Adolescents’ behavior is often a matter of concern, given their increased likelihood of enacting antisocial behaviors, which cause disruptions in the social order and are potentially harmful for the adolescents themselves and for the people around them. In this six-wave longitudinal study we sought to examine the interplay among maternal empathy, multiple indicators of mother-adolescent relationship quality , and adolescent antisocial behaviors rated both by adolescents and their mothers. Participants for the current study were 497 Dutch adolescents  followed from age 13 to 18, and their mothers. A series of cross-lagged panel models revealed reciprocal associations between maternal empathy and mother-adolescent relationship quality and between mother-adolescent relationship quality and adolescent antisocial behaviors. Interestingly, we also found some indirect effects of adolescent antisocial behaviors on maternal empathy mediated by mother-adolescent relationship quality. Overall, this study further highlights a process of reciprocal influences within mother-adolescent dyads. Adolescent antisocial behavior is often a matter of concern for parents, educators, and for the society at large . AdolescA consistent literature has shown that parent-adolescent relationship quality and adolescent antisocial behaviors are strongly related –5. In paTherefore, in this study we sought to study the interplay of maternal empathy, mother-adolescent relationship quality , and During adolescence, parent-child relationships undergo substantial changes. Support tends to decline from early to middle adolescence and stabilizes thereafter, while conflict is likely to increase from early to middle adolescence and to stabilize during late adolescence , 10–11. The extent to which parents and adolescents engage in relationship patterns that are appropriate for this period can affect adolescent psychosocial functioning . In contImportantly, the association between parent-child relationship quality and adolescent antisocial behaviors can be conceptualized as a dynamic process of reciprocal influences . In factIn line with these considerations, in this study we examined the longitudinal interplay of quality of mother-adolescent relationships and adolescent antisocial behaviors. Specifically, we focused on both concurrent associations and cross-lagged effects. Drawing from the literature reviewed above, we hypothesized to find reciprocal negative associations (at baseline and over time) between indicators of a warm mother-adolescent relationship  and adolescent antisocial behaviors .Differences in family relationships might be understood looking at different determinants of parenting . In partEmpathy is a fundamental social skill, representing the ability to understand and to share the emotional state of another person –33. MothFurthermore, the association between maternal empathy and mother-adolescent relationship quality can be conceived as a reciprocal one. In fact, as discussed in the previous paragraph, empathy can improve relationship quality in several ways and, at the same time, a warm mother-adolescent relationship can provide positive feedbacks to mothers, increasing their perception of being able to understand and respond appropriately to adolescents’ needs. So, mother-adolescent relationship quality and maternal empathy can reinforce each other in a positive loop.Drawing from these considerations, in this study we sought to examine associations between maternal empathy and mother-adolescent relationship quality over the course of adolescence. We expected to find positive reciprocal associations between maternal empathy and indicators of mother-adolescent relationship quality .Maternal empathy might also affect adolescent antisocial behaviors, both directly and indirectly. For instance, according to the social learning theory , maternaAdditionally, in line with transactional models of development , 19–20, Thus, in this study we sought to uncover associations between maternal empathy and adolescent antisocial behaviors. We expected to detect negative reciprocal associations between maternal empathy and adolescent antisocial behaviors. In addressing this hypothesis, we tested both direct and indirect effects to uncover whether maternal empathy has a direct effect on adolescent antisocial behaviors and vice versa  and their mothers, reciprocal associations among maternal empathy, mother-adolescent relationship quality , and adolescent antisocial behaviors . We usedData were drawn from the ongoing longitudinal RADAR-Y study (Research on Adolescent Development and Relationships–younger cohort). The RADAR study is a population-based prospective cohort study conducted in the Netherlands aimed at examining adolescent development. Because of an interest in both normal and abnormal development, a dual selection procedure was used to oversample adolescents at high-risk for externalizing problem behaviors. In a first step, teacher ratings of behavior problems were collected through the Teacher’s Report Form (TRF ). For thMage at T1 = 13.03, SDage = 0.46) and their mothers , for a total of 994 respondents. All participating adolescents were attending secondary schools. Most adolescents were native Dutch (95%), lived with both parents (86%), and came from families classified as medium or high socioeconomic status (89%).Participants for the current study were all 497 adolescents . Of the original sample, 425 families (86%) were still involved in the study at Wave 6, and the average participation rate over the six waves was 90%. Results of Little’s  Missing in Mplus .The RADAR study has been approved by the Medical Ethical Committee of Utrecht University Medical Centre (the Netherlands). Before the start of the study, adolescents and their parents received written information about the study and they all gave informed consent. Within each year of the study, trained research assistants made appointments for annual home visits. During these visits, participants completed a battery of questionnaires. Research assistants provided verbal instructions in addition to the written instructions that accompanied the questionnaires.doesn’t describe me at all) to 4 (describes me very well). Sample items include: “I often have tender, concerned feelings for people less fortunate than me” and “I try to look at everybody’s side of a disagreement before I make a decision”. In this study, average Cronbach's alpha across six waves was .82 (range: .79-.83).Mothers reported on their own empathy by completing the Dutch version  of the IBalanced relatedness refers to the extent to which adolescents feel that their mothers respect their opinions, whishes, and needs. It was rated by both informants with the Balanced relatedness scale ([absolutely disagree) to 4 (absolutely agree). Sample items include: “My mother respects my decisions”/“I respect my child’s decisions”. In this study, average Cronbach's alphas across six waves were .86 (range: .85-.89) and .88 (range: .85-.90) for adolescent and maternal reports, respectively.Mother-adolescent relationship quality was rated both by the adolescents and their mothers considering multiple indicators . s scale  to 5 (more is not possible). Sample items include: “How much does your mother really care about you?”/“How much do you really care about your child?” ; “How much do you and your mother/child get on each other’s nerves?” . In this study, average Cronbach's alphas across the six waves were and .81 (range: .78-.85) and .93 (range: .90-.95) for the adolescent reports of support and conflict, and .73 (range: .70-.78) and .92 (range: .90-.92) for the maternal reports of support and conflict, respectively.ry . Each pnever’, 1 = ‘sometimes’, and 2 = ‘often’). In this study average Cronbach's alphas across six waves were .89 (range: .87-.91) and .91 (range: .89-.92) for adolescent and maternal reports, respectively.Both adolescents and their mothers rated adolescent antisocial behaviors. Specifically, adolescents filled out the Dutch version  of the aplus 7.31 [Preliminary analyses were conducted in SPSS 22. We computed means and standard deviations for each study variable and bivariate correlations. To address our main research questions, we conducted longitudinal statistical analyses in Mlus 7.31 , using tSpecifically, we tested cross-lagged panel models to examine longitudinal associations between study variables. In each model, we included maternal empathy, one indicator of mother-adolescent relationship quality , and adolescent antisocial behaviors. In addition, we tested models separately for maternal and adolescent reports of mother-adolescent relationship quality and antisocial behaviors. Therefore, we tested a total of six models in which we examined cross-lagged associations controlling for one-year and two-year stability paths and concurrent associations.We tested model fit relying on multiple indices : the ComSB2) significant at p < .05, ΔCFI ≥ -.010, and ΔRMSEA ≥ .015 [plus [To model the longitudinal associations as parsimoniously as possible, we tested for each model whether cross-lagged effects and T2-T6 correlations were time-invariant. Thus, we compared the model in which these paths were free to vary across time with the constrained model in which they were fixed to be the same. In order to determine significant differences between models at least two out of these three criteria had to be matched: a Satorra and Bentler’s  scaled dA ≥ .015 –53. Fina15 [plus .Means and standard deviations of the study variables are reported in To unravel the longitudinal interplay among maternal empathy, mother-adolescent relationship quality, and adolescent antisocial behaviors, we tested a series of cross-lagged models. First, for each of the six models, we found that the version of the model in which cross-lagged effects and T2-T6 correlations were time-invariant was not substantially different from the version of the model in which these parameters were allowed to vary across time . Thus, wEach model fit the data very well . One-yeaAs shown in Cross-lagged effects indicated significant reciprocal positive associations between maternal empathy and balanced relatedness . Furthermore, a significant negative effect of adolescent antisocial behaviors on later balanced relatedness was detected with both adolescent and maternal reports.As displayed in Cross-lagged effects pointed to a significant negative effect of conflict on later maternal empathy for adolescent reports. Furthermore, bidirectional positive effects between conflict and adolescent antisocial behaviors were found for both informants.As reported in Cross-lagged effects indicated significant positive reciprocal associations between maternal empathy and support . Furthermore, we found a significant negative effect of support on later adolescent antisocial behaviors with adolescent reports, while the opposite effects  was detected with maternal reports.We tested indirect effects to examine mediational mechanisms. Findings indicated that mother-effects  were not statistically significant. In contrast, partial support was found for child-effects . More specifically, as reported in Adolescence has been traditionally described as a period of turmoil, in which antisocial behaviors reach a peak . In thisIt is worth noting that cross-lagged effects and concurrent T2-T6 correlations were found to be time-invariant, allowing us to model parsimoniously the pattern of associations among study variables over the course of adolescence (13 to 18 years). This suggests that while indicators of relationship quality , 11 and We found very strong support for the association between indicators of mother-adolescent relationship quality and adolescent antisocial behaviors. In fact, both at T1 and at T2-T6, balanced relatedness and support were negatively related, while conflict was positively related, to adolescent antisocial behaviors. Importantly, these