Single nucleotide polymorphisms (SNPs) have become the marker of choice for genome-wide association studies. In order to provide the best genome coverage for the analysis of performance and production traits, a large number of relatively evenly distributed SNPs are needed. Gene-associated SNPs may fulfill these requirements of large numbers and genome wide distribution. In addition, gene-associated SNPs could themselves be causative SNPs for traits. The objective of this project was to identify large numbers of gene-associated SNPs using high-throughput next generation sequencing.Transcriptome sequencing was conducted for channel catfish and blue catfish using Illumina next generation sequencing technology. Approximately 220 million reads (15.6 Gb) for channel catfish and 280 million reads (19.6 Gb) for blue catfish were obtained by sequencing gene transcripts derived from various tissues of multiple individuals from a diverse genetic background. A total of over 35 billion base pairs of expressed short read sequences were generated. Over two million putative SNPs were identified from channel catfish and almost 2.5 million putative SNPs were identified from blue catfish. Of these putative SNPs, a set of filtered SNPs were identified including 342,104 intra-specific SNPs for channel catfish, 366,269 intra-specific SNPs for blue catfish, and 420,727 inter-specific SNPs between channel catfish and blue catfish. These filtered SNPs are distributed within 16,562 unique genes in channel catfish and 17,423 unique genes in blue catfish.For aquaculture species, transcriptome analysis of pooled RNA samples from multiple individuals using Illumina sequencing technology is both technically efficient and cost-effective for generating expressed sequences. Such an approach is most effective when coupled to existing EST resources generated using traditional sequencing approaches because the reference ESTs facilitate effective assembly of the expressed short reads. When multiple individuals with different genetic backgrounds are used, RNA-Seq is very effective for the identification of SNPs. The SNPs identified in this report will provide a much needed resource for genetic studies in catfish and will contribute to the development of a high-density SNP array. Validation and testing of these SNPs using SNP arrays will form the material basis for genome association studies and whole genome-based selection in catfish. Single nucleotide polymorphisms (SNPs) are alternative bases at any given position of DNA. They are among the most abundant type of genetic variations and widely distributed within genomes. Theoretically, SNPs can have four alleles in the population, but they most often exist as bi-allelic markers. Because of their potential for high genotyping efficiency, automation, data quality, genome-wide coverage and analytical simplicity , SNPs haIn species where the whole genome has been sequenced, SNPs have been identified from genome sequencing efforts. In most cases, SNPs were identified by sequence variations between the two alleles of a single diploid individual whose genome was sequenced . More reTo identify larger numbers of gene-associated SNPs, higher throughput expressed sequence reads are needed to increase coverage and depth and ensure sequence accuracy. Next generation sequencing technologies such as Roche/454, Illumina/Solexa, and ABI/SOLiD sequencing platforms are particularly adapted to producing high coverage of expressed sequences within contigs . TranscrIctalurus punctatus) and blue catfish (I. furcatus) in order to identify gene-associated SNPs for the development of SNP arrays in catfish.While the 454 sequencing technology has been widely used for transcriptome analysis, Illumina sequencing technology is being gradually accepted for its dramatically improved sequencing throughput and quality ,24. PairIllumina sequencing was conducted to generate short sequence reads of expressed sequences. Two cDNA libraries were made from pooled RNA samples prepared from a total of 11 tissues of 47 channel catfish and 19 blue catfish, respectively, representing major strains used in commercial production. The cDNAs were sequenced with one lane each using Illumina GA-II and Illumina HiSeq 2000 that generated 48.6 million 36-bp paired-end reads and 173.9 million 100-bp paired-end reads for channel catfish, and 66.9 million 36-bp paired-end reads and 216.6 million 100-bp paired-end reads for blue catfish . As shown in Table Assembly of the expressed short reads was conducted in several ways. First, reference assemblies of channel catfish expressed short reads and blue catfish expressed short reads were conducted separately using all existing catfish ESTs as a reference. Such assemblies would allow establishment of contigs for channel catfish expressed short reads and blue catfish expressed short reads separately to allow identification of intra-specific SNPs that are anchored (scaffold) by longer EST reference sequences. Such an assembly is superior to the total de novo assembly of these remaining reads was conducted. As shown in Table de novo, generating 420,165 contigs and 420,953 contigs for channel catfish and blue catfish, respectively. However, the average contig length was much shorter than those in the reference assembly, with 298 bp and 315 bp for channel catfish and blue catfish, respectively. These contigs are also useful resources for the identification of intra-specific SNPs.Despite generating an efficient reference assembly, over 66 million channel catfish reads and 90 million blue catfish reads were not assembled with the reference assembly. These reads could represent additional genes that were not represented by the EST reference sequences, or they could come from gene regions that were not represented by the EST references. In order to make them useful resources for SNP identification, de novo assemblies were conducted using combined channel catfish and blue catfish expressed short reads in order to identify inter-specific SNPs. A total of 493.4 million expressed short reads from both channel catfish and blue catfish  were used. The reference assembly of all catfish expressed short reads placed 336.0 million reads (68%) into 104,870 contigs, with an average contig length of 686 bp. Similarly, the de novo assembly of all catfish expressed short reads generated 421,229 contigs, with an average contig length of 340 bp  had BLAST hits to the Uniprot database, and matched 17,766 unique protein accessions. As expected, a lower percentage of the contigs from de novo assembly had BLAST hits to Uniprot proteins. Of the 421,229 contigs, 24,168 (5.7%) had BLAST hits to the Uniprot database, with matches to 12,331 unique proteins. Altogether, of the 526,099 contigs, 56,518 (10.7%) had significant BLAST hits to the Unitprot database, and matched 24,440 unique protein accessions. Larger numbers of contig hits but fewer matches to unique proteins were observed when compared to the zebrafish Refseq protein database  annotations in catfish was compared with that of zebrafish.The unique genes from the catfish and the zebrafish annotated database were analyzed using generic GO-slim terms with Blast2GO ,27. The As summarized in Table Our previous research suggested that SNPs identified from contigs with at least four sequences at the SNP sites with the minor allele being represented at least twice are more reliable . In thisSince the information on minor allele frequency (MAF) is an important consideration in choosing which SNPs to be included in SNP arrays, the minor allele frequencies of SNPs in the discovery populations were estimated from the sequence data. As shown in Figure While the number of SNPs is important, their distribution in contigs and genes within the genome is also important when used for genetic analysis. A total of 168,458 channel catfish contigs and 190,197 blue catfish contigs were found to contain putative filtered SNPs, of which 13,414 contigs contain SNPs at same positions in both channel catfish and blue catfish. The number of unique Uniprot accessions hit by contigs containing SNPs was 16,562 for channel catfish, and 17,423 for blue catfish, suggesting that putative filtered SNPs were identified from the vast majority of catfish genes.One important aspect of using the inter-specific hybrid system is to identify inter-specific SNPs. From this work, a total of 232,972 contigs were identified to contain 420,727 inter-specific SNPs, i.e., sequence variations between the two species, channel catfish and blue catfish. These SNPs were from at least 18,085 distinct genes as determined by unique hits to the Uniprot protein database , produced significant hits to 6,674 previously uncaptured genes, and covered thousands of additional transcript regions currently without annotation.A second challenge is the over representation of highly expressed gene tags in transcriptome analysis. As shown in Figure Pooling of RNA samples from multiple individuals followed by transcriptome analysis using next generation sequencing is among the most efficient methods for SNP identification. Through many years of efforts, a total of approximately 303,000 putative catfish SNPs were previously identified . HoweverOne major challenge for SNPs is the problem caused by paralogous sequence variants (PSVs) and multisite sequence variants (MSVs) . PutativGiven the large numbers of SNPs generated that meet these minimal requirements, more stringent parameters can be applied in picking SNP sets for different applications. Average depth at putative SNP positions is greater than 100 sequences, providing high confidence in accuracy of identified SNPs within the pooled samples. Re-sequencing or limited validation of these samples by low-throughput SNP genotyping is costly and is unlikely to generate additional information. Ultimately, SNPs need to be validated by genotyping in a variety of reference mapping families and trait-selected populations using a high-density screening array. In catfish, the use of homozygous gynogenetic catfish  as contrGenome-wide association studies of complex traits require a large number of SNPs. However, for research communities focused on non-model organisms, it is cost-prohibitive to genotype all SNPs in an association study with the throughput of current technologies. Selection of uniformly distributed SNPs across the genome for association studies is therefore very important . Gene-asThe approach to sample animals of diverse genetic backgrounds and sequence to sufficient depth for reliable SNP identification allowed the ability to detect many common SNPs across the entire genome. We have demonstrated that transcriptome analysis of pooled RNA samples from multiple individuals using Illumina sequencing technology is both technically efficient and cost-effective for generating expressed sequences. Such an approach is most effective when coupled to existing EST resources generated using traditional sequencing approaches because the reference ESTs facilitate effective assembly of the expressed short reads. The SNPs identified in this report will provide a much needed resource for genetic studies in the catfish scientific community and will contribute to the development of high density, cost-effective genotyping