There is high demand for forensic pedigree searches with Y-chromosome short tandem repeat (Y-STR) profiling in large-scale crime investigations. However, when two Y-STR haplotypes have a few mismatched loci, it is difficult to determine if they are from the same male lineage because of the high mutation rate of Y-STRs. Here we design a new strategy to handle cases in which none of pedigree samples shares identical Y-STR haplotype. We combine next generation sequencing (NGS), capillary electrophoresis and pyrosequencing under the term ‘NGS+’ for typing Y-STRs and Y-chromosomal single nucleotide polymorphisms (Y-SNPs). The high-resolution Y-SNP haplogroup and Y-STR haplotype can be obtained with NGS+. We further developed a new data-driven decision rule, FSindex, for estimating the likelihood for each retrieved pedigree. Our approach enables positive identification of pedigree from mismatched Y-STR haplotypes. It is envisaged that NGS+ will revolutionize forensic pedigree searches, especially when the person of interest was not recorded in forensic DNA database. Y-STRs locating on the Y chromosome non-recombination region (NRY) allow to characterize paternal lineages2. Male relatives typically share an identical Y-STR haplotype. This knowledge serves as the basis for the identification of suspect pedigrees by demonstrating haplotype matches3. However, due to the high mutation rate of Y-STR, paternal relatives may suffer from inconsistent Y-STR haplotypes. As more Y-STR loci employed, as well as more distant relationship between two profiles is, the probability of encountering any mutation increases3 - individuals with discordant Y-STR haplotypes can still be relatives of possible suspects. False exclusion may occur due to potential Y-STR mutations. To solve this issue in forensic pedigree searches, it is of ultimate importance to develop an effective approach that can minimize the false negatives.Y chromosome short tandem repeats (Y-STRs) have been used to identify male pedigrees in large-scale crime investigation. The use of Y-STR profiling to narrow down the investigation scale has proven to be successful in solving some cold caseshttp://isogg.org/wiki/Genetics_Glossary), the haplotype is defined as a combination of Y-STR alleles inherited directly from male ancestors, and the haplogroup is defined as a set of similar haplotypes derived from the co-ancestors carrying same Y-chromosomal single nucleotide polymorphisms (Y-SNPs). Paternal relatives are also assumed to share identical haplogroups. Since the Y-SNP mutation rate is quite lower (~1.0 × 10−9 per year), pedigree signatures are kept much longer at Y-SNPs than Y-STRs3. Time estimates of the most recent common ancestor (MRCA) provided by Y-SNP haplogroup analysis are used to study evolution, human origin, and human migrations9, which leads to predict potential geographic or ancestral origin12. Searching for relatives or pedigrees with identical ancestral origin is the goal of both pedigree searches and identification of human remains or descendants. The latter has already been implemented using the joint analysis of Y-SNP haplogroup and Y-STR haplotype15, albeit with rough resolution. Next generation sequencing (NGS) allows us to subdivide the current up-to-date haplogroup tree19 to trace back to the MRCA within the shortest possible generations. This enables the application of the high-resolution Y-SNP haplogroup analysis to forensic pedigree searches. In crime scene evidence comparisons, the presence of a discordant haplogroup in the reference sample indicates that both samples are derived from different MRCAs and the involved pedigree should be excluded from the scope of further investigations.According to the Y Chromosome Consortium (YCC) , capillary electrophoresis (CE) and pyrosequencing, under the term ‘NGS+’, for typing Y-SNPs and Y-STRs. We further developed a new data-driven decision rule, FSindex, for estimating the likelihood for each retrieved pedigree. The new strategy was validated using 1) 100 father-son pairs; 2) 296 male samples from Han, Tibetan, Uygur and Hui ethnic groups in China and 3) a difficult actual case. Our approach enables positive identification of pedigree from mismatched Y-STR haplotypes. It is envisaged that NGS+ will revolutionize forensic pedigree searches, especially when the person of interest was not recorded in forensic DNA database.21 analysis , another mutation at DYS439 was observed through Yfiler haplotype22 analysis  in another father-son pair, and a total of 6 mutations occurring in 5 father-son pairs were seen in the 38 loci haplotype analysis   showed that 5 of the 8 mutation events were observed at RM Y-STRs, accounting for 62.5% of all mutations , was applied to 100 father-son pairs to test for Y-STR mutations in paternal relatives. One mutation at DYS385 was observed in the minimal haplotypehttps://yhrd.org), International Society of Genetic Genealogy (ISOGG) (http://www.isogg.org/tree/index.html) and the 1000 Genomes Project (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/) and constructed a high-resolution haplogroup tree aiming at major Chinese populations for forensic pedigree searches.Due to its extremely low mutation rate, Y-SNP is an optimal marker for forensic pedigree searches. We screened Y-SNPs from the Y Chromosome Haplotype Reference Database (YHRD) . A total of 33 terminal haplogroups were observed . 296 unrelated individuals randomly sampled from Han, Tibetan, Uygur and Hui ethnic groups in China were genotyped with the Ion Torrent PGMIn this study, the Y-STR haplotype analysis was based on CE, while the high-resolution Y-SNP haplogroup analysis was based on NGS and pyrosequencing. Y-SNP haplogroup analysis showed that all of the 100 father-son pairs had identical Y-SNP genotypes, including those with Y-STR mutations Table . Most ofet al.15 presented two different methods for Y-STR likelihood calculations, which were modified and applied to the analysis of Y-STR and Y-SNP respectively in this study (see Supplementary Note). The new hypotheses are proposed as follows: the null hypothesis is that the suspect does not come from the involved reference pedigree but fortuitously shares the same Y-SNP haplogroup and similar Y-STR haplotype as well as the involved pedigree can be excluded from the scope of further investigation; the alternative hypothesis is that the suspect maybe comes from the involved reference pedigree and Y-STR mismatches are caused by mutations as well as the involved pedigree has to be included in the scope of further investigation. The FSindex likelihood formula for forensic pedigree searches is as follows:12. n is the number of Y-SNPs used, S equals one minus the per mutation rate of Y-SNP of 0.00000000112, reflecting the necessary lack of mutation at all the detected Y-SNPs. A denotes the Y-STR haplotype frequency of the reference pedigree in the target population. B denotes the Y-SNP haplogroup frequency of interest in the target population. PAB is the correlation coefficient and set as 0.9969, according to the four studied ethnic groups . The police investigators faced with the dilemma of whether there was a necessity to keep searching the suspect locally.NGS+ results showed that Y-SNP genotypes of five pedigrees were identical with the crime scene evidence, carrying haplogroup O1a1a2-CTS52. We calculated the FSindex of these five pedigrees, respectively. As very few haplogroup frequencies exceeded 0.05 in the overall Chinese population in the high-resolution Y-SNP haplogroup analysis, the frequency of the haplogroup O1a1a2-CTS52 was conservatively estimated as 0.05. All male members of the pedigrees with an FSindex greater than 1 were recommended to undergo autosomal STR analysis. This was successful in revealing a matching suspect. The suspect pedigree had two branches of closely related descendants  can provide sufficient information30 without showing too many discrepancies between relatives. Mutations also occur as the number of meiosis events increase or the genetic relationship becomes more distant31. Undoubtedly, the phenomenon will be more common in pedigree searches since it is more likely that the suspect and the reference sample are distantly related. When more possible mismatches between two profiles from relatives are expected, the investigation will be placed on a larger scale. China has the largest population in the world and each pedigree has been through many generations. Thus, large-scale investigations are time- and effort-consuming and less successful than expected. It is important to find a more efficient approach of searching potential pedigrees. Liu et al.31, after testing more than 20,000 individuals, proposed the hypotheses that two Yfiler profiles with less than two mismatched loci were potentially related, and two Yfiler Plus profiles with no more than four mismatched loci or five mismatched steps were potentially related. These theories served as guidelines for forensic pedigree searches, but still required a more solid scientific foundation to support. In this study, we proposed a new forensic pedigree search strategy with NGS+. The mutation rate of Y-SNP is 100000 times lower than Y-STR32, therefore, we focused on constructing a high-resolution Y-SNP haplogroup tree specific to the Chinese population for forensic pedigree searches. The tree contained 139 Y-SNPs and was constructed based on NGS and pyrosequencing. Rather than applying a fixed minimum number of detected loci for exclusion, we developed a flexible likelihood formula FSindex for biostatistical support. All of the retrieved pedigrees with identical Y-SNP genotypes with crime scene evidence will go through the FSindex. Only pedigrees with FSindex greater than 1 will be treated as the potential source of the suspect pedigree. In the actual case, we narrowed down the investigation scope and successfully identified the suspect’s pedigree. This example demonstrates the power of the new approach for excluding most of the unrelated pedigrees through the high-resolution Y-SNP haplogroup analysis. Most importantly, the FSindex helps to generate positive identification of pedigrees from mismatched Y-STR haplotypes and guide further investigations.Y-STR has been successfully used to differentiate male DNA in sexual assault cases in forensicsThe new approach is based on the ‘NGS+’ - combining NGS, CE and pyrosequencing together. NGS has great advantages in accurate typing and high throughput, and is the optimal choice for multi-samples and multi-Y-SNP detection. Taking NGS as the core removed the need to limit the number of detected Y-SNPs when constructing the high-resolution Y-SNP haplogroup tree and kept the marker set integrated in each single test, thus avoiding errors in haplogrouping. In this study, two samples from the Hui population unexpectedly carried extra mutations. Sample Hui58 belonging to haplogroup I-M170 carried a P203.1 mutation defining haplogroup O1a1a-P203.1. Another sample, Hui60, belonged to haplogroup N-M231 but was observed with a M117 mutation which defined haplogroup O2a2b1a1-M117. The exactly same Y-SNP genotype with the extra mutation is unlikely to occur among other pedigrees, so the extra variations inconsistent with original haplogroups can be regarded as distinctive features of their pedigrees and flow through generations steadily. CE was used not only to analyze Y-SYRs in the first step of a forensic pedigree search, but also as the mainstream technology nowadays in forensic autosomal STR typing for personal identification after the scope of the investigation has been determined. ‘NGS+’ brought the advantages of CE into full play and created a usable approach for police investigators.The efficiency of the new approach depended directly on the resolution level of the Y-SNP haplogroup tree. Further sub-haplogrouping had to be carried out, because the 74 Y-SNPs panel could not have enough power of resolution. Human genome sequencing is now a mature technology, however, it is too extravagant to be used for evaluating evidence. Therefore, pyrosequencing was used to screen polymorphic Y-SNPs. 67 sets of primers were designed for pyrosequencing, and 53 Y-SNPs were observed to be polymorphic in 296 Chinese