Meat and egg-type chickens have been selected for several generations for different traits. Artificial and natural selection for different phenotypes can change frequency of genetic variants, leaving particular genomic footprints throghtout the genome. Thus, the aims of this study were to sequence 28 chickens from two Brazilian lines (meat and white egg-type) and use this information to characterize genome-wide genetic variations, identify putative regions under selection using Fst method, and find putative pathways under selection.A total of 13.93 million SNPs and 1.36 million INDELs were identified, with more variants detected from the broiler (meat-type) line. Although most were located in non-coding regions, we identified 7255 intolerant non-synonymous SNPs, 512 stopgain/loss SNPs, 1381 frameshift and 1094 non-frameshift INDELs that may alter protein functions. Genes harboring intolerant non-synonymous SNPs affected metabolic pathways related mainly to reproduction and endocrine systems in the white-egg layer line, and lipid metabolism and metabolic diseases in the broiler line. Fst analysis in sliding windows, using SNPs and INDELs separately, identified over 300 putative regions of selection overlapping with more than 250 genes. For the first time in chicken, INDEL variants were considered for selection signature analysis, showing high level of correlation in results between SNP and INDEL data. The putative regions of selection signatures revealed interesting candidate genes and pathways related to important phenotypic traits in chicken, such as lipid metabolism, growth, reproduction, and cardiac development.In this study, Fst method was applied to identify high confidence putative regions under selection, providing novel insights into selection footprints that can help elucidate the functional mechanisms underlying different phenotypic traits relevant to meat and egg-type chicken lines. In addition, we generated a large catalog of line-specific and common genetic variants from a Brazilian broiler and a white egg layer line that can be used for genomic studies involving association analysis with phenotypes of economic interest to the poultry industry.The online version of this article (10.1186/s12864-018-4444-0) contains supplementary material, which is available to authorized users. Chickens are one of the most important animals in the world, not only because of the intensive production of meat and eggs, but also because they are a model for developmental and genomic studies. The chicken genome has become an important tool for the worldwide avian research community since 2004, with the release of the first draft of the genome from a single female red jungle fowl, which is the wild ancestor of domestic chicken .The domestic chicken has hundreds of different breeds, but commercial chickens can be divided into two main groups – broilers (meat-type) and layers (egg-type), which have been artificially selected for centuries. These two types have different phenotypic and genotypic profiles as a consequence of distinct genetic backgrounds and intensive genetic selection for different traits . BroilerDifferent selection criteria applied for broiler and layer lines resulted in considerable differences in growth, development, and metabolic mechanisms during embryogenesis and hatching . This inRecently, millions of SNPs and INDELs have been identified from different chicken genomes based on next generation sequencing data (NGS) –8, and iIn Brazil, Embrapa Swine and Poultry National Research Center maintains several chicken lines under multi-trait selection in Brazilian climatic and nutritional conditions for more than 20 years. Two of those lines, a white-egg type (layer) and a meat-type (broiler), due to considerable difference in their growth rates and carcass yields, were utilized to generate an F2 experimental population for QTL mapping studies , 14. TheIn order to achieve a greater understanding of the genetic differences between these two Brazilian white-egg layer and broiler lines, a deep catalog of genetic variants (SNPs and INDELs) were generated by sequencing the genome of 28 chickens (14 per line) at medium sequencing coverage. SNPs and, for the first time in chicken, INDELs were utilized for detecting potential selection signatures using Fst approach. Genes located in candidate regions under selection and genes with variants of potential functional effect were further analyzed to consider their possible influence on economically important traits in chickens, such as fat deposition, growth and reproduction. In summary, this study provides novel insights into selection footprints that can help elucidate the functional mechanisms underlying important traits relevant to broiler and layer chicken lines. In addition, this study presents detailed information about variants in the Brazilian broiler and layer chicken lines that could be important for genetic studies involving association analysis with relevant phenotypes.Approximately 5 billion short reads were generated from 14 broilers, and 14 white-egg layers. After quality trimming, ~ 78% of the reads were retained. About ~ 98.8% of the quality-processed reads could be aligned to the chicken reference genome . After removal of PCR duplicates, the average sequencing coverage for the 28 individuals was ~ 11.2 X (average of 11.4 X for broiler line and 10.9 X for white-egg layer line).The initial variant call resulted in 15,944,063 SNPs and 1,997,771 INDELs from 28 individuals analyzed, including autosomes 1–28 and 32, two linkage groups, mitochondrial, sex chromosomes (W/Z), and unplaced scaffolds. The genome-wide average density of variants considering variants from all 28 chickens was 16.84 SNPs/kb and 1.72 INDELs/kb. SNP densities for each line were 13.15 SNPs/kb for broilers and 10.82 SNPs/kb for layers. INDEL densities for each line were 1.41 INDELs/kb for broilers and 1.20 INDELs/kb for layers. Additional file Filtration of the variants based on several criteria see  removed The transition and transversion (TS/TV) ratio for SNPs initially detected in the whole-genome was 2.17, while the ratio in the filtered set was 2.31.n = 11,856,959 SNPs and 1,200,710 INDELs in the filtered set) than in the layer line . About 26%  of all the SNPs were detected exclusively in the broiler line while ~ 12%  were detected only in the layer line  than in the layer line (average 44.8% of SNPs and 38.8% of INDELs)  of SNPs and INDELs were estimated within the two lines. Our data shows that most of the SNPs (54%) and INDELs (46.3%) from the broiler and the layer lines had low frequency (≤ 0.3) Fig. . HoweverAnnotation showed that most of the variants belonged to non-coding regions of the genome, such as intergenic (38.5% SNPs and 39.4% INDELs), and intronic (42.1% SNPs and 42.9% INDELs) regions. Some of the variants (11.6% SNPs and 10.9% INDELs) had multiple annotations as those could be classified into multiple categories. Only about 1.2% of the SNPs were located in exonic regions, including synonymous (69.9%), non-synonymous (29.8%) and stop gain/loss SNPs (0.3%)  were investigated using the QIAGEN’s Ingenuity® Pathway Analysis software (IPA) .The analysis of all genes containing intolerant SNPs from the white-egg layer and the broiler lines combined resulted in eight significant networks Table . A totalWhen the same analysis was performed separately for the broiler and the white-egg layer lines using genes harboring intolerant SNPs specific to a line, clearly different networks were observed Table . In the The metabolic pathways obtained from the analysis of coding INDELs showed a total of 257 genes participating in nine metabolic pathways, mainly associated with diseases and disorders, for example: embryonic development, connective tissue disorders, gastrointestinal, metabolic, neurological and hepatic diseases, cardiovascular development, and cancer  and 5 to 114 INDELs (average of 26/window).Genome-wide weighted Fst distribution was examined Fig.  based onr = 0.66; p < 0.001) between the Fst values from the same window of putative selection signatures  of SNPs and INDELs analysis.Even though the number of INDELs per window was smaller than those in the SNP dataset, similar regions were identified in the SNP and INDEL analyses, with a significant positive correlation  were found to be common between the two analyses.Out of the 307 genes from the SNP-based analysis and the 363 genes from the INDEL-based analysis, 220 genes were common. When all merged signature regions  obtained from both analyses (345 and 425 regions) were compared, most of the regions , ADCY2 (GGA2), LPGAT1 (GGA3), AKAP6 (GGA5), IGFBP2 and PLA2R1 (GGA7), and CLPS (GGA26). Candidate genes identified for muscle development are ACTC1 and AKAP6 (GGA5), IGFBP2 (GGA7), NOS1 (GGA15), MYOCD (GGA18), and MAPK13/14 (GGA26).Thus, candidate genes for fat deposition from SNP-based selection signature analysis include LPIN1 (GGA3), ADCY9 (GGA14), and APOA1/4/5 (GGA 24). For muscle development, the same candidate genes were identified in SNP and INDEL analyses, except the MYOCD gene, detected only in the SNP analysis, and IGF1R, detected only in the INDEL analysis.All the above mentioned candidate genes for fat metabolism were detected by both SNP and INDEL-based analyses, with the addition of five more candidates detected only by INDEL analysis: AKAP6, NOS1, MAPK13, and CACNA1S.Furthermore, we also checked genes containing any intolerant SNP that overlapped with selection signature regions. Sixty-three intolerant SNPs were found within selection signature regions, and they represented 39 genes  [In order to to achieve greater accuracy in the detection of the putative selection signatures, the merged regions  were compared with known QTL regions in chicken according to the Animal QTLdb  . About 9p-value < 0.05, Fig. Also, a statistical test based on permutation sampling was used to access the significance of the observed overlaps between the putative selection signature regions and QTLs. Based on the regions under selection from the SNP analysis, traits related to fat deposition, body weight, muscle weight, growth, egg production, bones, and others showed significant overlaps  15 M SNPs detected in a previous study by Gheyas et al. , and  in NGS even though their variant qualities were high. The error rate in SNP genotype call (considering the non-concordance of genotypes between NGS and array data for SNPs that could be compared) was only 1.51% for the layer chicken and 1.81% for the broiler.We also validated a large number of functional variants - intolerant non-synonymous and stopgain/loss SNPs against the 600 K array data. These included 757 intolerant SNPs (380 from layer and 377 from broiler chickens), and 37 stopgain/loss SNPs (18 from layer and 19 from broiler chickens). Importantly, over 41% of the 63 intolerant or stopgain/loss variants detected in the selection signature regions were present in the 600 K genotyping array and could be validated.n = 1,335,889) were also validated by comparison with results from two major recent studies [n = 1,031,932) of the INDELs in our study were common with the previous studies. When the alleles were also compared along with positions, 62.62% of the INDELs were validated .The INDELs identified in this study ( studies . We combA genome-wide SNP and INDEL identification in the Brazilian white-egg layer (CC) and broiler (TT) lines was performed to obtain a detailed map of genetic variation in these lines. The initial variant call resulted in approximately 16 million SNPs and 2 million INDELs with an average density of 16.84 SNPs/kb and 1.72 INDELs/kb. Our results are consistent with recent studies, which reported similar densities in chicken .After filtration, a total of 13.93 million SNPs and 1.36 million INDELs were retained, with more variants detected from the broiler