platforms. Validation and testing of SNPs using high-density arrays will subsequently lead to the production of a SNP array with well-spaced SNPs providing a powerful genotyping tool for the study of performance and production traits in catfish.Channel catfish of 47 individuals from five different aquaculture populations/fingerling sources  and blue catfish of 19 individuals from two different strains (7 Rio Grande and 12 D&B) were used for this study. Samples of 11 tissues including brain, gill, head kidney, intestine, liver, muscle, skin, spleen, stomach, heart, and trunk kidney were collected. The fish were euthanized with tricaine methanesulfonate (MS 222) at 300 mg/l before tissue collection. Tissue samples from each species were collected, pooled, immediately placed in 5 ml RNA later™  and kept at 4°C for 2-4 days until RNA extraction. Equal weight of each tissue from individuals of each species were combined, ground to a fine powder with mortar and pestle in the presence of liquid nitrogen and thoroughly mixed. A fraction of the tissue samples was used for RNA isolation. Total RNA was isolated using the RNeasy plus Mini Kit  with DNase I  treatment following the manufacturer's protocol.Sequencing was conducted commercially in HudsonAlpha Genomic Services Lab . Briefly, 100 ng of total RNA was used to prepare amplified cDNA using Ovation RNA-seq, a commercially available kit optimized for RNA sequencing . The produced double-stranded cDNA was subsequently used as the input to the Illumina library preparation protocol starting with the standard end-repair step. The end-repaired DNA with a single 'A'-base overhang is ligated to the adaptors in a standard ligation reaction using T4 DNA ligase and 2 μM-4 μM final adaptor concentration, depending on the DNA yield following purification after the addition of the 'A'-base. Following ligation, the samples were purified and subjected to size selection via gel electrophoresis to isolate 350 bp fragments for ligation-mediated PCR (LM-PCR). Twelve cycles of LM-PCR were used to amplify the ligated material in preparation for cluster generation. For each species of channel catfish and blue catfish, the prepared cDNA library was sequenced with 36-bp paired-end reads on one flow cell lane of the Illumina Genome Analyzer II platform and 100-bp paired-end reads on one flow cell lane of the Hiseq 2000 platform, respectively. The image analysis, base calling and quality score calibration were processed using the Illumina Pipeline Software v1.4.1 according to the manufacturer's instructions. Reads were exported in the FASTQ format and has been deposited at the NCBI Sequence Read Archive (SRA) under accession number SRA025099.de novo assembly. A reference-based assembly was firstly executed using a set of catfish unique sequences generated from ~500,000 Sanger-ESTs of both channel and blue catfish as a reference. For the reference assembly, the default local alignment settings were used to rank all potential matches, with mismatch cost of 2, deletion cost of 3 and insertion cost of 3. The highest scoring matches that shared ≥ 80% similarity with the reference sequence across ≥ 50% of their length were included in the alignment. This permissive alignment ensured that even reads derived from highly mutated orthologs between channel catfish and blue catfish would not be discarded. Reads that were not assembled into contigs in the reference assembly were entered into a subsequent de novo assembly with a higher stringency minimum match similarity (90%). Three separate assemblies were generated: channel catfish assembly, blue catfish assembly, and all catfish assembly . The raw reads were cleaned by trimming of adaptor sequences, ambiguous nucleotides ('N' in the end of reads) and low quality sequences with average quality scores less than 20. Trimmed reads less than 15 bp were also discarded from further analysis, the remaining reads were used in subsequent assembly. The approach of assembly in this study was based on a combination of reference assembly and y Figure .Unique consensus sequences from the all catfish assembly were compared against the Uniprot database and the zebrafish Refseq protein database (NCBI) using BLASTX  to obtain the putative gene identity. To estimate the proportion of annotated contigs that matched to unique genes in the known protein database, all BLASTX hits were filtered for redundancy in protein accessions. Assignment of Gene Ontology terms to annotated unique sequences was conducted using the program Blast2GO . OntologAssembled contigs were scanned for SNPs utilizing SNP detection software included in CLC Genomics Workbench . The central base quality score of ≥25 and average surrounding base quality score of ≥20 were set to assess the quality of reads at positions for SNP detection. Under the criteria of minimum coverage (read depth) of four and the minimum variant frequency of two, the variations compared to the reference sequence were counted as SNPs. Three lists of SNPs were generated from channel catfish, blue catfish and all catfish assembly, respectively. The identification of intra-specific SNPs for both channel and blue catfish, and inter-specific SNP between channel and blue catfish was achieved by comparing these three lists of SNPs. Inter-specific SNPs were defined as those that have sequence variations between channel catfish and blue catfish, but no sequence variations within channel catfish or within blue catfish; similarly, intra-specific SNPs were identified within channel catfish or within blue catfish; and intra-specific SNPs for both channel catfish and blue catfish were identified within both channel catfish and blue catfish at the same SNP position.All the unique sequences were used to search for microsatellite makers using Msatfinder  with a rIn order to identify quality SNPs, putative SNPs identified as mentioned above were further screened following specific criteria based on the read depth, minor allele frequency, the quality of flanking regions and absence of other SNPs within 15-bp flanking regions: only those SNPs with minor allele sequences representing no less than 10% of the reads aligned at the polymorphic loci were declared as quality SNPs; no extra SNPs or indels within 15-bp flanking regions were allowed; SNPs located in repetitive regions were also not considered. Potential repetitive elements were detected by RepeatMasker , SNPs loSL prepared the samples, conducted the bioinformatic analysis and was involved in writing the manuscript. ZZ, JL, FS, SW, HL, YJ, LK and HK were involved in one or more processes of samples collection, RNA extraction or bioinformatic analysis. EP provided assistance for fish collection, data analysis and manuscript preparation. ZL supervised the entire study and provided assistance for data analysis and manuscript preparation. All authors read and approved the final manuscript.Length distribution of contigs from the all catfish assembly with hits to the Uniprot database.Click here for fileCategorization of different types of SNPs identified from the all catfish assembly. (1) Intra-specific SNPs identified from positions where there were SNPs within channel catfish, but not within blue catfish; (2) Intra-specific SNPs identified from positions where there were SNPs within blue catfish, but not within channel catfish; (3) Intra-specific SNPs identified from positions where there were SNPs within both channel catfish and blue catfish; (4) Inter-specific SNPs identified from positions where there were no SNPs in channel catfish or in blue catfish, but the sequence differed between the two species; (5) Intra-specific SNPs identified from positions where there were SNPs within channel catfish and there were fewer than four blue catfish sequences; (6) Intra-specific SNPs identified from positions where there were SNPs within blue catfish and there were fewer than four channel catfish sequences. Intra-specific SNPs in channel catfish = (1) + (3) + (5); Intra-specific SNPs in blue catfish = (2) + (3) + (6); Intra-specific SNPs shared by the two species = (3); Inter-specific SNPs between the two species = (4).Click here for file


A linear mixed model was used to test two dimensions of SNP interactions affecting abdominal fat weight. With a threshold of P<1.2×10−11 by a Bonferroni 5% correction, 52 pairs of SNPs were detected, comprising 45 pairs showing an Additive×Additive and seven pairs showing an Additive×Dominance epistatic effect. The contribution rates of significant epistatic interactive SNPs ranged from 0.62% to 1.54%, with 47 pairs contributing more than 1%. The SNP-SNP network affecting abdominal fat weight constructed using the significant SNP pairs was analyzed, estimated and annotated. On the basis of the network’s features, SNPs Gga_rs14303341 and Gga_rs14988623 at the center of the subnet should be important nodes, and an interaction between GGAZ and GGA8 was suggested. Twenty-two quantitative trait loci, 97 genes (including nine non-coding genes), and 50 pathways were annotated on the epistatic interactive SNP-SNP network. The results of the present study provide insights into the genetic architecture underlying broiler chicken abdominal fat weight.We performed a pairwise epistatic interaction test using the chicken 60 K single nucleotide polymorphism (SNP) chip for the 11 Epistasis – the interactions between polymorphic loci, such as SNPs, genes or quantitative trait loci (QTLs) – is a hot topic in quantitative genetics Much progress has been made using genome-wide association studies (GWASs). Performing genome-wide SNPs interaction analysis represents the next step for detecting the variations of quantitative traits, because single-locus tests cannot identify the interactions among SNPs, genes or other genetic or environmental factors Gallus gallus), it has been suggested that epistatic interactions between genes (or QTLs) are important for quantitative traits such as growth and fatness traits In chickens . Pairwise epistatic interaction effects among genome-wide SNPs were detected. A network of significant SNP pairs was analyzed and annotated. The results provide further insight into the genetic network controlling abdominal fat deposition in chickens.All animal work was conducted according to the guidelines for the care and use of experimental animals established by the Ministry of Science and Technology of the People’s Republic of China  and was approved by the Laboratory Animal Management Committee of Northeast Agricultural University.th generation population of NEAUHLF, were used in this study.The experimental chickens were from the Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) Genotyping was carried out using the Illumina Inc.  chicken 60 K SNP chip, which contains 57,636 SNPs. Markers with minor allele frequencies less than 5% or monomorphic loci were filtered out. Individuals were removed who had 5% or more missing SNP genotypes. Finally, 45,611 SNPs in 475 individuals were used for the pairwise interaction analyses. SNP genotypes were provided by Zhang et al. http://animalgene.umn.edu/episnp/download.html) was used to test for epistatic effects The EPISNP3 module in epiSNP_v4.2_Windows software package , μ is the population mean, SNP1 and SNP2 are the two single-locus