Of the identified SNPs, the eight nearest genes were sonic hedgehog (SHH), limb region 1 homolog (mouse) (LMBR1), dipeptidyl-peptidase 6, transcript variant 3 (DPP6), thyroid-stimulating hormone, beta (TSHB), sal-like 4 (Drosophila) (SALL4), par-6 partitioning defective 6 homolog beta (Caenorhabditis elegans) (PARD6B), coenzyme Q5 (COQ5), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, etapolypeptide (YWHAH). The GWAS supports earlier reports of the importance of SHH and LMBR1 as regulating genes for polydactyly in chickens and other species, and identified others, most of which have not previously been associated with limb development. The genes and associated SNPs revealed here provide detailed information for further exploring the molecular and developmental mechanisms underlying polydactyly.Polydactyly occurs in some chicken breeds, but the molecular mechanism remains incompletely understood. Combined genome-wide linkage analysis and association study (GWAS) for chicken polydactyly helps identify loci or candidate genes for the trait and potentially provides further mechanistic understanding of this phenotype in chickens and perhaps other species. The linkage analysis and GWAS for polydactyly was conducted using an F2 population derived from Beijing-You chickens and commercial broilers. The results identified two QTLs through linkage analysis and seven single-nucleotide polymorphisms (SNPs) through GWAS, associated with the polydactyly trait. One QTL located at 35 cM on the GGA2 was significant at the 1% genome-wise level and another QTL at the 1% chromosome-wide significance level was detected at 39 cM on GGA19. A total of seven SNPs, four of 5% genome-wide significance (P < 2.98 × 10 LMBR1 was strongly associated with polydactyly in chickens. Another study has shown complete association of this same single-nucleotide polymorphism  with the polydactyly phenotype in a manner that was concordant with the dominant and incompletely penetrant nature of this trait , and limb region 1 homolog (mouse) (LMBR1) are important regulatory genes for limb development  for the polydactyly trait were also found in different species (Zic3 in regulating limb digit number via its modifying effect on GLI3 and SHH expression levels in mice. ZNF141) associated with autosomal-recessive postaxial polydactyly type A through whole-genome sequencing combined with homozygosity mapping and array comparative genomic hybridization analysis. These studies found the gene for one polydactyly allele/locus, but the molecular mechanism and other polydactyly alleles/loci remain incompletely understood  can be used to map the quantitative trait loci (QTL) underlying complex traits. The GWAS, based on high-throughput SNP genotyping technologies, has revealed loci underlying a variety of interesting traits in domestic animals  and it iIn the present study, both linkage analysis and GWAS were performed for the polydactyly trait using the Chinese Academy of Agricultural Science (CAAS) chicken F2 resource population derived from the cross between a local Chinese chicken breed  and a commercial broiler line  . The CAAS chicken F2 resource population was derived from a cross between BJY and CB chickens. The BJY is a Chinese indigenous breed with a 25−31% incidence of five digits on one or both feet. The CB (Cobb-Vantress) is a commercial broiler breed with four digits on each foot. Six BJY males were each mated to 12 CB females. Six males and 20 females of their offspring, the F1 generation, then produced the F2 chickens via artificial insemination and only nonrelated females to avoid inbreeding. The chickens from the three generations were raised in stair-step cages under the same routine conditions of nutrition and management at the conservation farm of the Institute of Animal Sciences  were used. Blood was collected into acid citrate dextrose anticoagulant tubes from the brachial vein on d 56. The number of digits, four or five on one or both feet, was recorded. Two of six BJY males in the F0 generation had polydactyly. The pedigrees with polydactyly traits are shown in -6 and SNPs with no assigned chromosome or linkage group. After quality control measures, 42,585 SNPs distributed among 29 chromosomes (including the Z chromosome) and one linkage group (LGE22) remained . The average physical distance between two neighboring SNPs was approximately 20.4 kb . A total of 39 were excluded due to sample call rate <90%. A total of 13,985 SNPs were removed for failing to meet one or more of the following conditions: SNP call rate <90%, minor allele frequency <3%, Hardy-Weinberg test P of < 10The genetic linkage map was constructed using 19 full-sib families  and 42,585 SNPs with the improved version of CRI-MAP . This method has been described in previous studies (2 threshold of 0.2 (To reduce the effect of linkage disequilibrium (LD) on the results, 6518 independent SNPs were acquired in all autosomal chromosomes using the indep-pairwise option, with a window size of 25 SNPs, a step of 5 SNPs, and rd of 0.2 . A totalWhere Ga is the length genetic map of each chromosome and Gc is the length of the genetic map of all autosomal chromosomes. Confidence intervals for QTL positions were estimated by bootstrapping, as presented by The population structure was assessed by multidimensional scaling (MDS) analysis using PLINK software 1.07 , Gm is the effect of the SNP, Kn is the random effect of the relative kinship matrix, which was constructed by matrix simple matching coefficients based on the independent SNPs, and this step was followed by compression  for suggestive significance, and 2.98 × 10-6  for 5% genome-wide significance. The Manhattan plot, showing genome-wide P-values of GWAS, was produced using R 2.13.2 software with the ‘‘gap’’ package , about 74% of all markers on the SNP beadchip, and 337 individuals from three generation and 19 full-sib families were used to construct the linkage map. As shown in Table S3) in autosomal chromosomes were used in the linkage analysis for polydactyly. Quantitative trait loci locations and effects for the polydactyly trait are shown in Figure S1); it explained 9.34% of the phenotypic variation. Another QTL at the 1% chromosome-wide significance level was detected at 39 cM on GGA19 and it explained 5.35% of the phenotypic variation.A total of three hundred chickens from the F2 generation and 6518 independent SNPs  from the F2 generation were used for further analysis.The MDS analysis of the 16,760 SNPs using the first two MDS components showed that the chickens within each full-sib family were clustered together (As shown in -7), 9.5 kb downstream of dipeptidyl-peptidase 6, transcript variant 3 (DPP6). Another SNP, Gga_rs14135036, located on GGA2 at 7628.2 kb was suggestively associated with polydactyly (P = 5.96 × 10-5). This SNP was in close proximity to other candidate genes for polydactyly, 113.9 kb downstream of sonic hedgehog (SSH) and 240.7 kb upstream of limb region 1 homolog (mouse) (LMBR1).The most significant SNP, GGaluGA132178, was located on GGA2 at 6641.2 kb (P = 7.94 × 10TSHB) gene, had highly significant association with the polydactyly (P < 2.98 × 10-6). Two other SNPs , located within a 13.3-kb segment on GGA20, were of genome-wide significance (P < 2.98 × 10-6); one was 176.3 kb upstream of the Sal-like 4 (Drosophila) (SALL4) gene and the other was 176.4 kb from the Par-6 partitioning defective 6 homolog beta (C. elegans), (PARD6B) gene.The SNP Gga_rs13606164, located at 2579.1 kb on GGA26, is 35.6 kb downstream of the thyroid stimulating hormone, beta (−5) with polydactyly were located within a 484.99-kb segment on GGA15; one was 22.9 kb downstream of coenzyme Q5 (COQ5), and the other was 2.8 kb upstream of tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, etapolypeptide, (YWHAH).Two SNPs (Gga_rs15023766 and Gga_rs14093104) with suggestive association  were identified through combined linkage analysis and GWAS for the polydactyly phenotype in chickens. In addition to confirming earlier studies in chickens and other species that SHH and LMBR1 are important regulating genes for polydactyly, the other genes identified here provide additional information for potentially exposing the molecular mechanisms and developmental biology underlying polydactyly in chickens. As new candidate genes, especially the SALL gene, they will be targets for future work.In conclusion, two QTLs and eight genes (


Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines.A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.The online version of this article (doi:10.1186/s13059-015-0606-4) contains supplementary material, which is available to authorized users. Wheat genomic variation is shaped by the interplay of multiple factors including two recent polyploidization events -3 Figura, domestA detailed description of DNA sequence variation across the genome is a prerequisite for the systematic analysis of variants underlying trait variation in wheat and critical for understanding the role of various evolutionary factors in shaping genome diversity. Recently, low to medium density genotyping arrays were used to characterize SNP variation and linkage disequilibrium (LD) in wheat populations ,14 and iArabidopsis, and several crops [Genome sequencing of populations of individuals has been undertaken in a number of species including humans, al crops -23 and hal crops ,24 and gal crops ,26 was sal crops ,28 can nHere we used the newly developed genome assembly of the cultivar Chinese Spring  based onThe WEC assay probes were designed with 107 Mb of non-redundant low-copy genic regions  targetinVariant calling was performed in the regions of the wheat genome covered by reads in >46 lines (>75%). We identified 1.57 million single nucleotide polymorphisms (SNPs) and 161,719 small insertions-deletions (indels) distributed across all 21 chromosomes, producing an average density of 1 variant every 175 bp  varied along chromosomes with lower values found near the telomeres  and elevated diversity in the D genome  was lower than in the A (ρ = 2.9 × 10-4/kb) and B (ρ = 3.2 × 10-4/kb) genomes  and B  genomes between the meiotic  and historic recombination rates suggesting stability of chromosomal recombination rates over time. However, no significant correlation was found between R and ρ in the D genome. This fact is most likely explained by the polyploidy-associated population bottleneck, which can impact the estimates of historic recombination [Using re-sequencing data, we now have the possibility to assess historic recombination rate (parameter ρ) , which id humans ,41,48, wbination  and alsorsp2 = 0.15, P <10-4), a trend previously reported in maize, and humans [ST estimates along the duplicated genomic regions of homoeologous chromosomes. This outcome is most