line, including heterozygous variants.The transition and transversion (TS/TV) ratio for SNPs increased after the filtration process, showing that SNP filtration improved the TS/TV ratio, as higher ratios indicate better accuracy and lower false-positive rates . In chicHeterozygous variants are more difficult to be identified than homozygous ones. It has been estimated that a sequencing coverage of 6–10 X is sufficient to detect 99% of all variants (homozygous and heterozygous); however, some studies reported that a higher coverage (> 20 X) is necessary for detecting 99% of heterozygous SNPs , 27.A recent study  analyzinEven though there are millions of SNPs and INDELs along the genome, only a few are expected to explain the phenotypic differences observed between the two lines. As expected, annotation of the variants showed that most of them belonged to non-coding regions of the genome, and just 1.2% of the SNPs and 0.2% of the INDELs were located in exonic regions, including 122,502 non-synonymous SNPs. Non-synonymous SNPs and SNPs located in regulatory regions are known to have the highest phenotype impact .Although, most of the variants were located in non-coding regions, we identified 7255 intolerant non-synonymous SNPs, 512 stopgain/loss SNPs, 1381 frameshift and 1094 non-frameshift INDELs that may alter protein functions., can affect gene regulation by modulating transcription or silencing genes [Both frameshift and non-frameshift INDELs affect protein sequence by being located within coding regions. Frameshift mutations, however, can also have a major deleterious impact on protein function as these alter the amino-acid reading frame. On the other hand, non-frameshift INDELs do not change the reading frame, but they can still alter the protein function by inserting or deleting one or more amino-acids in the protein sequence. The impact of INDELs on transcription has not been well investigated, but it has been suggested that this may be considerable . Non-codng genes .The chicken genome is still poorly annotated for functional non-coding regions , which cIn order to gain insights into how the polymorphisms identified could explain the phenotypic changes observed in the two Brazilian chicken lines, metabolic pathways of genes harboring variants of high potential functional effects were investigated.The analysis of all genes containing intolerant SNPs from the white-egg layer and the broiler lines combined resulted in eight networks related mainly to connective and metabolic disorders, embryonic development, cardiovascular diseases, and carbohydrate metabolism.The effects of the potential disruptions of these important pathways caused by intolerant SNPs may explain various disorders observed in commercial broiler and layer chickens. Broiler chickens, for instance, have been selected for weight gain, feed efficiency, breast and carcass yields, which results in a substantial increase of growth in a short period. This particular scenario for meat-type chickens can result in heart ventricular hypertrophy and overload of the cardiopulmonary system, leading to pulmonary hypertension syndrome (ascites), which can cause chicken mortality or whole carcass condemnation during slaughter .. FLS is a result of excessive accumulation of fat in the liver when lipoprotein transport is disrupted during high egg production and cause haemorrhage and sudden death [On the other hand, layer chickens, which have been mainly selected for high egg production, have a reduced body weight than the broiler chickens and can develop diseases such as fatty liver hemorrhagic syndrome (FLS) and osteoporosis . FLS is en death .PRL is a candidate gene for egg production in chicken [When the analysis was performed using genes harboring intolerant SNPs specific to a line, clearly different networks were obtained for each line. In the layer line, networks were identified mainly related to reproductive system development, cellular functions, and the endocrine system. The latter in layers is particularly important for reproductive aspects, which involve different hormones and their receptors e.g. growth hormone (GH), prolactin (PRL), thyroids, luteinizing hormone (LH), melatonin, and others , 36. For.In the broiler line, networks were obtained mainly related to lipid and carbohydrate metabolism, metabolic and dermatologic diseases, neural development, and organism injury. It is well known that commercial broiler lines gain much more weight and muscle mass than layer chickens. It was observed that at 41 days of age, the Brazilian broiler chicken line weighed on average 2395 g, while the layer chicken line weighed only 513 g, when reared as broilers, resulting in about a five-fold difference between these two lines. Also, the breast yield for the broiler line was 6% higher compared to the layer line , 16. ThiAnother important difference between these two lines is lipid metabolism and fat deposition. Broiler chickens accumulate more fat than layers, and lipid metabolism differences were detected during chicken embryogenesis, e.g. higher triglyceride content in the liver of broilers . It was Frameshift and non-frameshift INDELs are very important as well, because they are located in coding regions, and both can affect protein sequence. The metabolic pathways obtained from the analysis of coding INDELs showed pathways mainly associated with diseases and disorders. Many diseases in humans have been found to be associated with INDELs (e.g. different types of cancer), and it was estimated that INDELs are responsible for 24% of the inherited diseases in humans . PreviouIntensive artificial selection can cause high degree of genetic differentiation between populations in specific genomic regions, which can result in selection footprints. There are different methods and tools available to detect evidence of selection. Traditionally, the Fst method has been a suitable choice based on allele frequency between populations .Fst values were obtained from SNP and INDEL datasets separately using an overlapping sliding window of 20 kb, which was also utilized in previous studies in chicken , 43. AltWe obtained a sufficient number of markers per window with an average of 268 SNPs/window and 26 INDELs/window. In similar studies in chicken, Qanbari et al.  obtainedA majority of the windows had low Fst values (< 0.3), although a few windows showed extremely high Fst values (> 0.9). Our results corroborate with similar Fst-based studies in chicken. For instance, Gholami et al.  obtainedSeparate Fst analyses, with SNPs and INDELs, were performed to check if different putative signature regions would be detected. Most of the signature regions identified were found to be common between the two analyses (SNPs and INDELs), with a high level of correlation. This shows that even though the INDEL dataset was only ~ 10% of the SNP dataset in size, both datasets detected practically the same regions under selection. Our study is the first in chicken to use INDEL variants to detect footprints of selection. There are, however, a few studies in other species, for example in humans . In thisGenetic drift is a well-known factor that can also cause divergences in genomic regions. It is almost impossible to determine if a putative selection signature was caused by drift . BesidesADCY2/9, APOA1/4/5, AKAP6, CLPS, IGFBP2, ITPR2, LPGAT1, LPIN1, and PLA2R1. Candidate genes associated with muscle development were ACTC1, AKAP6, IGFBP2, IGF1R, NOS1, MAPK13/14 and MYOCD.We analyzed all genes under putative selection using network and gene enrichment analyses, and gene function by literature search, and we identified candidate genes for fat deposition and muscle development. There were 307 from SNP-based analysis and 363 from INDEL-based analysis. Candidate genes associated with fat deposition were AKAP6 gene is present in both muscle development and lipid metabolism pathways. A recent study suggests that this gene is an important regulator of muscle regeneration and myoblast differentiation in mice [The  in mice , in addi in mice . This geIGFBP2 identified in our study (with variants mainly fixed in the layer line), was previously reported in a comparison of various layer chickens [The 30 kb selective sweep region , covering the gene chickens . This gechickens  and fat chickens  from preIGF1R with variants mainly fixed in the layer line. Recent studies in chicken, using the same method described here, also identified the same gene under selection in a commercial broiler [A putative selection signature region of 20 kb size was identified  with 24 INDELs overlapping the gene  broiler  lines. T broiler  and may  broiler .insulin receptor gene (INSR) was identified in our study with variants mainly fixed in the broiler line  overlapping the us study .MEIS2 gene is related to the development of brain, heart, eyes, cartilage and hematopoiesis [MEIS2 gene, which were merged into one large region of 200 kb, possibly indicating a strong selection region. In the study of Gheyas et al. [MEIS2 gene.Apart from fat deposition and muscle development traits, genes within the putative signature selection regions were found to be candidates for other important traits in chicken, such as cardiac, skeletal and embryonic development, broodiness, energy metabolism, and reproduction. For instance, the opoiesis . In our s et al. , they idITPR2 and VIPR1, were identified in the putative selection signature regions, which are possibly related to layer reproductive traits. One recent GWAS study in chicken revealed that the ITPR2 gene was associated with eggshell ultrastructure [VIPR1 gene was found to be associated with broodiness [In addition, two genes, tructure . On the oodiness , and eggoodiness .TSHR (affecting reproductive behaviors) and the BCDO2 (for yellow skin) as have been discussed in a number of previous studies [TSHR and BCDO2 genes, there were 812 and 187 SNPs, respectively and 57 and 23 INDELs, respectively. Most of these variants were fixed (homozygous) in both lines, especially in the BCDO2 gene, showing that these selection signatures are clearly present in both chicken lines.Certain regions or genes are known to be selected in domesticated chickens, such as  studies , 7, 44. In order to gain confidence in detecting selection signatures and linking these regions to potential phenotypes, we compared the identified selection signature regions with known QTL regions according to the Animal QTLdb .Approximately 97% of the selection signature regions overlapped with one or more QTLs for different traits. This provides an independent support for the validity of the selection signature regions detected in this study. These QTLs corresponded to 190 traits related to fat deposition, growth, carcass, egg production and quality, bones, blood parameters, and organs, among others.In addition, a statistical test was performed to check if overlaps of the selection signature regions with QTLs were significant and not just by chance. This comparison revealed that a higher proportion of the putative signature regions identified in this study were located in QTL regions associated with relevant traits in chicken, and should be further investigated.The results showed that almost all the selection signature regions overlapped with different QTLs, providing additional confidence of the putative selection regions identified, and also may be an indication of genetic correlation between traits due to pleiotropy.Availability of variant information from previous NGS studies and high throughput genotype data from 600 K arrays offer an excellent opportunity to carry out cross-study and cross-platform (NGS vs array) comparisons to achieve greater confidence in the variant and genotype calls. We took this opportunity to validate SNPs and INDELs detected in the present study.SNPs were validated by comparing NGS data against two