Grb10 and downstream of Meg3, Rtl1as and Rian in the Dlk1-Dio3 cluster, an imprinted domain implicated in development and pluripotency. We detected paternal allele-specific transcripts downstream of Nespas, Peg3, Peg12 and Snurf/Snrpn. These imprinted transcript extensions were not unique to MEFs, but were also present in other somatic cells. The 5′ end points of the imprinted transcript extensions did not carry opposing chromatin marks or parental allele-specific DNA methylation, suggesting that their parental allele-specific transcription is under the control of the extended imprinted genes. Based on the imprinting signature of MEFs, these cells provide valid models for understanding the biochemical aspects of genomic imprinting.Mouse embryo fibroblasts (MEFs) are convenient sources for biochemical studies when cell number in mouse embryos is limiting. To derive the imprinting signature of MEFs and potentially detect novel imprinted genes we performed strand- and allele-specific RNA deep sequencing. We used sequenom allelotyping in embryo and adult organs to verify parental allele-specific expression. Thirty-two known ubiquitously imprinted genes displayed correct parental allele-specific transcripts in MEFs. Our analysis did not reveal any novel imprinted genes, but detected extended parental allele-specific transcripts in several known imprinted domains: maternal allele-specific transcripts downstream of  Imprinted genes exhibit allele-specific transcription depending on parental origin of the chromosomes . ImprintJF1XOG2 (JXO) embryos were generated using the inbred JF1/Ms inbred strain (purchased from The Jackson Laboratory) and the TgOG2  transgenRNA was isolated using RNA-Bee (Tel-Test Inc.). DNAse I digestion was applied to remove DNA contamination. Ten microgram of total RNA was treated with the Ribominus kit (Life Technologies) to remove ribosomal RNA contamination.Escherichia coli DNA polymerase I and dATP, dGTP, dCTP, dUTP nucleotides in the presence of E. coli ligase and RNase H. Ends were repaired using ‘End It Enzyme mix’, and dATP 3′ overhangs were created using Klenow exo- and then R2 adapters were ligated to the double-stranded cDNA fragments. The second strand was degraded using USER™ (Uracil-Specific Excision Reagent) enzyme complex. Only the first strand was used as PCR template for paired-end deep sequencing. The read 1 sequences (F2R1 or R1F2) represent the antisense strand, and the read 2 (F1R2 or R2F1) sequences represent the sense strand of the RNA. The single-stranded cDNA containing the adapter was amplified by 15 cycles of PCR using two different index primers. Reads resulting from the JXO cross MEF started with the sequence ACTTGA (AC) and reads resulting from the OXJ cross MEF started with the sequence CCGTCC (CC). The two MEF samples were sequenced on half a lane each of the Illumina sequencer.Sample preparation and deep sequencing was done from one JXO and one OXJ MEF line according to Illumina protocols with proper clean up between each step. Briefly, first-strand synthesis was done using Superscript II reverse transcriptase, dNTPs and the Illumina RNA seq library preparation kit. The enzyme was inactivated, and the dNTPs were removed. To enable strand-specific analysis, the second strand cDNA was labeled with dUTP before ligating the adapter. The second strand was synthesized using P-value and odds ratio were calculated using Fisher’s exact test based on the coverage information of reference and alternative alleles in AC and CC samples, and P-value was adjusted by false discovery rate (FDR) method  will not be detected by this option. Aligned reads were used to generate pileup for each base with Samtools v0.1.18. The pileup reads were used to identify the single nucleotide polymorphisms (SNPs) that are present in either AC or CC with ≥80% alternative allele frequency (AF) and total coverage ≥10. Only bases with quality score ≥13 were considered. The reference allele and alternative alleles in either AC or CC samples were then counted for these candidate SNPs, and SNPs with ≥80% in both AC and CC were excluded. The ) method . Final SSupplementary Table S1.We designed multiplex sequenom allelotyping assays using the SNPs derived from the deep sequencing analysis, including known and putative imprinted genes. Sequenom allelotyping was performed as we described earlier  using cDRian and potential 5′ end of Rian extension, respectively. The 3′ end of Rian was correctly amplified, but no PCR product was obtained from the 5′ end of Rian extension. A 3-kb long PCR product was obtained between Rian and Rian extension, which was subcloned and verified by DNA sequencing.We used SMARTer™ RACE cDNA amplification kit (Clontech) and primers 5′-TGGGTGGATCGTACCTCGGCCTAA-3′ and 5′-CCTGTGAAAAGCAAACTGAGGCGAGA-3′ to find the 3′ end of Supplementary Table S1.The methylated fraction of sonicated genomic DNA from MEFs was captured using recombinant MBD2b and MBD3L1 proteins as described earlier . ParentaPou5f1 promoter-driven EGFP expression in germ cells but not in somatic cells  mice with male TgOG2 (OG2) transgenic mice (JXO cross), and by switching the parents we generated the reciprocal (OXJ) cross. The OG2 transgenic line was originally made by injecting (CBA/CaJ × C57BL/6J)Fransgene . Both thbspecies  and provbspecies . We prepP-value and odds ratio were calculated using Fisher’s exact test based on the coverage information of reference and alternative alleles in the JXO and OXJ samples, and P-values were adjusted by FDR method (Supplementary Table S2) were selected based on the following cutoff criteria: the FDR in one sample was <0.05 and the reciprocal sample had an alternative AF <20%. Out of the 1247 TRUE SNPs, 813 and 434 SNPs qualified in the JXO and OXJ MEFs, respectively. The number of SNPs below this cutoff (FALSE) was 17 858 total (11 800 in JXO and 6058 in OXJ MEFs).Sequencing reads were aligned to the mm9 genome with Tophat v1.2.0. We obtained 95 517 702 and 117 618 284 total reads resulting 76 712 608 (80.3%) and 86 369 632 (73.4%) aligned reads from the JXO and OXJ MEFs, respectively. In total, 90.2% and 89.1% of the aligned reads were in the sense orientation along exons (coding and non-coding combined). The aligned reads were used to generate pileup for each base with Samtools v0.1.18. The pileup reads were used to identify the SNPs that are present in either JXO or OXJ with ≥80% alternative AF and total coverage ≥10. The reference allele and alternative alleles in either JXO or OXJ samples were then counted for these candidate SNPs. SNPs with ≥80% in both JXO and OXJ were excluded. The Supplementary Table S2, the JXO cross has revealed maternally expressed imprinted genes and the OXJ cross confirmed them. On the other hand, the OXJ cross has revealed paternally expressed imprinted genes and the JXO cross confirmed them. Therefore, the number of TRUE SNPs in the JXO or OXJ cross, respectively, only depends on the number of maternally or paternally expressed imprinted transcripts, respectively, and the number of SNPs along their lengths.There is a substantial discrepancy between the numbers of TRUE SNPs in the two crosses. This is due to our method of identifying SNPs. The JXO and OXJ datasets were complementary: one cross was used for calling each TRUE SNP , and the other cross was used to confirm it . As one can see from Supplementary Table S2). For example Igf2 and Igf2as transcripts occurred from (−) and (+) DNA strands, respectively. Imprinted transcripts usually harbored more than one of the allele-specific SNPs. Grb10 and Kcnq1ot1 had the most support with 205 and 201 SNPs, respectively. The OG2 strain has performed well against the JF1 inbred strain in identifying imprinted transcripts. The OG2 sequence aligned in general with the C57BL/6J reference genome. For the known maternally expressed ubiquitous imprinted genes, 502 of 527 (95%) SNPs identified the JF1 as alternative allele (in the JXO cross). For the paternally expressed ubiquitous imprinted genes 330 of 334 SNPs (99%) identified JF1 as alternative allele (in the OXJ cross), and each of these calls were confirmed in the reciprocal cross (Supplementary Table S2).The SNPs were annotated with mm9 refseq database. Of 1251 TRUE SNPs, 861 mapped to 32 known imprinted genes , many ofSupplementary Table S3). Of 152 known imprinted transcripts . Each of these extensions was represented by a minimum of 2 and a maximum of 209 SNPs. A total of 267 SNPs predicted eight imprinted transcript extensions. Using the Integrative Genomics Viewer (IGV) . The extension of the maternally expressed Grb10 imprinted gene is displayed in Supplementary Figure S1A. The paternally expressed Snrpn/Snurf gene was extended toward the paternally expressed D7Ertd715e transcript . The paternally expressed Nespas RNA . In addition, Peg3 and Peg12 paternally expressed imprinted genes were extended beyond their known 3′ ends   we couldand Rian . The intspas RNA ,29 exten 3′ ends . The PegSupplementary Table S2 and S4). Some predicted transcripts had no annotation in mm9 and we named them MegDT1-MegDT5 and PegDT1. These transcripts presented a warning for such deep sequencing analysis. The mapping of these six transcripts with a total of 94 TRUE SNPs was ambiguous. MegDT5 had an unusually high SNP coverage  in chr14. Most of the SNPs occurred in both JXO and OXJ MEFs. The two TRUE SNPs, however, were exclusive to JXO MEFs, predicting maternal allele-specific transcription. We used NCBI blast to align the underlying sequence and found perfect match to the mRNA sequence of the paternally expressed imprinted gene, Snrpn. Because all the reads of MegDT5 matched the Snrpn transcript but were different from the aligned sequence in chr14, we determined that we uncovered a silent pseudogene for the imprinted Snrpn gene on chr14. MegDT1 with 66 SNPs was mapped to chr1 but had a paralogous region in the mitochondria chromosome (chrM). The remaining 28 TRUE SNPs predicted 22 unambiguously annotated novel imprinted transcripts with mostly 1 and maximum 3 SNPs each. When we looked at the annotated predicted transcripts in IGV, we found that even though the TRUE SNP indicated allele-specific expression, it was in minority among the SNPs along the same transcript. The majority of SNPs in the predicted transcripts appeared equally in both crosses, suggesting biallelic transcription. Therefore, these predicted transcripts were not genuine imprinted genes. For comparison we also display a few SNPs where the prediction program called a FALSE SNP even though the allelic frequency was at the 80% cutoff (Supplementary Table S2 and S4).Of 1247 TRUE SNPs, only 123 predicted putative novel imprinted transcripts .Sequenom allelotyping confirmed that c organs A, but dic organs B. This imeninges . Very li  was biallelically expressed in MEFs and also in 13.5 dpc embryo organs, confirming our observations in IGV. Similarly, biallelic expression was detected at the