The wheat NDR-type AGC kinase TaAGC1 positively regulates host defence response to the necrotrophic phytopathogen Rhizoctonia cerealis through modulating expression of ROS-related and defence-associated genes. Triticum aestivum). The necrotrophic fungus Rhizoctonia cerealis is the major pathogen of the destructive disease sharp eyespot of wheat. In this study, the wheat AGC kinase gene TaAGC1, responding to R. cerealis infection, was isolated, and its properties and role in wheat defence were characterized. R. cerealis-resistant wheat lines expressed TaAGC1 at higher levels than susceptible wheat lines. Sequence and phylogenetic analyses showed that the TaAGC1 protein is a serine/threonine kinase belonging to the NDR (nuclear Dbf2-related) subgroup of AGC kinases. Kinase activity assays proved that TaAGC1 is a functional kinase and the Asp-239 residue located in the conserved serine/threonine kinase domain of TaAGC1 is required for the kinase activity. Subcellular localization assays indicated that TaAGC1 localized in the cytoplasm and nucleus. Virus-induced TaAGC1 silencing revealed that the down-regulation of TaAGC1 transcripts significantly impaired wheat resistance to R. cerealis. The molecular characterization and responses of TaAGC1 overexpressing transgenic wheat plants indicated that TaAGC1 overexpression significantly enhanced resistance to sharp eyespot and reduced the accumulation of reactive oxygen species (ROS) in wheat plants challenged with R. cerealis. Furthermore, ROS-scavenging and certain defence-associated genes were up-regulated in resistant plants overexpressing TaAGC1 but down-regulated in susceptible knock-down plants. These results suggested that the kinase TaAGC1 positively contributes to wheat immunity to R. cerealis through regulating expression of ROS-related and defence-associated genes.Considerable progress has been made in understanding the roles of AGC kinases in mammalian systems. However, very little is known about the roles of AGC kinases in wheat ( Triticum aestivum L.) is a widely cultivated and consumed staple crop, feeding ~35% of the world’s population (http://www.agri.cn/V20/bchqb/201501/t20150121_4344729.htm). The main agent of wheat sharp eyespot is Rhizoctonia cerealis van der Hoeven, a soil-borne fungus with bi-nucleated hyphal cells , several AGC kinases have been implicated in growth, morphogenesis and defence responses to pathogens  is an AGC VIIIb kinase and is required for immunity against the pathogens Hyaloperonospora arabidopsidis (an oomycete) and Pseudomonas syringae pv. tomato DC3000   and hydrogen peroxide (H2O2)   than in susceptible wheat line Wenmai 6 inoculated with R. cerealis. Then, the TaAGC1 gene was cloned from resistant wheat line CI12633, and further characterized. The TaAGC1 protein was evidenced to have kinase activity. The role of TaAGC1 in host defence responses to R. cerealis was explored using both TaAGC1 silencing- and overexpressing- wheat plants. The results showed that TaAGC1, acting as a positive regulator, mediated wheat resistance responses to R. cerealis.In this study, to isolate kinase genes involved in wheat defence response to T. aestivum) lines/cultivars—sharp eyespot-resistant CI12633 and Shanhongmai, moderately-susceptible Yangmai 158 and Yangmai 20, and susceptible Wenmai 6 (Supplementary Table S1)—were used in this research. A major Jiangsu virulent isolate of the pathogen R. cerealis, R0301, was provided by Profs Huaigu Chen and Shibin Cai of Jiangsu Academy of Agricultural Sciences, China.Five wheat . Additionally, the leaves of CI12633 seedlings at the three-leaf stage were sprayed with 10mM H2O2 solution for 0, 1, 3, 6 and 12h, following the protocol of Wheat plants were grown in a 14h light/10h dark (23°C/10°C) regime. At the tillering stage, the second base sheath of each wheat plant was inoculated with small toothpick fragments harbouring well-developed mycelia of Genomic DNA for each sample was isolated from the wheat leaves using the CTAB method  available from Agilent by using the default variables. Data files were loaded into GeneSpring GX 11.5 (Agilent Technologies). The signal values between both experimental groups (CI12633 or Shanhongmai probe vs Wenmai 6 probe) were assessed for each gene based on the normalized probe signals. Differentially expressed kinase genes between CI12633 or Shanhongmai and Yangmai 12 were filtered out by a fold change threshold ≥2.0.For each sample total RNA was extracted from the sheaths of 10 seedlings from sharp eyespot-resistant wheat lines (CI12633 and Shanhongmai) or the susceptible wheat cv. Wenmai 6 at 4 or 21 dpi with TaAGC1 cDNA, was expressed at a significantly higher level in the resistant wheat CI12633 than in the susceptible wheat Wenmai 6. TC411796 was homologous to a wheat AGC kinase gene WNDr1A (GenBank accession AB179755) in the National Center for Biotechnology Information (NCBI) database. The 3′-untranslated region (UTR) and full-length open reading frame (ORF) sequences of TaAGC1 were separately amplified using 3′-Full RACE Core Set kit v.2.0 (TaKaRa) or RT-PCR from cDNA of CI12633 stems inoculated with R. cerealis for 4 d. The deduced protein sequence was analysed using the Compute pI/Mw tool (http://web.expasy.org/compute_pi/) to determine the theoretical iso-electric point (pI) and molecular weight (MW), interPro-Scan (http://www.ebi.ac.uk/interpro/) to identify domains and Smart software (http://smart.embl-heidelberg.de/) to predict conserved motifs. A phylogenetic tree was constructed using a neighbor-joining method implemented with MEGA 5.0 software followed by an alignment with other protein kinases using ClustalX software.The microarray analysis indicated that a probe (TIGR number: TC411796) of the Agilent wheat microarray, the 3′-terminal partial sequence of TaAGC1 or the D239A mutant was separately subcloned in-frame to the 3′-terminus of a glutathione S-transferase (GST) gene in the pGEX-4T-1 vector . The resulting pGST-TaAGC1 and mutant pGST-D239A fusion constructs were transformed into competent cells of Escherichia coli BL21 (DE3), respectively. The recombinant GST-TaAGC1 and GST-D239A proteins were separately expressed after treatment with 0.1mM isopropyl-β-D-thiogalactopyranose at 16°C for 19h, and purified using a MicroSpin module .The residue 239 aspartate (D) at the Ser/Thr kinase active-site of TaAGC1 protein was replaced with alanine (A) to generate the D239A mutant of TaAGC1. To investigate if the TaAGC1 protein is an active kinase and the D239 is important for the kinase activity, the ORF sequence of 2, 0.1mM EGTA, 2mM dithiothreitol, 100mM [γ32P]ATP and ~100ng of GST-TaAGC1 or GST-D239A. For phosphorylation experiments of protein substrates, 1 μg MBP was incubated with ~100ng of GST-TaAGC1 or GST-D239A under the phosphorylation conditions described above. The reaction was incubated at 30°C for 30min. Then, 2 μl SDS sample buffer was added to the phosphorylation mixture to stop the reaction. The autophosphorylated and phosphorylated proteins were resolved by SDS-PAGE and analysed by autoradiography. After autoradiography, the proteins were stained for 10min by Coomassie Brilliant Blue (CBB) and then photographed.Autophosphorylation and myelin basic protein (MBP) phosphorylation assays were carried out using a reaction mixture (10 μl) consisting of 40mM Hepes-NaOH (pH 8.0), 5mM Mg(CH3COO)TaAGC1, lacking a stop codon, was amplified using the primers TaAGC1-GFP-F/TaAGC1-GFP-R. The amplified fragment was sub-cloned in-frame with the 5′-terminus of the green fluorescent protein’s (GFP) coding region in the p35S:GFP vector, resulting in the TaAGC1 fusion construct p35S:TaAGC1-GFP.The coding region of The resulting p35S:TaAGC1-GFP and p35S:GFP constructs were separately bombarded into white onion epidermal cells, following the protocol of NheI restriction site of the RNAγ of BSMV . Following the protocols described by CP) gene, and to evaluate the transcript changes of TaAGC1. At 14 d after BSMV infection, the BSMV-infected CI12633 plants were inoculated with R. cerealis. They were scored at 14 and 40 dpi with R. cerealis, respectively.The virus-induced gene silencing (VIGS) assay in barley and wheat was developed using the barley stripe mosaic virus (BSMV)  promoter and terminated by 3′ non-transcribed region of Agrobacterium tumefaciens nopaline synthase gene (Tnos). A total of 1500 immature embryos of the wheat cultivar Yangmai 20 were transformed by biolistic bombardment using pUBI:myc-TaAGC1 following the protocol described by The r pAHC25  with a cTaAGC1-overexpressing transgene in the transformed wheat plants was monitored by PCR using the specific primers, TaAGC1-1584F (located in TaAGC1 coding sequence) and Tnos-R (in Tnos of the transformation vector). PCR was performed in a 20-μl volume containing 200ng genomic DNA, 1×PCR buffer (TaKaRa), 0.4 μM each primer, 200 μM each dNTP and 1U Taq polymerase (TaKaRa). The amplified product (386bp) specific to the introduced TaAGC1-Tnos chimera was resolved on a 1.5% agarose gel and visualized by ethidium bromide staining.The presence of the The myc-TaAGC1 fusion protein in the overexpressing transgenic wheat lines was visualized by western blotting analysis. Total proteins were extracted from 0.3g of ground leaf powder. Total soluble proteins (~12 μg) for each line were separated on 12% SDS-PAGE and transferred to polyvinyl difluoride membranes (Amersham Biosciences). The western blots were incubated with 100-fold diluted anti-c-myc antibody followed by secondary antibody conjugated to horseradish peroxidase . The myc-TaAGC1 proteins were visualized using the ECL Western Blot Detection and Analysis System .TaAGC1 and the genes encoding ROS-scavenging enzymes  and ROS-producing enzyme NADPH oxidase (NOX), and four defence-associated genes  in wheat. qRT-PCR was performed using SYBR Green I Master Mix (TaKaRa) in a volume of 25 μl on an ABI 7300 RT-PCR system (Applied Biosystems). Reactions were set up using the following thermal cycling profile: 95°C for 5min, followed by 41 cycles of 95°C for 15 s and 60°C for 31 s. The products were examined using a melting curve analysis program. Three biological replications were run. The relative expression of the target genes was calculated using the 2−ΔΔCT method  was used to investigate the relative expression levels of T method , where tSupplementary Table S2.The accession number of all the genes and sequences of all the primers in the study are listed in 1−T3 generations, 30 plants for each line of the TaAGC1-overexpressing transgenic and WT wheat were inoculated with small