likely a consequence of the sensitivity of these summary statistics to historic demographic and selection events that likely had different impacts on each wheat genome  in our population within a 2-kb window was 70% to 71% for all three genomes. Consistent with the observed LD levels in the wheat genomes , marker density affects the probability of finding variants in linkage disequilibrium (LD) with a causal variant. A SNP-hiding test  showed tDiversity maps have proven to be a powerful tool for imputing genotypes ,20 allowFirst, we sequentially selected each cultivar from our panel of 62 lines and, after ‘hiding’ all SNP sites besides those overlapping with the public 90 K SNP array , we usedLr67 and Yr51, and one represents an uncharacterized locus  also showed selection scan test statistic scores approaching the extremes  in the extreme tails of the selection scans compared to synonymous variants. Regions identified by multiple selection scans were not common   associated with resistance to stem, leaf, and stripe rust, respectively, without the concomitant enrichment in the XP-CLR scan. Since the PHS test preferentially detects on-going selection events that have not reached fixation in a population [Non-synonymous, that is, likely functional, variants were significantly enriched , identified by both selection scans and located on the short arm of chromosome 1D; its expression was shown to be associated with resistance to a fungal pathogen causing powdery mildew in wheat . GlutathArabidopsis [The development of the chromosome-specific wheat genome assemblies and population-scale whole exome re-sequencing data now provides the unique opportunity to investigate the impact of selection on duplicated copies of homoeologous genes. We have used the ordered sets of homoeologous genes to establish the syntenic relationships between the selection targets on different genomes. Inter-genomic comparison of selection targets associated with transition from landraces to cultivars revealed that only a single syntenic region shared the signature of selection between the B and D genomes  to identify LGC contiguous sequences that were similar to Brachypodium exon sequences. Second, we used the same LGC sequence library  to identify LGC contiguous sequences that matched a set of non-redundant wheat cDNA and EST sequences [A wheat exome capture (WEC) assay targeted the 107 Mb of non-redundant low-copy regions in the wheat genome. The capture probes were designed using the low-copy number genome assembly (LGC) of the wheat cultivar Chinese Spring . The LGCequences  and tranequences . Finallyequences . The WECequences .260/280 ratio 1.7 to 2.0, and LM-PCR yield >500 ng were pooled and used for sequence capture. Several levels of DNA sample pooling have been used in our study  from a single 3-week-old seedling. One μg of DNA was fragmented with the Covaris S220 to obtain an average fragment length of 300 bp. The NEBNext DNA Library Prep Kit (NEB) for Illumina and Illumina TruSeq (TS) indexed (barcoded) adapters were then used for sample library preparation according to NEB protocol with the following exceptions. The PCR Enrichment step from the NEB was replaced with the Ligation Mediated PCR (LM-PCR). The TS-PCR Oligo1 (5′-AATGATACGGCGACCACCGAGA-3′) and TS-PCR Oligo2 (5′- CAAGCAGAAGACGGCATACGAG-3′) were used in the LM-PCR. The LM-PCR products were purified with the QIAquick PCR Purification Kit (Qiagen) followed by the size selection using Agencourt AMPure XP Beads (Beckman Coulter). The libraries were tested on 2100 Bioanalyzer (Agilent Technologies) and the NanoDrop Spectrophotometer (Thermo Scientific). Only samples with average fragment lengths of 200 to 400 bp, Aet al. [Complexity-reduced sequencing was performed according to the previously described protocol  modifiedet al. . A pooleFor mapping reads to the polyploid wheat genome we have developed a three-step iterative alignment strategy with parameters optimized to map reads uniquely to the different wheat subgenomes. Parameters were optimized using a subset of 29,716 Illumina 2 × 100 bp reads generated for cultivar RAC875. These reads map to 100 homoeologous sets of genes (three copies per gene) from the wheat CSS assemblies . We mappRaw paired-end Illumina reads were quality filtered using the default setting of NGS QC toolkit v2.3 , retainiMore than 4.7 billion paired-end reads were generated from the population of 62 accessions. On average 62% of quality-filtered reads generated by sequence capture were mapped covering more than 321 Mb of the hexaploid wheat genome ; one hit per genome. There were 47,739 homoeologous gene sets that fit these criteria. The log2 ratio of average coverage depth for each of these gene sets in pair-wise genome comparisons  was distributed normally with the mean centered at 0, suggesting that homoeologous targets are captured with equal efficiency , genotype calling was performed only for alleles that were present in at least two different accessions in the population. The high level of genotype calling concordance between the WEC and GBS datasets was indicative of the high accuracy of the applied GBS genotype calling approach  calling accuracy assessed in a set of 12,426 singletons unique to cultivar Chinese Spring depended on the read coverage depth  obtained using restriction enzyme-based sequencing can deviate from true values [Recently, it was demonstrated that the estimates of commonly used diversity statistics . The ordered CSS contigs were used to perform sliding window analyses. π and Tajima’s D estimates for 2 Mb sliding windows (1 Mb step) with >10 kb of CSS contigs covered by reads were calculated using LDHat (v. 2.2) [π and Tajima’s D were defined by the 2.5 percentiles of the test statistic distribution. FST was calculated by SNP using the R package adegenet (cran.us.r-project.org) by contrasting cultivars and landraces. A sliding window analysis (2 Mb with a 1 Mb step) was used to generate average estimates of FST. Outliers were calculated as the 97.5 percentile of all window values within the genome. Adjacent outlier windows were merged. The thresholds for the cultivar-landrace comparison were 0.158 for the A genome, 0.108 for the B genome, and 0.0925 for the D genome.The order of the CSS contigs along the wheat chromosomes was established using the combination of ‘genome zipper’  and a hi-3, 8.5 × 10-3/kb, and 9.7 × 10-3/kb for the A, B, and D genomes, respectively.For estimating recombination rate we selected 10,517 CSS contigs that contained at least 20 non-singleton SNPs. The historical recombination parameter (ρ = 4Ne) was estimated using a composite likelihood approach implemented in the Maxhap program . Regionsr2 as previously described [Pair-wise estimates of LD were obtained for SNPs with a MAF ≥0.05 by measuring escribed . The aveescribed . The genescribed .T. aestivum were mapped against the wheat survey sequence using GMAP [The nucleotide sequences of the CSS gene models for ing GMAP . Output ing GMAP  and HMMEing GMAP . The PFAA total of 20,000 SNPs were randomly selected from the dataset to estimate pairwise distances among the 62 accessions using the R package ‘ape’ V3.0.6 . The booAe. tauschii AL8/78 genotype [To determine which of the D genome alleles corresponds to the AL8/78 genotype, 100 bp flanking sequences of each D genome SNP were extracted and compared against the genomic sequence of genotype . In totaet al. [P <10-3.The PHS statistic was calculated as described by Toomajian et al. . Utiliziet al.  than thoFST = 0.03). This low FST was observed previously [FST) or XP-CLR test statistics between spring and winter wheat. Out of 372 XP-CLR outliers in the landrace-cultivar comparison only nine (2.4%) partially overlapped with the outliers in the spring-winter wheat comparison  overlapped with the FST outliers in the spring-winter wheat comparison. No overlap of genetically differentiated genomic regions between spring and winter wheat with the outliers of the genetic diversity scan was found. None of the genes located in the genomic regions that showed the signature of selection in at least two scans reported in Table S10  pathotype 134 E16A+; P. triticina (Pt) pathotypes 104-1,(2),3,(6),(7),11 + Lr37; 104-1,(2),3,(6),(7),11,13; 104-1,(2),3,(6),(7),9,11; 76-3,5,9,10 + Lr37; 10-1,3,(7),9,10,11,12 and P. graminis f. sp. tritici (Pgt) pathotypes 98-1,2,3,5,6 and 34-1,2,7 + Sr38. The pathotype designations are provided according to McIntosh et al. [Following seed bulk up for the single plant selections, the purified 678 accessions were grown under field conditions during 2006 and 2007 at the Lansdowne site of the University of Sydney. Each accession was grown as a single 1 m row. Field trials were artificially inoculated with h et al. . These ph et al. . For stret al. [The 678 landrace accessions were genotyped using the Infinium iSelect 90 K SNP assay, the content of which is reported to have minimal ascertainment bias for the analysis of diverse wheat landraces . Genotypet al. . Monomoret al. .P value <1 × 10-3 used to filter markers was selected based on the previous studies of agronomic traits in wheat and barley demonstrating that this threshold provides adequate accuracy for detecting marker-trait associations, as was validated independently in bi-parental mapping populations [Mixed model variance component analysis of mean phenotypic values was performed using the R package GAPIT . The infulations ,63,109. Lr67 included 124 F3/F4 lines derived from a cross between Thatcher and RL6077 [Yr51 was comprised of 89 F6 RILs derived from a single heterozygous F3 plant #5515 from a cross between Watkins’ line PBI769 and Westonia [6 RILs derived from a cross between Watkins’ line PBI562 and Yitpi .Mapping in the populations of recombinant inbred lines (RIL) was used to validate several marker-trait associations identified in the GWAS including two characterized and one previously uncharacterized disease resistance loci. The population segregating for leaf rust resistance gene d RL6077 . The popWestonia . The popSNPs associated with rust resistance genes segregating in each mapping population were identified using bulk segregant analysis (BSA)  and seleet al. [1 samples was about halfway between that of the other samples.For BSA, resistant and susceptible bulks were prepared by pooling equal amounts of genomic DNA from at least 20 plants for each phenotypic class. An artificial F1 sample was prepared by combining an equal amount of DNA from each of the two bulks. The bulked DNA samples, artificial F1, and parental lines were genotyped using a custom Infinium iSelect bead chip assay on the iScan instrument following the manufacturer’s instructions (Illumina Ltd.). The Thatcher/RL6077 and PBI No. 769/Westonia were genotyped using the 9 K and 90 K iSelect genotyping arrays, respectively. The SNPs were assessed for putative linkage by comparing the normalized theta values for each sample as described in Hyten et al. . PolymorFor SG, at least 15 resistant and 15 susceptible plants from PBI No. 562/Yitpi cross and its parents were genotyped using the 90 K iSelect bead chip assay. Polymorphism was considered to be linked to a rust resistant gene when the majority (>90%) of individuals within each phenotypic class were fixed for the expected parental allele.The genomic location of SNPs associated with rust disease resistance in the GWAS analysis were compared with those linked to mapped rust resistance genes in each of the bi-parental mapping crosses. GWAS-associated SNPs were considered to co-locate with the mapped resistance genes when the linked SNPs in each study were located at (or very near) the same position in the 90 K consensus SNP genetic map . In instThe sequencing data have been deposited in the NCBI Short Read Archive under accession number SRP032974. Datasets used for diversity analyses, genotype imputation, and GWAS are available from the project website . Variant