findings were very consistent, being detected with both adolescent and maternal reports of mother-adolescent relationship quality and antisocial behaviors. Overall, these results are convergent with a wide cross-sectional literature  shobalanced relatedness, we found that higher levels of antisocial behaviors were related to a decrease in balanced relatedness over time. This result was replicated with both adolescent and maternal reports. It suggests that adolescent antisocial behaviors undermine mothers’ propensity to accept and respect adolescent needs and opinions, since mothers can find their children behaviors unacceptable and harmful. For conflict, we found bidirectional associations between conflict and adolescent antisocial behaviors. Thus, conflictual relationships between adolescents and their mothers increase the likelihood that adolescents enact antisocial behaviors, and these behaviors lead to increasing conflicts in a negative loop. Also this set of evidence was confirmed with both adolescent and maternal reports. In contrast, results regarding cross-lagged associations between support and adolescent antisocial behaviors differed for adolescent and maternal reports. More specifically, with adolescent reports we found a significant negative effect of support on adolescent antisocial behaviors, while with maternal reports, we found a significant negative effect of adolescent antisocial behaviors on support. This difference seems to point to a sort of “blaming” effect, in which for mothers a relative decrease in support is mainly driven by adolescent negative behaviors while for adolescents it is the other way around. Taken together, results uncovering over-time associations are consistent with transactional models of development [In our study, we further disentangled this association by looking at the direction of cross-lagged effects. In the model with elopment , 19–20, elopment ).In this study, we further expanded our understanding of adolescent antisocial behaviors and mother-adolescent relationship quality by taking into consideration maternal characteristics . In linepositive characteristics and behaviors of the offspring . Mo empathy  is stronRegarding effects of adolescent antisocial behaviors on maternal empathy, we did not find direct effects but some support for indirect effects. We found that negative effects of antisocial behaviors on maternal empathy were mediated by balanced relatedness (in the model with adolescent reports) and support . Taken together, these results indicate that adolescent behaviors lead to a progression erosion in the quality of the mother-adolescent relationships that, in turn, undermine mothers’ capacity of remain empathic and sympathetic with their children. Thus, this evidence points to the active role played by adolescents within the dyadic relationship with the mother , 21, 23.This study should be considered both in the light of its strengths and shortcomings, which can suggest venues for future research. A first strength of this study was its fully-recursive longitudinal design. This design allowed us to examine concurrent and over time associations among study variables throughout adolescence. We should note that the cross-lagged effects we found were generally small. However, as clearly explained by Adachi and Willoughby , such smA second strength of this study was the inclusion of both adolescents and their mothers. In this way, we could test if findings were replicated using adolescent and maternal reports of mother-adolescent relationship dimensions and adolescent antisocial behaviors. Overall, results were very consistent across different informants. An important suggestion for future research would be to examine whether these findings could be replicated using observational measures of mother-adolescent interactions.A further strength of this study was the inclusion of maternal empathy. Doing so, we expanded the focus on the linkages between parental relationships and adolescent antisocial behaviors to examine how this pattern of associations can be further explained taking into account parental characteristics . We founIn addition to this, we should note that a limitation of our study was the solely focus on maternal empathy and the mother-adolescent relationship. Thus, we could not test whether the same results would be replicated considering paternal empathy and indicators of paternal-adolescent relationship quality. Future studies are needed to clarify whether mothers’ and fathers’ effects are comparable.Finally, our study involved mainly Dutch intact families with medium or high socio-economic status. Thus, we could not deepen to what extent the family social situation could account for some of the effects documented in this study. A main direction for future research could be to disentangle the interplay of parental characteristics, relationship quality, and adolescent antisocial behaviors across different types of families . In this way, it would be possible to further clarify how family and contextual factors  could moderate some effects documented in this study.This study provides new insights into our understanding of the interplay among maternal characteristics, mother-adolescent relationship quality, and adolescent antisocial behaviors. In fact, in this multi-wave and multi-informant longitudinal study, we found that indicators of mother-adolescent relationship quality  were reciprocally associated with adolescent antisocial behaviors. Moreover, considering maternal empathy we could gain a better understanding of why some mothers are better able to establish supportive relationships with their offspring and we also found that maternal empathy is influenced (indirectly) by adolescent antisocial behaviors. Overall, this study further highlights the interactive dynamic characterizing the mother-child relationship suggesting that adolescent antisocial behaviors are both fostered by family relationships and, at the same time, modify them, even influencing mothers’ own level of empathy.


We previously identified the Eps-Am1 locus from Triticum monococcum and showed that the allele from cultivated accession DV92 significantly delays heading time and increases the number of spikelets per spike relative to the allele from wild accession G3116. Here, we expanded a high-density genetic and physical map of the Eps-Am1 region and identified the wheat ortholog of circadian clock regulator EARLY FLOWERING 3 (ELF3) as a candidate gene. No differences in ELF3 transcript levels were found between near-isogenic lines carrying the DV92 and G3116 Eps-Am1 alleles, but the encoded ELF3 proteins differed in four amino acids. These differences were associated with altered transcription profiles of PIF-like, PPD1, and FT1, which are known downstream targets of ELF3. Tetraploid wheat lines with combined truncation mutations in the A- and B-genome copies of ELF3 flowered earlier and had less spikelets per spike than the wild-type control under short- and long-day conditions. Both effects were stronger in a photoperiod-sensitive than in a reduced photoperiod-sensitive background, indicating a significant epistatic interaction between PPD1 and ELF3 (P < 0.0001). By contrast, the introgression of the T. monococcum chromosome segment carrying the Eps-Am1 allele from DV92 into durum wheat delayed flowering and increased the number of spikelets per spike. Taken together, the above results support the hypothesis that ELF3 is Eps-Am1. The ELF3 alleles identified here provide additional tools to modulate reproductive development in wheat.Wheat cultivars exposed to optimal photoperiod and vernalization treatments still exhibit differences in flowering time, referred to as earliness The online version of this article (doi:10.1007/s10142-016-0490-3) contains supplementary material, which is available to authorized users. More than 700 million tons of wheat is grown every year in very diverse environments, providing a major source of calories and proteins to the human population. The ability of wheat to adapt to these different conditions has been favored by a rapidly evolving genome combined with the buffering effect of recent polyploidization events  gene family, which upregulates the expression FT1 under long days   and vernalization (prolonged exposure to low temperatures). The photoperiod response is mainly regulated by per se (Eps)  was proposed as a promising candidate gene for the Earliness per se 3 (Eps-Am3) locus in Triticum monococcum L.  locus in barely  and short-day (SD) conditions. The early flowering in both mutants was associated with arrhythmic transcript patterns of central and output circadian clock genes under SD and constant ambient conditions and the upregulation of PPD1 and FT1 transcript levels  mutants in barley show similar phenotypic effects as the eam10 mutants described above. The eam8 gene encodes a homolog of the Arabidopsis circadian clock gene EARLY FLOWERING 3  and PIF5  genes PRR7 and PRR9   was associated with delayed heading time and increased number of spikelets per spike relative to the allele from wild T. monococcum ssp. aegilopoides (accession G3116). The effect of this locus was larger when plants were grown at 16 °C compared to 23 °C, suggesting a role of temperature on the modulation of the effects of this gene  into the cultivated T. monococcum ssp. monococcum accession DV92 (spring growth habit). The Eps-Am1 allele from G3116 is associated with early heading and is referred to as Eps-Am1-e, whereas the allele from DV92 is associated with late heading and is referred to as Eps-Am1-l  for the Triticum turgidum ssp. durum, 2n = 28, genomes AABB) was used to generate the TILLING population analyzed in this study  and SMP (SM-LIKE PROTEIN). The isogenic background of these lines reduced genetic variability and facilitated the precise mapping of the Eps-Am1 locus as a Mendelian trait rather than as a quantitative trait locus (QTL). Young seedlings were screened with molecular markers for MOT1 and SMP, and only plants carrying recombination events between these two markers were phenotyped.Seeds for the high-density map were generated from BCEps-Am1-e and Eps-Am1-l alleles can be maximized by exposing the plants to fluorescent lights at a low fluency (160 μM m−2 s−1), a long-day photoperiod (16 h of light and 8 h of darkness), and constant cool temperatures (16 °C) , where the differences were smaller but still significant. Heading times were registered from sowing to spike emergence.Previous studies have shown that the differences in flowering time between the T. monococcum physical map (see next section) and sequences from wheat genes homologous to Brachypodium distachyon genes located in the region colinear to Eps-Am1  and SMP were used to search a genomic database of B. distachyon (http://www.phytozome.net) and establish the colinear target region. The genes detected in this region were then used as queries to search a T. aestivum L.  database of flow-sorted chromosomes arms developed by the International Wheat Genome Sequencing Consortium (IWGSC http://www.wheatgenome.org/). A reverse BLASTP search from the best wheat candidate to the Brachypodium proteome was done, to confirm that the correct homolog was found.Molecular markers used for the construction of the high-density genetic map are listed in Supplementary Table T. monococcum ssp. monococcum accession DV92 . The nonrepetitive sequences were then annotated using a combination of tools, including comparative genomics analyses, BLAST searches of other annotated grass genomes, and the annotation pipeline Triannot . Once sequences were assembled, repetitive elements were identified using the Triticeae Repeat Sequence Database  and sequenced. GenBank accession numbers are listed in Supplementary Table T. urartu (PI 428198) protein, followed by the position of that amino acid from the initial methionine in the T. urartu