toothpicks harbouring the well-developed mycelia of R. cerealis following the protocol of Supplementary Fig. S2), ITs were categorized from 0 to 5: 0, no lesion; 1, the lesion appeared on the sheaths rather than stems; 2, the width of the lesion is <50% of the infected stem perimeter; 3, the width of the lesion is >50 and <75% of the infected stem perimeter; 4, the width of the disease lesion is >75% of the infected stem perimeter; 5, white spike or dead plant). Disease index ={(0×X0+1×Xl+2×X2+3×X3+4×X4+5×X5)/[(X0+X1+X2+X3+X4+X5)×5]}×100, where X0−X5 indicated plants with IT: 0−5.In TIn situ H2O2 and O2− accumulations were examined via histochemical staining by 3, 3′-diaminobenzidine (DAB) and nitroblue tetrazolium (NBT), respectively, following the protocols of R. cerealis infection, and floated in a solution of 1mg ml−1 DAB-HCl (pH 3.8) at 25°C for 8h or in NBT (pH 7.8) at 25°C for 30min, and then chlorophyll was removed with 95% ethanol in a boiling-water bath.R. cerealis, the hybridization data of the Agilent wheat microarray chips were mined for wheat kinase genes that were differentially expressed between sharp eyespot-resistant wheat lines CI12633/Shanhongmai and the susceptible wheat line Wenmai 6 inoculated with R. cerealis for 4 and 21 dpi. Among differentially expressed kinase gene sequences identified from microarray data (GEO accession number GSE69245), the transcriptional level of TC411796 (probe), a cDNA 3′-terminal fragment of a wheat AGC kinase, was higher in resistant wheat lines CI12633/Shanhongmai than in susceptible wheat line Wenmai 6. For example, the probe TC411796 showed a 3.01-fold and 2.90-fold transcriptional increase in the resistant wheat line CI12633 than in the susceptible wheat line Wenmai 6 at 4 and 21 dpi, respectively. Being homologous to AGC kinases, this protein kinase gene was designated as TaAGC1. qRT-PCR analyses showed that the transcription of TaAGC1 was induced after R. cerealis inoculation, and the transcriptional induction was higher in resistant CI12633 than in susceptible Wenmai 6 at 4, 7 or 21 dpi with R. cerealis  was cloned from a R. cerealis-infected stem of CI12633. A sequence analysis showed that the cDNA sequence (2077 nucleotides) of TaAGC1 includes an ORF consisting of 1674 nucleotides (from 67 to 1740) . The deduced TaAGC1 protein consists of 557 amino acid residues, with a MW of 64.14 kD and pI of 8.65. As shown in Supplementary Fig. S3, the TaAGC1 protein contains an N-terminal S100B family-binding region (amino acid 96–113), the 12 Ser/Thr protein kinase subdomains (amino acid 116–427) that harbours an ATP-binding domain (amino acid 122−146), the kinase active-site (amino acid 235−247) and an activation loop (A-loop) motif (amino acid 257−331), and a C-terminal hydrophobic motif (amino acid 479−488).The full-length of 60STVGTPDYIAPE) of the NDR subclass of the AGC kinase family . The reconstructed phylogenetic tree analysis , radish NDR kinase RsNdr1 (GenBank accession no. BAC76894), Lotus japonicus NDR kinase PKL01 (GenBank accession no. BAD95893), human NDR kinase STK38 (GenBank accession no. NP_009202.1) and Mus musculus NDR kinase MmNdr1 (GenBank accession no. AAP44997), were clustered on the same branch. All of them belong to AGC VII subfamily. AtOXI1, OsOXI1, Adi3 and three unnamed wheat AGC kinases  were clustered in the other clade, which belonged to the AGC VIIIb subfamily. Another unnamed wheat AGC kinase (GenBank accession no. CDM85216.1) belonged to the PDK1 subfamily. However, the leucine-rich repeat receptor-like kinases, such as rice Xa21 (GenBank accession no. AAC49123.1) and Arabidopsis FLS2 (GenBank accession no. Q9FL28.1), and receptor-like cytoplasmic kinases, such as tomato PTO, Arabidopsis PBS1 (At5g13160) and BIK1 (At2g39660), were separately clustered into two out-groups, which do not belong to the AGC kinase family  at the Ser/Thr kinase domain active-site was replaced with Ala (A). The in vitro kinase assay showed that through detection by autoradiography, TaAGC1 autophosphorylated itself and phosphorylated the substrate MBP, but the D239A mutant did not display the autophosphorylation and phosphorylation activity. These results indicated that TaAGC1 is a functional kinase with autophosphorylation activity and phosphorylation activity, and that the Asp-239 residue of TaAGC1 is required for the TaAGC1 kinase activity  fusion protein with 93.2 KD and the GST-D239A (mutant) protein were separately expressed in activity .To explore the subcellular localization of TaAGC1, 35S:TaAGC1-GFP was transiently expressed either in onion epidermal cells or in wheat mesophyll protoplasts. At the same time, the transient expression of 35S:GFP was used as control. In the transient transformed onion epidermal cells, the TaAGC1-GFP protein was localized predominantly in the cytoplasm and nucleus . After pTaAGC1 transcript level was substantially reduced in BSMV:TaAGC1-infected CI12633 plants, indicating that TaAGC1 was successfully silenced in BSMV:TaAGC1-infected CI12633 plants  was markedly elevated compared with untransformed recipient  Yangmai 20  generations showed that, compared with WT Yangmai 20, all three TaAGC1-overexpressing lines  displayed significantly enhanced resistance to R. cerealis infection , suggesting that TaAGC1 might be involved in ROS signalling. To explore if the accumulation of ROS occurs in wheat challenged by R. cerealis, levels of H2O2 and O2− in the infected-wheat Yangmai 20 were assayed using DAB and NBT staining, respectively. The results showed that levels of H2O2 and O2− increased in the infected-wheat leaves from 1 dpi to 7 dpi , suggesting that the R. cerealis infection promoted the generation of H2O2 and O2−. To unravel whether TaAGC1 regulates ROS homeostasis, levels of H2O2 and O2− in TaAGC1-overexpressing and WT Yangmai 20 leaves after R. cerealis infection were further assayed. Under normal growth conditions, either WT or TaAGC1-overexpressing plants produced a small amount of H2O2 and O2−  and ROS-producing enzyme (NOX) in TaAGC1-overexpressing, TaAGC1-knock-down and control wheat plants were investigated. Under normal conditions, the transcriptional levels of POX2, CAT1 and SOD3 genes were significantly higher but the NOX transcript level was significantly lower in TaAGC1-overexpressing lines than in WT plants , suggesting that TaAGC1 positively regulated the transcription of POX2, CAT1 and SOD3 genes but negatively regulated the expression of NOX. Furthermore, following R. cerealis inoculation for 7 d and 40 d, the induced expression levels of POX2, CAT1 and SOD3 genes were significantly higher in TaAGC1 overexpressing wheat lines than in the WT plants and were lowest in the TaAGC1 knock-down plants. However, the NOX transcript level was significantly lower in TaAGC1-overexpressing lines than in WT plants and was the highest in TaAGC1-knock-down plants . The data suggested that the overexpression of TaAGC1 increased the expression of POX2, CAT1 and SOD3, but decreased the expression of NOX, resulting in a reduced ROS content in the overexpressing transgenic wheat plants infected by R. cerealis.To explore if TaAGC1 and four wheat defence-associated genes, defensin, nsLTP1, PR10 and chitinase 2, was significantly induced in WT wheat Yangmai 20 after R. cerealis inoculation for 7 d, 14 d or 40 d . To explore if TaAGC1 regulates the expression of these defence-associated genes in wheat, qRT-PCR was used to examine their transcription in TaAGC1-overexpressing, TaAGC1-knock-down, and control wheat plants. Under normal conditions, the transcriptional levels of wheat defensin and nsLTP1 genes were significantly higher but those of PR10 and chitinase 2 were significantly lower in TaAGC1-overexpressing lines than in WT plants . Furthermore, following R. cerealis inoculation for 7 d and 40 d, the transcriptional levels of wheat defensin and nsLTP1 genes elevated significantly more in TaAGC1-overexpressing lines than in WT plants and were the lowest in the TaAGC1-knock-down lines . However, the transcriptional levels of PR10 and chitinase 2 were decreased in TaAGC1-overexpressing resistant lines than in WT plants and were the highest in the TaAGC1-knock-down plants . The data suggested that TaAGC1 positively regulated the expression of wheat defensin and nsLTP1 genes but negatively regulated the expression of chitinase 2 and PR10.qRT-PCR analysis showed that the expression of TaAGC1, was isolated based on the microarray analysis and the qRT-PCR analyses of transcription levels in resistant and susceptible wheat genotypes. It was expressed at a higher level in R. cerealis-resistant wheat line CI12633 than in susceptible wheat line Wenmai 6. To understand the relationship between the gene transcriptional variance and the sequences in the resistant and susceptible wheat genotypes, we cloned the TaAGC1 sequence from the R. cerealis-resistant wheat line CI12633 and the homologous sequence from R. cerealis-susceptible wheat line Wenmai 6, respectively. Comparison of TaAGC1 sequences from CI12633 and Wenmai 6 showed that four single-nucleotide polymorphisms existed at their 3ʹ-terminal sequences , which may be a reason for the different expression patterns in the resistant and susceptible wheat genotypes. The protein TaAGC1 was predicted to be a Ser/Thr protein kinase and it shows the sequence characteristics  of NDR-type AGC kinases. For human NDR-type AGC kinases, three regulatory phosphorylation sites, Thr-74 in the S100B-binding domain, Ser-281 in the catalytic domain and Thr-444 in the hydrophobic motif, were previously reported to be crucial for NDR1 activity . A phylogenetic analysis provided additional evidence that the TaAGC1 protein is an NDR-type AGC kinase and is distinct from receptor-like kinases or receptor-like cytoplasmic kinases. The majority of receptor-like kinases contain transmembrane and extracellular domains and localize to the plasma membrane, which is consistent with their function in sensing extracellular signals  in the T1−T3 generations displayed significantly enhanced resistance to sharp eyespot disease compared to untransformed recipient plants, while the down-regulation of TaAGC1 in resistant CI12633 significantly impaired the host resistance. These data indicated that the wheat AGC kinase TaAGC1 positively contributes to host resistance to the necrotrophic fungus R. cerealis. Interestingly, compared with host wheat plants, the TaAGC1-overexpressing and knock-down wheat plants either did not show obvious phenotypic changes in development or did not alter resistance to powdery mildew disease . It would be interesting