genotypic effects, SNP1×SNP2 is the two-locus interaction effect, F (family effect) is a random effect, and e is the random error.where The two-locus interaction effect was partitioned into four individual epistatic effects using the extended Kempthorne model, which allows Hardy-Weinberg disequilibrium and linkage disequilibrium (LD): Additive×Additive (AA), Additive×Dominant (AD), Dominant×Additive (DA), and Dominant×Dominant (DD) epistatic effects 9; independent tests using the same data set, with a significance threshold of P<0.05), and the significance threshold of the test was determined as P<1.20×10−11.The P-value of model (1) was adjusted by a Bonferroni correction , and pVar is the phenotype variance.The contribution rate of every significant epistatic interactive SNP pair to phenotypic variation was calculated by the formula,−11 were loaded into Cytoscape to visualize the network. Based on entropy theory w is the importance of the subnet, n is the edge number (i.e. the number of SNP pairs), ip is the P-value of the interactive effect being tested for the ith SNP pair, and ic is the contribution rate of the ith SNP pair.The figure showing the SNP-SNP network with significant epistatic effects was drawn using the Cytoscape 2.8 software package http://www.animalgenome.org/cgi-bin/QTLdb/GG/i-ndex), and mapped in terms of QTLs. Second, the network was annotated with genes and pathways. A mean value of r2>0.8 http://genome.ucsc.edu/). Genes in the same network were put into the KEGG program (http://www.genome.jp/kegg/) to identify pathways that might affect the traits.The purpose of this step was to annotate significant SNPs and mine QTLs or gene networks affecting abdominal fat traits. First, the significant interactive SNPs were put into the Chicken QTL database  and Gga_rs14988623 on GGA13 reached 2.54×10−14, which was the most significant effect detected. There were six pairs of SNPs on the same chromosome . The other 46 pairs of SNPs were distributed on different chromosomes. The contribution rate of the significant epistatic interactive SNP pairs ranged from 0.62% to 1.54%, with 47 pairs having a contribution rate of more than 1%. The 52 pairs of significant SNPs comprised 68 single SNPs covering 18 chromosomes. The position of each SNP is shown in EPISNP3 To investigate the complex mechanism of epistatic effects on AFW, a network of SNPs having epistatic interaction affecting AFW was constructed. SNP epistatic interaction subnets containing more than three nodes are shown in The degree of nodes refers to the number of edges connecting it, and an edge stands for a two-way interaction. For example, the degree of SNP Gga_rs14988623 was 4, and the degree of SNP Gga_rs14303341 was 7 . SNP GgaInterestingly, in some subnets, the SNPs were adjacent to one another on the same chromosome. For example, in subnet A, seven SNPs on GGA3 were physically close to one another. This phenomenon was also observed for subnets C, D, E, G and H. Thus, there seemed to be a concentration of adjacent SNPs in these subnets. This indicated that the interaction takes place in the corresponding regions, especially for subnet C.The importance of the subnets, as evaluated by formula (3), is shown in First, the significant epistatic interactive SNPs were mapped to QTLs affecting chicken AFW . Twenty-Second, fragments of 0.4 Mb were defined, and the fragments’ centers were the 68 single SNPs in the significant epistatic interactive SNP pairs. The distances between some SNPs were small, and the fragments may overlap. A union set was used when the fragments overlapped. Regions were determined according to the fragments and union sets . The 97 GRB2, PDPK1, PIK3CA and SOCS3 are in the insulin-signaling pathway; and GRB2, PIK3CA, SOCS3 and IL21R are in the Jak-STAT-signaling pathway.Third, genes in every subnet were submitted to the KEGG database. The 97 genes were located in 50 pathways . Some geAbdominal fatness traits in chickens are important quantitative traits, and are under complex genetic control. Understanding the molecular mechanisms underlying them will help efficient growth selection in broiler chickens. In this study, analyses using genome-wide SNPs were performed to examine the genetic contribution of epistatic effects to the phenotypic variation of chicken abdominal fatness traits.GWASs have identified many significant association markers. However, many studies showed that the identified markers explain only a fraction of the heritable variance The features of scale-free networks are several nodes with large degrees and many nodes with few connections. Further, the nodes with large degrees should be the hub sites of the network. Our SNP-SNP network displayed the features of a scale-free network. The degree of one node was 7, and of another was 4; and 47 nodes had a degree of 1 in our SNP–SNP network. Thus, the nodes (Gga_rs14988623 and Gga_rs14303341) with larger degrees should be the important ones. Scale-free behavior has been demonstrated in many biological interaction networks, such as protein or gene interaction networks The formula for evaluating the importance of subnets proposed in the present study was based on the entropy theory GOSR2, GJC1, CCDC43, WNT3, NSF and EFTUD2 are within the defined 0.4 Mb region. These QTLs and genes should receive attention in a future study.Subnet A was radial, with seven SNPs connecting the center. A previous study concluded that many significant epistatic effects involving one locus were less likely to be random than was a single epistatic effect Subnet C was a loop graph, and contained six SNPs: three on GGAZ and three on GGA8. Between the two chromosomes, nine (3×3) pairs of SNP interactions were obtained, and eight of these interactions were significant. In addition, the SNPs were adjacent to one another on GGAZ and GGA8. The result indicated that the interaction occurs between the two regions on GGAZ  and GGA8 .The epistatic interactions of SNPs might provide the basis for identifying interactions among genes, QTLs, or even pathways; therefore, we annotated the network. The result showed that 35% (24/68) of the SNP markers are located in regions containing 22 QTLs associated with chicken AFW . The radGene-by-gene interaction plays a major role in genetic studies of quantitative traits; however, the detection of gene–gene interaction has been traditionally assessed by SNP interactions. Single SNPs cannot capture the total variation of a gene; therefore, SNP interactions cannot represent gene–gene interactions. Thus, Cui et al. BMPR1A, GIPR, GRB2, LITAF, SOCS3, WNT3 and PDPK1, were identified in these regions. Based on the literature, some genes are associated with obesity; for example, BMPR1A is associated with human obesity GIPR on GGA18 is associated with fat droplet formation LITAF plays an important role in the regulation of tumor necrosis factor alpha (TNF-α), which is important in adipose tissue WNT3 is part of the Wnt-signaling pathway, which is one of the pathways operating in fat cells PDPK1 affects the fat-pad mass in mice In this study, we detected 26 regions on 12 chromosomes. Ninety-seven genes (including nine non-coding genes), including Fifty-two pairs of significant epistatic interactive SNPs and their epistatic networks were determined. Two important SNPs and their interaction regions were proposed based on the network of significant epistatic interactive pairs. Relevant genes, pathways and QTLs were annotated. The results advanced our understanding of the genetic architecture of abdominal fatness traits, hinted at their molecular mechanisms, and will contribute to faster progress in the artificial genetic selection of broiler chickens.Table S1SNPs contained in the significant SNP pairs.(DOC)Click here for additional data file.Table S2SNPs mapped to QTLs. The QTL information was obtained from http://www.animalgenome.org/cgi-bin/QTLdb/GG/i-ndex.(DOC)Click here for additional data file.Table S3Information on protein coding genes. The information was obtained from http://www.genatlas.org/.(DOC)Click here for additional data file.Table S4Genes in pathways. Pathway information was obtained from http://www.genome.jp/kegg/.(DOC)Click here for additional data file.Table S5Results of the genome-wide association study of significantly interacting SNPs. The threshold of GWAS is P<2.7×10−7 by a Bonferroni 5% correction.(DOC)Click here for additional data file.


When selecting mates, many vertebrate species seek partners with major histocompatibility complex (MHC) genes different from their own, presumably in response to selective pressure against inbreeding and towards MHC diversity. Attempts at replication of these genetic results in human studies, however, have reached conflicting conclusions.HLA-DQA1 and -DQB1 genes. We also found that genomic regions with significant similarity between spouses show excessive homozygosity in the general population (assessed in the HapMap CEU dataset). Conversely, loci that were significantly dissimilar among spouses were more likely to show excessive heterozygosity in the general population.Using a multi-analytical strategy, we report validated genome-wide relationships between genetic identity and human mate choice in 930 couples of European ancestry. We found significant similarity between spouses in the MHC at class I region in chromosome 6p21, and at the odorant receptor family 13 locus in chromosome 9. Conversely, there was significant dissimilarity in the MHC class II region, near the This study highlights complex patterns of genomic identity among partners in unrelated couples, consistent with a multi-faceted role for genetic factors in mate choice behavior in human populations. When selecting mates, individuals of many vertebrate species favor partners with heterologous major histocompatibility complex (MHC) genes -6. TheseThe recent availability of highly efficient genome-wide genotyping platforms affords deeper marker saturation in regions of interest as well as the performance of hypothesis-neutral screens. In this study, a screen with 309,100 single nucleotide polymorphisms (SNPs) in 930 unrelated couples of European ancestry was used to assess genetic similarity between spouses. Quality-controlled genotypic data was obtained from the International Multiple Sclerosis Genetics Consortium (IMSGC) from a study performed to identify multiple sclerosis (MS) susceptibility genes . In a hy2 measures similarity at individual SNPs. A third approach seeks the preponderance extreme R2 values across regions. In this way, we allowed for a region to exhibit similarity and dissimilarity independently. This is important for gene-rich regions such as the MHC, which could potentially have an intricate role in mate preference. Relatedness coefficients are positive when spouses are more similar than random pairs of individuals, and negative when spouses are more dissimilar.Genetic similarity/dissimilarity between spouses was assessed using three parallel approaches. Relatedness coefficient R1 (defined in methods) measures similarity across genetic regions, while Rp < 10-6, using 106 permutations). In apparent contrast, Chaix et al. [p = 0.739). Is their population different from ours? To answer this question, similarity (R1) was measured