datasets: 15 M SNPs detected in a previous study by Gheyas et al.  and SNP We validated more than 78% of our SNPs by comparison with the catalog from Gheyas et al. [In addition, we observed high levels of concordance with 600 K SNP array data. We noticed a slightly higher percentage of genotype concordance for homozygous SNPs than for heterozygous ones, which were also observed previously in a study with bovine variants .The majority of the discordant genotypes checked had poor genotype quality in our NGS data even though their variant qualities were high. A possible explanation of this could be that the sequencing coverage was not sufficient in these cases for accurate prediction of the genotype.We observed low error rates in our study (considering the non-concordance of genotypes between NGS and array data for SNPs), showing that alignment and SNP calling were efficient.The 600 K array allowed us to validate a large number of functional variants, intolerant non-synonymous and stopgain/loss SNPs, thereby providing confidence in their detection.Moreover, INDELs identified in this study were also validated by comparison with two major studies , 22, whiIn this study, we performed a genome-wide identification and characterization of genetic variations, and found putative genomic footprints of selection from two different chicken lines, a white egg layer and a meat-type (broiler) line, which have been under multi-trait selection in Brazilian climatic and nutritional conditions for more than 20 years. Approximately 15 million genetic variants were identified. Of which, over 10 thousand are expected to alter protein functions. Important pathways related to lipid metabolism, growth, and reproductive traits were detected from the analysis of SNPs and INDELs, especially the line-specific variants.Fst-based analyses were used to identify putative regions of selection showing population differentiation. This approach revealed a number of highly plausible candidate genes and pathways under selection for fat metabolism, growth, reproduction, and cardiac development. Moreover, this is the first study to utilize genome-wide INDEL variants in chicken to identify selection signatures, showing high level of correlation in results between the SNP and INDEL based data.To minimize the effect of genetic drift, different approaches were applied to identify high confidence regions of putative selection. However, despite the effort to avoid detecting regions caused by random drift, some of the regions detected in this study may represent false positives, especially the ones that did not overlap with genes having known phenotypic association with QTLs. Further analysis in different populations would be required to confirm the accuracy of those regions.In summary, this study provides novel insights into selection footprints that can help elucidate the functional mechanisms underlying important phenotypic traits relevant to broiler and layer chicken lines. In addition, we have presented a detailed genetic catalog of variants both line-specific and common from two Brazilian broiler and layer chicken lines that can be used for future genomic studies involving association analysis with relevant phenotypes, and consequently, facilitate the identification of causative mutations in chicken, and an important resource for marker-assisted or genomic selection for important traits in chicken.2 Resource population for QTL mapping studies [Twenty eight chickens from two different experimental lines with pedigree control and multi-trait selection by Embrapa Swine and Poultry National Research Center (Brazil) were sequenced: 14 individuals from a paternal broiler line called TT , and 14 from a layer line called CC . Previously, these two experimental lines were used to generate a reciprocal F studies , 14.The broiler line (TT), originating from the White Plymouth Rock, New Hampshire and White Cornish breeds, has been selected since 1992 for body weight, feed conversion, carcass and parts yields, chick viability, fertility, hatchability of fertile eggs, and reduced fat deposition and metabolic disorders . On the Each chicken was sequenced individually using a paired-end protocol in the HiSeq2500 sequencer (Illumina) with paired read length of 101 bases. Further details on library preparation and sequencing can be obtained from Moreira et al. .mpileup option with mapping and base qualities (Phred score) ≥ 20. All 28 sample BAM files were analyzed together to improve the variant calling in low coverage regions. After the initial calling, different filtration criteria were applied to reduce the number of false-positives and to avoid copy number variation regions. The filtration criteria included (1) Phred-based variant quality score of at least 40; (2) minimum depth of coverage at the variant site of 5; (3) maximum depth of coverage of not more than mean coverage plus 3 standard deviations; (4) variant supported by both forward and reverse strands (at least one read on the forward strand and one on the reverse strand); (5) variant supported by at least 3 reads, and (6) SNP clusters (> 10 SNPs in 50 bp), and INDEL clusters (gap of 1 bp between two INDELs) were removed.First, the quality of the sequencing reads was checked with FastQC tool . Read quA SNP/INDEL was referred to as homozygous when only a non-reference allele was observed and heterozygous when both the reference and non-reference alleles were observed. The alternate allele frequencies (AAF) of SNPs/INDELs were calculated with VCFtools v. 0.1.12 , and AAFSets of unique SNPs and INDELs from the 28 chickens were annotated using Variant Effect Predictor tool v.75  against p-values and calculated using Fisher’s exact test. A network score of > 3  indicates a > 99.9% confidence that a network is not generated by random chance; however, a network score ≥ 30 was considered for determining significant results. Similar cut-off scores were utilized in previous studies to ensure that only highly significant networks were identified [QIAGEN’s Ingenuity® Pathway Analysis (IPA®) software  was usedentified , 71.n = 12,806,643) and INDEL  datasets considered separately and using overlapping windows of 20 kb and a step size of 10 kb. Weighted Fst values were calculated for windows with at least 10 SNPs or 5 INDELs. Windows with the top 1% Fst values were considered as candidates of selection signatures, while the extreme top 0.1% windows were considered candidates with strong evidence of selection. The selection signature regions obtained were annotated against the chicken gene database from Ensembl (release 84). All genes from regions under selection (top 1%) were further analyzed to predict the function of these genes with GeneMANIA prediction server [The Weir and Cockerham pairwise Fst (Fixation index) method  was appln = 1000). In every permutation, the overlap with the QTLs was recomputed based on the total genomic size (Mb) that was overlapped. The observed number of overlaps was compared with the empirical distribution to obtain the p-values. A p-value < 0.05 was considered to determine significant associations.QTL overlap analysis was carried out using the regioneR package . From 19The SNPs and INDELs detected in the present study were compared with variants detected from different populations in previous NGS studies as a means of validation. The filtered SNP set was compared with the 15 M SNPs detected in Gheyas et al. , which hThe accuracy of the NGS-based SNP genotype calls was investigated by comparing sequence data from two chickens against the genotype calls from 600 K Affymetrix® Axiom® HD genotyping array from the same chickens, which have been previously genotyped with the array. The chickens chosen for this analysis had intermediate sequencing coverage of ~ 11 X/chicken.From the NGS dataset, we compared 13,218,246 SNPs with 11.2 X coverage from the layer and 13,229,324 SNPs with 11.5 X average coverage from the broiler chicken. From the chip dataset, the total of 575,599 (layer) and 574,482 (broiler) SNPs were used, excluding the SNPs located on the linkage groups. In both datasets, the chromosomes analyzed were GGA1–28 and sex W/Z. Both heterozygous and homozygous genotypes were considered for comparison of the two datasets. The comparison was performed based on a methodology previously proposed  by calcuAdditional file 1:A figure with the substitution types of SNPs initially identified from the 28 chickens (broiler and layer chickens combined). (PDF 1108 kb)Additional file 2:A figure with the proportion of heterozygous and homozygous SNPs (A) and INDELs (B) observed in each individual of layer and broiler lines. (PDF 7169 kb)Additional file 3:http://www.ingenuity.com/) with default parameters were used to find metabolic pathways of genes with relevant biological functions. (XLSX 10 kb)An excel file with the top nine networks from frameshift and non-frameshift INDELs from the layer and broiler lines combined. QIAGEN’s Ingenuity® Pathway Analysis (IPA®) software  with default parameters were used to find metabolic pathways of genes with relevant biological functions. (XLSX 10 kb)An excel file with the top six networks from frameshift and non-frameshift INDELs exclusively from the layer or broiler line. QIAGEN’s Ingenuity® Pathway Analysis (IPA®) software Additional file 6:An excel file with enrichment analysis results including the enriched GO terms from genes located in top 1% regions under selection (SNP dataset). The enrichment analysis was performed with GeneMANIA prediction server considering human database. (XLSX 9 kb)Additional file 7:An excel file with enrichment analysis results including the enriched GO terms from genes located in top 1% regions under selection (INDEL dataset). The enrichment analysis was performed with GeneMANIA prediction server considering human database. (XLSX 9 kb)Additional file 8:An excel file with the intolerant SNPs located within genes under selection (top 1%). (XLSX 11 kb)Additional file 9:An excel file with the SNP validation comparison results showing the number and percentages of SNPs with same position (POS-CONC) and genotype (GEN-CONC) from broiler and layer chickens of NGS and chip dataset. (XLSX 9 kb)


