There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale .Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression.We used the Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression.The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change. Research utilization refers to the "process by which specific research-based knowledge (science) is implemented in practice" . In receInstrumental research utilization is a direct use of research knowledge. It refers to the concrete application of research in clinical practice, either in making specific decisions or as knowledge to guide specific interventions related to patient care. For instrumental use, the research is often translated into a material and useable form  [onceptual research utilization (CRU) refers to the cognitive use of research where the research findings may change one's opinion or mind set about a specific practice area but not necessarily one's particular action. It is an indirect application of research knowledge [Symbolic (or persuasive) research utilization is the use of research knowledge as a political tool in order to influence policies and decisions or to legitimate a position [overall research utilization as the use of any kind of research in any way in clinical practice and conceptualized it as an omnibus and multidimensional construct [Research utilization is a multidimensional construct that consists of three kinds of research use: instrumental, conceptual, and symbolic (or persuasive) ,6, each ideline) ,6. Concenowledge ,6. An exposition ,6. For iposition  embarkedposition  defined onstruct ,7.inform their decision-making process [While the number of studies examining research utilization has increased significantly in the past decade, the majority continue to examine research utilization as a general construct or instrumentally . Concept process -12. As a process . Further process ,15-19.Conceptual Research Utilization Scale  -to measure conceptual research use by healthcare aides.We located 11 articles published between 1989 and 2009, whose authors had measured CRU by nursing care providers. All studies measured CRU by registered nurses and/or licensed practical nurses ,15-24, wValidity refers to the extent to which a measure achieves the purpose for which it is intended, and is determined by the "degree to which evidence and theory support the interpretations of test scores entailed by proposed users of tests" [Reliability refers to the consistency of measurement obtained when using an instrument repeatedly on a population of individuals or groups [Acceptability refers to ease of use of an instrument [Standards for Educational and Psychological Testing (the Standards) [Assessment of the psychometric properties of a new instrument involves testing the instrument for: (1) validity, (2) reliability, and (3) acceptability -28. Valif tests"  (p. 9). r groups . Acceptastrument . In thisandards)  to guideStandards, considered best practice in the field of psychometrics [The ometrics , follow ometrics -33, who ometrics ,35. SincResponse processes evidence refers to empirical evidence of the fit between the concept under study (CRU) and the responses given by respondents on the item(s) developed to measure the concept [Content evidence refers to the extent to which the items included in an instrument adequately represent the content domain of the concept of interest [a priori efforts by scale developers  and, (2) a posteriori efforts  using a panel of content experts to evaluate the relevance of the scale's items to the concept of interest [Internal structure evidence refers to the relationships between the items in an instrument. Factor analytic approaches are frequently used to assess internal structure. Finally, relations to other variables evidence, refers to analyses of the relationships between scores obtained for the concept of interest (CRU) and variables external to the concept. External variables may include measures, which the concept is expected to predict, as well as other scales hypothesized to measure the same concept, and related or different concepts. No one kind of relations to other variables evidence is always desired; the specific evidence sought will depend on the score interpretations desired. This type of evidence is most often expressed in the form of bivariate correlations, statistical (predictive) models, and/or multi-group-comparisons [relations to other variables evidence that could be obtained in this study. However, evidence does exist to suggest that CRU  is: (1) correlated with instrumental research utilization and symbolic research utilization [relations to other variables evidence. concept . Respons concept . Contentinterest . Contentinterest ,38. Inteparisons .With resry) and,  a posterry) and,  a posterlization . ConfirmTranslating Research In Elder Care (TREC) program [The CRU scale was developed as part of a larger research program - the  program . Develop program  to evalu program ,6. Six i program  checklisWe used three samples to conduct the validation study presented in this paper. A description of the samples, data collection and analytic approaches taken are described next.response processes validity evidence from healthcare aides to determine fit between the items comprising the scale and the concept of CRU. Participants consisted of 10 healthcare aides from two general units in two nursing homes in Alberta Canada. All healthcare aides employed on the two units were invited to participate. The healthcare aides completed the CRU scale during work time in a private room (located outside of their work unit). Data collection occurred in three iterations  between December 6, 2008 and December 21, 2008. The data collection process included reviewing a study information letter with each healthcare aide; obtaining signed informed consent; administration of the CRU scale by a member of research team by reading it aloud to the healthcare aide; and an informal conversation with a research team member following completion of the scale. All questions or comments regarding the scale made by the healthcare aides were recorded.The first sample collected Following each iteration, two research team members analyzed all comments recorded during the scale administration and informal conversation sessions using content analysis. Findings from the content analysis were then discussed and used to inform any changes to the scale items/response options prior to the next testing (iteration). The final form of the CRU scale (post-iteration 3), presented in Additional File content validity evidence. This phase of the study occurred concurrently with the pilot test (sample 3). A content validity survey was developed, which asked respondents (experts) to rate each of the five items comprising the CRU scale with respect