individuals. These Y-SNPs were selected for gradual subdivision testing. The development of haplogroup frequencies in each pyrosequencing test showed the effectiveness of each Y-SNP for differentiation at various levels. In accordance with that, Y-SNP density was adjusted over time. Pyrosequencing costs are much lower for single tests and the primers are much easier to be designed, making this method superior to NGS for adjusting marker sets. It is suitable for single Y-SNP detection in small size samples. Y-SNP sets can also be adjusted for pyrosequencing when the target population is changed. NGS+, which combines NGS, CE and pyrosequencing, can be deemed to be the most suitable approach to construct a reasonable and efficient forensic pedigree search approach. In the future, the entire final Y -SNP panel will be tested via NGS, therefore then, the forensic pedigree search approach will be based on NGS plus CE.AB reflects the correlation specific to the target population between Y-SNPs and Y-STRs. The estimate of PAB is affected by many factors, such as sample size and the resolution level of the Y-SNP haplogroup analysis. In this study, based on the new analysis approach, we obtained the mean PAB derived from four ethnicities as 0.9969 . The general correlation formula of continuous variables was applied to adjust the FSindex. P31. To determine the number of meiosis in each retrieved pedigree is critical, both theoretically and empirically. Though the relationship between the suspect and the representatives from the reference pedigree cannot be predicted a prior, we are able to obtain the maximum number of meiosis in each pedigree accordingly to the known genealogy. After this correction, the FSindex will be much more conservative. If the value is still greater than 1, it is reasonable to infer that the pedigree maybe is the suspect’s pedigree and further investigation is needed to confirm. To minimize the interference on forensic pedigree searches due to possible Y-STR mutations, it is necessary to adjust X and carry out specific estimates for each retrieved pedigree. Since the number of potential pedigrees after haplogroup screening will be very limited, adjusting the X value doesn’t complicate the calculation but enables the FSindex formula to be more general and accurate. The FSindex takes into account the Y-STR haplotype frequency and the Y-STR mutation rate, as well as the resolution level of the Y-SNP haplogroups and the haplogroup frequency in the target population. In addition, it also quantifies the correlation between the haplogroup and haplotype, rendering the formula more appropriate for forensic pedigree searches. Evaluating all the retrieved pedigrees via an FSindex avoids the subjective judgment by investigators due to Y-STR mismatches, and more reliably indicates the possibility of the involved pedigree as the suspect’s source. It provides solid biostatistical support for forensic pedigree searches to narrow down the scope of the investigation. It is worth noting that, since the FSindex dependent on the number of Y-STR mismatches in the case of the identical haplogroup, it is not appropriate for likelihood comparison between reference pedigrees.The variable X, which is equal to the average Y-STR mutation rate per generation times the maximum meiosis of the involved pedigree, reflecting the possibility that the presumed mutation must have occurred in a certain number of meiosis. The probability of mutation increased with the number of meiosis30, blood samples of 296 unrelated healthy male individuals including 127 Han, 47 Tibetan, 62 Uygur and 60 Hui were collected upon approval of the Ethics Committee at the Institute of Forensic Medicine, Sichuan University with informed consent. All the methods were carried out in accordance with the approved guidelines of Institute of Forensic Medicine, Sichuan University. The Ethics Committee of Sichuan University approved this study.According to the distribution of Y chromosome variation along administrative as well as ethnic divisions in the mainland territory of the People’s Republic of ChinaDNA was extracted by salting out method or with the QIAamp DNA Blood Mini Kit . Typical control DNA of 2800 M , 9948  and 007  human cell line samples were used as reference samples in every single run.https://yhrd.org), International Society of Genetic Genealogy (ISOGG) (http://www.isogg.org/tree/index.html) and the 1000 Genomes Project (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/). The Y-SNP inclusion criteria for initial genotyping on the Ion Torrent PGMTM system  are as follows: (1) Y-SNPs of the major haplogroups in Chinese population and the marker number of each haplogroup is determined on the haplogroup frequency, (2) phylogenetically key intermediate Y-SNPs which increase integrity of the Chinese-specific tree, (3) primers could be designed for NGS and amplicons are smaller than 200 bp. Finally, 74 Y-SNPs providing the basic structure of the haplogroup tree for forensic use were screened out but two were excluded according to the latest ISOGG Y-SNP haplogroup tree version 2017.All the candidate Y-SNPs were obtained from the Y Chromosome Haplotype Reference Database (YHRD)  polymorphic in Chinese populations, and (2) primers could be designed for Pyrosequencing and amplicons are smaller than 150 bp. Finally, 67 additional Y-SNPs for the expansion of our forensic haplogroup tree were screened out. Details of these additional Y-SNPs are shown in Supplementary Table http://www.ampliseq.com) in a BED file (Thermo Fisher White Glove Approach ID: IAD97812).AmpliSeq Designer was employed to conduct single-pool DNA Hotspot designing, and the 74 candidate Y-SNPs were submitted to Thermo Fisher AmpliSeq primer design tool . One of each pair of amplification primers was biotinylated at the 5’-end and purified by HPLC to eliminate free biotins . Experimental steps included library preparation, emulsion PCR and sequencing.The initial haplogroup determination of 74 Y-SNPs was conducted on the Ion Torrent PGMTM Library Kit 2.0  on a ProFlexTM 96-well PCR System . The library-PCR system were performed in 20 μl total volume each containing: 4 μl of 5X Ion AmpliSeqTM HiFi Mix; 10 μl of 2X Ion AmpliSeqTM Primer Pool; 5 μl of Nuclease-free Water; 1 μl of 10 ng/μl genomic DNA dilution. Thermal cycling consisted of: enzyme activation of 2 min at 99 °C; followed by 19 cycles of 15 sec at 99 °C, 4 min at 60 °C; finally 10 °C for hold. The PCR products were treated with 2 μl FuPa  to partially digest primer sequences and were continued in the thermal cycler with 10 min at 50 °C; 10 min at 55 °C; 20 min at 60 °C; and finally a 10 °C hold. Considering that multiple libraries would test on a single chip, all libraries were assigned a unique barcode using the Ion XpressTM Barcode Adapters  following the manufacturer recommendations. Then the ligation reaction was performed by adding 4 μl of Switch Solution ; 2 μl of diluted barcode adapter mix prepared before addition; 2 μl of DNA Ligase  to each digested sample in 30 μl total volume and loaded in the thermal cycler following 30 min at 22 °C; 10 min at 72 °C; and a 10 °C hold. After purifying the unamplified library using Agencourt® AMPure® XP Reagent , the unamplified Ion AmpliSeqTM library was eluted in 50 μl of Low TE Buffer . Library quantification was conducted on the 7500 Real Time PCR System  with Ion Library TaqManTM Quantitation kit  following 2 min at 95 °C and 40 cycles of 15 sec at 95 °C with the Ion Library Quantitation Kit . All the libraries were unified into a same concentration and the final library was established by collecting 5 μl dilutions from each dilution library.10 ng of genomic DNA was used for library preparation using the Ion AmpliSeqTM Particles (ISP). Emulsion PCR (emPCR) was conducted using Ion PGMTM Hi-Q OT2 Kit on the Ion One TouchTM 2 System  following the recommended protocol. The template-positive ISPs were enriched on the Ion One TouchTM ES complying with the description in the user guide.The final library was attached to Ion SphereTM system  with the Ion PGMTM Sequencing 200 kit v2 (Life Technologies) using the Ion 318TM Chip V2 BC. A final volume of 30 μl was loaded onto the chip, containing sequencing primer, Control Ion SpheresTM Particles and sequencing polymerase of the Ion PGMTM sequencing kit . These runs were set in order to achieve a 500X coverage for each sample. We took Hg19 as the human reference genome. Raw sequencing data were collected as DAT files which were processed on the Torrent_Suite Server (v4.6). VariantCaller (v4.6.0.7) and CoverageAnalysis (v4.6.0.3) plugins. The BAM files were verified using IGV_2.3.59.Finally, sequencing was performed on the Ion Torrent PGMAdditional 67 Y-SNPs were analyzed by pyrosequencing. Below is a step-by-step summary of the experimental steps.TM 96-well PCR System . Thermal cycling comprised: enzyme activation of 15 min at 95 °C; followed by 50 cycles of 30 s at 94 °C, 30 s at 62 °C and 30 s at 72 °C; final extension of 10 min at 72 °C and hold at 10 °C. Non-specific amplicons were not observed by electrophoresis in 6% polyacrylamide gels with silver nitrate staining.The PyroMark PCR Kit  was used for amplification according to the manufacturer’s recommendations on a ProFlexTM High Performance  were mixed and vortexed for 10 min at room temperature. The dynabeads were adsorbed to the filter tips using a microfilter and then placed in 70% alcohol for 5 s, PyroMark Denaturation Solution  for 5 s, and 10-fold diluted PyroMark Washing Buffer, 10X  for 10 s using the PyroMark Q96 Vacuum Workstation (Biotage AB) for single strand PCR products preparation. After turning off the vacuum workstation, the microfilter was placed into the PSQ96 Plate Low  containing 2 μl the PSQ primer and 43 μl PyroMark Binding Buffer . The plate was placed in an incubator at 80 °C for 2 min and then cooled to room temperature.For pyrosequencing 25 μl of the biotin-labeled PCR amplicons, 47 μl PyroMark Binding Buffer  and 3 μl Streptavidin SepharoseFinally, the PCR amplicons were sequenced in a PyroMark Q96 ID system . For an obvious qualification result, the PyroMark ID 1.0 software  was utilized to arrange the sequencing dispensation order of nucleotides with the SNP mode.TM Kit , the AmpFLSTR YfilerTM Plus Kit  and the AGCU Y SUPP  on a ProFlexTM 96-well PCR System  according to the manufacturer’s recommendations. The PCR products were separated by CE on the ABI 3500xl Genetic Analyzer  and all haplotypes were determined with GeneMapper ID v3.2 software (Thermo Fisher Scientific). Four samples failed to produce genotypes.DNA amplification of all the individuals was performed using the AmpFLSTR YfilerThe authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information. Extra data are available from the corresponding author upon request.Supplementary InformationSupplementary Table S1