control FALSE SNPs . In addition, reverse-transcription PCR amplified a fragment across the junction with an antisense primer coming from the extension. These results collectively suggested that the transcript between Rian and Mirg does not initiate at the 3′ end of Rian but is an extension of Rian.The deep sequencing analysis of MEFs failed to detect novel imprinted transcripts apart from the transcript extensions in established imprinted domains. We wondered if these extensions are independent transcripts or they arise from missed termination of the known imprinted transcripts. We did not observe any deep sequencing reads between We wondered whether the imprinted transcript extensions are specific to MEFs or could be general components of known imprinted domains. To this end we compared the deep sequencing results obtained in JXO and OXJ MEFS with male and female somatic cells  of the embryonic testis and ovary at 15.5 dpc, as assessed recently . We founD7Ertd715e and the junction of Rian and Rian extension, which could potentially serve as promoters. The methylated fraction was captured from the MEF DNA and was subjected to sequenom allelotyping at the specific genomic loci. We found no evidence for allele-specific DNA methylation at these positions -SNuPE  at the positions  suggestiMEFs are convenient sources for biochemical studies when cell number may be limiting from mouse embryos. To validate this model system with respect to imprinted gene expression, we carried out an allele- and strand-specific deep sequencing experiment using MEFs from reciprocal mouse crosses and performed validation using sequenom allelotyping of the same MEF samples, embryo organs from the same crosses and adult organs from two additional mouse crosses. We verified the parental allele-specific transcription of 32 ubiquitous imprinted transcripts. Based on the imprinting signature of MEFs, they provide valid models for understanding the biochemical aspects of genomic imprinting at these 32 imprinted genes.in silico analysis found that several intergenic ESTs in the Dlk1-Dio3 domain transcribed in the same direction as Rian. They confirmed maternal allele-specific expression in one EST, CE2, downstream of Rtl1as   promoter  [RO1GM064378 to P.E.S.]; City of Hope [Excellence Award to P.E.S.]. Funding for open access charge: NIH [RO1GM064378 to P.E.S.].Conflict of interest statement. None declared.


Increased disease resistance through improved immune capacity would be beneficial for the welfare and productivity of farm animals. To identify genomic regions responsible for immune capacity traits in swine, a genome-wide association study was conducted. In total, 675 pigs were included. At 21 days of age, all piglets were vaccinated with modified live classical swine fever vaccine. Blood samples were sampled when the piglets were 20 and 35 days of age, respectively. Four traits, including Interferon-gamma (IFN-γ) and Interleukin 10 (IL-10) levels, the ratio of IFN-γ to IL-10 and Immunoglobulin G (IgG) blocking percentage to CSFV in serum were measured. All the samples were genotyped for 62,163 single nucleotide polymorphisms (SNP) using the Illumina porcineSNP60k BeadChip. After quality control, 46,079 SNPs were selected for association tests based on a single-locus regression model. To tackle the issue of multiple testing, 10,000 permutations were performed to determine the chromosome-wise and genome-wise significance level. In total, 32 SNPs with chromosome-wise significance level (including 4 SNPs with genome-wise significance level) were identified. These SNPs account for 3.23% to 13.81% of the total phenotypic variance individually. For the four traits, the numbers of significant SNPs range from 5 to 15, which jointly account for 37.52%, 82.94%, 26.74% and 24.16% of the total phenotypic variance of IFN-γ, IL-10, IFN-γ/IL-10, and IgG, respectively. Several significant SNPs are located within the QTL regions reported in previous studies. Furthermore, several significant SNPs fall into the regions which harbour a number of known immunity-related genes. Results herein lay a preliminary foundation for further identifying the causal mutations affecting swine immune capacity in follow-up studies. Increasing robustness by improving resistance/tolerance to pathogens is an important selection objective in animal breeding. In the past 30 years, selection for growth, carcass leanness, meat quality and prolificacy has been highly effective in pigs Cytokines are important mediators in the regulation and activation of the adaptive immune response in various infections, inflammation, and even cancer development et al. (1997) found that different inbred strains of rats and mice were extremely different in their capacity of producing Th1 and Th2-type cytokines, which caused them to be different in susceptibility to different kinds of diseases, such as diabetes, experimental autoimmune encephalomyelitis , rheumatoid arthritis and infectious diseases (Mycobacteria) et al. (2003) reported that a Th1-inclined cytokine profile leading to an exacerbated local inflammation at the early installation stage of the cysticercus may interfere with their successful establishment in the serum antibodies against total cysticercus antigens There are a positive feedback of IFN-γ and IL-10 on their own production and a negative control of each other's production Immunoglobulin G (IgG) is important in immune responses. IgG antibodies are involved in predominantly the secondary immune response. IgG is the most common immunoglobulins circulating in the blood. The presence of specific IgG corresponds to maturation of the antibody response http://www.animalgenome.org/cgi-bin/QTLdb/index). However, the resolution of these QTLs are generally low with confidence interval 20∼30 cM and the identification of the relevant genes and quantitative trait mutations (QTMs) remains great challenge although a few prominent successful cases have been reported In order to include immunocompetence in selection for improved health, a major challenge is to find the key genes controlling immune traits in animals with inter-individual variability in response to various pathogens. Up to now, a large amount of QTLs for immune traits have been detected and mapped to different pig chromosomes . The structure of experimental population was given in  The animal resource used in this study consists of 562 piglets from three different breeds  according to the manufacturer's instructions. All samples were arranged randomly in each plate and a standard curve was fitted for each plate and used to calculate IFN-γ and IL-10 concentrations in each serum sample.IgG blocking percentage in the serum was measured using the commercial CSF virus antibody test kit  according to the manufacturer's instructions. DNA was extracted from ear tissue samples of all pigs, including piglets and parental individuals. DNA was quantified and genotyped using the Illumina PorcineSNP60 BeadChip containing 62,163 SNPs, which is a multi-sample genotyping panel powered by Illumina's InfiniumH II Assay. Features of the Illumina PorcineSNP60 BeadChip have been detailed previously To assess the technical reliability of the genotyping panel, a randomly selected DNA sample was genotyped twice and over 99% identity of called genotypes (two mismatches) was obtained. This demonstrates the technically robust feature of the 60 K SNP BeadChip panel employed herein. All the samples included are with a minimum of 95% call rate.Quality control procedures were as follows. First, only samples with a minimum of 90% call rate were included. Second, out of the initial full-set of 62,163 SNPs, we discarded: (1) SNPs with a call rate <90% ; (2) those deviating from Hardy–Weinberg equilibrium (HWE) in controls ; and (3) those having a minor allele frequency (MAF) <03 in the total sample . Therefore, 46,079 SNPs were available for the subsequent analyses, and the distribution is presented in Similar to the study of GWAS before b and the corresponding sampling variances df  = 1 was constructed to examine whether the SNP is associated with the trait.The variance components involved in the model were estimated by using the REML method and the software DMU  The effect of a SNP on a trait was measured as proportion of the phenotypic variance of the trait explained by the SNP. The phenotypic variance was estimated based on the model described above with the SNP genotype vector excluded. The variance explained by a SNP was calculated as We employed Fortran 95 to code the computing program for the method and it is available upon request.th percentile value of the genome-wise (chromosome-wise) empirical distribution.For the analyses above, the permutation method was adopted to generate the empirical distribution of the test statistic and to adjust for multiple testing from the number of SNPs tested as well. In our method, the phenotypes of all individuals were randomly resampled 10,000 times without replacement along with all their related fixed and random effects except SNP genotypes. The critical region was formed by the greatest values of the test statistics in 10000 permutation tests. The genome-wise significance was determined from the critical region which was formed by the greatest values of the test statistic among all tested SNPs in the whole genome from each of the 10,000 permutations. The chromosome-wise significance was determined in the same way except that the critical region was build for each chromosome respectively, i.e., the highest test statistic values among the SNPs on the chromosome were picked up to form the critical region. We declared a genome-wise (chromosome-wise) significant SNP if its raw test statistic value was larger than the 95 Linkage disequilibrium (LD) block analyses were performed for the chromosomal regions with multiple significant SNPs clustered to further pinpoint potential candidate genes. The LD levels were detected using Haploview (Version 4.2) The descriptive statistics of IgG and cytokine concentration in peripheral blood on day 20 (the day before vaccination) and day 35 (the day two weeks after vaccination) are shown in Compared with the measurements on day 20, the IL-10 and the IgG concentration in blood on day 35 decreased obviously while the IFN-γ concentration in blood on day 35 increased. However, the ratio of IFN-γ to IL-10 on day 35 changed only slightly after challenge.P values (in terms of –log10P) of all tested SNPs for the four traits are shown in The profiles of the  For the IFN-γ concentration, 6 significant SNPs, including one at genome-wise significance level, were identified, and three of them are harbored in a narrow region (43.83 to 45.38 Mb) on SSC11. For the IL-10 concentration, 15 significant SNPs (including one SNP at genome-wise significance level) were identified. Six of them are harbored in a narrow region (40.75 to 42.27 Mb) on SSC6 and 4 of them are harbored in a narrow region (0.35 to 0.67 Mb) on SSC 13. LD analysis showed that SNPs in each of the two regions were in one LD block, respectively. For the ratio of IFN-γ to IL-10, 5 and 2 significant SNPs were identified on SSC8 and SSC12, respectively. For the IgG concentration, 6 significant SNPs (including two at genome-wise significance level) were found, and four of them are on SSC2.2 = 0.41 to 1.0) g<0.3) GWAS has been considered as a promising tool for gene identification for complex traits. So far GWAS for domestic animal are largely focused on economically important growth and production traits, such as milk production in dairy cattle, backfat in swine, etc. In this study, we carried out a GWAS to explore potential causal genes for the IgG and cytokine in swine. To our knowledge, this is the first study aiming at unrevealing the genetic mechanism of those immune traits in swine based on a high density SNP chip panel. Heritability estimates of the cytokine (IL10 and IFN-γ) levels were moderate to high after PMAIONO and CONA stimulations , in this region, which can be considered as a promising candidate gene, although it is not the nearest gene to any of the 6 SNPs.32 significant SNPs with chromosome-wise level were detected to be associated with the four traits investigated, 4 of them reached genome-wise level. These SNPs account for 3.23% to 13.81% of the total phenotypic variance individually. For the four traits, the numbers of significant SNPs range from 5 to 15, which jointly account for 37.52%, 82.94%, 26.74% and 24.16% of the total phenotypic variance of IFN-γ, IL-10, IFN-γ/IL-10, and IgG, respectively. However, some of the significant SNPs for a trait are very close to each other and may represent one SNP. Therefore, the total effect of the significant SNPs for a trait should be much less than the sum of the individual SNP effects. For example, of the 15 significant SNPs for IL-10, four are located in the region of 0.35 Mb to 0.67 Mb on SSC13 and six in the region of 40.7 Mb to 42.2 Mb on SSC6. LD analysis revealed that they are in one LD block, respectively. In particular, the 6 SNPs on SSC6 are in complete LD, suggesting their effects on IL-10 may be due to a single gene in this region. Indeed, there is only one immune-related gene, Of the significant SNPs, 17 fall into QTL regions of immune-related traits previously reported, furthermore, some of the QTL are responsible for the same traits considered in the present study. Specifically, on SSC11, the three significant SNPs with effect on the IFN-γ concentration are located within the reported QTL region for IFN-γ IL-11 gene harbored in the LD block on SSC6, there are several other significant SNPs which fell into the regions harboring known immune-related genes (not necessarily the nearest to the SNPs). For IFN-γ, the significant SNPs  were found in the region which harbors the PIBF1 (progesterone immunomodulatory binding factor 1) gene and the KLF5 ) gene. For IgG, the significant SNP (ASGA0009314) was found in the region which harbors the MADD (MAP-kinase activating death domain) gene, the SPI1 (spleen focus forming virus (SFFV) proviral integration oncogene spi1) gene and the NR1H3  gene. On SSC1, the SNP with genome-wise significance level (MARC0056499) for IgG falls in the region which harbors the LRRC8A  gene. The LRRC8 family genes encode components of the pre-B cell receptor or proteins that are activated by crosslinking of the pre-B cell receptor. Defects in these genes result in a block in B-cell differentiation at the pro-B to pre-B cell transition LRRC8A can be considered as a candidate gene for IgG in the further studies.In addition to the In summary, our study revealed 32 SNPs associated with four traits (including Interferon-gamma (IFN-γ) and Interleukin 10 (IL-10) levels, the ratio of IFN-γ to IL-10 and Immunoglobulin G (IgG) blocking percentage to CSFV in serum) at chromosome-wise significance level of which 4 reached genome-wise significance level. These SNPs account for 3.23% to 13.81% of the total phenotypic variance individually. 17 of them are located within the immune-related QTL regions reported in previous studies. Furthermore, 11 of them fall into the regions harboring known immunity-related genes. Findings herein lay a preliminary foundation for further identifying the causal mutations affecting swine immune capacity in follow-up studies.


Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various sources, observed as “bright spots”, “dark clouds”, and “shadowy circles”, etc. It is crucial that these image defects are correctly identified and properly processed. Existing approaches mainly focus on detecting defect areas and removing affected intensities. In this article, we propose to use a mixed effect model for imputing the affected intensities. The proposed imputation procedure is a single-array-based approach which does not require any biological replicate or between-array normalization. We further examine its performance by using Affymetrix high-density SNP arrays. The results show that this imputation procedure significantly reduces genotyping error rates. We also discuss the necessary adjustments for its potential extension to other oligonucleotide microarrays, such as gene expression profiling. The R source code for the implementation of approach is freely available upon request. Oligonucleotide microarrays have been commonly adopted in various biomedical researches, such as gene expression profiling, single nucleotide polymorphism (SNP) genotyping, and copy number estimation, etc et al. studied the impact of blemished images on gene expression microarrays by directly applying MAS5 and GCRMA et al. conducted a similar comparison by using packages MAS5 and RMA All fluorescence images may have spatial defects to some extent, due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc et al. developed an approach, referred to as “harshlight”, which was able to detect and mask three types of defects: localized blemishes affecting a few probes, diffuse defects affecting larger areas, and extended defects which may invalidate an entire chip et al. used replications for identifying the abnormal spatial structures, referred to as “blob”, “lines”, “rectangular enhancement” and “coffee rings” et al. proposed a software package, referred to as Microarray Blob Remover (MBR), which allowed visualization, detection and removal of various ‘blob’ like defects To ensure the image quality, researchers were always recommended to visually examine all CEL images et al. and Arteaga-Salas discussed possible correction for the affected intensities in some samples These approaches have showed great promise for image processing. However, the existing approaches commonly masked affected intensities with missing. Previous studies indicated that correction of the affected intensities could improve the reliability of data, and thus improve the reproducibility of the results In this article, we propose an imputation approach for the intensities from defect areas. The proposed approach is single-array-based, which does not require any biological replicate. It models the cross-hybridization between probes and targets, and imputes the intensities by the binding affinities between them. In the following, we first explain our approach and further examine its performance with Affymetrix high-density SNP arrays. The performance of imputation is evaluated by genotyping accuracies.Among current platforms, Affymetrix DNA microarrays have been commonly used for gene expression profiling, SNP genotyping and copy number estimation at a relatively high resolution with low-cost. In this article, we introduce our approach by using Affymetrix high density SNP arrays. We used SNP arrays as an example, because the performance of imputation can be easily examined by genotyping accuracies.The Affymetric SNP array is a major platform that was used in the international Haplotype Map (HapMap) projects A and B. Each quartet consists of 4 types of probes that are 25-mer in length, either perfectly matched to the target or mismatched at a particular SNP site for each allele: perfect match A, mismatch A, perfect match B and mismatch B, denoted respectively PA, MA, PB and MB for short. The quartets have different shifts (k) of the nucleotide  from the center of the probe sequence (k  =  0 at position 13 of the 25-mer), see Affymetrix SNP arrays use multiple probe_sets to capture the property of each SNP. Here, we use Affymetrix Mapping 100 K arrays as an example to illustrate the design. In a 100 K array, ten quartets are used to interrogate a single dimorphic SNP site with alleles commonly denoted as In order to account for potential spatial effect on an array, the probe_sets of each SNP are usually distributed evenly over the array. The intensities for these probe_sets are highly correlated and inherently determined by SNP alleles. Unless the majority of array is defective, most of the probe_sets will not be affected. Therefore, it is possible to impute the affected intensities with those that are unaffected.A could hybridize to PA probe through perfect match binding and hybridize to PB, MA or MB probes through mismatch binding. Importantly, the hybridization to mismatch probes can have two mismatch nucleotides when shift k ≠ 0  model In the past few years, a number of genotyping approaches, such as BRLMM and CRLMM, have been proposed for Affymetric high density SNP arrays ft k ≠ 0 . Given tet al., we first briefly introduce PICR model, the detail of which can be found elsewhere A could hybridize to PA probe through perfect match binding, the binding free energy of which is :Following the same notation with Wan A could hybridize to MA, PB, MB (k = 0) probes through mismatch binding with one unique mismatch. For example, the binding free energy between TA and MA isFurther, a target sequence TA with allele where A could hybridize to MB probes through mismatch binding with two mismatches when shift k ≠ 0. The mismatch would occur at both the center and shift k of the probe. The binding free energy is: Finally, a target sequence TA with allele The term k. E is the binding free energy between the probe and the target. A and allele B, respectively. The model intercept b represents the baseline intensity. The error terms Eventually, a linear regression model was used to model the probe intensities of quartets: The PICR model is a single-SNP, single-array approach, which was shown to have greater genotyping accuracies than a commonly used approach, CRLMM N SNPs, each has M probe_sets (M = 40 for 100 K SNP arrays). Each probe_set has binding affinities j-th probe is a PA probe with shift k, thenj-th probe_set of the i-th SNP with a mixed effect model:Assuming we have a SNP array with a large number of j-th probe of SNP i ; i = 1, 2 ……N; j = 1, 2, ……40; and j-th probe of SNP i with respect to two allele A and B, respectively. Here, A and copy number abundance for allele B, respectively; N SNPs of interest. Further, i-th SNP. The random effect i, but varies across different SNPs, the variance of which are estimated by all probe intensities of N SNPs in the model.Based on the above mixed effect model, an imputation procedure can be conducted as follows: 1) First, defect areas are identified by examining a CEL image or using existing image processing software, such as harshlight and BMR. The intensities from defect areas will be set as missing. 