in 100,000 randomly selected sets of 28 couples from our dataset. The random set of 28 couples had R1 lower (less similarity) than that observed in the Chaix et al. study in 7.06% of the permutations. Although the power to assess differences in genetic identity patterns between the two populations is limited, it appears that the two populations are different. Further, the Chaix et al. results may be influenced by a few outlying couples [Genome wide, the partners in the 930 couples of European descent included in this study are more genetically similar than expected by chance , there were 21,665 regions with at least 300 SNPs. The top 100 regions exhibiting excessive similarity or dissimilarity  are shown in Additional file On the other hand, using Pearson Correlation (R2) for SNPs that showed significant similarity or dissimilarity among couples in the screen, 38 individual SNPs passed the genome-wide significance threshold fdr < 0.1) . When considering the results emerging from the MHC region, however, it is important to bear in mind the origin of our dataset: couples with an offspring affected with MS, an autoimmune disease with a genetic component, and a known strong association with the MHC, specifically with the relatively common HLA-DRB1*15:01 allele. Other genes within the HLA class I region have been also proposed to be independently linked to MS by conferring protection. In apparent contrast, Chaix et al reported significant dissimilarity in this region  for the 28 HapMap couples. Once again we ask whether their population was different from ours. When R1 was measured in 100,000 random sets of 28 couples chosen from our study, a value of R1 lower than -0.043 was only observed 0.853% of the time, suggesting significant differences between the two populations and reflecting the greater diversity of the dataset used in this study as well as the sampling variability of the HapMap samples [Using R1, we observed an overall trend of similarity across the entire MHC that is not significant  was 291st on the list (sorted by significance) . Upon breaking down the MHC into three classes, significant genetic identity among couples was found in class I  (Table HLA-E and RPP21 (a gene involved in maturation of rRNA), and to a lesser extent to another series of markers near MDC1 (a mediator of DNA damage checkpoint), including the non-synonymous SNP rs9262152  and its non-synonymous nature. Observed similarity among couples at rs9262152 was confirmed by PCR  in a subset of the same couples but results were not replicated in independent couples , indicating that regional class I similarity is not a consequence of this SNP.Using the Fisher meta value, the MHC region as a whole showed a greater excess of significantly similar SNPs than the rest of the genome , SFTPG (surfactant associated protein), DPCR1  and VARS . The identity pattern detected using Affymetrix arrays was confirmed by a dense Illumina custom array of 1536 MHC SNPs performed on 920 of the 930 couples  and DQB1 (p = 0.044).Next, imputation of 6 classical HLA alleles  was performed using this dense coverage  is bounded by SMC2 and NIPSNAP3A. This region exhibits the greatest abundance of SNP-level allelic similarity between couples in the entire genome . The bulk of genetic similarity is found in a non-coding region toward the centromere, 200-300 Kbp upstream from the OR13F gene cluster  was confirmed by PCR-based genotyping in 387 couples from the original analysis  and 393 new couples .Functional odorant receptor (OR) families 13 and 18) are located in clusters on various chromosomes. Only one such cluster (from family 13) was large enough for analysis. A 500 Kbp region covering 8 consecutive  and 18 aEleven non-MHC SNPs covered in this study are reportedly associated with MS risk. Similarity between couples (R2) is not significant at these SNPs, after a correction for 11 multiple hypotheses  and significant deviation from Hardy-Weinberg equilibrium (p < 0.024) , further validating our observations . The twOdorant receptor family 13 region on chromosome 9; (iii) 38 SNPs whose alleles showed significant correlation between couples , 10 of which are upstream from eight consecutive OR13 genes on chromosome 9. This, to our knowledge, is the first genome-wide study of its size with regard to human mate selection. We report a complex but statistically significant role for genetic similarity in mate choice, in particular the genes of the MHC and odorant receptors. In the MHC the interaction is different from class to class and from gene to gene, indicating that the disparity in the literature regarding the role of the MHC in mate choice may be resolved with inspection of smaller genetic windows.We performed a genome-wide analysis of genomic identity in 930 White couples of European ancestry, and report (i) a significant similarity in couples' genotypes at MHC class I (ii) novel significant similarity among couples in SNPs linked to the In mice, MHC genotypes have been shown to be a determining factor in mate selection in some strains but not in others . AlthougOur results contribute to this ongoing exploration by finding an abundance of genetic similarity among couples (parents of children with MS) across class I of the MHC. Within class I, much of the similarity occurs near the HLA-E gene. HLA-E is a member of the non-classical class I genes (Ib), with characteristically low polymorphism across primates. Assortative mating may be driving the relatively low polymorphism in this gene. One of the drivers of high polymorphism in HLA genes is "crossing over" events during meiosis. Crossing over fails to increase polymorphism in homozygous individuals. Homozygous individuals are more frequent in the population when mating is associative.This study sampled much older couples than the majority of previous studies. For 73% of the couples in this study, the mating event (successful breeding) occurred in the 1950's and 1960's. Just as we recognize that our findings of MHC class I similarity may be specific to the ethnicity of our population, we must allow that the results may be specific to this post-war generation.HLA-DRB1. Our dataset is thus enriched for the set of common risk alleles of this gene  potentially leading to biased observations, but we report significant dissimilarity at this locus. While HLA-DRB1 confers the greatest susceptibility to MS, it has been proposed that other HLA genes, in this case conferring resistance , may exe  become available, deeper analysis of the roles of individual genes and functional pathways in mate selection and implications for health and disease will become possible.a) 334,923 Single Nucleotide Polymorphism (SNPs) from the International Multiple Sclerosis Genetics Consortium (IMSGC) dataset, typed by Affymetrix 500K GeneChip in 931 European and European American couples . Samples came from collections the University of California, San Francisco (417 couples), the Cambridge University Hospital Multiple Sclerosis Center (453 couples), and the Brigham and Women's Hospital (61 couples). The mean age was 71.2 (sd = 9.5) for males and 68.6 (sd = 9.2) for females. For 73% of couples, the mating event (successful breeding) occurred in the 1950's and 1960s. Divorce status and length of marriage was not recorded. One couple was removed because one parent was affected with multiple sclerosis (MS). 930 couples, each with a child afflicted with MS were used for analysis presented here. We included only autosomal SNPs with minor allele frequency above 5%, missing no more than 10% of genotypes in males or females . This dataset is available on dbGAP (phs000139.v1.p1) .b) 1,536 SNPs in the extended MHC region from the International MHC and Autoimmunity Genetics Network (IMAGEN)  dataset,c) A set of HLA types for 6 of the main HLA genes imputed from the IMAGEN SNP genotypes by the original authors.d) Candidate SNPs were genotyped by PCR in 387 couples from the IMSGC dataset and 393 new European American couples. Both datasets were collected using the same inclusion criteria and have a similar distribution of age and ethnicity. Notably, these 393 new couples are also parents of children with MS.The following approaches for measuring genetic similarity were used in parallel.p value is the proportion of permutations (where spouses are shuffled) in which the mean R1 of permuted couples is more extreme than the mean R1 of real couples. 100,000 permutations were performed. This approach was used for HLA types and regions of SNPs by Chaix et. al. [a) In the IMSGC dataset, a relatedness coefficient R1 was defined for each couple at each variant as a ratio of probabilities of identity in state R1 = (Qc -Qm)/(1 -Qm), where Qc is the proportion of identical variants between the two spouses and Qm is the mean proportion of identical variants in the sample . For SNPs, the proportion of identical variants between two individuals is 1 if both individuals are homozygous for the same allele, 0.5 if either individual is heterozygous, and 0 otherwise. For larger genetic regions of at least 300 SNPs, Qc and Qm were averaged across all SNPs in the region. The significance of R1 was assessed using a permutation approach: the two-sided  et. al.  and the  et. al. .b) In the IMSGC dataset, another relatedness measure R2 was defined at individual SNPs across all couples as the Pearson correlation between fathers' and mothers' genotypes . A comparison of R2 and R1 at individual SNPs is discussed in Additional file p values of all SNPs in the region which were similar between couples (R2 > 0 from part b) using Fisher's method [p value is assigned to each candidate region as the percent of regions across the genome that had Fisher meta values smaller than the Fisher meta value of the candidate region. In the hypothesis-neutral approach , we apply a Benjamini Hochberg correction for multiple comparisons.c) In the IMSGC dataset, we next tested the hypothesis that an abundance of significant positive similarity exists among SNPs in a given region, say the MHC. For that purpose we combined the s method . A low FWe also tested the hypothesis that an abundance of significant negative similarity exists among SNPs in a given region. For this, we repeated the above steps using only SNPs showing dissimilarity (R2 < 0). In this way, we allowed for a region to exhibit similarity and dissimilarity independently. This is important for gene-rich regions such as the MHC, which could potentially have a multifaceted role in mate preference.p value of that region.d) Validation of observations in the MHC region was performed using the IMAGEN study. For each SNP, significance of the similarity score was assessed by 50,000 permutations. Regional scoring of the 3 MHC classes  was done in the same manner as the IMSGC. However, the procedure for assigning significance to the Fisher meta value was necessarily different from that used for the IMSGC dataset; with the IMAGEN dataset, we did not have the entire genome to use as a background. Instead, we created a regional background by shuffling couples 50,000 times, each time calculating the Fisher meta value on each of the 3 MHC classes. The percent of random Fisher meta values from the background that are lower than the observed Fisher meta value of