reference protein . The potential effect of amino acid changes was predicted using multiple tools including BLOSUM62 matrix scores . T. monococcum NILs carrying the Eps-Am1-e (early flowering) and the Eps-Am1-l (late flowering) alleles were grown in a growth chamber under continuous temperature (16 °C) and long-day photoperiod (16 h fluorescent lights at 160 μM m−2 s−1). Five weeks after sowing, when plants carrying the Eps-Am1-e allele were at the terminal spikelet stage and plants carrying the Eps-Am1-l allele were at the double ridge stage, tissue was collected from young leaves every 4 h during a 24-h period. Six individual plants were sampled at each time point.The expression levels of the ACTIN was used as an endogenous control. Transcript levels for all genes are expressed as linearized fold-ACTIN levels calculated by the formula 2ACTIN CT − TARGET CT)( ± standard error (SE) of the mean. The resulting number indicates the ratio between the initial number of molecules of the target gene and the number of molecules of ACTIN.RNA samples were extracted using the Spectrum Plant Total RNA Kit (Sigma-Aldrich). First-strand cDNAs were synthesized from 1 μg of total RNA using the High Capacity Reverse Transcription kit (Applied Biosystems). Quantitative PCR was performed using SYBR Green and a 7500 Fast Real-Time PCR system (Applied Biosystems). Primers used for SYBR GREEN quantitative PCR are listed in Supplementary Table ELF3 were obtained from available sequences from T. aestivum cultivar Chinese Spring (IWGSC http://www.wheatgenome.org), and from T. turgidum ssp. durum cultivar Kronos transcriptome . Genome-specific primers were developed  and control plants homozygous for the wild-type ELF3 alleles (ELF3-WT) were selected from the segregating BC2F2 plants.A screening of DNAs from 1536 TILLING lines was performed using 384 pools, each including four DNAs. PCR amplification products of the target region were digested with elf3-null lines were also crossed to a Kronos near-isogenic line carrying the wild-type photoperiod-sensitive PPD-A1b allele, developed before  and LD (16 h light) photoperiods using fluorescent lights and a constant temperature of 16 °C. Heading time and number of spikelets per spike were determined for each plant, and the effects of the individual alleles and their epistatic interactions were statistically evaluated using two by two factorial ANOVAs. Statistical analyses were performed using SAS program version 9.4.BCEps-Am1 from T. monococcum accession DV92  was introgressed into T. turgidum ssp. durum cultivar CBW0112 . To generate this material, we first intercrossed T. turgidum ssp. durum cultivar Langdon and T. monococcum ssp. monococcum cultivar DV92 and treated the ABAm hybrid with colchicine to double the chromosome number. We backcrossed the resulting AABBAmAm amphyploid to CBW0112 for five generations, selecting the Eps-Am1 allele in each crossing cycle with flanking markers wg241 and NDK3 (NUCLEOSIDE DIPHOSPHATE KINASE 3) , and heading time and spikelet number were registered.CBW0112 BC3F2, seven SSR markers and three CAP markers previously mapped to wheat chromosome 1A were tested to determine the size of the introgressed 1Am chromosome segment , we decided to add ELF3 to our previous map and revisit the critical recombination events  and G3116 (GenBank KU570055) and used one of the discovered SNPs to develop a codominant marker  in the same T. monococcum segregating population used before to map Eps-Am1  and identified 22 lines with recombination events in the target region. Based on the heading time of BC6F3 progenies of the selected recombinants grown under controlled environment conditions, the Eps-Am1 locus was mapped 0.20 cM proximal to SMP and 0.18 cM distal to MOT1  and SMP (Bradi2g14260) define a 47-kb genomic region including the Brachypodium orthologs of ADK1 (Bradi2g14310), ELF3 (Bradi2g14290), and five additional annotated genes , but was not present in the Brachypodium colinear region. GRIK1-like encodes a serine/threonine-protein kinase similar to GRIK1 (GEMINIVIRUS REP INTERACTING KINASE 1) from Arabidopsis.The es Table . These Bce IWGSC . Three wion Fig. . The wheSH4 Bradig14330 anEps-Am1 region was delimited to a 0.1-cM interval flanked by ADK1/CK1-like (CASEIN KINASE I ISOFORM DELTA-LIKE) on the proximal side and ACA7 (ALPHA CARBONIC ANHYDRASE 7-LIKE) on the distal side. In the high-density mapping population, Eps-Am1 was completely linked to ELF3, GRIK1-like, and PWWP1 (PWWP domain-containing protein)  Fig. , which aELF3 is a circadian clock gene involved in the regulation of flowering time in Arabidopsis, rice, legumes, and barley , orthologous to Bradi2g14260, Bradi2g14270, and Bradi2g14275, were detected in close proximity, within an 8.6-kb region present in both BACs  has its closest homolog in a different wheat chromosome. Since all the genes annotated in the colinear region in Brachypodium have been accounted for, any additional wheat genes found in the two gaps still present in the T. monococcum physical map  to DV92 (ELF3 haplotype-C) and then backcrossing the F1 to DV92. These lines were grown in a controlled environment (16 °C and 16 h of light), and heading time and spikelet number were registered. This experiment confirmed that PI 355522 carries the Eps-Am1-e allele for early flowering and reduced number of spikelets , the shoot apical meristems were more advanced in the early flowering NILs  than in the late flowering NILs . No significant differences in the transcription profiles of the three candidate genes were detected between Eps-Am1 alleles during a 24-h time course  the comparison of the expression of known downstream targets of ELF3, (ii) the determination of the effect of the introgression of the Eps-Am1 allele in tetraploid wheat, and (iii) the determination of the effect of the elimination of all functional copies of ELF3 in tetraploid wheat.(i)Expression profiles of downstream genes: Previous studies of the barley elf3 mutants showed significant differences in the transcription profiles of PPD1, FT1, and central circadian clock genes, so the same genes were compared between the Eps-Am1 alleles. The peak of the PPD1 expression in the NIL carrying the Eps-Am1-l allele occurred 4 h earlier (Zeitgeber time ZT4) than in the NIL carrying the Eps-Am1-e allele (ZT8), and those differences were highly significant    than in those carrying the Eps-Am1-e allele (ZT12)  Fig. , PRR73  Fig. , GI  Fig. .Eps-Am1 alleles is consistent with an effect of ELF3. It should be noted that many of the changes observed in the T. monococcum NILs are not as severe as those reported for the elf3 null mutants in barley. This is not an unexpected result since the barley mutations are loss-of-function mutations Determination of the effect of the introgression of the Eps-Am1 allele into tetraploid wheat: The GRIK1-like proteins encoded by the diploid T. monococcum DV92 and the tetraploid Kronos A-genome copy alleles are identical, but the ELF3 and PWWP1 proteins are not. Therefore, a significant effect of the introgression of the DV92 Eps-Am1-l allele into tetraploid wheat would suggest that GRIK1-like is not likely a good candidate gene for Eps-Am1.5F3 lines of tetraploid wheat CBW0112 carrying a distal 1Am chromosome segment  that actively delays flowering time and increases the number of spikelets per spike that is effective both in diploid and tetraploid wheat.Under controlled environmental conditions , BC(iii)Loss-of-function mutants of ELF3 in tetraploid wheat: We hypothesized that if ELF3 was the correct candidate gene for Eps-Am1, and the ELF3 allele from DV92 was actively delaying flowering and increasing the number of spikelets per spike, then an elf3-null mutant should show the opposite phenotypic effects. We selected mutations that generated premature stop codons in the A and B homoeologs of ELF3 in the TILLING population of the tetraploid wheat variety Kronos  were evaluated under controlled environmental conditions . Under these conditions, the elf3-null Kronos plants flowered on average 7.2 days earlier than sister lines carrying wild-type ELF3 .In barley, loss-of-function mutations and wild-type alleles of P < 0.0001) were found between elf3-null plants and ELF3-WT sister lines both in heading time and in the number of spikelets per spike  and number of spikelets per spike (P < 0.05). These interactions can be visualized as nonparallel lines in the graphs presented in Fig. PPD-A1a and PPD-A1b alleles (P < 0.0001) in the presence of the wild-type ELF3 allele disappeared in the elf-null mutant background  but not for spikelet number  and PPD-A1b (53.6 days) alleles .Under SD conditions, there was also a significant epistatic interaction for heading time between nes Fig. . When thles Fig. . Headingelf3 were sufficient to abolish the differences in flowering time associated with the presence of the PPD-A1a or PPD-A1b alleles, or with different photoperiods.In summary, loss-of-function mutations in T. monococcum is a different species from T. urartu, the A-genome donor to the polyploid wheat species  to map Eps-Am1 within a 0.1-cM interval in the distal region of T. monococcum chromosome arm 1AmL , that was mapped on the distal region of chromosome arm 1HL colinear with Eps-Am1 . This domain is found in numerous proteins involved in cell division, growth, and differentiation. It binds to Histone-4 methylated at lysine-20, H4K20me, suggesting that it is a methyl-lysine recognition motif. The PWWP1 proteins encoded by the DV92 and G3116 alleles differ in five amino acids. The S251W and T294M polymorphisms are unlikely causal mutations for PRR7 and PRR9  and higher temperatures (day 20 °C/night 17 °C), the differences between the same PPD1 alleles in the same Kronos background were only 4 days under LD  and PPD1 (=PRR37) acts as a repressor of FT-like genes  and SD (25.3 days)  may have favored the selection of this allele during the domestication of T. monococcum, explaining its high frequency among the cultivated T. monococcum ssp. monococcum accessions  represents an encouraging result. However, this preliminary experiment was performed under controlled environmental conditions that are different from the ones observed in natural environments. The potential impact of this introgression on tetraploid wheat yield will require replicated field experiments in multiple environments. We are currently introgressing this T. monococcum chromosome segment into different tetraploid and hexaploid backgrounds to perform these experiments.The significant increase in spikelet number associated with the Eps-Am1-l allele introgression into tetraploid wheat are very preliminary for practical application, this experiment provided some valuable information. In previous studies using diploid wheat segregating populations, it was not possible to determine which of the Eps-Am1 alleles represented the ancestral stage. We did not know if the Eps-Am1-l allele delayed flowering time or if the Eps-Am1-e allele accelerated the transition to the reproductive stage  have been used commercially in Scandinavia, in regions with very short growing seasons and long photoperiods . Although the current production of wheat at high latitudes is limited, global warming may open new geographic areas to wheat cultivation. In this context, it would be interesting to test the performance of elf3 wheat mutants in high-latitude regions of North America, Europe, and Asia.In addition to the elf3 loss-of-function wheat mutants find a special niche in high-latitude regions, it is unlikely that they will be useful in other regions because of the negative pleiotropic effects in the number of spikelets per spike. In our controlled environment experiments, the elf3 Kronos mutants showed significant reductions in the number of spikelets per spike both under both LD (up to 6.4 spikelets) and SD photoperiods Below is the link to the electronic supplementary material.