to further study how TaAGC1-overexpressing and knock-down wheat plants perform in response to other wheat pathogens in the future. In previous studies, three AGC VIIIb kinases in other plant species were reported to participate in the immune responses to biotrophic and hemibiotrophic pathogens, including the Arabidopsis AtOXI1 to H. arabidopsidis and P. syringae pv. tomato  is increased in uninfected wheat plants overexpressing TaAGC1, suggesting that TaAGC1 is involved in ROS signalling. Furthermore, after R. cerealis infection, the transcription levels of TaAGC1, SOD3, POX2 and CAT1 genes were more elevated in the TaAGC1-overexpressing wheat lines compared with the WT line, and were lower in TaAGC1 knock-down wheat plants than in the control plants, further indicating that TaAGC1 positively regulated the transcription of these ROS-scavenging enzyme genes but down-regulated the expression of the gene NOX encoding ROS-producing enzyme. Consequently, following R. cerealis infection, the ROS level was lower in TaAGC1-overexpressing wheat lines compared with the WT line. These data suggested that TaAGC1 maintained ROS homeostasis by regulating the expression of ROS-scavenging and -producing enzyme genes.In early responses to pathogen invasion, plants often generate an excessive amount of ROS. During plant immunity against biotrophic pathogens, ROS not only functions in toxicity and the oxidative cross-linking of plant cell walls, but also has a signalling role in mounting a defence response (TaLTP5 (a wheat LTP gene) displayed enhanced resistance to the fungal pathogens Cochliobolus sativus and Fusarium graminearum  in TaAGC1-overexpressing and knock-down wheat plants, and control wheat plants with or without R. cerealis inoculation. These four wheat defence-related genes were reported to participate in transcription factor-mediated defence responses to pathogens in previous studies  construct, BSMV with green fluorescent protein fusion construct (BSMV:GFP), and the construct of the recombinant virus expressing the wheat AGC gene TaAGC1, BSMV:TaAGC1.Supplementary Fig. S2. The representative symptoms of different infection types of sharp eyespot disease at 40 dpi with Rhizoctonia cerealis.Supplementary Fig. S3. Nucleotide sequence and deduced amino acid sequence of the wheat AGC kinase gene TaAGC1.Supplementary Fig. S4. Expression patterns of the wheat AGC kinase gene TaAGC1 in wheat responding to exogenous hydrogen peroxide (H2O2) treatments.Supplementary Fig. S5. Analysis of hydrogen peroxide (H2O2) and superoxide anion (O2−) accumulation in wheat leaves.Supplementary Fig. S6. Transcriptional analysis of ROS-related and defence genes in the wheat AGC kinase gene TaAGC1-overexpressing and untransformed wheat Yangmai 20 plants under normal growth conditions.Supplementary Fig. S7. Transcription analysis of ROS-related and defence genes in the wheat AGC kinase gene TaAGC1-overexpressing and knock-down, as well as control wheat plants with Rhizoctonia cerealis inoculation for 40 d.Supplementary Fig. S8. Transcription analysis of ROS-related and defence genes in the wild type wheat Yangmai 20 plants after Rhizoctonia cerealis inoculation for 7, 14 and 40 d.Supplementary Fig. S9. Alignment of 3ʹ terminal sequences of TaAGC1 in resistant wheat line CI12633 and of its homologue in susceptible wheat line Wenmai 6.Supplementary Fig. S10. The powdery mildew symptoms of the wheat AGC kinase gene TaAGC1-overexpressing and knock-down, and control wheat plants.Supplementary Table S1. Rhizoctonia cerealis responses of four wheat lines.Supplementary Table S2. Primers used in this study.


Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion.The online version of this article (doi:10.1007/s10519-015-9735-5) contains supplementary material, which is available to authorized users. Extraversion is a personality trait characterized by the tendency to experience positive emotions, to be active and feel energetic, to be talkative and to enjoy social interactions. Extraversion is associated with numerous psychosocial, lifestyle and health outcomes, such as academic and job performance, well-being, obesity, substance use, physical activity, bipolar disorder, borderline personality disorder, Alzheimer’s disease, and longevity  personality inventories  analysis using test linking  gene. A subsequent meta-analysis of GWA results for the NEO personality traits, conducted in 17,375 subjects, also did not yield any genome-wide significant associations for extraversion  studies for extraversion have been published, aimed at detecting specific single nucleotide polymorphisms (SNPs) that explain part of the heritability. The first GWA study for personality, which focused on the five NEO personality traits, was conducted in 3972 adults  cohort was included as a replication sample . A brief overview of all cohorts is provided in Table A harmonized latent extraversion score was estimated for all participants in all 29 cohorts that were included in the GWA meta-analysis. This score was based on all available extraversion item data for each individual  was performed, followed by imputation of genotypes. QC of genotype data was performed in each cohort separately, with comparable but cohort specific criteria. Standard QC checks included tests of European ancestry, sex inconsistencies, Mendelian errors, and high genome-wide homozygosity. Checks for relatedness were conducted in those cohorts that aimed to include unrelated individuals only. Other checks of genotype data were based on minor allele frequencies (MAF), SNP call rate (% of subjects with missing genotypes per SNP), sample call rate (% of missing SNPs per subject) and Hardy–Weinberg Equilibrium (HWE). Genotype data were imputed using the 1000Genomes phase 1 version 3  reference panel with standard software packages such as IMPUTE, MACH, or Minimac, see Supplementary Table 1.GWA analyses were conducted independently in each cohort. Since the cohorts used different research designs , GWA methods were optimized for each cohort. Extraversion scores were regressed on each SNP under an additive model, with sex and age included as covariates. Covariates such as ancestry Principal Components (PCs) were added if deemed necessary for a particular cohort. In all analyses, the uncertainty of the imputed genotypes was taken into account, either using dosage scores or mixtures of distributions. In those cohorts that included related individuals, the dependency among participants was accounted for using cohort-specific methods. Standard software packages for GWA analyses were used (see Supplementary Table 1).http://www.sph.umich.edu/csg/abecasis/metal/index.html). Excluded from meta-analysis were poorly imputed SNPs (r2 < 0.30 or proper_info < 0.40) and SNPs with low MAF (MAF < √(5/N), which corresponds to less than 5 estimated individuals in the least frequent genotype group, under the assumption of HWE). This resulted in a total number of 7,460,147 unique SNPs in the final meta-analysis (with 1.1–6.6 M SNPs across cohorts). For 2182 SNPs, SNP locations could not be matched with rs names. For an additional 516,362 SNPS, results were based on one cohort only and therefore left out of the analysis, so that the results are based on 6,941,603 SNPs. Genomic control inflation factors (lambda), Manhattan plots and quantile–quantile plots per cohort are provided in Supplementary Table 2 and Supplementary Figs. 1, 2. A P value of 5 × 10−8 was used as the threshold for genome-wide significance.A meta-analysis of the GWA results was conducted with the weighted inverse variance method in METAL  were used as the input to compute P-values at the gene level. We performed these analyses in KGG  cohort (8648 subjects). Polygenic risk scores for this cohort were estimated using LDpred  adult cohort , GCTA software  is found in Supplementary Table 3. The results of all SNPs can be downloaded from www.tweelingenregister.org/GPC. A gene-based test showed one significant hit for LOC101928162, a long non-coding RNA site, P = 2.87 × 10−6. A list with the top five genes from the gene-based analysis is provided in Table BDNF, P = 0.0003), a gene also implicated, though not genome-wide significant, in Terracciano et al. (P = 0.0001).Meta-analysis of GWA results across the 29 discovery cohorts did not yield genome-wide significant SNPs associated with extraversion. The lowest o et al. , as was P-value <1 × 10−5 see Supplementary Table 3. Of the 74 SNPs tested in the replication cohort, three SNPs showed nominal evidence of association (P < 0.05), which is less than the number expected based on chance alone (0.05 × 74 = 3.7).Results of the follow-up analysis of the top five SNPs in the GS:SFHS cohort can be found in Table B = 0.059, X2(1) = 27.30, P < 0.001.There were 8201 persons individuals with polygenic scores for prediction of extraversion. The LDpred-based genetic risk scores significantly predicted extraversion in the target cohort, P = 0.24). In the QIMR cohort, 0.0001 % (SE = 15) of the variance was explained by SNPs (P = 0.46).In the NTR cohort, an estimated 5.0 % (SE = 7.2) of the variance in extraversion was explained by all SNPs, but this estimate was not significantly different from zero , harks back to an interesting extraversion SNP (rs7088779) in a previous GWAS on personality  has been reported to increase the risk for schizophrenia and bipolar disorder  was previously found to be involved in schizophrenia