TP53 mutation at diagnosis.Neuroblastoma is a pediatric cancer that exhibits a wide clinical spectrum ranging from spontaneous regression in low-risk patients to fatal disease in high-risk patients. The identification of single nucleotide polymorphisms (SNPs) may help explain the heterogeneity of neuroblastoma and assist in identifying patients at higher risk for poor survival. SNPs in the TP53 pathway are of special importance, as several studies have reported associations between TP53 pathway SNPs and cancer. Of note, less than 2% of neuroblastoma tumors have a We selected 21 of the most frequently studied SNPs in the TP53 pathway and evaluated their association with outcome in 500 neuroblastoma patients using TaqMan allelic discrimination assays.MYCN status, and stage of the disease in 500 neuroblastoma patients. A missense SNP in exon 10 of the CASP8 gene SNP D302H was associated with worse overall and event-free survival in patients with MYCN-amplified neuroblastoma tumors.We investigated the impact of 21 SNPs on overall survival, event-free survival, age at diagnosis,  TP53TP53 is mutated in more than 50% of human cancers TP53 mutation at diagnosis Neuroblastoma (NB) is a complex and heterogeneous pediatric cancer that can manifest with tumors that regress spontaneously, and also with tumors that metastasize and acquire resistance to therapy, leading to severe illness and death TP53 gene itself  U test. Association of the SNPs with MYCN status or stage of the disease was done using Pearson's Chi-square test. Statistical tests were two-sided and results with corrected p-values  <0.05 were considered statistically significant. All statistical analyses were conducted using SPSS version 20 software. All statistical analyses were followed by Benjamini-Hochberg multiple testing correction in R version 3.0.2 using “stats” statistical package.Comparisons of Kaplan-Meier survival curves between different genotype groups were done using the log-rank test. Survival time was defined as the time from diagnosis until death from the disease or until the date of the last follow-up of living patients. Patient samples were divided into two groups based on SNP status . Comparisons of age at diagnosis between the different groups were done using the Mann-Whitney MDM2 SNPs  were genotyped by Sanger sequencing and the remaining 17 SNPs in TP53 pathway genes were genotyped using the TaqMan allelic discrimination assay. NB tumor DNA was first amplified using illustra GenomiPhi V2 DNA amplification kit  according to the manufacturer's protocol. Genotyping was carried out using 10 ng of amplified genomic DNA per reaction, with 2.5 µL of 2X TaqMan Genotyping Master Mix and 160 nM primers  in a 5 µL reaction volume. All genotypes were determined by endpoint calling on an ABI 7900 real-time PCR instrument . PCR cycling conditions were 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 92°C for 15 s and 60°C for 1 min. Sanger sequencing was carried out by PCR amplification and subsequent sequencing. The primers used for PCR amplification were tagged with universal M13 sequencing tags (underlined): forward primer CACGACGTTGTAAAACGACTGGCTTTGCGGAGGTT and reverse primer CAGGAAACAGCTATGACCTCGGAACGTGTCTGAACTT. The PCR was performed in a 25 µl reaction mix using 20 ng of DNA, 1x KAPA2G buffer, 2.5 mM MgCl2, 0.2 mM dNTP mix, 0.5 µM forward and reverse primers, and 1 U KAPA2G Robust HotStart DNA polymerase. PCR conditions were as follows: initial denaturation at 94°C for 4 min, denaturation at 94°C for 20 s, primer annealing at 68°C for 15 s, and extension at 72°C for 1 min for 12 cycles. This was followed by another 34 cycles: denaturation at 94°C for 30 s, primer annealing at 55°C for 30 s, and extension at 72°C for 30 s. Finally, an extension step at 72°C for 4 min was performed, followed by cooling at 15°C for 1 min. The size of the amplified PCR product was verified using Caliper Labchip GX . The sequencing was done using Sanger DNA sequencing service . The generated data was analyzed using Seqscape software v.2.7 . 18 SNPs were genotyped in 31 NB cell lines (amplified and non-amplified DNA) to ensure the quality of the DNA amplification procedure.SNPs in potentially important TP53 pathway genes were selected from previously reported studies www.primerXL.org). The sequence of the primers can be found in RT-qPCR was performed on duplicate samples using 5 ng of template cDNA, 2.5 µL of 2x Soo Advanced Reaction Mix (Roche), and 0.25 µL of a 5 µM solution of each primer in a 5 µL total reaction volume. The PCR conditions were as follows: 10 min at 95°C, followed by 45 cycles of denaturation (10 s at 95°C), and elongation (45 s at 60°C) using LightCycler480 (Roche). Primers for caspase-8 and the reference genes  were designed using an in-house web tool . The clinical features of the patients are shown in CASP8 SNP D302H was the only SNP that showed an association with worse overall (OS)  and event-free survival (EFS)  in NB patients with MYCN amplification . This additional analysis makes it highly unlikely that different SNP status exists in the tumor compared to the blood of the patients. We performed also multivariate Cox proportional hazard analysis and investigated the effect of CASP8 SNP D302H on the survival of neuroblastoma patients taking into account the different clinical covariates . Our results show that CASP8 SNP D302H is an independent prognostic factor . CASP8 SNP D302H was not correlated to age at diagnosis  and also in an analysis stratified by iagnosis . The sam disease . SimilarCASP8 SNP D302H could affect the expression of caspase 8. We measured caspase 8 mRNA expression in two independent cohorts of NB tumor samples. Cohort 1 included 162 samples treated according to the International Society of Pediatric Oncology Europe Neuroblastoma Group . Cohort 2 included 161 samples from the Neuroblastoma Research Consortium (NRC), which is a collaboration between several European NB research groups. All samples from cohorts 1 and 2 were derived from patients that had been included in the entire cohort of 500 patients analyzed in CASP8 SNP D302H had no effect on caspase 8 mRNA expression levels, regardless of MYCN status or the stage of the disease  TP53 and TP53 pathway genes are associated with cancer risk and clinical outcomes HACE1 (rs4336470) LIN28 (rs17065417) BARD1  LMO1 (rs110419) DUSP12 (rs1027702), DDX1 (rs2619046), IL31RA (rs10055201), and HSD17B12 (rs11037575) MYCN status of 500 NB patients. We did multiple testing correction using Benjamini-Hochberg method and considered only the q-values  less than 0.05 as significant. Our results showed that one SNP, CASP8 SNP D302H (rs1045485) was associated with worse overall and event-free survival in NB patients with MYCN amplification. Of note, we analyzed 41 representative blood samples from the patients in our cohort and observed that all these patients have the same SNP status in their blood as in their tumor (data not shown), indicating that CASP8 SNP D302H is a germline risk factor for NB patients with MYCN amplification. CASP8 SNP D302H is a missense variant located in exon 10 of the CASP8 gene. CASP8 encodes for inactive procaspase 8 that is later activated by dimerization to form caspase 8. Caspase 8 plays an important role in execution of the extrinsic cell death pathway (death receptor-mediated killing) and is involved in several other cell signaling pathways CASP8 is located on 2q33, a region frequently associated with loss of heterozygosity (LOH) in NB MYCN-amplified NB tumors MYCN amplification and reduced caspase 8 expression) are closely related in NB, it has been shown that MYCN overexpression does not affect the methylation status or expression of CASP8CASP8 SNP D302H on caspase 8 mRNA expression in two independent cohorts. Our results showed that CASP8 SNP D302H had no effect on caspase 8 expression; being a non-synonymous SNP, it may therefore have a possible role in the activation of procaspase 8 or in the interaction of caspase 8 with elements that form the death-inducing signaling complex (DISC). CASP8 SNP D302H encodes an amino acid residue that is localized on the external surface of procaspase 8 and that could affect the processing of procaspase 8 or caspase-8 interaction with other apoptosis regulators. However, until this is confirmed, the effect of this SNP on caspase 8 activity remains elusive. The well-known MDM2 SNP309 was also included in our targeted analysis of 21 SNPs. Previous studies reported an association of this SNP with neuroblastoma NB is a heterogeneous disease with a broad clinical spectrum ranging from spontaneous regression and excellent outcome in low-risk patients to widely disseminated disease and poor survival in high-risk patients. The phenomenon of spontaneous NB regression has been attributed to successful delayed apoptosis of tumor cells, whereas progression towards uncontrolled proliferation and tumor formation largely reflects defects in the apoptotic process CASP8 gene, is associated with worse overall survival and event-free survival in NB patients with MYCN amplification.In conclusion our study shows that SNP D302H, a missense SNP in exon 10 of S1 FileSupporting tables. S1 Table, RT-qPCR primers. S2 Table, Comparison of genotyping data on amplified DNA (A) and non-amplified DNA (NA) for 18 SNPs in 31 NB cell lines. S3 Table, Stratified survival analysis of all the SNP. S4 Table, multivariate cox proportional hazards analysis . S5 Table, multivariate proportional hazards analysis . S6 Table, CASP8 SNPD302H and age at diagnosis. S7 Table, CASP8 SNPD302H association with Stage 4 and MYCN status.(DOCX)Click here for additional data file.


