Genome-wide association studies provide important insights to the genetic component of disease risks. However, an existing challenge is how to incorporate collective effects of interactions beyond the level of independent single nucleotide polymorphism (SNP) tests. While methods considering each SNP pair separately have provided insights, a large portion of expected heritability may reside in higher-order interaction effects.We describe an inference approach  designed to probe collective interactions while treating both genotypes and phenotypes as random variables. The genotype distributions in case and control groups are modeled separately based on empirical allele frequency and covariance data, whose differences yield disease risk parameters. We compared pairwise tests and collective inference methods, the latter based both on DDA and logistic regression. Analyses using simulated data demonstrated that significantly higher sensitivity and specificity can be achieved with collective inference in comparison to pairwise tests, and with DDA in comparison to logistic regression. Using age-related macular degeneration (AMD) data, we demonstrated two possible applications of DDA. In the first application, a genome-wide SNP set is reduced into a small number (∼100) of variants via filtering and SNP pairs with significant interactions are identified. We found that interactions between SNPs with highest AMD association were epigenetically active in the liver, adipocytes, and mesenchymal stem cells. In the other application, multiple groups of SNPs were formed from the genome-wide data and their relative strengths of association were compared using cross-validation. This analysis allowed us to discover novel collections of loci for which interactions between SNPs play significant roles in their disease association. In particular, we considered pathway-based groups of SNPs containing up to ∼10, 000 variants in each group. In addition to pathways related to complement activation, our collective inference pointed to pathway groups involved in phospholipid synthesis, oxidative stress, and apoptosis, consistent with the AMD pathogenesis mechanism where the dysfunction of retinal pigment epithelium cells plays central roles.The simultaneous inference of collective interaction effects within a set of SNPs has the potential to reveal novel aspects of disease association.The online version of this article (doi:10.1186/s12864-016-2871-3) contains supplementary material, which is available to authorized users. A key focus of modern genetic research is the relationship between genomic variations and phenotypes, including susceptibilities to common diseases . Recent m2, where m is the number of SNPs that are considered simultaneously, with m=1 and 2 corresponding to the independent-SNP and pairwise tests, respectively. To prevent model overfitting, high-dimensional inference with limited sample sizes requires regularization, whose values can be determined by cross-validation. Ayers and Cordell performed a comprehensive study of the performance of different penalizer choices on noninteracting SNP inferences , where ε=0 corresponds to the independent-SNP limit with no interaction. The PL method uses λ>0 and has the advantage that it can be easily parallelized. We implemented parallel computations of PL using the message passing interface protocol.where the first summation is over genotype data of mentclass2pt{minimOnce genotype distributions of case, control, and pooled (whole sample) groups have been inferred, Bayes’ theorem allows one to obtain disease risk: One can show that .We used cross-validation to determine the penalizer λ in Eq. . We firsp1=n1/n for cross-validation because the training and test sets have the same sampling biases. In predicting risks with a prospective sample, however, this probability would have to be adjusted to known population phenotype frequencies. We implemented a software feature such that the disease prevalence, which affects the disease risk parameter α, can be re-specified when a parameter set inferred from case-control data is applied to an independent test set.We used disease prevalence In contrast to DDA outlined above, the logistic regression uses α,βi, and γij are then directly determined by maximizing the likelihood of Pr(y|a) only: instead of Eq. , assuminn=n0+n1, with respect to α, βi, and γij. In general, these disease risk parameter values from logistic regression are different from those obtained via genotype distribution parameters ψy in DDA with Eq. (6); the latter contains the effects of the nonuniform marginal genotype distribution Pr(a) of the sample, while logistic regression does not. For comparison, we also implemented this multivariate logistic regression with an l2-penalizer. The logistic regression can yield higher prediction AUC than DDA because by maximizing Eq. . The statistic was obtained by adding the log-likelihood values of case and control groups and subtracting that of the pooled group (see Text S1). We tested the significance of the single-locus contribution to disease association from site i by calculating the likelihoods i were prescribed as their pooled values (restricted model), and evaluating the differences against the likelihood of the full model . Analogous tests were performed for SNP pair i,j with λ by permutation resampling: the phenotype data of a given sample with a certain penalizer λ value was randomly reshuffled to obtain realizations of the likelihood ratio statistics. This sampling was repeated multiple times (up to ∼104) to construct empirical cumulative distribution functions of the statistic for each site, or SNP-pair, from which the p-values were estimated. For the single-locus contribution statistics, we calculated p-values using the asymptotic χ2-distribution.We performed likelihood ratio tests to assess the statistical significance of the overall collective inference and individual loci/interactions. The m=10 or 20 loci and n=2n0 individuals were generated under randomly assigned parameters {ψ0,ψ1}. The model parameters were chosen with m for binary models) possible genotypes to calculate their probability distribution using Eq.  were obtained and used to derive θ. In contrast, in logistic regression, θ was obtained directly. The mean square error was calculated for inferred θ relative to true values of all distinct single-SNP and interaction parameters. We performed different inferences using a common set of data for each realization of parameters. The mean square error was then averaged over 100 realizations of parameters. We also compared pairwise test results using PLINK [In testing the inference algorithms using simulated data, samples of case-control genotypes containing sing Eq. . For a gng PLINK  epistasip-value >10−6, and genotyping rate >0.05 [We obtained AMD case-control genotype data from the National Eye Institute Study of AMD Database (dbGaP accession number phs000684.v1.p1), which consisted of 2, 159 case and 1, 150 control individuals. Autosomal SNPs were filtered with the criteria of MAF >0.01, Hardy-Weinberg equilibrium te >0.05  to yieldte >0.05  in additλ (or ε for MF) was first determined from cross-validation  and later used consistently for parameter estimation and p-value calculation. Interaction p-values were obtained for a given SNP selection and λ∗ by resampling.Except otherwise stated, inferences on AMD data used the genotypic model. In all cases, pc-based selection . We then used the NIH Roadmap reference epigenome chromatin state annotations [m=96 in Fig. p-values were calculated by the binomial test.In performing the epigenetic state