The Lundehund is a highly specialized breed characterized by a unique flexibility of the joints and polydactyly in all four limbs. The extremely small population size and high inbreeding has promoted a high frequency of diseased dogs affected by the Lundehund syndrome (LS), a severe gastro-enteropathic disease.LEPREL1 1.2 Mb proximal to the region of the genome-wide association, which was shown to be significantly associated with LS. LS-affected Lundehund harbored the mutant LEPREL1:g.139212C>G genotype A/A whereas all controls of other breeds showed the C/C wild type.Comprehensive analysis of bead chip and whole-genome sequencing data for LS in the Lundehund resulted in a genome-wide association signal on CFA 34 and LS-specific runs of homozygosity (ROH) in this region. Filtering analysis for variants with predicted high or moderate effects revealed a missense mutation in NOD1 potentially involved in LS breed disposition.In addition, ROH analysis for the Lundehund indicated a high enrichment of genes in potential signatures of selection affecting protein activation and immunoregulatory processes like LEPREL1 and suggests it as a precursor of the inflammatory process. Analyses of ROH regions give an insight into the genetic background of characteristic traits in the Lundehund that remain to be elucidated in the future.Sequencing results for Lundehund specific traits reveal a potential causative mutation for LS in the neuropeptide operating gene The online version of this article (doi:10.1186/s12864-016-2844-6) contains supplementary material, which is available to authorized users. The Norwegian Lundehund represents a specific but small group in the variety of dog breeds which have evolved since domestication . It stanCaspase Recruitment Domain Family, Member 9 (CARD9), which is known to be associated with Crohn’s disease and ulcerative colitis in human [limb development membrane protein 1 (LMBR1) mutation DC-2, which was shown to be associated with preaxial polydactyly in western dogs, revealed the mutant genotype (A/A) in all tested Lundehund [In the Lundehund, a signature of selection for LS was suggested to be located on CFA 9 in the region of in human , 17. In undehund , 19. In undehund .In this study whole-genome sequencing was performed in two Lundehund pools comprising LS-affected and LS-unaffected individuals. Sequence data were investigated for runs of homozygosity (ROHs) and variants predicted to have high or moderate effects in order to elucidate the genetic background of LS.Samples of six Lundehund dogs with typical characteristics of a puffin hunter phenotype including an additional toe and joint flexibility Fig.  were choInspection of the pedigree data of all Lundehund under study revealed close relationships among affected dogs  and mab-21 domain containing 2 . The highest -log10P value (−log10P = 7.7) could be shown for one SNP at 24,152,349 bp whereas further 30 SNPs in the associated region reached -log10P-values of 5.6-5.9. Expected versus observed -log10P-values (quantile-quantile-plot) showed that inflation due to stratification effects had not increased -log10P-values. The distribution of genotypes suggested a recessive effect as proposed by segregation analysis.Genome-wide association analysis for LS in 17 LS-affected and 8 LS-unaffected Lundehund showed a highly significant peak on CFA 34 at 26,384,304-27,498,705 bp (CanFam 2.0) corresponding to 23,373,982-24,488,983 bp in CanFam 3.1 assembly  and 14.48X Lundehund pool 2 (LS-unaffected). In total 10,584,482 SNPs and 3,223,567 insertions/deletions (INDEL) could be detected for pool 1 as well as 10,530,165 SNPs and 3,207,163 INDEL for pool 2 in raw data after variant calling. Stringent filters for read depth and quality of each variant led to a set of 8,815,395 SNPs (pool 1) as well as 8,799,793 SNPs (pool 2) for ROH analysis. For these SNPs the mean heterozygosity per site was 0.11 in both pools.Coiled-Coil Domain Containing 50 (CCDC50), FGF12 and MB21D2  and GREM2  on CFA 7. Furthermore, we detected a ROH region on CFA 14 at 42,891,687-43,618,177 bp harboring Nucleotide-Binding Oligomerization Domain Containing 1 (NOD1). In contrast, no ROH region could be detected harboring the candidate gene LMBR1 (limb development membrane protein 1) for canine polydactyl mutations [LMBR1 SNP DC-2 revealed the mutant DC-2 allele homozygous in all analyzed Lundehund but also in the Norwegian Buhund, Bernese Mountain Dogs, Briard and Irish Wolfhound  which matched the region of genome-wide association. In total 2036 intron variants with a homozygous mutant genotype in LS-affected Lundehund, one 3′UTR and 13 5′UTR variants could be found in FGF12 with predicted low or modifier effects only. In MB21D2 no LS-associated variant could be detected.Detection of SNPs or INDEL predicted to have high or moderate effects in ROH regions was performed to identify LS-specific or Lundehund-specific variants. No variant with predicted high or moderate effects could be detected within COL28A1:g.159951T>A on ECA14 was predicted to be deleterious (SIFT) as well as probably damaging (PolyPhen).Analysis of private LS-specific ROH regions resulted in one variant with a homozygous mutant genotype in LS-affected Lundehund, a heterozygous genotype in LS-unaffected Lundehund and a wildtype genotype in controls. This missense variant PRRX1 and GREM2 did not harbor variants with predicted high or moderate effects. Nevertheless, ten intronic variants could be detected in PRRX1 with mutant homozygous genotypes exclusively found in the Lundehund pools.In ROH regions detected in all Lundehund (pool 1 and 2) seven variants with predicted tolerated or deleterious (low confidence) effects could be found. The candidate genes Functional gene classification of human orthologues detected in ROH regions showed a particularly high involvement in biological processes affecting cellular processes GO:0009987), localization GO:0051179), response to stimulus (GO:0050896) and metabolic processes (GO:0008152) in all data sets  in other genomic regions which were predicted to be deleterious (SIFT [leprecan-like 1 (LEPREL1), centrosomal protein 164 kDa (CEP164), collagen, type XXVII, alpha 1 (COL28A1) and keratin 3 (KRT3) in 36 Lundehund revealed a significant association of CEP164:g.57380G>T and LEPREL1:g.139212C>G with LS (Table Based on these results we chose the missense mutation us (SIFT ) for valLEPREL1:g.139212C>G and COL28A1:g.159951T>A to be breed specific mutations for the Lundehund , which could be found to harbor a Lundehund specific ROH region, have been shown to be highly susceptible to inflammatory bowel disease [NOD1 plays an important role in colonic epithelial protection against intracellular organisms similar to mucin genes. In Lundehund affected with atrophic gastritis an abnormal presence of mucous neck cells could be detected in the gastric mucosa [Variants in the candidate gene  disease . It was c mucosa . We suppLMBR1 (DC-2) in western dog breeds [GREM2 was shown to be expressed in osteoblasts during in vivo skeletogenesis and involved in the regulation of bone formation genes [GREM2 was bidirectionally regulated by bone morphogenetic protein 2 (BMP2) together with GREM1, a gene which was shown to be expressed along with sonic hedgehog (SHH) and play an important role in limb bud development [PRRX1 (PRX1). Loss-of-function mutant PRRX1/PRRX2 mice resulted in shortened zeugopods of the forelimbs and hindlimbs as well as postaxial polydactyly in the forelimb [PRRX2 in long-range homozygous haplotypes suggesting this gene as a candidate for Lundehund-polydactyly [Further potential signatures of selection could be detected in the region of genes that might be involved in breed specific conformational traits like joint flexibility or polydactyly. According to Lundehund breed standard, the Lundehund was reported to have at least six toes at fore and hind limps , 18, 20.g breeds . The homg breeds . We founon genes , 36. It elopment . A similforelimb . EHH anaydactyly . We assuLEPREL1:g.139212C>G as a potential causative mutation for LS as well as NOD1 as a potential precursor gene which might play a role in LS breed predisposition. In addition, we identified potential signatures of selection for characteristic breed specific traits whose targeted selection might have increased genetic risk factors for disease development.In conclusion, our study of whole-genome sequencing data of Lundehund dogs suggests The results of our analysis represent a significant step to identify the genetic background of Lundehund specific traits which still remain to be further investigated for proper understanding of the underlying complex genetic mechanisms.Genomic DNA was obtained from blood samples of 36 Lundehund and 186 dogs of different dog breeds including Norwegian Buhund and Norrbottenspets using a standard saline precipitation method . Lundehuversus expected -log10P-values were calculated to control for population stratification using SAS/Genetics.In total 25 Lundehund (17 LS-affected and 8 LS-unaffected) were genotyped on the canine Illumina high density bead chip (Illumina) according to manufacturer’s protocols. Quality of genotyping data was controlled using a minor allele frequency (MAF) >0.05 and a genotyping rate per SNP >0.90. Filtering resulted in 93,882 SNPs with a mean genotyping rate of 99 %. Genome-wide association analysis was run using TASSEL version 3.0 . Generalhttp://darwin.cwru.edu, 2012) to test the most likely mode of inheritance for LS. We tested the hypothesis for the μ model without any genetic component, monogenic inheritance, polygenic inheritance and mixed major gene inheritance with a polygenic component and an independently segregating major gene locus with two alleles.Pedigree analysis was performed using regressive logistic models and the procedure SEGREG of S.A.G.E. . Furthermore, we locally realigned reads and performed quality score recalibration and SNP calling using GATK [Whole genome sequencing of Lundehund samples was performed on the Illumina MiSeq  in paired-end mode. Libraries were prepared for one DNA-pool of three LS-affected Lundehund and for one DNA-pool of three LS-unaffected Lundehund using the Illumina Nextera DNA Sample Prep Kit according to standard protocols. We performed four runs for each pool with v2 Reagent Kits (Illumina) on a single lane flow cell (2 × 250 bp reads). Fastq-files were quality controlled using fastqc 0.11.3 . Mappinging GATK . In addiing GATK .http://pngu.mgh.harvard.edu/purcell/plink/). Based on the genome size covered with SNPs divided by the number of SNPs we estimated the minimum distance of SNPs as 0.3. A maximum of three SNPs with missing genotypes and three heterozygous SNPs were admitted in each window. ROHs were filtered for LS-specific and Lundehund specific ROH regions based on hom.summary data outputs from ROH analysis for each dog using SAS/Genetics, version 9.4 . The homozygosity information of each SNP  was detected and compared to the other individuals in order to identify overlapping and private regions, the number of SNPs and length of the regions. These specific ROH regions were searched for variants predicted to have high or moderate effects (SNPEff).ROHs were detected using a stringently quality-controlled autosomal dataset of 8,585,517 SNPs showing a minimum read depth of 3, a maximum read depth of 60, a minimum mean read depth of 12 and minor allele frequency 0.01 for all samples. Sliding windows of 500 SNPs were chosen for ROH analysis. Homozygous regions of >150 kb were detected as ROHs using PLINK, version 1.07  [Chromosomal positions of ROH regions were merged with Ensembl gene predictions (ensGene) using Galaxy intersection tool (xy.org/) , 47. Thexy.org/) , 