In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest.Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50–80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP The online version of this article (doi:10.1186/s12918-017-0403-7) contains supplementary material, which is available to authorized users. Alcohol dependence is characterized by increasing tolerance to and consumption of alcohol, even in the face of adverse effects . Almost Alcoholism is very difficult to overcome once it initiates, and thus there has been much interest in preventing the onset of alcoholism altogether . The conThere have been several association studies that have sought to identify a small number of susceptibility loci for alcoholism . Howeverp-value of the likelihood ratio. Similarly, a logistic regression model SNP+E+(SNP×E) was used in [p-values of the interaction term was used to declare the significance of interaction. Limitations of linear or logistic regression analysis in detecting SNP-SNP interactions have been discussed elsewhere [Various explanations have been suggested for the missing heritability , e.g., e used in  to identlsewhere . In partlsewhere –15. Therlsewhere ), or dralsewhere ).Here, in an effort to discover plausible epistasis, i.e., non-additive SNPs association with alcoholism phenotype, we propose the use of first-order dependence tree of maximum weight. Although this technique has been proposed for the first time by Chow and Liu in , but its10  is displayed on the y-axis. Markers above the horizontal black line (p<0.0005) have been used in subsequent analysis for construction of the SNP×SNP×E network of alcoholism (see Methods Section for more details). Figures The Manhattan plot in Fig. Figures We sought to rank the genes, demographic factors, biological pathways, and the interactions represented in our SNP.g., see . Howeverentclass1pt{minimaAlcohol has a variety of effects on the body; many of these arise from alcohol’s activation of receptors in the brain . A numbep-value (see Methods Section for details). Some of these factors and interactions are explicitly stated in prior studies. For example, a prior study [Table or study  has demoor study  providesor study .p<0.04). While we used an AUC-metric-based approach to highlight this interaction, one may realize the importance of such link by examining the distribution of rs8225 among cases and controls in both races. As presented in Table p<10−15 and in cases p<10−15 as determined by Cochran-Armitage test [p<0.005 and this difference for EAs has p<0.0002 as determined by Cochran-Armitage test [There is also an interesting interaction between race and rs8225 in Table age test ). The wiage test ). AnotheTwelve of the 14 biological pathways detected in our analysis  denote the probability mass function of a random vector x. The mutual information between two variables (here SNP1 and SNP2) is given byFirst-order dependence tree of maximum weight is proposed initially by Chow and Liu  and furtI measures the amount of information that SNP1 carries about SNP2 and vice versa. In a graphical representation of dependency among SNPs, we assume the dependencies have a tree structure (meaning each node has a single parent and one node (the root) has no parent), and assign to every edge of the tree an mi denotes the parent node of node i and n is the number of SNPs under study. Note that there is no difficulty to maximize Intuitively, posed in  resolvesIn a case–control study, we can define a “class” variable C to measure the amount of information between SNPs given the phenotype (case or control). In this case, the maximum weight first-order dependence tree becomes the one with the maximum P, under the first order dependence tree structure . In praure (see ). This imentclass2pt{minimn (here 652 SNPs selected as described before), there will be 2n-1 potential maximum weight networks that can be constructed on any subset of n variables. Of course one may choose to grow the tree on all n SNPs but here we propose a complementary step to further narrow down the list of potential genetic factors used in the proposed network of alcoholism. To do so, we use the network as a classifier and use the AUC to rank a set of potential networks (see next subsection) and choose the one with the highest AUC. Unless otherwise stated, we employ 3-fold cross-validation procedure to compute AUCs. Nevertheless, since for the initial dimensionality reduction step, we use the CMH test on the full training data, we shall not interpret AUC as the predictive ability of our constructed network on a subset of SNPs and/or other factor. In other words, the use of AUC here is merely a measure to rank constructed sub-networks of interactions.From the previous section, we have to note that the MWDT guarantees the maximum likelihood estimate of the joint distribution given the true tree dependency among a set of given SNPs. However, for a set of SNPs of size p-values were removed and a new network was constructed using the reduced list of SNPs. The best network was the one with the highest AUC in differentiating cases from controls. The LD analysis-based approach sought to eliminate redundant SNPs. LD analysis was performed and SNPs that were strongly linked (i.e. frequently co-occurred in both the cases and the controls) were grouped into bins. The approach outlined by Carlson, et al. [2 threshold lowered from 0.8 to 0.4, was used to produce a single tag SNP for each LD bin. Only the tag SNPs were maintained, and the iterative approach was applied to them. This approach ensures that multiple SNPs that are proxies due to low LD distance are not selected. The tag SNP acts as a proxy for all SNPs in that region. The best networks from the two approaches were compared, and the one with the highest AUC was selected as the SNP×SNP×E network.To construct the optimal network of interactions, two approaches were employed: one is a backward sequential iterative approach described below, and the other is an approach based on a combination of linkage disequilibrium (LD) analysis  and the , et al. , with thTo study the gene level interactions with the phenotype based on SNP level variations, we enumerate all genes with at least one SNP in the network. For each gene, we construct a sub-network of SNPs involved in the full SNP×SNP×E network located on that gene and record the AUC of a newly constructed sub-network. We consider race and sex as part of each sub-network. This would unlock the full potential of race- or sex-specific SNPs. In a sense, this analysis is similar to the adjustment for sex and age in the classical regression analysis.p-value associated with each AUC. Although here ranking based on AUC or p-value leads to the same result, we use the p-value threshold of 0.05 (non-adjusted) to narrow down the list.We next considered important demographic features. To evaluate the importance of each demographic factor, we calculated the decline in resubstitution AUC (AUC on the training set) upon removal of that factor and all edges connected to it from the full SNP×SNP×E network. Resubstitution was used because the response of cross-validation AUC to minor changes is relatively imprecise due to larger variance of cross-validation estimators . We usedp-value for each gene- or pathway-specific network, we constructed 1,000 networks, each with the same number of nodes for which the AUC in question was calculated, and determined their AUCs. The set of genetic features for each of the 1,000 networks was drawn randomly from the background set of SNPs. Race and sex were included as features in all 1,000 networks in order to ensure parity with the procedure used to generate the gene- or pathway-specific network. The list of 1,000 random AUCs enabled the calculation of a p-value for the AUC in question.To determine a p-value for the decline in AUC of interest. Each gene, demographic factor, pathway, and interaction relationship was now associated with a p-value.To determine the statistical significance of each decline in AUC (used for quantifying the importance of each demographic variable and the interactions in the SNP×SNP×E network), we used the same background set to construct 1,000 random networks with the same set of demographic factors and the same number of SNPs as in the SNP×SNP×E network. For each randomly generated model, we recorded the decline in AUC upon removal of a random SNP (in the case of the declines in AUC for demographic factors) or a random edge (in the case of the declines in AUC for interaction). The 1,000 random declines in AUC enabled the calculation of a Additional file 1:Supplementary Notes: The first section in this file provides the evidence for functional ties between some of the implicated genes/pathways and alcoholism. The second section in this file details the maximum likelihood estimate of the conditional mutual information. (DOCX 170 kb)Additional file 2:Table S1. This file provides the list of SNPs with CMH test p-value < 0.0005. (XLSX 52 kb)Additional file 3:Table S2. This file provides the complete list of interactions in the SNPxSNPxE network. (XLSX 56 kb)


Noninvasively collected samples are a common source of DNA in wildlife genetic studies. Currently, single nucleotide polymorphism (SNP) genotyping using microfluidic arrays is emerging as an easy-to-use and cost-effective methodology. Here we assessed the performance of microfluidic SNP arrays in genotyping noninvasive samples from grey wolves, European wildcats and brown bears, and we compared results with traditional microsatellite genotyping. We successfully SNP-genotyped 87%, 80% and 97% of the wolf, cat and bear samples, respectively. Genotype recovery was higher based on SNPs, while both marker types identified the same individuals and provided almost identical estimates of pairwise differentiation. We found that samples for which all SNP loci were scored had no disagreements across the three replicates (except one locus in a wolf sample). Thus, we argue that call rate (amplification success) can be used as a proxy for genotype quality, allowing the reduction of replication effort when call rate is high. Furthermore, we used cycle threshold values of real-time PCR to guide the choice of protocols for SNP amplification. Finally, we provide general guidelines for successful SNP genotyping of degraded DNA using microfluidic technology. However, the extraction and subsequent analyses of DNA from noninvasively collected samples present considerable challenges8. The recovered DNA is often degraded and quantities are usually low, resulting in decreased amplification success and increased processing efforts to obtain genotypes of sufficient quality11. Co-extracted PCR inhibitors, especially from scat samples, may further complicate the generation of reliable genotype data.Noninvasively collected samples play an important role in wildlife genetic studies. The collection of animal residues, such as hair, scats, saliva and feathers, enables the study of wild and elusive species that are otherwise difficult to sample, whilst minimizing disturbance to the animals and their habitats17. SNPs are valued as an efficient and cost-effective addition to the toolkit of genetic markers18. Assessing population structure or hybridization, individual identification, as well as sex, parentage and relatedness determination are examples of key applications in population genetic studies for which SNP loci have been selected19. Consequently, SNP marker panels have been used in a number of conservation genetic studies to date . However, the technology has not reached routine genetic monitoring for many species, which would be a prerequisite for joint cross-laboratory conservation efforts5.In the past years, genome-wide single nucleotide polymorphisms (SNPs) have become increasingly popular as a marker of choice in population genetic studies22. Unlike microsatellite scores, SNP data do not require calibration across different laboratories and therefore the data can be readily compared. Also, the mutation modes are better known for SNPs, and thus ambiguities due to null alleles or variable mutation patterns are less frequent than in microsatellite studies19. Genotype calls are rather straight-forward and the bi-allelic nature of the markers leaves little room for subjective scoring decisions. Using microfluidic platforms, genotype data for up to 192 samples can be generated within a few hours, allowing for high-throughput analyses. With the advent of next generation sequencing (NGS) technologies, panels of SNPs can easily be generated . Thus they might be particularly suitable for the amplification of fragmented DNA, such as DNA extracted from noninvasively collected or historical material.The requirement of samples with high DNA quantity and quality has complicated the implementation of SNP markers in wildlife monitoring for a long timeIn this study, we address the issue of how to produce, assess and apply SNP data to samples with low DNA quantity and quality using microfluidic platforms. We focus on how reliable SNP data can be generated from this type of samples, and how the data can be assessed in order to minimize replication. Finally, we compare the success in obtaining SNP- versus microsatellite-based genotypes.To answer these questions, we generated SNP data for hair and scat samples of grey wolves, European wildcats and brown bears. We assessed SNP genotyping performance across replicates and in relation to real-time PCR (RT-PCR) measurements. In addition, we compared the performance of SNP and microsatellite genotyping