enrichment analysis, for each SNP considered, we used the 1000 Genomes reference haplotypes  of Europotations  to constp<10−3 from Fig. p-values were calculated using the binomial test.Analogous calculations were performed for the SNP interaction enrichment analysis. We first selected statistically significant SNP pairs with m<6×103, DDA independent-SNP and collective inference (ε-optimized MF) inferences were applied to each SNP set without further filtering to derive 5-fold cross-validation AUC. For larger pathways, pc-based filtering was incorporated into cross-validation to reduce the model sizes.Pathway-based SNP sets were generated for human pathways in Reactome database . We compAMD, age-related macular degeneration; AUC, area under the curve; CFH, complement factor H; ChREBP, carbohydrate response element-binding protein; DDA, discrete discriminant analysis; EE, exact enumeration; GeDI, genotype distribution-based inference; GWAS, genome-wide association study; LD, linkage disequilibrium; MDA, malondialdehyde; MF, mean field; MSC, mesenchymal stem cell; pAUC, pseudo-AUC; PL, pseudo-likelihood; PRR, pattern-recognition receptor; RPE, retina pigment epithelium; SASP, senescence-associated secretory phenotype; SNP, single-nucleotide polymorphism; TLR, toll-like receptor; TSS, transcription start siteAdditional file 1Text S1. Mathematical formulation of inference algorithms. Table S1. Independent-SNP inference comparison of logistic regression (from PLINK) and DDA (GeDI). (PDF 283 kb)Supplementary material. Additional file 2Figure S1. Inference properties of a single independent SNP. Log odds ratio (OR) and power (level of significance 0.05) inferred from case-control data of size n=2n0=2n1 using DDA  and logistic regression  are shown for the dominant model. Equation (S29) of Text S1 was used. The minor allele frequency ϕy for control and case groups were set such that ϕy= such that fy) and hy) for control and case groups, respectively, and β=1.1987. (PDF 6.99 kb)Additional file 3Figure S2. Examples of true versus inferred parameters. a Dominant model with m=10 SNPs and inference on a sample of size n=103. b Genotypic model with m=10 SNPs and inference on a sample of size n=105. In all cases, the penalizer value was determined by cross-validation. (PDF 18.1 kb)Additional file 4Figure S3. Determination of penalizer λ via cross-validation. The data set is one realization of simulations shown in Fig. λ as sample size n increases. (PDF 6.17 kb)Additional file 5Figure S4. Distributions of interaction likelihood ratio statistics under the null hypothesis. Empirical cumulative distribution functions (CDF) in terms of the interaction statistics q were obtained by resampling. Simulation conditions were as described in Fig. Additional file 6Figure S5. Whole-genome p-value profile of AMD data. Independent-SNP DDA with genotypic model was used. (PDF 25.7 kb)Additional file 7Figure S6. Regional views of AMD data. Independent-SNP DDA results (light blue) are compared to logistic regression from PLINK (yellow). Genotypic model was used. (PDF 10.9 kb)Additional file 8Figure S7. Marginal pairwise interaction p-values. PLINK epistatic module was used to m=96 AMD SNPs. SNP pairs with strongest p-values near HTRA1 have p∼10−9. Genotypic model was used. The bottom panel shows the independent-SNP p-values. (PDF 54.6 kb)Additional file 9Figure S8. Linkage disequilibrium r2 within m=96 AMD SNPs from PLINK. (PDF 48.3 kb)


We identified more than 800 Solanum lycopersicum specific k-mers (32 nucleotides in length) from 42 different chloroplast genome regions using the developed method. We demonstrated that identified k-mers are also detectable in whole genome sequencing raw reads from S. lycopersicum. Also, we demonstrated the usability of taxon-specific k-mers in artificial mixtures of sequences from closely related species. Developed method offers a novel strategy for fast identification of taxon-specific genome regions and offers new perspectives for detection of plant taxa directly from sequencing raw reads.Polymerase chain reaction and different barcoding methods commonly used for plant identification from metagenomics samples are based on the amplification of a limited number of pre-selected barcoding regions. These methods are often inapplicable due to DNA degradation, low amplification success or low species discriminative power of selected genomic regions. Here we introduce a method for the rapid identification of plant taxon-specific  The molecular identification of plant species is one possible approach when processed material has to be analyzed and the visual inspection of morphology is not possible. Using DNA-based tests for authentication plays an important role in the detection of mislabelled species and inadvertent or intentional species substitutions in food products , detectiDNA barcoding is a widely used method for the identification of plant, animal or fungi taxa by sequencing a standardized short DNA fragment. Barcoding methods identify taxa very efficiently within metagenomics samples of different origin , but theDNA metabarcoding method bases on next-generation sequencing of pre-amplified DNA barcodes for identification of multiple taxa simultaneously from a single metagenomics sample and has also been applied for the identification of plant and animal species from the various metagenomics samples . HoweverDNA mini-barcodes with significantly reduced length barcode sequences (100–250 bp) have been introduced to improve PCR amplification success of samples with degraded DNA . Mini-baMore innovative approaches for the identification of species from metagenomics samples include the deep sequencing of total genomic DNA from samples with various taxon composition. These potentially avoid some of the problems associated with targeted PCR-based methods. Therefore, these has also been suggested as a valuable tool for species identification and quantification in food testing .However, the taxonomic classification of metagenome sequencing reads can be challenging, especially when analyzing short reads derived from next generation sequencing. One approach for the identification of taxa is comparing the pre-assembled reads to the reference genomes. However, metagenomics assemblies and the quantification of taxa from contigs is computationally challenging and assembly free methods that are based on sequence alignment are too slow to cope with the increasing amount of available genomic data .k-mers (short exact-matching substrings of a fixed-length k) contained in the reference genomes are stored in an index for fast lookup and the k-mers in each sequencing read are searched in this index. The read is assigned to a taxon based on the matching genomes  (The advantage of using taxon-specific DNA d fungi) . Chloropd fungi) .k-mers from the chloroplast genome, that can be used for the qualitative detection of plant taxa directly from raw sequencing data. We used the plant species Solanum lycopersicum (tomato plant) as an example for finding species-specific k-mers. We also analyzed the presence and number of taxon-specific k-mers (identified from chloroplast genome) in whole genome sequencing raw reads of different Solanaceae species.In this work, we introduce a fast pipeline for the identification plant taxon-specific k-mers is creating the k-mer lists of every target sequence and k-mer list of all non-targets . Target sequences and non-target sequences used as inputs can be assembled chloroplast genome sequences or any other genome regions for target and non-target taxa.The first step for selecting the taxon-specific k value (the length of oligomers) depends