Mimulus guttatus and M. nasutus). This hybrid incompatibility, which causes one sixteenth of F2 hybrid seedlings to lack chlorophyll and die shortly after germination, occurs between sympatric populations that are connected by ongoing interspecific gene flow. Using complimentary genetic mapping and gene expression analyses, we show that lethality occurs in hybrids that lack a functional copy of the critical photosynthetic gene pTAC14. In M. guttatus, this gene was duplicated, but the ancestral copy is no longer expressed. In M. nasutus, the duplication is missing altogether. As a result, hybrids die when they are homozygous for the nonfunctional M. guttatus copy and missing the duplicate from M. nasutus, apparently due to misregulated transcription of key photosynthetic genes. Our study indicates that neutral evolutionary processes may play an important role in the evolution of hybrid incompatibilities and opens the door to direct investigations of their contribution to reproductive isolation among naturally hybridizing species.Hybrid incompatibilities play a critical role in the evolution and maintenance of species. We have discovered a simple genetic incompatibility that causes lethality in hybrids between two closely related species of yellow monkeyflower ( The evolution of hybrid incompatibilities (gene interactions that cause hybrids to be sterile or inviable) is a common outcome of genomic divergence between lineages. However, evaluating the importance of hybrid incompatibilities for speciation requires that we identify the causal genes and evolutionary forces in recently diverged, wild species. We discovered that hybrid seedling lethality between two closely related sister species of yellow monkeyflower is caused by duplicate copies of a gene critical for chloroplast development. Because each lineage carries its one functional gene copy in a distinct genomic location, some hybrids inherit only inactive (or missing) alleles. We infer that hybrid lethality in this young species pair has arisen through divergent resolution of gene duplicates by degenerative mutations and (likely) genetic drift. These findings are an important step toward understanding the evolutionary dynamics of hybrid incompatibility genes in nature, as well as the role of such genes in species divergence. Drosophila, Arabidopsis, and rice. However, insight into the evolutionary forces acting on hybrid incompatibilities during the speciation process requires a focus on young species pairs with natural populations.Across diverse taxa, hybrid incompatibilities arise as a byproduct of genetic divergence among incipient species. The basic genetic underpinnings of this process are well understood: two or more mutational differences between species interact epistatically to cause hybrid inviability or sterility –3. HowevOver the past two decades, genetic dissection of diverse incompatibilities has provided some hints about their evolutionary origins (reviewed in –7). OfteOryza sativa [Drosophila melanogaster-D. simulans hybrids [O. sativa and wild species [Arabidopsis thaliana [O. sativa [The idea that gene duplication might play a key role in hybrid incompatibilities was initially proposed by Muller as a variant of his original model . He expla sativa , and mora sativa , 25. The hybrids  and the  species , 22. Genthaliana , 28 and . sativa . HoweverMimulus guttatus and M. nasutus. These recently diverged species  in central California, where both species coexist within centimeters of one another. Species at DPR are strongly isolated by divergence in flowering time and mating system [Mimulus hybrid lethality. As the first case of hybrid incompatibility genes identified between naturally hybridizing species, this study opens the door to direct investigations of their evolutionary dynamics and contribution to reproductive isolation.In this study we investigate the molecular genetic basis of hybrid seedling lethality between two closely related sister species of yellow monkeyflower, -500kya; ) co-occu-500kya; –33 and p-500kya; –40. Desp-500kya; , 31, 41.g system . Using hM. guttatus and M. nasutus from the sympatric DPR site and is easily characterized by seedlings that completely lack chlorophyll  and DPR104-nas (M. nasutus). We then examined phenotypic ratios of white and green seedlings among their selfed progeny and reciprocal F1 and F2 hybrids . Segregation of white seedlings in reciprocal F2 hybrids suggests a nuclear, rather than cyto-nuclear, genetic incompatibility. Chi-squared tests rejected several genetic models that could potentially explain the observed phenotypic ratios, but could not reject a two-locus model involving only recessive alleles in either F2 population, or when their ratios were combined . In the first round, we pooled DNA from green and white F2 seedlings into eight separate tubes . Because incompatibility alleles act recessively, our expectation was that pooled white seedlings should be homozygous  at markers linked to hybrid lethality loci, whereas green seedlings should segregate 1:2:1 (for DPR102-gutt homozygotes: heterozygotes: DPR104-nas homozygotes). Of the 126 size-polymorphic markers (spanning much of the Mimulus genome) that we used for genotyping, four showed an association with seedling phenotype: the four tubes with white seedlings carried only (or mostly) DPR104-nas alleles, whereas green seedlings carried both parental alleles. All four markers map to a region of roughly 40 cM on linkage group 14 .To genetically map ition in ), which hl14. We generated 60 F3 families by self-fertilizing green F2 hybrids that were homozygous for DPR104-nas alleles at hl14 (determined by genotyping flanking markers); these F3 families segregated green and white seedlings in ratios of either 3:1 or 1:0. We reasoned that if hybrid lethality is caused by hl14 and a single interacting locus, white F3 hybrids should be homozygous for DPR102-gutt alleles at the partner, whereas green F3 families that do not segregate white seedlings should be homozygous for DPR104-nas alleles. Based on this logic, we formed two separate pools of DNA from F3 hybrids: one with 34 white seedlings and one with 26 green seedlings from non-segregating families. Note that each F3 seedling was derived from a different family  so that at markers unlinked to hybrid lethality, both pools should carry each of the two parental alleles at ~50% frequency. For the two pools, we performed whole genome sequencing, generated a genome-wide SNP dataset, and calculated average allele frequency difference in 200-SNP sliding windows (100-SNP overlap between windows). Using this approach, we discovered that the top 5% most divergent windows were located in contiguous windows along the distal end of chromosome 13 .To identify the partner locus, we performed a second round of BSA controlling for genotype at osome 13 , which whl13 and hl14, we generated a large DPR104-nas x DPR102-gutt F2 mapping population, oversampling white seedlings to roughly equalize frequencies of the two phenotypes . Each F2 individual was genotyped with two pairs of markers targeted just outside of the hybrid lethality loci: M208 and M236 at hl13, and M241 and M132 hl14. As expected, nearly all white seedlings were homozygous for DPR102-gutt alleles at the hl13-linked markers and homozygous for DPR104-nas alleles at the hl14 markers , whereas green seedlings never carried this genotype (N = 1478)  that allowed us to flank the locus. We genotyped informative recombinants at additional size-polymorphic and SNP-based markers designed in each interval. Although white seedlings must be destructively sampled for DNA, green seedlings were allowed to grow into adult plants so that informative recombinants could be self-fertilized and phenotyped via progeny testing. In this way, we determined if green F2 hybrids were heterozygous for hl13 and/or hl14 . Using this strategy, we mapped the hl13 locus to a 72.2 kb-region at the distal end of chromosome 13 that contains 24 genes  in Arabidopsis thaliana, gene ontology terms, known mutant phenotypes, and predicted functions (To fine-map  = 1478) . Because24 genes . At the e genome . For eacunctions .hl13 interval, we discovered several strong functional candidates for hybrid lethality including Migut.M02023, a homolog of pTAC14 (PLASTID TRANSCRIPTIONALLY ACTIVE CHROMOSOME 14). In A. thaliana, pTAC14 is essential for proper chloroplast development and mutants show a chlorotic lethal phenotype [-gutt x DPR104-nas F2 hybrid lethality. In addition to Migut.M02023 on chromosome 13, we also identified a highly similar and slightly truncated protein homolog of pTAC14 , Migut.O00467, located on an unmapped scaffold of the IM62 M. guttatus reference genome (v2.0 scaffold_193). To investigate the possibility that this additional copy of Mg.pTAC14 resides on chromosome 14, we turned to several large-insert IM62 genomic libraries (six fosmid and two BAC libraries) that were generated and end-sequenced as part of the reference genome assembly effort [Migut.N01489, a gene within the mapped interval of hl14. This finding provides evidence that a second copy of Mg.pTAC14 is located on chromosome 14 in IM62, despite it being absent from the current genome assembly. Using PCR, we confirmed that the DPR102-gutt genome also contains two copies of pTAC14 , but that only DPR102-gutt carries a copy at hl14 (referred to as Mg.pTAC14_2).To determine if R104-nas . RemarkaMimulus pTAC14 genes, we obtained full-length genomic sequences from both DPR parents and generated a neighbor-joining tree (pTAC14 from DPR102-gutt and IM62 cluster together (Mg.pTAC14_1 and Migut.M02023). Likewise, chromosome 14 copies of pTAC14 from DPR102-gutt and IM62 cluster together (Mg.pTAC14_2 and Migut.O00467). However, somewhat counterintuitively, the DPR104-nas copy (Mn.pTAC14_1), which is located on chromosome 13, clusters more closely with M. guttatus copies on chromosome 14 than with copies on chromosome 13  contains a frameshift mutation in exon 7, which results in the production of numerous premature stop codons in downstream sequence  and hl14 from DPR104-nas (containing no copy of pTAC14). Using qPCR and primers that amplify both Mimulus pTAC14 duplicates, we found strong expression in green parental and F2 seedlings, but not in white F2 seedlings  correspond to Mg.pTAC14_2; consensus sequences generated in the same manner from DPR104-nas reads correspond to Mn.pTAC14_1. Moreover, RNAseq SNP variation in green F2 seedlings suggests they express only Mg.pTAC14_2 from DPR102-gutt and/or Mn.pTAC14_1 from DPR104-nas. In contrast, read coverage of pTAC14 transcripts in white F2 seedlings is exceptionally low: of the 1,092 genes that are significantly differentially expressed between white F2 seedlings and green seedlings , the duplicate copies of pTAC14 are the two most underexpressed  are significantly misexpressed in all three pairwise comparisons between white and green seedlings (N = 209), we found a significant enrichment of genes involved in photosynthesis and/or located within the thylakoid and photosynthetic membranes. Among overexpressed transcripts (N = 883), we observed an enrichment of heat shock proteins and glutathione peroxidase proteins  RNA polymerase, which leads to reduced transcription of some chloroplast-encoded genes, particularly those involved in photosynthesis , and increased transcription of others such as the rpo genes [Mimulus chloroplast genes, those involved in photosynthesis, and thus likely to be transcribed by PEP RNA polymerase, were often significantly underexpressed in white F2 seedlings. In contrast, homologs of A. thaliana rpo genes were significantly overexpressed. Several additional putative chloroplast genes  that are likely transcribed by both PEP and the nuclear-encoded phage-type (NEP) RNA polymerase [Mimulus F2 white seedlings, which show a remarkable similarity to patterns observed in A. thaliana pTAC14 knockouts [pTAC14 duplicates in Mimulus hybrid lethality.Comparison of genome-wide RNAseq patterns among DPR102eedlings . Among tproteins . Furthereedlings , S5 Fig.po genes . Of 52 plymerase –50 were nockouts , provideMimulus pTAC14, a gene critical for chloroplast development in A. thaliana [M. guttatus and M. nasutus at the DPR site. We fine-mapped hybrid lethality to hl13 and hl14, two small nuclear genomic regions on chromosomes 13 and 14. In DPR102-gutt (M. guttatus), pTAC14 is present in each of these genomic intervals, but only the hl14 copy is expressed. In DPR104-nas (M. nasutus), pTAC14 is present only in the hl13 interval, consistent with either of two possibilities: the hl13 copy is ancestral and this line lacks the duplication, or a large deletion has removed all trace of the gene from hl14. As a consequence of divergent resolution of these duplicate genes, F2 hybrids that are homozygous for DPR102-gutt alleles at hl13 and homozygous for DPRG102-nas alleles at hl14 contain no functional copy of Mimulus pTAC14. These hybrids fail to produce chlorophyll and die in the cotyledon stage of development, remarkably similar to what is observed in pTAC14 knockouts in A. thaliana [Identifying the molecular genetic basis of hybrid incompatibilities between recently diverged, wild species is a critical first step toward understanding their evolutionary origins and role in speciation. We have shown that duplicate copies of thaliana , causes thaliana . To our Mimulus pTAC14 is the cause of hybrid lethality between DPR M. guttatus and M.nasutus. But what causes the lack of pTAC14 expression in white hybrid