2) Second, for each affected SNP, a large number of unaffected SNPs (e.g. 100 SNPs) will be randomly selected to fit a mixed effect model. 3) The missing values can be imputed by the predicted values based on the mixed effect model.We considered 90 Affymetrix Mapping_100 K_Xba arrays from the HapMap study. D-ChIP was first used to examine the CEL image for each sample In order to examine the quality of imputation, PICR was applied to conduct genotyping for both the original CEL and the imputed CEL. According to the number (proportion) of affected probes, we further classified all SNPs into four groups, including 0 affected probe, 1–4 affected probes (<10%), 5–8 affected probes (10%–20%) and >8 affected probes (>20%). The result was summarized in In this study, we have proposed a multi-SNP approach for imputing the intensities from defect areas on Affymetrix SNP arrays. The approach can be viewed as an extension of previously proposed PICR model. Similar to PICR, it is a single-array approach, which does not require any biological replicate or between-array normalization. It would be especially helpful for small studies with limited sample sizes. Further, the results have showed that this approach is effective to impute intensities from defect areas. The genotyping error rates were significantly reduced.et al. have investigated the sequence-specific binding and non-specific binding for gene expression data, and provided estimation for the corresponding binding free energy In this article, we have focused on Affymetrix SNP arrays, because the performance can be easily examined by genotyping error rates. However, our approach models the fundamental mechanism of physical binding between DNA nucleotides. It can potentially be extended to other oligonucleotide microarrays with a similar design. For example, the gene expression arrays have similar PM/MM probe_sets, with the number of pairs ranging from 11–16. To extend the proposed approach to gene expression microarrays, the binding free energy will need to be re-calculated. Zhang In our study, we also found that the genotyping error rates for a few arrays remained similar after imputation, e.g. NA10853. One reason was that the defect area was relatively small and only a small number of SNPs were affected. Given the large number of total SNPs on the array, it did not have a great impact on the overall genotype calling. Another possible reason is that the genotyping errors can be caused by multiple sources. For those arrays, there might be other possible factors affecting genotyping accuracies.One remaining problem of the proposed approach is to efficiently identify the boundary for defect areas. The defect areas on an array may have irregular shapes. In this article, we have used a rectangle area to cover the defect area for imputation. However, these rectangles were slightly larger than the bright spots and some unaffected intensities are set to missing as well. It will be helpful to identify the boundary of defect areas effectively and accurately. In addition, other studies have shown that some outliers can not be easily identified by checking CEL images. Several computational algorithms have emerged as promising tools for detecting defect areas on a CEL image, such as ‘harshlight’ and ‘BRB’. These available packages are potentially helpful to identify the outliers automatically.Figure S1The CEL image before and after imputation for sample NA07056.(TIF)Click here for additional data file.Figure S2The CEL image before and after imputation for sample NA10835.(TIF)Click here for additional data file.Figure S3The CEL image before and after imputation for sample NA10863.(TIF)Click here for additional data file.Figure S4The CEL image before and after imputation for sample NA12005.(TIF)Click here for additional data file.Figure S5The CEL image before and after imputation for sample NA12056.(TIF)Click here for additional data file.Figure S6The CEL image before and after imputation for sample NA12144.(TIF)Click here for additional data file.Figure S7The CEL image before and after imputation for sample NA12146.(TIF)Click here for additional data file.Figure S8The CEL image before and after imputation for sample NA12155.(TIF)Click here for additional data file.Figure S9The CEL image before and after imputation for sample NA12236.(TIF)Click here for additional data file.Figure S10The CEL image before and after imputation for sample NA12239.(TIF)Click here for additional data file.Figure S11The CEL image before and after imputation for sample NA12813.(TIF)Click here for additional data file.


Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world.The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits.The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity. Zea mays L.) is one of the most important crops in the world, being one of the main sources of human food, animal feed, and raw material for some industrial processes [Zea genome is in constant flux, with transposable elements changing the genome and affecting genetic diversity [Maize  compare this new sequencing technology with other available options, 2) explore the potential of this new technology to help with curation and use of germplasm, 3) evaluate genetic diversity and population structure both across the genome and between groups of germplasm, 4) investigate the history of recombination and LD through the different breeding groups, and 5) explore the potential of the collection as a resource to study the genetic architecture of quantitative traits.The germplasm set examined in this experiment comprised 2,711 available maize inbred accessions preserved in the USDA-ARS NCRPIS collection (some of them with more than one source), another 417 candidates to be incorporated into the USDA collection as new sources of diversity, and the 281 maize inbred lines from the Goodman maize association panel [The average base-call error rate based on repeated samples was 0.18%. An additional level of quality control was provided by approximately 7,000 SNPs that overlapped with those obtained with a large genotyping array  for the SA24, used as a check, was analyzed more than 25 times and had only 16% missing data. In addition, coverage was highly dependent on the genotype. A substantial number of the total reads could not be aligned to the reference genome, some because of limited sensitivity of the Burrows-Wheeler Alignment (BWA) software, but most because of presence/absence variation (PAV). Use of the B73 reference genome resulted in inbreds more closely related to B73 achieving values of less than 20% missing data with only two samples, whereas more distant inbreds maintained values of around 30% missing data even after several replicate sequencing runs.The average coverage  by sample was 35%, with values ranging from 2 to 75%. However, when samples were sequenced more than once, coverage improved substantially. For example, the Goodman association panel was evaluated twice, and reduced the average missing data from 63% based on a single run to 35% for the merged data. The nested association mapping (NAM) parents , coveredZea samples), allowing for a 5% mismatch. If the requirements were not met, the SNP was not imputed, leaving only about 10% of the data unimputed. When comparing the imputed GBS data with the results from the genotyping array [Imputation of missing data was performed using an algorithm that searched for the closest neighbor in small SNP windows across our entire maize database  for all pairs of lines Figure , GBS datB73 shares more than 97% of its genome with more than 50 inbreds  for the entire collection showed that most of the SNPs in the Ames inbred panel (68%) had a minor allele frequencies (MAF) less than 0.1, with more than half of all SNPs being rare MAF < 0.05)  contains only 45% of the total number of polymorphic SNPs. Moreover, private breeding efforts have favored the divergence between three main heterotic pools . In analyzing the network relationships for the ExPVP inbreds, only 2% of the pairwise IBS relationships with greater than 90% IBS were found to be between inbreds from different heterotic pools Figure , and onlWe also analyzed pairwise fixation indexes (Fst) between different groups of accessions. The small Fst estimates, averaging only 0.06, indicated that there is moderate differentiation  between Within chromosomes, all groups consistently displayed smaller values of Fst and lower MAF in the pericentromeric regions versus the remainder of the genome.To evaluate the levels of diversity and divergence in the entire collection and within different groups of germplasm, we calculated LD, haplotype length, and population differentiation (Fst) across the entire maize genome. We also calculated the correlation between those measurements and previous recombination rates across the genome estimated with NAM  Figure .r2 of 0.2 within about 1 Kb   for the entire collection, with a smaller length within the tropical materials (44 SNPs) and a much larger length in the non-stiff stalk (152 SNPs) and stiff stalk (495 SNPs) groups. The ExPVP group also displayed a large average haplotype length of 200 SNPs (around 5.1 Mb), with mean haplotype lengths greater for lines developed by breeding programs now owned by Monsanto than for Pioneer lines. Core collections such as the Goodman association panel or NAM parents, which were selected to maximize diversity, had the smallest haplotype lengths  . However, the fixation indexes between both temperate groups and tropical materials showed an In addition, when analyzing the entire chromosome with all samples, chromosome 4 was found to have a larger haplotype length (sites) compared with the rest of the chromosomes Table . When loP = 10-86) with kernel color was found within the Y1 gene that reduces the presence of carotenoid pigments in the endosperm [The germplasm set conserved in the USDA collection is extensive and publicly available, and contains a high amount of allelic diversity and rapid LD decay. For these reasons, we wanted to explore its possible use as a panel to study quantitative traits, combined with a strategy of low-coverage data in multiple samples. We used a simple Mendelian trait, namely, kernel color, with an approximate frequency of 20% for white kernels in our population, to perform GWAS using GBS markers. The SNP with strongest association  defined a 14 Mb interval containing Su1, a gene that participates in kernel starch biosynthesis [Because the power to detect