on the specific task. Reducing the length of k-mers when selecting taxon-specific k-mers would increase the number of k-mers that are present in the different target-taxon sequences, but with the cost of increasing the probability of finding those k-mers in sequences from non-target taxa  sequences .To get an overview of the available data for chloroplast genome sequences, we constructed a tree containing all 1,719 chloroplast genome sequences downloaded from the GenBank database. According to the tree, all five S. lycopersicum specific k-mers from assembled chloroplast sequences in five S. lycopersicum as the target taxon sequences and 1,714 other plant species as the non-target taxa sequences, we detected 882 S. lycopersicum specific k-mers (32 nucleotides in length). The tomato plant chloroplast genome is approximately 155,000 bp and there were 129,812 k-mers that were present in at least two chloroplast genome sequences from S. lycopersicum. After removing the k-mers that were not present in at least 2 S. lycopersicum sequences  and the unspecific k-mers that were present in the assembled chloroplast genome sequences of non-target species or whole genome sequencing raw reads for S. pimpinellifolium and S. tuberosum, we detected 882 S. lycopersicum specific k-mers . We used k-mer length 32 nt, which gave us the maximum number of S. lycopersicum specific k-mers  for the identification of S. lycopersicum in sequencing raw reads.Using our pipeline for the identification of S. lycopersicum specific k-mers (detected from plastid genome sequences) are detectable also in whole genome sequencing raw data, we analyzed the number of S. lycopersicum specific k-mers detected in whole genome sequencing raw reads from different Solanaceae species . The reads were downloaded from NCBI SRA database . To also consider the influence of the number of sequencing reads on k-mers detection, we simulated new FASTQ files with different numbers of sequencing reads (103–108) from original FASTQ files for all the samples.To test whether S. lycopersicum specific k-mers, that originated from the chloroplast genome, are also detectable in whole genome sequencing raw reads from S. lycopersicum. The number of detected S. lycopersicum specific k-mers from S. lycopersicum sequencing data increased with the number of sequencing reads. Almost all 882 k-mers from the pre-selected set of taxon-specific k-mers were detected, when the number of sequencing reads was at least 105 . Assuming that the sequencing coverage of different genomic regions is almost equal, 105 sequencing reads provides coverage for S. lycopersicum nuclear genome of approximately 0,01×. The chloroplast genome coverage is difficult to estimate because of the variable copy number of plastome in individual plant cells.The results showed that S. lycopersicum specific k-mer list containing k-mers that were present in at least 2 S. lycopersicum chloroplast genome sequences, we performed similar experiments with the k-mer lists that contained S. lycopersicum specific k-mers that were present in at least 1 or at least 5  S. lycopersicum chloroplast sequences to analyze the influence of universality cut-off value on k-mers detectability. The results for the different sets were similar . The results showed that the number of detected S. lycopersicum specific k-mers in whole genome sequencing raw data from phylogenetically close species started to increase as the read number exceeded 106, it reached to 548 in the whole sequencing raw data from S. pimpinellifolium when the sequencing read number was 108 . Therefore, for a metagenomics sample with an unknown proportion of S. lycopersicum and other taxa , detecting less than 550 S. lycopersicum specific k-mers will generate complications for determine if these k-mers originate from S. lycopersicum or from other Solanaceae species.To analyze the specificity of the k-mers for Oryza sativa and Zea mays and analyzed the number of O. sativa and Z. mays specific k-mers detected in whole genome sequencing raw reads from different samples of target taxa and also phylogenetically close non-target taxa , it would be helpful, if we could also consider the frequency of every k-mer in the sequencing reads. Although, the copy numbers of chloroplast genome may vary in different samples, we assumed that all the k-mers from the S. lycopersicum chloroplast genomes are represented in raw sequencing reads from one sample with similar frequency, which reflects the copy numbers of chloroplast genomes in the sample. This could help to distinguish the k-mers truly from S. lycopersicum (should be with similar frequency) and k-mers from close non-target species (caused by sequencing errors etc.).In the detection of taxon-specific k-mers with very low frequency can be the result of sequencing errors, we tried to increase the k-mer frequency cut-off value (from 1 to 10) to increase the specificity. The increased cut-off value decreased the number of detected S. lycopersicum specific k-mers in S. tuberosum and S. pimpinellifolium, even when the read number was more than 107 (specificity increased). However, at in the same time we were not able to detect S. lycopersicum specific k-mers in some of the S. lycopersicum samples when the sequencing read number was 2.5∗105 or less (sensitivity of S. lycopersicum detection decreased) . Therefore, it is possible to increase the specificity but with the price of sensitivity. Thus the appropriate choice of the frequency cut-off value depends on the specific case.Assuming that S. lycopersicum specific k-mers identified in this work using our pipeline are described . The S. lycopersicum specific k-mers were located along the entire chloroplast genome as 42 clustered taxon-specific regions along the single copy regions in the S. lycopersicum chloroplast genome (most of them were located in intergenic spacer regions or introns). The k-mers that were located adjacent to each other and differ by only one nucleotide belonged in the same cluster. The sequences in the k-mers clusters contain at least 1 mismatch per 32 bp of S. lycopersicum sequence compared to all non-target sequences.The locations of all 882 S. lycopersicum chloroplast genome varied from 33 to 62 bp , meaning that there was 2–31 k-mers in one cluster. The sequence lengths bp) between two adjacent cluster regions (region between the end of the last k-mer from one cluster and the start of the first k-mer sequence from the next cluster) varied from 30 to 39,507 bp.The lengths of the 42 detected taxon-specific regions in the k-mer based methods, that do not require prior primer design and the pre-amplification of specific regions, genome assembly and mapping of sequencing reads to a reference genome, have already been applied for the identification of microbial species or strains in metagenomics samples   and deep samples .k-mers-based approach to detect plant taxa from raw sequencing reads. We showed that it is possible to identify a set of plant taxon-specific k-mers from the chloroplast genome, that are also detectable from whole genome sequencing raw reads of target taxon. Different pieces of available softwares enabled fast identification and counting of taxon-specific k-mers from genome sequences from different taxa . To get overview of the available data of target and non-target sequences we constructed a tree containing more than 1,700 chloroplast genome sequences.To find a correct set of taxon-specific S. lycopersicum specific k-mers from the chloroplast genome. The set of taxon-specific k-mers can be identified from the chloroplast genome using available software in only a few minutes, and