A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios.FIA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90.Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb  yielded imputation accuracies (2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.Combining 60K and 9K panels with imputation in F Designipulation .2 individuals, different genotyping scenarios were tested. In the first scenario, data consisted of F0 and F1 genotypes at HD and F2 at LowD, and average IA was 0.99. Similarly, Weigel et al. [IA > 0.95.Data from a 9K chip (average distance between SNP = 0.30 Mb) were used as a LowD panel to impute to a HD 60K panel. Using the experimental data from Fl et al.  imputed 1 genotyped at 9K, between the generations of grandparents and grandoffspring, and it was observed that IA of F2 decreased to 0.9. In our last scenario F0 and F1 were genotyped at HD and F2 at LowD but the relationships between the F2 and the reference panel were ignored, resulting in an average accuracy of imputation of 0.9. Badke et al. [IA of 0.90 and 0.95 using reference groups of 16 and 64 animals, respectively.Our second scenario included the Fe et al.  used the2 and in the reference population, were evaluated by means of simulated data. Accuracies of haplotyping were calculated as the number of erroneous inference of phase between consecutive heterozygous markers, as in Druet and Georges [0 and F1 were genotyped at HD and F2 was genotyped at LowD, the fraction of non-tagSNP with uncertain phase was 4%, whereas this statistic was 30% when the F0 was genotyped at HD, and the F1 and F2 were genotyped at LowD. The corresponding IA were 0.97 and 0.85, respectively. These results suggest that accuracies of imputation in the current study were affected by knowledge of the phase of non-tagSNP. Moreover, when the amount of genotypes from related individuals  increases, the accuracy of haplotyping goes up, and the accuracy of imputation also increases. These results apply to genotyping designs with a pedigree with a small number of founder individuals genotyped in HD and a large number of progeny genotyped in LowD. If the phase is known in the founders, it is easy to accurately follow transmission of chromosomal segments to the remainder of the population using linkage information. In practice, however the phase needs to be ascertained using LD information. Such information is very limited in cases such as our F0 because of reduced sample size. In that case, the researchers can follow two paths. First, as presented with large pedigrees, having extra animals from the same founding population(s) can help in using LD to accurately phase those animals. Second, as presented here, two consecutive generations can be genotyped in HD to use the information in grand-parents (F0) to accurately phase the parents (F1) and then use linkage information to impute genotypes within the progeny (F2). For such approaches to work, full pedigree information (three generations) and two generations of HD genotypes are needed. The approach is still cost effective in typical F2 populations [Habier et al.  indicate Georges . In all ulations ,32. ThesR2 when MAF < 0.10, and the drop was higher when the masked genotypes were >84% of total SNP. Similarly, Lin et al. [R2) was employed in the current research to evaluate the effect of MAF on imputation accuracy. Our results showed that markers with MAF < 0.10 in the founders were imputed with reasonably good accuracy in the F2 , one can genotype 1.9 times more individuals in a scenario with F0 and F1 at HD, and F2 at LowD than in a scenario with F0, F1 and F2 at HD. The imputed genotypes can then be used for association or for meta-analysis studies.The cost of genotyping is an important consideration. At present, the cost of commercial HD genotyping (60K) for pigs is more than twice as much as the cost of genotyping with the 9K chip. Assuming a population with a structure similar to the one used here  resulted in IA of 0.99 when the F0 and F1 were genotyped at HD and the F2 at LowD. The cost of such genotyping scheme would be less than half the cost of using HD genotypes for all individuals. The correlation between observed and imputed genotypes was high (R2 = 0.94), so that the power for future association studies would be high. Thus, under a genotyping strategy of high accuracy of imputation , information on imputed genotypes from more animals that is similar to that from a HD panel can be obtained at a lower cost. These results apply to the imputation of markers in the SNP60 beadchip, in populations where a small number of founders can be genotyped at HD and phase of parents of imputed animals can be derived with certainty. Translation of LD-based results, on the other hand, are constrained to pig populations showing similar levels of LD as in the founding animals [Designing custom SNP panels for each F animals .0) were four unrelated Duroc boars mated to 15 Pietrain sows by artificial insemination. From all resulting F1 animals, 50 females and 6 males (progeny of 3 F0 sires) were selected as parents for the F2 generation, by avoiding full or half sib matings. A total of 1,259 F2 piglets were born alive from 142 litters out of 11 farrowing groups. Animal protocols were approved by the Michigan State University All University Committee on Animal Use and Care (AUF# 09/03-114-00).The experimental population was raised at the Michigan State University Swine Teaching and Research Farm, East Lansing, MI . ParentsN = 411 . Genotyping was performed at a commercial laboratory  using the Illumina PorcineSNP60 beadchip [M = 62,163 SNP, 6,422 SNP were eliminated as their physical positions were unknown. Mendelian inconsistencies (≤ 0.01%) were taken as missing genotypes, and 12 animals (1 F1 and 11 F2) with more than 10% of SNP missing were not used in any analysis. By similar consideration, 3,038 SNP were removed from the analyses due to presenting more than 10% missing data. Additionally, 10,139 SNP were excluded as their minor allele frequency (MAF) was below 0.01. These editing policies resulted in a data set comprising 399 pigs with 45,003 SNP per animal. This editing procedure followed that of Badke et al. [0 and F1 animals, genotypes for 932 F2 animals were simulated conditional on the real pedigree using a gene-dropping model. Simulated genotypes were used to assess alternative tagSNP selection procedures while experimental genotypes on a subset of animals (n = 336) were used to assess imputation accuracy using a SNP list for a 9K commercial chip that has recently been publicly released by GeneSeek Inc.  from the real F2 population. The haplotypes were estimated at a high accuracy from the genotypes of the F1 parents and 19 F0 ancestors (4 Duroc boars and 15 Pietrain sows), using the software MERLIN [1 haplotypes were drawn from a Poisson distribution with mean equal to the length of the given chromosome in Morgans (M) by assuming 1 Mb = 1 cM [1 genotypes to their F2 progeny.A stochastic simulation was performed to evaluate two different methods of selecting tagSNP for imputation on the accuracy of the resulting F-dropping theory, e MERLIN . The numb = 1 cM . The posb = 1 cM ,42. As ab = 1 cM , the numrt2) [rt2 based on pair-wise LD of the F1 haplotypes was selected, so that all SNPs above the chosen threshold were selected as tagSNP. 2) The second method consisted of selecting evenly-spaced markers. The chromosome was divided into k segments of equal length, and then the SNP that was closest to the center of the segment was selected. In cases where there were no SNP lying in a segment, no selection was performed resulting in the number of tagSNP≤k in segments of approximately equal length.Two different methods were used for tagSNP selection: 1) The first one consisted of a statistical search built into the software FESTA  and usedrt2) . A minim2 genotypes of non-typed markers were imputed using the algorithm of Lander and Green [2 litter, a three generation pedigree was built [0 grandparents, the two F1 parents, and up to a maximum of 10 F2 animals. When the litter had more than 10 progeny, a new “family” was formed with the four F0 grandparents, the two F1 parents and the remaining F2 animals. The resulting “families” were analyzed separately and genotypes were imputed with MERLIN [For simulated data, Fnd Green  that preas built  using thh MERLIN . Breakin2 genotypes of non-typed markers were imputed using the algorithm built into the software AlphaImpute [For experimental data, FhaImpute . The alghaImpute  uses infhaImpute . The alg2 individuals for all methods was evaluated using two different statistics. First, the mean of the difference between observed and imputed allelic dosage was calculated [Irrespective of data generation , the accuracy of genotype imputation in Flculated ,13 as foN is the total number of animals imputed, Mi represents the number of markers with observed genotype in animal i, gij is the observed  allelic dosage in animal i and SNP j, and R2 statistics of Huang et al. [R2 statistics were calculated as follows:In this expression, g et al. . DenotinThe statistic is interpreted as a squared correlation coefficient.JPS, RJCC, JLGD: performed and supervised statistical and simulation analyses and wrote the manuscript. ROB, CWE: designed the resource population and led collection of phenotypic data. CWE, NER: performed DNA extraction and coordinated genotyping with commercial laboratory. JPS, ROB, CWE: designed high density genotyping scheme. All authors read and approved the paper.