Bos indicus) in Brazil, has always raised concerns about inbreeding, which affects conservation of genetic resources and sustainability of this breed. Here, we investigated the distribution of autozygosity levels based on runs of homozygosity (ROH) in a sample of 1,278 Nellore cows, genotyped for over 777,000 SNPs. We found ROH segments larger than 10 Mb in over 70% of the samples, representing signatures most likely related to the recent massive use of few sires. However, the average genome coverage by ROH (>1 Mb) was lower than previously reported for other cattle breeds (4.58%). In spite of 99.98% of the SNPs being included within a ROH in at least one individual, only 19.37% of the markers were encompassed by common ROH, suggesting that the ongoing selection for weight, carcass and reproductive traits in this population is too recent to have produced selection signatures in the form of ROH. Three short-range highly prevalent ROH autosomal hotspots (occurring in over 50% of the samples) were observed, indicating candidate regions most likely under selection since before the foundation of Brazilian Nellore cattle. The putative signatures of selection on chromosomes 4, 7, and 12 may be involved in resistance to infectious diseases and fertility, and should be subject of future investigation.The use of relatively low numbers of sires in cattle breeding programs, particularly on those for carcass and weight traits in Nellore beef cattle ( Bos indicus) is deemed to have mimicked all these triggers of autozygosity, especially considering the increasing use of artificial insemination over the decades. As inbreeding has been incriminated in reduced fitness and reproductive performance in other cattle populations under artificial selection  genotypes to scan individual genomes for contiguous homozygous chromosomal fragments has been proposed as a proxy for the identification of identical-by-descent haplotypes . These animals comprised part of the genomic selection reference population from a commercial breeding program. These dams were born between 1993 and 2008, being under routine genetic evaluation for weight, carcass and reproductive traits by the DeltaGen program, an alliance of Nellore cattle breeders from Brazil. Data filtering was performed using PLINK v1.07 , according to the manufacturer's protocol , and chromosomal segments were declared ROH under the following criteria: 30 or more consecutive homozygous SNPs, a density of at least 1 SNP every 100 kb, gaps of no more than 500 kb between SNPs, and no more than 5 missing genotypes across all individuals. In order to account for genotyping error and avoid underestimation of long ROH  the matrix P, whose elements of column l are equal to 2pl. The diagonal elements of G  represent the relationship of an animal with itself, and thus encapsulate autozygosity information. Following VanRaden et al. . Thus, matrix G was computed using allele frequencies fixed at 0.5.Where n et al. , Gi, i cSNP & Variation Suite v7.6.8, which identifies clusters of contiguous set of SNPs with size > smin, where every SNP has at least nmin samples presenting a run. Clusters were identified based on a fixed minimum cluster size of smin = 0.5 Mb for varying minimum number of samples: 127 (10%), 255 (20%), 319 (25%), and 639 (50%). In order to assess the sensitivity of the algorithm to parameter settings in ROH detection, we repeated the analysis using minimum numbers of 30 or 150 SNPs in a run, maximum gap sizes of 100 kb or 500 kb, and 0 or 2 heterozygous genotypes as variable parameters.Chromosomal segments presenting ROH hotspots were defined as ROH islands or common ROH. In order to identify such genomic regions, we used two different strategies. First, we used the clustering algorithm implemented in FL) following Kim et al. , thus it summarizes the level of local autozygosity in the sample.Alternatively, we calculated locus autozygosity  are shown in Figure FROH  for FROH > 2 Mb and increasing to 827 (64.71%) for FROH > 16 Mb. Under the assumption of the relationship between ROH length and age of autozygosity, these findings show that varying the minimum ROH length in the calculation of FROH can be useful to discriminate animals with recent and remote autozygosity.The distributions of FROH > 1 Mb and Gi, i (r = 0.69) was close to the ones reported by Ferenčaković et al. (FROH > 1 Mb derived from the HD panel and pedigree estimates in Brown Swiss (r = 0.61), Pinzgauer (r = 0.62), and Tyrol Gray (r = 0.75). Similar correlations were observed when the X chromosome was included in the analysis  exhibited 69 ROH > 1 Mb encompassing 465.66 Mb of the total autosomal genome extension covered by markers (2.51 Gb), with a mean ROH length of 6.75 ± 9.20 Mb, and a maximum segment length of 43.79 Mb. The least inbred animal presented 8 ROH > 1 Mb, summing up only 10.72 Mb, with an average length of 1.34 ± 0.46 Mb and a maximum of 2.43 Mb.The minimum, average, median, and maximum autosomal FROH > 1 Mb distribution was 37.5%, indicating moderate variability in autozygosity levels in this sample. In spite of the average genome coverage by ROH of 4.58% may seem to indicate moderate inbreeding levels for classical standards, it has to be considered that incomplete pedigree data usually fails to capture remote inbreeding, so that traditional inbreeding estimates based on pedigree are only comparable with FROH calculated over large ROH lengths, which in the present study were close to 0%.The coefficient of variation (here denoted as the ratio of the standard deviation to the mean) of the FROH > 1 Mb of 15.1%, 6.2%, and 6.6% for samples of the Bos taurus breeds Brown Swiss, Pinzgauer, and Tyrol Gray, respectively. Also, the effective population size estimated for this Nellore sample was approximately 362 animals , which is consistent with the low genome average LD reported by other studies  cattle imported since the late 15th century after the discovery of America  contained a ROH. The common ROH on the X chromosome are difficult to be discussed as they span several millions of bases, encompassing hundreds of genes and making functional explorations unfeasible. Besides, the assembly status of X chromosome is poorer than the autosomal ones. Hence, we focused on the three autosomal regions on chromosomes 4, 7, and 12. The three regions were relatively short, ranging from 0.73 to 1.43 Mb. For this range of ROH length, the expected number of generations since the common ancestor is estimated between 35 and 69 (Howrigan et al., The calculations of locus autozygosity were consistent with the cluster analysis using 150 SNPs and 2 heterozygous genotypes, regardless of permitted gap size Figure . Seven dBos taurus and indicus breeds via FST analysis (Bovine HapMap Consortium, Trypanosoma congolense (Hanotte et al., SPOCK1, a candidate gene for puberty both in humans (Liu et al., The most autozygous locus was found at chromosome 7:51605639-53035752. This region was previously reported in genome-wide scans for signatures of selection in cattle through the comparison of Bos taurus and Bos indicus cattle (Gautier et al., BRCA2, involved in Fanconi anemia in humans (Howlett et al., The common ROH at 12:28433881-29743057 identified in the present study also overlaps a common ROH hotspot (Sölkner et al., We used high-density SNP genotypes to successfully characterize autozygosity in Nellore cows under artificial selection for reproductive, carcass and weight traits. We have shown that, although the massive use of relatively few sires and artificial insemination has generated long stretches of homozygous haplotypes in the genomes of over 70% of these animals, inbreeding levels were considerably low in this population. We also found few genomic regions with high homozygosity across individuals, suggesting that the ongoing selection for reproductive, weight and carcass traits in this population is not very intensive or too recent to have left selection signatures in the form of ROH. Furthermore, the current common breeding practices of avoiding inbreeding in the mating schemes are antagonistic to additive trait selection, making it hard to maintain ROH signatures in the herds. The three candidate regions under selection identified herein were likely to be contributions from remote ancestors, predating the foundation of the Nellore breeding programs. The selective pressure effects and the genes involved in these regions should be subject of future investigation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genotype imputation has become a standard practice in modern genetic research to increase genome coverage and improve the accuracy of genomic selection (GS) and genome-wide association studies (GWAS). We assessed accuracies of imputing 60K genotype data from lower density single nucleotide polymorphism (SNP) panels using a small set of the most common sires in a population of 2140 white layer chickens. Several factors affecting imputation accuracy were investigated, including the size of the reference population, the level of the relationship between the reference and validation populations, and minor allele frequency (MAF) of the SNP being imputed.22 and Ref62). Animal-specific imputation accuracy corrected for gene content was moderate on average (~ 0.80) in most scenarios and low in the 3K to 60K scenario. Maximum average accuracies were 0.90 and 0.93 for the most favourable scenario for Ref22 and Ref62 respectively, when SNPs were masked independent of their MAF. SNPs with low MAF were more difficult to impute, and the larger reference population considerably improved the imputation accuracy for these rare SNPs. When Ref22 was used for imputation, the average imputation accuracy decreased by 0.04 when validation population was two instead of one generation away from the reference and increased again by 0.05 when validation was three generations away. Selecting the reference animals from the most common sires, compared with random animals from the population, considerably improved imputation accuracy for low MAF SNPs, but gave only limited improvement for other MAF classes. The allelic R2 measure from Beagle software was found to be a good predictor of imputation reliability (correlation ~ 0.8) when the density of validation panel was very low (3K) and the MAF of the SNP and the size of the reference population were not extremely small.The accuracy of imputation was assessed with different scenarios using 22 and 62 carefully selected reference animals  contains supplementary material, which is available to authorized users. Using dense single nucleotide polymorphism (SNP) panels, genomic selection (GS) and genome-wide association studies (GWAS) have become common in animal and plant genomic breeding programs. Both GS and GWAS exploit linkage disequilibrium (LD) between SNPs and causative mutations. Increasing the density of SNP panels is therefore expected to contribute to improved accuracies of genomic prediction and GWAS , 2. HoweAnimals may be genotyped for different SNP chips due to the expansion of available genotyping technologies, for design reasons, or due to the coexistence of several genotyping products . Thus faSeveral factors influence the accuracy of imputation including the genetic relationship between the animals in the reference and validation populations , the siz.g. LD and effective population size (Ne)) differ substantially across studies. In general, high imputation accuracies were found in broiler chickens (ranging from 0.94 to 0.99) , [0.1-0.2], [0.2-0.3], 0.3-0.4], and [0.4-0.5] , computed as the correlation between the true genotypes  and the imputed genotype (the most likely genotype minus the mean gene content) as suggested by Mulder et al. [1A1, A1A2, and A2A2, respectively. We then compared the square of SNP-specific imputation accuracy (“true” imputation reliability) with allelic R2 generated by Beagle. Allelic R2 is the squared correlation between the allele dosage of the most likely imputed genotype and the allele dosage of the true genotype. The estimated A2-allele dosage was obtained from the imputed posterior genotype probabilities as: 0 * P(A1A1) + 1 * P(A1A2) + 2 * P(A2A2) [corrected were given and discussed throughout this paper as the main measure of imputation accuracy for different scenarios. Allelic R2 was compared with true imputation reliability in a separate section  . The restion see .Ne was estimated from the observed LD values (r2) between SNPs. The r2 was related to Ne based on Sved’s equation [equation :\documenc, in Morgan units) was obtained by converting the physical distances (in base-pairs) to genetic distances (in Morgan) using the recombination rate values as reported by International Chicken Genome Sequencing Consortium (ICGSC) [Ne has been obtained under the assumption of constant population size [The genetic distance between SNPs ( (ICGSC) . This esion size .Blood samples were collected as part of routine data and sample collection in a commercial breeding program. According to the local legislation, it was not needed to have permission from the ethics committee.