49 and xy.org/) .LEPREL1 and further 23 SNPs were investigated for functional effects using the Variant Effect Predictor [Analysis of sequencing data was performed identifying variants with SNPEff-predicted high or moderate effects with a homozygous mutant genotype in the LS-affected pool, a heterozygous or homozygous wild-type genotype in LS-unaffected pool and a homozygous wild-type genotype in the other five dogs of different breeds using SAS/Genetics 9.4. Variants flagged with warnings about possible annotation accuracy problems were omitted from analysis. The candidate SNP in redictor  for SIFTredictor  predictiredictor . RegionsLEPREL1:g.139212C>G, KRT3:g.2584C>T as well as the polydactyly-associated polymorphism in LMBR1 (DC-2) was performed using restriction fragment length polymorphisms according to standard protocols [CEP164:g.57380G>T and COL28A1:g.159951T>A were validated using Kompetitive Allele Specific PCR (KASP) genotyping assays  [Genotyping of the candidate SNPs rotocols . DC-2 prrotocols . In addisex, UK) . After tMB21D2 and LEPREL1 was performed in one LS-affected Lundehund and one Great Dane as control dog. RNA was obtained from hair roots in stabilized RNALater reagent , transcribed into cDNA and used for PCR amplification according to a standardized protocol [FGF12 could not be amplified from these hair root samples at all.Sanger sequencing of the cDNA of protocol  (AdditioCARD9, Caspase Recruitment Domain Family, Member 9; CCDC50, Coiled-Coil Domain Containing 50 ; cDNA, complementary DNA; CEP164, centrosomal protein 164 kDa; COL28A1, collagen, type XXVII, alpha 1; EHH, extended haplotype homozygosity; FGF12, fibroblast growth factor 12; GLM, generalized linear model; GREM2, gremlin 2, DAN family BMP antagonist; GWAS, genome-wide association analysis; KRT3, keratin 3; LEPREL1, leprecan-like 1; LMBR1, limb development membrane protein 1; LS, Lundehund syndrome; MB21D2, mab-21 domain containing 2; NOD1, Nucleotide-Binding Oligomerization Domain Containing 1; PLE, protein-losing enteropathy; PRRX1, paired related homeobox 1; ROH, runs of homozygosity; SHH, sonic hedgehog; SNV, single nucleotide variant


ZmARF31, which plays a key role in low P responses and root architecture regulation. Significant phenotypic variation was found in each of 11 tested traits under both P and no-P treatments, and 30 single nucleotide polymorphisms (SNPs) and 14 insertion–deletions (InDels) were detected in ZmARF31 among the 331 maize inbreds. The 5′-untranslated region (UTR) of ZmARF31 showed a small linkage disequilibrium (LD) block under significant purifying selection, whereas the 3′-UTR showed the most abundant diversity and a larger LD block. Thirty, fourteen, and nine natural variations were identified in ZmARF31 that were associated with P-deficiency-tolerance traits (P ≤ 0.01) by using the general linear model (GLM), GLM incorporated with population structure, and mixed linear model, respectively. Four SNPs were significantly associated with the total dry weight (TDW) in the three models, of which SNPs S410 and S462 were located in a complete LD block. A further verification conducted in a recombinant inbred line population revealed that favorable allele G/G of non-synonymous mutation S410 and favorable allele with a 38 bp insertion of InDel S1442 exhibited positive genetic effects on the TDW and total root tips, respectively. Expression analysis further confirmed that ZmARF31 was highly expressed in the roots of low-P-tolerant inbred 178. The protein encoded by ZmARF31 was located both in the nucleus and cytoplasm. Haplotypes carrying more favorable alleles showed a greater effect on phenotypic variation than single loci. Such haplotypes should be helpful to develop valuable genetic markers and perform maize molecular breeding.Low-phosphorus (P) stress is one of the major factors constraining plant growth and yield. Improving plant tolerance to P starvation through molecular breeding is an efficient alternative to increase grain production. In the study, 331 diverse maize inbreds were used to detect nucleotide diversity and favorable alleles of  In plants, phosphorus (P) is a critical macronutrient and plays an important role in various basic biological functions such as energy generation, glycolysis, nucleic acid synthesis, enzyme activation/inactivation, redox reactions, signaling, and carbon metabolism . Low avaAtTIR1 (OsPHR2 (ZmPTF1 (AtARFs (ARF), one of the transcription factors (TFs), plays a key role in low P responses and phenotypic variation by altering gene expression and function  in Arabidopsis  further validate gene expression in low-P-tolerant and low-P-sensitive inbreds and the genetic effect of favorable alleles on P-deficiency-tolerance traits in a recombinant inbred line (RIL) population. Molecular markers designed based on favorable allelic variations and haplotypes are useful in molecular breeding to improve maize tolerance to P deficiency.The findings of such studies provide more information to accelerate molecular breeding for selecting P-deficiency-tolerance traits by using useful molecular markers. Based on high density of genetic polymorphisms and linkage disequilibrium (LD), association analysis provides a novel approach for dissecting complex trait loci in maize . Maize sactivity . The assolerance . With hi4H2PO4 was added to the solution for NPA treatment. The plants were watered once every 3 days with 300 mL of the solution per pot. The P-deficiency-tolerance traits were evaluated at the five-leaf seedling stage, i.e., 25 days after germination.In total, 331 diverse core maize inbred lines , selected from different heterotic groups, stiff stalk, non-stiff stalk, and tropical or subtropical group , were usThe WinRhizo Pro 2008a image analysis system  equipped with a professional scanner  was used to analyze the root morphology under PA and NPA conditions, such as total root length, average root diameter (RD), total root volume, total root tips (RT), and total root fork. The other characteristics measured included number of leaves (NL), length of the longest root (LLR), total dry weight (TDW), root dry weight, shoot dry weight, and root to shoot ratio. The plant material was separated into shoot and root, stored in paper bags, and heated at 105°C for 30 min to kill the living cells, and then dried at 75°C until constant mass was obtained. The field experiment conducted in 2012 has been reported previously . DescripZea perennis, Zea mexicana, and Zea huehuetenangensis, two from Zea nicaraguensis, and three from Zea parviglumis, were used for the analysis of nucleotide diversity and molecular evolution (Supplementary Table A RIL population (178 × 9782) containing 196 lines generated using low-P-tolerant inbred (178) and low-P-sensitive inbred (9782) was used for the validation of significant association sites. Maize inbreds 178 and 9782 were screened from over 400 maize inbred lines with extreme tolerance and sensitivity to low-P stress in multiple experiments . Plant gZmARF31 (GRMZM2G023813), including the 5′- and 3′-untranslated regions (UTRs), were obtained from 331 maize inbreds and eight teosinte lines by designing three pairs of primers using Primer Premier 5 (Genomic DNA was extracted from seedlings at the three-leaf stage using the cetyltrimethyl ammonium bromide (CTAB) method. Sequences of remier 5 . PCR wasZmARF31 polymorphisms among the 331 maize inbreds and eight teosinte lines were detected by conducting multiple sequence alignments by using ClustalX v2.0.11  ≥ 0.05 in TASSEL v3.0 , was generated using the neighbor-joining method in MEGA v6.0 , were used to distinguish the presence of favorable alleles in ZmARF31 by using PCR. The primers are listed in Supplementary Table 2 (25 mM), 4.0 μL dNTP (2.5 mM), 0.2 μL Taq polymerase (5 U/μL), 1 μL template DNA (approximately 100 ng/μL), and 0.5 μM primers. The PCR conditions were as follows: 94°C for 3 min; 35 cycles at 94°C for 30 s, 58°C for 30 s, 72°C for 30 s, and 72°C for 10 min. The genotypes of the InDel locus were detected based on the amplification products separated on 1.5% agarose gels, stained with ethidium bromide and visualized under UV light, in an electrophoresis systems (Bio-Rad). Two alleles of the SNP locus were distinguished by performing high-resolution melting (HRM) curve (TM EvaGreen® supermix (Bio-Rad) and Bio-Rad CFX96 detection system. The reaction volume and cycling conditions for HRM were as per the manufacturer’s instructions, and HRM curves were analyzed using the manufacturer’s software. The t-test was used to detect the correlation between allele variation and investigated traits combined with SNP/InDel genotyping and phenotypic data of the RIL population. The favorable alleles containing improved low-P tolerant traits and increased tolerance to P starvation were then identified as described by The RIL population (178 × 9782), including 196 lines, was used to detect allele variation in M) curve  by using4H2PO4 (PA) in a phytotron to eliminate other influencing factors. At the three-leaf stage, plant seedlings were assigned to PA and NPA conditions. Roots and leaves were separately collected from the maize inbred lines under PA and NPA conditions at 0, 6, 12, 24, 48, and 72 h. Total RNA was isolated from each sample by using TRIZOL reagent (Invitrogen) and RNase-free DNase (Takara). Next, iScript cDNA synthesis kit (Bio-Rad) was used to synthesize cDNA by using 1 mg total RNA from each sample. Subsequently, quantitative real-time reverse transcription PCR (qRT-PCR) was performed using the SsoFastTM EvaGreen® supermix (Bio-Rad), and the expression of housekeeping gene 18S was used as an internal control. Three replicates were used to calculate the average and SD of expression levels by using the 2-ΔΔCT method  was amplified using the specific primers (Supplementary Table ® II system (Vazyme). A CaMV35S:ZmARF31-eGFP construct was used to assess protein localization. PCXSN2RFP vector, including a nuclear location signal (NLS), was used as a control. The loaded vector pCAMBIA2300-P35S:ZmARF31-eGFP and control vector were transformed into Agrobacterium tumefaciens strain GV3101. Tobacco (Nicotiana benthamiana) leaves were used for agroinfiltration by using the transformed GV3101, as described by The protein localization of P < 0.01; and descriptive statistics exhibited a large range of phenotypic variation for each trait . There were 44 polymorphic loci identified with MAF ≥ 0.05  contained less genetic variation . Although the 3′-UTR showed the highest nucleotide diversity (π = 12.36 × 10-3), exon 1 showed the lowest