Prunus mume Sieb. et Zucc.) is a famous ornamental plant and fruit crop grown in East Asian countries. Limited genetic resources, especially molecular markers, have hindered the progress of mei breeding projects. Here, we performed low-depth whole-genome sequencing of Prunus mume ‘Fenban’ and Prunus mume ‘Kouzi Yudie’ to identify high-quality polymorphic markers between the two cultivars on a large scale.Mei  cultivar, producing a set of informative diversity data. We also analyzed the frequency and distribution of detected InDels and SSRs in mei genome and validated their usefulness as DNA markers. These markers were successfully amplified in the cultivars and in their segregating progeny.A total of 1464.1 Mb and 1422.1 Mb of ‘Fenban’ and ‘Kouzi Yudie’ sequencing data were uniquely mapped to the mei reference genome with about 6-fold coverage, respectively. We detected a large number of putative polymorphic markers from the 196.9 Mb of sequencing data shared by the two cultivars, which together contained 200,627 SNPs, 4,900 InDels, and 7,063 SSRs. Among these markers, 38,773 SNPs, 174 InDels, and 418 SSRs were distributed in the 22.4 Mb CDS region, and 63.0% of these marker-containing CDS sequences were assigned to GO terms. Subsequently, 670 selected SNPs were validated using an Agilent’s SureSelect solution phase hybridization assay. A subset of 599 SNPs was used to assess the genetic similarity of a panel of mei germplasm samples and a plum (A large set of high-quality polymorphic SNPs, InDels, and SSRs were identified in parallel between ‘Fenban’ and ‘Kouzi Yudie’ using low-depth whole-genome sequencing. The study presents extensive data on these polymorphic markers, which can be useful for constructing high-resolution genetic maps, performing genome-wide association studies, and designing genomic selection strategies in mei. Prunus mume Sieb. et Zucc., 2n=2x=16) is a member of Rosaceae, sub-family Prunoideae  . These aWhole-genome sequencing allowed us to detect 200,627 candidate SNP markers in ‘Fenban’ and ‘Kouzi Yudie’. The density of these SNP markers was 847 SNPs/Mb in mei assembly sequences, which was notably lower than that in potato   and sorgTo validate the quality of identified SNPs for a genotyping system, we randomly selected a set of 670 SNPs, which were assembled into an Agilent’s SureSelect solution phase hybridization assay. The 670 SNPs contained 581 SNPs at an average spacing of about 340 Kb widely distributed across eight mei pseudo-chromosomes and 89 SNPs located in assembly sequences which were not anchored to any mei pseudo-chromosome Figure .Captured DNA was sequenced on an Illumina GA II instrument, generating 4.2 G sequencing data with 78 bp reads from the 24 libraries that had been prepared with the SureSelect method (NCBI database under accession SRA063161), and 3.4 G reads passed through the Illumina chastity filter to produce automatic allele calling for each locus. Each library was sequenced to a specific depth, providing a mean ~20-fold mapped coverage of the targeted region. Of 670 SNPs, 89.4%  produced non-ambiguous data containing 513 SNPs distributed across eight mei pseudo-chromosomes and 86 SNPs located in assembly sequences that were not anchored to mei pseudo-chromosomes ] for SNPs are low , 1.2 μl of 2.5 mM dNTP, and 0.6 U of Taq DNA polymerase . The different mixtures were as follows: 0.9 μl of 10 uM each of forward and reverse primers for SSRs, and 1 μl of these for InDels and added ddH2O to the total volume. The PCR amplifications of SSRs and InDels were performed with the following program: 5 min at 95°C; followed by 25 cycles of 40 s at 95°C, 30 s at the optimized annealing temperature for each primers , and a regular reverse primer. The PCR reactions of SSRs and InDels were respectively conducted in a 10 μl mixture. The same mixtures included 50 ng of the genomic DNA, 1 μl of 10 × buffer [20 mM Tris–HCl (pH 8.4), 20 mM KCl, 10 mM  and ‘Kouzi Yudie’ (KZYD) and their F1 hybrids (HB): (A) and (B) loci heterozygosity in the ‘Fenban’, two alleles; (C) loci heterozygosity in the ‘Kouzi Yudie’, two alleles.Click here for fileRelative frequency for mono-, di-, and trinucleotides of SSR repeat motifs.Click here for fileExamples of amplifications of SSR primers labeled with FAM fluorescent dyes. Panels indicated data from ‘Fenban’ (FB) and ‘Kouzi Yudie’ (KZYD) and their F1 hybrids (HB): (A) locus heterozygosities in the ‘Fenban’, two alleles; (B) locus heterozygosities in the ‘Kouzi Yudie’, two alleles; (C) locus heterozygosities in parental lines, two alleles; (D) locus heterozygosities in parental lines, three alleles; (E) locus heterozygosities in parental lines, four alleles; (F) locus homozygosity in parental lines.Click here for file


Ascaris suum (large roundworm of pigs) is a parasitic nematode that causes substantial losses to the meat industry. This nematode is suitable for biochemical studies because, unlike C. elegans, homogeneous tissue samples can be obtained by dissection. It has large sperm, produced in great numbers that permit biochemical studies of sperm motility. Widespread study of A. suum would be facilitated by more comprehensive genome resources and, to this end, we have produced a gonad transcriptome of A. suum.A. suum gonad, respectively. 86% of the high-quality (HQ) reads were assembled into 9,955 contigs and 69,791 HQ reads remained as singletons. 2.4 million bp of unique sequences were obtained with a coverage that reached 16.1-fold. 4,877 contigs and 14,339 singletons were annotated according to the C. elegans protein and the Kyoto Encyclopedia of Genes and Genomes (KEGG) protein databases. Comparison of TES and VAS transcriptomes demonstrated that genes participating in DNA replication, RNA transcription and ubiquitin-proteasome pathways are expressed at significantly higher levels in TES tissues than in VAS tissues. Comparison of the A. suum TES transcriptome with the C. elegans microarray dataset identified 165 A. suum germline-enriched genes (83% are spermatogenesis-enriched). Many of these genes encode serine/threonine kinases and phosphatases (KPs) as well as tyrosine KPs. Immunoblot analysis further suggested a critical role of phosphorylation in both testis development and spermatogenesis. A total of 2,681 A. suum genes were identified to have associated RNAi phenotypes in C. elegans, the majority of which display embryonic lethality, slow growth, larval arrest or sterility.Two 454 pyrosequencing runs generated 572,982 and 588,651 reads for germline (TES) and somatic (VAS) tissues of the A. suum. By comparison with C. elegans datasets, we identified sets of genes associated with spermatogenesis and gonad development in A. suum. The newly identified genes encoding KPs may help determine signaling pathways that operate during spermatogenesis. A large portion of A. suum gonadal genes have related RNAi phenotypes in C. elegans and, thus, might be RNAi targets for parasite control.Using deep sequencing technology, this study has produced a gonad transcriptome of  Ascaris, also known as the "giant intestinal roundworms", contains the largest intestinal nematode species. Ascaris lumbricoides causes the commonest helminth infection of humans, whereas a closely related species, Ascaris suum, typically infects pigs and causes substantial financial losses to the meat industry. The A. suum female is capable of producing more than 200,000 eggs per day and these eggs can survive and remain infective after many years in soil [e.g., RNA interference) for parasite control.The genus  in soil . At presA. suum [Caenorhabditis elegans biology provides opportunities to elucidate functionally conserved mechanisms in nematode biology. Employment of NGS technologies has greatly accelerated the 959 Nematode Genomes project http://www.nematodes.org/nematodegenomes/index.php/Main_Page. Genome sequencing of A. suum somatic cells is ongoing http://www.sanger.ac.uk/resources/downloads/helminths/ascaris-suum.html, and a draft genome and transcriptome of A. suum is now available http://www.nematode.net/NN3_frontpage.cgi?navbar_selection=home&subnav_selection=asuum_ftp. To date, there have been 38 A. suum EST libraries with ~55,000 sequences available in the NEMBASE4 database http://www.nematodes.org/nembase4/ and these ESTs were obtained using conventional cDNA library sequencing technology.To better understand the molecular and biochemical basis of nematode development, nematode EST projects have generated more than 250,000 ESTs from 30 species, including A. suum ,5. This A. suum gonad, the organ for reproduction. A. suum males have a large gonad that can be readily isolated by dissection to provide large numbers of sperm that are suitable for biochemical and cell biological studies [A. suum gonad is composed of three distinct regions; the testis and seminal vesicle form germline tissue and the glandular vas deferens forms somatic tissue. Sperm are stored in the seminal vesicle. During copulation, the spherical, non-motile sperm are activated into bipolar, amoeboid spermatozoa by an unknown component secreted by the glandular vas deferens. The motility of amoeboid sperm is driven by the regulated assembly and disassembly of major sperm protein (MSP) cytoskeleton [e.g., MPOP, MFPs and PP2A) that participate in the dynamics of the MSP cytoskeleton have been identified [A. suum, there have been few studies focusing on sperm chromatin or on distinctions between germline and somatic tissues in A. suum. In addition, chromatin diminution in A. suum represents a fascinating exception to the general rule of the constancy of the genome. However, the complex mechanism of this phenomenon, involving DNA degradation and new telomere addition remain an enigma [A. suum.In the present study, we applied 454 pyrosequencing technology to unravel the transcriptome of the male  studies . The malskeleton ,8. The mentified -12. Despn enigma -18. One A. suum, we acquired the transcriptomes of germline and somatic tissues of A. suum gonad using the RNA-seq approach. Comparison of these two tissues showed that the nucleic acid metabolic and proteasome-ubiquitin pathways are more active in the germline than in the soma. Further comparison with C. elegans microarray data identified 165 conserved germline-enriched genes in A. suum. We also categorized the RNAi phenotypes for A. suum gonadal genes, taking advantage of the C. elegans RNAi phenotype database. Therefore, these A. suum transcriptome data provide a valuable platform for both fundamental biological studies  and for research concerning parasite control (e.g. use of RNAi).To facilitate diverse studies concerning reproductive biology in A. suum gonad was dissected into two parts: testis and seminal vesicle (TES) and glandular vas deferens (VAS), and both samples were subjected to total RNA extraction followed by cDNA synthesis. Second-strand cDNAs with trimmed poly(A) tails were used for high-throughput sequencing on a 454 GS FLX Titanium platform. We performed two runs that produced ~1.16 million raw reads constituting a total of ~0.4 billion base-pairs (bp). The majority of the reads were over 400 bp and the average length of the reads was ~356 bp. The size distribution of the raw reads from both samples is shown in Figure de novo assembly using Newbler (Version 2.3). 