CLU), rs6691117 (CR1), rs4938933 (MS4A), rs9349407 (CD2AP), rs1160985 (TOMM40), rs4945261 (GAB2) and rs5984894 (PCDH11X); Protective SNPs consisted of rs744373 (BIN1), rs1562990 (MS4A), rs597668 (EXOC3L2), rs9271192 (HLA-DRB5/DRB1), rs157581 and rs11556505 (TOMM40). Among positive SNPs presented above, we found the interaction between rs4938933 (risk) and rs1562990 (protective) in MS4A weakened their each effect for LOAD; for three significant SNPs in TOMM40, their cumulative interaction induced the two protective SNPs effects lost and made the risk SNP effect aggravate for LOAD. Finally, we found rs6656401-rs3865444 (CR1-CD33) pairs were significantly associated with decreasing LOAD risk, while rs28834970-rs6656401 (PTK2B-CR1), and rs28834970-rs6656401 (PTK2B-CD33) were associated with increasing LOAD risk. In a word, our study indicates that SNP-SNP interaction existed in the same gene or cross different genes, which could weaken or aggravate their initial single effects for LOAD.Recently, a number of single nucleotide polymorphisms (SNPs) were identified to be associated with late-onset Alzheimer disease (LOAD) through genome-wide association study data. Identification of SNP-SNP interaction played an important role in better understanding genetic basis of LOAD. In this study, fifty-eight SNPs were screened in a cohort of 229 LOAD cases and 318 controls from mainland China, and their interaction was evaluated by a series of analysis methods. Seven risk SNPs and six protective SNPs were identified to be associated with LOAD. Risk SNPs included rs9331888 ( PSEN1), presenilin 2 (PSEN2) and amyloid precursor protein (APP) genes [APOEε4 allele [Alzheimer’s disease (AD) is a clinically complex neurodegenerative disorder, affecting up to 81.1 million people worldwide . AccordiP) genes . As the 4 allele .APOE was limited to account for approximately 50% of individuals with LOAD [TREM2, rs145999145 in PLD3 and rs137875858 in UNC5C, which were recently recognized as risk variants for LOAD [APOE CLU, ABCA7, SORL1; (2) Immune response: CR1, CD33, MS4A, EPHA1, CLU, ABCA7, HLA-DRB5/DRB1 and INPP5D; (3) Endocytosis: BIN1, PICALM, CD2AP, EPHA1, RIN3, SORL1, MEF2C and MADD [However, ith LOAD , indicatith LOAD ,8. The ffor LOAD . With reand MADD –16.However, in most cases, the identified single nucleotide polymorphisms (SNPs) have small or moderate effect sizes, and the proportion of heritability explained is quite modest. Like other complicated diseases, a polygenic analysis has been suggested to explain genetic contribution to the pathogenesis of the majority of LOAD cases. We hypothesized that SNPs might interact in subtle ways that led to substantially greater effects than the effect of any single SNP. Therefore, in this study, we adopted a series of statistical analysis methods to evaluate SNP-SNP interactions in a Chinese cohort consisting of 547 individuals. It was found that SNP-SNP interaction could weak or aggravate their single effect for LOAD. In addition, even if some variants had no effect on LOAD, their interactions could become significant effects associated with LOAD.A total of 547 subjects were recruited in this study, including 229 patients with LOAD  and 318 controls . All patients diagnosed as probable or definite AD met with the NINCDS-ADRDA criteria, and they were collected from the department of neurology, Xiangya Hospital. Analyses also included all unaffected individuals of matched geographical ancestry as healthy controls. The study was approved by the Ethics Committee of Xiangya Hospital, Central South University in China . Written informed consent was obtained from all subjects .Genomic DNA was extracted from peripheral blood leukocytes of all subjects. We used a fluorometer to evaluate the quality and quantity of DNA, which were normalized to 50 ng/μl for sequencing. APOE genotype was identified through polymerase chain reaction (PCR), and the primer information was listed in Table A in http://www.alzgene.org/) [APOE genotypes (rs7412 and rs429358) were also screened, and the result of this method was consistent with that of Sanger sequencing, The 56 SNPs involved BIN1 , CLU , ABCA7 , CR1 , PICALM , MS4A , CD33 , CD2AP , EPHA1 , TOMM40 , LRAT (rs727153), TNK1 (rs1554948), ARID5B (rs2588969), GAB2 , PCDH11X (rs5984894) SORL1 PTK2B (rs28834970), ATP7B (rs1801243), EXOC3L2 (rs597668), SLC24A4-RIN3 (rs10498633), ZCWPW1 (rs1476679), CELF1 (rs10838725), MEF2C (rs190982), NME8 (rs2718058), HLA-DRB5/DRB1 (rs9271192), FERMT2 (rs17125944), ECE1 (rs213045) and CASS4 (rs7274581) (Table A in We then selected a list of AD candidate genes based on published GWAS paper and Alzgene.org (ne.org/) ,12–16. THardy-Weinberg equilibrium was analyzed using SHEsis software . LogistiFinally, Lasso-multiple regression (LMR) method was used to identify SNP-SNP pairs’ interaction. The calculation involved two steps: In step one, the Lasso was used to select candidate SNPs with all pair-wise interaction terms in the regression model, which was conducted under the R package glnetmi. Ten-fold cross validation was performed in this step. Interaction terms were ranked by the absolute value of the coefficients from high to low. Thus, it could achieve the aim of feature selection. In the second step, multiple linear regression was performed on the candidate SNPs selected in the first step to evaluate the pure interaction effect. The variable significance was measured by the p-value of cross-product term. Stepwise AIC selection was applied on the multiple linear regression to obtain an optimal regression model –20.APOE alleles and genotypes of all subjects were listed in The frequencies of CLU, rs6691117-A in CR1, rs4938933-C in MS4A, rs9349407-C in CD2AP, rs1160985-C in TOMM40, rs4945261-A in GAB2, rs5984894-A in PCDH11X, and the protective SNPs involved rs744373-T in BIN1, rs1562990-C in MS4A, rs597668-T in EXOC3L2, rs9271192-C in HLA-DRB5/DRB1, rs157581-G and rs11556505-T in TOMM40  and TOMM40 . With regard to two SNPs in MS4A, a total of four subhaplotypes were grouped, while no significant difference was identified between cases and controls  was significantly susceptible to LOAD risk. (It was noted that both risk and protective SNPs were identified in controls . In addiAD risk. CR1-CD33), rs28834970-rs6656401 (PTK2B-CR1), rs28834970- rs6656401 (PTK2B-CD33). The pair (rs6656401-rs3865444) with combination  reduced the LOAD risk by 0.10 compared to , while the other two SNP pairs had an increase risk for LOAD. The pair (rs28834970-rs6656401) with genotype  increased the LOAD risk by 10.90 compared to  and genotype  on (rs28834970-rs3865444) had an increased risk by 1.54 compared to .We further used LMR method to analyze SNP-SNP pairs’ interaction: in the first step, 20 SNPs were selected as candidates from 56 SNPs  in the future.MS4A, although they had independent reverse effect on LOAD, both of their respective effects were loss when being considered together. With regard to rs157581, rs11556505 and rs1160985 in TOMM40, although they were involved in decline or increasing risk for LOAD, the only significant subhaplotype was G-T-C that could increase LOAD risk, which indicates the SNPs effect on LOAD could be influenced by other SNPs in the same gene.Another goal for current study was to identify whether SNP-SNP interactions existed in the pathogenesis of LOAD. We first analyzed the interactions between positive SNPs in the same gene. For rs4938933 and rs1562990 in PTK2B-CR1 gene should be concerned due to the high Odds Ratio score (10.90) for LOAD risk, suggesting these two genes may have an strong interaction effect in the pathogenesis pathway. Previous study has identified two significant SNP-pairs for LOAD risk: rs386544-rs670139 from CLU-MS4A4E and rs11136000-rs670139 from CD33-MS4A4E. Taken these results together presented above, we offered evidence that SNP-SNP interaction played a pivotal role in LOAD susceptibility. As we know, AD is a complicated disease with gene-environment interaction. Therefore, although three pairs PTK2B-CR1, PTK2B-CD33, CD33-CR1 interaction appeared to have strong effects on LOAD, there may be unmeasured environment factors participating in these interaction. In current study, three new SNP-SNP pairs left their inter-molecular mechanism unsolved. CD33 is a transmembrane protein which has been implicated as a negative regulator of myeloid cells. CR1 is found on myeloid cells as well. Both of them are key molecules in inflammatory cells, such as microglia [CR1 and CD33 variants to uncover their functionally link susceptibility loci [PTK2B, which was one of recent identified new susceptible gene in 74046 individuals from diverse ethnicities [PTK2B was located on chromosome 8 near the CLU gene, we did not find a SNP-SNP interaction in these two genes. However, we found PTK2B had a strong interaction with NEDD9, which was important for lymphocyte signaling and migration and played a role in T cell-mediated inflammation [PTK2B might participate in immune inflammation through regulating NEDD9 protein function, which was involved in CR1 and CD33.In addition, we further used LMR analysis method to identify three SNP pairs significantly associated with LOAD risk. However, the significant effect was not to be found in any of them during the previous univariate analysis. Among them, one pair rs28834970-rs6656401 from nicities , it was In summary, we examined 56 candidate SNPs in a cohort of Chinese subjects using a series of analysis methods. We identified seven risk and six protective variants for LOAD. With regard to SNP-SNP interaction, firstly, we found out some SNPs in the same gene could be influenced by each other to weaken or aggravate their single effect. Secondly, although some variants had a weak effect, a strong interaction effect was found after interacted with each other.S1 File(DOCX)Click here for additional data file.S2 File(TXT)Click here for additional data file.S3 FileA multivariable logistic analysis in seven risk SNPs. Table B, A multivariable logistic regression analysis in the six protective SNPs.(DOCX)Click here for additional data file.S4 File20 Candidate SNPs were selected in the first step using LMR method. Table B, Multiple linear regression model after stepwise selection.(DOCX)Click here for additional data file.