the set of taxon-specific k-mers can be easily updated if additional genomic sequences are available in biological databases for either target or non-target species.Although the variability of chloroplast genome sequences between very close plant species can be quite low and finding barcoding regions for amplification has been restricted , we idenS. lycopersicum specific k-mers that were 32 nucleotides in length . Our results showed that the identified S. lycopersicum specific k-mers were located in 42 different regions along the S. lycopersicum chloroplast genome single copy regions, which is in accordance with the previous findings that the mutation rate in the plastome single copy regions is much higher than in the IR regions , petN-psbM, rpl32-trnL, rpoC1-rpoB, trnA-trnI, trnK-rps16, and ycf1 (parts 1–3)  . Most ofS. lycopersicum specific k-mers in the whole genome sequencing raw reads from S. lycopersicum showed that k-mers from chloroplast genomes are also detectable from whole genome sequencing raw reads, and the number of detected S. lycopersicum specific k-mers increases with the increase in the number of sequencing reads. Almost all 882 k-mers were detected when read number was at least 100,000.The detection of pre-selected S. lycopersicum specific k-mers differed from S. pimpinellifolium or S. tuberosum chloroplast genome sequences by only one nucleotide. Therefore, probably due to sequencing errors  and small amount of reads from S. lycopersicum. However, it would be possible to detect S. lycopersicum from raw sequencing reads using taxon-specific k-mers from the chloroplast genome, even from metagenomics samples containing tomato, currant tomato, potato, eggplant, and pepper, if the read number from S. lycopersicum is at least 100,000 and approximately 600 k-mers (from 882 S. lycopersicum specific k-mers) are detected.Most of the g errors , the numk-mers to increase the specificity or to estimate the amount of S. lycopersicum in a sample, as the frequency of k-mers from the chloroplast genome is influenced on many factors   .k-mers, we analyzed the frequency distribution of S. lycopersicum specific k-mers in different S. lycopersicum and other Solanaceae species samples. Although the average frequency varied between different samples, the overall frequency of detected S. lycopersicum k-mers in S. lycopersicum samples was substantially higher than in other (non-target) Solanaceae samples. Using increased k-mer frequency cut-off values increased the specificity , though the sensitivity of S. lycopersicum detection was decreased (we were not able to detect S. lycopersicum specific k-mers in some of the S. lycopersicum samples if the sequencing read number was 2.5∗105 or less). Therefore, the appropriate choice for the frequency cut-off value depends on the specific case.To find ways to increase the detection specificity while also considering the frequency of detected k-mers from nuclear genomes which would be useful, when discriminating very close species, subspecies or cultivars with very little or lack of chloroplast genome sequence variability.The increasing number of available plant whole genome sequences probably in the future gives opportunity to identify additional plant taxa specific k-mers from all over the genome for the identification or qualitative detection of plant taxa would give improved resolution at the species level as well as aid in analyzing complex and degraded samples that sequence barcoding or other traditional methods fail to resolve. However, to apply the identified S. lycopersicum specific k-mers to detect S. lycopersicum (tomato plant) from real metagenomics samples , additional testing with real metagenomics samples is required.Using hundreds of taxon-specific short Accurate and fast methods for the identification of plant(s) from degraded metagenomics samples play an important role in identifying the composition of complex mixtures of processed biological materials, including food, herbal products, gut contents, environmental samples, etc., PCR and different barcoding methods commonly used for plant identification are based on the amplification of a limited number of pre-selected barcoding regions. These methods are often inapplicable due to the degree of DNA degradation, low PCR amplification success or low species discriminative power of the selected barcoding regions.k-mers from the chloroplast genome. By applying our method to select a set of S. lycopersicum (tomato plant) specific k-mers, we identified 882 S. lycopersicum specific k-mers that were present in at least two chloroplast genome sequences from S. lycopersicum and none of the 1,714 chloroplast sequences from non-target species. In silico experiments using raw sequencing data from the S. lycopersicum, S. tuberosum (potato), S. pimpinellifolium (currant tomato), S. melongena (eggplant), and C. annuum (pepper) whole genomes showed that S. lycopersicum specific k-mers found in the chloroplast genome can also be detected from S. lycopersicum whole genome sequencing raw reads as well as in other Solanaceae species. If sequencing data from metagenomic samples contain at least 105 reads from the S. lycopersicum genome, it is possible to discriminate k-mers from S. lycopersicum and k-mers from other Solanaceae species.Our method enables rapid identification of plant taxon-specific k-mers that could be used in the future for developing diagnostic tests for the fast detection of different plant taxa in raw sequencing reads from metagenomics samples.We are providing a valuable method here for the identification of plant taxon-specific k-mer analysis pipeline for finding a set of taxon-specific k-mers. GListMaker converts assembled chloroplast sequences or raw sequencing reads (FASTA or FASTQ) from the sample to k-mer lists and GListCompare compares different lists of k-mers to find intersections, differences or unions between the lists for detecting a set of taxon-specific k-mers.By combining different programs  from the GenomeTester4 software package  and in-hSolanum species, and five sequences from S. lycopersicum, which were downloaded from the GenBank database . We created a k-mer list for S. lycopersicum and a k-mer list for other (non-target) species. The final set of S. lycopersicum specific k-mers containing 882 k-mers was identified using two steps for removing non-specific k-mers from the S. lycopersicum k-mer list. The first step used assembled plastome sequences from non-target taxa to remove non-specific k-mers that were represented in the chloroplast genome of non-target taxa. The second step used whole genome sequencing raw data from the two phylogenetically close non-target species S. pimpinellifolium and S. tuberosum with a frequency at least 10 to remove additional non-specific k-mers that may be represented in the nuclear or mitochondrial genomes of non-target taxa S. pimpinellifolium and S. tuberosum. A cut-off value of 10 removed most of the non-specific k-mers present in the nuclear, mitochondrial or chloroplast genome regions of the non-target taxa, but did not include k-mers caused by sequencing errors in the sequencing reads. The raw sequencing reads from 1 S. pimpinellifolium and 3 S. tuberosum  were downloaded from the NCBI SRA database  k-mers that were present in all five S. lycopersicum chloroplast sequences, (2) k-mers that were present at least in 2 S. lycopersicum chloroplast sequences, and (3) k-mers that were present at least 1 S. lycopersicum chloroplast sequence. We selected one of these lists of k-mers for the subsequent analysis.We created the following three lists of S. lycopersicum specific k-mers