each region is reported as the P values were assigned using a cumulative normal distribution, with mean and standard deviation assessed from the background. Normality of the background distributions was assessed by visual inspection and the Anscombe-Glynn test of kurtosis.e) For each of the six HLA genes from the IMAGEN study, a similarity score was calculated as follows. Each gene was given one point for each couple that shares one common allele at that gene, and two points for each couple that has both alleles in common. The similarity score for each gene was the total number of points across 920 couples. Couples were reassigned 20,000 times and similarity scores recalculated, creating a background distribution. f) For SNPs genotyped by PCR, significance of the similarity measure (Pearson correlation) was assessed by using a normal distribution with mean and standard deviation estimated from 200,000 random measurements (where the spouses were randomly re-assigned). Normality of the background distributions was assessed by visual inspection and the Anscombe-Glynn test of kurtosis.2 thresholds of 0.25, 0.5, and 0.75 using software Plink.We checked that results are not affected by varying SNP density and linkage pattern across the genome by re-doing our analyses on reduced sets of approximately independent SNPs. Haplotype block tagging SNPs were selected genome-wide at rAn earlier study with a large sample size  that also found HLA similarity between couples suggested a possible confounding issue. The existence of ancestral or ethnic stratification with characteristic HLA types may influence the degree of genetic identity between couples . AncestrIn this paper, we chose to present the results from all 930 couples. When filtering for a more homogeneous western European population, we are removing a number of inter-group spouses (i.e. where one spouse is western European and the other is not). Inter-group mating events are a real phenomenon that we want to capture in the analysis.PCR validations and replications were done with a made-to-order Applied Biosystems TaqMan SNP genotyping assay, and carried out in 384-well plates using Applied Biosystems TaqMan genotyping Master Mix on an ABI Prism 7900HT Sequence Detection System using SDS 2.1 software.Imputation of HLA Alleles from dense SNP coverage was performed by the authors of the IMAGEN paper . HLA genJRO, SLH, SEB, PAG, and PK conceived and designed the study. SJC carried out the rt-PCR assays. AS recoded HLA types and provided data management. PK performed the statistical analysis. JRO, SEB, and PK wrote the manuscript. All authors read and approved the final manuscript.Table S1. Top similarities at the regional level using R1. Each of the top regions was compared against all regions from the genome with lower recombination rate if the region is lower than average, or higher recombination rate if the region is higher than average. On the far right, we see what results would look like using the more homogeneous subset (803 couples of Western European descent) of the population (see Methods).Click here for fileFigure S1. Average relatedness coefficient R1 between spouses at 3.6 Mb regions throughout the genome versus recombination rate. More extreme values of R1 are seen in regions of lower recombination rates. The HLA region is shown in red. Compare to Figure 2 of Chaix et al.Click here for fileTable S2. Genome-wide SNP-level results. Approximately 4,000 most highly correlated SNPs among the 930 IMSGC couples. This includes positive and negative correlation. All SNPs with a one-tailed p value of 0.01 or better are highlighted by filter.Click here for fileTable S3. Top similarities at the regional level using Fisher values. Regions exhibiting an abundance of significantly similar SNPs (R2 > 0).Click here for fileTable S4. Top dissimilarities at the regional level using Fisher values. Regions exhibiting an abundance of significantly dissimilar SNPs (R2 < 0).Click here for fileFigure S2. Validation on MHC results with the IMAGEN dataset. In the screening IMSGC dataset, the MHC region (663 SNPs) was identified in the candidate-region approach as a mosaic of similarity and dissimilarity. 920 of the 930 couples were re-genotyped by a dense custom Illumina Platform . (A) The pattern of similarity found in the IMAGEN dataset is comparable to that found in the screening . (B) 150 MHC SNPs were in common between IMAGEN and IMSGC. For each SNP passing quality control (94 SNPs), similarity between couples was calculated separately in both datasets. The correspondence of similarity scores between the two datasets was high (r2 = 0.94).Click here for fileTable S5. IMAGEN regional p values. Regional scoring of the 3 MHC classes  was done in the same manner as the IMSGC. P values are obtained by shuffling couples 50,000 times.Click here for fileTable S6. Imputed classical HLA alleles. Two of the class II genes, DQA1 and DQB1 showed significant dissimilarity between couples. Two-digit allele designations were used.Click here for fileTable S7. Multiple-Sclerosis-associated SNPs. Spousal identity (R2) and uncorrected p-value for 11 SNPs associated with multiple sclerosis. After correction for 11 multiple comparisons, the spousal identity is not statistically significant.Click here for fileFigure S3. Parental similarity versus offspring heterozygosity. When parents choose mates that are similar to self at a given SNP, the result is excessive homozygosity in the children (an excess of homozygous genotypes at that SNP). Conversely, when parents choose mates that are dissimilar to self, the result is excessive heterozygosity in the children. In a simulation, random genotypes for 22,500 SNPs ) were generated for 1,000 sets of parents. At each SNP, the similarity measure (Pearson correlation) was calculated between the vectors of parental genotypes (shown on the y-axis). For each SNP, the genotypic frequencies of the offspring of the 1,000 sets of parents were calculated based on Mendelian inheritance. The observed frequency of heterozygotes in the offspring was divided by the expected frequency of heterozygotes, assuming Hardy Weinberg equilibrium (x-axis). A value higher than 1 on the x-axis means that offspring have a greater than expected frequency of heterozygotes, while a value smaller than 1 on the x-axis means that offspring display excessive homozygosity. These plots show that SNPs which show similarity between parents  are more likely to show excessive homozygosity in the offspring . To extend the concept: if parents select mates that are similar to self at a given SNP, over many generations we expect excessive homozygosity in the general population compared to Hardy Weinberg equilibrium.Click here for fileText S1. Comparison of two measures of similarity.Click here for fileText S2. Comparison of two methods of assessing significance of Pearson Correlation as a measure of similarity.Click here for fileFigure S4. Hierarchical clustering. Using IBD distances calculated in software Plink, Ward agglomerative clustering (done in R) reveals a large cluster (A) of Scandinavian and western Europeans on the left. Smaller clusters on the right include (B) Eastern European (Russian and Polish) Ashkenazi Jews, (C) Mediterranean/Western European, (D) Hispanic with some Mediterranean, (E) Mediterranean, (F) non-Ashkenazi Eastern European. Self-reported ethnicity data was available for about 1/3 of the samples. This data is shown below the clusters. A red dot on the "Polish" row means that the person reports being Polish. A black dot means that the person did not report being Polish. The grey background means that no self-reported data was available for that person. Just above the self-reported ethnicity rows (black and red) is a single row showing cohort. Each sample belonged to one of three cohorts . Note that nearly all samples from the non-western European group (B-F) came from the UCSF cohort.Click here for file


We have recently developed a first set of 768 SNPs assayed by the Golden Gate Genotyping Technology for the highly heterozygous genome of Eucalyptus from a mixed Sanger/454 database  were used for linkage analysis and map construction. DNA was extracted through CTAB and picogreen quantified. The DNA was used for SNP genotyping on an Illumina BeadStation 500 GX. SNP data were analyzed using GenomeStudio V2009.1 and a GeneTrain score cutoff of 0.25 and call rate ≥0.95 were initially applied to the whole dataset. After that, every single SNP was manually checked for genotyping failures and potential calling errors that would bias the overall analysis and segregation ratios. Linkage analysis, individual and consensus map construction for both segregating populations was performed using Joinmap, v. 3.0 [Two inter-specific segregating populations of The proportion of informative segregating SNPs out of the 768 assayed were similar in the two mapping populations: 215 SNPs in DGUGL (28%) and 239 in IP (31%). SNPs were mapped on top of existing microsatellite maps. The linkage map of the IP population with 409 markers on the 11 expected linkage groups had 215 microsatellites and 194 SNPs, with an observed length of 1,581.3 cM and average distance between markers of 3.9 cM. The DGUGL map also had 11 linkage groups and 430 markers being 236 microsatellites and 194 SNPs with an estimated length of 1,252.4 cM and average distance of 2.9 cM. The consensus map, constructed using both segregating populations included 624 unique markers, of which 320 were microsatellites and 304 SNPs with 1,451.4 cM and average distance of 2.3 cM. The proportion of segregating SNPs in each population individually (28 to 31%) was consistent with a within-species level observed heterozygosity of ~50% for this set of 768 SNPs , reminding that none of these parents were used in the generation of the EST database wherefrom these SNP were derived. The rate of mappable SNPs was enhanced to almost 40% (304 SNPs mapped out of 768) by using two mapping populations suggesting that by sampling more full-sib families it should be possible to map most if not all SNPs in this panel. No evident clustering of SNPs was observed suggesting that these SNPs are randomly distributed in the Eucalyptus genome.This study shows that large numbers of informative SNPs can be developed directly from in silico sequence databases involving unrelated individuals to the parents of mapping populations. Evidently, by being biallelic, SNPs will be less efficient than multiallelic microsatellites for linkage mapping purposes. This drawback however is clearly compensated by the much higher throughput, automation and lower cost of SNP genotyping. While this is the first reported sizeable scale SNP mapping effort in Eucalyptus, a larger number of informative SNPs mapped at regular intervals will be necessary for broader applications. For example, to implement Genomic Selection in most Eucalyptus breeding programs some 4,500 to 6,000 informative SNPs will be necessary to provide a marker density of 3 to 5 markers/centiMorgan and reach selection accuracies above 70% [Financial support. Brazilian Ministry of Science and Technology (CNPq Grant 577047-2008-6), FAP-DF NEXTREE Grant 193.000.570/2009 and EMBRAPA Macroprogram 2 project grant 02.07.01.004.


ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles.We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately.iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB. In a diploid organism, each somatic cell has two copies of the genome. At certain genomic loci, gene expression, DNA methylation, transcription factor (TF) binding or histone modification (HM) can be allele-specific. In other words, the two alleles can behave differently. These phenomena, also known as allele-specific expression (ASE), allele-specific DNA methylation (ASM) and allele-specific binding , can contribute to phenotypic diversity and may play important roles in adaptive evolution -3. Many Early methods for analyzing AS events rely on low-throughput technologies such as real time quantitative PCR . Later, ChIP-seq, a technology that couples chromatin immunoprecipitation with high-throughput sequencing, has become the state-of-the-art approach for mapping genome-wide TF binding sites and HMs -28. HoweChIP-seq data in public domains grow rapidly. A recently developed database hmChIP, for instance, has compiled over 450 human and mouse ChIP-seq datasets representing approximately 2000 samples from 140+ different TFs and HMs ,30. The In this article, we present an integrated solution to this problem by developing a new approach, iASeq, for jointly analyzing allele-specificity in multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to describe unknown correlation patterns of allele-specificity among multiple datasets. These patterns can be discovered automatically from the data by fitting the model using an Expectation-Maximization (EM) algorithm. Using the identified correlation patterns, the model allows one to integrate information from multiple datasets to improve the ASB detection. Applying this approach, we analyzed 40 ENCODE  ChIP-seqD ChIP-seq datasets generated using cells from the same individual and the same cell type. Each dataset d corresponds to one TF or HM, and has Jd replicate samples  or not (bid=0) in dataset d. If bid=1, then SNP i is assumed to be SR in all replicate samples in dataset d. Similarly, we introduce another binary indicator cid to indicate whether SNP i is SN or not in dataset d. bidand cid cannot be equal to one at the same time. If bid=0 and cid=0, then SNP i is NS in dataset d. The configuration at each SNP i can be described by two vectors Bi= and Ci= , or equivalently , are the observed data for SNP i in sample , whereas the indicators bid and cid are unobserved.After read mapping and data preprocessing  usually are small.Our primary goal is to infer for each SNP whether there is allelic imbalance in each dataset. This is equivalent to inferring If one knows how different datasets are correlated in terms of allelic imbalance, this knowledge may be used to improve the data analysis. For instance, if the allelic imbalance of two proteins A and B are closely correlated, then observing skewed read counts for protein A will provide information for inferring the allelic imbalance of protein B. Integrating the data from both A and B will increase the effective number of reads available for statistical inference, which will then lead to increased statistical power.D datasets, there are 3D possible configurations in total. From studying many SNPs, one can know the relative frequencies (or mixing proportions) of these 3Dconfigurations. The mixing proportions will tell how different datasets are correlated. For instance, let  be the skewness configuration of a SNP in the D datasets. If the mixing proportions for three configurations ,  and  are 0.9, 0.05 and 0.05, then no other configurations exist in the data and all datasets are perfectly correlated in terms of the allelic imbalance. In other words, at a particular SNP, if one dataset is SR, then all the other datasets are also SR. If one is SN, then all the others are also SN. On the other hand, if other configurations have non-zero mixing proportions, then not all datasets are perfectly correlated, and at a particular SNP, one allows the possibility that only a subset of datasets are correlated. For instance, if the mixing proportion for a configuration  is 0.03, then there will be 3% of SNPs that are skewed to the reference allele in the first two datasets but not skewed in the other datasets. Therefore, knowing the mixing proportions of all 3Dconfigurations will tell one the correlation structure in the data. This knowledge can then be used to improve statistical inference at each individual SNP by facilitating information sharing across datasets. For example, if the configuration  has a much higher mixing proportion than , then observing strong skewness towards the reference allele of a SNP in the first two datasets will imply that, a priori, the SNP is highly likely to be skewed to the non-reference allele in the third dataset and has much lower probability to be non-skewed for both alleles. The principle here is the same as the principle represented by the Bayesian hierarchical models in the statistical literature.In reality, how different proteins are correlated is usually unknown. However, one may learn it by studying the data from many SNPs. Each SNP has three possible states in each dataset: SR, SN and NS. For D AS configurations in order to describe the correlation. As the number of datasets increases, the number of possible configurations increases exponentially. Thus this approach does not scale well with the increasing D. Later, in our analysis of GM12878 data, D=40 and 3D>1019. This simple approach is clearly intractable.A limitation of this approach is that one has to enumerate all 3Dconfigurations, iASeq employs a technique that can describe the major correlation patterns in the data using a few probability vectors whose values vary from 0 to 1 rather than being dichotomous . This approach significantly reduces the model complexity but keeps the flexibility to account for all 3Dconfigurations. It is easily scalable to increasing dataset number. The correlation structure in the model can then be used to improve the statistical inference of allelic imbalance at each SNP in each individual dataset.To circumvent the difficulty of documenting the frequencies of all 3K + 1 classes with different allele-specificity patterns (K≪3D), and the observed data are viewed as generated as follows: iASeq is based on the Bayesian hierarchical mixture model below that uses several probability vectors to describe the major correlation patterns among multiple datasets Figure . The modaiis randomly assigned to each SNP i according to a probability vector Π=. Here, Πk=Pr(ai=k) is the prior probability to assign a SNP to class k. ● First, a class label ai=0, then Bi= and Ci=. In other words, all SNPs in class 0 are background SNPs, and they are NS in all datasets. If ai=kand k≠0, then SNP i can be skewed, and its s in different datasets are generated independently according to the following probabilities: Pr=vkdand Pr=wkd. We assume vkd + wkd<1, i.e., Pr=1−vkd−wkd>0. The model implies that each class is associated with two vectors of probabilities Vk= and Wk=. For SNPs in class k, Biand Ciare generated according to the probabilities in Vkand Wk.● If the class label Bis and Cis. Consider SNP i and dataset d. If bid=1, then  in each replicate sample  is generated according to a probability distribution Pr=PrPr≡Pr(nidj)fidj1(xidj). Here we assume that the marginal distribution of nidjdoes not depend on bidand cid, and we use fidj1(xidj) to denote the conditional distribution Pr. Data in different replicate samples are assumed to be generated independently. Similarly, if cid=1, then s are generated according to Pr=Pr(nidj)fidj2(xidj). If bid=0 and cid=0, then s are generated according to Pr=Pr(nidj)fidj0(xidj).● Next, the observed read counts are generated based on the AS configurations specified by i and dataset d, we organize data from all replicates j=1,⋯,Jd into Xid= and Nid=. For SNP i, Xi= and Ni= contain data from all datasets. The final observed data are X= and N= which are the ensemble of data from all SNPs.For SNP A= be the collection of class membership indictors of all SNPs, and let B= and C= be the SR and SN indictors for all SNPs. A, B and C are the unobserved missing data one wants to infer.Let Vkand Wk from different classes into two matrices V, W, and the probability vector Π that describes the class abundance are the unknown model parameters. K is assumed to be fixed. The choice of K and specification of data generating distributions Pr(nidj), fidj0(xidj), fidj1(xidj) and fidj2(xidj) will be discussed later.Organize the probability vectors k (k≠0) is associated with two vectors of probabilities Vkand Wk which characterize the allelic imbalance preferences in different datasets for SNPs belonging to class k. For example, if a class has  = , then SNPs in this class have high probability to be SR in datasets 1 and 2, and high probability to be SN in dataset 3, but they have low probability to be allele-specific in dataset 4. Since Vk and Wkare probabilities rather than 0-1 vectors, each class k can generate all 3D AS configurations. Therefore, SNPs in the same class are not required to have the same AS configuration , although they usually have similar AS configurations because SNPs in the same class are all generated using the same probability vectors. Meanwhile, there are K different classes, and each class has a different  which specifies a different preference to generate the skewing configurations. Thus, whereas SNPs in the same class tend to have similar  configurations, SNPs from different classes tend to have very different configurations. Conceptually, this is similar to a model-based clustering analysis in which SNPs are grouped into K + 1 clusters based on their  configurations. However, an important difference here is that s are unknown.Based on this model, each SNP class bid;cid]s of the same SNP in different datasets are a priori independent conditional on the class membership ai. However, s from different datasets are not independent marginally if one integrates out the class label ai. For example, the marginal probability Pr=Bi;Ci], then each cluster will represent a co-occurrence pattern of allele-specificity across multiple proteins. The marginal correlation among multiple datasets is described by multiple clusters, whereas within each cluster the data in different datasets are generated independently. In real data, a small K  usually is sufficient to describe the major correlation structure among datasets. Using Π, V and W to describe the correlation among datasets only requires O(KD) parameters, which is significantly less complex than O(3D) parameters. At the same time, the iASeq model still provides the flexibility to accommodate all 3D possible  configurations as all of them have non-zero probability to occur.Our model assumes that  in Figure pdj0=αdj/(αdj + βdj) is defined as above. After integrating out pidj, the distribution of xidjconditional on bid=1 and cid=0 is If bid=0 and cid=1, SNP i is SN in dataset d, and we assume that pidj follows a uniform distribution U  in Figure pidj, the distribution of xidjconditional on bid=0 and cid=1 is If Based on the model above, the complete data likelihood can be derived as: δ(.) to be an indicator function. δ(.)=1 if its argument is true, and δ(.)