The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined. Genome-wide association analysis (GWAS) is an efficient way to discover QTLs associated with phenotypes. A common method for GWAS is to sequentially fit all SNPs one at a time as a fixed effect in a mixed model that includes the pedigree or genomic relationship matrix to control for polygenic background effects  for broiler chickens were provided by Cobb-Vantress Inc.  for a dam line across 5 generations . The total number of animals with phenotypes was 274,776, and the average BW6 was 2.40 ± 0.33 kg. Complete pedigrees were available for all individuals. For generations G1–G4, 4732 broilers were genotyped with 57,636 SNP markers on a SNP panel across the whole genome developed by Groenen et al. .Quality control (QC) procedures were applied to remove genotyped individuals with pedigree errors and SNP genotypes that were either monomorphic, or displaced segregation distortion according to Wiggans et al.  with metA−1 matrix replaced by H−1 , and q is a weighting factor. The weighting factor can be derived either based on SNP frequencies : û = qDZ′(ZDZ′q)−1â, where â is the GEBVs of animals which were also genotyped.Convert GEBVs to SNP effects (di = û2i2pi(1−pi), where i is the i-th SNP.Calculate weight for each SNP: Normalize SNP weights to remain the total genetic variance constant.Loop to 2. (ssGWAS1) or 4. (ssGWAS2).The ssGWAS method is a modification of BLUP with the numerator relationship matrix Vanraden  as:G = SNP weights were calculated iteratively either looping through steps 4–6 (ssGWAS1) or through steps 2–6 (ssGWAS2). Iterations with both scenarios increase weights of SNP with large effects and decrease those with small effects, essentially regressing them to the mean. Experiences with simulated data using ssGBLUP  and BayesB. The re-rankings in CGWAS was greater compared with the other methods. Additionally, the fraction of explained variance varied greatly among methods. Because of the way BayesB partitions variances among a fraction of the total SNPs, it is expected that BayesB will always assign a greater proportion of genetic variance to a SNP in any GWAS comparison.The comparison between ssGBLUP and CGWAS is more direct. Part of the reason that CGWAS accounts for less genetic variance than ssGWAS may be because CGWAS does not take into account all relationships among subjects but only for genotyped individuals, which might lead to detection of spurious associations due to incompleteness , which might not match every pattern of haplotype blocks. Thus, over- or under-estimation of window variances were possible. Moreover, window variances were calculated based on SNP effects at each locus, which probably contains estimation errors, and translates into more variation in results for ssGWAS2. The noise due to the estimation process could be reduced by using sliding average values for SNP windows rather than point estimates.Results showed that interpretation of GWAS using BayesB can be misleading. BayesB is based on a mixture model of those SNPs that explain genetic variance and those that do not (π). While the proportion of SNPs that explains genetic variance may become small, the total genetic variance remains constant, and is thus distributed among fewer SNPs resulting in what appears to be an inflated estimate of genetic variation accounted for by a SNP.Every methodology for GWAS has a weakness. The ssGWAS1 method seems a more useful methodology compared with CGWAS and BayesB when a large number of phenotyped subjects are not genotyped, and obtaining deregressed proofs is difficult or impossible. A limitation of ssGWAS is that the number of iterations is dictated by heuristics at this time. Additional studies (unpublished) indicate that GWAS accuracy with ssGWAS1 is maximized at 2–4 iterations, with a single iteration creating noisy plots, and with more iterations suppressing signals from smaller QTLs. Another weakness of ssGWAS1 is the inability to determine the significance level. Possibilities to address this issue are the permutation test  no pseudo values are required, (2) complex modeling and multiple-traits are possible, and (3) computing is fast and implementation is simple.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.


New SNP marker platforms offer the opportunity to investigate the relationships between wheat cultivars from different regions and assess the mechanism and processes that have led to adaptation to particular production environments. Wheat breeding has a long history in Kazakhstan and the aim of this study was to explore the relationship between key varieties from Kazakhstan and germplasm from breeding programs for other regions.The study revealed 5,898 polymorphic markers amongst ten cultivars, of which 2,730 were mapped in the consensus genetic map. Mapped SNP markers were distributed almost equally across the A and B genomes, with between 279 and 484 markers assigned to each chromosome. Marker coverage was approximately 10-fold lower in the D genome. There were 863 SNP markers identified as unique to specific cultivars, and clusters of these markers (regions containing more than three closely mapped unique SNPs) showed specific patterns on the consensus genetic map for each cultivar. Significant intra-varietal genetic polymorphism was identified in three cultivars . Phylogenetic analysis based on inter-varietal polymorphism showed that the very old cultivar Erythrospermum 841 was the most genetically distinct from the other nine cultivars from Kazakhstan, falling in a clade together with the American cultivar Sonora and genotypes from Central and South Asia. The modern cultivar Kazakhstanskaya 19 also fell into a separate clade, together with the American cultivar Thatcher. The remaining eight cultivars shared a single sub-clade but were categorised into four clusters.The accumulated data for SNP marker polymorphisms amongst bread wheat genotypes from Kazakhstan may be used for studying genetic diversity in bread wheat, with potential application for marker-assisted selection and the preparation of a set of genotype-specific markers.The online version of this article (doi:10.1186/s12870-014-0258-7) contains supplementary material, which is available to authorized users. Genotyping of cultivars and breeding material using molecular markers is a very important tool for modern plant breeders. As a component of marker-assisted selection (MAS), molecular markers improve and accelerate the process of development and release of new cultivars -4. MolecTriticum aestivum L. [Single nucleotide polymorphism (SNP) markers are one of the most important types of molecular markers and there have been significant recent developments in this technology for cultivated bread wheat, tivum L. -9. The itivum L. -13 and Btivum L. . Howevertivum L.  through tivum L. ,16, parttivum L. ,18. The tivum L.  has beentivum L. -24. Thretivum L. . Multipltivum L. . In our tivum L. ,27 to intivum L. -30, emmetivum L. , synthettivum L. ,33.SNP genotypes have been useful for both linkage and association mapping approaches to associate markers with agronomic traits. Using KASPar technology, high density SNP genetic maps have been established for doubled-haploid populations  and for Aegilops tauschii, the progenitor of the wheat D genome, 7 K - 185 K polymorphic and high-confidence SNP markers were reported and used for studying of the genome evolution, genetic diversity and geographic origin of various accessions [Further modifications of NGS-SNP technology were developed and applied to the study of genetic diversity in durum wheat. Single strand conformation polymorphism (SSCP) and KASPar technology were exploited for fine mapping ‘Gene of Interest’ in durum wheat lines with bulked segregant analysis . Sequenccessions ,40-42. Bcessions ,44.The aims of the current study were: (1) assess the suitability of the SNP platforms to explore the origins and basis of selection for relatively isolated wheat improvement programs using Kazakhstan as a model; (2) identify polymorphic and unique SNP markers and examine their distribution across the genome and chromosomes of a set of ten bread wheat cultivars from Kazakhstan, including eight cultivars recently bred and two historical reference-pedigree cultivars; and (3) assess intra- and inter-varietal genetic diversity among the selected cultivars to explore the origins of key Kazakh wheat lines. This initial study can be used as a basis for further a more extensive germplasm study and to develop a set of SNP markers useful for breeding programs in Kazakhstan.Seeds of ten cultivars of bread wheat were received from the Kazakh Research Institute of Agriculture and Crop Production, Almalybak, Kazakhstan, and are listed with their pedigree description in Table A small panel of wheats of wide geographic origin was selected from 2,994 wheat accessions with published data  for thei−1 and the quality of DNA was checked both by gel electrophoresis and with PCR. DNA samples  were submitted to the Department of Primary Industries (DPI), Victoria  for genotyping using the 9 K Infinium SNP array [Plants were grown in a greenhouse until tillering and five uniform plants were selected for each cultivar, with the exception of cultivar TZE-3C, where six plants were chosen. DNA was extracted from a single young leaf from each plant, using a phenol-chloroform extraction method . DNA conNP array ,22,27 wiAll SNP markers were assessed and polymorphic markers were identified where alleles differed in at least one cultivar. Unique SNP markers were distinguished from the polymorphic ones, defined as being diagnostic for a single cultivar (Singleton). Additionally, clusters of three or more unique markers, which mapped to small regions of a single chromosome on the consensus map (2.5 cM or less between neighbouring SNPs), were defined and identified for each cultivar. Unique SNP markers with identical locations on the genetic map were counted as a single marker when identifying SNP marker clusters. All unique SNP markers and their clusters were assessed and checked manually. Markers, which were polymorphic among the 5–6 plants within a given cultivar were classified as ‘intra-varietal’, and not used for ‘inter-varietal’ analysis.The computer software program FlapJack  was usedERY-841), and PIC values ranged between 0.3832 and 0.4224.The 9 K Infinium SNP analysis identified 8,632 SNP markers, of which 5,898 were effective across the ten Kazakh wheat cultivars. The data set supporting the results of this article is included in Additional file However, only 2,730 of the polymorphic SNP markers (46.3%) could be mapped to a chromosome location in wheat and used for further study  of the total number of mapped markers  were specific to the old, reference-pedigree cultivar .6% were ERY-841, with the highest number of unique SNP markers, also had 35 clusters of unique SNPs covering almost all chromosomes. ERY-841 is not shown in Figure SAR-29 were particularly small, amounting to 0.3 cM fragments each on the very distal parts of chromosome 2A  had no or very low intra-varietal polymorphism, and plants of these cultivars can be described as genetically uniform . The remaining eight genotypes were categorised into four clusters of a single sub-clade C2. While the cultivar KAZ-15 (cluster C2-1) was developed at the same breeding centre as KAZ-19, they are genetically divergent. Two cultivars (AST-2 and the reference-pedigree cultivar SAR-29) fell into cluster C2-2, while two cultivars (AKT-39 and KAZ-R) were classified in cluster C2-3. The three remaining cultivars  fell into cluster C2-4, where PAV-93 and KAR-90 were closely related , but also included the modern Kazakh cultivar KAZ-19 (sub-clade B2). Clade C was quite diverse, with all remaining cultivars from Kazakhstan falling into a single sub-clade C2. Genetic differences between clusters in sub-clade C2 were significant but much less when compared to genotypes in other clades. Only cultivars from Australia and the USA were found in clade D, indicating minimal genetic relationships with the genotypes from Kazakhstan  [In the present study, 2,730 (46.3%) of the polymorphic SNP markers could be mapped to a chromosomal location, higher than the proportions reported in similar studies, of 37.1%  and 22.5hii (DD) . By comphii (DD) . The relhii (DD) .There were no strong trends noted for the frequencies of SNP markers in each of the different chromosomes. Table The ten cultivars from Kazakhstan were specifically chosen for this study for their productivity potential in drought-prone environment and reports of stable yields across multiple generations . Eight pERY-841) is one of the oldest reference-pedigree cultivars and may actually represent a landrace. Its original form was found as local germplasm in the Ashkhabad region (Turkmenistan) in 1913 and released as a cultivar in 1929 . It is possible that some cultivars used in this study were developed with breeding material derived from early crosses made using ERY-841, but pedigree analysis indicates that ERY-841 is genetically distant from all of the other studied genotypes  is geographically close to Uzbekistan, China, India and Pakistan, and therefore, their similarity is not unexpected. Sonora is one of the oldest wheat cultivars, with originating as far back as 1770 in Mexico prior to its transfer to the USA [ERY-841 and other Asian genotypes. We can speculate that Asian genotypes in clade A with ERY-841 had common ancestry with Sonora. The modern American cultivar Survivor (released in 1991) may help shed more light at this phylogenetic story but current record of the pedigree does not point to Sonora [Erythrospermum 841 , which has a shorter history than ERY-841 (released in 1957). This cultivar was extremely popular in the former USSR but it has been replaced by more advanced cultivars based on the same germplasm, such as Saratovskaya 60 . SAR-29n Russia . In our er Table  and SNP 995 . SARSAR-29 clustered in the middle of sub-clade C2 much closer to the eight modern cultivars in the phylogenic tree than ERY-841  contained cultivars from North America (USA and Canada), and from Australia and the USA, respectively, demonstrating that studied genotypes from Kazakhstan were distributed in other clades. Interestingly, Chinese cultivars were relatively widely spread across clades A, C1 and C2 in our study Figure .ERY-841 and KAZ-19  and 56.Aegilops tauschii using 7,185 SNP markers [The unique SNP markers and their clusters tended to be distributed on the distal regions of both arms for each chromosome rather than in centromeric regions, reflecting the distribution of recombination events. Our results are similar to those reported for the D genome of 225 accessions of  markers , and con markers .SAR-29. AST-2 had three clusters  while four clusters were found in each of KAZ-15 and KAR-90. The most genetically diverse cultivars were AKT-39, KAZ-19 and ERY-841 with 11, 29 and 35 clusters of unique SNP markers, respectively. Identification of unique SNP markers and knowledge of the locations of unique clusters can be used as a first step in the preparation of cultivar-specific markers for bread wheat. The identification of similar unique SNP cluster regions in other populations may facilitate studies of the ancestry of such segments and indicate their significance in adaptation to specific environments.To our knowledge, this is the first description of the distribution of unique SNP clusters to specific chromosomal regions within a set of germplasm Figure . In our This SNP marker platforms provide a powerful tool for the molecular-genetic analysis of important agricultural crops such as bread wheat, due to the accuracy of the data and the relatively high-throughput. SNP markers can be employed for careful and precise identification of genetic phylogeny and differences between genotypes. The distribution of unique SNP markers and their clusters (regions containing more than three closely mapped SNPs) across chromosomes is an additional defining characteristic of particular genotypes. The current results, reporting SNP markers from ten cultivars of bread wheat from Kazakhstan, support the potential application of 9 K Infinium SNP marker technology for the study of larger panels of Kazakh wheat genotypes and for use in breeding programs in the country.This initial analysis of current and historical wheat cultivars grown in Kazakhstan has confirmed the relative isolation of Kazakh germplasm from other wheat growing regions of the world. However, the study has also revealed some likely incursions of external germplasm notably from North America. The implication of these finding is that there may be considerable opportunity for genetic gain in Kazakh breeding program through systematic introgression and evaluation of elite lines from other programs. With a very large production area but currently relatively low yields, there is a significant opportunity to improve wheat production in Kazakhstan. It also appears that Kazakh breeders have made little use of global elite wheat germplasm and, therefore, there is an opportunity for significant genetic gain. The situation in Kazakhstan is likely to be reflected in a number of other regions where access to novel germplasm has been limited.