999,214 reads from either TES or VAS were assembled into 9,955 contigs. These contigs range from 100 bp to 6,649 bp and 97.6% of them were assembled from three or more reads. The size distribution of the contigs is shown in Figure http://159.226.118.206/miaolab/index.htm. The contigs longer than 200 bp have been deposited in the GenBank Transcriptome Shotgun Assembly (TSA) database under the accession numbers JO467643-JO475858. A summary of 454 sequencing and assembly is shown in Table The male A. suum testis EST library in NEMBASE4 has collected 2,868 ESTs. These ESTs correspond to 595 homologous genes in C. elegans . In contrast, our A. suum gonad transcriptome corresponds to 4,207 homologous genes in C. elegans and 3,686 novel gonadal genes were identified  protein databases using BLASTX . A total of 4,877 contigs (49%) and 14,339 singletons (20.5%) were annotated. A large portion of the 454 sequences have not been functionally defined. Some sequences can be annotated by increasing the E-value and others may represent A. suum specific genes. In summary, 9,822 unique sequences  were annotated in the TES dataset and 12,123 unique sequences  were annotated in the VAS dataset , whose activity is essential for gonad and body morphology in C. elegans [Tubulin genes  are the most abundant transcripts in the  elegans , are alsA. suum gonad metabolic pathways, we mapped the C. elegans homologues of A. suum genes to the KEGG pathways. A total of 5,426 unique A. suum sequences corresponding to 850 homologous genes in C. elegans were assigned to metabolic pathways  having significantly higher levels of expression in TES than in VAS for semi-quantitative RT-PCR analysis. These genes are involved in processes including, protein processing, ubiquitin-proteasome pathways, Wnt signaling, cell division and nucleic acid metabolism. The results  [C. elegans germline-enriched genes and 139 (32.8%) of the soma-enriched genes have homologues in A. suum TES and VAS tissues, respectively . Substantial numbers of serine/threonine kinases and phosphatases (KPs), as well as tyrosine KPs were identified, suggesting pivotal roles of phosphorylation during spermatogenesis. It should be noted that the genes encoding KPs are over-represented among the sperm-enriched genes in C. elegans [Using microarray technology, Reinke ormBase) . BLAST ad 139 32.% of the  elegans .When comparing the TES and VAS datasets, we noticed an enriched expression of genes encoding KPs in TES tissues. There are 242 contigs in the TES dataset encoding KPs that have levels of expression 10-fold higher than those in VAS; in contrast, there are only 17 contigs encoding KPs with VAS/TES expression ratios above 10  [A. suum gonad transcriptome  analysis showed most of them regulate reproduction, embryo development, growth, locomotion and development of anatomical structures, as shown in Figure Through a genome-wide RNAi screen, Kalis ormBase) . BLAST aC. elegans [A. suum larval development has been reported [C. elegans and A. suum to query the C. elegans RNAi phenotype database. RNAi phenotypes associated with 2,681 genes were identified  was first discovered in  elegans  and has reported . TherefoA. suum causes massive losses to the swine industry worldwide. The 959 Nematode Genomes Project has included this species for whole-genome sequencing and a draft genome and transcriptome of A. suum has just become available. In this study, we adopted 454 sequencing technology to determine the transcriptome of the A. suum gonad so as to facilitate further studies of this organism.Nematodes are one of the most diverse phyla and they make up approximately 80% of all individual animals on earth ,26. As tC. elegans exome (26 million bp). 86% of the high-quality reads were assembled into longer contigs, suggesting that these sequencing data had a high coverage. We annotated half of the contigs and 20% of the singletons; a large fraction of sequences have not been functionally assigned. The unannotated 454 ESTs may contain precursor non-coding RNAs  as well as the polyadenylated ncRNA classes; for example, over 13% and 26% of full-length cDNAs in mice and human, respectively, are proposed to be polyadenylated mRNA-like ncRNAs [A total of 0.4 billion bp were obtained by 454 sequencing, which were assembled into 25 million bp, which is equivalent to the e ncRNAs -29. The C. elegans [A. suum germline. MSP comprises 10-15% of the total proteins in nematode sperm [To investigate germline-soma distinctions, we compared the digital transcriptomes of TES and VAS, and identified numerous TES-specific pathways, including DNA replication and proteasome and ubiquitin-mediated proteolysis pathways. These pathways might be required to regulate germline proliferation and differentiation. The proteasome has been documented to regulate the balance between cell proliferation and meiotic entry in  elegans . The gende sperm  and sperde sperm ,8,32,33.A. suum spermatogenesis. Based on the C. elegans microarray data, 165 A. suum genes (83% are spermatogenesis-enriched) were identified as conserved genes controlling germline development. The most abundant proteins involved in A. suum spermatogenesis consist of sperm specific proteins, PDZ domain proteins, tyrosine kinases and phosphatases (KPs), and serine/threonine KPs. Identification of the genes encoding KPs in this analysis underpins the essential role of phosphorylation in the regulation of spermatogenesis. As the most common posttranslational modification, phosphorylation has been established to link to sperm function in a variety of species. In mammalians, the processes regulated by phosphorylation include capacitation, hyperactivated motility, zona pellucida binding, acrosome reaction and sperm-oocyte binding and fusion [C. elegans, the genes encoding KPs are over-represented among the sperm-enriched genes [A. suum. A phosphorylated membrane protein (named MPOP) recruits a soluble casein kinase 1 (named MPAK) to the inner leaflet of the plasma membrane to initiate sperm motility [A. suum. We provide evidence that a ~45 KD protein in sperm is associated with strong tyrosine phosphorylation (pY). Because tyrosine phosphorylation and dephosphorylation act as a molecular switch to regulate MSP assembly [A. suum spermatogenesis/spermiogenesis.We have a particular interest in d fusion -36. In Ced genes . Clues tmotility ; MPAK, imotility ,11. A pumotility . The newassembly , we propC. elegans genes that are homologous to genes represented in the A. suum gonad transcriptome were examined for associated RNAi phenotypes. Although the genes involved in spermatogenesis are possibly insensitive to RNAi [C. elegans database. This RNAi phenotypic categorization is in line with the functional classification of the gonad developmental genes, the majority of which control reproduction, embryo development and growth. Due to the growing concern of anthelminth resistance, RNAi provides a new means to combat parasitic nematodes. RNAi has been successfully used to knock down target genes in a few parasitic nematode species, including B. malayi [H. glycines [G. pallida [O. volvulus [T. colubriformis [A. suum [A. suum gonad transcriptome sequencing data will provide opportunities to use RNAi as a novel anti-parasite agent for parasite control.Lastly,  to RNAi , a varie. malayi , H. glycglycines , G. pall pallida , O. volvvolvulus , T. coluriformis  and nota[A. suum . Recentl[A. suum . TherefoA. suum males were collected from the intestines of infected hogs at Zhongrui Pork Processors  and were stored in worm buffer . A. suum gonads were dissected into two parts: (1) testis and seminal vesicle; (2) glandular vas deferens. Dissected samples were immediately frozen in liquid nitrogen prior to storage at -80°C.A. suum testis and vas deferens were prepared using TRIzol reagent  before removal of trace genomic DNA using DNAse I . Poly A+ RNA was purified using an Oligotex Direct mRNA Kit  followed by first and second-strand cDNA synthesis using the Universal RiboClone cDNA Synthesis System (Promega) with a modification as follows. We designed a poly(T) adaptor with a BsgI site flanking the poly(T) sequence (5'-CGTGTGCAGT(20)VN-3') for cDNA synthesis. The purified second-strand cDNAs were digested by BsgI and recovered with a QIAquick PCR Purification Kit (Qiagen). Both double-strand cDNA samples were subjected to 454 pyrosequencing using the GS FLX Titanium Kit.Total RNAs from C. elegans confirmed protein database (derived from Wormbase) as well as the KEGG protein databases for all organisms http://www.genome.jp/kegg/download/ by BLASTX; the BLAST cutoff E-value was set at < 1e-5. The A. suum gonad developmental genes were functionally assigned by GO Slimer http://amigo.geneontology.org/cgi-bin/amigo/slimmer?session_id. The A. suum genes were mapped onto metabolic pathways according to C. elegans pathways http://www.genome.jp/kegg/download/. Total read numbers of TES and VAS datasets were normalized to equal levels, and the relative gene abundance was defined by log10 of the normalized read number. Heat-maps were generated using R software (version 2.12.0).High-quality sequences (> 99.5% accuracy on single base reads) were filtered to remove short ESTs (< 50 bp) before assembly using Newbler (version 2.3). For assembly, the quality score threshold was set to 40. All unique sequences containing both contigs and singletons were compared against the A. suum testis and vas deferens were isolated before being reverse transcribed into cDNA using the SuperScript III First-Strand Synthesis System (Invitrogen). The cDNA products were diluted 10-fold for use as RT-PCR templates. PCR was performed using High Fidelity PCR SuperMix  under the following conditions: 94°C for 3 min; 30 cycles of 94°C for 30 s, 57°C for 30 s, 72°C for 1 min; 72°C for 8 min. The genes Act-1 and eIF-4A were used as controls.Total RNAs from A. suum TES and VAS tissues were disrupted in HKB buffer  in a homogenizer. An equal volume of lysis buffer  was added and samples were then placed on ice for 30 min before centrifugation for 30 min at 20,000 rpm. Supernatants were then collected for Western blot analysis. To prepare sperm extracts (S100), frozen sperm were thawed on ice for 1 hr and centrifuged for 10 min at 13,000 rpm. Supernatant was centrifuged at 100,000 × g for 1 hr at 4°C. Supernatant (S100) was further analyzed by Western blotting.Fresh SDS-PAGE and Western blotting were performed as previously described . WesternA. suum sample collection. XM prepared ds cDNA samples and performed RT-PCR. CL conducted library construction and 454 pyrosequencing. XM and YS extracted total proteins from TES and VAS tissues and performed Western blot analyses. XM and YZ performed data analyses. XM and LM wrote the manuscript. All authors have read and approved the final manuscript.XM and LM conceived and designed the experiments. SC helped to conceive and coordinate the experiments. FM participated in A. suum testis EST library in NEMBASE4 and for the A. suum gonad transcriptomeSummary of gene assignments for the . Sheet GenBank EST summarizes the A. suum testis EST library; Sheet Gonad 454 data summarizes the A. suum gonad transcriptome.Click here for fileA. suum germline (TES) and soma (VAS) transcriptomesAnnotation of .Click here for fileA. suum TES and VAS transcriptomesMetabolic pathway assignment of .Click here for fileTranscripts with relatively high expression levels in TES and VAS. Sheet TES summarizes transcripts having a 10-fold or higher level of expression in TES compared with VAS; Sheet VAS summarizes transcripts having a 10-fold or higher level of expression in VAS compared with TES.Click here for fileGene/Protein association network for genes highly expressed in TES. The functions of the highlighted gene clusters are as follows: gray, proteasome pathway; yellow, ubiquitin pathway; green, nucleotide metabolism; pink, ribosome members; blue, MSP family.Click here for fileRT-PCR verification of highly expressed genes in TES. Genes Act-1 and eIF-4A are used as controls. Gene codes are from C. elegans.Click here for fileA. suumPredicted germline and soma-enriched genes in . Datasets were acquired by comparison of the A. suum gonad transcriptome with published C. elegans microarray data.Click here for fileA. suum candidate genes conserved in nematode germline developmentSummary of the 165 .Click here for fileContigs encoding kinases and phosphatases that are highly expressed in TES and VAS tissues. Sheet TES summarizes the transcripts of KPs having a 10-fold or higher level of expression in TES compared with VAS; Sheet VAS summarizes the transcripts of KPs having a 10-fold or higher level of expression in VAS compared with TES.Click here for fileA. suumGenes predicted to control gonadogenesis in . Dataset was acquired by comparison of the A. suum gonad transcriptome with published C. elegans genes that regulate gonad development.Click here for fileA. suum gonadal genesRNAi phenotypic categorization of . Each A. suum sequence ID is in bracket. RNAi phenotype codes are from Wormbase.Click here for file