Improving meat quality is a high priority for the pork industry to satisfy consumers’ preferences. GWAS have become a state-of-the-art approach to genetically improve economically important traits. However, GWAS focused on pork quality are still relatively rare.Sus scrofa chromosome (SSC) 1, SSC5, SSC9, SSC16 and SSCX, were associated with meat colour. However, the most promising region was detected on SSC15 spanning 133–134 Mb which explained 3.51% - 17.06% of genetic variance for five measurements of pH and colour. Three SNPs  in very strong LD were considered most likely to account for the effects in this region. ASGA0070625 is located in intron 2 of ZNF142, and the other two markers are close to PRKAG3, STK36, TTLL7 and CDK5R2. After fitting MARC0083357 (the closest SNP to PRKAG3) as a fixed factor, six SNPs still remained significant for at least one trait. Four of them are intragenic with ARPC2, TMBIM1, NRAMP1 and VIL1, while the remaining two are close to RUFY4 and CDK5R2. The gene network constructed demonstrated strong connections of these genes with two major hubs of PRKAG3 and UBC in the super-pathways of cell-to-cell signaling and interaction, cellular function and maintenance. All these pathways play important roles in maintaining the integral architecture and functionality of muscle cells facing the dramatic changes that occur after exsanguination, which is in agreement with the GWAS results found in this study.Six genomic regions were shown to affect loin pH and Minolta colour a* and b* on both loin and ham through GWAS in 1943 crossbred commercial pigs. Five of them, located on PRKAG3 that have an important effect on pH and colour. The potential markers and their interactions with PRKAG3 require further investigationThere may be other markers and/or genes in this region besides The online version of this article (doi:10.1186/s12863-015-0192-1) contains supplementary material, which is available to authorized users. This is vital for the swine industry to sustain profitability and enhance its competiveness in terms of both swine genetics and sales of pork (including export markets). Minolta colour and pH are two common industry measures of pork quality that are directly correlated with glycogen and glycolytic potential . Colour Subunit) ,4,5, havMost of the QTLs identified were detected using linkage mapping and cover large regions of the genome. With the availability of high-density SNP chips and genome wide analysis methodology, GWAS have been increasingly used to identify more precisely the genomic regions and markers associated with quantitative traits, and have begun to be used in dairy cattle and poultry breeding programs as reviewed by Goddard and Hayes . RecentlTherefore, the aims of this study were to detect genomic regions and identify potential candidate genes and markers affecting pork pH and colour by combining GWAS and post-GWAS bioinformatics analysis. The results will contribute useful information for marker based genomic improvement of these traits. Knowledge of the gene networks will provide new insights for the biological mechanisms underlying these traits.Population stratification with Identity-by-State (IBS) showed that all animals from the two commercial populations could be classified into two clusters, which correspond to the populations from which the individuals were derived. Therefore, population was fitted as a fixed effect to optimize the association analysis model.quadriceps femoris on the ham, colour b* measured on the thawed loin (TMCOL b*), pH 24 h post-mortem measured on fresh loin (FpH24) and pH measured on thawed loin (TMpH), respectively. Other genomic regions on SSC1, SSC5, SSC9, SSC16 and SSCX also explained a relatively large proportion of genetic variance of colour. For example, the region on SSC5 (5_107) explained 4.44% of the genetic variance for QFCOL a*, and each of the others explained about 1% of the total genetic variance for QFCOL a* and TMCOL b*.Genomic regions affecting both fresh and thawed loin pH, colour a* and b* on fresh ham, and colour b* on thawed loin muscle were detected by Bayes B 1 Mb window analysis. Accordingly, a larger proportion of genetic variance explained by the region indicates a stronger effect of the corresponding region on the target trait. The genomic regions that explained more than 1% of the total genetic variance for the traits studied are shown in Table PRKAG3 which was previously reported as a candidate gene associated with meat quality. The LD between block 2 and SNP27 is also strong.The region of SSC15_133 spans 898,069 base pairs and includes a total of 28 SNPs on the 60K SNP panel. Two SNPs (1 and 17) with low genotype call rates were filtered out from the analysis. An overview of the distribution of all the SNPs and their relationships is shown in the LD haplotype map in Figure For further study, window (15_133) GEBVs for the traits were summed based on the marker effects of the 26 SNPs, and then regressed on the genotype covariate for each SNP (0/1/2). Three SNPs  with almost complete LD captured an average of 98.2%, 99.2% and 99.0% variances of window GEBVs. These results indicated that any of the three SNPs could be affecting the traits. To further analyze these markers, SNP21 (MARC0083357) was chosen as a fixed effect in the animal model (ASREML) to estimate its allele substitution effect. The results  of the haplotypes for other traits are shown in Figure For QFCOL a*  and the other is close to CDK5R2 (SNP27). For the other six SNPs, four are intragenic with ARPC2, TMBIM1, NRAMP1 and VIL1, and two are close to RUFY4 and CDK5R2.A total of 29 genes including 17 of unknown function were identified in a 1 Mb region on SSC15. The nine significant SNPs  were found to be intragenic or close to ten genes as shown in Table NRAMP1 was replaced by the human orthologues of SLC11A1 during the process. Except for RUFY4, all the genes (coloured by pink and red) were strongly connected through the functional pathways which are involved in cell-to-cell signaling and interaction, cellular function and maintenance. Usually the genes with darker colour indicate they have more contributions to the pathway and have stronger connection to the network. For example, ZNF142 and PRKAG3 were coloured dark red indicating the greatest contribution to these pathways compared to the other genes that were coloured light red and pink. Gene annotation  was detected in this region where MARC0017490 was the most promising marker (RSQ = 0.975) for QFCOL a*. Three genes were found for SSC9_147 where three SNPs with high LD (r2 = 0.63 - 0.89) contributed the major effect (each of them explained more than 87% variance of the window GEBV). Only one gene was identified for the region on SSC16 and the most promising SNP was ALGA0091417. For the region on SSCX, three genes were identified and ASGA0083984 was the best marker explaining almost all the variance of window GEBV (RSQ = 0.998).Candidate genes in the regions identified on other chromosomes are presented in Table PRKAG3, located within this region, can be considered as one of the few major genes known to affect meat pH and colour. Four missense substitutions  in this gene were found to significantly affect meat quality [PRKAG3 to the effect of the region on the traits remains unknown. Therefore, it is essential to extend the analysis of the regions flanking PRKAG3 and investigate the genomic architecture of this region affecting meat pH and colour.In this study, a 1 Mb region 133 – 134 Mb) on SSC15 explained the majority of the genetic variance for loin pH, loin and ham colour a* and b*. Previous linkage mapping studies have reported that SSC15 is enriched for QTLs affecting meat pH and colour. These include five QTLs between 56 cM to 119 cM for ham and loin pH 24 h post-mortem  as well  Mb on SSFive other regions, on SSC1, SSC5, SSC9, SSC16 and SSCX, were also found to capture a relatively large proportion of genetic variance for meat colour in this study, indicating their important effects on these traits. However, the limited gene and map information in these regions restricted further analysis of these QTLs. For example, thirteen genes including 8 of unknown function were identified in the region on SSC1. Only a few (1 - 3) genes were detected in the other regions and none of them harbours SNPs on the 60K SNP panel. Previous GWAS studies in the pig have reported a few QTLs besides SSC15 to affect meat quality in different pig breeds/populations. Nonneman et al.  identifiPRKAG3 in the 60K panel, showed significant effects on all of the traits studied. PRKAG3 was previously reported as a candidate gene affecting meat quality. The most important substitution (R200Q) in the gene caused a 70% increase in muscle glycogen in RN− homozygous and heterozygous pigs that resulted in lower ultimate pH and water-holding capacity [PRKAG3 also showed significant effects on pH, and Minolta L* and b* for both loin and ham [PRKAG3 detected several novel mutations by sequencing the coding and promoter regions, and the haplotype (g.-157C - g.-58A - 24E - 199I) was found to have a positive effect on meat quality in pigs. In our study, the most significant effects of SSC15_133 region reported here are on a*, b* and pH, but not lightness (L*)  in very strong LD captured nearly all of the genetic variance of this region. SNP21 (MARC0083357), one of the closest SNPs to capacity . Another and ham  on PRKAGPRKAG3. The three SNPs  might be in high LD with some of the mutations in PRKAG3 that affect pH and colour. SNP20 (ASGA0070625), in complete LD with SNP21 in this study, was previously reported to affect meat pH in Yorkshire (n = 220) and Finnish Landrace (n = 230) [PRKAG3, but was in complete LD with some novel mutations such as g.-157C > G, g.-58A > G and K24E. These results indicate that there may be other polymorphisms and/or genes associated with variation in meat quality traits. Additional genes in this region may contribute to variation of meat quality independently or by interacting with PRKAG3. Three other known genes  are much closer to SNP21 than PRKAG3, and furthermore, SNP20 (ASGA0070625) is located in the intron 2 of ZNF142 (zinc finger protein 142). ZNF142 was reported as a putative candidate gene for both developmental and malignant disorders in human [ZNF142 is involved in calcium ion binding and cell membrane integrity. The STK36 (Serine/Threonine Kinase 36) gene product is related to the hedgehog signaling pathway and activation of cAMP-dependent PKA. TTLL7 is Member 7 of the Tubulin Tyrosine Ligase-Like Family. SNP27 (MARC0039273) is close to CDK5R2 ), a neuron-specific activator of CDK5 kinase which is associated with cyclin-dependent kinase activating proteins. All these functions are associated with signal transduction, energy supply or cellular maintenance, which may play important roles on the activity of muscle cells post-mortem.The most likely hypothesis is that the main effect of the QTL detected on SSC15 is due to the effect of polymorphism in n = 230) . HoweverARPC2, TMBIM1, NRAMP1 and VIL1, and two are close to RUFY4 and CDK5R2. ARPC2  encodes one of seven subunits of the human Arp2/3 protein complex, which has been implicated in the control of actin polymerization in cells. Actin polymerization and actin/myosin interaction are strongly related to the toughening of muscle during the first 24 h post-mortem [NRAMP1  in human and other species is SLC11A1, which is a member of the solute carrier family 11 and encodes a multi-pass membrane protein that regulates intracellular pathogen proliferation and macrophage inflammatory responses [VIL1 (Villin 1) encodes a member of a family of calcium-regulated actin-binding proteins, it is a major actin-modifying protein associated with the microvillar actin filaments, and regulates epithelial cell morphology, actin reorganization, and cell motility through the functions in the capping, severing, and bundling of actin filaments [RUFY4 (RUN and FYVE domain containing 4) is affiliated with the lncRNA class of proteins with roles in metal ion binding. Therefore, the main functions of these genes are related to membrane components and functionality, transporter activities and calcium ion binding, which play important roles in muscle activity related to structural integrity. These may therefore be involved in the dramatic micro-environmental changes that occur post-mortem [When SNP21 was fitted in the model, another 6 SNPs in this region were still significant for at least one of the studied traits. When the phenotypic LSMs were compared among the different haplotypes constructed with SNP21 and these SNPs, the differences increased compared with the difference among the three genotypes of SNP21. These results indicate that the other SNPs in this region contributed some effect on the traits, and these effects might be due to other genes in this region. Four of the six SNPs are intragenic with t-mortem , consequesponses . VIL1  which is an important energy-sensing enzyme that monitors cellular energy status and responses to cellular metabolic stresses. Activated