to water and a corn and soybean meal-based diet33. More details of the diet content can be found on Nones et al.9.A total of 529 chickens from the Embrapa F2-TCTC generation was previously used to map numerous QTLs for performance, carcass and chemical component traits75 using microsatellite markers. For the study reported in this paper, selection of families for genotyping and GWAS was based on F1 males that appeared to be heterozygous for QTL effects reported in previous studies.This F2 population were slaughtered and eviscerated at 42 days of age, after 6 h of fasting to avoid contamination of the carcass during slaughter and post-slaughter processing. The body weight at 42 days of age (BW42) was measured and carcasses were stored at 4 °C for 6 h. Then, the carcass and its cuts were weighed. Abdominal fat was removed from chilled carcass for weighing . Abdominal fat percentage (ABFP) was calculated dividing ABF by BW42 and multiplying by 10010.A total of 502 chickens from the F9. Carcass fat content weight (CFC) was estimated by multiplying the percentage in the sample by BW42. Carcass fat content on dry matter basis (CFCDM) was estimated dividing sample fat by carcass dry matter content and multiplying by 100. More details about trait measurement can be found in Campos et al.10, Nunes et al.3 and Nones et al.9.Fat (ether extract) was measured by near-infrared reflectance spectroscopy (NIRS) and estimated as percentage according to the weight of each sample (250 g of ground and homogenized carcass)® (Thermo Fisher Scientific), and diluted to the final concentration of 20 ng.µL−1. Diluted genomic DNA was prepared for genotyping following Affymetrix protocols and genotyped with the 600 K Axiom Chicken Genotyping Array. That array comprises SNPs chosen to be segregating for different chicken lines66.Genomic DNA was extracted from blood samples with DNAzol® protocol. After extraction, DNA integrity was evaluated on agarose gel (1%), quantified in spectrophotometer NanoDropNCBI). Missing genotypes were replaced by their average covariate value at that locus76.Quality control analysis and genotype calling were performed with Affymetrix Power Tools v1.17.0 (APT). Samples that exhibited DishQC ≥0.82 and call rate ≥90% were kept for further analysis. Among those high-quality samples, the most accurate and polymorphic marker SNPs, with call rate ≥98% and minor allele frequency (MAF) ≥2%, were kept for further analysis. In this step, R scripts from the package SNPolisher were used. SNPs located in the sex chromosomes, unmapped linkage groups, without genomic annotation and those that were monomorphic were removed from the analysis. SNP annotation was based on the Gallus_gallus-5.0 chicken assembly (http://www.r-project.org/). The estimation of variance components was performed using a Bayes C model in GenSel software18. The resultant posterior means of the variance components were used as priors in a Bayes B model to estimate genomic heritability for each trait using GenSel. Sex and hatch were included as fixed effects in the model, and BW42 was used as a fixed covariate for ABF and CFC.The mean and standard deviation of each phenotype were calculated using in-house scripts in R software  when δj = 1 but βj = 0 when δj = 0, with δj being a random variable 0/1 indicating the absence (with probability π) or presence (with probability 1-π) of the locus j in the model, and e is the vector of residual effects assumed to be normally distributed N . Sex and hatch were included as fixed effects in the model, and BW42 was used as a fixed covariate for ABF and CFC.The SNPs retained after quality control were used in the GWAS analysis with a Bayesian approach, performed with the GenSel software79. Many SNPs were fitted simultaneously in the model and due to high linkage disequilibrium, the QTL effect can be distributed across these markers18, and these previous studies showed that the 1 Mb windows can capture the effects.We assumed π = 0.9988 in the Bayes B models and obtained 41,000 Markov Chain Monte Carlo (MCMC) samples with the first 1,000 samples being discarded. A map file was used to position the SNPs into 943 non-overlapping windows of 1 Mb. We adopted 1 Mb non-overlapping windows in our study in agreement with other recent GWAS studies in chickens reported in the literature using genomic prediction methodology20, and windows that explained five times more than the expected value (0.53%) were considered to be biologically significant. Thus, we selected only biologically significant windows to characterize and identify PCGs. Additionally, within each significant window (QTLs), we selected the SNP most frequently included in the model.Each window is expected to explain 0.106% of the genetic variance (100%/943) based on an infinitesimal model7, using Chicken QTLdb - release 33, accessed in September, 2017. We used the search tool in Chicken QTLdb website, using our QTL coordinates based on Gallus_gallus-5.0 chicken genome assembly. Previously mapped QTLs overlapped were reported by their respective QTL ID numbers. The genomic windows that did not overlap with previously annotated QTL regions were considered novel discoveries.We checked the overlaps of all genomic windows detected with QTLs previously mapped in chickenEnsembl Genes 90 Database and the Gallus_gallus-5.0 (NCBI) chicken genome assembly.The goal of this study was to investigate PCGs based on literature information to provide new insights for further studies, and also to better understand the genetic architecture of fat deposition traits. In this context, the list of annotated genes within each associated genomic window were searched using NCBI and OMIM databases, the BioMart tool and literature to find GO terms and biological processes related to abdominal fat, lipid metabolism, fat content, and fat deposition. For all the analyses, we considered the gene annotation from 2 population analyzed in our study21. That study used whole genome sequence to identify genetic variants and applied the Fst method80 to estimate the divergence between populations and identify regions under selection (TT vs. CC lines). We used the CrossMap tool (http://crossmap.sourceforge.net/) to convert selection signature coordinates  to the Gallus_gallus-5.0 chicken genome assembly (NCBI).To refine the list of candidate genes, we compared our list of PCGs against selection signature regions identified in a previous study with 28 parental chickens from the two lines that generated the F81 software was used to detect the haplotype blocks, with default parameters.We also compared our list of PCGs against the haplotype blocks that harbored the SNPs with the highest model frequency within each associated genomic window. PLINK v.1.921.Additionally, to identify potential candidate genetic variants for fat deposition in chicken, we performed a screening of SNPs located in PCGs, using the same dataset of sequencing SNPs used to detect the selection signature regions 82 implemented by the VEP tool83. SIFT scores were predicted for all non-synonymous and stop codon (gained/lost) variants located in the PCGs.In order to refine our list of PCG variants, we searched for genetic variants predicted as deleterious and high impact. To predict whether SNPs in coding regions are deleterious or not (may affect the protein function), we obtained the SIFT (sorting intolerant from tolerant) scores. That score is an assessment of the level of conservation in homologous protein sequences83 that provides an estimation of the putative impact of the variant classified as high impact, i.e. annotating all the mutations annotated as transcript ablation, splice acceptor, splice donor, stop gained, frameshift, stop loss, start lost and transcript amplification, mutations that may cause protein truncation, loss of function or trigger nonsense mediated decay (http://www.ensembl.org/info/genome/variation/predicted_data.html).The prediction of high impact SNPs was also performed using the VEP toolSupplementary Information FileSupplementary Spreadsheet S1Supplementary Spreadsheet S2Supplementary Spreadsheet S3