E. coracana subsp. africana. Both STRUCTURE and genetic diversity analysis assisted with the correct identification of the new germplasm collections. These polymorphic SSR and SNP markers are a significant addition to the existing 82 published SSRs, especially with regard to the previously reported low polymorphism levels in finger millet. Our results also reveal an unexploited finger millet genetic resource that can be included in the regional breeding programs in order to efficiently optimize productivity.Finger millet is an important cereal crop in eastern Africa and southern India with excellent grain storage quality and unique ability to thrive in extreme environmental conditions. Since negligible attention has been paid to improving this crop to date, the current study used Next Generation Sequencing (NGS) technologies to develop both Simple Sequence Repeat (SSR) and Single Nucleotide Polymorphism (SNP) markers. Genomic DNA from cultivated finger millet genotypes KNE755 and KNE796 was sequenced using both Roche 454 and Illumina technologies. Non-organelle sequencing reads were assembled into 207 Mbp representing approximately 13% of the finger millet genome. We identified 10,327 SSRs and 23,285 non-homeologous SNPs and tested 101 of each for polymorphism across a diverse set of wild and cultivated finger millet germplasm. For the 49 polymorphic SSRs, the mean polymorphism information content (PIC) was 0.42, ranging from 0.16 to 0.77. We also validated 92 SNP markers, 80 of which were polymorphic with a mean PIC of 0.29 across 30 wild and 59 cultivated accessions. Seventy-six of the 80 SNPs were polymorphic across 30 wild germplasm with a mean PIC of 0.30 while only 22 of the SNP markers showed polymorphism among the 59 cultivated accessions with an average PIC value of 0.15. Genetic diversity analysis using the polymorphic SNP markers revealed two major clusters; one of wild and another of cultivated accessions. Detailed STRUCTURE analysis confirmed this grouping pattern and further revealed 2 sub-populations within wild  Eleusine coracana subsp. coracana) is an annual grass that is widely cultivated as a staple food in eastern Africa and south Asia. It is tetraploid (2n = 4x = 36) and belongs to the Poaceae family and Chloridoideae sub-family [E. coracana subsp. africana. Other wild species include E. kigeziensis, E. floccifolia, E. intermedia, E. tristachya, E. jaegeri and E. indica. Finger millet is a nutritious cereal high in protein, methionine and other essential amino acids. The small seeds can be stored for years without damage, making it an important food reserve in times of famine. The grain is used for bread, porridge, beer, soup and pudding. In countries where it is grown, it is commonly referred to as Wimbi (Swahili), Bulo (Uganda), Tellebun (Sudan) and Ragi (India) [Cultivated finger millet  ,12, InteCloning and hybridization-based SSR libraries and Sanger sequencing, which were used to develop the first 45 SSR markers for finger millet , allowinOn the other hand, SNPs have become the markers of choice for crop genotyping due to their abundance with up to 1 SNP per 140 bp being observed in rice . They arFinger millet genotypes KNE755 and KNE796 were used to generate sequence data for SSR and SNP marker development. Ten diverse finger millet genotypes  that werPstI methylation sensitive restriction endonuclease for 1 hour at 37°C in order to enrich for genic regions. After end-repair of both Covaris-sheared and enzyme digested DNA, sequencing libraries were prepared following the TruSeq protocol  and sequenced on an Illumina Hi-Seq 2000.For Roche 454 sequencing, leaves of each genotype (KNE755 and KNE796) were sampled 2–3 weeks after planting, dried using silica gel and sent to Ecogenics  for DNA extraction, SSR enrichment and sequencing (Roche 454/FLX). For Illumina sequencing, DNA was extracted from two weeks old seedlings of KNE755 and KNE796 and sent to Georgia Genomics Facility at the University of Georgia (USA). A 1-μg portion of each DNA sample was fragmented using Covaris  ultrasonication. A second DNA portion of 5μg of each sample was digested using De novo assembly of all non-organelle sequences was done using Velvet software [Fastq-mcf  was usedsoftware  to creatsoftware  with a msoftware –37 and esoftware  and lesssoftware ,40 and tsoftware ,42.http://picard.sourceforge.net) before running FreeBayes [For SNP identification, BWA software  was usedreeBayes  to identreeBayes  based onreeBayes . The rawhttp://www.repeatmasker.org/) and aligned using blastx [http://www.uniprot.org/) as well as the non-redundant plant protein database retrieved from the Genbank (ftp://ftp.ncbi.nlm.nih.gov/refseq/release/plant/plant.1.protein.faa.gz) setting an e-value cutoff of 1e-5 and minimum similarity of 80%. A non-redundant protein list generated from the two databases was compiled and submitted to PANTHER [http://pantherdb.org/) classification system for Gene Ontology (GO) term annotation (molecular function).Reference sequence contigs that were at least 200bp long were masked for repetitive elements using RepeatMasker (g blastx  against  PANTHER  (http://Sequence assembly of Roche 454 data and identification of SSRs was done by Ecogenics (Switzerland). Primers were designed for 101 of the identified SSRs using the following parameters: primer length between 18–23 with an optimum of 21 bp, PCR products of 100 to 300 bp, primer TM between 58°- 64°C with an optimum of 60°C and GC content from 45–70%. All forward primers contained an M13-tag (5’- CACGACGTTGTAAAACGAC—3’) on the 5’ end that was fluorescently labelled to allow detection of amplification products . SSR marhttp://www.lgcgenomics.com/genotyping/kasp_technical_resources/), UK. The data generated was viewed graphically as cluster plots using SNPviewer V2 (www.lgcgenomics.com). For SNP validation, two weeks-old seedlings of the 89 genotypes (www.lgcgroup.com). The tubes were sealed in a plastic bag with desiccant and immediately shipped to LGC Genomics  for genotyping.One hundred and one SNP markers were selected randomly and submitted for Competitive allele-specific PCR (KASPar) genotyping at LGC Genomics  algorithm implemented in STRUCTURE 2.3.3 software [K ancestral populations. The estimated proportions of each individual’s genotype originating from each of the K ancestral populations (q) was calculated for K ranging from 1 to 10 with 10 runs for each K value. For each run, a burn-in period of 10000 and MCMC replications of 100000 was used. The optimum K value was calculated using STRUCTURE HARVESTER [ad hoc statistic Δk based on the rate of change in LnP(D) between successive k [The UPGMA based clustering was computed using TASSEL  and rootsoftware . The admARVESTER , which cessive k . Resultsessive k .KNE755 was more abundant in homeologous SNPs with a frequency of 1/657 bp compared to a frequency of 1/956 bp in KNE796. The most abundant homeologous SNP in both genotypes was CT/AG (~62%) while CG was the rarest SNP at about 7.5%. The Ts/Tv ratios of the homeologous SNPs were comparable across the two genotypes but slightly higher in KNE755 (1.8) than in KNE796 (1.5).We identified 10,327 SSRs   and 23,2Searches against the plant protein and rice gene databases revealed 6,821  and 6707  sequences respectively containing putative genes. While 9,175 and 11,632 of the reference contigs contained SSRs and SNPs respectively, only 36 and 564 of the putative genic sequences contained SNPs and SSRs respectively. Out of the 5,094 rice genes that showed sequence similarity with 6,707 finger millet sequences, 4,240 GO terms were retrieved for biological processes, 2,835 related to molecular functions and 1,147 to cellular components. For the 564 SNP-containing putative genic sequences, 519, 346 and 146 GO terms for biological processes, molecular functions and cellular components could be assigned respectively. Of the 101 SSRs tested , 49 wereWe developed 8,740 KASPar assays from 92 SNP regions across 93 finger millet accessions . The assNinety five % (76 out of 80) of the SNP markers showed polymorphism across 30 wild accessions while only 27.5% (22 out of 80) were polymorphic across the 59 cultivated genotypes revealing low variability within the cultivated finger millet. However, the 4 SNP markers  that were monomorphic across wild accessions all showed polymorphism among the cultivated accessions. These polymorphisms resulted either from a single genotype harboring a heterozygous allele  or from a few segregating genotypes (in the case of ICECSNT72). KNE796, which was also one of the genotypes used to generate the sequencing data, uniquely harbored the only heterozygous alleles for loci ICECSN99 and ICECSNT78 while P224, a popular high yielding variety, harbored the only heterozygous allele for locus ICECSNT33 and one of the 2 alternative homozygous alleles of locus ICECSNT72. The average PIC of the 80 SNP markers tested across 89 diverse genotypes was 0.29 . The wilE. kigeziensis accessions . Cluster I comprised mainly of cultivated E. coracana subsp. coracana accessions while the second cluster comprised of E. coracana subsp. africana accessions (24 accessions). Accessions MS19, MS17, MS21, EDL34, MS18, UG10, LEN24, MS16 and EDL25, which were previously classified as wild, grouped closer to the subsp. coracana accessions, suggesting they may have hybridized with cultivated accessions.The dendrogram  generateE. kigeziensis accession MD48 as an outgroup. We generated two dendrograms  from the cultivated  revealed using UPGMA maintained a distinct grouping from the E. coracana subsp. africana accessions (E. kigeziensis.The 89 accessions were further classified into two sub-populations (delta K = 2)  using STltivated . We furtcessions . Althougcessions  confirmiE. kigeziensis and 24 of E. coracana subsp. africana) and attempted to correctly identify the two accessions (EDL9 and UG3) that were previously classified as wild but which now clustered with the cultivated accessions under both UPGMA clustering (K at K = 3. The 30 “wild” accessions were classified into 3 sub-populations–one in E. kigeziensis and two in subpopulations of subsp. africana. Cultivated accession MS16 formed a distinct cluster (E. kigeziensis accessions (LESK10 and EDL16) with the other accessions in comparison with MD48 (We subsequently explored the sub-populations within the wild accessions (three from ustering  and STRUustering  grouping cluster . EDL9 anith MD48 . This isE. coracana subsp. coracana), for which there are currently very few polymorphic markers. As expected, SNP markers were more abundant (1/3.3 kbp) than SSRs (1/7.5 kbp) despite the stringent filtering criteria used to eliminate homeologous SNPs. Homeologous SNPs were much more abundant (at least 1/kbp) suggesting a recent polyploidization event within the cultivated species. Recently formed polyploids have not undergone extensive genetic or genomic changes, and therefore their genomes would be additive with respect to their parental species [AA and BB sub-genomes. The allotetraploid origin of finger millet remains unresolved and the detection of homeologous loci in the nuclear genome can provide critical information to elucidate its evolutionary history. Future evolution studies should include E. indica (AA), which is believed to be the maternal genome donor [E. coracana.We report here the simultaneous mining of SSR and SNP markers from cultivated finger millet  [We focused on di-, tri- and tetra-nucleotide SSRs due to their abundance in plant genomes –39 and s(101/Mb)  but lowe(101/Mb)  even tho(101/Mb)  and citr(101/Mb) . However(101/Mb) .Japonica rice varieties (346.6 SNPs/Mb) [Japonica and Indica genomes [Japonica varieties that included a landrace (Omachi) and an improved line (Nipponbare), whilst the current study included only improved finger millet varieties (KNE755 and KNE796), suggesting that higher numbers of SNP markers could be identified if more diverse genotypes were included in the discovery process. Stringent SNP identification criteria would need to be maintained to eliminate homeologous SNPs and reduce the numbers of false positives that have been observed in other polyploid crops [Eleusine species, which are likely to be exploited in future breeding programs.The distribution of SNPs across the two genotypes (1/3.3 kb or 303 SNPs/Mb) was comparable to those observed between two SNPs/Mb)  but much genomes . Howeverid crops . Future Eleusine species. This conversion rate was quite compared to similar studies in hexaploid wheat (67%) [Spartina pectinata (78.5%) [Eleusine species. However, for high-throughput SNP assays in the future, all available SNP genotyping platforms [KBiosciences allele-specific PCR (KASPar) technology was used to convert a random set of the identified SNPs into assays, as this system is flexible and was shown to work well with other polyploids ,73. Nineat (67%)  and polylatforms –76, as wlatforms ,78 shoulIn most plants, di-nucleotide SSRs have been reported to be the most polymorphic , followeThe allelic diversity of the SNP markers identified in this study was relatively low (mean PIC of 0.29) and remarkably different across wild (mean PIC = 0.30) and cultivated (mean PIC = 0.15) germplasm. Narrow genetic diversity had been observed in cultivated finger millet ,80 due tEleusine species and enabled correct classification of unknown genotypes. Most wild accessions used in the current study were new collections that were expected to be cross-compatible with cultivated species except MD48. The clustering pattern observed when all accessions were analysed together clearly distinguished between E. coracana subsp. coracana and that of subsp. africana confirming the distinctness of the two subspecies. We were also able to classify ten “unidentified” new collections  correctly based on the phylogenetic and STRUCTURE analysis. Although MS16 was previously considered a wild accession belonging to E. coracana subsp. africana, our clustering confirmed it was a cultivated accession with mixed ancestry. Although Lesk10 and EDL16 were previously classified as E. coracana subsp. africana, their consistent clustering with MD48 (E. kigeziensis) left no doubt that these two genotypes belong to the same (E. kigeziensis) species. Due to the tight clustering between Lesk10 and EDL16, it was difficult to conclude whether the samples were contaminated or if the genotypes were indeed genetically very similar. More studies are needed to combine both morphological and genetic analysis to correctly distinguish these two genotypes (Lesk10 and EDL16) in the future.The 80 polymorphic SNP markers developed in the current investigation successfully discriminated various africana subspecies, most of the accessions from Uganda  and western Kenya (at the border with Uganda) clustered together, while the Kenyan (names starting with GBK) and Tanzanian (names starting with TZA) cultivated accessions also clustered together. The deviation of some of the improved varieties from the major cluster may have been due to gene flow between subsp. africana and coracana, as well as the recent history of finger millet breeding in eastern Africa, which was influenced by Indian accessions. Some finger millet varieties released in Africa (such as P224) were bred through hybridization of African and Indian germplasm [coracana accessions. Both EDL9 and UG3 were previously classified as belonging to subsp. africana using morphological features. Given the high levels of gene flow between africana and coracana, it is likely possible that these two genotypes have crossed with coracana and are therefore genetically more similar to subsp. coracana but morphologically similar to subsp. africana.Some degree of geographical clustering was also observed within the two major clusters. For example, within the ermplasm ,82,83. MK = 10  that clustered under E. kigeziensis, it was only MD48 that displayed some degree of admixture. MD48 was originally collected from Uganda [E. kigeziensis genomes. While earlier studies within E. coracana species reported two subpopulations of coracana (Asian and African subpopulations) [africana. The two subpopulations of africana observed did not reveal any geographical clustering pattern and therefore need to be investigated further to determine the basis for grouping.Eastern Africa, being the center of diversity for finger millet, contains a large number of landraces as well as finger millet wild relatives. Using STRUCTURE software, we identified four distinct sup-populations when K = 10  represenm Uganda , but hadlations) , this isE. coracana subsp. coracana accessions formed one distinct grouping and also enabled further identification of previously misidentified accessions, such as MS16. We were also able to confirm that both EDL9 and UG3 were admixtures and contained DNA from subsp. coracana. Clearly, UPGMA and morphological characterization alone are not enough for correct classification of accessions within a genus where gene flow is rampant. Other studies that included both Asian and African accessions in the past [coracana that reflected the two geographies. Our study specifically included only eastern African collections of subsp coracana, and this may explain the low levels of genetic diversity and the observation of only one subpopulation of coracana.All the past  have revThis study illustrated the power of NGS to advance research in previously under-studied crops such as finger millet and demonstrated the immediate application of such resources in breeding programs. Although genic molecular markers are considered more useful due to their likely association with functional genes and wider application across related species, genomic markers are still extremely useful in finger millet due to their abundance and high polymorphism levels that would facilitate the immediate implementation of genomics-assisted breeding. No doubt, future marker development studies in finger millet would need to exploit the decreasing sequencing costs in order to generate higher numbers of genic markers as has been done in other polyploids ,73. WhilS1 Fig(TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


SCD (SSC14) and to reveal an association of IMF and fatty acid composition in the LEPR region (SSC6). The association of LEPR was later validated with an independent set of 853 pigs using a candidate quantitative trait nucleotide. The SCD gene is responsible for the biosynthesis of oleic acid (C18:1) from stearic acid. This locus affected the stearic to oleic desaturation index (C18:1/C18:0), C18:1, and saturated (SFA) and monounsaturated (MUFA) fatty acids content. These effects were consistently detected in gluteus medius, longissimus dorsi, and subcutaneous fat. The association of LEPR with fatty acid composition was detected only in muscle and was, at least in part, a consequence of its effect on IMF content, with increased IMF resulting in more SFA, less polyunsaturated fatty acids (PUFA), and greater SFA/PUFA ratio. Marker substitution effects estimated with a subset of 65 animals were used to predict the genomic estimated breeding values of 70 animals born 7 years later. Although predictions with the whole SNP chip information were in relatively high correlation with observed SFA, MUFA, and C18:1/C18:0 (0.48–0.60), IMF content and composition were in general better predicted by using only SNPs at the SCD and LEPR loci, in which case the correlation between predicted and observed values was in the range of 0.36 to 0.54 for all traits. Results indicate that markers in the SCD and LEPR genes can be useful to select for optimum fatty acid profiles of pork.Intramuscular fat (IMF) content and fatty acid composition affect the organoleptic quality and nutritional value of pork. A genome-wide association study was performed on 138 Duroc pigs genotyped with a 60k SNP chip to detect biologically relevant genomic variants influencing fat content and composition. Despite the limited sample size, the genome-wide association study was powerful enough to detect the association between fatty acid composition and a known haplotypic variant in  Intramuscular fat (IMF) content and fatty acid (FA) composition affect both organoleptic quality and nutritional value of pork and, thus, there is increasing interest in including these traits in the selection objectives of pigs bred for quality pork markets. Saturated (SFA) and monounsaturated FA (MUFA) are related to better sensory attributes and technological properties , while pSus scrofa chromosome (SSC) 14 affecting SFA and MUFA in purebred Duroc [SCD) gene [During the last decades, a lot of efforts have been put into the detection of quantitative trait loci (QTL) affecting IMF content and FA composition using low-density microsatellite linkage maps –8. Howeved Duroc ,11, whicCD) gene .SCD haplotype on SFA and MUFA, which segregates in the studied population. The potential use of genomic prediction for these traits is discussed in light of the results obtained.The onset of high-density SNP genotyping arrays has enabled a more precise scanning of the genome to detect quantitative trait loci (QTL) and nucleotides (QTN) and to make genomic predictions of breeding values. Genome-wide association studies (GWAS) on FA composition have been performed mostly in experimental crosses –15, but The experimental protocol was approved by the Committee on the Ethics of Animal Experiments of the University of Lleida.ad libitum with a pelleted finishing