data generated from the same samples. Finally, based on our results, we provide general SNP genotyping guidelines for noninvasive samples that may be applicable to other projects aiming to use microfluidic SNP genotyping arrays.37. We removed loci that failed to amplify in ≥70% of the reactions  to minimize missing data, and calculated the percentage of samples that got called for 0–100% of the remaining loci, averaged over the three replicates  and microsatellite amplification rate against cycle threshold (Ct) value from real-time PCR (RT-PCR) Fig. . The perBoth SNPs and microsatellites identified the same individuals, with no differences between the two marker types . Similarly, we compared the original microsatellite marker sets  with decreasing numbers of microsatellite loci , selected based on highest heterozygosity and highest FST value (the latter only for wild and domestic cats). No major differences were found among the PCoA plots obtained from randomly selected SNPs  and the probability of identity between siblings (PIDsib) changed according to the number of loci considered Fig. . In the 11. Some of the advantages posed by noninvasive sampling schemes include the low disturbance to the species of interest and the relative ease in which animal residues that are frequently shed or deposited into the environment can be collected38. However, noninvasively collected samples also present challenges due to the potentially low quantity and quality of target DNA. This problem is usually overcome by a multiple-tube approach7, which in turn leads to a rise in analysis cost and time. In this study, we show that recently developed SNP marker panels implemented on microfluidic arrays provide reliable results for noninvasively collected samples. We further show that SNP call rates, as well as Ct values derived from RT-PCR experiments, are good indicators of sample quality and of the number of replicates that should be conducted in order to obtain reliable genotype data.Wildlife management decisions are increasingly informed by genetic analyses, for which noninvasive sampling has become a prominent source of DNA38. To date, the multiple-tube approach7 is widely applied in genetic studies based on noninvasive samples40. Keeping the number of required replicates as low as possible is often desirable, because available funding is generally scarce and the consumption of precious DNA extracts rises with the number of replicates42. Therefore, it was important to investigate how noninvasive samples performed when genotyped with SNP marker panels implemented using a microfluidic approach, in order to assess the reliability of the genotypes obtained and propose recommendations for its implementation. Based on our analyses, we propose a classification of the samples based on their SNP call rates, and use this as a proxy of sample quality and of the reliability of the genotype. Samples with a SNP call rate of 100%  showed nearly no disagreements in the genotypes among the three replicates. We found only one case in which one out of the three replicates disagreed for one wolf sample at one locus, out of 74 samples that had no missing data across the three species studied. Based on this, samples that reach call rates of 100% in a first run can be excluded from further replication and the genotype considered as reliable. In the case of our study, this was the situation for 26–50% of the investigated samples . Samples that reached 95–99% SNP call rates in first runs may need to be duplicated, since up to 17% of the generated genotypes might have disagreements. Ultimately, the researcher will need to decide which data require further validation through duplication, depending on the questions to be answered. Samples with call rates of 71–94% should be at least triplicated to detect potential errors. In our data set, we detected 34% of disagreements in this sample class. Finally, samples with call rates ≤70% may be subject to additional replication, if needed, or be discarded. When applying call rate as a proxy, it is important to consider that technical issues may very rarely hamper successful amplification. For instance, while two of the three replicates of one bear sample obtained call rates of 99%, the third replicate had a call rate of almost 0%. This is most probably explained by an air bubble introduced into the chip inlet during pipetting. This particular case led to an averaged call rate of 67% for that sample; while only one disagreement was detected between the two well-performing replicates  or had been successfully genotyped using microsatellite markers (brown bears), which may explain why SNP call rates were overall relatively high. Because mtDNA analysis is the primary means to determine species identity, we expect that most molecular ecology laboratories working with noninvasively collected samples will have similar pipelines of pre-selection in order to exclude samples from non-targeted species. Also, we increased DNA volume (from 1.25 µl to 3.2 µl) and STA-PCR cycles (from 14 to 18) as compared to the manufacturers’ protocol, which further aids successful amplification.43. Nussberger et al.36 calculated genotyping error rates  for their SNP markers and subsequently selected hair samples adequate for SNP genotyping based on DNA concentration. Kraus et al.35 reported an overall error rate of about 1% for dilution series of tissue and blood samples (to concentrations as low as 0.2 ng/µl) and suggested that there may be no need for PCR replication. Norman et al.3 reported genotyping error rates as low as 0.00038 (based on pre-selected samples that had worked for microsatellites). A follow-up study, which investigated population size and pedigrees in bear scat samples, reported no replication for the implemented SNP genotyping approach of noninvasive faecal samples37. Notably, the reported error rates for SNP genotyping of noninvasive samples described so far are below microsatellite error rates using noninvasive sampling44.Previous studies have applied the SNP marker panels that we used here to noninvasively collected samples45. Also, SNP genotype disagreements of up to 1% (wolves), 3% (wildcats) or even 35% (one case in the bears) occurred for samples with Ct values <30.Predicting SNP genotyping performance of noninvasive samples may be useful when selecting a subset of samples of the best quality for genotyping when many samples are available. We show here that the Ct value of RT-PCR of noninvasively collected samples may constitute a good indicator. We targeted an autosomal product of similar size to the loci being amplified in the SNP array (127 bp). Our results, based on different sample types (hair and scats), showed that both SNP and microsatellite genotyping success strongly decreased for samples with Ct values >30 and that the occurrence of SNP genotype disagreements increased. Nevertheless, it must be noted that samples with Ct values >30 occasionally performed very well. This may be due to a delay in amplification because of the presence of inhibitors or very small amounts of DNA in the extractset al.46 extracted DNA from single hairs and assessed sample quality using RT-PCR quantification. They reported a minimal DNA concentration of 50 pg/µl as a threshold for SNP amplification, favouring extracts with a minimum of 200 pg/µl. Here, we decided not to attempt to determine DNA concentration because dilution series of standard DNA might not be available, particularly for non-model species, and because standards might produce different results since they are usually pristine, ultra-clean and non-degraded gDNA products. In our experience, comparing the performance of noninvasively collected samples in RT-PCR to a good quality tissue sample permitted the assessment of sample quality adequately, as was the case for the bear samples in this study.Nussberger Furthermore, we used RT-PCR results to guide the genotyping protocol conditions, in order to maximize scoring success. For samples with good quantity and quality of DNA  the STA pre-amplification protocol was run following the manufacturers’ protocol including 1.25 µl of DNA and 14 cycles. Samples with lower quality and quantity of DNA were performed with our modified STA protocol of 3.2 µl DNA and 18 cycles of amplification.49. In terms of amplification success  SNP markers performed better when compared to microsatellite markers. This increased success might be explained by the shorter size of the SNP fragments (<120 bp), which favours PCR success when using small quantities of degraded DNA templates. We found no inconsistencies in the identification of individuals based on SNP and microsatellite data, proofing the suitability of the investigated SNP panels for individual identification. We show here that approximately 20 of the most heterozygous SNPs in our data sets were sufficient to reach PIDsib < 0.0001 and thus should distinguish even closely related individuals with high certainties. Panels of approximately 12 microsatellites performed similarly well, except for the relatively homozygous Central European wolf population, where PIDsib calculations showed that inbred individuals cannot be differentiated when applying a strict PIDsib < 0.0001 cutoff.SNP genotyping is increasingly applied to investigate wild populations and to inform management decisions, and it is thus of utmost interest to compare the performance of novel SNP panels to more traditionally applied panels of microsatellites when genotyping noninvasively collected samples57. While many studies use thousands of SNP markers derived from high-density SNP chips or NGS data with high statistical power59, others have shown that few selected SNPs may provide enough information to answer questions on individual identification, population origin or introgression62. In summary, the number of required markers will depend on the question posed, the diagnostic power of the markers, their variability and the particular history of the population under study. Morin et al.52 propose the software POWSIM63 for an estimation of sample size, number of loci and types of SNPs needed for a study as well as testing the statistical power for a given SNP marker set. A pilot study that assesses the adequacy of the markers is advisable, as it is the case with microsatellites. However, to make cross-laboratory comparisons possible, the same SNP panels should be used to aid marker harmonization  or if they are applied to a small sample size and/or a population with little genetic differentiation 49 or SNaPshot61. The microfluidic arrays introduced by Fluidigm66 combine, in our view, several advantages. Some of the major benefits include the suitability to genotype low quality and quantity DNA, due to a pre-amplification step and the small size of the amplicons; the few microliters of DNA extract required, 3.2 µl in our adapted protocol; and the relatively straight-forward methodology, provided standard molecular laboratory setup and expertise are available. The MassARRAY technology (Sequenom) may present a comparable alternative to the microfluidic approach; however, only up to 42 SNP loci could be co-amplified with the MassARRAY platform using noninvasive samples49. Finally, the implementation of massive parallel sequencing has markedly improved microsatellite genotyping68, which may present another powerful tool for genotyping of noninvasive samples, but may also have similar constraints.With the advent of NGS in the last decades, and the associated arrival of high-throughput SNP genotyping, analyses based on high numbers of markers are no longer an issue. A number of different genotyping platforms for diverse sample throughputs and incorporating various kinds of multiplexing capabilities and chemistries have become availableCosts. – SNP genotyping may incur higher costs than microsatellite genotyping in the case of a single or a few samples, but costs dramatically decrease for larger sample sets.Choice of markers. – SNP marker selection should be preceded by a validation phase; in our experience, approximately 10% of SNP markers that performed well with tissue samples continually failed to amplify when applied to noninvasively collected samples.Genotyping protocol. – Ct