alleles at lower frequencies is expected to be less, we decided to test another Mendelian trait, sweet corn versus starchy corn, where the sweet phenotype is present at a much lower frequency (5%) than the white kernel type. This trait has been affected by strong selection pressure, both during domestication and the breeding process , resultiynthesis   are located on chromosome 8, surrounding the region where Vgt1, one of the major flowering time QTL for maize is located [P = 10-14) does not have any identified candidate gene association, but overlaps with one of the flowering time QTL detected using NAM [P = 10-12) also overlaps with one of the NAM flowering time QTL [DLF1-DelayedFlowering1 [Arabidopsis FRI-Frigida gene [teosinte-branched1 and dwarf8 flank one side, while PhytochromeA1 flanks the other side [CCT domain is also located in the region, only 0.2 Mb away from our hit. Recent work has suggested that dwarf8 has been a target of selection in early flowering lines [Finally, we tested the power of this association panel with a complex trait, the number of growing degree days from planting to the day that 50% of the plants show silk . GBS has been shown to be a less expensive method for genotyping large numbers of samples, and provides many more SNPs than do SNP arrays. Although the use of a reference genome for calling SNPs from GBS data might cause bias and underestimate the amount of diversity from the groups more distant from the reference, the diversity picture obtained when analyzing the distance matrix seems to be closer to the expectations from simple sequence repeats studies [The enormous progress in sequencing technologies that has occurred over the past few years has allowed better understanding of the maize genome. High-density genome sequencing has been used to study maize diversity ,23-25. Iet al  describe studies , whole-gApeKI and the levels of coverage obtained here may be a problem for some applications, especially GWAS and GS. Although better coverage can be achieved with more repetitions of the samples, this will increase cost, and quickly reaches a point where there is little reduction in missing data with increased investment in repeated sequencing runs. Given the importance of PAV in maize [B73 genome in other inbred lines. As shown here, simple imputation procedures based on identifying the most similar haplotype can be used to supply some of those missing data, and this imputation may be sufficiently accurate provided that similar haplotypes are present in the sample of genotypes. This kind of procedure may work better as the total number of maize samples in the GBS database increases, but it may also cause over imputation of data that are actually biologically missing as a result of a PAV. Alternative methods for handling missing SNP data in GBS datasets include an approach that avoids using a reference genome, such as the one recently used for switchgrass [The percentage of missing data from GBS with enzymes such as in maize ,3,24,43 tchgrass , or one tchgrass .Another important difference between the results obtained with GBS and the results from SNP array methods seems to be the MAF distribution. Whereas array assays seem to oversample SNPs with intermediate frequencies  even wheGWAS studies have shown that variation in some traits is related to rare alleles, and that those rare variants could explain an additional fraction of the missing heritability . HoweverB73, the main founder of the stiff stalk group, is closely related to more than 50 other inbred lines from different programs in the collection. Several germplasm sources were used to generate the male pool . However, the visualization of the genetic relationships through the MDS shows that even if the non-stiff stalk group forms a larger cluster , an overlap between the stiff stalk and non-stiff stalk group still exists.A complication of using the entire USDA-ARS maize inbred collection for breeding or GWAS is the close relationships between some of the lines. When the seed yield of a few inbreds derived from the Iowa Stiff Stalk Synthetic and their derivatives facilitated the transition to single-cross hybrids, these inbreds became the female parents of choice for many breeding programs . For exaet al. [Mo17 decreased, whereas that of Oh43 increased. Our analysis shows that the ExPVP inbreds tend to cluster into three main groups, with B73, Mo17/Oh43, and PH207 being the principal connectors within each cluster. Although all of the major private seed companies are represented within each group , Pioneer germplasm is represented more in the iodent group (including PH207) and more of its germplasm falls outside the three main clusters . This result is in concordance with the observed smaller average haplotype length of Pioneer germplasm.As shown by the MDS plot and Fst values, most of the germplasm from classic breeding programs of the Corn Belt region is closely related. The bottleneck is even narrower when ExPVPs are examined. Using a much smaller sample of SNP markers, Nelson et al.  reportedet al.  studied Although the recycling of elite lines as breeding parents has markedly reduced the amount of diversity used by maize breeders over the past few decades, breeders have also been aware of the importance of maintaining and introducing diversity into their programs . The detet al. [et al. [ZmCCT, was targeted with only one SNP, meaning that it could easily have been missed. By contrast, the Vgt1 peak showed more than 80 SNPs associated with the trait . All these results for our candidate regions support the hypothesis of the presence of some multigene complexes that may have evolved together during the process of maize domestication and adaptation. Further studies to unravel these regions and better understand the genetic architecture of flowering time are needed. Flowering time and adaptation to temperate climates are complex traits that seem to be controlled by several genes with small effects, organized in clusters across the genome.In summary, our GWAS results for days to silking showed that this association panel combined with the GBS information can help to dissect the genetic architecture of important agronomic complex traits. Our best association signals corresponded to regions in which bidopsis  and ricebidopsis , the resbidopsis . In our ZmRap2.7  and ZCN8.7 [ZCN8 . A similreported . Within 1 and d8 ,36 and tAs previous studies have suggested ,8,39, thLeaf samples from the entire available collection of maize inbred lines conserved at the USDA Plant Introductory extension in Ames (IA), including several sources for the same accession, and from other collaborators, were collected from an experiment planted near Columbia-Missouri (MO) in 2010. Several checks across the experimental design were planted in order to collect accurate phenotypic data. Leaf samples from those checks were also collected to serve as controls during the DNA manipulation process. DNA extractions were performed on leaf punches from a single plant using a commercial kit . DNA from the Goodman association panel was provided by the Institute for Genomic Diversity  This panel was sequenced twice to serve as technical replicates for quality control. Another 95 additional samples from the entire collection were selected to maximize diversity, and sequenced several times with the same purpose and as sources of data for imputation.ApeKI as restriction enzyme and multiplexing 96 samples on each Illumina flow cell lane. Raw reads from the machine for the samples reported here were analyzed in conjunction with approximately 18,000 additional maize samples, including NAM and other linkage populations. The GBS sequencing data has been submitted to NCBI SRA (study accession number SRP021921). The GBS discovery pipeline for species with a reference genome, available in TASSEL (version 3.0) [O/HE, where HO = observed heterozygosity and HE = expected heterozygosity) of 0.8, and MAF of 0.2%. For the 'biparental error correction' step that uses the information of biparental populations present in the overall sample, we used a maximum error rate  of 0.01, and a minimum median r2 for LD with markers in the local genome region across biparental families of 0.5. For the latter parameter, the r2 for each individual biparental family in which a SNP was segregating (minimum MAF of 0.15) was calculated as the median r2 in a window centered on the SNP in question and consisting of one-twentieth of the SNPs on the corresponding chromosome. SNPs within 100 Kb of the SNP in question were excluded from the calculation, as they could alter the result because of possible errors in the order of the sequenced bacterial artificial chromosomes.Genotype data was generated following the GBS protocol , using Aion 3.0) , was useZea database  was used to search for the closest sample.The imputed data used for the GWAS was generated using a custom Java script that divided the entire SNP dataset into 1,024 SNP windows and looked for the most similar inbred line within each window to fill the missing data. The algorithm takes advantage of small IBD regions shared between pairs of inbred lines in the collection; if the window from the closest neighbor has more than 5% difference from the line being imputed, the data point is left as missing. The entire GBS Both GBS SNP datasets (raw and imputed) are publicly available through Panzea r2 threshold was 0.2. The resulting number of remaining SNPs was approximately 200,000.IBS and IBD were calculated for all possible pairwise comparisons using PLINK (version 1.07) . For eacNetwork diagrams were generated using the open-source network visualization platform Gephi (version 0.8) .MDS through principal coordinates analysis for two dimensions was performed on the IBS matrix using the isoMDS option of the package MASS from R . AccessiMAF were calculated using the 'Geno Summary by Site' analysis tool in TASSEL (version 4.0) . Taxa anet al.