There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations. Genotype-phenotype association studies, from both targeted and genome-wide data, have contributed to our ability to identify genetic variants that are associated with disease. Although an increasing number of studies have found single-nucleotide polymorphisms (SNPs) that have statistically significant association with diseases, most of them either have small effects on disease risk There are two challenges in finding SNP combinations that are highly associated with a phenotype from a large number of SNPs. The first arises from the combinatorial nature of the problem, i.e. there are exponentially increasing number of combinations as the order goes higher. This is even more problematic if a large number of permutation tests are used to correct for multiple hypothesis tests The second challenge is that existing approaches for high-order SNP combination searches lack statistical power. Specifically, due to the use of statistics with high degree of freedom In this paper, we aim to address both the above challenges.To improve computational efficiency, we leverage the discriminative pattern mining framework  constraints within the DPM framework. While this approach has been used in existing work to improve computational efficiency, we quantitatively evaluate its effect on enhancing statistical power in conjunction with the DPM framework.The improved computational efficiency and statistical power further enables the discovery of significant high-order SNP combinations from the three real datasets and then allows the exploration of functional interactions in high-order SNP combinations. Specifically, we study the functional interactions among the genes covered in high-order SNP combinations with an integrated human functional gene network. We find a positive connection between the increase of discriminative power of a SNP combination over its subsets and the functional coherence among the genes covered in the combination. Such an observation is beyond the disease-specific functional interactions studied by existing work that are based on datasets covering a small number of genes Survival), (ii) acute rejection [MIM: N/A] (within in six months) vs. non-rejection (within eight years) after kidney transplant Kidney), (iii) lung cancer [MIM: 211980] vs. non-lung cancer (both heavy smokers) Lungcancer). The three datasets were all collected with a chip We use three SNP datasets designed for studying different types of disease phenotypes: (i) short (less than one year) vs. long (greater than three years) survival of multiple myeloma [MIM: 254500] patients http://www.ecog.org/, accessed 2012 Feb 20). In addition to the three real datasets, We also use a synthetic dataset with 70 cases and 70 controls, 2172 SNPs without differentiation between the cases and controls, and four synthetic high-order SNP combinations of size 3, 4, 5 and 6 respectively, that are associated with case-control groupings. (See the methods section for simulation details).Preprocessing and quality control steps are described in the method section. Note that, the above four datasets have much larger number of SNPs (ranging from 2172 to 3428) than the datasets used in previous studies on high-order SNP interactions (tens or hundreds of SNPs). With these four datasets, we will show that the proposed framework is substantially more efficient and scalable than existing approaches. Although the proposed approach could not directly handle datasets with more than 10,000 SNPs due to the intrinsic computational complexity of high-order SNP combination search, it is worth noting that tag SNP selection X) with three genotypes (homozygous minor (mm), heterozygous (Mm) and homozygous major (MM).), we create three binary variables as mm, mM or MM respectively. We use a binary coding scheme of SNP genotypes, where we create three binary columns for each SNP . For a sX, Y and Z, a combination might be pattern in this paper, where we use the terms “pattern”, “combination” and “SNP combination” interchangeably. Following the traditional setup in discriminative pattern analysis, a pattern is said to be present in a subject only if the subject’s genotypes match all the SNP genotypes in the pattern, and absent otherwise. Thus, a combination of SNP genotypes  is also encoded as a binary variable (present or absent). Again, we use this setup to allow DPM to efficiently perform the search of combinatorial pattern space. The frequency of a pattern  has a mathematical property named anti-monotonicity, which can be leveraged by DPM to prune most of the combinatorial search space and only investigate those patterns that are more likely to have strong association with a disease phenotype Based on the binary coding for each SNP genotype, a combination of SNPs is essentially a combination of SNP genotypes. For example, for three SNPs With this binary encoding of a SNP combination, a i .It is worth noting that Ma et al. http://www.epistasis.org/software.html) and the framework presented in http://sourceforge.net/projects/mdr/) and used the standard coding, in which each SNP is represented by a categorical value with three possible values (genotypes). For DPM and FPC, we use the binary coding. FPC requires an input for the parameter We compare the DPM framework with two representative existing tools for high-order SNP combination discovery: MDR In the synthetic dataset (described in the method section), there are 2172 SNPs. The three frameworks need to search through size-2, size-3, size-4, size-5 and size-6 combinations in order to discover the four embedded patterns of size-3 to 6. After five hours, MDR was still enumerating size-3 SNP combinations, and thus failed to identify the embedded size-4, size-5 and size-6 patterns. FPC could reach size-6 within five hours, but only with a The discovered SNP combinations are of size 2 to 10, including all the four embedded patterns. The substantially better efficiency of SMP is also observed on the three real datasets, which have 2755–3428 SNPs . The subNote that, the synthetic datasets used above  are representatives of the three real datasets used in the paper. For datasets with smaller number of SNPs (e.g. tens or hundreds of SNPs), MDR and FPC (as well as other similar approaches) have been compared with other approaches Among the set of discovered SNP combinations discovered by DPM, some have better discriminative power than their corresponding subsets like  in  while soWith the above definition, the Intuitively, it would be ideal if an algorithm like SMP can directly differentiate combinations with positive and negative values and then prune the ones with negative values as early as possible in the searching process. However, this is a non-trivial task because the We ran DPM on the three real datasets . We observe that there are several significant patterns with FDR  enforcing a proper threshold of In P of size-k and a high This comparison demonstrates that As discussed earlier, As discussed in the introduction, gene-set constraints can reduce the number of hypothesis tests and thus have the potential to enhance the statistical power of high-order SNP combination search. However, the reduction of search space based on prior knowledge also risks missing novel combinations that are not supported by known gene sets. This calls for a quantitative estimation of the tradeoff. Leveraging the efficiency and scalability of the proposed framework, we design the following experiments to explore how gene-set constraints improve the statistical power of high-order SNP combination search (see methods section about how to incorporate gene-set constraints in DPM), where the power is measured indirectly by the number of combinations and unique SNPs discovered with respect to a false discovery rate based on permutation tests as described in the method section. We use the 1892 gene sets from the Molecular Signature Database  with two variations of with-constraint setups, one with a A  and the other with a threshold  that is lower to demonstrate that the gain of computational efficiency with gene-set constraints allows the search for combinations with lower frequency. The latter two setups are denoted as B and C respectively. To study how the size of gene sets affects the statistical power of the proposed framework, we use a parameter ) for this Lungcancer pattern is also shown. This molecular subnetwork is associated with cell death and cell cycle function with the top enriched disease being cancer, and therefore supports the functional interaction among the set of genes and their joint association with the risk of lung cancer. The other pattern in The five SNPs in the Lungcancer pattern are mapped to the five genes that are closest (chromosome location) to them respectively, It is worth noting that such statistically significant and biologically relevant discriminative SNP combinations are mostly high-order combinations of common variants . While the current focus in the exploration of the genetics of rare diseases is mostly on individually rare variants, these high-order SNP combinations indicate that common variants could also be the cause of rare diseases because combinations of common variants can be a rare composite variant.We presented a computational framework for searching high-order SNP combinations with strong disease association from case-control datasets with thousands of SNPs. The framework is substantially more efficient and scalable than existing techniques that usually handle tens of or hundreds of SNPs and mostly up to size-3 combinations. We further showed that, while most high-order combinations are trivial extensions of their subsets, there are indeed high-order combinations in real datasets and they have stronger associations with some disease phenotypes beyond single SNPs and low-order SNP combinations. We also evaluated the effect of two strategies for enhancing the statistical power of high-order SNP combination search: filtering out SNP combinations with lower or similar discriminative power than their subsets and constraining the search space with known biological gene sets. Further leveraging the improved statistical power of this framework, we explored the functional interactions within the SNP combinations discovered from three real case-control datasets and revealed a positive connection between the increase of discriminative power of a SNP combination over its subsets and the functional coherence among the genes covered by the combination. Last but not least, we investigated two representative high-order SNP combinations (one of size-5 and the other of size-4) discovered from a lung cancer case-control dataset and a kidney transplant-rejection case-control dataset respectively, and showed that the genes covered by the two patterns are enriched with molecular interaction networks that are highly relevant to the risk of lung cancer and risk of rejection after kidney transplant, respectively. These results demonstrate the ability of our approach to find statistically significant and biologically relevant high-order, patterns, but we likely find only a subset of all possible SNP patterns of interest. In particular, some interesting patterns could be eliminated during the discriminative pattern mining step or in the Given that there has been a lack of tools for higher-order combination analysis due to computational and statistical challenges, the proposed framework is expected to