AMPK is expected to inhibit glycogen synthesis and stimulate glycogen degradation. Studies in pig suggested that PRKAG3 may play a key role in the regulation of energy metabolism in skeletal muscle [UBC (ubiquitin C). The important biological processes associated with UBC include activation of mitogen-activated protein kinase, cellular response to hypoxia, energy homeostasis and ion transmembrane transport, which are strongly associated with the muscle cell activities that are likely to be impacted post-mortem. These two major hubs are linked by APP (amyloid beta (A4) precursor protein) through AMPK subunits (e.g. PRKAB1 and PRKAB2) and kinesin (e.g. KLC1 and KLC2), also through some mediators associated with energy homeostasis like Ca2+ and ATP. These findings indicate that the genes identified in this region of SSC15 have strong interactions with PRKAG3 and UBC. Based on their annotations, they may work together to impact meat quality through processes related to the regulation of actin filament polymerization, protein/ion/ATP binding, kinase activities and maintaining the cytoskeleton  with almost complete LD were the markers most strongly associated with the effect in this region and could be considered for marker assisted selection for meat quality improvement. An additional six SNPs also showed significant effects. Ten known genes have been identified to contain or be close to these markers, including the previously reported candidate gene All animal procedures related to this project were reviewed and approved by the Canada Animal Care and Use Committee.ad libitum and harvested from 2010 to 2012. All animals were sent to the same processing plant when the body weight was close to 115 kg. Carcasses were processed within 24 h post-mortem and fresh meat measurements taken at that time, and pork loins (4th - 7th ribs) were harvested from each pig, vacuum packaged, and chilled (2 - 4°C) within 24 hours after exsanguination. All the packaged loins in the same batch were simultaneously frozen (−20°C) within 24 hours after exsanguination and maintained frozen until meat quality measurement. Prior to testing, the pork loins were thawed for 72 hours at 4°C. All samples were treated in the same way.A total of 1943 crossbred commercial pigs from two populations (961 samples from Hypor and 982 samples from Genesus) were obtained for this study. All animals were from a typical Canadian three-way cross consisting of a Duroc sire line on an F1 hybrid female (Landrace * Large White). Pigs in both populations were fed longissimus dorsi muscle (FMCOL) and subcutaneous fat above the longissimus dorsi muscle (FCOL) on the fresh loin, and on the muscle of gluteus medius (GMCOL), quadriceps femoris (QFCOL) and iliopsoas (ICOL) on the fresh ham face. Thawed muscle pH (TMpH) and colours  were measured on the thawed loin muscle. pH was measured using a portable pH meter  equipped with a glass electrode  calibrated at room temperature using standards of pH 4.01 and 7.01. The pH probe was inserted about 5 cm into the centre of the loin three times through a small slit in three different locations within 2.5 cm from the posterior of the longissimus loin. The average was used as the final value for the analysis. Minolta colours were taken from three different locations on the surface of the muscle, and the average value was used for the final analysis. All the colour assessment was made using the colour system values specified by the Commission Internationale de L’eclairage (CIE) L* (lightness), a* (redness) and b* (yellowness), using a Konica Minolta Chroma-meter CR-400 . Other traits including drip loss on both fresh (FMDL) and thawed (TMDL) loin muscle, cooking loss (CL) and shear force (SF) on thawed muscle were also measured in this project. The details of the measurement were described previously [Fresh meat pH 24 h post-mortem (FpH24) was measured at two different locations on the surface of the loin muscle at the 10th rib. The average was used for the final statistical analysis. Minolta colours L*, a* and b* were measured at 24 h after exsanguination on the eviously .2 by proc allele program in SAS 9.3. The haplotype block pattern was built using haploView software [Population stratification was analyzed using an identical-by-state (IBS) distance clustering method using the PLINK program . Linkagesoftware .Genomic DNA was isolated from tissue samples following the DNA Extraction instruction manual . Genotyping was conducted by Delta Genomics  using Illumina PorcineSNP60 V2 Genotyping Beadchip according to the Illumina Infinium Assay .Quality control for genotypes was performed with the criteria of genotyping call rate ≥ 95%, Chi-square of Hardy-Weinberg equilibrium test < 600, and minor allele frequency (MAF) ≥ 5%. After filtering, 1943 individuals and 42456 SNPs remained in the model for the final analysis.SNP positions were updated according to the newest release from Ensembl (Sscrofa 10.2 genome version). Map information was adjusted based on the LD decay for each chromosome. Only the SNPs with reliable positions were used in this study. The missing genotypes were imputed by Fimpute v2.2 .yijkl is phenotypic observation, μ is overall mean, si is the effect of the ith sex , gj is the effect of the jth contemporary group (j = 1 - 57 for Genesus and 58 - 103 for Hypor), rk is effect of the kth room (k =1 - 10 for Hypor), pl is the effect of the lth pen (l = 1 - 13 for Hypor), and eijkl is the random residual error Traits of TMpH and FMDL did not exhibit normal distribution and were transformed using cosine and natural logarithm functions, respectively. All the records were adjusted within population using GLM procedure of SAS 9.3. A contemporary group consisting of slaughter batch, year and group during the test (~70 – 115 kg) was treated as a fixed effect in the model. There were 57 contemporary groups for Genesus and 46 for Hypor. Fixed effects of sex and contemporary group were fitted in the model below. For the Hypor population, the room and pen are fitted in the model as well, as they are available and have significant effect on the traits. The interactions among these factors were not significant and ignored in the model.y = vector of adjusted phenotypic observations, X = incidence matrix relating fixed factors to phenotypes, b = vector of fixed effects (mean and population), zj = vector of the genotype covariate for SNP j (j = 1 to k) based on the number of B alleles using Illumina’s  genotype calling , αj = allele substitution effect for SNP j, and δj = indicator for whether SNP j was included (δj = 1) or excluded (δj = 0) in the model for a given iteration of the Markov chain Monte Carlo (MCMC). A total of 50 000 iterations were run for each analysis, with the first 5000 iterations discarded as burn-in. The probability of δi = 0 was set equal to π = 0.99. The Bayesian model was implemented using method Bayes B. Genomic regions associated with traits were identified using 1 Mb, non-overlapping windows using Ensembl build 10.2 of the swine genome.The adjusted phenotype for each population were combined together and considered as a new phenotype for the genome-wide association. Population (Hypor and Genesus) was fitted as a fixed effect in the model. The marker effects were estimated by fitting all SNPs simultaneously using Bayesian methods as follow, as implemented in the online software GenSel.Y = Xb + Za + e, where Y is the vector of adjusted phenotypes, b is the vector of fixed effects including mean, population and fitted SNPs, a is the random polygenic effects with G is the relationship matrix that was constructed based on pedigree information, and e is the vector of residual errors with a distribution of I is the identity matrix and X and Z are the incidence matrices associated with b and a.Further analysis on the SNPs within a 1 Mb window was implemented by a mixed animal model in ASREML . The modWindow genomic estimated breeding value (GEBV) was calculated by sum of the marker effects of the SNPs within the window. And then the window GEBV was regressed on each SNP variant (0/1/2) within that window. The R-squared (RSQ) for the regression model, defined as the percentage of the GEBV variance explained by the SNP, was used to detect the most likely SNP in that window. The marker effect for the most likely SNP was estimated by fitting the SNP as fixed factor in the above animal model in ASREML. Then each of the remaining SNP was included one at a time in the model to test their significance after accounting for the effect of the most likely SNP. Finally, all the significant SNPs were simultaneously fitted in the model to retest their significance for the traits. Only the significant SNPs from the multiple marker association were used to construct the haplotypes. The least squares mean (LSMs) of the phenotype for each haplotype were estimated and contrasted by GLM procedure in SAS9.3. The significant threshold was set at P < 0.05.http://www.ensembl.org/biomart/martview). Gene networks were explored by the online software Ingenuity Pathways Analysis (IPA) (http://www.ingenuity.com/products/ipa). Human genes were used as background in pathway and gene network investigation, because some genes have not been characterized in pigs and translational gene aspects are of high interest. The functions and related information about candidate genes were summarized using the database of GENECARDS the human gene compendium (http://www.genecards.org/).Candidate gene identification and functional annotation for the significant SNPs were obtained using Ensembl annotation of Sscrofa 10.2 genome version (The original data sets supporting the results of this article are available. They are not public, but are able to be accessed by request for result verification only.


The closure positive shift (CPS) event related potential (ERP) is commonly used as a neural measure of phrase boundary perception in speech. The present study investigated whether the CPS was elicited by acoustic cues at phrase boundaries in English. ERPs were recorded when participants listened passively to sentences with either early or late phrase boundaries.The closure positive shift (CPS) ERP was elicited at both early and late phrase boundaries. However, the latency, amplitude, and scalp distribution of these passive CPS ERPs in English sentences differed to active CPS ERPs measured in non-English sentences in previous studies.These results show that acoustic cues at the phrase boundaries in English are sufficient to elicit the CPS, and suggest that different processes might be involved in the generation of the CPS in active and passive conditions. Most adults understand the speech of other people with little apparent effort. This is an amazing feat given the complexity of the speech signal, which contains a continuous stream of words that are seldom separated by perceptible gaps. To make the meaning of a word-stream clearer to a listener, the speaker groups the words into separate meaningful phrases . In EnglRob or Sandra and Mary will come” has different meanings depending on whether a phrase boundary is placed after “Rob” or after “Sandra”. Second, phrase boundaries can define grammatical boundaries in speech. For example, in the sentence “The little girl over there, is the one who likes snails”, a careful speaker of the language will insert a phrase boundary between the words “there” and “is”. By dividing the sentence into two meaningful chunks - a noun phrase and a verb phrase – the speaker clarifies the syntax of the language , lower in intensity  and was followed by a pause . The pre-boundary syllable was also characterised by a rise in pitch  for 146 ms (SD = 111 ms) on average. The acoustic analysis confirmed that the phrase boundary was created by duration, intensity and pitch cues.Acoustic analysis data of the pre-boundary syllable was compared with the same syllable in the non-phrase boundary condition. The comparison revealed that the syllable at a phrase boundary was longer in duration [The reporters were frustrated by the politician’s answers to their questionsThe tourists were extremely dispirited before they reached the HimalayasNinety-six filler sentences were created that did not include a phrase boundary. The filler sentences were approximately the same length as the experimental sentences. They were included to prevent any ERP effects resulting from subjects habituating to the same type of sentence structures. The ERP responses to these filler sentences were not analysed. Two examples of filler sentences are given below.The experimental and filler sentences were presented in pseudo-random order, with the constraint that the same sentence was not presented twice in a row . The stimuli were presented diotically via Sennheiser HD 280 Pro headphones . The sentences were presented with an inter-stimulus-interval  of 2.5 seconds. The stimuli were divided into two blocks of 96 sentences each. Shorter stimuli blocks are recommended for obtaining EEG data with less movement related artifacts .Each participant was seated in a comfortable chair placed 