values obtained from RT-PCRs can be used to determine the best SNP genotyping protocol. Generally, high quality samples with low Ct values perform well with the manufacturers’ STA pre-amplification protocol, while performance of samples with high Ct values benefited from our modified STA protocol (see Methods section).Sample selection. – The exact Ct value indicative of good SNP genotyping performance may need to be validated through a small pilot experiment. This will help to exclude low quality samples.Genotyping success. – To keep costs to a minimum, we found that the number of required replications for a sample can be determined after a first run, based on call rate (see above for thresholds). This may result in avoiding replication in the case of noninvasively collected samples of high quality, or reduce replication to the necessary minimum.Reference libraries. – We recommend setting up a reference set of high quality samples that will ideally include homozygotes and heterozygotes for all loci in the marker panel. The genotypes of these samples can be used to create a reference library, which is then applied as a reference to subsequent runs of noninvasive samples to improve clustering and facilitate scoring.Here we have shown that noninvasively collected samples selected using proposed thresholds and treated with specific protocols may provide good quality SNP data Fig.  while ken = 60), and wildcat and domestic cat hair  that were selected randomly using the sample function in R from samples collected in the frame of the German wildlife monitoring program between 2004 and 2015 (Supplementary Table n = 72) were analysed as part of a larger genetic study focusing on brown bears from the Balkans and were selected for SNP genotyping based on successful performance in microsatellite genotyping. Wolf scats were stored in 96% ethanol at room temperature until DNA extraction, while cat hairs were stored at room temperature between dry filter paper within a plastic sample bag containing silica as a desiccant agent until DNA extraction. We extracted DNA using the QIAamp DNA Stool Mini Kit (Qiagen) and the QIAamp DNA Investigator Kit (Qiagen), respectively. Bear hair samples were stored and extracted as described in69. For shipment between laboratories, bear DNA extracts were dried after microsatellite genotyping and reconstituted prior to SNP analyses.We SNP-genotyped wolf scats . PCR reactions with a total volume of 10 µl contained 3.2 µl DNA, 5 µl 2x SensiFAST SYBR No-Rox Mix (Bioline), 1.4 µl molecular grade water and 0.2 µl of forward and reverse species-specific primers (10 µM each) . Brown bear hair samples were checked macroscopically in order to avoid wild boar hairs. Unlinked autosomal microsatellite data for grey wolves and European wildcats were obtained as part of the regular genetic monitoring program implemented in our laboratory and from brown bears as part of another study69. Detailed protocols are provided in the Supplement and elsewhere .Prior to SNP typing, the species identity of scat and hair samples was determined using mtDNA sequencing as described in previous studies  or discriminate between wild and domestic taxa (wildcats36). The genotyping protocol involved two consecutive PCR reactions. In the first one, all 96 loci were pre-amplified in a single reaction with locus-specific primers (STA reaction) in order to generate sufficient amounts of template for the subsequent genotyping reaction. Next, fluorescently labelled allele-specific primers were used to target both alleles in a genotyping PCR. Finally, the fluorescence signal of each reaction was measured in an endpoint reader  in order to determine whether the individual was homozygote or heterozygote for that locus.DNA samples were genotyped using 96.96 Fluidigm Dynamic Arrays (Fluidigm) with integrated fluidic circuits (IFCs). All marker panels consisted of 96 SNPs and were developed to maximize individual information  of DNA, 4 µl 2x QIAGEN Multiplex PCR Master Mix (Qiagen) and 0.8 µl of 10x SNPtype Assay STA primers (500 nM). Thermal conditions included an initial denaturation step of 95 °C for 15 min, followed by 18 (instead of 14 cycles) of 95 °C for 15 s and 60 °C for 4 min. STA products were diluted 1:10 (instead of 1:100) with DNA Suspension Buffer . The subsequent genotyping PCR was conducted according to the manufacturer’s protocol, but in addition to measuring the fluorescent signal after 34 cycles, a second measurement was performed after four more cycles to ensure sufficient fluorescence strength. All samples were run as triplicates along with four NTC reactions per plate to monitor for potential contamination.35. To facilitate genotype calling and improve clustering analysis, assay reference libraries were incorporated, which should typically feature good quality samples and all homozygote types.We scored SNPs using the Fluidigm SNP genotyping analysis software v4.1.2, manually validating the automatically generated scatter plots. First, sample replicates with a call rate of ≤70% over all SNP loci were invalidated and thus excluded from the clustering algorithm. Next, we eliminated the loci in which at least one NTC overlapped with the samples’ genotype clusters or in which the clusters were not clearly separated from one another, as this made those loci unreliable. In addition, we invalidated NTCs that showed significant fluorescence (≥0.2) to ensure proper fluorescence data normalization over all loci. Occasional fluorescence of NTCs is a known phenomenon in the Fluidigm system, but is no cause for particular concern, because target DNA is favoured in PCRs containing sample DNAn = 4 and n = 2, respectively). This resulted in 16 loci, 37 and 25 loci being removed from the wolf, wildcat and brown bear data sets, respectively.To prevent the influence of locus-specific performance on the assessment of replicate genotypes, we excluded loci that worked in <70% of the reactions and, in the case of the bears, also the mtDNA and Y-linked SNP loci  per sample and locus. In contrast to the assessment of genotype consistency across replicates described above, the quality thresholds were in this case applied to consensus data only, thus resulting in slightly differing numbers of samples and loci  and the bear dataset . This resulted in data sets comprising 52 samples and 85 loci for the grey wolf, 43 samples and 65 loci for European wildcats and domestic cats, and 70 samples and 69 loci for the brown bear. We determined the microsatellite consensus genotype for wolf and wildcat microsatellite data with the custom R script described above. We applied a multiple-tubes approach with four (wolf) or three (cats) PCR replicates each. Consensus genotypes with more than four missing loci, an allelic dropout rate of >0.4 and a call rate of <50% were excluded from further analyses. The microsatellite consensus genotypes for brown bears were determined as described in ref. We determined SNP consensus genotypes for each sample over two to three replicates with a custom R package DNA TOOLS74 in the case of wolves and cats and ConGenR73 in the case of bears. We accepted one mismatch at one locus to consider genotypes as belonging to the same individual. Based on SNP data, we identified 41 individual grey wolves, 39 individual wildcats and domestic cats and 70 individual brown bears. Using microsatellite data, we identified 34 individual grey wolves, 25 individual wildcats and domestic cats and 71 individual brown bears  and were therefore excluded from the figures and further analysis . Furthermore, we tested subsets of microsatellite markers  selected based on highest heterozygosity and highest FST (the latter only for wild and domestic cats). We assumed that, in studies in which a minimum number of markers are desired and a pool of available markers to select from exists, the markers chosen may either be the most heterozygous ones or, in the case of hybridization studies, those that best allow to differentiate two taxa.We used the PCoA implemented in GenAlEx v.6.577. After an initial burn-in of 250,000 steps, 500,000 MCMCs were run using the admixture model with correlated allele frequencies with no prior information. Ten independent iterations were run for each K = 1–10 and combined using the GREEDY or LARGEKGREEDY algorithm as implemented in CLUMPP78. The Evanno method79 as implemented in STRUCTURE HARVESTER80 was used to select the most likely K value , observed heterozygosity (HO) and (unbiased) expected heterozygosity (HE) for sampling groups with n ≥ 5 and for total sample pools of each species. Pairwise FST calculations were performed using AMOVA for sampling groups with n ≥ 5 and significance assessed using a permutation approach81. We calculated probability of identity (PID) and probability of identity between siblings (PIDsib) for SNP and microsatellite loci for all three investigated species according to84. For PID and PIDsib calculations loci were ranked according to highest (unbiased) expected heterozygosity (HE).Analyses of basic population genetic measures and calculations of probabilities of identity were performed with GenAlEx v6.5The datasets generated and analysed during the current study are available from the corresponding authors on reasonable request.Supplementary Information


P < 1.40E-06) and 11 suggestive (P < 2.79E-05) single nucleotide polymorphism (SNP)-trait associations. Six SNPs were located in genomic regions where quantitative trait loci (QTLs) have previously been reported for feeding behavior and eating efficiency traits in pigs. Five candidate genes  with biochemical and physiological roles that were relevant to feeding behavior and eating efficiency were discovered proximal to significant or suggestive markers. Gene ontology analysis indicated that most of the candidate genes were involved in the development of the hypothalamus . Our results provide new insights into the genetic basis of feeding behavior and eating efficiency in pigs. Furthermore, some significant SNPs identified in this study could be incorporated into artificial selection programs for Duroc-related pigs to select for increased feeding efficiency.Efficient use of feed resources is a challenge in the pork industry because the largest variability in expenditure is attributed to the cost of fodder. Efficiency of feeding is directly related to feeding behavior. In order to identify genomic regions controlling feeding behavior and eating efficiency traits, 338 Duroc boars were used in this study. The Illumina Porcine SNP60K BeadChip was used for genotyping. Data pertaining to individual daily feed intake (DFI), total daily time spent in feeder (TPD), number of daily visits to feeder (NVD), average duration of each visit (TPV), mean feed intake per visit (FPV), mean feed intake rate (FR), and feed conversion ratio (FCR) were collected for these pigs. Despite the limited sample size, the genome-wide association study was acceptable to detect candidate regions association with feeding behavior and eating efficiency traits in pigs. We detected three genome-wide ( Pork is a major meat resource for humans, providing over ~37% of all meat in 2012–2014 and Haploview [http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview] were utilized for haplotype block analysis. Linkage disequilibrium blocks were defined using Haploview 4.2 based on SNPs with MAF values of > 0.05, Mendel errors of < 2, and P-value in the HWE test of < 10−3. We performed haplotype block analysis, which requires at least two suggestive SNPs in a chromosome[The software Plink v1.07 [hromosome.Sus scrofa 10.2 genome version were downloaded from www.animalgenome.org/pig/. The Ensembl annotation of the Sus scrofa 10.2 genome version was employed to find genes that were nearest the significant SNPs [http://ensembl.org/Sus_scrofa/Info/Index]. To annotate significant SNP positions to previously mapped QTLs in pigs, all QTL data in pigs were downloaded from http://www.animalgenome.org/cgi-bin/QTLdb/SS/download?file=gbpSS_10.2 [http://geneontology.org/].P < 10−6) were excluded from the analysis. A total of 6,002 SNPs located on the sex chromosome and unknown chromosomal regions were thus removed. A final set of 35,791 SNPs and 338 pigs