in the raw sequencing reads from whole genome samples of the following different Solanaceae species: (1) 16 S. lycopersicum (tomato), (2) 10 S. tuberosum (potato), (3) 4 C. annuum (pepper), (4) 2 S. pimpinellifolium (currant tomato), and (5) 1 S. melongena (eggplant) . All the datasets downloaded from NCBI SRA database  from the raw sequencing data files for all the samples. The reads were evenly selected from an original FASTQ file [downloaded from NCBI SRA database  for the frequency of the detected k-mers from the final set of S. lycopersicum specific k-mers in raw sequencing reads from 16 different S. lycopersicum and 17 other plant species whole genomes.We also analyzed the frequency of individual k-mers in the chloroplast genome were found using the S. lycopersicum chloroplast sequence NC_007898.3 using Python scripts. A physical map of the tomato plant plastome was drawn using the GenomeVX software , k-mer detection pipelines, wrote in-house Python scripts, analyzed the sequence data and wrote the manuscript. KR and MR designed the experiments and interpreted the results of all analysis. MR edited the manuscript. All authors read and approved the final manuscript.KR constructed the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lp1-SST encoding sucrose:sucrose 1-fructosyltransferase and LpGA20ox encoding gibberellin 20-oxidase were associated with LFW and LDW, and LpACO1 encoding 1-aminocyclopropane-1-carboxylic acid oxidase was associated with LFW. Associations between Lp1-SST and HT, Lp6G-FFT encoding fructan:fructan 6G-fructosyltransferase and Fv/Fm, LpCAT encoding catalase and HT were also detected under submergence stress. Upon de-submergence, Lp1-SST, Lp6G-FFT, and LpPIP1 encoding plasma membrane intrinsic protein type 1 were associated with LFW or LDW, while LpCBF1b encoding C-repeat binding factor were associated with HT. Nine significant SNPs in Lp1-SST, Lp6G-FFT, LpCAT, and LpACO1 resulted in amino acid changes with five substitutions found in Lp1-SST under submergence or recovery. The results indicated that allelic diversity in genes involved in carbohydrate and antioxidant metabolism, ethylene and gibberellin biosynthesis, and transcript factor could contribute to growth variations in perennial ryegrass under submergence stress and recovery after de-submergence.Perennial ryegrass is a popular cool-season grass species due to its high quality for forage and turf. The objective of this study was to identify associations of candidate genes with growth and physiological traits to submergence stress and recovery after de-submergence in a global collection of 94 perennial ryegrass accessions. Accessions varied largely in leaf color, plant height (HT), leaf fresh weight (LFW), leaf dry weight (LDW), and chlorophyll fluorescence (Fv/Fm) at 7 days of submergence and in HT, LFW and LDW at 7 days of recovery in two experiments. Among 26 candidate genes tested by various models, single nucleotide polymorphisms (SNPs) in 10 genes showed significant associations with traits including 16 associations for control, 10 for submergence, and 8 for recovery. Under submergence,  Plant species or cultivars differ in growth responses to submergence stress. Escape or quiescence can be a strategy for plant survival from submergence stress . Escape Numerous physiological and molecular alterations are involved in genotypic variations in response to submergence stress and recovery . CarbohySUB1A and SKs, gibberellin-mediated shoot elongation either increased to grow out of the water (escape) or was suppressed for carbohydrate conservation during submergence (quiescence). The stimulated elongation of petioles under submergence stress was associated with accumulated ethylene in submerged petioles, a fast and substantial decrease of the endogenous abscisic acid (ABA) concentration, and a certain level of endogenous auxin and gibberellin in marsh dock  significantly associated with submergence response and involved in many physiological processes including carbon starvation and fermentation  plastic containers with tap water kept at 5 cm above the top of the grasses. The stress treatments began on November 14 of 2009 for Experiment 1 and were repeated on January 29 of 2010 for Experiment 2, lasting 7 days for both. After 7 days of stress, the submerged plants were taken out of the water to allow recovery for 7 days. The environmental conditions during the treatment period were described previously .Leaf color, plant height (HT), leaf fresh weight (LFW), leaf dry weight (LDW), and chlorophyll fluorescence (Fv/Fm) were assessed for indications of plant growth and physiological responses to submergence stress. Leaf color was visually rated on a scale of 1 (yellow) to 9 (dark green). Grass were cut to a similar height prior to stress, the measured HT referred to plant growth occurring only during the 7-day treatments. Leaves corresponding to this HT were cut as measurement of LFW, and LDW was determined after the tissues were dried in an oven at 80°C for 3 days. Fv/Fm was determined in the dark on randomly selected leaves using a fluorescent meter . Only HT, LFW and LDW were evaluated after de-submergence, but not leaf color and Fv/Fm due to minimum differences in these two measurements for both experiments.h2) was calculated using PROC MIXED . The h2 was calculated as follows:The experiment was a split plot design with three replications for both experiments . The mai2ge for genotype-by-environment, r is the number of replications, and l is number of environment. Based on the outcome of h2, least square means were estimated for each accession across two experiments.where The population was genotyped by using 109 published genome-wide simple sequence repeat markers in perennial ryegrass . The detLpCAT, LpChl Cu-ZnSOD, LpCyt Cu-ZnSOD, LpGPX, LpMnSOD, LpFeSOD, LpAPX, LpMDAR, LpDHAR, LpGR), kinase (LpMAPK), dehydration (LpLEA3), aquaporin (LpPIP1 and LpTIP1), and transcription factor  for this perennial ryegrass population were obtained previously .Twenty-six candidate genes in the functions of gibberellic acid biosynthesis, ethylene biosynthesis, kinase, dehydration protection, aquaporin, transcription factor, and anaerobic, carbohydrate and antioxidant metabolisms related to growth and submergence tolerance were selected for sequencing. Of them, sequences of genes involved in antioxidant metabolism , nucleotide polymorphism (𝜃), and Tajima’s g TASSEL . The LD P-value lower than the Pthreshold-value, calculated using Pthreshold = 0.05/N, where N was the number of SNPs in a candidate gene.Quantile-quantile (Q-Q) plots for model comparisons of simple linear (S), Q, K and Q + K across all traits were analyzed using R, and the best fit model was selected for association analysis of each trait. Associations between candidate genes and traits of leaf color, HT, LFW, LDW, and Fv/Fm were analyzed using TASSEL 2.1 software with the following three data set: (1) well-drained control (C); (2) submergence stress (S); (3) Recovery after de-submergence (R). Minor alleles with frequency < 5% were removed prior to association analysis. Associations were considered to be significant only at a Brachypodium distachyon, wheat, barley, rye, maize, rice, oats, and foxtail millet.The putative functional amino acid substitutions were analyzed only for genes significantly associated with traits. A full length gene sequence was obtained from NCBI or from perennial ryegrass genome assembly  for anchTable 1). Specifically, C-color ranged from 3.7 to 9.0 with a mean of 6.0 and S-Color ranged from 1.7 to 6.8 with a mean of 3.5. Fv/Fm varied from 0.71 to 0.82 for the control plants