1 m away from computer screen. They were fitted with an electrode cap that held sintered Ag-AgCl electrodes placed at 30 positions on the scalp in line with the 10–20 system . The ground electrode was positioned between FPz and Fz. Electrical activity was recorded from both the mastoids, with the left mastoid (M1) acting as the online reference. Vertical eye movements (VEOG) were measured with electrodes placed above and below the left eye. Horizontal eye movements (HEOG) were measured with electrodes on the outer canthi of each eye. The electrodes were adjusted until the impedance was below 5 kΩ.After the electrodes had been fitted, headphones were placed on the participant’s ears, which presented the experimental sentences diotically. The subjects were told that they should ignore the sounds in the headphones, and focus their attention on the silent video on the computer screen. This video did not include subtitles because there is evidence that commas in the written text generate a CPS-like component .While participants were watching the video, and ignoring the sentences, we measured their EEG from each electrode. The signal from the scalp electrodes was amplified 20,000 times , sampled at 500 Hz, and low-pass filtered at 100 Hz online. This activity was recorded continuously until all stimuli in the two stimulus blocks have been presented.Within each participant’s EEG, the onset of each sentence was marked with a “trigger”. Additional triggers were placed on the experimental sentences to mark 1) the onset of the second phrase in the sentence  and ERPerplab/; ) toolboxt(23) = 0.52, p = 0.61), ensuring no systematic signal to noise ratio differences across conditions. Individual ERP waves were averaged to get grand averaged ERP for each condition. These were our “whole sentence ERPs” for early and late phrase boundaries.In line with previous studies ,18, the A limitation of this approach is that if a positivity was seen at early or late phrase boundaries, this could reflect (1) a genuine CPS to the phrase boundary, (2) an enhanced N1-P2 response to the onset of the first word in the second phrase which follows a silent pause, or (3) or a combination of both. In order to disentangle the CPS and N1-P2 responses, a second analysis was performed where we divided the EEG into epochs that started 500 ms before the start of the second phrase, and ended 1000 ms after the onset of the second phrase . Similar time intervals were selected for the sentence portion without the phrase boundary .This time window allowed us to differentiate the ERP effects that started prior to the onset of the second phrase (−250 ms to 0 ms) where the acoustic cues of phrase boundary are available  from the ERPs effects that started after the onset of the second phrase (0 to 250 ms) where the N1-P2 response to the second phrase onset is overlapped with the ERP response to phrase boundary (if any).Each epoch was baseline corrected from −200 to 0 ms relative to the onset of the sentence. We took the baseline before the onset of the sentence (detached baseline) because the more commonly used immediate baseline  is problematic especially for the late phrase boundary: Since the CPS for early phrase boundary happens in the immediate baseline period for the late phrase boundary, there are systematic differences in ERPs in the immediate baseline period. The detached baseline period also has an advantage in excluding the activity related to the last syllable of the pre-boundary word, which contains frequency, intensity and duration cues for phrase boundary.Epochs with amplitude exceeding 100 μV in a 200-ms moving window with a 100-ms window step were removed from the analysis, and remaining epochs were averaged separately for early phrase boundary and late phrase boundary sentences. More than 80% of the trials were accepted for every participant . A 2 × 2 repeated measures analysis of variance (ANOVA) on percentage of accepted trials with factors boundary type  and trigger  showed no significant effect , ensuring no systematic signal to noise ratio differences across conditions. Individual ERPs waves were averaged to get grand averaged ERP for each condition. These were our “second phrase ERPs”.Sentence ERPs were measured by computing the mean amplitude in 200-ms time-windows that started at the onset of the phrase boundary. Three time windows were analysed after each phrase boundary . It is noteworthy that from 1225–1825 ms, the early phrase boundary condition contained a phrase boundary while the late phrase boundary condition did not. Similarly, between 1986–2586 ms, the late phrase condition contained a phrase boundary but the early phrase boundary condition did not. Since early and late phrase boundary sentences contained the same words, the comparison of ERPs in these time windows would reflect the effect of phrase boundary .Second phrase ERPs were measured by computing amplitudes for the time window −250 to 0 ms (preceding the second phrase onset) and the time window 0 to 250 ms (following the second phrase onset). A positive response in both windows would suggest that the CPS at the phrase boundary started before the second phrase onset, and later merged with the N1-P2 response to that phrase onset.Separate analyses were done for the whole sentence ERPs and second phrase ERPs. Separate analyses were also done midline and lateral electrode sites. Most of the data sets (around 85%) were normally distributed and followed the assumption of homogeneity of variance. Hence parametric statistics were used to analyse the data. For midline electrodes , a three-way repeated measures ANOVA was performed using the factors boundary type , condition  and electrode . For lateral sites, electrodes were grouped into four regions of interest (ROIs): right anterior , right posterior , left anterior  and left posterior . A four-way repeated measures ANOVA was performed with the factors boundary type , condition , hemisphere  and location . These ANOVAs were performed separately for each time interval.df) in the numerator, Greenhouse-Geisser (G-G) correction was applied to account for potential violations of sphericity. An alpha level of .05 was set as criterion for statistical significance. Partial ŋ2 was computed as a measure of effect size.If a significant interaction was found between condition and any other factor, post-hoc one-way ANOVAs were computed to understand the effect of electrode, hemisphere or location for each condition separately. In case of more than one degree of freedom .Grand averaged ERPs to early phrase boundary and late phrase boundary sentences at the electrode Cz are shown in Figure 2 = 0.17; 200–400 ms: F = 18.74, p = 0.001, partial ŋ2 = 0.45; 400–600 ms: F = 12.76, p = 0.002, partial ŋ2 = 0.36). In all time windows, ERPs were significantly more positive for the phrased condition compared to unphrased condition . Therefore, the phrase boundaries generated a statistically significant CPS at central electrodes for all time windows.The ERPs to whole sentences with early and late phrase boundaries showed a positive shift starting immediately after the onset of a phrase boundary which lasted around 600 ms. This observation was supported by the statistical analysis. The three-way ANOVA at the midline electrodes revealed a main effect of condition for all three time windows  = 4.64, p = 0.04, partial ŋ2 = 0.46) and 400–600 ms  = 15.40, p = 0.001, partial ŋ2 = 0.40), where the ERPs for phrased condition showed a significantly larger positivity as compared to unphrased condition . There was a significant condition × hemisphere interaction between 0–200 ms F = 4.25, p = 0.050, partial ŋ2 = 0.16. To disentangle the interaction, follow-up one-way ANOVAs were computed for each hemisphere separately with the factor condition. These ANOVAs showed the main effect of condition only at right hemisphere F = 4.73, p = 0.04, partial ŋ2 = 0.17, where the ERPS were more positive for the phrased condition .The four-way ANOVA at lateral electrodes showed a main effect of condition at 200–400 ms  = 19.32, p = 0.001, partial ŋ2 = 0.17. Follow-up one-way ANOVAs conducted at each location showed a main effect of condition at both anterior  = 18.37, p = 0.001, partial ŋ2 = 0.44) and posterior locations  = 13.36, p = 0.001, partial ŋ2 = 0.37). At both locations, the phrased condition generated a larger positivity compared to the unphrased condition .There was also a significant condition x location interaction between 200–400 ms F = 4.62, p = 0.04, partial ŋIn sum, the analysis on lateral electrodes showed a CPS over right hemisphere between 0–200 ms and at all locations between 200–400 ms and 400–600 ms time intervals. There were no main effects or interactions for boundary type at either midline or lateral electrodes indicating that CPS did not depend on the position of the boundary.The ERPs time locked to the onset of the second phrase are shown in Figure Figure 2 = 0.23; and 0–250 ms F = 18.65, p = 0.001, partial ŋ2 = 0.45. Phrased stimuli elicited larger positivity compared to unphrased stimuli at both time windows .The three-way ANOVA of midline electrodes showed a main effect of condition between for −250-0 ms F = 6.89, p = 0.015, partial ŋ2 = 0.16; and 0–250 ms F = 14.66, p = 0.001, partial ŋ2 = 0.39. Similar to the findings at medial electrodes, the phrased condition generated larger positivity at lateral electrodes . Therefore the positivity at the phrase boundaries started before the start of the second phrase and later merged with the N1-P2 responses to the second phrase.The four-way ANOVA at lateral electrodes also showed a main effect of condition for −250-0 ms F = 4.41, p = 0.047, partial ŋThe aim of this study was to use passive ERPs to test if acoustic cues for phrase boundaries in normal English sentences elicit a CPS. We presented 24 English-speaking adults with two types of sentences: sentences with an early phrase boundary, and sentences with a late phrase boundary. We found that both early and late phrase boundaries elicited a significantly more positive ERP from 0 to 600 ms after the onset of the phrase boundary at midline and right hemisphere electrodes, and from 200 to 600 ms at left hemisphere electrodes, than the ERP to the same sentence portion without a phrase boundary.The positivity seen at phrase boundaries might indicate either an ERP effect for phrase boundary processing (CPS), an enhanced N1-P2 response to the second phrase onset (speech onset following a pause is expected to generate a larger N1-P2 response) or a combination of the two. In order to disentangle the CPS from the N1-P2 response, a second analysis was conducted between −250 ms and 250 ms with respect to the onset of the second phrase ,18. ThisThis is the first study to investigate and show the presence of a passive CPS to unattended normal sentences in adult English speakers. The results confirm that a CPS can be elicited under passive listening conditions, and suggest that acoustic cues that mark phrase boundaries do indeed elicit a CPS in normal English sentences. These findings are similar to previous studies ,12,13 whThere are two obvious methodological differences that could explain these slight differences in CPS latency and amplitude between studies. First, the previous studies analysed the ERPs in 500-ms time windows, while we analysed the ERPs in smaller 200-ms time windows. Our smaller time window should have allowed a finer analysis of the temporal processing of phrase boundaries, which could reveal different results. Secondly, the current study used a passive auditory paradigm, while the previous studies used an “active” ERP paradigm that asked listeners to actively comprehend the presented sentences. Overt sentence comprehension may trigger additional linguistic processing  which may extend up to 600 ms . These aAn alternative account for the early passive CPS might be the changes in F0 contour  at the phrase boundary. Roll et al. ,37 foundThe third difference between the results of the present study and previous CPS studies is the topography of the CPS. Centro-parietal distribution for the active CPS is previously reported for German sentences ,12. UsinInterestingly, the location of the phrase boundary did not have an effect on the CPS. Both early and later phrase boundaries elicited CPS with similar magnitude and scalp distribution. This contrasts with the findings of Holzgrefe et al. , who fouIn conclusion, the present study shows that a passive CPS is elicited by acoustic cues that mark phrase boundaries normal English sentences. The timing, amplitude, and topography of the passive CPS are different to the active CPS measured in previous studies. These results show that acoustic cues at the phrase boundary are sufficient to elicit the CPS, and suggest that different processes might be involved in the generation of the CPS in active and passive conditions. The passive CPS that we observed appears to primarily reflect the processing of the acoustic cues in normal English sentences, whereas the active CPS might be reflecting acoustic and linguistic processing.