was retained for subsequent GWAS analysis. The number of markers on each chromosome and average distances between two markers after QC are presented in A summary of the statistics of the seven traits are presented in Sus scrofa Genebuild 10.2 as well as genes neighboring the tag SNPs are listed in Manhattan plots of GWAS of all traits after QC and the Q–Q plots are shown in A total of four SNPs associated with DFI were identified within the genomic region where QTLs for feed intake traits and/or feed conversion ratio were previously been mapped in pigs . One SNPSERPINA3), myc proto-oncogene protein (MYC) were correlated to DFI; lymphoid enhancer binding factor 1 (LEF1), which is a potential functional candidate gene, was associated with FPV; paired-like homeodomain 2 (PITX2), a potential functional candidate gene, was associated with NVD; and mitogen-activated protein kinase kinase kinase 14 (MAP3K14) was correlated with FCR. We then conducted gene ontology analysis of the five candidate genes, which indicated that most of the genes were involved in the development of the hypothalamus .The aim of the present study was to identify and characterize genes that were related to novel feeding behavior and eating efficiency. To obtain more credible results, we attempted to reduce the number of potential genes based on biochemical and physiological roles that were relevant to feeding behavior and eating efficiency traits. Ultimately, a total of five candidate genes were obtained. Of these, serpin family A member 3  associated with TPV for average daily gain in Yorkshire pigs in SSC1. Two SNPow family. The cloComparative mapping may facilitate in the validation of our results, as well as refine QTL regions and target candidate genes for complex traits such as feeding behavior. In addition, by comparing our results with those of previous QTL studies, we have determined that a large proportion of feeding behavior and eating efficiency traits located that have been previously reported. These observations are suggestive of a genetic correlation between feeding behavior and eating efficiency traits, as well as the occurrence of regional pleiotropic effects on feeding traits.Furthermore, compared to conventional breeding, the marker-assisted selection (MAS) can speed up the breeding programs. The MASSERPINA3 gene  in young pigs[SERPINA3 on SSC7 could be a candidate gene for DFI. The MYC gene was situated proximal to the significant SNP ASGA0018324, which is associated with DFI  near the significant SNPs for DFI with unknown function. These three genes have not been studied to date and thus its function in pig has not been established. Moreover, the pig genome has not been completely annotated; therefore, additional research studies to better elucidate the association between these genes and DFI is warranted.The marker ALGA0037698 on SSC6, which is located on fourth intron of the with DFI . Previouic cancer, ewing Sic cancer and metaic cancer. HoweverLEF1) was localized proximal to the SNP ALGA0049273  was located proximal to the suggestive SNP ASGA0087256, which is associated with NVD  was located proximal to the top SNP, and this gene was related to human height[MAP3K14 gene, whereas the other marker, MARC0059507, was located on the MAP3K14 gene and was associated with FCR , total daily time spent at feeder per day (TPD), Time spent to eat per visit (TPV) and mean feed intake rate (FR), respectively.The inserted quantile–quantile (Q–Q) plots in the right show the observed versus expected log p-values. In the Manhattan plots, negative log10 (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Phvul.001G214500, Phvul.007G229300 and Phvul.010G142900.1 on Pv01, Pv07 and Pv10, respectively. These results indicate that GWAS-based QR was able to enhance the understanding on genetic architecture of phenological traits (DFF and DTF) in common bean.Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest. The objectives of this study were to estimate genetic parameters and to use QR to identify genomic regions for phenological traits  in common bean. A total of 80 genotypes of common beans, with 3 replicates were raised at 4 locations and seasons. Plants were genotyped for 384 SNPs. Traditional single-SNP and 9 QR models, ranging from equally spaced quantiles (τ) 0.1 to 0.9, were used to associate SNPs to phenotype. Heritabilities were moderate high, ranging from 0.32 to 0.58. Genetic and phenotypic correlations were all high, averaging 0.66 and 0.98, respectively. Traditional single-SNP GWAS model was not able to find any SNP-trait association. On the other hand, when using QR methodology considering one extreme quantile (τ = 0.1) we found, respectively 1 and 7, significant SNPs associated for DFF and DTF. Significant SNPs were found on Pv01, Pv02, Pv03, Pv07, Pv10 and Pv11 chromosomes. We investigated potential candidate genes in the region around these significant SNPs. Three genes involved in the flowering pathways were identified, including  Phaseolus vulgaris L.) is the world’s most important grain legume for human consumption T represents the unknown parameters.Prior to performing genome-wide association (GWA) analyses, the genetic structure of the populations used in this study was assessed by principal component analysis (PCA) using the ion in R . PCA wasθτ) by solving the following optimization problem:τ(·) is denoted as a check function [The SNP effects were estimated at different portions of a probability distribution of the traits by quantile regression . This mefunction , and is 10P) of 3.88 and 4.58 were calculated based on the Bonferroni method for multiple testing to identify SNPs significant at 5% and 1%, respectively.The markers effects were also obtained using the traditional GWA approach, by fitting one SNP at a time . Thresholds  and ranges of the phenological traits are presented in P<0.01) for all 3 traits , and the genotype dispersion based on the first two PCs are presented in P>0.1x10-4) associations between SNPs and phenological traits . The Manhattan plots for DFF, DTF and DEF are showed in Association between SNPs and (adjusted) phenotypes were performed using traditional single-SNP and quantile regression (QR) methodologies. The traditional single-SNP approach did not result in any significant (P<0.1x10-4) using the lowest quantile (τ = 0.1) evaluated in this study (P<0.1x10-4) for DFF  SNPs were identified on Pv01 (scaffold00005_1715227), Pv02 (scaffold00020_86873), Pv03 (scaffold00083_261143), Pv07 (scaffold00028_145636), Pv10 (scaffold00036_329244), and Pv11 (scaffold00007_368524). Altogether, these SNPs explained 12.5% of the variability in DTF (P > 0.1x10-4).The location of these SNPs are shown in y in DTF . One of After that, we investigated potential candidate genes in the region around these significant SNPs . Three gHeritability estimates for DFF (0.58), DTF (0.49) and DEF (0.32) were consistent with those reported in the literature. Specifically, for DFF and DTF, estimates were close to those reported by , with 0.In this study, we aimed to enhance the understanding of the genetic architecture of phenological traits  in the common bean under a QR approach. The proposed method allows investigating the relationship between explanatory variables (i.e. SNPs in our study) and phenotype at different portions of a probability distribution of the traits, which resulted in more informative results by taking into account the extreme tails of trait distributions. QR uses the same principle used in sampling from the extremes of the trait distribution , 9, 10, Although the main objective of this manuscript was just to introduce the QR for plant breeders under a GWAS approach, we believe that this concept can be better exploited under a small sample size framework. The fact of QR take into account any quantile of phenotypic distribution can be considered as a robust tool to make inference in the presence of small population size. Although selective samples (sampling from the extremes) have been effective for QTL detection , under aPhvul.001G221100, located on Pv01, as a candidate gene for controlling photoperiod sensitivity and flowering in common bean. Moghaddam et al. [Phvul.001G064600, Phvul.001G192200, Phvul.001G192300, and Phvul.001G087900 with potential role on days to flowering, because of their function on regulates Flowering Time [The number of reports including GWAS for economically relevant traits in the common bean is still very limited. Kamfwa et al. , using 2ing Time ; change ing Time ; suppresing Time ; regulatIn the present study, no associations were found when analyses were performed using traditional GWA (single-SNP GWAS) methodology. The lack of associations using this methodology was expected because of the limited power of this study, which is limited due to the small number of markers and population sizes available . HoweverPhvul.001G214500, located on Pv01 (~50Mb). The functional annotation on Phytozome indicated that Phvul.001G214500 is a flowering time control protein FCA-related. This protein plays a role in the regulation of flowering time in the autonomous flowering pathway by decreasing expression levels of flowering locus C [Phvul.007G229300, which was located on Pv07 (~47Mb), is classified as protein terminal flower 1. A BLASTn search on NCBI data resulted in a best score to a gene TFL1z. Flowering time (FT) acts in part downstream of transcription factor gene, Constans (CO), and mediates signals for flowering in an antagonistic manner with the TFL1 gene. Phvul.010G142900.1 that is mapping near the Pv10 (~41Mb) is a protein early flowering 3 [Among the significant SNPs, 2 located on Pv01 and Pv07 are closest (<3.5Mb) to 2 genes involved in the flowering pathways. The first one,  locus C . Accordiwering 3 . This prwering 3 . The repIn general, these results indicate that using quantile regression to perform GWAS for flowering traits in the common bean resulted in the identification of QTLs. In contrast, this was not the case for when we used traditional single-SNP analyses. Indeed, these results are reasonable, once QR allows the estimation of effects for all portions of a probability distribution of the trait, using of the same principle used in sampling from the extremes of the trait distribution, increase the statistical power of the analysis , 9, 10. Quantile regression-based GWAS was able to detect true QTL for phenological traits (DFF and DTF) in the common bean that were missed when using traditinal single-SNP. The associations were identified using one extreme quantile (τ = 0.1), using the same principle used in sampling from the extremes. Finally, genomic regions identified in this study were enriched with candidate genes involved in the flowering pathways.S1 Table(PDF)Click here for additional data file.S1 Fig(A) Scree plot and variance explained (%) by each component. (B) Plot for PC1 against PC2 illustrating the population structure of 80 common bean genotypes.(TIFF)Click here for additional data file.S2 FigThe solid and dashed lines show the Bonferroni-adjusted thresholds of 3.88 and 4.58 for alpha equals to 5% and 1%, respectively.(TIFF)Click here for additional data file.S3 FigThe solid and dashed lines show the Bonferroni-adjusted thresholds of 3.88 and 4.58 for alpha equals to 5% and 1%, respectively.(TIFF)Click here for additional data file.S4 FigThe solid and dashed lines show the Bonferroni-adjusted thresholds of 3.88 and 4.58 for alpha equals to 5% and 1%, respectively.(TIFF)Click here for additional data file.S5 FigResults are sorted by quantile (τ), from 0.1 (A) to 0.9 (I). The solid and dashed lines show the Bonferroni-adjusted thresholds of 3.88 and 4.58 for alpha equals to 5% and 1%, respectively.(TIFF)Click here for additional data file.S6 Figτ), from 0.1 (A) to 0.9 (I). The solid and dashed lines show the Bonferroni-adjusted thresholds of 3.88 and 4.58 for alpha equals to 5% and 1%, respectively.Results are sorted by quantile ((TIFF)Click here for additional data file.S7 FigResults are sorted by quantile (τ), from 0.1 (A) to 0.9 (I). The solid and dashed lines show the Bonferroni-adjusted thresholds of 3.88 and 4.58 for alpha equals to 5% and 1%, respectively.(TIFF)Click here for additional data file.