help discover novel genotype-phenotype associations missed by existing approaches that mostly take the route of univariate analysis, pathway/network enrichment analyses that are based on univariate statistics, or epistasis analysis of low-order SNP combinations. In addition to the proposed framework itself, some general observations made in this study could also help the development of other computational techniques that search for high-order SNP combination and exploit functional insights, namely: 1) two strategies for enhancing statistical power to cope with multiple hypothesis testing in the combinatorial search could be leveraged by other approaches, 2) the observed positive connection between the increase of discriminative power of a combination beyond its subsets and the within-pattern functional coherence, both of which may guide more comprehensive exploration of functional insights of high-order interaction, and 3) the observation that many significant associations are rare combinations of common variations, which suggests an alternative direction to explore the genetics of rare diseases for which current focus is on individually rare variations.The three real datasets used in this paper represent a type of studies that have a different perspective from the typical disease-control designs used in most genome-wide association studies (GWAS). Specifically, the case-control designs used in the three studies are the short vs. long survival of multiple myeloma patients , acute rejection after kidney transplant  and patients with lung cancer and normal subjects . Studies with such or similar designs enforce strict additional criteria in sample selection and thus normally have much fewer samples compared to most GWAS studies. Given the limited sample sizes, the three studies adopted a SNP chip that targets a set of SNPs selected on the basis of biological candidacy in order to have better statistical power. Therefore, we expect the proposed framework to help other studies that also use targeted SNP chips to search for high-order SNP combinations that provide insights beyond univariate or lower-order analysis.The proposed framework is able to efficiently search high-order combinations for focused studies with thousands of SNPs, but not directly suitable for focus studies with even more SNPs (e.g. tens of thousands) or genome-wide data. However, note that, this limitation is not specific to the proposed approach but to high-order interaction discovery in general, because there it is computationally infeasible to search for high-order interactions directly from genome-wide SNP datasets. After all, the state of the art methods for discovering high-order interactions could only handle less than a thousand SNPs as reviewed in the paper. Nevertheless, a practical solution to handle genome-wide datasets is to apply the current framework on a subset of SNPs selected by some prioritization strategy There are several possibilities for future work. First, we used a binary encoding for SNP-genotype combinations which differentiates the present of all the SNP genotypes in a pattern in a subject from the mismatch of any one genotype, but not further distinguish different numbers of mismatches. A more generalized encoding We carefully checked the race and gender information in the three datasets to make sure the high-order combinations are not due to spurious allelic association as suggested by http://www.hapsample.org, accessed 2012 Feb 20) to simulate genotype data with the 3404 SNPs from a recent study on multiple myeloma We first used Hap-Sample simulator (F is anti-monotonic if the following equation holds:t)  combination with strong differentiation shows zero differentiation in all of its subsets decreases dramatically when k increases . Therefore, in practice, we can use a threshold on Using real patterns. Next, in each permutation test, we randomly shuffle the grouping of subjects into cases and controls while maintaining the original sample-size ratio between cases and controls, and then use SMP with exactly the same setup as for the original case-control grouping to discover a set of patterns. If random patterns. For each pattern , we compute a c and of size-k) is then calculated as follows: if there are m real patterns of size-k with c and there are n random patterns of size-k with c, then the false discovery rate is Because of the large number of high-order SNP combinations tested in the search process, correction for multiple hypotheses testing is needed for a reasonable estimation of the statistical significance of the discovered SNP combinations. We follow the widely used empirical permutation-based approach Click here for additional data file.Figure S2Four synthetic discriminative patterns of size-36 that we embed in the synthetic dataset as described in the method section, with similar description as (TIF)Click here for additional data file.Figure S3The scalability of SMP with respect to sample size (cases and controls combined). The computational time of SMP increases linearly with the sample size (Note that the x-axis is not linearly spaced).(TIF)Click here for additional data file.Table S1Summary of the three real datasets. The second column lists the number of SNPs for each dataset after filtering out the SNPs with more than 5% missing values.(DOC)Click here for additional data file.Table S2The effect of sample sizes on the FDRs of the four synthetic SNP combinations shown in  embedded(DOC)Click here for additional data file.


Current breeding approaches in potato rely almost entirely on phenotypic evaluations; molecular markers, with the exception of a few linked to disease resistance traits, are not widely used. Large-scale sequence datasets generated primarily through Sanger Expressed Sequence Tag projects are available from a limited number of potato cultivars and access to next generation sequencing technologies permits rapid generation of sequence data for additional cultivars. When coupled with the advent of high throughput genotyping methods, an opportunity now exists for potato breeders to incorporate considerably more genotypic data into their decision-making.~28-29 Mb for each cultivar. Using the Maq SNP filter that filters read depth, density, and quality, 575,340 SNPs were identified within these three cultivars. In parallel, 2,358 SNPs were identified within existing Sanger sequences for three additional cultivars, 'Bintje', 'Kennebec', and 'Shepody'. Using a stringent set of filters in conjunction with the potato reference genome, we identified 69,011 high confidence SNPs from these six cultivars for use in genotyping with the Infinium platform. Ninety-six of these SNPs were used with a BeadXpress assay to assess allelic diversity in a germplasm panel of 248 lines; 82 of the SNPs proved sufficiently informative for subsequent analyses. Within diverse North American germplasm, the chip processing market class was most distinct, clearly separated from all other market classes. The round white and russet market classes both include fresh market and processing cultivars. Nevertheless, the russet and round white market classes are more distant from each other than processing are from fresh market types within these two groups.To identify a large number of Single Nucleotide Polymorphisms (SNPs) in elite potato germplasm, we sequenced normalized cDNA prepared from three commercial potato cultivars: 'Atlantic', 'Premier Russet' and 'Snowden'. For each cultivar, we generated 2 Gb of sequence which was assembled into a representative transcriptome of The genotype data generated in this study, albeit limited in number, has revealed distinct relationships among the market classes of potato. The SNPs identified in this study will enable high-throughput genotyping of germplasm and populations, which in turn will enable more efficient marker-assisted breeding efforts in potato. Solanum tuberosum Group Tuberosum, is an autotetraploid (2n = 4x = 48) and the world's third most important food crop in overall production, after rice and wheat [Verticillium wilt [The most widely cultivated potato species, nd wheat . Potato nd wheat  includinnd wheat -9, they nd wheat -15, but nd wheat , Potato nd wheat -19, potand wheat  and Vertium wilt . DevelopWith the emergence of genomics in the late 1990s, Expressed Sequence Tag (EST) projects were initiated for potato in which Sanger-based sequencing was used to catalog transcripts in an array of tissues and genotypes -26. To d~250 potato clones, which includes many representatives of each of the major market classes, Solanum species, genetic stocks, and represents a broad genetic base to assess the allelic distribution of a subset of SNPs and the population structure and relationships between market classes.Due to the high throughput and low costs, next generation sequencing methods provide a powerful means to generate large sequence datasets that can be used to characterize sequence diversity ,29. In ade novo assembly of quality-filtered reads. For the GA2 transcript data, de novo assembly resulted in a combined total of 86.9 Mb of contigs for potato , it was determined that the number of subpopulations in the set of 248 diverse genotypes was four  species, and 4) diploid breeding lines derived from various Solanum species and genetic stocks . We were initially surprised that the cultivated tetraploid germplasm differentiated into two groups based on market class , rather than one group  or six groups (one for each market class). Over the past fifty years, chip processing germplasm has undergone intense selection for processing characteristics such as low reducing sugar content, high starch, shallow eyes and round tuber appearance, which may partially explain why this market class is genetically distinct from all other market classes.Intra- and intervarietal SNP diversity exists in elite cultivated germplasm. With the diversity of a germplasm panel and the availability of a set of random SNP markers, the opportunity exists to examine population structure in elite breeding germplasm. US potato breeding efforts currently center on improving six distinct market classes , where most  hybridizations for varietal selection are made between clones within a market class. Over time one might expect these six market classes to diverge, not only in terms of the few traits that define each class, but also in terms of unlinked, selectively neutral DNA markers. To assess whether the market classes have diverged significantly, 82 high quality SNPs (selected from the 96 SNPs used in BeadXpress genotyping described above) were used to evaluate the 248 clone germplasm panel. Using the likelihood of the observed genotypes given the number of populations in the model for each of the values of s Figure . It is nS. tuberosum germplasm. The wild species group thus serves as an outgroup for analysis of elite potato germplasm. The diploid breeding line/genetic stock group clustered more closely to the cultivated germplasm, presumably because members of this group often contain substantial amounts of S. tuberosum Group Tuberosum and/or Phureja. Even so, the diploid breeding/genetic stock group was still clearly distinct from cultivated germplasm. Interestingly, within cultivated germplasm the two russet market classes - French fry processing and table russet - clustered more closely to each other than they did to either the round white chip processing and round white table groups. We had expected the French fry processing and chip processing market classes to group more closely because of similar selection for processing traits. Russet germplasm, common in North America but not elsewhere, is characterized by long tuber shape and russet skin. The group with red or purple skin formed its own cluster, while the remaining cluster included various round tuber classes . The yellow flesh, pigmented skin, and round white table groups clustered separately from the chip processing