and from 0.66 to 0.81 for the submerged plants. The minimum and maximum HT were 2.0- and 16.3-cm for control, 1.44-and 11.7-cm for submergence, and 0.77- and 11.6-cm for recovery, respectively. LFW and LDW spanned from 0.34- to 1.7-g and 0.04- to 0.23-g for the control, 0.19- to 1.57-g and 0.01- to 0.19-g for submergence, and 0.17- to 1.62-g and 0.02- to 0.17-g for recovery, respectively.For all accessions across two experiments, large variations in traits were observed under the control, submergence stress, and recovery periods (h2) was calculated across two experiments. The h2 values of all traits were higher than 0.5 except for S-Fv/Fm (Table 1). The average h2 for traits under control, submergence, and recovery were 0.69, 0.62, and 0.65, respectively. The high repeatability over testing two environments allowed least square means (LSmeans) of individual traits to be calculated and used for association analyses of genes and traits.Repeatability . Subgroup 1 contained accessions mainly from southern Europe and northern Africa, and subgroup 2 consisted of accessions mainly from eastern Europe and western Asia. Subgroup 3 was the largest one with more diverse geographic locations mainly including accessions from northern Europe, western Europe, USA, Canada, Australia, and New Zealand. Accessions from subgroup 4 showed mixed geographic locations from northern Africa, eastern and southern Europe. The average values of all traits differed in four subgroups except for C-Fv/Fm, S-Fv/Fm, and S-color . On average, C-color was 5.3, 6.6, 6.2, and 5.0 in subgroup 1, 2, 3, and 4, respectively. Generally, the highest values of HT, LFW, and LDW were found in subgroup 4 and the lowest in subgroup 2 . No differences in traits were noted between subgroup 1 and 3. The separation in trait values of subgroups could be related to geographical locations of accessions.Four population structures (subgroups) were identified previously in this population with no obvious kinship . There wLpPIP1 (0.34) and the lowest in LpCBF1b (0.004) with a mean value of 0.091 (Table 2). Watterson’s 𝜃w ranged from 0.0086 for LpCBF1b to 0.15 for LpCBF3c with a mean value of 0.042 (Table 2). Positive Tajima’s D values were seen in all genes except for LpCBF1b, LpCBF3c, and LpGPX. No Tajima’s D was detected in LpCBF3b, LpMAPK, and LpMnSOD. For the LD pattern, the mean r2 for the pair of SNPs with all candidate genes was 0.16, ranging from 0.034 (LpCBF3c) to 0.48 (LpCBF1b) (Table 2). Overall, a rapid LD decay was shown in perennial ryegrass across all genes . LD decay decreasingly extended to more than 900 kb within r2 = 0.1. Within the sequencing length, a total of 863 SNPs were detected in all genes, ranging from 2 (LpCBF3b) to 121 (LpACO1) after minor SNPs (<5%) were removed (Table 2). The average SNP frequency was 1/48.3 bp, ranging from 1/5 bp in Lp1-SST to 1/357 bp in LpCBF3b.Across 26 candidate genes, the highest value of nucleotide diversity (π) was found in Figure 3). The results between the observed and expected -log 10 (P) for associations of 26 genes with traits showed that either the S, Q, K or Q + K implemented model was suitable for analyzing gene-trait associations, depending on individual traits.Quantile-quantile plots verified the adequate model for controlling false positives for each gene-trait association for the control, submergence, and recovery periods . The mean values of S-HT and S-LDW under submergence at 1124 bp in Lp1-SST were significantly higher in the accessions carrying heterozygous C:T than the accessions carrying homozygous T:T and C:C. Similarly, at 1091 bp, accessions with heterozygous C:T had higher R-LFW and R-LDW after recovery than the accessions with homozygous T:T and C:C.Sequence variations and phenotypic differences of different alleles of Table 4). Five SNPs at position 871-, 1087-, 1091-, 1116-, and 1124-bp in Lp1-SST had nucleotide substitutions from TCA to GCA, GCC to TCC, GTC to GCC, GAC to GAG, and CTG to CCG, causing amino acid substitutions from tyrosine (Y) to aspartic acid (D), alanine (A) to serine (S), valine (V) to A, D to Glutamic acid, (E) and leucine (L) to proline (P), respectively. A SNP at 835 bp in Lp6G-FFT showed one nucleotide substitution from GTC to ATC, resulting in an amino acid change from V to isoleucine (I), while a SNP at 938 bp had a substitution from TAC to TTC with the amino acid change from Y to phenylalanine (F). Nucleotide substitution also occurred at 1093 bp for LpCAT and 469 bp for LpACO1, leading to amino acid changes from V to methionine (M) and P to S, respectively.The predicted amino acid substitutions were analyzed only for significant SNPs identified under submergence and recovery. Nine SNP positions in four genes resulted in amino acid changes . Based on LpCAT sequences, perennial ryegrass was closer to wheat, barley, and Brachypodium distachyon than that in rice, maize, foxtail millet, and tall fescue . Lp6G-FFT in perennial ryegrass was more closely related to foxtail millet than that in Kentucky bluegrass, wheat, tall fescue, and barley , indicating that structured traits may be related to a geographical origin of the accessions. Larger variations in these traits found in submerged plants more than in the control plants, except for HT, suggested a wide range of submergence response in the perennial ryegrass collection. The differences in HT among accessions may be due to their variation in natural growth habits, although submergence-induced elongation was observed in some perennial ryegrass accessions . In particular, multiple amino acid substitutions caused by SNP changes indicated the importance of this gene in conferring submergence tolerance and recovery. Associations of Lp6G-FFT with S-Fv/Fm and with R-LFW after recovery were also identified, especially amino acid changes occurring at SNPs at 835 and 938 bp for R-LFW. The results indicated that Lp1-SST and Lp6G-FFT could influence fructan biosynthesis and assist in submergence tolerance and regrowth in perennial ryegrass after recovery from de-submergence.Natural variations in plant response to submergence stress provide an important basis for analyzing gene and trait associations. Carbohydrate metabolisms play a critical role in submergence tolerance. In temperate grasses, fructan biosynthesis is mainly controlled by fructosyltransferases (FTs) including sucrose:sucrose 1-fructosyltransferase (1-SST), fructan:fructan 1-fructosyltransferase (1-FFT), fructan:fructan 6G-fructosyltransferase (6G-FFT), and sucrose:fructan 6-fructosyltransferase (6-SFT) . 