Fibrinogen alpha C chain 5.9 kDa fragment (FIC5.9) is a new serum biomarker for chronic hepatitis that was discovered by proteomics analysis. Previous studies have shown that FIC5.9 is derived from the C-terminal region of fibrinogen alpha chain and the serum levels of FIC5.9 decrease in chronic hepatitis. It also have been reported that FIC5.9 cannot be detected in the blood stream of the systemic circulation and it is released from fibrinogen during blood clotting in collecting tube. However, the mechanism of FIC5.9 releasing from fibrinogen is unclear.We formulated a hypothesis that FIC5.9 is released by enzymes that are activated by post-blood collection and may be coagulation and fibrinolysis factors. In this study, we analyzed the mechanisms of FIC5.9 releasing from fibrinogen in healthy blood.Our analysis showed that thrombin acts as an initiator for FIC5.9 releasing, and that mainly plasmin cleaves N-terminal end of FIC5.9 and neutrophil elastase cleave C-terminal end of FIC5.9.FIC5.9 reflects minute changes in coagulation and fibrinolysis factors and may be associated with pathological conditions.The online version of this article (doi:10.1186/s12014-016-9129-6) contains supplementary material, which is available to authorized users. Many biomarkers have been discovered by proteomics analysis, but fewer have been developed for clinical use , 2. MostFibrinogen alpha C chain 5.9 kDa fragment (FIC5.9) is a new serum marker for chronic hepatitis that was discovered in samples from alcoholic liver disease using SELDI-TOF MS . FIC5.9 Based on the studies above, we formulated a hypothesis that FIC5.9 is released from fibrinogen or fibrin by the enzyme that is activated post-blood collection. Especially we focused on the coagulation and fibrinolysis factors. FIC5.9 sequence includes amino acids 576–629 of the fibrinogen alpha chain  in PBS buffer was incubated with thrombin , plasmiStageTips C18  was used for desalting the degradation products of fibrinogen . Obtaineg for 10 min at 4 °C. The level of FIC5.9 was measured using a FIC5.9 ELISA kit described in [Serum collection tubes  with added thrombin , hirudin , plasmiribed in . WrittenStatistical analysis was performed by Mann–Whitney U-test with SPSS software, version 18.0 . A P value <0.05 were considered significant.http://merops.sanger.ac.uk. Accessed 21 September 2013). and we finally choose thrombin, plasmin, and neutrophil elastase [To reconfirm that FIC5.9 is released from fibrinogen in the serum collection tube during blood clotting , we studelastase –19. We dTo determine if thrombin, plasmin and neutrophil elastase can release FIC5.9 from fibrinogen in blood, we analyzed the time course of FIC5.9 releasing in an evacuated blood collection system. We firstly examined the time course of FIC5.9 releasing in plain and silica-coated tubes by measuring the levels of FIC5.9 in serum after 0, 5, 30, 60, 90 min of clotting. The rate of FIC5.9 releasing in a silica-coated tube was significantly faster than that in a plain tube , plasmin, tranexamic acid (a plasmin inhibitor), or sivelestat sodium (a neutrophil elastase inhibitor) Fig. a–c. In aIn this study, we formulated a hypothesis that FIC5.9 is released from fibrinogen by coagulation and fibrinolysis factors. We firstly analyzed in vitro released fragments from fibrinogen by digestion with thrombin, plasmin or neutrophil elastase using LC–MS/MS Fig. . SuccessLC–MS/MS analysis showed that N-terminal end of FIC5.9 (RGK/SSS) is cleaved by thrombin or plasmin, and C-terminal end of FIC5.9 (RPV/RGI) is cleaved by neutrophil elastase Fig. . To confOn the other hands, addition of plasmin sufficiently accelerated the releasing of FIC5.9 and increased substantially the amount of FIC5.9. While thinking thrombin is an initiation element of FIC5.9 releasing, these results indicate that plasmin is the major enzyme that cleaves the N-terminal region of FIC5.9 and affects the amount of FIC5.9. In nature, activation of plasminogen to plasmin occurs after activation of coagulation factors including thrombin , 23. TheUp to here, we discussed enzymes cleaving N-terminal end of FIC5.9. Our results also indicate neutrophil elastase play an important role in cleaving C-terminal end of FIC5.9 Figs. , 4. ResuIn the experiments of serum collection tubes, FIC5.9 was detected at 0 min of clotting or inhibitor added tubes Fig. . We estiOur results showed that the mechanism of FIC5.9 releasing in healthy people is strongly related to coagulation and fibrinolysis factors. And our results suggest why FIC5.9 is a marker of alcoholic liver disease and liver fibrosis –8. NeutrThrombin (seems to be an initiator of FIC5.9 synthesis) and plasmin  are molecular markers of liver disease, partly because both enzymes are secreted from liver –29.The presence of other factors related to FIC5.9 releasing should also be considered. Recently, Marfa et al. reported that TGF-β reduces the expression level of fibrinogen alpha chain mRNA , which iMeasurement of degradation products in blood circulation is commonly used in clinical tests , 35. Mos


Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken’s carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds.JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs.ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel , we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.The online version of this article (10.1186/s12864-018-4779-6) contains supplementary material, which is available to authorized users. The chicken was the first domesticated animal species that was whole-genome sequenced and it has emerged as an excellent model for genomic studies in agriculture, developmental biology, fatness and leanness . The maiBroiler chicken lines have been selected for rapid growth, and carcass yield , 8. RapiSome studies have been conducted to map genomic quantitative trait loci (QTLs) associated with variation in abdominal fat , 9–13, aPrevious genome-wide association studies (GWAS) have been performed for abdominal fat weight and fat percentage in an F2 chicken population’s using a 60 K SNP chip (Illumina) , 17 and Fat deposition is an economically-relevant trait in fast-growing chickens, and knowledge about the genetic regulation of this trait is essential for breeding programs. Based on this fact, the main goal of this study was to perform GWAS analysis using a high-density SNP panel (600 K) to identify QTLs and positional candidate genes (PCGs) and possibly candidate mutations for fat deposition in broiler chickens.All experimental protocols related to animal experimentation in this study were performed in agreement with resolution number 011/2010 approved by the Embrapa Swine and Poultry Ethics Committee on Animal Utilization (CEUA) in Concordia, Santa Catarina State – South of Brazil, in agreement with the rules of National Council of Animal Experimentation Control (CONCEA) to ensure compliance with international guidelines for animal welfare.This study was conducted with a paternal broiler line (TT) belonging to the Chicken Breeding Program of EMBRAPA Swine and Poultry National Research Center, in Concordia, Santa Catarina State – South of Brazil. This line, originating from the White Plymouth Rock and White Cornish breeds, has been under multiple trait selection since 1992 mainly for body weight, feed conversion, carcass and cuts yield, viability, fertility, hatchability and reduced abdominal fat , 20–23. After 6 h of fasting, the chickens at 42 days of age were weighted (BW42) and then euthanized by cervical dislocation. In this step, a blood sample from each chicken was immediately collected for subsequent DNA extraction then, the carcass was cooled. After 4 h of cooling, the weights of the carcass, skin covering each carcass part  and abdominal fat  were measured. The percentage of each trait was calculated dividing the weigh by BW42 and multiplying by 100. Total skin weight and percentage were used as indicators of subcutaneous fat, as discussed by Zerehdaran et al. . More deGenomic DNA from 1430 blood samples were extracted using the PureLink® Genomic DNA  kit and were quantified using Qubit® 2.0 Fluorometer . After extraction, DNA integrity was evaluated on agarose gel (1%) and diluted to 10 ng/μL. Diluted genomic DNA was prepared following recommended Affymetrix protocols in order to perform the genotyping analysis using 600 K Affymetrix Axiom Genotyping Array . This genotyping array comprises segregating SNPs for different chicken populations, including four commercial broilers (broiler chicken lines), as detailed by Kranis et al. .p-value < 0.000001). Single nucleotide polymorphisms located in the sex chromosomes, and those SNPs not mapped in the chicken assembly  were excluded from the analysis. Only the SNPs annotated to autosomal chromosomes from GGA1 to GGA28 were used in statistical analyses. After all the filtering steps, the few remained missing genotypes were replaced by the average of covariate values at that particular locus, as described by Cesar et al. [Initially, Axiom™ Analysis Suite (Affymetrix®) software was used to filter genotypes based on the DishQC parameter, after which PLINK v.1.9  softwarer et al. .From a total of 1430 genotyped chickens, 22 samples were removed from the analysis after applying the DishQC criteria, and a filter on sample call rate ≥ 90% loci. From the total of 580,961 SNPs available on the SNP array, 355,027 informative polymorphic SNPs on the autosomal chromosomes (GGA1–28) were kept after filtering. The average density of SNPs was 520 SNPs/Mbp, with the lowest chromosome-wise density observed on GGA2 (268 SNPs/Mbp), and the highest chromosome-wise density on GGA21 (898 SNPs/Mbp)  was performed using a Bayes C model in GenSel software  using thpriors to run a Bayes B model as performed by Cesar et al. [The SNPs that passed the quality control filters were used in the GWAS analysis using genomic prediction methodology with a Bayesian approach in GenSel software . In the r et al. . The Bayr et al. . The maty represents the vector of phenotypic values; X is the incidence matrix for fixed effects; b is the vector of fixed effects; K is the number of SNPs; aj is the column vector representing the SNP as a covariate in locus j coded with the number of B alleles; βj is the random substitution effect for locus j assumed to be normally distributed N  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 residual associated with the analysis. Sex and hatch were included as fixed effects in the model and BW42 (slaughter age) as a fixed covariate for ABF and SKINW.In this model, We assumed π = 0.9970 in the BayesB models and obtained 41,000 Markov chain Monte Carlo (MCMC) samples with the first 1000 samples being discarded. A map file was used to position the markers into 947 non-overlapping 1 Mb windows. The windows that had the marker with higher model frequency in the MCMC interactions had their effect predicted as mentioned by Van Goor et al. . Each