seedlings? In M. nasutus (DPR104-nas), because pTAC14 is missing entirely from the hl14 interval, the hl13 copy (Mn.pTAC14_1) is the only one expressed. In M. guttatus (DPR102-gutt), the situation is less clear. Although both copies (Mg.pTAC14_1 at hl13 and Mg.pTAC14_2 at hl14) are present and highly similar in exons , pTAC14 copies on chromosome 13 are most related and pTAC14 copies on chromosome 14 are most related  and the M. nasutus copy on chromosome 13 (Mn.pTAC14_1) would have arisen from a similar genetic variant (M. guttatus is high [pTAC14_1. Both the duplicated copy in M. guttatus (Mg.pTAC14_2) and the ancestral copy in the selfing M. nasutus (Mn.pTAC14_1) would be expected to carry only a small subset of ancestral variation. Unfortunately, we have not yet been able to assess molecular patterns of Mimulus pTAC14 variation in a wider sample of M. guttatus and M. nasutus. Although whole genome resequence data are available from a number of lines [Mimulus pTAC14 align equally well to both annotated copies in the IM62 reference genome (Migut.M02023 and Migut.O00467). Once Mimulus pTAC14 is sequenced from a broader sample of individuals, we speculate that Mn.pTAC14_1 from M. nasutus and Mg.pTAC14_2 from M. guttatus will be included in distinct monophyletic groups nested within the greater diversity of sequences present at the ancestral Mg.pTAC14_1 from M. guttatus. Interestingly, white seedlings are often observed segregating at low frequencies within M. guttatus populations, which manifest as epistatic inbreeding depression [pTAC14 alleles within species will be required to determine whether the hl13-hl14 incompatibility plays a role in such phenotypes outside of the DPR population.In addition to showing that  related . However variant . Standin is high , 54–57, of lines , 54, 57,pression , 59 thatpTAC14 underlies hybrid lethality among sympatric Mimulus species, we now have a natural system in place to test broader questions regarding the evolutionary significance of hybrid incompatibilities in speciation.Our study provides the first detailed study of hybrid incompatibility genes from naturally hybridizing species and contributes to a growing body of literature that suggests hybrid incompatibilities might result from neutral evolutionary change within species. Going forward, it will be important to address whether these barriers can persist in the face of ongoing gene flow. Theoretical treatments of this question have consistently concluded that the maintenance of hybrid incompatibility alleles between hybridizing populations relies heavily on a selective advantage within species –64. If sM. guttatus and M. nasutus derived from wild individuals collected from Don Pedro Reservoir (DPR) in central California [M. nasutus is a selfer, the DPR104-nas line used in this study was naturally inbred. To generate the DPR102-gutt inbred line, we self-fertilized M. guttatus for three generations, which is expected to result in a genome that is 87.5% homozygous. The DPR102-gutt and DPR104-nas inbred lines were intercrossed to generate reciprocal F1 and F2 hybrids . To ensure that both inbred lines were homozygous at hl13 and hl14, we analyzed white:green seedling ratios in 32 F2 families ; indeed, all F2 families segregated white seedlings at a frequency of one sixteenth, confirming homozygosity at both hybrid lethality loci.In this study, we used inbred lines of lifornia . Wild-coM. guttatus and M. nasutus [DNA was extracted from seedlings and adult leaf tissue using a standard CTAB-chloroform protocol  modified nasutus , 67, 68  nasutus  and SequM. guttatus (IM62) reference genome (https://phytozome.jgi.doe.gov), along with previously generated whole genome re-sequence data for DPR104-nas [-nas using the samtools mpileup function, which provided a list of SNPs that differentiate these two lineages. We used the samtools mpileup function to estimate the frequency of each SNP  within white and green BSA pools. Since SNPs were not based on differences between DPR104-nas and DPR102-gutt, our analysis assumes that M. guttatus lines IM62 and DPR102-gutt (which is not sequenced) share a common set of SNPs.For whole genome sequencing of bulked segregants, we generated equimolar amounts of DNA from green (N = 26) and white hybrid seedlings (N = 34). Green and white DNA was pooled separately and sent to the Duke Center for Genomic and Computational Biology, where Illumina libraries with unique barcodes were prepared and sequenced using the Illumina Hi-seq platform (100bp single-end reads). Reads from both pools were aligned to the R104-nas . Reads wR104-nas ) with a R104-nas ). We idehl13 and hl14  followed by cDNA synthesis with GOscript Reverse Transcriptase . We designed exon-specific primers to amplify fragments of each gene  and a single class of white seedlings (white F2s). Seedlings with fully expanded cotyledons began to emerge within 3 days and continued to emerge for a week thereafter. We collected pools of 10 seedlings from each biological class directly into 2mL Eppendorf tubes filled with liquid nitrogen. We then extracted RNA from these pools using the Zymo Quick-RNA microprep kit  and estimated RNA concentration using a qubit fluorometer . High quality RNA was subsequently submitted to the Duke Center for Genomic and Computational Biology, where Kapa Stranded mRNA-Seq libraries  were prepared and samples were sequenced across a single lane of Illumina Hiseq 4000 with single-end 50 bp reads. In total, our analysis involved three replicates each of DPR102-gutt and DPR104 green seedlings, five replicates of green F2 seedlings, and six replicates of white F2 seedlings, where each replicate was a pool of 10 seedlings.We performed an RNAseq experiment to compare genome-wide expression profiles between white and green seedlings. We used lines of DPR102-gutt, DPR104-nas, green F2s, and white F2s). We aligned trimmed and filtered reads (Q>20) to the M. guttatus IM62 reference genome in TopHat2 [https://phytozome.jgi.doe.gov). For GO term analyses, we used all annotated genes in the v2.0 IM62 M. guttatus reference assembly to serve as the background population and used a Bonferroni cutoff value of 0.05 to test for significant GO term enrichment in our subset of differentially expressed genes. For our analysis of chloroplast-encoded genes, we generated a list of putative chloroplast genes, since no chloroplast genome assembly is currently available for M. guttatus. We generated this set by first downloading a list of 135 genes present in the chloroplast genome in A. thaliana from the TAIR database (www.arabidopsis.org). We used this list to identify M. guttatus homologs in the Phytomine database (https://phytozome.jgi.doe.gov). This approach yielded a set of 52 putative Mimulus chloroplast genes that are currently included  in the nuclear genome (no homologs were identified for the other 83 genes used in our search). A substantial fraction of these genes (69%) occur along chromosome 4 from positions 6,719,000–7,985,375, which contains 183 genes total.We utilized the cufflinks pipeline  to asses TopHat2 , which r TopHat2 , and the TopHat2 . Gene on TopHat2  and GO:: TopHat2  implemenpTAC14 sequences from DPR102-gutt and DPR104-nas, we amplified both genomic and cDNA using primers designed within conserved exonic sequence. PCR fragments were amplified using Phusion High-Fidelity DNA Polymerase  and either directly sequenced or sequenced after cloning into the TOPO TA Vector . Additionally, we extracted full-length transcript sequences from RNAseq data by performing de novo assemblies on reads that mapped to candidate genes using the Geneious Assembler . When reads mapped to duplicated genes, they were combined into a single de novo assembly and 95% confidence consensus sequences were constructed. Using PHYML [pTAC14 using 4,319 bp of genomic sequence (excluding 5’ and 3’ UTRs and insertions/deletions coded as single variants) with branch support determined with 1000 bootstraps. For the tree presented in To obtain full-length ng PHYML , we consS1 FigM. guttatus (DPR102-gutt) and M. nasutus (DPR104-nas). Photos kindly provided by Adam J Bewick.White seedlings segregate in 1:15 in reciprocal F2 hybrids of (TIF)Click here for additional data file.S2 FigMimulus chromosomes) was calculated in 200-SNP windows with 100-SNP overlap. The 0.5% most divergent windows are highlighted as black dots, which are all located at the distal end of chromosome 13 in contiguous windows and represent the candidate hl13 region. Red dots, which overlap with the previously mapped interval for hl14, show the 0.5% least divergent windows .Difference in average allele frequency between green and white pools Click here for additional data file.S3 FigMg.pTAC14_1, Mg.pTAC14_2, and Mn.pTAC14_1 in DPR104-nas x DPR102-gutt F2 seedlings. Red box indicates site of frameshift mutation in Mg.pTAC14_1.Exons are highlighted blue. Black boxes show ten SNPs that were used to positionally map copies (JPEG)Click here for additional data file.S4 FigPCR products run on 1% agarose gel with 2-log ladder. Control transcript is Migut.M00195 (ACYL-COENZYME A OXIDASE-LIKE PROTEIN). Note that DNA and RNA was extracted from pools of 10 seedlings for each genotype.(TIF)Click here for additional data file.S5 FigpsaA, psaB, psbA, psbB, petB, petD), PEP and NEP (atpA and ndhB), and NEP . Green: average log2 fold-change in all pairwise comparisons among green seedlings . White: average log2 fold-change in pairwise comparisons between white F2 seedlings and green seedlings. *Significantly down-regulated in white seedlings . **Significantly up-regulated in white seedlings .Differential expression among chloroplast genes transcribed by PEP ((TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


Arachis hypogaea) consists of two subspecies, hypogaea and fastigiata, and has been cultivated worldwide for hundreds of years. Here, 158 peanut accessions were selected to dissect the molecular footprint of agronomic traits related to domestication using specific-locus amplified fragment sequencing (SLAF-seq method). Then, a total of 17,338 high-quality single nucleotide polymorphisms (SNPs) in the whole peanut genome were revealed. Eleven agronomic traits in 158 peanut accessions were subsequently analyzed using genome-wide association studies (GWAS). Candidate genes responsible for corresponding traits were then analyzed in genomic regions surrounding the peak SNPs, and 1,429 genes were found within 200 kb windows centerd on GWAS-identified peak SNPs related to domestication. Highly differentiated genomic regions were observed between hypogaea and fastigiata accessions using FST values and sequence diversity (π) ratios. Among the 1,429 genes, 662 were located on chromosome A3, suggesting the presence of major selective sweeps caused by artificial selection during long domestication. These findings provide a promising insight into the complicated genetic architecture of domestication-related traits in peanut, and reveal whole-genome SNP markers of beneficial candidate genes for marker-assisted selection (MAS) in future breeding programs.Peanut ( Arachis hypogaea L.), is one of the most important edible oil crops in the world. Cultivated peanut is an allotetraploid , harboring homoeologous A and B genomes putatively derived from the natural hybridization of two wild diploid species, A. duranensis  and A. ipaensis   has become much more straightforward, allowing analysis of the impact of domestication on genomic variation. Specific-locus amplified fragment sequencing (SLAF-seq) is an efficient method of large-scale single nucleotide polymorphism (SNP) identification and genotyping using high-throughput sequencing, with many advantages such as lower costs and reduced genome complexity  have also been used to determine the genetic basis of traits underlying domestication in a wide range of