nucleotide diversity (π = 3.52 × 10-3) in maize. The nucleotide diversity ratio (maize/teosinte) was 36%, ranging from 19% (intron) to 80% (exon), indicating that the maize inbred lines have lower genetic variation .Nucleotide diversity analysis using sliding windows indicated that natural variants in D test and Fu and Li’s test for different gene regions were used to understand selection mechanism of ZmARF31 during maize domestication and improvement. Significant negative Tajima’s D values over the entire sequence in maize were consistent with those of Fu and Li’s statistics, suggesting the existence of purifying selection in ZmARF31 (Table 1). Tajima’s D test indicated that, except for introns with a positive value, all other regions had negative values in both maize and teosinte. However, the 5′-UTR had significant negative value in maize, indicating that purifying selection and/or population size expansion had occurred in this region. As an additional statistic, Fu and Li’s test using the outgroup species of Z. perennis indicated the presence of significant negative values for all genic regions, excluding some non-significant positive values in the introns (Table 1). Fu and Li’s test also revealed the signature of purifying selection in some regions of ZmARF31.Tajima’s ZmARF31 gene. Under the assumption of only a single favorable haplotype at the neutral loci fixed by past selection, maize sequences would form a single clade during domestication as expected in a previous phylogenetic analysis . In particular, all maize inbreds were clustered into a subclade along with their wild ancestor, Zea mays ssp. parviglumis; which was consistent with the expectation of selective pressure analysis.A phylogenetic tree was constructed for elite maize and teosinte accessions . An LD block was observed at the 5′-UTR, where the signature of purifying selection were detected. However, a larger LD block was observed in the 3′-UTR that also showed the most abundant diversity . Plots of r2, representing of LD level, indicated that LD declines to 0.1 within 1 kb in maize lines , which is considerably faster than the average of the whole maize genome , 220, including 146 under PA and 74 under NPA conditions, were significant at P ≤ 0.05. Using GLM, GLM + Q, and MLM (Q + K), 91, 73, and 56 associations were significant at P ≤ 0.05, 30, 14, and 9 of which were significant at P ≤ 0.01, respectively (Table 2). Among the 44 polymorphic sites, 18 (including two InDels) were found to be significantly associated at P ≤ 0.01 with at least one of the P-deficiency-tolerance traits . Detailed information on the location, genotype, frequency, and statistical value for each site is presented in Supplementary Table The association between natural variation in ZmARF31. Five SNPs/InDels (MAF ≥ 0.05) with moderate to strong LD levels that were significantly associated with RT under PA condition (P ≤ 0.01) were evaluated, and eight haplotypes were identified . Of them, five haplotypes (Hap1–Hap5) with an MAF of ≥5% were analyzed on a P-deficiency-tolerance trait, RT. Significant differences in RT were found between pairwise haplotypic comparisons  at P < 0.05. The haplotypes with more favorable alleles  showed significantly (P < 1.23 × 10-5) higher RT than those with less favorable alleles . Haplotypes had higher explained phenotypic variation for RT (7.65%) compared to single loci (from 2.31 to 3.60%). Another gene region, which was at the extremely strong LD level (r2 > 0.95), contained five SNPs  and two InDels (S2029 and S2045) and captured only two haplotypes with MAF ≥ 0.05, showed significant effect on TDW under the NPA condition (P < 0.05). Moreover, haplotypes from the SNPs and InDels that were significantly associated with other tested traits were also analyzed without considering LD. At P < 0.01, four informative haplotypes with MAF ≥ 0.05, consisting of five SNPs significant for NL  and one nearby SNP S1998 (or nearby SNP 492), showed significant haplotypic effect on the target trait, NL. Four informative haplotypes from six SNPs and one InDel  were significantly associated with NL and RD. Five haplotypes consisting of three SNPs and two InDels  had significant effect on LLR. The combinations of the loci that were significantly associated with target traits explained higher proportions of phenotypic variation than single loci and also represented functional haplotypes.The effects of gene polymorphisms on root trait response to low P stress were determined by analyzing haplotypes with significant SNPs/InDels in ZmARF31 on low-P tolerance in maize was elucidated using an RIL population with 196 lines and its two parental lines. The alleles from significant loci in the tolerant inbred 178 and sensitive inbred 9782 were considered to be favorable/tolerant and inferior/sensitive, respectively. The DNA polymorphisms at significant SNPs S410 (T/G) and S462 (A/G) associated with TDW and InDel S1442 (0/38 bp) associated with RT were detected . The allele “G” at SNP locus S410 and the large insert fragment with 38 bp located in the intron region at S1442 were found in the low-P-tolerant inbred 178 . Two pairs of special primers, designed based on the flanking sequences at the two significant loci , were used to successfully distinguish the presence of favorable alleles in ZmARF31 in the RIL population . For S410 (T/G), two line groups with alleles “T” and “G” in the RIL population were identified using the normalized melting curve of HRM . For S1442 (0/38 bp), “insertion” and “deletion” with 38 bp were distinguished and revealed through a fragment length polymorphism in the RIL population .The genetic effect of natural variations within Figure 4D). The line group with SNP G/G had higher mean value than the group with SNP T/T, implying that the SNP G/G was favorable/tolerant. The low-P-tolerant inbred 178 had favorable/tolerant allele GG at SNP S410 (T/G), while the low-P-sensitive inbred 9782 had allele TT. A significant difference in RT was observed between the line groups with homozygous allelic insertions and deletions . As expected, compared with NPA treatment, the RIL population showed substantially increased mean values for both traits under PA condition. Further, the lines with favorable/tolerant allele insertion were more tolerant to P starvation than those with inferior/sensitive allele deletions. Thus, natural variation at the tested SNP and InDel in ZmARF31 contributed to the improved low-P tolerance in maize.The genetic effects of SNP S410 (T/G) and InDel S1442 (0/38 bp) on the associated traits TDW and RT were examined. A significant difference in TDW between the line groups with homozygous allelic SNP T/T and G/G was found under both PA and NPA conditions . The highest relative expression levels were observed in the roots of low-P-tolerant inbred 178, which were gradually and constitutively induced after NPA treatment, with an increased expression of over 10-fold at 48 h. However, stable mRNA levels were observed for the leaves of 178 across different treatment time points. This finding was considerably similar to the expression trend observed for the roots of low-P-sensitive inbred 9782, where the expression levels of ZmARF31 were lower in the leaves than those in the roots. These results suggested that ZmARF31 was possibly involved in maize low-P tolerance.The expression patterns of 35S:ZmARF31-eGFP and control vector PCXSN2RFP into tobacco leaves using transient transformation. LSCM revealed strong red fluorescence signals only in the nucleus of the tobacco leaves transformed by CaMV35S:NLS-RFP as the control , whereas the fusion protein, ZmARF31-eGFP, showed green fluorescence signals in both nucleus and cytoplasm . This result confirmed the prediction by the CELLO v.2.5 program  every 49 bp. Different levels of nucleotide polymorphism detected across different studies might be attributed to gene properties in specific plant species and population size (ZmARF31 (Table 1) was diverse compared to that in PSY1 . Furthermore, HRM-based verification for non-synonymous SNP mutation S410 (T/G) revealed that favorable/tolerant allele G/G had a positive genetic effect on TDW . This is similar to a previous report on co-segregation of the ZmDREB2.7 tolerant allele with improved drought tolerance . Compared with low-P-sensitive inbred 9782, a higher expression level was detected in the roots and leaves of low-P-tolerant inbred 178. The results also indicate that the candidate gene association mapping within ZmARF31 gene region might be helpful to mine natural variation associated with low-P stress. As reported by Further haplotype analysis with five significant variation loci, including InDel S1442, revealed that the haplotypes with more favorable SNPs/InDels had a greater effect on phenotypic variation than single loci . The expZmCCT, was detected in nucleus by CaMV35S:ZmCCT-GFP introduced into onion epidermal cells (NAC26 was located in nucleus (ZmARF31 was located in both nucleus and cytoplasm, which was different from the general phenomenon that TFs are only located in the nucleus. The difference can be explained by the special biological function of ZmARF31. The subcellular distribution of ZmARF31 is regulated by various cellular processes, which might include the degree of phosphorylation during P starvation (ZmARF31 associated with low-P tolerance might allow the elucidation of the molecular mechanism underlying response to low-P stress under various maize genetic backgrounds. The detailed phenotypic and molecular characterization of allele variations in ZmARF31 will be useful for accelerating improvement of tolerance to abiotic stress and increasing molecular breeding efficiency in maize.To determine the subcellular localization of the functional gene, the target fragment is fused with eGFP/GFP forming a fusion protein, and then an expression system can be used to determine its location based on the protein signal. Subcellular localization with the heterologous system using CaMV35S as a promoter, especially in tobacco leaves or onion epidermal cells is widespread used in different species including maize. For example, photoperiod sensitivity gene in maize, al cells . Tobacco nucleus . In our arvation , time-/darvation , as wellarvation . HoweverYLu and FW conceived the experiments and designed the study. FW, ZL, JX, and LL performed the experiments and analyzed the data. SG and HL provided some maize lines and the RIL population. FW, YLi, and YLu drafted the manuscript, which was reviewed by all authors.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.