[To analyze genetic diversity, each inbred was considered a random sample of a single maize haplotype from the populations being examined. Hence, heterozygous SNP genotypes were set to 'missing'. With the resulting dataset, pairwise IBS for all pairs of individuals from each set of populations being compared was calculated for each 1 Mb window. Average nucleotide difference was defined as 1 minus average IBS. To estimate average haplotype length, we followed the procedure proposed by Hufford et al.. Choosinet al., and an For the LD analysis, SNPs with more than 25% missing data and with a MAF less than 0.05 were filtered before the analysis, resulting in a total set of 21,806 SNPs. To avoid the bias that differences in sample sizes of the different populations could cause, one random set of 180 inbreds from each of the tropical, ExPVP, and overall populations was selected. LD was calculated using TASSEL , and outThe GRIN database contains public information for different descriptors for each of its entries. When these analyses were performed, kernel color phenotypes were available for 1,595 accessions . We first performed a GWAS for kernel color, with white kernels coded as 0 and yellow as 1. In addition, information about kernel type was used to analyze starchy corn (0) versus sweet corn (1), with 2,520 entries in the first category and 140 into the second. Data on flowering time were collected from plants grown in randomized augmented designs in three environments  during summer 2010. Growing degree days were calculated using climate data from weather stations located near the farms. Best linear unbiased predictors for each line across environments were constructed with ASREML software (version 3.0) . BlockinGWAS analyses were performed on the imputed dataset using the GAPIT package for R . For theBWA: Burrows-Wheeler Alignment; ExPVP: Expired Plant Variety Protection; Fst: Fixation index; GBS: Genotyping by sequencing; GEM: Germplasm Enhancement of Maize; GRIN: Germplasm Resources Information Network; GS: Genomic selection;; GWAS: Genome-wide association study; IBD: Identity by descent; IBS: Identical by state; LD: Linkage disequilibrium; MAF: Minor allele frequencies; MDS: Multidimensional scaling; NAM: Nested association mapping; NCRPIS: North Central Regional Plant Introduction Station; PAV: Presence/absence variation; PCoA: Principal coordinate analysis; QQ: Quantile-quantile; SFS: Site frequency spectrum; SNP: Single-nucleotide polymorphism; USDA-ARS: USA Department of Agriculture - Agricultural Research ServiceThe authors declare that they have no competing interests.MCR analyzed the data and drafted the manuscript. MJM curates the germplasm collection: participated in the design and coordination of the study, and provided phenotypic information. JCG contributed with new analysis tools and analyzed data. JAP analyzed data. KLS and TMC contributed with new analysis tools. RJE, CBA, and SEM coordinated and performed the GBS lab protocol. SAF-G and MDM conceived of the study, participated in the study design and coordination, and collected data. JBH conceived of the study, participated in the project design and coordination, collected data, and helped to draft the manuscript. ESB and CAG conceived of the study, managed its design and coordination, collected data, and helped to draft the manuscript. All authors read and approved the final manuscript.Table S1. Details for the 2,815 accessions  value for all the samples, percentage of missing data, breeding program, and pedigree group).Click here for fileTable S2. The 10 closest neighbors for each unique entry in our maize list based on identical by state (IBS) values. IBS value for each neighbor is presented between brackets.Click here for fileFigure S1. Network diagram showing the relationships of maize inbred lines with identical by state (IBS) values greater than 0.96.Click here for fileFigure S2. Median linkage disequilibrium (LD) decay measured as pairwise r2 between all single-nucleotide polymorphisms (SNPs) in the collection. Each line represents a different group of germplasm.Click here for fileFigure S3. Relationships between nested association mapping (NAM) recombination rate (log10 cM/Mb), average haplotype length (bp), average linkage disequilibrium (LD) (r2), and fixation index (Fst) between stiff stalk, non-stiff stalk, and tropical lines at the NAM genetic map bin scale for each chromosome. The numbers indicate the coefficient of determination (r2) calculated using Spearman's rank correlation.Click here for fileFigure S4 Quantile-quantile (QQ) plot for kernel color genome-wide association study (GWAS) analysis.Click here for fileFigure S5 Quantile-quantile (QQ) plot for sweet corn genome-wide association study (GWAS) analysis.Click here for fileFigure S6 Quantile-quantile (QQ) plot for flowering-time genome-wide association study (GWAS) analysis.Click here for file


A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity  on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program  to understand their effects on detecting known autozygosity.Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods. With the advent of high-density genome-wide SNP arrays, examination of individual genetic data has revealed that runs of homozygosity (ROHs) - many homozygous SNPs in a row - are a common occurrence in all populations worldwide . ConsequROH analyses to date have investigated questions relevant to both basic population genetics theory and disease risk. Population genetics studies have analyzed the distribution, prevalence, and location of ROHs across various sub-populations to infer population structure, history, and natural selection ,3-8. PheA central limitation to current studies analyzing ROHs is the lack of consensus criteria or even guidelines for defining a ROH . For exaIn a recent study , we founOur analysis simulated sequence data that mimicked LD and polymorphism properties found in modern European heritage populations, thus allowing the sequence to resemble expected autozygosity in an outbred population as well as provide perfect information about truly autozygous segments. SNP data was obtained from the sequence by sampling common polymorphisms that mimicked the allele frequency distribution and SNP density found in a modern dense SNP chip , adding error rates and missingness patterns that were also empirically derived. Using this SNP data, we evaluated the performance of existing ROH detection programs to detect known autozygous segments. There are three primary ROH detection programs that have either been used in previously cited ROH studies and/or that have been the focus of a recent publication on detection of autozygosity: PLINK , GERMLINWhile low type 1 and type 2 error rates are always preferred, they cannot be minimized simultaneously: an inherent trade-off exists such that an increase in the type 1 error rate leads to a decrease in the type 2 error rate and vice versa. Determining which ratio of type 1 to type 2 errors should be preferred is not obvious; here, we used a second, independent simulation to find which ratio of type 1 and type 2 error rates would maximize power to detect an association between autozygosity burden and a simulated phenotype. We started by simulating a phenotype associated with autozygosity, and from this population drew a sample containing autozygous segments at the rate found in our simulated sequence data . We then regressed the simulated phenotype on the sum of segments identified as "autozygous, " which included truly autozygous segments (influenced by the type 2 error rate) as well as non-autozygous segments (type I errors), as indicated by the type 1 and type 2 error rates found for the program/thresholds from the previous analysis. Power in this case is defined as the proportion of significant results observed in the simulation. By comparing power across programs and across thresholds within those programs, we have an empirical, objective foundation for deciding which program and which thresholds are most suitable for detecting autozygous ROHs.In order to test the performance of detecting autozygous segments for each program, we needed genomic data that identified which segments of some arbitrary length were truly autozygous from a common ancestor within some time frame . One method would be to use real sequence data in existing samples, but detecting autozygosity in sequence data given current small samples, low pass coverage, and high error rate estimates  poses aWe used the forward-time simulation program FREGENE  to simulLD in data simulated under a neutral mutation-drift model is known to have much lower LD than is observed in human data ,35. BothWe then turned our attention to generating simulated data that led to similar lengths and frequency of ROHs as seen in our empirical SNP data. We found that reducing population size to 6, 500  and selective sampling of individuals from that reduced population best mimicked the length of ROHs seen in our real data. In particular, we chose 1, 000 individuals from the sequence data that closely matched the overall proportion of ROH seen in our empirical data across various ROH analyses. Thus, we simulated genetic sequence data that mimics as closely as possible the two parameters - LD and distribution of ROHs - central to the present investigation. Our sample of simulated sequence data contained 669, 219 total variants, with 436, 564 having a minor allele frequency (MAF) > 1%, and on average one variant per 179 bp.g Morgans, where g is the number of generations since the common ancestor. Thus, the expected length of an autozygous segment caused by sib-sib inbreeding  is 1/4 Morgan or 25 cM, while the expected length of an autozygous segment originating from a common ancestor 50 generations in the past is 1 cM  threshold captured 80% of autozygosity originating in common ancestors within the past 20 generations (~600 years in humans), and the second (short) threshold captured 80% of autozygosity originating within the past 50 generations (~1500 years ). As shop < 0.0001) [We extracted a subset of variants from the simulated sequence data to mimic several properties found in empirical SNP data measured on a modern, commonly used SNP platform (the Affymetrix 6.0 SNP chip). We first sampled a subset of SNPs that matched the MAF distribution from our empirical data  , resultihttp://pngu.mgh.harvard.edu/~purcell/plink/ and Table The -homozyg option in PLINK v1.07  makes RO2 > 0.5  with all other SNPs in the window, removing 24, 700 SNPs  within the low error SNP data, and removing 24, 422 SNPs  within the high error SNP data. Heavy LD pruning removed SNPs within a 50 SNP window that had r2 > 0.09 with other SNPs (correspond to a VIF > 1.1), removing 28, 743 SNPs  within the low error SNP data, and removing 28, 732 SNPs  within the high error SNP data. We used VIF LD pruning because we found this procedure led to more consistent SNP densities across different SNP platforms than LD pruning based on pairwise comparisons of SNPs. Using these two levels of LD-pruned SNP data along with the unpruned SNP data, we ran a total of 192 ROH analyses in PLINK , with specific parameters detailed in Table PLINK does not account for MAF or LD in its algorithm. Aside from the ROH tuning parameters available in PLINK, taking into account MAF and LD in SNP data will also affect how ROH are identified. In particular, many low MAF SNPs in a row can increase the probability of chance (non-autozygous) ROH segments, and high LD within dense SNP regions can also have this effect. To minimize the probabilities of spurious ROH calls, we used LD-pruned data , such that we first removed SNPs with MAF < 0.05, and then used PLINK's --indep command to prune for LD at two levels, which we term "moderate" and "heavy" LD pruning. Moderate LD pruning removed SNPs within a 50 SNP window that had rhttp://www.cs.columbia.edu/~gusev/germline/). Also like PLINK, GERMLINE