=0 otherwise. We have Define Π, V and W, we employ a Bayesian approach by imposing a Dirichlet prior D on Π and imposing independent Dirichlet priors D on all triplets . The joint posterior distribution of unknown parameters and indicators given the observed data is: To infer Pr(N) is a constant that does not contain parameters of interest, therefore it is absorbed into a proportionality constant not shown in the formula above. Using this joint posterior, an EM algorithm can be derived to search for posterior mode A, B and C are all integrated out    after summing over all possible values of ai and Pr. The one with the larger value determines the direction. Finally, the posterior probabilities of top N SNPs can be converted to an estimate of false discovery rate (FDR) using Using Pr: (1) Pr, which is determined using information from all D datasets, and (2) Pr, which only uses information specific to dataset d conditional on Π, V and W. Thus for each particular dataset d, the dataset-specific information is weighted by information obtained from other datasets to determine the SNP ranking. Intuitively, if allelic imbalance in two datasets are correlated, then observing an AS event in one dataset will suggest that a relatively weak skewing event observed at the same SNP in the other dataset is very likely to be a true AS event. In contrast, if no AS event is observed in one dataset, then a relatively weak skewing event observed at the same SNP in the other dataset is likely to be a false positive. This is the underlying nature of using Pr to re-weigh information in Pr, and it provides the foundation for improving SNP ranking by borrowing information across datasets. In real applications, Π, V,W are unknown, and they are replaced by the posterior mode obtained from the EM algorithm.Formula 7 shows that two types of information contribute to We collected 40 ENCODE  ChIP-seqpdj0 which models the background skewing probability and is estimated using all reads mapped to heterozygote SNPs in each sample. If there is reference mapping bias, pdj0will take a value different from 0.5 to adjust for the bias. One may remove reference bias before the analysis by masking SNPs in the reference genome during the alignment or by aligning reads to a diploid personal genome. This situation will also be automatically recognized by iASeq through the estimation of pdj0 from the data . Therefore, regardless of whether the reference bias has been removed from the data in the preprocessing or not, the iASeq model is able to automatically handle it and adjust the inference accordingly.As previously described in , there aThe intrinsic bias is a different type of bias. As shown by , even ifWe began with 1,704,166 heterozygote SNPs and filtered out 149,996 (8.8%) SNPs with inherent bias. Next, we eliminated SNPs that were not bound by any TF or associated with any HM in any dataset >0.5 as cutoff. To mimic the real background noise, these SNPs were resampled by a bootstrap procedure to create the background SNPs in the simulations, and we kept the read counts  of each background SNP as is in the simulated data. Next, we simulated ASB SNPs and added them to the background. Simulations were carried out under two different scenarios . SNPs in class 2 were SN in datasets 1 to 30 . In datasets 31 to 40, no SNPs had allelic imbalance. Class 2 can be viewed as the mirror image of class 1. This symmetric design reflects the symmetry of allele-specificity, that is, the skewing to the reference allele and to the non-reference allele is approximately symmetric. The class abundance  roughly matched the abundance observed in the analysis of real GM12878 data.● Scenario 1: Two types of ASB SNPs (classes 1 and 2) were created in addition to the background SNPs (class 0). The SNP number for class 0, 1, and 2 was 85,069, 4,725 and 4,725 respectively. Thus the true ● Scenario 2: Four correlation patterns (classes 1-4) were created in addition to the background class (class 0). Class 1 and class 2 were the same as in simulation 1. Classes 3 and 4 were two new patterns. SNPs in class 3 were SR in datasets 21-40, and SN in datasets 1-10. Class 4 was the mirror image of class 3. The abundance of the classes 0 to 4 was .Bi;Ci] configurations, we then simulated the read count data for ASB SNPs as described in detail in Additional file Given the simulated = , it is possible to have one SNP with configuration  and at the same time another SNP with configuration . Therefore, SNPs in the same class are not required to have the same AS configuration, even though they tend to have similar AS configurations. The probabilistic patterns are used here to provide a parsimonious description of the complex correlation structure in the data, so that one can circumvent the difficulty of handling 3D AS configurations whose complexity increases exponentially. As a consequence of using this parsimonious model, multiple weak correlation patterns without strong enough data support could be merged into a bigger class. For instance, consider two AS patterns =  and = . Suppose both patterns are equally likely to occur in the data. If each pattern is only associated with a small number of SNPs, then a parsimonious model will prefer merging them together into one single class with =. For this reason, iASeq only discovers correlation patterns that have sufficient data support so that they can be distinguished from other patterns. It will not report weak patterns, which could be real but do not have enough data support to allow them to be robustly recovered. For users, this means that at the cluster level, they may not be able to see weak but real AS correlation patterns if these patterns are not associated with enough number of SNPs. On the other hand, for the purpose of inferring whether or not each SNP is allele-specific in each dataset, these parsimonious correlation patterns are sufficient for describing the correlation structure in the data and serving as a prior to guide the information sharing across datasets. The information sharing will lead the increased ASB detection power, and the eventual AS configuration at each individual SNP will be determined by the posterior probabilities of  ;]). As a result, only looking at the pattern represented by Vk;Wk, one cannot tell the details of protein-protein interactions, such as these interactions only exist between datasets 1 and 2, or between 3 and 4, but not between the other pairs of datasets. What one can tell from this merged pattern is that, when the allelic imbalance occurs in these four datasets, they will be skewed toward the same direction, i.e., the reference allele in this example.Consistent with , in the In summary, while the correlation patterns in iASeq provide some insights on the correlation of allelic imbalance among different datasets, one should not over-interpret them. The primary goal of these patterns is to describe the correlation structure in the data so that information from different datasets can be shared in a principled way to increase the power of statistical inference. This also points to an important difference between this study and previous studies that reported coordinated allele-specificity among multiple proteins. The previous studies only reported the correlation as a biological finding, but did not provide a statistical method to further utilize the correlation structure to improve the statistical inference. In contrast, iASeq provides a general and rigorous statistical method that utilizes the automatically discovered correlation patterns to increase the statistical power of AS detection. As such, it represents a novel development for the analysis of allele-specificity.Unlike tools such as AlleleSeq which mainly focus on the preprocessing steps for the AS analysis , iASeq is developed as a general model working downstream of the preprocessing pipelines. The input data for iASeq are the read counts in the format shown in Figure K=1 on a machine with 2.7 GHz CPU and 4Gb RAM. To fit a single model with K=10 on the same machine, the EM took 16 hours. Running the EM for all 10 Ks between 1 and 10 on a single core took 4.6 days. However, when we run these 10 jobs in parallel on 10 cluster nodes, we were able to select the best model within 1 day. Therefore, running the algorithm on a single machine is a little time-consuming, but the computation time can be reduced by parallelization. Also, our analysis of GM12878 data indicates that the optimal K in that real data was 2. For a K not extremely large, even if running the full BIC selection on a single machine takes some time, it usually requires less than a week, which is acceptable compared to the time devoted to preparing samples and generating data.In iASeq, we used an EM algorithm to find the posterior mode of parameters and carried out statistical inference accordingly. In principle, one may also use a full Bayesian approach and Markov Chain Monte Carlo (MCMC) to perform the posterior inference. However, since MCMC usually takes much longer to run for a big dataset and it is not easy for users to monitor convergence, we decided to use the posterior mode and EM-based approach in our implementation. For analyzing the GM12878 data with 94,519 SNPs, iASeq took 5 hours to run the EM algorithm to fit a single model with In principle, the statistical model developed in iASeq may also be applied to analyze other types of AS events, such as ASE and ASM. In the future, we plan to improve the model by incorporating information from the spatial correlation among closely located SNPs. For example, for the ASE analysis, one may jointly model SNPs from the same gene, similar to .The analysis of AS events using the high-throughput sequencing data frequently faces the problem of low statistical power due to the limited amount of information available at heterozygote SNPs. One way to increase the power is to increase the sequencing depth for one data type . An alternative approach is to spend the same amount of money to generate data for multiple different but related data types , each with a lower coverage. One can then integrate the multiple datasets to increase the statistical power of allele-specificity analysis. The merit of the second approach is that one can collect multiple different types of information which might be useful for other purposes . If the second approach is used in the study design, then iASeq will offer a flexible, powerful and scalable framework for better analyzing the AS events in the data. As ChIP-seq data continue to grow rapidly, this integrative approach will allow us to use the data more efficiently to characterize the allele-specificity.In summary, we have proposed a Bayesian hierarchical mixture model iASeq to integrate multiple ChIP-seq datasets for analyzing allele-specificity. The primary goal of iASeq is to increase the statistical power of AS detection, and it does so by taking the advantage of correlations among datasets. Since the correlation structure may not be known before the data analysis, iASeq learns it from the data automatically. Application of iASeq to the ENCODE GM12878 data shows that allelic imbalance of most analyzed TFs and HMs have strong preference to be skewed toward the same direction. Analysis of both the simulated and real data show the effectiveness of iASeq to improve detection of allele-specificity compared to single-dataset based methods.AS: allele-specific; ASB: allele-specific binding, including both allele-specific TF binding and allele-specific histone modifications; ASE: allele-specific expression; ASM: allele-specific DNA methylation; AUC: area under receiver operating characteristic curve; EM: Expectation-Maximization algorithm; FDR: false discovery rate; HM: histone modification; NS: not allele-specific; ROC: receiver operating characteristic curve; SN: skewed to the non-reference allele; SR: skewed to the reference allele; TF: transcription factor.The authors declare that they have no competing interests.Conceive study: HJ. Develop model: HJ, YW. Implementation: YW. Data collection: XL, QW. Data analysis: YW, XL. Write paper: HJ, YW, XL, QW. All authors read and approved the final manuscript.Supplemental Methods. A PDF file including: data preprocessing procedures; method of moment estimation in the beta distribution; parameter choice for the Dirichlet prior; derivation of the EM algorithm for iASeq; Bayesian Information Criterion for choosing K; data generation procedure in simulation studies; single dataset based EM analysis.Click here for fileTable S1. Description of all GM12878 ChIP-seq and RNA-seq studies. An excel file showing the name of TF and HM, the number of replicates for each dataset in GM12878 cells.Click here for fileTable S2. Raw read count data for 94,519 analyzed SNPs.Click here for fileSupplemental comparison of defining allele-specific SNPs as SNPs that have RNA-seq exonic ASE SNPs in their 1kb neighborhood. Supplemental Figure Click here for fileFigure S1. ROC curves for GM12878 using Yale Exonic RNA-seq ASE SNPs as gold standard.Click here for fileFigure S2. ROC curves for GM12878 using Yale autosomal exonic RNA-seq ASE SNPs as gold standard.Click here for fileTable S3. Comparison of iASeq and AlleleSeq using Yale RNA-seq exonic ASE SNPs as gold standard.Click here for fileFigure S7. The ROC curves for comparison between AlleleSeq and iASeq.Click here for file