diet from 160 days of age until slaughter. The average composition of the diet was 16.9% crude protein, 6.6% fiber, and 6.7% fat . Animals were slaughtered in the same commercial abattoir at 215.6 (7.9 SD) days of age and 127.7 kg (10.9 SD) of body weight. Carcass backfat thickness  at 6 cm off the midline between the third and fourth last ribs was measured by an on-line ultrasound automatic scanner . Immediately after slaughter, a sample of subcutaneous fat  at the level of the third and fourth ribs was collected. After chilling for about 24 h at 2°C, samples of the muscles gluteus medius  and longissimus dorsi at the level of the third and fourth ribs  were also collected. The IMF content and FA composition of the samples were determined in duplicate by quantitative determination of the individual FA by gas chromatography [We genotyped 138 purebred Duroc barrows from the commercial line described in  using thtography  and exprg.2228T>C in the SCD promoter [LEPR gene (NM_001024587:c.1987C>T) [Given the GWAS results, two candidate QTN from the most associated regions were genotyped posteriorly for validation purposes. These two additional SNPs were genotyped in an independent set of 853 pigs sampled from all years since 2002 until 2013. The first SNP was the polymorphism AY487830:promoter , which w1987C>T) . All samSus scrofa Build 10.2 were also excluded. The remaining data comprised 135 individuals and 36,432 SNPs.The PLINK software  was usedy is the phenotypes vector, X is the incidence matrix relating fixed factors to phenotypes, b is the vector of fixed effects, zj is the vector of (coded) genotypes for a SNP at locus j , αj is the allele substitution effect of the SNP at locus j, δj is a random 0/1 variable that represents the absence or presence  of SNP j in the model for a given iteration of the Markov chain Monte Carlo procedure, and e is the vector of random residuals, assumed to be normally distributed. Alternate homozygous genotypes were coded as -10 and 10, heterozygotes as 0, and missing genotypes as the average value in the population. Fixed effects included batch as a class variable and age at slaughter as a covariate. Intramuscular FA composition traits were analyzed with and without IMF content as an additional covariate. Due to the limited number of animals in the study, the prior proportion of SNPs considered to have no effect on the trait (δj = 0) was fixed to π = 0.997, so that the model fitted ~110 SNPs per iteration. Variance components used as priors were estimated as in [j (BFj) [j of being included in the model at a given iteration of the Markov chain Monte Carlo procedure. Evidence of association was considered "substantial" for BFj above 3.2, "strong" above 10, and "decisive" above 100 [Associations of SNP genotypes with the phenotypes were analyzed using the Bayes B approach  implemened as in  with the[j (BFj) ,26 asBFbove 100 . Linkagebove 100 . The perbove 100 . The avebove 100 .SCD g.2228T>C and LEPR c.1987C>T to the SNPs of the chip. The association of SNP LEPR c.1987C>T with leptin concentration in plasma and with the studied traits was analyzed in an independent set of 853 pigs using a model with age at slaughter as a covariate and batch and genotype as class variables. This analysis was performed under a Bayesian setting with Rabbit software [In view of the results, genome-wide associations were reanalyzed after adding software .i in the testing dataset was predicted asij is the genotype of animal i for a SNP at locus j  coded as above, and j based on the analysis of the training dataset. The correlation between GEBV and the adjusted phenotypic values of the testing dataset was used as a measure of the prediction accuracy. Phenotypes were adjusted for batch and age at slaughter using a fixed model. In view of the GWAS results, five different sets of SNPs were evaluated for their predictive ability: (1) all SNPs in the chip, (2) only SCD g.2228T>C, (3) only LEPR c.1987C>T, (4) only these two SNPs together, or (5) all SNPs in the chip but excluding those in the SCD and LEPR regions detected by GWAS.We used the animals born in 2002–2003 as training data to estimate the SNP effects and then to predict the genomic estimated breeding values (GEBV) of the animals born in 2009–2010. The effect of each SNP was estimated using the same procedure as for GWAS but using only the training set (n = 65). The GEBV of an individual The posterior means of variance components and heritabilities based on the genotypic data are given in NFKBIE  and SLC29A1  lie in its vicinity. Interesting candidate genes for BT were found in the SSC8 region at 144–148 Mb (1.3%), containing AGPAT9, GK2, and SCD5, all three related to FA and triglyceride metabolism, and BMP3, which is involved in adipogenesis.Strong individual marker associations were detected for carcass BT (BF up to 22.0), but the highest values of explained genetic variance for the 1-Mb windows hardly reached 2% (data not shown). The SSC7 at 46–47 Mb region (1.9% of genetic variance) was the one showing the highest association. No candidate genes were found in this region, but In contrast, strong signals for IMF content and FA composition traits were located on SSC6 (BF up to 738.3) and SSC14 (BF up to 97.3), which are zoomed in in Figs LEPR) and the leptin receptor overlapping transcript (LEPROT), which share the two first exons  has been reported as the probable causative mutation associated with increased feed intake and fatness [CART (negative regulation of appetite) and the upregulation of NPY (positive regulation of appetite) [LEPR in pigs [LEPR/LEPROT locus and finishing downstream the JAK1 gene. Interestingly, the signal transductor coded by JAK1, which maps to 135.9 Mb  . The samppetite) –38. Ther in pigs –41. The 135.9 Mb , is alsoNPY gene . Mutatioet al. [In our study, the 135–136 Mb window was also strongly associated with FA composition, both in GM and LD. In particular, the 135–137 Mb extended region explained the greatest percentage of genetic variance for SFA (17.6%), PUFA (16.9%), and SFA/PUFA (21.7%) in GM and, to a lesser extent, in LD . However, when adjusting for IMF, the genetic variance explained by this region decreased to 0.4–6.1%, indicating that, at least in part, the observed associations of the SNPs in this region with SFA and PUFA are an indirect effect of differences in IMF, as also noted by Galve et al. .LEPR locus with fat-related traits was further evaluated on an independent set of 853 pigs using the putative causative mutation LEPR c.1987C>T [LEPR genotypes also for the FA composition of SF, not detected by GWAS but in line with the findings by Muñoz et al. [et al. [LEPR gene; Genbank accession number FN677933.1). In fact, the four SNPs in the chip showing the strongest associations were the ones in greatest linkage disequilibrium with LEPR c.1987C>T  the LEPR gene affects the leptin regulatory system, similarly to what has been observed in humans [The association of the .1987C>T . Pigs wiz et al.  and Galv [et al. . Similar.1987C>T , for whin humans ,45. As aSCD gene , CHUK , CYP17A1 (direct role in steroidogenesis), and PITX3 . The percentages of genetic variance explained by this region for SFA (16.1% in GM and 16.3% in LD), MUFA , C18:1 , and C18:1/C18:0  were close to those obtained when only accounting for the effect of the SCD haplotypes [SCD g.2228T>C if included in the analysis was 15.0 for SFA, 15.3 for MUFA, 27.1 for C18:1, and 7.1 for C18:1/C18:0. This polymorphism could affect the expression of the SCD gene by disrupting the RXR:RARα and PPARG transcription factor binding sites [On the other hand, the region on SSC14 at 120–124 Mb was found to be strongly associated with SFA, MUFA, C18:1, and the desaturation index C18:1/C18:0. The most associated window was located either at 121–122 Mb, for the muscles, or at 122–123 Mb, for SF. This region, which was estimated to capture up to 44.8% of the genetic variance of C18:1/C18:0, corresponds to the location of the SCD gene , therebyposition , the sigRB1, involved in adipogenesis, and CYSLTR2, involved in lipid homeostasis. No functional candidate genes mapped to the other regions.Other regions explaining more than 2.5% of genetic variance were found at SSC1, 3, 5, 7, 9, 11, 12, 13, 15, and 18. The region in SSC11 at 19–21 Mb, which was the most associated with IMF in LD (3.7% of genetic variance), contained the genes et al. [2 cross and a larger population size, did not reach much different results from ours, with the SCD locus being the only reported QTL for major FA in IMF. Zhang et al. [SCD locus and FA in LD in commercial Duroc × (Landrace × Yorkshire) hybrids but not in the Chinese breeds Erhualian and Laiwu. Similarly, in the QTL genome scan with microsatellites performed by Uemoto et al. [SCD nor the LEPR loci were detected. This result can be expected for the SCD locus, because there is no evidence that the investigated SCD polymorphism segregates in Iberian, Landrace, or any breed other than Duroc and Large White [LEPR c.1987C>T polymorphism, though, was first described in an Iberian × Landrace intercross [A previous GWAS performed by Yang et al. , using ag et al.  found ano et al.  in purebo et al. ,14. In tge White ,46. The tercross , but postercross ,37 or crtercross ,38 or Ibtercross .SCD and LEPR loci were 0.42–0.45, and did not change substantially between the two time periods in the study. Thus, the three genotypes at each loci and time period were similarly represented (SCD locus) while revealing another one (at the LEPR/LEPROT locus).Our results highlight that, even if only a limited number of animals are available, GWAS can be a successful strategy to detect polymorphic regions with relatively high effects which are segregating at intermediate frequencies. The minor allele frequencies at the resented . Our resSCD g.2228T>C and it improved for C18:1. Similarly, using only LEPR c.1987C>T raised the correlations of the predictions for IMF, PUFA, and SFA/PUFA to 0.46–0.49, although that for SFA was halved as compared to whole genome predictions. The combination of the two SNPs in SCD and LEPR provided similar or better accuracies than the whole chip for traits IMF, SFA, PUFA, C18:1/C18:0, and SFA/PUFA, with correlations ranging from 0.43 to 0.50. For MUFA and C18:1, including the LEPR genotype as a predictor worsened their prediction, probably because these traits were not associated with LEPR. For these two traits, prediction accuracies using only SCD g.2228T>C were similar to those with the whole chip. Consistently, the rest of SNPs in the chip predicted the phenotypes very poorly.We used the animals born in 2002–2003 as training data to re-estimate the SNP effects and then to predict the genomic estimated breeding values (GEBV) of the animals born in 2009–2010. The correlation between the GEBV and the adjusted phenotypic values of the testing dataset are given in SCD and LEPR loci and, because pigs in the predicted set were separated by a span of seven years from those in the training set, that their effects are consistent across generations. On the other hand, these results suggest that using many SNPs does not necessarily lead to improved predictive ability. To our knowledge, the only attempts to assess the value of genomic prediction for IMF FA composition have been in beef cattle using the BovineSNP50 BeadChip [SCD was also found by GWAS as one of the most influencing genes on IMF FA composition. In this population the correlations between GEBV and phenotypes were also greater for C18:1, MUFA and SFA (0.21–0.28) than for PUFA and SFA/PUFA (0.18–21).These results, on one hand, confirmed the predictive ability of the SNPs at the BeadChip ,48. InteBeadChip  between SCD gene with FA composition and to reveal an association that had not yet been detected in the Duroc population under study between the LEPR/LEPROT region and IMF and FA composition. The SNPs in these two loci can be used conjointly for marker-assisted selection for IMF and FA composition. The other minor candidate regions detected require further research, but they provide a good basis for exploring the development of custom low-density SNP arrays aimed at improving meat quality.We have been able to confirm the association of known SNPs at the S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