1-SST cACS1 and ACO1 and proteins were all up-regulated in bog yellowcress  regulates plant growth and development, including stem elongation and leaf expansion . GA20oxiertility . The mutterility , and ovet growth . Deepwateepwater . Our resCBF genes in perennial ryegrass, similar to either the HvCBF3 or HvCBF4 subgroups in barley  genes encoding transcriptional activators control the expression of genes containing the C-repeat-dehydration responsive element DNA regulatory element in their promoters . There wn barley . Overexpt growth , which mof maize . Four LpBrachypodium distachyon ecotypes reveals oxidative stress as a major tolerance factor  and ascorbate-glutathione cycles at different cellular locations (SOD or CAT were noted in maize (Lpcyto Cu-Zn SOD and LpCAT with HT detected under non-stress or submergence indicated contributions of these genes to HT variations in perennial ryegrass accessions. In particular, a SNP at 1093 bp in LpCAT causing an amino acid change after recovery suggested a link between LpCAT and plant regrowth by potentially diminishing oxidative injury upon re-oxygenation.Transcriptomic analysis of submergence-tolerant and sensitive e factor . Among a to H2O2 , and H2Oocations . The incin maize  and in cin maize . The assLp1-SST, Lp6G-FFT, LpCAT, and LpACO1 caused amino acid substitutions, especially for Lp1-SST with multiple substitutions. The results suggest that allelic diversities of genes involved in carbohydrate and ethylene biosynthesis, antioxidant metabolism, and transcription factor may contribute to variable plant growth responses to submergence stress and recovery after stress in the perennial ryegrass population. This discovery illustrated an important genetic mechanism underlying submergence response, which will be valuable for further studies of functional and regulatory genes involved in submergence tolerance in perennial ryegrass or other perennial grass species with a more complex genome.Significant associations were identified between candidate genes and growth and physiological traits in perennial ryegrass under the non-stress, submerged and recovery after de-submergence conditions. Allelic variations in XW conducted gene sequence and wrote the manuscript; YJ designed the experiments and led analyzing data and writing of the manuscript; XW, XZ, XS, XX, ZP, and HL analyzed data. All authors approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Efficient use of feed resources for farm animals is a critical concern in animal husbandry. Numerous genetic and nutritional studies have been conducted to investigate feed efficiency during the regular laying cycle of chickens. However, by prolonging the laying period of layers, the performance of feed utilization in the late-laying period becomes increasingly important. In the present study, we measured daily feed intake (FI), residual feed intake (RFI) and feed conversion ratio (FCR) of 808 hens during 81–82 weeks of age to evaluate genetic properties and then used a genome-wide association study (GWAS) to reveal the genetic determinants.FOXO1) that is involved in the insulin-signaling pathway, which influences nutrient metabolism in many organisms. Additionally, some suggestively significant variants, located on GGA3 and GGA9, were identified to associate with FI and RFI.The heritability estimates for the investigated traits were medium and between 0.15 and 0.28 in both pedigree- and genomic-based estimates, whereas the genetic correlations among these traits were high and ranged from 0.49 to 0.90. Three genome-wide significant SNPs located on chromosome 1 (GGA1) were detected for FCR. Linkage disequilibrium (LD) and conditional GWA analysis indicated that these 3 SNPs were highly correlated with one another, located at 13.55–45.16 Kb upstream of gga-miR-15a. Results of quantitative real-time polymerase chain reaction (qRT-PCR) analysis in liver tissue showed that the expression of gga-miR-15a was significantly higher in the high FCR birds than that in the medium or low FCR birds. Bioinformatics analysis further revealed that gga-mir-15a could act on many target genes, such as forkhead box O1  contains supplementary material, which is available to authorized users. The previous findings indicate that feed efficiency as a variably quantitative trait requires a more accurate and comprehensive strategy to reveal the genetic factors for birds under several conditions.People are always paying attention to animal feed efficiency because of the large effect on farm profitability. For the poultry industry, feed efficiency represents its competitive position against other animal protein sources, and to food economists, efficiency places less demand on global feed resources . The advIn the egg-type chicken industry, a trend has developed to prolong the laying cycle, which is related to animal welfare, the ecological footprint of animal production and the use of natural resources . HoweverThe descriptive statistics of daily feed intake (FI), residual feed intake (RFI), feed conversion ratio (FCR), body weight (MBW) and daily egg mass (EM) are presented in Table Estimates of heritability and genetic correlations among these traits are listed in Table MIR15A) was the only gene near these 3 genome-wide significant loci. Several genes also harbored or were near the 11 suggestively significant SNPs, including gga-miR-16a-1 (MIR16–1), deleted in lymphocytic leukemia 2 (DELU2), SPRY domain containing 7 (SPRYD7), potassium channel regulator (KCNRG) and tripartite motif containing (TRIM13). To further test the possible secondary association signals at the locus, conditional GWA analysis was conducted with the top associated SNP rs13553102 as a covariate. All SNPs were hidden below the suggestively significant line  plots for FCR, FI and RFI are shown in Fig. lue Fig. . The genFARP2 , KIF1A (kinesin family member 1A) and SNED1 . Compared with the FI, only two SNPs, rs314723494 and rs313839239, with genome-wide suggestive significance were detected for RFI. The two SNPs were located on GGA3 and GGA9. The identical SNP rs313839239 identified for FI and RFI might support the high genetic correlation between FI and RFI  in the current population. However, the substitution of variant C to T caused a significant difference for both FI . Chickens with the CC genotype of rs314723494 were more efficient with a − 1.88 g/d RFI than TC and TT genotypes with 0.27 and 6.53 g/d RFI, respectively  were detected for FI on chromosome 9 (GGA9) spanning from 4.36 to 4.52 Mb. All SNPs had a positive effect on FI. Linkage disequilibrium (LD) analysis showed that the 5 SNPs were in a high linkage phase  enriched to 9 biological pathways  was significantly enriched, with 12 target genes involved. Interaction analysis was then performed on these 12 genes one by one using RNA hybrids. Minor free energy (MFE) was selected as an indicator to identify the reliable bind between microRNA and target mRNA. A total of 9 interactions were found between gga-miR-15a and three target genes with an MFE less than −20. These 3 genes were forkhead box O1 (FOXO1), 3-phosphoinositide dependent protein kinase 1 (PDPK1) and protein kinase cAMP-dependent type II regulatory subunit alpha (PRKAR2A)  genes were predicted using these two tools, respectively  was in this region and close to the three significant SNPs. Given that FCR is related to energy homeostasis and egg production, we constructed cDNA from liver tissue, which is vital to glucose, glucagon, lipid and protein metabolism in chickens [MIR15A was significantly lower in the medium and low FCR birds, which suggested that MIR15A was a promising candidate gene involved in the regulation of FCR. MicroRNAs are small non-coding RNAs that have been highly conserved during evolution and have been implicated in multiple molecular interactions [MIR15A, accompanied by MIR16–1 and DELU2 nearby, forms a DLEU2/miR-15a/16–1 cluster to affect chronic lymphocytic leukemia in cancer research [FOXO1 was identified as the most reliable target gene of gga-mir-15a among 12 target genes. FOXO1, a member of the forkhead box transcription factor class O (FOXO) family, is a direct transcriptional regulator of gluconeogenesis and glycolysis, reciprocally regulated by insulin, and has profound effects on hepatic lipid metabolism [FOXO1, gluconeogenic gene activity is upregulated, promoting glucose production in the liver and accounting largely for the hyperglycemia observed in diabetic individuals [FOXO1 by binding to the 3′-UTR of FOXO1 mRNA and then inhibit the protein expression of FOXO1 involved in the insulin-signaling pathway, resulting in the alteration of FCR in chickens.A genomic region of 31 kb on chromosome 1 (GGA1) that harbored 3 genome-wide