organisms  accessions were examined in the present study, including 36 hypogaea type (group I) with no floral axes on the main stem, and 122 fastigiata type (group II) with flowers growing on both branch and the main stem  were selected based on uniqueness and uniformity of simulated fragment alignments to the reference genome sequence of two diploids, A. ipaensis  and A. duranensis . Different length fragments of genomic DNA after digestion were then simulated in silico. Oryza sativa indica (http://rapdb.dna.affrc.go.jp/) was selected as the control genome to test the accuracy of the restriction enzyme digestion protocol using SOAP software  then the PCR products were purified with a Quick Spin column  and electrophoresed on 2% (w/v) agarose gel. Fragments with expected lengths were isolated using a Gel Extraction Kit (Qiagen) and diluted for sequencing. Fragments of 314–344 bp were isolated for use as SLAF tags.https://github.com/lh3/seqtk). High quality paired-end reads were mapped onto the reference genome (A. ipaensis and A. duranensis) using the Burrows-Wheeler Aligner (BWA)  and compressed mixed linear model (MLM) with TASSEL software  was used to explain the phenotypic variation of each marker. It was calculated as SS Marker  / SS Total, where SS stands for sum of squares .where Q is the population structure derived from ADMIXTURE software , is a measure of population differentiation or genetic distance based on genetic polymorphism data  ratios were evaluated using a 100-kb sliding window with 10-kb steps  and the top 5% of π ratios were defined as potential selective sweeps . Sequencing depths ranged from 32.42 to 4.94 X, with an average of 8.06 X. Polymorphic SLAFs defined by both GATK and SAMtools were recorded as reliable SNPs, resulting in a total of 268,889 SNP markers among the 158 accessions. After filtering SNPs located on scaffolds, 17,338 high quality SNPs with an MAF > 0.05 and integrity >0.5 were selected for further analyses , while chromosome A08, the shortest chromosome at 48.94 Mb, contained the least . The average number of SNPs/Mb was seven on both the A and B subgenomes, while average genes/Mb were 35 and 31, respectively. Chromosome B03 had the highest number of genes , while its counterpart, chromosome A03, contained the second highest .r2 value, revealing uneven distribution of SNPs on each chromosome. r2 values of the A subgenome ranged from 0.071 on chromosome A03 to 0.356 on chromosome A02, while those of the B subgenome ranged from 0.029 on chromosome B03 to 0.251 on chromosome B05. Average r2 values of the A and B subgenomes were 0.177 and 0.137, respectively, revealing differences in the level of LD between different chromosomes and subgenomes.LD was estimated as the hypogaea accessions (group I) forming a separate cluster, and fastigiata accessions (group II) forming another large cluster  = 6.238)  Figure . A 100-kAs a result, a total of 13 significant SNPs were associated with height of the main stem, with the peak SNP A03-26481539 explaining 27.55% of the phenotypic variation. The Aradu 52T5J gene was located ~32 kb from this SNP, and is known to encode a malate dehydrogenase, which is thought to be related to biomass and plant height in maize  and a π I / π II ratio threshold of 2.03 -encoding genes are of particular interest because they confer resistance against pests and disease. Possible resistant genes in the highly differentiated genomic regions were therefore analyzed. Ten and 25 genes containing the NB-LRR domains were identified on the A and B subgenomes, respectively. Of the 10 NB-LRR-encoding genes on the A subgenome, eight were located on chromosome A03, and of the 25 on the B subgenome, 18 were located in two genomic regions of chromosome B07. One region was located within 282 kb of chromosome B07 (between 1714315 and 1996823) and harbored 15 NB-LRR-encoding genes Table , suggestArachis are widely distributed across tropical, subtropical and warm temperate zones, but only the cultivated peanut (A. hypogaea) is an important food crop. Peanut evolved morphologically during domestication, allowing it to adapt to various agroecological environments  of the selected accessions  represents the most ancient variety due to its runner habit, lack of floral spikes and branching patterns, which are similar to the characteristics of wild Arachis species. In this study, analysis of phylogenic relationships, population structure and PCA among 158 peanut accessions revealed that accessions in subsp. fastigiata contain unique genomic regions that differ from those in hypogaea  were used as the reference genomes. In order to accurately allocate marker tags to the correct genome, sequencing depth was combined with integrity and MAF to filter SNPs. There are not too much SNPs found in exceptionally high homologous regions, which would not influence the following GWAS results. Finally, a total of 17,338 high quality SNPs were identified across the whole genome by reduced representation sequencing technology (the SLAF-seq method)  Table . The aved) Table .Arabidopsis thaliana, it was found to play a role in cell division and cell polarity  ratios. The existence of major selective sweeps on chromosome A03 indicated that this chromosome was subjected to primary selection pressure , late leaf spot (caused by Cercosporidium personatum), and Tomato spotted wilt virus , may cause significant yield loss in peanut production  on linkage group AhII with one QTL for LS, qF2LS1. Another QTL, qF5LS10 for LS, was identified between GM1254 and seq15C10 on linkage LGT17. The two linkage groups, AhII and LGT17, were colinearized with B07 of reference consensus genetic map (the three QTLs mentioned above covered 24 cM), suggested that there is a cluster of resistance QTLs on B07. Pandey et al. (A. duranensis) than in the “B” genome (345 genes in A. ipaensis). Though function of these genes needs to be further validated, these findings suggest that SNPs located in these major selective sweeps will facilitate future breeding of resistant cultivars in peanut. The major selective sweep on chromosome B03 harbored a number of genes related to both resistance and plant-type-related traits, similar to a recent study (Zhou et al., There are several major diseases, like early leaf spot(caused by y et al.  found thy et al. , who repIn summary, this study provides the first insight into the complex genetic relationship between agronomic traits and domestication processes in peanut. Chromosomes A03 and B03 harbored major genes related to peanut domestication, while chromosome B07 contained a cluster of NB-LRR-encoding genes. Further studies are now needed to understand the genetic mechanisms underlying yield- and seed-related traits and identify potential resistant genes for future peanut breeding programs.XZ, JZ, XH, DY carried out phenotyping and genotyping. XZ, YW, DY managed the project. XZ, XM, DY analyzed the data. XZ wrote the paper. All of the authors read and 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.


Milk β-lactoglobulin (β-LG) content is of interest as it is associated with nutritional and manufacturing properties. It is known that milk β-LG content is strongly affected by genetic factors. In cattle, most of the genetic differences are associated with a chromosomal region on BTA11, which contains the β-LG gene. The aim of this study was to characterize this region using 777 k SNP data (BovineHDbeadChip) and perform a haplotype-based association study. A statistical approach was developed to build haplotypes that capture the genetic variation associated with this genomic region.1, A2, B1 and B2.The SNP with the most significant effect on β-lactoglobulin content was one of the 2 causal mutations responsible for the β-lactoglobulin protein variants A/B. Haplotypes based on 2 to 5 selected lead SNP were clustered in groups with different effects on β-lactoglobulin content. Four different groups were identified suggesting that β-lactoglobulin variant A and B can be further refined in AThis study showed that β-lactoglobulin protein variants A/B do not explain all genetic variation associated with the tail part of BTA11 but this region contains more than one mutation with an effect on β-lactoglobulin content. These findings can be used for selection of cows with higher cheese yield, which is desirable for the dairy industry. Bovine milk contains around 3–4% protein, which consists of caseins and whey proteins. Around 80% of the milk proteins are caseins and the remaining fraction is made up of soluble proteins of which β-lactoglobulin (β-LG) is the most important , 2. β-LGTwo distinct forms of the β-LG protein (A and B) were described in 1955  and seveMilk β-LG content is strongly affected by genetic factors: 80% of the differences are due to genetics . A genomThis study aims to fine map the chromosomal region on BTA11 associated with β-LG content using 777 k SNP data and to investigate if one or multiple mutations are responsible for the observed effects.The average protein content of the milk samples was 3.50% (w/w%) and 8.34% of the protein consists of β-LG (data not shown). Table 10 of the association between SNP located on the tail part of BTA11 and the unadjusted β-LG  >350. The lead SNP, i.e. Q-Tag SNP1 (rs110066229) is located in the third exon of β-LG gene (PAEP) and is one of the 2 mutations responsible for the difference between β-LG protein variants A and B [2) between Q-Tag SNP1 and the other SNP. Table 1, the additive genetic variance drops from 1.121 to 0.111 . In other words, Q-Tag SNP1 explained 91% of the additive genetic variation of the unadjusted β-LG content. Herd variation slightly increased after adjusting for the effect of Q-Tag SNP1 and as a consequence of the decrease in additive genetic variation, the part of phenotypic variation explained by the herd variation increased. Figure 1. Q-Tag SNP2 (rs110144148) had a highly significant effect on β-LG1 with a –log10 of 12.9. This shows that not all variation associated with this chromosomal region is captured by the difference between the β-LG protein variants A and B. Q-Tag SNP2 is located at 107.3 Mb, i.e. distal from the PAEP gene. The additive genetic variation is further reduced after adjusting for Q-Tag SNP2 to 0.090, i.e. 8% of additive genetic variance of the unadjusted β-LG content (Table 2. Q-Tag SNP3 (rs136463816) is located at 104.8 Mb and has a –log10 of 4.6. The additive genetic variation for β-LG3 is 0.090. Figure 3. Q-Tag SNP4 (rs136800235) is located at 107.7 Mb and has a –log10 of 3.86. Figure 4. Q-Tag SNP5 (rs17871095) is located at 103.2 Mb and has a –log10 of 3.14. More details about the Q-Tag SNP can be found in Table Figure  A and B . The col rs11006629 is loc1, i.e. the β-LG protein variants  are also shown in Fig. Figure 1 and Q-Tag SNP2, haplotypes GG, GA, AG and AA can be distinguished. Pairwise comparisons of the predicted haplotype effects show that all four haplotypes have significantly different effects on β-LG content. Haplotype GG has the strongest positive effect on β-LG content and its effect is significantly different from haplotype GA, which also is associated with an increase of β-LG content. The GG and the GA haplotypes differentiate among β-LG protein variant A and will therefore be referred to as A1 (GG) and A2 (GA). The two other haplotypes AG and AA differentiate β-LG protein variant B and will be referred to as B2 (AG) and B1 (AA). These two haplotypes are associated with lower β-LG content. The estimated difference between haplotype GG and AA is 1.60% and therefore the expected difference between cows carrying two copies of haplotype GG versus those who have two copies of haplotypes AA is 3.20%.When considering Q-Tag SNP1, is differentiated in AGG and GGG haplotypes which have significantly different effects. Whereas the AGG haplotype is assigned to haplo-group A1, the GGG haplotype is assigned to haplo-group A2.Further refining the haplotypes by considering three Q-Tag SNPs results in one haplotype (AGA) which occurs at a very low frequency in the population (1%). Adding the third Q-Tag SNP does result in a further refinement: the GG haplotype, which was assigned to haplo-group A2) is not significantly different from effects of haplotypes in haplo-group A1 and the predicted effect of haplotype AAAC (haplo-group B1) is not significantly different from effects of haplotypes in haplo-group B2.When considering 4 or 5 Q-Tag SNP to build haplotypes, there is an increasing number of haplotypes that occur at low frequencies in the population. The predicted effects of haplotypes with frequencies smaller than 1% were not included in Fig. 1 (A1A1) and individuals homozygous for haplo-group B1 (B1B1) increased from 2.86 for considering only one Q-Tag SNP to 3.26 when considering 3 or more Q-Tag SNP. The analyses indicate that 89% of the additive genetic variation in β-LG content can be explained by the genomic region between 100 Mb and 110 Mb of BTA11.Table PAEP gene. Therefore it is expected that the phenotype-genotype relationship of milk β-LG content is relatively simple. The heritability of milk β-LG content was estimated to be 0.80 indicating that differences in β-LG content are strongly determined by genetic factors [PAEP explains most of the genetic variation in β-LG content [1, A2, B1 and B2, with distinct effects on β-LG content suggesting that this chromosomal region contains more than one mutation with an effect on β-LG content.