Genotyping-by-sequencing (GBS) is becoming an attractive alternative to array-based methods for genotyping individuals for a large number of single nucleotide polymorphisms (SNPs). Costs can be lowered by reducing the mean sequencing depth, but this results in genotype calls of lower quality. A common analysis strategy is to filter SNPs to just those with sufficient depth, thereby greatly reducing the number of SNPs available. We investigate methods for estimating relatedness using GBS data, including results of low depth, using theoretical calculation, simulation and application to a real data set.We show that unbiased estimates of relatedness can be obtained by using only those SNPs with genotype calls in both individuals. The expected value of this estimator is independent of the SNP depth in each individual, under a model of genotype calling that includes the special case of the two alleles being read at random. In contrast, the estimator of self-relatedness does depend on the SNP depth, and we provide a modification to provide unbiased estimates of self-relatedness. We refer to these methods of estimation as kinship using GBS with depth adjustment (KGD). The estimators can be calculated using matrix methods, which allow efficient computation. Simulation results were consistent with the methods being unbiased, and suggest that the optimal sequencing depth is around 2–4 for relatedness between individuals and 5–10 for self-relatedness. Application to a real data set revealed that some SNP filtering may still be necessary, for the exclusion of SNPs which did not behave in a Mendelian fashion. A simple graphical method (a ‘fin plot’) is given to illustrate this issue and to guide filtering parameters.We provide a method which gives unbiased estimates of relatedness, based on SNPs assayed by GBS, which accounts for the depth (including zero depth) of the genotype calls. This allows GBS to be applied at read depths which can be chosen to optimise the information obtained. SNPs with excess heterozygosity, often due to  polyploidy or other duplications can be filtered based on a simple graphical method.The online version of this article (doi:10.1186/s12864-015-2252-3) contains supplementary material, which is available to authorized users. The calculation (via pedigree records) or estimation (using genotype data) of relatedness either implicitly or explicitly underlies most genetic analyses. Pedigree relatedness is used in the improvement of agricultural species and the elucidation of human diseases. More recently, marker-based estimates have been used to account for population structure in genome-wide association studies (GWAS)  and to eThe first applications of GWAS and GS have relied on genome-wide data generated using ‘SNP chips’—assays that give genotype results for a pre-defined set of many thousands of single nucleotide polymorphisms (SNPs). An alternative method that is gaining popularity is to use genotype calls on the basis of next generation sequencing. This is usually implemented by sequencing a subset of the genome using restriction enzymes and size selection, so that more reads  of the sequenced regions can be obtained for a given sequencing effort . The infGBS is an attractive method for research in species for which SNP chips have not been developed, as it does not have the up-front development costs of the SNP chips. It may also be used in species without a reference genome sequence, although a reference genome is useful for sequence alignment, SNP ordering (if imputation or GWAS are to be used) and for quality control.One drawback of GBS is that it is possible that only one of the two alleles is seen, and that a heterozygous individual is observed as homozygous. Also, if the coverage of the sequenced region is low, there will be many missing genotypes . The usual analysis strategy is to filter the data so that most genotypes are called with sufficient depth to minimalize these errors and missing data (e.g. ). For a g* denote the genotype as observed, e.g. AA* denotes that only A alleles are observed , including the case where there is only a single read. SupposeSuppose a SNP has alleles A and B. Let K = 1/2k where k is the sequencing depth for the observation. For example, at k = ∞, K = 0 and the observed genotype is the true genotype. At k = 1, K = ½ and an AB genotype is seen as either AA or BB (each with probability ½). There may be other plausible models, for example ones where allele reads exhibit clustering such that observed homozygosity is higher (when depth exceeds one) than in the random reads case, but which still have K = ½ when k = 1.If allele reads are at random, and without error, then x be the number of A alleles (the score) in the observed genotype (e.g. x = 2 for AA*).Let i and i' will be used to denote (joint) genotypes or values relating to individuals i and i'. Let p be the A allele frequency (assumed known). Some commonly used estimates of relatedness are based on the quantities i = i ' and i ≠ i ').Subscripts ach SNP) . We now F, thenIf the individual has inbreeding Ki and Joint observed genotypes can be written in terms of joint true genotypes . θ, γ, δ and Δ can be found in [θ is the coancestry, also referred to as the kinship or half the relatedness, between two (different) individuals. We now derive expectations of relevant quantities; full details are given in Additional file Further details about the quantities found in , but in K, i.e. on the sequencing depth. On the other hand, E(Sii) is dependent on sequencing depth ; varying between 2p(1 − p)(1 + F) (for infinite depth) and 4p(1 − p) (for depth one). The latter result shows that E(Sii) increases with decreasing sampling depth—if sampling depth is not taken into account, the diagonals of the genomic relationship matrix will be inflated.Notice that Rearranging the above equations, we obtainThe divisor in the first of these equations is the one that is used  when there are no depth issues for the genotype calls. It is seen to be the variance of the scores, and therefore the estimator is sometimes referred to as a correlation-based estimator. We prefer to regard it as a methods of moments estimator, with the denominator being chosen to make the estimator unbiased.k = 1, K = ½. For this estimator, E(Sii) = 4p(1 − p), i.e., it cannot be used to construct an estimator of F.We also consider an estimator which discards multiple reads for a SNP and individual, by randomly sampling one read from those available. Such an estimator may be more robust to model assumptions and/or be diagnostic for data that deviates from the model. However, when the model is correct, because the estimator does not use all the information, it will be more variable than one where all reads are used. The calculations are carried out as above, with j index the SNP number, and where relevant  the above quantities will be subscripted further by j. Let Z be the matrix  of centred genotype scores, i.e. having elements (xij − 2pj), where i indexes the rows  and j indexes the columns (SNPs). Then a genomic relationship matrix (GRM) can be calculated [The above methodology relates to a single SNP. Let lculated  as\documG1 have expected value 2θii’, the relatedness between the individuals. Diagonal elements have expected values which depend on the Kij . To correct this, we replace the diagonal elements of G byIf the allele frequencies are considered as known, then from the above equations, off-diagonals of Fi . Note that if K = ½, the denominator in the first sum is zero. This relates to the case where E(Sii) does not depend on F, i.e., contains no information about F. Therefore, the two sums above are taken over the SNPs where K > ½.so that it is an estimator of 1 + G3, , sometimes referred to as ‘naïve imputation’ [G are calculated using only those SNPs which are scored in both of the corresponding individuals. Here we denote the matrices constructed in this manner without and with the diagonal correction described above as G4 and G5 respectively. We refer to this fully corrected method of estimation (G5) as kinship using GBS with depth adjustment (KGD).If individuals are sequenced to a low depth, then many genotypes will be missing. Common approaches for dealing with missing genotypes are to replace them with estimated values, either using imputation  or more utation’ . We propZ are replaced by zeros. This means that any SNP that is missing for either of a pair of individuals will not contribute to their corresponding element in ZZ' (zero is added for that SNP). Next create a matrix P0 with the same dimensions as Z with each row initially being identical and containing the allele frequencies, but then replace these by zero for individuals and SNPs with missing genotypes. Also create a matrix P1 in the same way, but using one minus the allele frequencies. ThenA drawback of this approach is that the set of SNPs in the calculation varies across relative pairs. An obvious calculation strategy would be to loop through each pair of individuals, take a copy of the genotype scores for those SNPs where both individuals are scored, and calculate the relatedness using those SNPs. However, software which enables matrix calculations is usually designed so that such computations are much faster than element-wise calculations. A strategy to take advantage of fast matrix computation (but that requires more memory) is as follows. Firstly, missing values in G5.will only use SNPs where both individuals are scored, as required. The diagonals then need to be corrected, as above, to obtain d  and drawing kij alleles at random, with replacement, from the alleles of individual i at SNP j, where kij ~ Poi(d) . Simulations were undertaken for average depths 1, 2 and 8. Any SNP which had a minor allele frequency of zero in the genotype or GBS data was removed from the analysis. Relatedness estimates which required a value of K used the random alleles calculation, K = 1/2k where k is the sequencing depth for the observation. Means and standard deviations (sds) of relatedness values were obtained over sets of full-sibs, parent-offspring, between each pair of individuals present as parents (unrelated) and selfs, for the various relationship matrix methods. We also calculate mean sample ‘call rates’  and mean ‘co-call rates’ between pairs of individuals . Although the simulation of the full-sibs is not realistic in that it does not account for linkage, the aim here was to generate sets of individuals with a common level of relatedness, to allow a benchmark for comparing methods.Several small simulations were undertaken to illustrate and compare methods. A set of 100 pairs of unrelated parents were generated along with two (full-sib) offspring of each pair of parents. A set of 10,000 SNPs were simulated with the allele frequencies sampled from a Uniform distribution. The SNPs were modelled as being unlinked. Parent alleles were sampled from a population with these allele frequencies, while progeny received one of their parent’s alleles at random, and independently of the alleles received at the other SNPs . Once the genotypes were created, GBS results were simulated by setting an average depth Another set of simulations were run to investigate the effect of sequencing depth on the sds of estimates, at a fixed total sequencing effort. The total effort was quantified as the number of SNPs times the mean depth. Total effort was fixed at 10,000 reads spanning a SNP and this effort was spread over between 500 and 40,000 SNPs (i.e. mean depth ranging from 0.25 to 20). For this simulation allele frequencies were sampled uniformly between 0.1 and 0.5 to ensure that all SNPs were retained in the analysis .G5 results are presented from these last two sets of simulations.A third set of simulations investigated the effect of allele frequency distribution on the sds. For this simulation mean depth was fixed at 2 which gave near optimal results in the previous simulation. All SNPs were modelled as having the same allele frequency for a particular run. Each combination of allele frequency, ranging from 0.01 to 0.5, and number of SNPs, ranging from 1000 to 50,000, was simulated. Only the Salmo salar) which were part of Stofnfiskur’s (http://stofnfiskur.is/) breeding programme. The management of these animals has been approved by the Icelandic Food and Veterinary Authority (MAST). The dataset included fish from a cohort of 122 full-sib families, each having a unique dam, but with 66 different sires each siring 1–3 families. Genotypes were obtained from all sires, 119 dams and 94 full-sib families . In addition, there were a set of 244 ‘unrelated’ fish , plus an ‘unrelated’ reference which was sequenced 27 times. Each time a sample was sequenced it consisted of approximately 2 million 100 bp reads (see below) and these sampled approximately 1.2 % of the genome or 30Mbp to an average depth of 5 fold.The methods were also compared using a real dataset of 2203 Atlantic Salmon  fluorescence was utilised to accurately quantify the DNA prior to generating the restriction enzyme digest fragment sequencing libraries. These GBS-libraries were prepared utilising the PstI restriction enzyme following the method outlined in Elshire et al. [PstI overhangs were purchased from MWG Operon (www.operon.com) and the 96 barcodes were designed by Deena Bioinformatics (http://www.deenabio.com/services/gbs-adapters)  were digested with 5 U PstI  in 20 μl volume containing 1x NEB CutSmart buffer and incubated at 37 °C for 2 h followed by heat inactivation (65 °C for 