significant SNPs was detected associated with the feed conversion ratio (FCR). Additionally, gga-mir-15a . SNP rs313839239 for RFI was overlapped with a QTL previously identified in commercial meat-type chickens [rs313839239) affecting RFI and FI simultaneously, which is similar to our findings in the laying period from 57 to 60 weeks in a previous study [FARP2 , a guanine nucleotide exchange factor in the Rho family of small GTPases, was a shared candidate gene for both FI and RFI. FARP2 was identified as a candidate gene in diabetes research [CNR1), which is referred to in an energy homeostasis and metabolic process revealed by pharmacological studies [In the present study, only 5 and 2 hits were identified for FI and RFI at a suggestively significant level, respectively, which indicated that the effect of genetic determinants was too weak to be identified for FI and RFI. Additionally, a pure line selection scheme resulted in effective variants homozygous in the current stocks. However, the suggestively associated SNPs were also promising candidates to some extent. The identified SNPs on chromosome 9 (GGA9) for FI were first reported in chickens according to the QTL database  trial period. In the 2-wk. feeding test period, feed was provided in individual containers for each hen. Feed consumption data were procured daily by manual collection to derive the daily feed intake (FI). Egg mass (EM) was obtained as total egg number multiplied by average egg weight for each hen. Daily egg number was recorded to calculate total egg number, and the weight of 3 consecutive eggs collected in each week was used to calculate average egg weight. Body weight was measured on a mid-test day for each recorded hen. Feed conversion ratio (FCR) was calculated as the ratio of FI and daily EM. After removing outliers  for FCR, the remaining 808 hens were used to calculate residual feed intake (RFI) as the residuals from a regression model of FI on EM and metabolic BW (BW raised to the power of 0.75) . The phehttp://www.affymetrix.com/). Autosomal SNPs of 808 samples were filtered by the criteria set in PLINK [P-value <1e-6). Afterward, the remaining SNPs and 808 birds were used for the imputation implemented in the Beagle Version 4 software package based on localized haplotype clustering [Genomic DNA was isolated from whole blood samples using phenol-chloroform methods. The qualified 808 hens were genotyped for 580,961 markers using Affymetrix 600 K chicken high-density genotyping array. In the quality control of raw data, all individuals passed the criteria with a missing SNP call rate < 5% using Affymetrix power tool (APT) provided by Affymetrix  method implemented in DMU software . The multability  and SNP-tability ) was caltability .2 threshold of 0.2. Second, pairwise identity-by-state (IBS) distances were calculated between all individuals using the independent SNP markers. Finally, we calculated multidimensional scaling (MDS) components using the mds-plot option based on the IBS matrix, which was included as a covariate in the subsequent association analyses. GWAS was performed using a mixed models approach [P-value was used as the criterion to screen SNPs associated with the investigated traits. Conditional GWA analyses were performed using the same mixed model with the addition of the dosage of the strongest associated SNP as a covariate [Before the genome-wide association study (GWAS), the eligible SNPs and birds were used to evaluate the population structure by PLINK. First, all SNPs were pruned to obtain independent SNP markers using the indep-pairwise option, with a window size of 25 SNPs, a step of 5 SNPs, and rapproach  implemenapproach . Associaovariate .P-value threshold of genome-wide significance, the simpleM method [http://cran.r-project.org/web/packages/gap/index.html) within the R software. Linkage disequilibrium (LD) analysis was performed for the chromosomal regions with many associated SNPs clustered implemented in Haploview version 4.2 [With respect to the M method  was usedsion 4.2  with thesion 4.2 .Gallus gallus 4.0 genome version, and genes within 500,000 base pairs flanking the associated SNPs were chosen for further analysis. Target genes of microRNA were predicted using TargetScan (http://www.targetscan.org) and miRDB (http://www.mirdb.org/miRDB/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to analyze target genes of microRNA implemented online with the DAVID platform (https://david.ncifcrf.gov/). Bioinformatics analyses of molecular interactions between microRNA and predicted coding genes were performed using RNAhybrid online [http://www.mirbase.org/), and the target sequence of coding genes was queried from Ensembl online (http://www.ensembl.org/).SNP positions and information were obtained using annotation of d online  in whichBirds were deeply anesthetized with sodium pentobarbital via cardiopuncture, and decapitated. Liver tissue samples were collected from chickens along with the regular quarantine inspection of the experimental station of China Agricultural University in accordance with the Guidelines for the Care and Use of Experimental Animals established by the Ministry of Agriculture of China . The entire study was approved by the Animal Welfare Committee of China Agricultural University (permit number: SYXK 2007–0023).-ΔΔCT were used for statistical analyses. Data were analyzed by pairwise Student’s t-tests implemented in the R software.Total RNA was extracted from the liver tissue using a mirVana™ miRNA Isolation Kit , and was reversely transcribed using a miRACLE cDNA Synthesis Kit  as described by the manufacturer. The quantitative real-time PCR (qRT-PCR) was performed on an ABI 7500 system (Applied Biosystems). Primers of gga-miR-15a and chicken 5 s rRNA were designed and synthesized by Genetimes Technology Inc. . The mature miRNAs were polyadenylated by polyA polymerase and reversely transcribed into complementary DNA (cDNA) using a reverse primer, which had a 3 prime degenerate anchor and a universal tag sequence on the 5 prime end. The cDNA template was then amplified using specific forward and universal reverse primers. The specific forward primer sequences of the miRNAs used in this study were the following: gga-miR-15a, forward 5′-TAGCAGCACATAATGGTTTGTAAAA-3′ and chicken 5 s rRNA, forward 5′-ACCGGGTGCTGTAGGCTTAA-3′. The universal reverse primer was included in the qPCR Kit of miRACLE qPCR miRNA Master Mix (Genetimes Technology). The optimum thermal cycling conditions were as follows: 95 °C for 10 min, 40 cycles of 95 °C for 10 s, 60 °C for 20 s, 72 °C for 1 min, 95 °C for 15 s, 60 °C for 30 s, and 95 °C for 15 s. All experiments were run in triplicate. Relative quantification of microRNA expression was analyzed using the ΔΔCT method  with 5 sAdditional file 1: Table S1.Table S2. Predicted target genes from TargetScan for gga-mir-15a. Table S3. Predicted target genes from miRDB for gga-mir-15a. (XLSX 37 kb)The information for SNPs suggestively associated with feed conversion ratio. Additional file 2: Figure S1.Linkage map of the associated genomic region for daily feed intake on chicken chromosome 9. (TIFF 166 kb)Additional file 3: Figure S2.rs13553102. The graph plots genomic position (x axis) against its significance expressed as -log10 P value (y axis). (TIFF 205 kb)Regional association plot for FCR after conditional analysis on Additional file 4: Figure S3.Molecular interactions between gga-mir-15a and target genes with minimal free energy less than −20 kcal/mol. Red letters indicate the 3′ UTR sequences of the target genes. Green letters indicate the matured sequences of gga-miR-15a. (TIFF 3113 kb)