group.Of the 248 potato clones genotyped, 244 could be readily categorized as either wild species, diploid breeding line/genetic stock, or as belonging to one of the following market classes: French fry processing, table russet, chip processing, yellow flesh, pigmented skin, round white table. Using allele frequency-based distances  with data mining of existing ESTs from three older cultivars , we were able to identify an abundance of SNPs in elite potato germplasm. These SNPs will facilitate future marker analyses by potato geneticists and breeders alike. Breeders, in particular, will soon be able to incorporate large amounts of genotypic data into their decision making. This will lead to deeper understanding of breeding germplasm, as well as more efficient QTL mapping, association mapping and marker-assisted selection, collectively resulting in more predictable and directed breeding.With stringent filtering of sequence data in combination with alignment to a reference potato genome, we were able to identify 69,011 high confidence SNPs for use with the Infinium genotyping platform. A subset of these SNPs was recently used to design a 8300 marker SNP array . The curSolanum species and accessions  that have previously been used for introgression into tetraploid germplasm were included in the panel to provide a taxonomic perspective. These clones were designated as "species" in our analyses. Sequences used in this study are listed in Table Solanum tuberosum Group Phureja DM1-3 516R44 (DM) potato were obtained from the Potato Genome Sequencing Consortium  were assembled separately using the Velvet assembler  in the pA. thaliana proteome  were searched against the   and Uni  with an  were us  terms.We computationally identified SNPs within our three GA2-generated transcriptomes  and designed a 96 SNP BeadXpress assay to 1) validate our computational predictions and 2) assess allelic diversity and population structure in a diverse set of potato germplasm.Intra-varietal and inter-varietal SNPs were identified by aligning the RNA-Seq reads from each variety to the Velvet-generated contigs using the Maq easyrun.pl pipeline in the paired-end mode Figure .K (number of populations) for K equals two through 10 using an admixture model with a burn-in time and replication number of 50,000. The population number with the maximum likelihood of the observed genotypes given the number of populations was used to determine population structure. PowerMarker version 3.25 [DNA was extracted from 248 potato lines using the Qiagen Qiaxtractor DX system . Samples were loaded at 50 ng/μl on an Illumina BeadXpress Analyzer  and data were analyzed using the Illumina GenomeStudio software. Cluster positions for three marker classes  were manually determined for each marker within the Illumina GenomeStudio software. Due to the difficulty of calling allelic dosage in the tetraploid clones, all heterozygous classes in tetraploids  were scored as AB. Of the 96 SNP markers, 14 were of low quality based on the tightness of clusters and/or signal intensity and were removed from downstream analysis , Premier (B), and Snowden (C) based on their annotations to terms in the gene ontology molecular function category are shown.Click here for fileHigh confidence SNPs for genotyping. This file lists the high confidence SNPs with their flanking sequence that were used in the genotyping experiment.Click here for fileGenes and SNPs represented on the 96 BeadXpress platform. This file lists the SNPs and the genes represented by the SNP with their functional annotation.Click here for fileK = 2, 3, 4, 5, 6, 7, 8, 9, and 10)Graphical display of population substructure for 248 genotypes at variable population numbers (. Population substructure was determined using STRUCTURE [TRUCTURE  with 82 Click here for fileGermplasm genotyped with the BeadXpress SNP assay. Population structure was determined using STRUCTURE [TRUCTURE  with 82 Click here for fileRogers genetic dissimilarity matrix [between market classes based on 82 high quality SNP markers and 244 genotypes with known market class designations. Genetic dissimilarity among market classes are shown.y matrix between mClick here for fileGenotypic data for 82 high quality BeadXpress SNP makers on 248 genotypes. Genotypes for potato clones with 82 SNP markers are shown.Click here for file


General, breed- and diet-dependent associations between feed efficiency in beef cattle and single nucleotide polymorphisms (SNPs) or haplotypes were identified on a population of 1321 steers using a 50 K SNP panel. Genomic associations with traditional two-step indicators of feed efficiency – residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG) – were compared to associations with two complementary one-step indicators of feed efficiency: efficiency of intake (EI) and efficiency of gain (EG). Associations uncovered in a training data set were evaluated on independent validation data set. A multi-SNP model was developed to predict feed efficiency. Functional analysis of genes harboring SNPs significantly associated with feed efficiency and network visualization aided in the interpretation of the results.For the five feed efficiency indicators, the numbers of general, breed-dependent, and diet-dependent associations with SNPs  were 31, 40, and 25, and with haplotypes were six, ten, and nine, respectively. Of these, 20 SNP and six haplotype associations overlapped between RFI and EI, and five SNP and one haplotype associations overlapped between RADG and EG. This result confirms the complementary value of the one and two-step indicators. The multi-SNP models included 89 SNPs and offered a precise prediction of the five feed efficiency indicators. The associations of 17 SNPs and 7 haplotypes with feed efficiency were confirmed on the validation data set. Nine clusters of Gene Ontology and KEGG pathway categories  including, 9nucleotide binding; ion transport, phosphorous metabolic process, and the MAPK signaling pathway were overrepresented among the genes harboring the SNPs associated with feed efficiency.The general SNP associations suggest that a single panel of genomic variants can be used regardless of breed and diet. The breed- and diet-dependent associations between SNPs and feed efficiency suggest that further refinement of variant panels require the consideration of the breed and management practices. The unique genomic variants associated with the one- and two-step indicators suggest that both types of indicators offer complementary description of feed efficiency that can be exploited for genome-enabled selection purposes. Feed efficiency is based on the relation between animal intake (input) and production (output). In beef cattle feedlot enterprises, the feed costs may represent up to 84% of the total costs, depending on the stage of production , and thuSeveral genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNP) associated with feed efficiency in beef cattle -16. The In addition to individual SNP, the study of haplotype associations in GWAS has benefits. Haplotype blocks may capture epistatic interactions between SNPs -21, and The objectives of this study were: 1) to identify general, breed- and diet-dependent associations between feed efficiency in beef cattle and SNPs or haplotypes and, 2) to compare the genomic associations with traditional two-step indicators of feed efficiency – residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG) – relative to two complementary one-step models of feed efficiency: efficiency of intake (EI) and efficiency of gain (EG). Associations uncovered in a training data set were evaluated on a validation data set. A multi-SNP model was developed to predict feed efficiency. Functional analysis and network visualization aided in the interpretation of the results.Animals used in this trial were managed according to the guidelines recommended in the Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching . ExperimA beef cattle population encompassing various breed compositions and receiving various diets was used to uncover general and breed- or diet-specific genomic variants associated with feed efficiency. A total of 1,321 feedlot steers obtained from five ranches in Montana representing calving years between 2005 and 2008 were studied. Steers entered the study with an average (± standard deviation) of 249.75 ± 29.91 days of age, and stayed for an average (± standard deviation) test period of 160.96 ± 16.50, 180.08 ± 15.20, 163.39 ± 13.31, and 158.68 ± 14.78 days, for years 2005 to 2008, respectively. Animals were harvested in different groups, from two to three groups per year and ranch. All ranches participated in this study during all years and one ranch was used only in the last year. The combination of these harvest groups, ranches, and years was used to create contemporary groups . The pedigree and breed information of all steers used in this study were accessed from the American Simmental Association Herdbook Service . The ped2), and back fat thickness . Adjusted final weight  was obtained as hot carcass weight divided by the average dressing percentage of the harvest group, and used to calculate individual average daily gain , as the difference between FW and IW, divided by the test period. The calculation of ADG in this study may not be optimal since the FW was estimated using the average dressing percentage of the harvest group. Individual feed intake records were collected using the GrowSafe automated feeding system . Chromatography paper was used to take an image of the longissimus dorsi for REA measures, and recorded using a planometer. Measures of BF were taken in a transverse orientation between the 12th and 13th ribs, at approximately 10 cm distal from the midline. The average (± standard deviation) of IW, ADG, DMI, REA, and BF were 310.10 ± 40.08 kg, 1.61 ± 0.24 kg, 10.48 ± 1.42 kg/day, 90.18 ± 10.19 cm2, and 1.26 ± 0.36 cm, respectively. A more detailed description of the diets, measurements of the traits, and slaughter procedures may be found elsewhere [After one week of adaptation to the diet, initial weight  of each animal was recorded and steers were measured for dry matter intake , rib eye area , REA, and BF. In a similar fashion, RADG  was calThe identification of SNPs associated with RADG or RFI is the result of a two-step approach. The first step consists of the estimation of ADG (or DMI) once the covariation due to MBW, DMI (or ADG), REA, and BF have been removed. The second step consists on the identification of SNPs or haplotypes associated with the resulting point residuals of ADG (or DMI). These residuals are a function of point predictions that ignore the uncertainty  associated with these predictions. The purpose of these calculations of RFI, RADG, and RIG is to minimize the effects of body weight . A one-sTwo complementary indicators of feed efficiency obtained from a one-step model for SNP associations are proposed. In the one-step model, the association between SNPs or haplotypes and feed efficiency is described in one model that includes the covariates used in the first step of the computation of RFI and RADG. The complementary one-step model overcomes the limitation of RFI and RADG by accommodating parameter estimate uncertainty. The one-step indicators used as complements for RFI and RADG are termed efficiency of intake (EI) and efficiency of gain (EG), respectively.Genotypes were obtained using the Illumina® BovineSNP50 BeadChips v1 and v2 platforms  that include 54,001 and 54,609 SNPs, respectively. The 52,340 SNPs presented in both versions of the platform were analyzed. Quality control was performed in two steps. First, SNPs not assigned to chromosomes, according to the Bos_taurus_UMD_3.1 assembly (519 SNPs) , and wit2 < 0.08) and is low at 100 kb (r2 < 0.18) [Using the genotypic data after quality control, haplotype blocks were and phased using PLINK. Blocks were estimated within a 200 kb window  using th < 