β-LG is a milk protein which is the product of the  factors . A genom content . However1) is identical to the lead SNP based on the 50 k array [1 is one of the 2 causal mutations for β-LG protein variants A/B [1 actually may be one of the causal mutations or at least located close to the causal mutation. Q-Tag SNP1 explains most but not all of the additive genetic variation associated with this genomic region. This suggests that either the causal mutation has not been identified or that this region contains multiple mutations with an effect on β-LG content.Fine mapping the genomic region between 75 and 110 Mb on BTA11 using the high density SNP array (777 k) resulted in a substantial increase in SNP density as compared to the 50 k array SNP panel. Therefore, the high density SNP array is expected to increase the probability of finding SNP in strong Linkage Disequilibrium (LD) with the causal mutation(s). However, the lead SNP based on the 777 k array  than single SNP and therefore may have increased power to detect QTL , 18. Then where n is the number of Q-Tag SNP.An important difficulty with a haplotype-based approach is that the number of haplotypes becomes very large when haplotypes are based on an increasing number of SNP. E.g. when haplotypes are constructed based on the lead SNP and 10 adjacent SNP (5 on each side) 13 haplotypes are segregating in the current data and when 20 adjacent SNP are used (10 on each side of the lead SNP) the number of haplotypes is 53. Having a large number of haplotypes reduces the number of observations per haplotype: several haplotypes have frequencies smaller than 0.1%. The small number of observations per haplotype will likely dilute association signals. Construction haplotypes based on Q-Tag SNP strongly limits the number of possible haplotypes while still capturing the variation associated with a region. However, even when building haplotypes on Q-Tag SNP, the number of haplotypes is 2Haplotypes can be considered as alleles of a single multi-allelic marker and as such can be used in an association. The maximum number of genotype effects of this “super” marker is ½ m(m + 1) where m is the number of haplotypes . For example, for 8 haplotypes there are at maximum 36 effects to be estimated which is a further risk of diluting association signals. Therefore, we restricted the number of effects to be estimated by assuming additivity of the haplotype effects. The design matrices of both haplotypes were combined and the statistical analysis results in one estimated haplotype effect.Even when using the described approach, inevitably a few common and several rare haplotypes will appear when the number of Q-Tag SNP increases Fig. . These l1, A2, B1 and B2. This is consistent with other study suggesting that the genetic variant A and B of PAEP can be further refined into 4 genetic variants in total through splitting both the A and B variants into 2 sub-variants [1, was split in haplotype AGG which was assigned to haplo-group A1 and haplotype GGG which was assigned to haplo-group A2.In the current study we were able to identify 4 groups of haplotypes with distinct effects on β-LG content: Avariants . Both th1 carries a “+” allele at locus 1 and a “+” allele at locus 2, haplo-group A2 carries a “+”allele at locus 1 and a “-” allele at locus 2, haplo-group B2 carries a “-” allele at locus 1 and a “+” allele at locus 2 and haplo-group B1 carries a “-”allele at locus 1 and a “-” allele at locus 2. Using the results from Table Having more than two groups of haplotypes suggests that this chromosomal region contains more than one mutation with an effect on β-LG content. Assuming that there are two mutations underlying the observed haplotype effects, i.e. locus 1 and locus 2, than haplo-group A2haplo-group = 0.00) but there is a large effect on the casein index (h2haplo-group = 0.57). The estimated effect on the casein index is −0.87 for haplo-group A1, −0.69 for haplo-group A2, 0.62 for haplo-group B2 and 0.94 for haplo-group B1 (haplo-groups based on 3 Q-Tag SNP). The difference in casein index between the β-LG protein variants BB and AA is 3.15% whereas the difference between extreme haplotype groups (B1B1 vs. A1A1) is 3.63%. The casein index is directly related to the efficiency of cheese production and therefore selecting for B1B1 is beneficial to the dairy industry.β-LG protein variants are not associated with protein content of milk but are strongly associated with the casein index . When an1A1 versus B1B1). Although knowledge on the causal mutations is currently lacking, the identified haplotypes can be used in selection.In order to find the causal mutations a possible next step is to sequence animals. The haplotypes can be used to design sequencing studies and individuals from different haplo-groups can be identified for sequencing . The existence of more than two groups of haplotypes suggests that this chromosomal region contains more than one mutation with an effect on β-LG content. These findings can be used for selection of cows with higher cheese yield.The lead SNP from the single SNP association using the high density SNP array is one of the 2 mutations responsible for the difference between β-LG protein variants A and B. The statistical approach developed can be used in fine mapping, haplotypes reconstruction and association studies with quantitative traits. A tool enabling to decide at which step to stop the stepwise association study has to be found. We constructed haplotypes based on 2 to 5 Q-Tag SNP and clustered in groups with significantly different effects on β-LG content. This study showed there are 4 different haplo-groups: AThe present study was part of the Dutch Milk Genomics Initiative. In this project Milk samples were collected from 1,713 primiparous cows on 383 commercial herds. These cows descended from one of five proven bulls representing five large half-sib families (782 cows), one of 50 test bulls representing 50 small half-sib families (760 cows), or from 15 other proven bulls (171 cows). In the last group of 171 cows, at least 3 cows per herd were sampled. The pedigrees of the cows were supplied by the CRV . Each cow was at least 87.5% Holstein-Friesian. The average age of the cows at first calving was 2.1 years and the cows calved between June 2004 and February 2005. Almost all the same animals were used in previous studies for the genetic analysis of milk protein , 11.Morning milk samples, collected between February and March 2005 on 1,713 Dutch Holstein-Friesian cows, were analysed for detailed milk protein composition. The β-LG content was determined by Capillary Zone Electrophoresis (CZE) as described by Heck et al. (2008). Protein content (wt/wt%) was predicted based on infrared spectroscopy by routine milk recording  was used to genotype cows as well as the sires of the cows using the Infinium assay  . In addiThe genotypes of the 2 SNP responsible for the amino acid changes in the β-LG variants A and B and 8 other SNP associated with β-LG content  were avaIn total, 1,647 cows had both phenotypic and genotypic information and were used for the association study. Based on previous results , we focuThe single SNP association study was performed using the following model:klmnowas the β-LG content, μ is the mean for β-LG content, dimklmno is the covariate describing the effect of the numbers of days in milk, modelled with Wilmink curve [klmno is the covariate describing the effect of the age at first calving as linear and quadratic, seasonk is the fixed effect calving season , scodel is the fixed effect of sire group , SNPm is the fixed effect of the SNP, animaln is the random additive genetic effect of the animal n, herdo is the random herd effect and eklmno is the random residual effect. The animal effects were assumed to be distributed as N, herd effects were assumed to be distributed as N and the residuals were assumed to be distributed as N, where A is the additive genetic relationships matrix, I is the identity matrix, σa2 is the additive genetic variance, σherd2 is the herd variance and, σe2 is the residual variance. The statistical package ASReml [where ynk curve  as explank curve , caklmnoe ASReml  was usedThe heritability and the proportion of variance due to herd were calculated based on estimates from model (1) without the SNP effect. The heritability was calculated asherd) was calculated as:The proportion of variance due to differences among herds . Subsequently variance components were re-estimated for the adjusted phenotype (y*klmno) and the association study was repeated with variance components fixed at their new values. This procedure was repeated until P > 0.01 for the most significant SNP. The significant level of P-values = 0.01 equivalent to –log10 = 2 was chosen. False positive test were performed to check for multiple testing issue. In analogy to “Tag SNP”, i.e. a limited set of SNP that capture the genetic variation associated with a genomic region [1 (lead SNP for the un-adjusted β-LG content) will be referred to as β-LG1, β-LG2 refers to β-LG1 adjusted for the effect of Q-Tag SNP2 (lead SNP for β-LG1) and so on.where yc region , we defi1, Q-Tag SNP2, Q-Tag SNP3 and so on). The association of haplotypes with β-LG content was estimated using the following animal model:Haplotypes were constructed based on Q-Tag SNP and effects of these haplotypes were estimated. The number of Q-Tag SNP that determine a haplotype was gradually increased by adding Q-Tag SNP in order of their number  where I is the identity matrix and σ2haplo is the variation due to haplotypes.where the variables are as described for model (1) with the SNP effect being replaced by haplotype effects haplo1t-test was used to test if haplotype effects differed significantly. The haplotype effects were considered to be significantly different when p < 0.01. If haplotype effects did not differ, they were grouped and such a group of haplotypes will be referred to as “haplo-group”. The proportion of the phenotypic variance explained by haplotype groups was calculated based on model (2) as:Predicted values for the haplotype effects were calculated in ASReml  and a t-2 was estimated using PLINK 1.07 [In order to determine the LD among the SNP between 75 Mb and 110 Mb of BTA11, the rINK 1.07 . By defaINK 1.07 .