30 min). Following digestion, ligation was carried out by adding a 30 μl ligase solution (containing 5 μl ligase buffer with ATP and 1 μl T4 ligase (NEB) and incubating at 22 °C for 1 h followed by heat inactivation (65 °C for 30 min). The individual barcoded samples were combined (5 μl of each), purified using a commercial kit  according to the manufacturer’s instructions and eluted to a final volume of 50 μl. The restriction fragment library was then amplified in 4 individual reactions  and 25 pmol of each primer; Additional file Tissue samples for DNA extraction were collected in the form of a fin clip from each animal which was stored in 96 % ethanol. A subsample of approximately 3 mme et al. . The amohttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). For each lane a random subsample of 10,000 reads was checked for contamination. The subsamples went through adapter removal using cutadapt [Salmo salar reference genome  with bwa version 0.7.9a-r786 [Raw fastq files were quality checked using FastQC v0.10.1 . The recorded pedigree was used to assess the effectiveness of the QC steps and of the various relatedness estimates. Finally, parentages which were not confirmed by the GBS results  were assumed unknown.The allele count data was analysed using the relatedness estimation methods discussed above. Allele frequencies were estimated using the total allele counts from all reads. A number of additional quality control (QC) diagnostics were also undertaken, and the data filtered on the basis of these results, with relatedness estimation applied both pre-filtering and post-filtering using a variety of filters. Samples were examined for sequence depth. The SNP ‘call rates’  and their minor allele frequencies (MAFs), based on genotype calls, were calculated. Hardy-Weinberg disequilibrium  was plotted against MAF, using a colour gradient to illustrate SNP average depth. We refer to this as a ‘fin plot’ because of the shape of the boundaries (upper bound of MAF-MAFG1) and the methods proposed here for use with GBS data (G3 and G5). All methods gave estimates close to zero for the unrelated.Mean relatedness estimates for the different relationship sets are shown in Fig. G1 with GBS data gave downwardly biased estimates for pairs of related individuals (due to the inflated denominator). This bias decreased with sequencing depth and had essentially disappeared when d = 8. Use of G1 with GBS data gave upwardly biased estimates for self-relatedness. The bias is due to an inflated numerator (not accounting for depth) which appears to exceed the effect of the inflated denominator. The most bias was seen at d = 2 and the least at d = 8. The use of method G4 removes the inflation in the denominator, and so results in more upwardly biased estimates for self-relatedness than did the use of G1. Additionally correcting for sample depth in the estimates for self-relatedness (method G5) gives results very close to true values for all the relationship types.Use of G1; labelled ‘Chip’) gave the lowest sds, while all the GBS methods except G3 gave similarly low sds when d = 8. Among the GBS methods, G3 gave the highest sds, followed by G5. However, these were the only two methods which gave unbiased mean estimates. Their higher sds reflect in the incompleteness of the GBS data which is not recognized by G1 and G4. For these two methods the sds decreased with increasing mean depth. However the sds for G3 with higher depth did not approach those for genotype data  of the relatedness estimates for the different relationship sets are shown in Fig. Figure The sds of estimated relatedness initially decline rapidly with increasing mean depth, and then slowly increase. For relatedness for pairs of individuals, the minimum (smoothed) values are around a depth of 2 to 4. For self-relatedness the minimum value is near a depth of 8, although any depth between 4 and 10 gave similar results. For self-relatedness, greater depth is needed to ensure a reasonable proportion of SNPs have depth of at least 2, which is the minimum required to allow an estimate of inbreeding.A contour plot of standard deviations of estimates of relatedness for parent-offspring pairs (true relatedness exactly 0.5) at a mean SNP depth of 2 is shown in Fig. The sequencing and conservative bioinformatics process, typical of what would be expected using a species with no available genome sequence, resulted in 30,923 SNPs for analysis. The mean SNP depth was 7.9 while 34 % of animal by SNP combinations had no results. The mean depths ranged from 0.38 to 38.10 for each animal, except for the reference (mean depth of 89.06). Although there was a wide range , none appeared to be ‘outliers’, so all were retained for further analysis. The distribution of MAFs is shown in Fig. G5 estimates of relatedness are shown in Table G5 (self-relatedness estimates) were plotted against the log-transformed mean depth for each sample as a diagnostic for the effectiveness of the depth adjustment used in G5. This is shown for the ‘All’ analysis in Fig. The diagonals of In view of these unexpected results, further diagnostics were undertaken, including a ‘fin plot’ Fig. . The uppWe have tried various filters based on this plot, such as proportional closeness to the lower boundary or a cut-off based on level of Hardy-Weinberg disequilibrium. The best results  of those investigated was to remove SNPs with Hardy-Weinberg disequilibrium below −0.05 . These results are shown in Table Although the plot of MAF suggests that it is only high MAF SNPs that may be causing a problem, a filter discarding SNPs with MAF ≥ 0.48  did not perform as well as HW-0.05 Table . AlthougEven after filtering, the relatedness estimated between related pairs was lower than expected. Examination of the distribution of relatedness estimates for parent-offspring pairs, using ‘HW-.05’ showed a bimodal distribution  which allows many samples to be sequenced for a subset of the genome . GBS is Drosophila, [GBS has already been investigated for genomic prediction in a number of species, such as wheat , Drosophsophila, , maize [sophila, , soybeansophila, , spruce sophila,  and ryegsophila,  with measophila, ). Crossasophila,  found thsophila,  weightinG1. Nevertheless it would seem sensible to make the adjustments proposed here, so that the GRM correctly reflects the relatedness. Further research is needed to investigate similar adjustments for other prediction methods.It is unclear whether these studies would find improved accuracies using the methods recommended here. Given that alternative prediction methods gave similar accuracies, it is possible that these other methods suffer from similar issues as when using a GRM (via GBLUP), but that the effects of (e.g.) naïve imputation and not adjusting self-relatedness for depth are indirect. Alternatively, the corrections we propose here may not affect prediction accuracy unduly. This is supported by the simulation study of Gorjanc et al. , who fouWe have shown that naïve imputation leads to downwardly biased estimates of relatedness. Any imputation method that is better at recovering the true genotype is likely to have less bias, but this needs further investigation. An attraction of GBS is that it can be applied with minimal genomic information, so imputation methods that do not require knowledge of SNP order , 27, or G4 . For self-relatedness there is only missingness for one individual to consider. However, the method only partly improved estimates of relatedness for full-sibs and parent-offspring groups (mean relatedness estimate of 0.31 for both groups). Other researchers have also noticed issues with GRM constructed from GBS data. Pérez-Enciso [When relatedness estimation is used only for data auditing, the focus is on a pair of individuals, and calculations are based on only those SNPs scored in both individuals . We havez-Enciso  found thz-Enciso  noted inz-Enciso  found inG1 GRM). Theoretical investigations showed that an adjustment was necessary for the diagonals of the GRM. We also argued that the divisor of each element in the GRM should only use SNPs that are in the corresponding calculation for the numerator. The adjustments were corroborated by a small simulation, where estimates of relatedness appeared to be unbiased, except at very low depth. The downward bias at low depth  is the probability of that genotype inflated by a constant K . This includes the random sampling situation with K = 1/2k, where k is the depth. It also includes cases where observations of alleles are not independent, but are ‘clustered’, i.e. for a true heterozygote, the probability of a second read being the same as the first read is greater than 0.5. It is possible that this is actually the case, as random imbalances in allele numbers could be exacerbated during DNA amplification. In any case, we have shown that this will not affect the estimation of relatedness between two individuals.We have shown that the numerator of the off-diagonal elements of G1 has an expectation which does depend on the depth, and we have shown how to correct this and demonstrated this correction in simulations using K = 1/2k in both the simulation and the calculations. The application of this method to real data found self-relatedness estimates which, after some SNP filtering, did not appear to be related to the mean depth of the individual. More research with additional data sets at varying depths is needed to investigate whether there is clustering in allele reads, but this initial evaluation suggests that there is not a strong clustering effect.On the other hand the diagonal elements of We are hopeful that our methods will place less reliance on filtering data for low SNP call rates or low depth, as is common in the studies using GBS cited above. Our application of these methods to a real data set has demonstrated that SNP filters should be investigated, but to exclude data that deviate from the assumed model . Atlantic salmon are known to have a genome which duplicated relatively recently, and still has regions of tetraploid inheritance . This waAlthough filtering for excess heterozygosity appears to have improved the estimation of relatedness, by giving results that better agree with the recoded pedigree, we still found estimates which appear to be low for highly related fish (full-sibs or parent-offspring). This could partly be due to incorrect assignment of samples to pedigree (either pedigree recording errors or sample recording errors). This is supported by the observation that some parents have very low estimated relatedness (near zero) with all or some of their putative progeny. Removing putative parent-offspring pairs where the mean estimated relatedness was less than 0.25 improved the mean estimated relatedness to 0.46 . These possible mis-assignments are still under investigation, but it appears that the GBS results have been useful in identifying these issues. Another reason for depressed estimates of relatedness is genotyping error and regions with non-Mendelian inheritance. We have tried to reduce errors in the data by excluding low MAF SNPs at the bioinformatics step, however, there are likely to still be some errors. Further investigation is required to see if allowing for sequencing error can improve estimates. If it is not possible to eliminate the effects of error and non-Mendelian inheritance, one possibility would be to use estimates of relatedness that are regressed towards pedigree-derived values (after appropriate pedigree cleaning), along the lines of the third GRM method given by VanRaden .Li et al.  argue thOur third simulation shows that it is mainly higher MAF SNPs that are contributing to the estimation of relatedness. Therefore it is likely to be beneficial to remove very low MAF SNPs  as these may contain a high proportion of incorrect reads.A possible drawback of our method is that it does not guarantee a GRM that is positive semi-definite, and so may not be invertable, which is a requirement for GBLUP. This will need investigation on a case by case basis. It may be possible to modify a non-invertible GRM to make it invertible  with onlIt may be possible to extend the approach to calculating GRM using GBS data outlined here to include calculations of linkage disequilibrium and linkage estimators. Such extensions or alternative approaches  would fuWe have addressed two major impediments to widespread use of genotyping by sequencing for routine use: the first is the ability to calculate unbiased estimates of the GRM using low coverage sequencing where genotypes are often missing and genotypes are probabilistic. The method makes maximal use of the available genotype data, is computationally efficient via use of matrix algebra and does not depend on problematic solutions such as imputation. Optimal sequencing depths for both relatedness and inbreeding were then calculated by simulation and the technique was also validated on a large real data set of pedigree recorded animals. The second impediment are SNPs with non-Mendelian inheritance based on observed GBS genotypes. A simple graphical method is given to illustrate this issue and to suggest an appropriate filter so they can be excluded from the GRM calculations. This works well for fixed differences between segmental duplications or allo-tetraploidy and also identifies SNPs that are segregating in one of the duplicated genome copies.https://github.com/AgResearch/KGD.git.R software for undertaking the calculations described here, example data and output are available from