0.18) ,35. A wiThe association between feed efficiency and non-overlapping blocks was tested. The lower number of non-overlapping blocks, relative to sliding window overlapping blocks, resulted in a substantial reduction in the number of tests and thus in less stringent multiple-test adjustment of the significance P-values. A potential drawback of the non-overlapping approach is that all SNPs in LD with the loci could be assigned to different blocks, thus resulting in loss of power. This situation may also arise for the sliding window approach when a limited number of window widths are applied . In thisr2 statistics [Haplotype alleles with posterior phasing probability below 1 were excluded to increase the reliability of the haplotype alleles. The second quality control step in the haplotype data used the same thresholds from the individual SNP analysis, and no blocks or steers were removed. The extent of LD between SNPs within haplotype blocks was computed in PLINK using the pairwise average atistics  in orderTwo data sets were obtained from the records that passed the quality control. The training data set consisted of 976 steers  and was used to identify SNPs and haplotypes associated with feed efficiency. The validation data set consisted of 336 steers  and was used to validate the findings. Steers from the same sire were assigned to only one of the two data sets to minimize the dependencies between data sets and attain a less biased validation ,38. The The general model used to identify associations between SNPs and the two-step feed efficiency indicators RFI, RADG, and RIG was:ijklm is the value of RFI, RADG, or RIG, μ is the overall mean, SNPi is the fixed effect of the SNP genotype, Bj is the fixed effect of breed (5 levels), Dk is the fixed effect of diet (5 levels), CGl is the random effect of contemporary group , and eijklm is the random error  and EG (Eq. 3):2, b3, b4, and b5 are the fixed effect regression coefficients for the covariates ADG, MBW, BF, and REA, respectively, for the EI model, and b6, b7, b8, and b9 are the fixed effect regression coefficients for the covariates DMI, MBW, BF, and REA, respectively, for the EG model. The additive and dominance effects of the SNPs were tested jointly and SNPs were considered significant at P-value < 0.0001 [where b< 0.0001 .Similar models were used to test the individual association between the 1,129 haplotype blocks and the five indicators by substituting SNP for haplotype in the models. The additive effect of haplotype was tested and considered significant at P-value < 0.001 to account for the lower number of haplotype hypotheses tested  compared to SNP hypotheses tested .Additive and dominance estimates are presented relative to the less frequent or minor allele. The additive effect was tested for SNPs located on BTA X, when testing the interactions with breed and with diet, and for the haplotype analyses. The additive estimates for SNPs interacting with breed or diet are the contrasts between each breed level relative to SM steers for breed-by-SNP associations, and each diet level relative to diet E, for diet-by-SNP associations. Before association analyses, the normality and homoscedastic of the residual estimates from each model without the SNP effect was confirmed assessing the Shapiro-Wilk’s test of normality using the UNIVARIATE procedure in SAS. All association analyses were performed using Qxpak v.5.05 .A SNP-based indicator of feed efficiency was developed by simultaneously considering significant SNPs and their ability to predict each of the five feed efficiency indicators evaluated using a stepwise selection approach. All the explanatory variables in Equations 1, 2, and 3 were kept in the model with the exception of the SNPs that underwent variable selection. For each feed efficiency indicator, the set of SNPs that were significantly associated at P-value < 0.001 in the whole-genome SNP association analysis were assessed for entering and staying in the model at P-value < 0.0001.The significant SNPs and haplotypes identified in the genome-wide analyses were evaluated in the validation data set using the same models. Both, the significance level and trend (sign of the estimates) of the SNP and haplotype were compared between the two independent data sets. Each SNP and haplotype was individually tested and considered validated at P-value < 0.05 . For theT and RMSEV are the root mean square errors of the models when using the training and validation data sets, respectively. In this formula it is possible to assess the drop in the model adequacy when fitting the set of selected SNPs using the validation data set in place of the training data set.where RMSEHeritability estimates were obtained using single trait analyses, whereas the genetic and phenotypic correlations were obtained using bivariate analyses. The five feed efficiency indicators were analyzed using the models described for whole-genome analysis, excluding SNP or haplotype and the interactions with other model factors. The variance components of the indicators were estimated using WOMBAT .The Gene Ontology (GO) categories that are overrepresented among the genes harboring the SNPs associated with feed efficiency were identified. This analysis and gene network visualization offered insights into molecular functions and biological processes that could be associated with feed efficiency in beef cattle. Gene Ontology  categoriEnrichment was identified in three gene lists: general SNP associations, breed-by-SNP associations, and diet-by-SNP associations that resulted from the combination of all five feed efficiency indicators. In order to identify background genes for the enrichment, the SNP-encompassing sequences  were mapped to the Bos Taurus genome assembly UMD_3.1 using BLASTN. Only annotated genes that contained the best hit of each SNP with an E-value < 1.0E-10 were retained as background genes for enrichment testing. An EASE score was applied to evaluate each functional category using the Fisher Exact test . The EASGene networks associated with feed efficiency were inferred based on the lists of genes corresponding to the significant GO terms. Networks were visualized using the BisoGenet plug-in  from theA summary of the total number of unique significant SNPs  and haplotypes  are presented in Tables The heritability estimates for RFI, RADG, RIG, EI, and EG were: 0.40 ±0.10, 0.17 ± 0.07, 0.40 ± 0.10, 0.40 ± 0.10, 0.16 ± 0.07, respectively. Consistent with SNP and haplotype results, the heritability for RFI and RADG are similar to EI and EG, respectively. The RFI heritability estimate is consistent with those previously reported . The RADAll absolute genetic and phenotypic correlation estimates, were above 0.9 with the exception of RADG with RFI and RIG. The high correlations are expected considering the similarity between indicators. The genetic and phenotypic correlation estimates between RADG and RFI were, 0.43 ± 0.09 and −0.34 ±0.03, respectively, and are in accordance with previous reports . The genper se or interacting with breed or diet that were mapped to gene regions are presented in Tables The SNPs significantly associated  with each indicator Significant associations between feed efficiency and SNP-by-breed or SNP-by-diet interactions uncover breed- and diet-dependent SNP associations. The identification of breed-dependent SNPs associated with feed efficiency has two uses. First, beef cattle crossbreeding systems can exploit the higher feed efficiency for SNPs with dominant mode of action. Second, the use of breed-dependent SNP information may improve selection and breeding within-breeds. The identification of diet-dependent SNPs associated with feed efficiency supports breeding and selection strategies optimized for specific feeding managements. In the absence of significant interactions, significant SNP association uncover general genetic variants that are associated with feed efficiency regardless of breed or diet and that are useful across beef cattle breeding systems.A total of 137 significant genomic associations with feed efficiency indicators, representing 93 SNPs, were detected. Associations were identified on all chromosomes with the exception of BTAs 21, 26, and 27. Ten or more associations were observed on BTAs 6, 8, 12, 15, and 17 (approximately 42% of the significant associations). Although the later chromosome had the highest number of associations (15), only three SNPs were located in gene regions.RFC5). The additive estimate of the minor allele G was consistent across the three indicators, with purebred SM being more efficient than 3/4AN steers. This SNP is located within a QTL region for RFI previously reported [UNC5C G > A allele on BTA 6 had higher feed efficiency than 3/4AN and AN. On BTA 12, rs109291606 and rs41611457, that map to ecto-NOX disulfide-thiol exchanger 1 (ENOX1), had a significant association with EG in a general and diet-dependent, respectively (Tables DACH1) showed breed-dependent associations: rs41625438 with RIG, and rs42456314 with RFI, EI, and RIG. On BTA 15, rs41620774, rs108964818, and rs109709275 corresponding to ELMO/CED-12 domain containing 1 (ELMOD1), Lys-Asp-Glu-Leu containing 1-like gene (KDELC2), and GRAM domain containing 1B (GRAMD1B), respectively, had significant general associations with feed efficiency  and breed that indicates that this SNP has a breed-dependent mode of action (Table  and breereported . There wCNTFR), CUB and Sushi multiple domains 2 (CSMD2), ELMO/CED-12 domain containing 1 (ELMOD1), glypican 5 (GPC5), Lys-Asp-Glu-Leu containing 1-like (KDELC2), replication factor C (activator 1) 5, 36.5 kDa (RFC5), transmembrane protein 40 (TMEM40), and unc-5 homolog C (UNC5C) genes were associated with two or more indicators (Table TMEM40 had the highest number of associations (seven) across indicators. The same SNP in this gene (rs108942504) exhibited significant diet-by-SNP association with RIG  pathway [TMEM40 and CNTFR and feed efficiency may be related to the role of these genes in the transport of substances that regulates energy expenditure inside the cell.From the single SNP analyses, 29 genes located on 18 BTAs were associated with feed efficiency indicators Table . In partIG Table  and dietne rs1089504 exhibHLX) and molybdenum cofactor sulphurase C-terminal domain containing 1 (MOSC1) genes. For RADG and EG, rs109945988 . For SNPs not mapped to genes interacting with diet, two polymorphisms showed high divergence between diets , aquaporin 11 (AQP11), chloride channel, nucleotide-sensitive, 1A (CLNS1A), and remodeling and spacing factor 1 (RSF1). In contrast, for steers fed the same diet C, the minor allele C for rs41256074 is associated with lower feed efficiency by increasing both RFI and EI. This SNP is located approximately 100 kb upstream the B-cell CLL/lymphoma 11A (zinc finger protein) gene (BCL11A) on BTA 11.Steers with genotypes AA and TT, for rs41619246 and rs41256074, respectively, showed higher feed efficiency in feedlot beef production systems that use dry-rolled corn and corn gluten feed diets.For rs41634631 and rs111010038, similar trends were observed for both RFI and EI . The protein encoded by this gene acts as a positive regulator of neuron projection development [RFC5 gene, rs29024448 is located between two regions previously associated with RFI [Genomic regions harboring QTL associated with RFI have been reported on all bovine chromosomes except BTAs 27 and X -14,46. Selopment . Among felopment . Feedlotwith RFI ,46. For with RFI , rs10919with RFI , rs41660with RFI , rs41856with RFI  on BTA 2Considering only the general association results . The three genes in this block were: microtubule-associated protein 6 domain containing 1 (MAP6D1), presenilin associated, rhomboid-like (PARL), and YNL107w, ENL, AF-9, and TFIIF small subunit (YEATS) domain containing 2 (YEATS2). This block had two SNPs located upstream of the genes, two located within 2 kb 5′ to the genes MAP6D1 