Cercosporidium personatum) is a major fungal disease of cultivated peanut (Arachis hypogaea). A recombinant inbred line population segregating for quantitative field resistance was used to identify quantitative trait loci (QTL) using QTL-seq. High rates of false positive SNP calls using established methods in this allotetraploid crop obscured significant QTLs. To resolve this problem, robust parental SNPs were first identified using polyploid-specific SNP identification pipelines, leading to discovery of significant QTLs for LLS resistance. These QTLs were confirmed over 4 years of field data. Selection with markers linked to these QTLs resulted in a significant increase in resistance, showing that these markers can be immediately applied in breeding programs. This study demonstrates that QTL-seq can be used to rapidly identify QTLs controlling highly quantitative traits in polyploid crops with complex genomes. Markers identified can then be deployed in breeding programs, increasing the efficiency of selection using molecular tools.Late leaf spot (LLS; Key Message: Field resistance to late leaf spot is a quantitative trait controlled by many QTLs. Using polyploid-specific methods, QTL-seq is faster and more cost effective than QTL mapping. Arachis hypogaea) is an important oil crop rich in protein, vitamins and minerals. It is grown in most temperate to sub-tropical regions of the world reaching a total of 40 million tons in global production in 20141. Late leaf spot  is a major fungal disease plaguing peanut production worldwide. In Georgia-United States, the disease starts around mid-August when the fungal spores germinate and penetrate peanut leaves through stomata located on the abaxial surface. Round-shaped black lesions become visible within 1 week of fungal germination. Since the spores are deposited in the soil, LLS disease usually starts from the bottom layer of the plant canopy and moves upward. As the fungal lesions enlarge, coalescence of lesions often is observed in highly susceptible lines  and restriction fragment length polymorphism (RFLP) markers . This meA. cardenasii  population not used for QTL-seq and by backwards selection. Four years of field data showed that these markers are suitable for marker-assisted selection.6 to provide seed for line increases. F6:8 RILs were used to initiate phenotyping. At the F2 stage, the population was divided so that half of the population was advanced in Tifton, GA, United States and the other half was advanced in Raleigh, NC, United States. Subsequently, a total of 192 and 191 RILs were independently increased at these two locations.The C1801 (Florida-07 x GP-NC WS 16) population was advanced using small plots of bulked seed to minimize attrition . A rando2) at a seeding rate of six seeds per 0.3 m in Georgia in 2012, 2013, 2014, and 2015. Tests for years 2012 and 2013 were conducted at the University of Georgia College of Agricultural and Environmental Sciences Gibbs Farm, Tifton, GA, United States and for years 2014 and 2015 at the CAES Bowen Farm, Tifton, GA, United States. Marker-selected NC advanced lines along with resistant checks Georganic , where σg2 was the genetic variance component among the RILs, σ2gxe was the RIL x environment interaction variance component and σ2e was the residual component, n was the number of environments and r was the number of replications . The broad sense heritability was estimated according to the formula: ications . Statist®. Equal amounts of DNA were pooled to form each bulk. Whole genome shotgun sequencing libraries were constructed using the Illumina TruSeq PCR-free kit starting with 2 μg of total DNA isolated from single plants using Qiagen DNAeasy Plant mini kit® and and paired-end 150 sequencing was performed on the Illumina HiSeq 2500 V4® sequencer at HudsonAlpha Institute for Biotechnology . Florida-07 and GP-NC WS 16 had been integrated in a group of 20 genotypes for whole genome re-sequencing, including 10 parents of RIL populations  with Bowtie2 using default parameters for sensitive local alignment reporting best alignment and zero mismatch in the 20 bp seed. Consequently, very similar overall alignment rate was obtained for both genomes, being 96.7% on average over the A. duranensis genome and 96.9% over the A. ipaensis genome. The SNP calling between all the genotypes based on the reference genomes, and filtering of homeologous SNPs was developed following the SWEEP Prime version program  using Bowtie 2 v. 2.2.3  under the BioProject ID An initial analysis was done without knowledge of parental SNPs. First, the resistant and susceptible bulk SNPs were filtered for ‘polymorphic’ loci using mpileup-generated genotypic probabilities using custom scripts and filtered for at least 10 reads per bulk covering a SNP. The SNP Index was calculated for each bulk by counting the number of reads with the SNP and dividing by the total reads mapped to the locus for each bulk. Then the susceptible bulk index was subtracted from the resistant bulk index to get the ΔSNP for each SNP.To clean up noise in the data from false-positive SNP calls, a new strategy was employed using parental SNPs identified with the polyploid-specific SNP filtering tool, SWEEP . SNPs wep < 0.05 and p < 0.01. The simulation python script is available in Supplementary File To generate a null model assuming no QTLs, a permutation test was carried out as in The G statistic was calculated for each SNP as described in The LLS rating data from all 4 years were shuffled 10,000 times and the top 16 individuals were selected to represent the ‘resistant’ lines and the bottom sixteen individuals were selected to represent the ‘susceptible’ lines. Sixteen was used because that is the number of individuals represented in the RIL population with resistant or susceptible alleles at each marker. For each iteration, the number of groups of shuffled individuals that had an average disease score below the empirical group of ‘resistant’ lines and an average disease score above the empirical group of ‘susceptible’ lines was recorded.Table 1). The RIL population demonstrated large continuous phenotypic variation across all three years  and transgressive segregation for leaf spot resistance was observed (Table 1). Both RIL and environment (year) significantly affected LLS resistance in the analysis of variance test whereas the effect of RIL × environment (year) was not significant (Table 2). The year to year variation in phenotype data is caused by the fluctuation of disease pressure in the natural field environment; however, the lack of RIL x environment effect indicates that the RILs performed consistently in response to varied levels of disease pressure. The broad sense heritability was 0.88 supporting high genetic influence on the phenotypic variation. Since the RIL data deviated from a normal distribution detected by the Shapiro test, square-root transformation was applied to normalize the data set.Late leaf spot ratings (AUDPC) were significantly different between Florida-07 and GP-NC WS 16 in 2012 and 2015 but the difference was not significant in 2014 (Table 3) The median bulk ranking for the “Resistant” bulk (R bulk) was 7.75 and the median ranking for the “Susceptible” bulk (S bulk) was 177.5. These bulks were subjected to whole genome shotgun sequencing.Selection of individuals to include in sequencing bulks was done by ranking each line for every year and selecting the top five and bottom five lines according to average rank  with most significant sliding window ΔSNP values of 0.6, 0.78, and –0.74, respectively (p < 0.05). The estimated regions for each QTL are 4.7 Mb (A05), 1.2 Mb (B03), and 3.4 Mb (B05). The candidate QTL on chromosomes A05 and B03 represented alleles from the resistant parent (GP-NC WS 16) as expected. The QTL on B05 represented alleles from the susceptible parent Florida-07. Three Kompetitive Allele Specific PCR (KASP) markers were developed representing significant SNPs within each QTL region  were detected in the bulks, using pipeline specifically designed to deal with the particular issues in polyploid genomes, SWEEP . SignifiFigure 3, top). As a confirmation of these results, the data were subjected to a bootstrapping algorithm. After 100 simulations across all 4 years of data, more than 95% of randomly selected pools of individuals had an average disease score greater than the ‘resistant’ group selected with the three markers (p < 0.05). Additionally, more than 95% of randomly selected pools of individuals had an average disease score less than the ‘susceptible’ group selected with the three markers (p < 0.05).To validate the identified regions, the three KASP markers with ΔSNP values for each SNP of 0.6 for A05, 0.78 for B03, and –0.8 for B05, were first used to genotype the entire population . The selected lines were significantly different in response to LLS.A blind validation test was conducted to further validate the identified markers. First, lines were selected among the RILs advanced in North Carolina that were not tested for LLS severity previously. The lines were selected only with the KASP markers to select for ‘resistant’ and ‘susceptible’ alleles for each marker. The selected lines were tested in an unsprayed field test with three replicates following the randomized block design. In addition, the lines used in each bulk for sequencing and resistant and susceptible checks were included in the test . There is an exception to this and that is when the trait of interest is localized within an alien introgression segment that was introduced using interspecific hybridization methods. In this case, when a segment from a foreign genome is controlling the trait of interest, it is routine to identify these introgressions easily  $50 per sample, then the genotyping cost is $9,550 plus labor and reagent costs for DNA extraction. Using QTL-seq, only two samples need to be sequenced. The genome size and genome complexity drives the sequence coverage needed, but as an example for peanut, sufficient data could be generated by sequencing each bulk to 10× coverage (used in this study). Using the Illumina coverage calculator3 two bulks could be sequenced on one NEXTSeq high output run or over two lanes of HiSeq 2500 high output (with 2× extra coverage). The cost of the library construction ($30 per library) and sequencing  will be less than genotyping the entire population. More cost savings can be recovered by combining many bulks that target multiple traits of interest. Further, because sequencing gives access to genotype-specific markers, the marker most tightly linked to the QTL can be identified and converted to a marker for marker-assisted selection. Using sets of SSR markers or a SNP array designed with markers of more broad applicability, there is less chance to identify markers as strongly linked to the trait.For a quantitative trait, such as LLS resistance, bulking individuals based on a single year of data would give spurious results. For example, in this study, bulking after 2012 would give much different results than using any of the other years. Bulking using just the data in 2015 would have yielded the same results, but knowledge Figure 4). The thresholds for significance varied across read depths. Read depth will vary greatly between different experiments based on experimental design. Variation also is due to sequencer sampling. Random sampling of sequenced reads aligning to each marker, a unique estimation of the null distribution should be established for each marker in a dataset-specific manner. This will control for experiments with high variability of sequencing depth between markers.It is important to note that the probability of the null distribution will change according to the specific read depth at each SNP position. By simulating mapping-by-sequencing datasets, the number of candidate mutations decreased according to increased read coverage and number of pooled individuals . AdditioIn this study, QTL-seq was used to identify multiple QTLs for LLS resistance in peanut. These QTLs were validated by QTL mapping, backward selection, and blind selection. Markers were designed using the most significant SNPs and it was shown that selection with these markers alone could lead to a significant increase in resistance in the field. The power of this method is its speed and low cost relative to QTL mapping. Further, markers can be designed straight from identified QTLs that are strongly linked to the trait and can be deployed immediately in breeding programs. One caveat is that these QTsL are background specific, as statistical power must be increased substantially to attain perfect linkage to the functional variation. That being said, QTL-seq provides the resolution necessary to find strong linkage in populations where the donor parents of the identified alleles are in the pedigree. QTL-seq can be used for marker-assisted selection even with highly quantitative traits where multiple experiments are used to identify bulks. In many breeding programs, historical data can be leveraged to advise bulk creation, further increasing the efficiency of marker identification.PO-A, JC, and YC conceptualized the research and revised the manuscript; TI, CH, and CC provided the genetic resources and data; JC, YC, CC, SB, and AC performed the experiments, conducted the data analysis, and curated the data; JC and YC wrote the original draft and were responsible for the data visualization; PO-A administered the project.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.