does not account for SNP MAF or LD. Thus, we included the same MAF and LD pruned data subsets used in the PLINK analysis. We ran at total of 192 ROH analyses in GERMLINE , with specific parameters detailed in Table The principal use of GERMLINE  is identBEAGLE's ROH detection algorithm  uses a fBecause BEAGLE's HBD program accounts for LD, we did not use pruned SNP data in the BEAGLE analysis. For prior parameters, we set the non-HBD to HBD transition rates of 0.0001, 0.01, and 0.1, and set the HBD to non-HBD transition rates at 1, 0.5, and 0.25. It should be noted that this is a large range of priors, and that they include the default priors . As sugghttp://www.r-project.org.All simulations, statistical programming, and graphing were done using R statistical software 2.11.1 d', an index of measurement sensitivity in signal detection that incorporates both type 1 and type 2 error rates . d' is estimated as: φ(1 - type 2 error rate) - φ(type 1 error rate), where φ is the distribution function of a standard normal, which converts a proportion to a Z-score value. Our estimated d' values are included in the table of our top regression power estimates (see Results).To get an estimate of the type 1 error rates (detecting a ROH that is not autozygous) and type 2 error rates (failing to detect an autozygous ROH) for each program, we compared known autozygosity from the sequence data (as described above) to detected ROH from each analysis. These two types of errors were called on a per-SNP rather than per-ROH basis, as calls can be correct for one part of an autozygous segment and wrong for another. To calculate the type 1 error rate, we summed the total number of SNPs that were type 1 errors and divided it by the total number of non-autozygous SNPs. To calculate the type 2 error rate, we summed the total number of SNPs that were type 2 errors and divided it by the total number of autozygous SNPs. We then estimated d' measurements are a good estimate of measurement sensitivity, it has limitations. First, as the type 1 or type 2 error rate approaches zero, d' approaches infinity. Second, two identical d' estimates can have very different ramifications on the actual number of errors made if the prior probabilities of the errors differ, making it difficult to know which ratio of type 1 to type 2 error rates is optimal. An alternative and preferable method to d' is to ask what ratio of type 1 to type 2 errors would maximize statistical power to detect a relationship between whole-genome autozygosity burden and a phenotype (assuming some base rate of autozygosity). This approach not only circumvents the limitations surrounding d' estimates, but also addresses a commonly tested hypothesis in clinical ROH research. Furthermore, power results derived from testing whole-genome ROH burden apply to single ROH association hypotheses (e.g. ROH mapping) as well because the error probabilities in detecting autozygosity are equivalent at the single ROH level and at the whole-genome level.While p-value < 0.05, so a power estimate of 0.5 would mean that 500 of the 1, 000 simulations positively associated the simulated phenotype with overall ROH burden.To estimate statistical power of a whole-genome ROH burden analysis informed by the type 1 and type 2 error rates, we simulated a sample of 2, 000 individuals, with every individual's genome split into 'potential' autozygous segments of equal length  and 18, 935 segments for the 50 generation autozygosity map ). Each segment had a probability of being autozygous at the rate observed in our simulated sequence data (0.36% within 20 generations and 0.91% within 50 generations). While the true level of autozygosity in modern outbred populations is unknown, these base rates are likely to be close to the true level because the simulation parameters and selective sampling of the sequence data were chosen to mimic the level of LD and distribution of ROHs found in modern European populations. A continuous phenotype was created for each individual such that the summed autozygous segments within individuals accounted for a small percentage (1%) of the variance of their phenotype score. The choice of the variance accounted for (1% in this case) is purely arbitrary; other choices would raise or lower the absolute levels of detected power but have no effect on the relative differences between various estimates of statistical power, and therefore would have no influence on our final conclusions. We then simulated true calls, false calls (type 1 errors), true non-calls, and false non-calls (type 2 errors) using the observed error rates from each ROH analysis. We summed the called ROHs for each individual (made up of ROHs that are both true positives and type 1 errors) and regressed the simulated phenotype on this sum. To derive our power estimates, we repeated our simulated regression 1, 000 times for each analysis. Regression power was defined as the proportion of trials associated with a positive slope and Overall, PLINK consistently generated the highest regression power estimates for detecting autozygosity, outperforming GERMLINE and BEAGLE. Figures Given these initial results, we decided to look in more detail within the PLINK ROH analyses for the optimum level of LD-pruning and minimum SNP threshold. In addition to moderate LD-pruning, we included "light" LD-pruning, where we removed SNPs that had r2 > 0.9 with other SNPs in a 50 SNP window (VIF > 10), removing 19, 662 SNPs  within the low error SNP data, and removing 17, 756 SNPs  within the high error SNP data. We also included "moderate-heavy" LD-pruning, where we removed SNPs that had r2 > 0.25 in a 50 SNP window (VIF > 1.33), removing 26, 375 SNPs  within the low error SNP data, and removing 26, 213 SNPs  within the high error SNP data. We also varied the minimum SNP threshold between 10 and 125 SNPs in increments of 5 SNPs.The fine-scale results from PLINK Figure  show thaRegression power estimates for GERMLINE were similar in pattern but consistently lower than power estimates for PLINK, which was likely driven by the lower start/end resolution of ROH calling in GERMLINE (see Methods). To ensure that the discrepancy between GERMLINE and PLINK results were not due to the different ways that ROH length thresholds were defined, we also looked at minimum SNP thresholds in GERMLINE (as opposed to cM length), finding virtually identical results to PLINK, but with slightly lower regression power estimates at all thresholds (data not shown).BEAGLE was very conservative in detecting autozygous segments, with consistently high type 2 error rates despite using the maximum posterior probability from 10 iterations and a reduced threshold for calling ROH to any posterior probability greater than 10% (a 50% posterior probability threshold was used in Browning & Browning ). In shoThere were major differences in computational time between the three programs. For our 120 Mb data using 1, 000 individuals, a single PLINK and GERMLINE ROH analysis took under 30 seconds, whereas a single BEAGLE HBD analysis took ~150 minutes . Both PLINK and GERMLINE analysis times scale up linearly with respect to distance, so a whole-genome analysis for 1, 000 individuals should take under 11 minutes. On the other hand, BEAGLE analysis run time scales exponentially, so while BEAGLE runs a separate analysis on each chromosome, chromosome size and sample size greatly affects the computational time.By simulating sequence and SNP data to match important population genetic properties found in empirical SNP data, we were able to compare the performance of ROH detection programs to identify true levels of autozygosity expected in large, outbred populations of European heritage. PLINK consistently outperformed both GERMLINE and BEAGLE, producing higher regression power estimates for detecting autozygosity within 20 and 50 generations, regardless of the genotyping error rate. While we found, as expected, that GERMLINE performed worse than PLINK due to the lower resolution of start/end points of ROHs, we were surprised by the lower performance of BEAGLE; we expected that the incorporation of LD information to result in higher accuracy to detect autozygosity. However, this did not appear to be the case. Rather, BEAGLE was consistently conservative, and changing the tuning parameters did not alter this. As currently designed, at least, BEAGLE appears optimized to detect rather long autozygous segments arising from a recent common ancestor, but has not been designed to detect the more ambiguous autozygous signals from distant common ancestors.Our results suggest that PLINK is the most suitable program for detecting autozygosity arising from distant ancestors  were not simulated and may affect estimates of ROH in real data. We did not investigate several additional questions, such as the effects of our recommendations for detecting ROHs in non-European heritage or isolated populations. We also did not investigate optimal thresholds/program for detecting more recent or more ancient autozygosity. However, autozygosity arising from more recent ancestors becomes increasingly easy to detect, and most programs/thresholds should detect it with very high fidelity. Autozygosity arising from more ancient common ancestors is more difficult to detect but may nevertheless be detectable as SNP chips become denser. However, the variation between individuals in overall burden of such ancient autozygosity becomes very small , and thuPLINK has been the most commonly used ROH detection program to date. However, only one study analyzed data that was pruned for LD , which wThe current study is the first of its kind to directly assess the ability of current ROH detection programs to estimate genome-wide autozygosity. Our results should apply equally well to research on autozygosity using whole-genome ROH burden or single ROH association mapping. None of the programs tested perfectly detects autozygosity, and new parameters and algorithms may further minimize detection error and increase sensitivity to detect autozygous segments. Until then, the results in this study represent an important step for developing working 'consensus criteria' for defining ROH.The authors declare that they have no competing interests.DPH designed the study, performed all statistical analyses, and drafted the manuscript. MCK conceived of the study, simulated the genetic data, participated in study design, and helped to draft the manuscript. MAS participated in the study design and helped to draft the manuscript. All authors have read and approved the final manuscript.MAF distribution of empirical SNP data, simulated sequence data, and simulated SNP data drawn from sequenceClick here for fileAfter MAF pruning of the simulated sequence data, SNPs were drawn to closely match SNP base positions observed in empirical SNP dataClick here for fileType 1 and type 2 errors for autozygosity within 20 generations using PLINK and GERMLINEClick here for fileType 1 and type 2 errors for autozygosity within 50 generations using PLINK and GERMLINEClick here for fileType 1 and type 2 errors using BEAGLEClick here for fileFigure legends for additional files 3, 4, and 5Click here for file