and PARL, and one in an intronic region of YEATS2 , negative regulation of microtubule depolymerization (MAP6D1), and negative regulation of release of cytochrome c from mitochondria (PARL) [The haplotype block 2  . The hema (PARL) , and proa (PARL) .r2 of 0.29. Comparing the two alleles with the most extreme additive estimates, higher efficiency is expected for feedlot beef cattle carrying the haplotype allele GCCG in place of GTCT. Block H03 located on BTA 2 had an average r2 of 0.66 and was associated with RFI, EI, and RIG. The SNPs in this block are located in the intronic region of the F-box protein 42 gene (FBXO42). This gene encodes for F-box proteins, which interact with other products to act as protein-ubiquitin ligases [SPRED2). This gene encodes for a protein of the Sprouty/SPRED family of proteins regulates the activation of the MAP kinase cascade [SPRED2 participates in the Jak-STAT signaling pathway, a signaling mechanism for a variety of cytokines and growth factors in mammals [Among the haplotypes with significant association at P-value < 0.0001 Table , H03 and ligases . In all  ligases . The hig cascade , and SPRNID2) and one in the intronic region of the DNA helicase homolog PIF1 gene (PIF1). This haplotype was in moderate-high LD , and serine/threonine-protein kinase TAO3 (TAOK3). The serine/threonine-protein kinase TAO3 participates in the MAPK signaling pathway and the JNK cascade [Four of the seven SNPs in H09 located on BTA 10 are in intronic regions of the nidogen-2 gene , and CCR4-NOT transcription complex, subunit 6-like (CNOT6L); for RADG, ATP-binding cassette, sub-family A (ABC1), member 1 (ABCA1), and family with sequence similarity 135, member B (FAM135B); for RIG, fer-1-like 5 (FER1L5), GTPase activating protein (SH3 domain) binding protein 2 (G3BP2), and spectrin, beta, non-erythrocytic 2 (SPTBN2); for EG, ArfGAP with GTPase domain, ankyrin repeat and PH domain 1 (AGAP1), cyclin M2 (CNNM2), glypican 5 (GPC5), glycerol-3-phosphate dehydrogenase 1 (soluble) (GPD1), and WDFY family member 4 (WDFY4).All SNPs significantly associated at P-value < 0.001 (data not shown) were evaluated in the stepwise selection in order to consider SNPs that may have weaker associations when considered alone, and stronger associations when considered simultaneously with other SNPs. The numbers of SNPs used in the multi-SNP analyses were 227, 164, 246, 219, and 143, for RFI, RADG, RIG, EI, and EI, respectively. The selected SNPs included in the final models as general or breed/diet-dependent associations for each feed efficiency indicator is presented in Table ABCA1) and was associated with RADG in a breed-dependent manner (Table CD3E); a gene that encodes for a protein that acts in immune-response related biological processes, including regulation alpha-beta T cell proliferation, interleukin-2 biosynthetic process, and interleukin-4 production [GPD1). This gene has NAD binding molecular function [+[The SNPs in the multi-SNP model mapped to several genes with known functions that could be potentially associated with efficiency. On BTA 8, rs41590616 is located on the intronic region of the ATP-binding cassette, sub-family A (ABC1), member 1 gene (er Table . This geoduction . This SNfunction  and playfunction . The pronction [+).The SNPs and haplotypes significantly  associated with feed efficiency indicators on the training data set were evaluated in the validation data set. Individual SNP and haplotype associations were confirmed in the validation data set at P-value < 0.05. The less stringent P-value threshold reflects the limited number of tests that are validated, the stringent threshold used in the training data set, and the expected lower false positive rate of validation tests. For the multi-SNP models, validation was assessed by the goodness-of-fit of the selected SNPs from the training on the validation data set.For the single-SNP analysis, the results for the validated SNPs are summarized in Additional file SPON2), C-terminal binding protein 1 (CTBP1), macrophage erythroblast attacher (MAEA), KIAA1530 ortholog (KIAA1530), stem-loop binding protein (SLBP), transmembrane protein 129 (TMEM129), and transforming, acidic coiled-coil containing protein 3 (TACC3) genes. On BTA 18, rs41856111 was associated with RFI and RIG. This SNP is located 70 kb downstream the v-maf musculoaponeurotic fibrosarcoma oncogene homolog (avian) gene (MAF) and approximately 1.5 Mb to a genome region previously associated with RFI [CFDP1), and steers carrying C alleles had higher efficiency when fed diet C compared to diet E. For the other three SNPs that had breed-dependent association with RIG, rs109195623 (BTA 3) is located 242 kb upstream the nuclear factor I/A gene (NFIA), rs41662450 (BTA 9) is within an uncharacterized loci 8 kb downstream the erythrocyte membrane protein band 4.1-like 2 gene (EPB41L2), and rs109137042 (BTA 10) is within 200 kb of the secretory carrier membrane protein 1 (SCAMP1), lipoma HMGIC fusion partner-like 2 (LHFPL2), and arylsulfatase B (ARSB) genes. The association between EG and rs110522962 was also validated. This SNP is located on BTA 17, approximately 100 kb upstream the predicted histone chaperone anti-silencing function-1 homolog B gene (ASF1B), and feedlot steers carrying the C > T substitution had higher EG and thus higher efficiency.The associations of rs109452133 and rs41856111 with feed efficiency were validated. On BTA 6, rs109452133 was associated with RFI and EI, and is located within 200 kb of several genes, such as the spondin 2, extracellular matrix protein (with RFI . Of the TMEM40 (rs108942504) was validated in five analyses, having a general association with RFI and RADG, a breed-dependent association with RFI and EI, and a diet-dependent association with EI. The other three gene-mapped validated SNPs were rs109053103, rs108964818, and rs42072585 and are harbored in genes bridging integrator 2 (BIN2), KDELC2, and ceroid-lipofuscinosis, neuronal 3 (CLN3), respectively. These SNPs had validated breed-dependent association with EI, general association with RADG, and diet-dependent association with EI, respectively. Although the SNPs in KDELC2 and CLN3 are located in introns, rs109053103 is within 2 kb 5′ of BIN2. CLN3 is located on BTA 25 and encodes for a minor lysosomal membrane protein. Steers carrying allele G and receiving diet E had lower EI and thus higher efficiency.The SNP that maps to The results of the haplotype analysis validation are provided in Additional file MYO9A) located on BTA 10. This promising haplotype is also located in a region previously associated with RFI [PPP1R12A), and one not mapped to any gene, formed H04 located on BTA 5. This gene encodes for a protein that acts in biological processes related to motility, such as vascular smooth muscle contraction, focal adhesion, and regulation of actin cytoskeleton [The validated haplotypes H07 was associated with multiple indicators. Block H07 had a breed-dependent association with RFI and EI and included four SNPs that are in moderate to low LD  indicated comparable multi-SNP model adequacy across data sets. The relative differences in multi-SNP model prediction are presented in Table A summary of the results from the functional enrichment analysis is presented in Table Nine clusters of Gene Ontology categories and KEGG pathways that had an enrichment score > 3 were identified among the genes encompassing general, breed-dependent, and diet-dependent SNPs. These clusters included the Gene Ontology molecular functions of nucleotide binding, protein kinase activity, and metallopeptidase activity, the Gene Ontology biological processes of ion transport, phosphorus metabolic process, membrane invagination, and proteolysis, and the MAPK signaling KEGG pathway.The enrichment of nucleotide binding is consistent with reports that have associated nucleotide binding proteins with endocrine diseases, metabolic syndrome, and type II diabetes . SimilarGSK3B). This gene is located on BTA 1, and encodes for a serine-threonine kinase protein involved in several metabolic and disease-related pathways [The identification of MAPK signaling pathway genes that either harbor or in the close proximity of SNPs are depicted in Figure pathways . Althougpathways . Consistpathways . In addiThe two complementary one-step indicators to assess feed efficiency, EI and EG, were responsible for the discovery of 49 unique SNPs  for EI and RFI was 63, and for EG and RADG was 37. For EI and RFI, 42 SNPs were simultaneously uncovered in these two indicators, whereas the number of overlapped associations between EG and RADG was ten. The trend (direction of the estimate) and type of association  of each of these associations were similar when comparing the results of the one-step indicators with their two-step counterparts . In addition, the comparable genetic parameters estimated for the pairs EI/RFI and EG/RADG indicate that the one-step indicators of feed efficiency account for the same portion of phenotypic and genetic variation accounted for the two-step indicators. The benefit in using EI/EG in place of RFI/RADG may be better observed comparing the RMSE of the multi-SNP Table  and nullGenomic SNPs and haplotypes associated with feed efficiency indicators in feedlot beef steers were identified. General, breed-dependent, and diet-dependent associations were characterized and validated. These findings support both general and targeted selection decisions. Single SNPs and haplotypes showed significant association with all the five feed efficiency measures considered. Although many SNPs were associated in a general manner, other SNPs showed significant associations dependent on the breed of the animals or on the diet provided. A multi-SNP panel that can be used to predict feed efficiency was developed for each indicator including SNPs with general, and breed and diet-dependent associations. The SNP panel developed for RFI showed robust results, indicating that the set of SNPs selected can be used across breeds and diets. The complementary one-step indicators of feed efficiency (EI and EG) had comparable genetic variance than traditional two-step indicators (RFI and RADG). The unique and overrepresented SNPs, haplotypes, and genes identified for each group of indicators suggest that the one-step indicators offer complementary description of feed efficiency that can be exploited for genome-enabled selection purposes. Functional and network analysis uncovered molecular functions and biological processes enriched among the genes associated with feed efficiency. In addition, the diet- and breed-dependent genomic associations can be exploited in different production systems.The authors declare that they have no competing interests.NVLS compiled the data, performed data processing, performed statistical analyses, contributed to the interpretation of results, and drafted the manuscript. JEB obtained funding for the study and helped in the drafting of the manuscript. DBF obtained funding for the study and helped in the drafting of the manuscript. DGP performed the genetic parameter analysis and helped in the drafting of the manuscript. BRS performed the gene annotation of the SNPs in the platform, and functional analyses. SRZ obtained funding for the study, participated in its conception, analysis, interpretation of results, and contributed to the final version the manuscript. All authors have read and approved the final version of this manuscript.Table S2. Additive estimates of SNPs that have breed-dependent association with feed efficiency; Table S3. Additive estimates of SNPs that have diet-dependent association with feed efficiency; Table S4. Description of the haplotype blocks significantly associated with feed efficiency; Table S5. Enriched functional categories, Gene Ontology (GO) terms, and genes by SNP association.Additive and dominance estimates of SNPs that have general association with feed efficiency; Click here for file