CRP gene in several tumor types, instead suggesting that locally produced CRP is a potential driver of tumorigenesis. However, it is unknown whether the -286 site is the sole SNP position of CRP gene targeted for mutation and whether there is any association between CRP SNP mutations and other frequently mutated genes in tumors. Herein, we have examined the genotypes of three common CRP non-coding SNPs  in tumor/normal sample pairs of 5 cancer types (n = 141). No recurrent somatic mutations are found at these SNP positions, indicating that the -286 SNP mutations are preferentially selected during the development of cancer. Further analysis reveals that the -286 SNP mutations of CRP tend to co-occur with mutated APC particularly in rectal cancer . By contrast, mutations of CRP and p53 or K-ras appear to be unrelated. There results thus underscore the functional importance of the -286 mutation of CRP in tumorigenesis and imply an interaction between CRP and Wnt signaling pathway.C-reactive protein (CRP) is an established marker of inflammation with pattern-recognition receptor-like activities. Despite the close association of the serum level of CRP with the risk and prognosis of several types of cancer, it remains elusive whether CRP contributes directly to tumorigenesis or just represents a bystander marker. We have recently identified recurrent mutations at the SNP position -286 (rs3091244) in the promoter of  Inflammation is essential for the development of cancer On the other hand, single nucleotide polymorphisms (SNPs) that associate with genetically elevated concentrations of CRP do not confer an increased cancer risk to the general population CRP is specifically mutated at the SNP position (rs3091244) 286 bp upstream the transcription start site in 109 out of 453 tumor samples but not in the matched normal controls CRP induction in tumors likely via disruption of the conserved CpG methylation motif. Moreover, most of the cancer types examined harbor the -286 mutation and the fraction of the mutated allele is high . These findings thus support the role of CRP produced in situ as a potential cancer driver that is probably involved in general mechanisms favoring tumorigenesis Serum CRP is produced by hepatocytes of the liver; however, accumulating evidence also reveals a local production of CRP by extra-hepatic cells CRP SNP sites, thus highlighting that the -286 mutations are highly specific to tumorigenesis. We further examined whether there is any correlation between the -286 mutations of CRP and other frequently mutated genes in tumors. The identified association between the -286 and APC mutations implies an interaction of CRP with Wnt signaling.Besides the -286 SNP, there are several additional common non-coding SNPs that significantly affect the baseline levels of serum CRP. The representatives include rs7553007, rs1205, and rs3093077 CRP: forward: 5′-AGGGGGGAGGGATAGCATTAGAA-3′; reverse: 5′-CGTCCTGCTGCCAGTGATACAAG-3′; human p53: forward: 5′-CTGTCCCTTCCCAGAAAACCT-3′; reverse: 5′-CCTGGGCATCCTTGAGTTC-3′; human APC: forward: 5′-TAATACCCTGCAAATAGCAGAAATA-3′; reverse: 5′-GTGGCAAAATGTAATAAAGTATCAG-3′; human K-ras: forward: 5′-ATGACTGAATATAAACTTGTGGTA-3′; reverse: 5′-CAACACCCTGTCTTGTCTT-3′) followed by sequencing. Genotyping of 24 SNPs  was performed by the mass spectrometry-based Sequenom service . Written informed consent was obtained from patients. All patients are Chinese. The study was approved by the Ethic Committee of the Gansu Provincial Tumor Hospital.Frozen tumor/normal tissue sample pairs were obtained from the tissue bank of Gansu Provincial Tumor Hospital. Genomic DNA was isolated from tissues or blood samples using DNAiso Reagent or Blood Genome DNA Extraction Kit (Takara) according to the manufacturer’s instructions. For the identification of gene mutations, genomic DNA was amplified with specific primers  together with 21 additional SNPs in 141 tumor/normal sample pairs of 5 cancer types, i.e. gastric, lung, esophagus, colon and rectal cancers. The frequencies of alleles associated with lower CRP levels are 48.9% for A allele of rs7553007, 47.9% for T allele of rs1205, and 79.1% for T allele of rs3093077 in normal samples (CRP-286 SNP (rs3091244) mutations CRP SNP sites assayed herein are only randomly mutated in tumors at the background mutation frequency.To see whether other non-coding SNP sites of  samples , thus pr samples . These iIL-1β and IL-8, respectively. Their low mutation frequencies (0.7–1.4%) argue that the promoter localization per se is not likely the cause of somatic hypermutation at the CRP-286 SNP -site in tumors; rather, the high incidence of the -286 mutation would be the result of functional consequences related to the enhanced induction of CRP, which may confer host cell clones sufficient advantage to survive and expand in the development of cancer.According to the genotyping results, the mutation frequencies of SNP sites with and without associated genes are 0.97% (95% CI: 0.35–1.59%) and 1.30% (95% CI: 0.78–1.82%), respectively. Although not statistically significant, this suggests that gene-associated SNP sites tend to be less prone to random mutation than those with unknown association, possibly due to constraints that limit damages to genomic loci with functional importance. Of the gene-associated SNPs, rs1143627 and rs4073 are two promoter SNPs that locate at 31 and 199 bp upstream of the transcription start sites of CRP-286 SNP mutation is most prevalent in colon cancers p53, K-ras and APC are among the most frequently mutated genes that promote tumorigenesis via distinct mechanisms p53, K-ras and APC were identified by sequencing of their respective hotspot mutation regions, i.e. 301–1044 of p53, 24–442 of K-ras, and 3922–4453 of APC in cDNA sequence ranges, according to the statistics of the COSMIC database. Despite their high incidences (about 50%), the CRP-286 SNP mutation shows no apparent association with mutated p53 . These results thus suggest that CRP and APC may cooperate in overlapping pathways during the development of colorectal cancer.By contrast, a two-fold enrichment of mutant  n = 38; . However n = 38; , which iin vitro activities of CRP in vivo or simply represents a nonspecific marker as hinted by its acute phase expression pattern. In this regard, the identification of the highly recurrent CRP-286 SNP mutations in multiple types of human cancer The TERTCRPCRP that the mutation occurs at a common SNP site. This raises the concern whether SNP sites are generally more vulnerable to genetic alterations, leading to the high incidence of passenger mutations. To address this concern, we genotyped 24 SNPs of 141 tumor/normal sample pairs. These SNPs are located on 9 distinct chromosomes, and consist of 3 SNPs of CRP, 2 promoter SNPs of inflammatory cytokines, 1 SNP of a non-coding gene, 18 SNPs with unknown association. Despite that, all of the SNP sites were found to be mutated in tumors with only low background frequency. Therefore, the highly recurrent mutation at the CRP-286 SNP site is most likely the result of the selection by cancer development, but not simply due to general properties associated with SNP site or genomic location. It is, however, still possible that the -286 mutation is just a consequence of tumorigenesis and further functional assays are required to clarify this point.Promoter mutations in CRP SNPs all affect the serum level of CRP, only the -286 SNP is targeted by tumorigenesis. This would suggest that the effects of the other 3 SNPs are secondary to the -286 SNP, which may in part be explained by the dependence of CRP expression on promoter CpG methylation, an essential epigenetic mechanism in gene silencing CRP promoter, the evolutionarily conserved -286 CpG appears to be the key, particularly for extrahepatic cell types, in determining the basal level of CRP expression CRP likely via lowering the inhibitory methylation signal and facilitating the binding of transcription factors to the underlying E-box sequence CRP SNPs.Nonetheless, it is intriguing that although the 4 examined CRP-286 SNP mutations in tumors suggest that locally produced CRP, instead of circulating CRP, drives the development of cancer. This paradox may be explained by the tight dependence of the actions of CRP on inflammatory microenvironments in situ production The high recurrence and pervasiveness of the APC or activating mutations of β-cateninAPC inactivation is the stabilization of β-catenin and the aberrant activation of the downstream target genes CRP has been shown to be a target of β-cateninCRP-286 SNP mutations tend to co-occur with mutant APC in colon and rectal tumors. These would imply that the two secretory molecules, i.e. CRP and Wnt, may act in feed-back and cooperative manners to promote tumorigenesis, which deserves further investigations. Given the aberrantly activated Wnt signaling and highly induced CRP expression in tumors, topical targeting both molecules may be a potential option for colorectal cancer therapy.The dysregulation of Wnt signaling pathway is the most frequent event observed in colorectal cancer, which is usually manifested by inactivating mutations of