The EXPEN 2000-SNP and EXPEN 2012 maps consisted of 3,503 and 3,687 markers representing 1,076 and 1,229 unique map positions (genetic bins), respectively. The EXPEN 2000-SNP map had an average marker bin interval of 1.6 cM, while the EXPEN 2012 map had an average bin interval of 0.9 cM. The EXPIM 2012 map was constructed with 4,491 markers  and an average bin interval of 0.8 cM. All three linkage maps revealed an uneven distribution of markers across the genome. The dense EXPEN 2012 and EXPIM 2012 maps showed high levels of colinearity across all 12 chromosomes, and also revealed evidence of small inversions between LA0716 and LA0121. Physical positions of 7,666 SNPs were identified relative to the tomato genome sequence. The genetic and physical positions were mostly consistent. Exceptions were observed for chromosomes 3, 10 and 12. Comparing genetic positions relative to physical positions revealed that genomic regions with high recombination rates were consistent with the known distribution of euchromatin across the 12 chromosomes, while very low recombination rates were observed in the heterochromatic regions.The concurrent development of high-throughput genotyping platforms and next generation sequencing (NGS) has increased the number and density of genetic markers, the efficiency of constructing detailed linkage maps, and our ability to overlay recombination and physical maps of the genome. We developed an array for tomato with 8,784 Single Nucleotide Polymorphisms (SNPs) mainly discovered based on NGS-derived transcriptome sequences. Of the SNPs, 7,720 (88%) passed manufacturing quality control and could be scored in tomato germplasm. The array was used to generate high-density linkage maps for three interspecific F Solanum lycopersicum L.) has been a model species for basic studies in plant biology. The strength of genetic resources anchored to high-density maps has permitted the map-based cloning of genes involved in disease resistance 2 population derived from a wide cross between S. lycopersicum and S. pennelliiS. lycopersicum x S. pimpinellifolium maps based on 434 PCR-based markers Tomato (The genomic resources available for tomato are rapidly expanding due to the increased throughput of next generation sequencing (NGS) technologies that have significantly reduced the cost and time of sequencing relative to the Sanger method and facilitated whole-genome sequencing, transcriptome profiling, and discovery of variation across genomes High-throughput SNP discovery has been paralleled by the development of genotyping platforms that permit cost-effective scoring of many thousands of SNPs in a highly parallel fashion 2 populations to provide details of genetic order, recombination, and their position relative to the draft assembly of the tomato reference genome sequence. The SNP array and high-density linkage maps will be useful for population level analysis, trait discovery, and selection for cultivar improvement in tomato.In order to facilitate genetic analysis and breeding, we developed the first large scale SNP genotyping array for tomato using 8,784 SNPs mainly discovered based on NGS-derived transcriptome sequences for six accessions We developed a genotyping array on the Illumina Infinium platform  based on 8,784 SNPs. These SNPs represented a highly filtered and selected set, optimized for polymorphism detection among cultivated germplasm and spread throughout the genome. Of these, 7,720 SNPs (88%) passed manufacturing quality control . A failu2 individuals from a cross between LA0925 (S. lycopersicum) and LA0716 (S. pennellii). Among 7,720 scorable SNPs on the array, 3,640 were polymorphic between the parental lines and were analyzed in the mapping population. 3,503 SNP markers could be placed as codominant markers on the linkage map representing 1,076 unique map positions (genetic bins) with an average marker bin interval of 1.6 cM and the largest gap of 9.7 cM on chromosome 12  The widely used tomato reference population EXPEN 2000 was used to develop a SNP map (EXPEN 2000-SNP) based on 79 Fosome 12 . Each ch2 individuals derived from a cross between Moneymaker (S. lycopersicum) and LA0716. Of 3,770 polymorphic SNPs between the parental lines, 3,687 markers  were mapped with an average marker bin interval of 0.9 cM (Since the EXPEN 2000-SNP map was based on relatively few (n = 79) individuals and the introgression lines did not cover all chromosomal regions, another linkage map (EXPEN 2012) was generated based on 160 Ff 0.9 cM . The twof 0.9 cM . OtherwiS. lycopersicum and the green-fruited S. pennellii, the EXPIM 2012 map was analyzed with 183 F2 individuals derived from a more narrow cross between Moneymaker (S. lycopersicum) and the red fruited LA0121 (S. pimpinellifolium). Among 4,792 polymorphic SNPs between the parental lines, 4,491 markers were mapped as codominant loci representing 1,358 genetic bins with an average marker bin interval of 0.8 cM and the largest gap of 8.7 cM on chromosome 11  suggesting that distorted segregation was not responsible for the expanded map.The total genetic distance of the EXPEN 2000-SNP map was estimated as 1,669.9 cM, or approximately 45% larger than the EXPEN 2012 map  and 59% larger than the EXPIM 2012 map  . AlthougAn alternative explanation for the map expansion observed for the EXPEN 2000-SNP map compared to the EXPEN2012 map relates to the large number of makers scored and the small population size. The accuracy of the calculations for genetic distance is influenced by population size since a falsely scored or incorrectly ordered marker has a larger effect in a smaller population. The EXPEN 2000-SNP map length may be overestimated as a result of population size which limits accurate estimation of marker order and genetic distances. To address this hypothesis, we repeated the EXPEN 2000-SNP map construction by selecting 307–325 markers that were separated by at least 5 cM interval and recalculated the genetic map. This resampling analysis led to estimates of map length that were reduced by an average of 25% (range 22–27%) relative to the EXPEN 2000-SNP map length based on all markers . This reP<0.001) longer for chromosomes 2, 4, 5, 6, 9, 10, 11 and 12. The EXPIM 2012 map was significantly (P<0.001) longer for chromosome 1 and 7. No differences were detected for average distances on chromosome 3 and 8, though there may be differences in recombination length between the two maps for the arms of chromosome 8.The approach of creating a series of resampled maps allowed us to compare map length between the EXPEN 2012 and EXPIM 2012 populations. The 10% difference between the two maps was significant based on over 100 iterations. The EXPEN 2012 map was significantly . A total of 758 Mbp of the tomato genome was covered by the SNP markers on the array with an average distance between markers of 0.12 Mbp  that have created an ascertainment bias.Given the physical length of chromosome 1 (largest chromosome), the number of markers is lower than expected while the number of markers on chromosome 11 is higher than expected. This distribution is not due to a lack of or excess of genes on these chromosomes but is likely due to the process of SNP marker selection. Alternatively, the distribution may reflect the introgression of highly polymorphic regions (e.g. containing disease resistance loci such as the http://solgenomics.net) S. lycopersicum Moneymaker x S. pennellii LA0716 population with 3,687 markers and the EXPIM 2012 map for the S. lycopersicum Moneymaker x S. pimpinellifolium LA0121 population with 4,491 markers. In total, we genetically positioned 5,621 SNP markers including common sets of 2,509–3,149 markers between the linkage maps.Despite the SNP selection for cultivated populations and the observed over- and under-representation, the SNP array provides a powerful resource for genetic map construction in interspecific populations. The EXPEN 2000 population has been used in the last ten years as a reference mapping population in tomato and 2,506 markers have been previously mapped (S. lycopersicum x S. pennellii) populations differed from each other with the EXPEN 2000-SNP map length estimated to be 1,670 cM and the EXPEN 2012 map length as 1,155 cM. We investigated whether a possible reason for the differences in map length could be differential distortion due to gametic phase selection. If such distortion occurred in favor of LA0716 alleles on one portion of the chromosome and in favor of cultivated alleles on another, recombination would be overestimated in the progeny. Although the EXPEN 2000-SNP map showed a number of distorted markers on several chromosomes including chromosome 1 where genes affecting self and unilateral incompatibility are located S. lycopersicum x S. pimpinellifolium) population, and these differences were significant based on iterative estimates of map length. We expected that a genetic map generated from two more closely related parents would display a generally higher level of recombination. Our observation of greater map distance in the EXPEN populations is even more surprising given the likely existence of several small inversions between S. lycopersicum and S. pennellii which suppress recombination in these regions. Comparing the EXPEN 2012 and EXPIM 2012 maps suggests that there could be regions on chromosomes 1, 3, 6, 7, and 9 where small inversions differentiate LA0716 and LA0121. A paracentric inversion on the distal end of chromosome 7 was previously reported in S. pennellii LA0716 relative to S. pimpinellifolium LA1589 S. lycopersicum and S. pennellii can lead to changes in chromosome structure presumably due to inversions and translocations The length of the genetic maps derived from the two EXPEN  for tomato breeding. For association mapping, accounting for population structure and/or familial relatedness is often necessary to avoid spurious marker-trait associations 2 progeny from the EXPEN 2000 population S. lycopersicum (LA0925) x S. pennellii (LA0716) which was previously published 2 progeny from a S. lycopersicum Moneymaker x S. pennellii (LA0716) cross and the EXPIM 2012 population of 183 F2 progeny derived from Moneymaker x S. pimpinellifolium (LA0121) S. pennellii introgression lines in the M82 background For genetic mapping, we used 79 FS. lycopersicum var. cerasiforme accession (PI 114490), and an S. pimpinellifolium accession (PI 128216) SNPs for the array were selected based on a multi-tier strategy that was optimized for polymorphisms within and among cultivated types. Briefly, SNP discovery was based on the Genome Analyzer II-derived transcriptome sequences of four cultivated tomato accessions , an 2 individuals of the EXPEN 2000 population was provided by Steven Tanksley  We also obtained DNA from the S. pennellii introgression lines in the M82 background from Dani Zamir . Genotyping with the array was performed according to the manufacturer’s instructions for Illumina Infinium assay. The resulting intensity data was processed using the genotyping module v1.7.4 of the GenomeStudio software  for SNP calling. In order to determine SNP genotype, a cluster file developed by TraitGenetics based on 92 hybrids facilitated allele calling in the Genome Studio software.Genomic DNA was isolated from fresh, young leaf tissue using a modified CTAB method Three different software packages were used for mapping of the markers: JoinMap 4.0 2 intercross, search linkage criterion P = 0.05, map function Kosambi, cross type line cross). The final map was drawn using MapChart 2.2.The final map position of the markers and the genetic distances between the markers were further optimized manually with respect to the number of crossovers (as low as possible) and the length of the linkage group (as short as possible) using the ABH mapping data file in Excel and MapManager QTX . Map construction followed the steps described above, and comparisons between total map length and individual chromosome lengths were based on Analysis of Variance.http://solgenomics.net/organism/Solanum_lycopersicum/genome) We determined the physical map position of the SNPs based on the flanking sequences used to develop the high-density Infinium array. These sequences were oriented relative to the genome sequence using the automated batch BLAST feature to search the Tomato WGS chromosome Click here for additional data file.Table S23,503 SNP markers in the EXPEN 2000 (LA0925 x LA0716) linkage map and their assignment on the introgression line population of S. pennellii (IL).(XLSX)Click here for additional data file.Table S33,687 SNP markers in the EXPEN 2012 (Moneymaker x LA0716) linkage map.(XLSX)Click here for additional data file.Table S44,491 SNP markers in the EXPIM 2012 (Moneymaker x LA0121) linkage map.(XLSX)Click here for additional data file.Table S53,149 SNP markers mapped on both the EXPEN 2000 and EXPEN 2012 linkage maps.(XLSX)Click here for additional data file.Table S62,509 SNP markers mapped on both the EXPEN 2000 and EXPIM 2012 linkage maps.(XLSX)Click here for additional data file.Table S72,841 SNP markers mapped on both the EXPEN 2012 and EXPIM 2012 linkage maps.(XLSX)Click here for additional data file.Table S85,295 SNP markers with both genetic and physical positions.(XLSX)Click here for additional data file.