Oncorhynchus nerka) that exhibit reproductive ecotypes (stream-spawning and shore-spawning) in lakes throughout western North America and northeast Asia. Current conservation and management strategies may treat these ecotypes as discrete stocks, however their recent divergence and low levels of gene flow make in-season genetic stock identification a challenge. The development of genome-wide SNP markers is an essential step towards fine-scale stock identification, and may enable a direct investigation of the genetic basis of ecotype divergence.High throughput next-generation sequencing technology has enabled the collection of genome-wide sequence data and revolutionized single nucleotide polymorphism (SNP) discovery in a broad range of species. When analyzed within a population genomics framework, SNP-based genotypic data may be used to investigate questions of evolutionary, ecological, and conservation significance in natural populations of non-model organisms. Kokanee salmon are recently diverged freshwater populations of sockeye salmon (We used pooled cDNA samples from both ecotypes of kokanee to generate 750 million base pairs of transcriptome sequence data. These raw data were assembled into 11,074 high coverage contigs from which we identified 32,699 novel single nucleotide polymorphisms. A subset of these putative SNPs was validated using high-resolution melt analysis and Sanger resequencing to genotype independent samples of kokanee and anadromous sockeye salmon. We also identified a number of contigs that were composed entirely of reads from a single ecotype, which may indicate regions of differential gene expression between the two reproductive ecotypes. In addition, we found some evidence for greater pathogen load among the kokanee sampled in stream-spawning habitats, suggesting a possible evolutionary advantage to shore-spawning that warrants further study.This study provides novel genomic resources to support population genetic and genomic studies of both kokanee and anadromous sockeye salmon, and has the potential to produce markers capable of fine-scale stock assessment. While this RNAseq approach was successful at identifying a large number of new SNP loci, we found that the frequency of alleles present in the pooled transcriptome data was not an accurate predictor of population allele frequencies. Biology is currently undergoing a genomics revolution . With thThe emerging field of population genomics has evolved in tandem with these novel technologies, incorporating genome-wide sequence data into the study of systems that historically would have been limited to a small number of neutral markers . CentralOncorhynchus nerka) that exist in lakes throughout western North America and northeast Asia. Two reproductive ecotypes of kokanee have been described based on spawning behaviour and location. Stream-spawning kokanee migrate into tributaries in early autumn, display pronounced secondary sex characteristics, site defence, and redd formation. Conversely, shore-spawning populations  breed much later in the autumn, lack secondary sex characteristics and site defence, and mate directly on the shoreline rather than migrating into tributaries , a pathogenic water mould that is associated with pre-spawning mortality in salmonids [Flavobacterium psychrophilum [GenBank: AM398681], which is a bacterial infection associated with high levels of salmonid mortality [Gyrodactylus salmonis [GenBank: JN230351], a pathogenic flatworm  as a reference to map the kokanee transcriptome reads .While there is not yet a fully assembled and annotated salmon nuclear genome, an annotated mitochondrial genome sequence is available for most salmonid species. To assess the prevalence of mitochondrial genes in the transcriptome data, we used the mitochondrial genome for sockeye salmon [-6, HSP length cut-off = 33). The top five hits for each contig were retained. Following the Blastx search, GO analysis was carried out in order to obtain hierarchical structure information with respect to the three GO domains . This analysis was carried out using Blast2Go v. 2 .The high coverage data set was subject to sequence similarity searches using Blast2Go v.2 ,39. For -5) searches were performed. Blast2Go v.2 was again used to assign GO annotations to contigs (same parameters as above).Sequence similarity searches were also carried out for all contigs in each of the ecotype unique data sets. Both Blastx (same parameters as above) and Blastn  was carried out using all contigs in the high coverage data set in order to identify contigs with overlapping portions. Redundancies among the contigs may indicate the presence of PSVs or be indicative of alternative splicing within the transcriptome data. Contigs that overlapped with one or more other contigs were removed from the high coverage data set.A CLC Genomics Workbench v. 5.5 . By carrying out mapping and SNP detection separately for the two ecotypes and then combining the resulting SNP tables, it was possible to determine the number and percent of reads that matched the reference contig sequence at each SNP site. The absence of a polyorphism (fixation) in one ecotype was evident as either 100% of reads matching the reference (in this case no SNP would be observed in the table for this ecotype) or 100% of reads possessing a nucleotide other than that of the reference sequence. For example, a fixed difference for a given location would be scored when one ecotype was 100% different from the reference contig sequence, while the other ecotype displayed no polymorphism at that site (i.e. 100% match to the reference). Using this approach we were able to characterize all SNPs as being either: (a) polymorphic in both ecotypes; (b) fixed in one ecotype, polymorphic in the other; or (c) fixed for different alleles in each ecotype. Polymorphic sites with more than two alleles and sites with read ambiguity were removed from the data. Variation in the form of indels was not examined in this study.The working data set of high coverage contigs was screened for SNPs using the A divergence value  was calPrimer3[A subset of 36 SNPs was used to genotype an independent sample of kokanee in order to validate this ascertainment procedure. Validation of candidate SNPs was carried out following a pipeline similar to that implemented by Seeb et al. . BrieflyPrimer3 such thaTaq polymerase (Kapa Biosystems), 20–100 ng of DNA template, and ultra pure water for a total reaction volume of 12.5 μl. For each reaction, a touchdown PCR procedure was implemented using a Veriti thermal cycler (Applied Biosystems). The program had an initial denaturation at 94°C for 2 minutes, followed by 10 cycles at 94°C for 30 seconds, 60°C for 30 seconds, and 72°C for 30 seconds, with the annealing temperature decreasing by 1.0°C per cycle. This was followed by 25 cycles at 94°C for 30 seconds, 50°C for 30 seconds, and 72°C for 30 seconds. The final cycle had an extension of 72°C for 2 minutes and was then held at 4°C. PCR products were run on a 1.5% agarose-gel in order to obtain a preliminary assessment of the quality and size of the amplicon. Loci that showed evidence for the presence of introns (larger PCR products then expected), or had multiple bands were not retained for subsequent analyses.Each PCR reaction contained 1.25 μl of 10× buffer, 1.25 μl of 2 mM dNTP mix, 0.5 μl of 10 mM forward and reverse primer, 0.5 units of Kapa Loci that produced a single clean PCR product of the anticipated size were then evaluated by HRM analysis using DNA samples from 32 stream-spawners and 32 shore-spawners from Okanagan Lake. For detailed sampling methods for the kokanee DNA samples see Russello et al. . In addiEach HRM reaction contained 7.2 μl of Precision Melt Supermix (Bio-Rad), 0.4 μl of each primer, 20–100 ng of DNA template, and ultra pure water for a total reaction volume of 20 μl. HRM analyses were run in 96 well plates on a Bio-Rad CFX96 Touch™ real time PCR detection system. A two-step touchdown PCR protocol was used starting with an initial denaturation step at 95°C for 2 minutes, followed by 9 cycles of 95°C for 10 seconds, 60°C for 30 seconds, with the annealing temperature decreasing by 1°C per cycle. This was followed by 43 cycles of 95°C for 10 seconds and 50°C for 30 seconds. The final PCR cycle consisted of 95°C for 30 seconds followed by 55°C for 1 minute. A plate read was obtained at the end of every PCR cycle. The melt curve data were obtained starting at 70°C and increasing by 0.2°C every 10 seconds to a maximum of 95°C. During the melting stage a plate read was obtained at every 0.2°C increment. Melt curve data were analyzed using the Bio-Rad CFX proprietary software.SNPs that demonstrated multiple clusters of HRM curves (i.e. polymorphic loci) were subjected to Sanger re-sequencing on an ABI 3130 Genetic Analyzer to confirm their genotype. Given that each HRM cluster should represent a single SNP genotype, 1–2 individuals from each cluster were sequenced in order to determine the genotype of each cluster.For each locus that was successfully genotyped, we calculated expected and observed heterozygosity, and tested for deviations from Hardy-Weinberg and linkage equilibrium. These analyses were carried out using GenePop v.4  followedThe authors declare that they have no competing interests.MR and ML designed the study. ML collected tissue samples, carried out lab work, analyzed data and drafted the manuscript. DD carried out lab work and assisted with data analysis. MR obtained the funding, assisted with analyses, and helped draft the manuscript. All authors read and approved the final manuscript.High coverage contig sequences. In FASTA format containing the sequence of all high coverage contigs used for SNP detection .Click here for fileCharacterization of high coverage contigs. Listing the length, coverage, number of reads, and top Blastx hit (NCBI) for each of the high coverage contigs.Click here for fileGO terms for each high coverage contig. Listing the contig name and GO annotation information for each of the three GO domains: Biological process (BP), molecular function (MF), cellular component (CC).Click here for fileSNP information. Characterizing the 32,699 SNPs identified in the high coverage data set.Click here for fileSNP primers. Containing the primer sequences and locus information for all loci for which HRM validation was attempted.Click here for fileEcotype unique contig sequences. In FASTA format containing the sequence of all contigs that were composed entirely of reads from a single ecotype .Click here for fileSample collection. Containing the sampling locations of all kokanee used to generate the two pooled transcriptome libraries.Click here for file