wiThe overlap of our genomic windows with previously mapped QTLs for fat-related traits in chickens was determined using the information available at Chicken QTLdb - release 33, accessed in September, 2017 . We usedA list of annotated genes within each QTL (genomic window) and their respective GO terms and biological processes were obtained using Ensembl BioMart tool , 34. Genp-value < 0.05 and p-value adjusted for multiple testing by Benjamini & Hochberg [Enrichment analysis of gene list was performed with the Functional Annotation Tool (FAT) in Database for Annotation, Visualization and Integrated Discovery software  to Hochberg  method <To refine our list of candidate genes, we screened our PCGs for genetic variants using a dataset of SNPs identified in the parental generation from our genotyped population, which were generated by next generation sequencing of 14 parental males (from 112) with approximately 13 X of coverage performed by HiScanSQ (Illumina) with a read length of 101 bp. Further details about library preparation and sequencing are available in Moreira et al.  and GodoSNP calling was performed using the most recent chicken genome assembly Gallus-gallus-5.0 (UCSC) with SAMtools software v.1.2 , with maVariants located in coding regions can lead to phenotypic changes , 39, 43.High impact SNPs were also evaluated in the candidate genes. The VEP tool  providesThe number of animals, averages and standard errors, variance components and estimated genomic heritability from the Bayes B model are given in Table A list with all the SNP windows analyzed including the proportion of the genetic variance explained by each one (even those with effects close to zero) is provided in Additional file The Manhattan plot of the posterior means of the proportion of genetic variance explained by each SNP window across the 28 autosomal chromosomes for ABF are presented in Fig. In order to support our findings, we checked the effect of the markers within the associated genomic windows. Manhattan plots of the SNP effect distribution within each significant SNP window for ABF are colored by chromosome and presented in Fig. Twenty-seven previously published QTLs for fat traits overlapped with five of the QTLs identified in our study. The QTL located on GGA5 at 38 Mb, associated with ABF and ABFP overlapped with three known QTLs: two QTLs associated with for ABF . While,The QTL identified on GGA27 at 3 Mb that was associated with SKINW and SKINP, overlapped with 10 QTLs previously reported to be associated with fat traits: three associated with ABF  and within this cluster, four GO terms were enriched: regulation of intestinal cholesterol absorption; high-density lipoprotein particle assembly, lipoprotein metabolic process and positive regulation of fatty acid biosynthetic process .We used a dataset of high quality SNPs from sequencing data identified in 14 parental chickens from TT Reference Population in order to screen for SNPs potentially affecting gene expression and/or function and identified 3639 SNPs located within the 13 PCG. SNP density (SNPs/kb) within each PCG and the functional annotation of the SNPs are presented in Fig. FITM2 and another located in ENSGALG00000000483 gene. In addition, two potentially deleterious variants were identified: one located in PLCG1 and another in ENSGALG00000000477 gene  were obtained using genotypes from a high-density SNP panel. Close relatives may have long chromosome segments in common, thereby sharing alleles and QTLs in the same pattern, which may lead to less bias and consequently, higher prediction accuracy for genomic heritability .Heritabilities estimates for ABF and ABFP have been reported to be 0.62 and higher, while the heritability of skin traits is between 0.24 and 0.28 , 53. TheUsing the same population reported here, Fornari et al.  observedBayesian approaches are commonly used in genomic prediction and selection studies , 57 as wFive out of the nine QTLs detected were previously identified in different populations, corroborating our results, and indicating that these QTLs probably originated from the founder lines used to generate the broiler TT line used in this study.The novel QTLs identified could be false positives, exclusive to our population, or a consequence of the number of animals and the higher SNP density compared to other QTL mapping studies for abdominal fat and skin traits in chickens , 8–14. TIn general, we observed small effects for the markers fitted simultaneously within the QTLs detected. This could be due to lack of power to capture the genetic variability in our population or, due to artificial selection. As mentioned before, the artificial selection can lead to fixation of specific loci , 61 and In contrast to the QTL mapped on GGA5 at 38 Mb, previously reported QTLs for the same trait were larger than 1 Mb , 46. TheThe QTL mapped on GGA27 at 3 Mb overlapped with known QTLs for fatness related traits, mapped in a Brazilian F2 population established by crossing a broiler male line (TT) and a layer line (CC), and these known QTLs are segregating in different families from the Brazilian F2 population , 49. FurWe identified only a few QTLs associated with fat traits in this population. For quantitative traits, a greater number of alleles are expected to present a small effect , and theJun dimerization protein 2 (JDP2) gene. This gene regulates lipid accumulation in adipose tissue acting as a repressor of adipocyte differentiation [We identified 13 PCG in five of the nine QTLs identified  gene is involved in the calcium signaling pathway. The expression of PLCG1 has been show to affect adipocyte triglyceride content [HNF4A) controls insulin metabolism and triglycerides level [Fat storage inducing transmembrane protein 2 (FITM2/FIT2) gene belongs to a family of proteins that play a role in fat storage [FITM2/FIT2 gene was reported to be associated with lipid droplets biogenesis and accumulation [We identified  content . Hepatoces level . Triglyces level . The Fat storage . In a stmulation , which cADIPOR1, ENSGALG00000000477 and ENSGALG00000000483 genes. In chickens, the adiponectin receptor 1 (ADIPOR1) gene is expressed in fat, liver and muscle, and this gene affect adipocyte differentiation [ADIPOR1 is the main receptor of adiponectin. It is negatively correlated with fat deposition [In the QTL on GGA26 at 1 Mb, we found ntiation . ADIPOR1position , and is position . ENSGALGPTPN11, MVK and ENSGALG00000005043 genes. The protein tyrosine phosphatase, non-receptor 11 (PTPN11) gene encodes for a Src homology-2 domain-containing protein tyrosine phosphatase 2 (SHP2). Its expression has been reported to affect energy balance and lipid and glucose metabolisms [SHP2 was reported to be associated with obesity [MVK) encodes for a mevalonate kinase enzyme that plays an important role at the beginning of cholesterol biosynthesis [In the QTL on GGA15 at 6 Mb, we found abolisms . Additio obesity . Mevalonynthesis . Changesynthesis . ENSGALGAPOA1, APOA4 and APOA5 genes. These three genes belong to a gene family (Apolipoproteins – APO) that encodes important regulators of lipid biosynthesis and metabolism [APOA1) is involved with cholesterol transport [Apolipoprotein A-IV (APOA4) and Apolipoprotein V (APOA5) are involved with triglycerides metabolism [APOA4 gene was also reported as a regulator of triglycerides metabolism in mice [In the QTL located on GGA24 at 5 Mb we found tabolism . Additioransport . While, tabolism . Additio in mice .Corroborating our findings, no overlap between our positional candidate genes, and genes under selective pressure reported in a previous study with the same dataset  was obseAPOA1. Possible explanations for the observed lack of overlap, are the different chicken lines used in these studies, and the removal of fixed SNPs in the current study.Additionally, comparing a dataset of SNPs and INDELs identified in Brazilian broiler and layer lines, our group recently identified regions under selection , harbourWe observed many SNPs annotated in intronic regions of the PCGs , splicing site and stop codon Additional file 2:An excel file with the characterization of all the SNP windows analyzed including the proportion of the genetic variance explained by each one. (ODS 137 kb)Additional file 3:Manhattan plot of the posterior means of the proportion of genetic variance explained by each 1-Mb SNP window across the 28 autosomal chromosomes for abdominal fat percentage (ABFP): (A) genomic windows located on macrochromosomes, and (B) windows located on microchromosomes. The X-axis represents the chromosomes and Y-axis shows the proportion of genetic variance explained by each window from Bayes B analysis. Red lines indicate the threshold to deem significant SNP windows. (DOCX 245 kb)Additional file 4:Manhattan plot of the posterior means of the proportion of genetic variance explained by each 1-Mb SNP window across the 28 autosomal chromosomes for skin weight (SKINW): (A) genomic windows located on macrochromosomes, and (B) windows located on microchromosomes. The X-axis represents the chromosomes and Y-axis shows the proportion of genetic variance explained by each window from Bayes B analysis. Red lines indicate the threshold to deem significant SNP windows. (DOCX 243 kb)Additional file 5:Manhattan plot of the posterior means of the proportion of genetic variance explained by each 1-Mb SNP window across the 28 autosomal chromosomes for skin percentage (SKINP): (A) genomic windows located on macrochromosomes, and (B) windows located on microchromosomes. The X-axis represents the chromosomes and Y-axis shows the proportion of genetic variance explained by each window from Bayes B analysis. Red lines indicate the threshold to deem significant SNP windows. (DOCX 243 kb)Additional file 6:Manhattan plot of the SNP effect distribution within each significant window for abdominal fat percentage (ABFP). The X-axis represents the significant SNP window represented by the number of the respective chromosome and Y-axis shows the SNP effect from Bayes B analysis. Their respective start and end positions are: GGA5 ; GGA10 ; GGA13 ; GGA26 . (DOCX 1425 kb)Additional file 7:Manhattan plot of the SNP effect distribution within each significant window for skin weight (SKINW). The X-axis represents the significant SNP window represented by the number of the respective chromosome and Y-axis shows the SNP effect from Bayes B analysis. Their respective start and end positions are: GGA15 ; GGA24 ; GGA27 . (DOCX 1425 kb)Additional file 8:Manhattan plot of the SNP effect distribution within each significant window for skin percentage (SKINP). The X-axis represents the significant SNP window represented by the number of the respective chromosome and Y-axis shows the SNP effect from Bayes B analysis. Their respective start and end positions are: GGA9 ; GGA15 ; GGA27 . (DOCX 1425 kb)Additional file 9:List with the 419 genes annotated within the nine QTL detected. (XLSX 61 kb)