to their relevance to the concept of CRU. A 4-point Likert scale was provided: 1 'not relevant'; 2 'item needs some revision'; 3 'relevant but needs minor revision'; and 4 'very relevant'. This is a modified version of Davis' scale [The second sample was comprised of an international panel of experts in research utilization in nursing, and was used to collect s' scale , which hs' scale . Responds' scale .content relevance. We chose to use content validity index (CVI) scores and intraclass correlation (ICC). CVI scores allow for item-level assessments  and are more easily interpreted and understood than are other methods of agreement [There are numerous methods of quantifying agreement on greement . For thigreement . The accgreement ,42. Secogreement . An S-CVgreement ,40. Becagreement . The meagreement .We also calculated the ICC . Intraclass correlations describe how strongly units in the same group resemble each other and are often reported as an assessment of consistency of quantitative measurements made by different observers observing the same behavior or measuring the same quantity. With respect to instrument content validity, this refers to ratings made by a number of experts on the relevance of an item to a concept (CRU).validity - internal structure evidence; (2) validity - relations to other variables evidence; (3) reliability; and, (4) acceptability. For this phase, a sub-analysis of data collected for the TREC program was used. TREC is a multi-level  and longitudinal research program designed to examine the impact of organizational context on research utilization by healthcare providers and the subsequent impact of research utilization on outcomes  in nursing homes across the Canadian Prairie Provinces. Data used in this paper come from the TREC survey, in which the CRU scale is embedded. Data were obtained from healthcare aides employed in 30 urban nursing homes that completed the TREC survey during the project's first year of data collection (July 2008 - June 2009). The 30 nursing homes were selected using stratified random sampling . Healthcare aides within each nursing home were recruited using a volunteer, census-like sampling technique. Inclusion criteria included: (1) ability to identify a unit where they have worked for at least 3 months; and, continue to work, and (2) work a minimum of 6 shifts per month on this unit. Additional details on the sampling employed in the original (TREC) study can be found elsewhere [The third sample was used to collect evidence on: (1) lsewhere .p > 0.05). Healthcare aides with English as their first language however scored significantly lower on all five CRU scale items in comparison to healthcare aides whose first language was not English   to determine homogeneity of the sample prior to conducting our psychometric assessment. No significant differences were found by age (r > .80) [Since this was the first field assessment of the CRU Scale, our assessment was largely exploratory in nature. Therefore, to examine the underlying dimensional structure of the CRU Scale, we performed: (1) item-total statistics (using PASW Version 18.0 ), (2) prr > .80) ,46. The r > .80) ,48.The items comprising the CRU Scale were selected during scale development to be similar yet explicitly non-redundant items, and hence the factor-structured models traditionally employed to assess internal structure are not precisely correct, though the similarity of items within the CRU scale renders the factor structure the most appropriate of the available model structures. We ran three confirmatory factor models. Model 1 was comprised of the five items loading onto one factor (CRU). When Model 1 failed to support a strict unidimensional structure, we did a more detailed investigation by setting up two alternate models: Model 2 comprised the five items loading onto one factor (CRU) but with correlated measurement errors between two sets of items based on error theory, and Model 3 was a modified version of Model 2, whereby one item was dropped from the model . We assessed model-data fit of all three models using the chi-square statistic and three fit indices: (1) the root mean square of approximation (RMSEA); (2) the standardized root mean square residual (SRMSR); and, (3) the comparative fit index (CFI). The chi-square statistic tests whether a model-implied covariance matrix is consistent with a sample covariance matrix; a non-significant chi-square value implies acceptable fit. A RMSEA < 0.06 and SRMSR < 0.09 ,49 and arelations to other variables validity we conducted the following analyses: (1) bivariate correlations between each CRU scale item and instrumental, persuasive, and overall research utilization; (2) assessment for change in mean scores for each CRU item at increasing levels of instrumental, persuasive, and overall research utilization; and, (3) a multivariate linear regression model with overall research utilization was the dependent variable.To examine To assess the reliability of the CRU scale we calculated three internal consistency coefficients: (1) Cronbach's alpha; (2) Guttman split-half reliability; and, (3) Spearman-Brown reliability. Coefficients can range from 0 to 1; a coefficient of 0.70 is considered acceptable for newly developed scales while 0.80 or higher is preferred and indicates the items may be used interchangeably ,27. We aEthics approval was obtained from the Human Research Ethics Board at the University of Alberta (Canada). Operational and administrative approvals were obtained from the research facilitation committee overseeing the participating facilities and the TREC research program.First, general wording changes were made to make the items more reflective of nursing homes and the work of healthcare aides. Examples of wording changes included using the word 'resident' instead of 'patient'. General wording changes were also made to the stem (lead-in) for the 5 items. For example, we changed the word 'research' to 'best practice' to reflect terminology commonly used and understood by healthcare aides. Second, item 3 was reworded from 'help to change your attitudes or beliefs about how to care for residents' to 'help to change your mind about how to care for residents' to increase clarity. Third, one of the original six items was removed. The item 'help you plan your workday better' was removed because its interpretation by the healthcare aides (according to the comments they provided) was not congruent with the concept of CRU. Fourth, changes were made to the response options used. We began with a 5-point frequency scale . However, the healthcare aides found these options difficult to interpret. In iteration 2 we trialed a 5-point Likert scale , which the healthcare aides interpreted more easily. Discussions with healthcare aides following iteration 2 resulted in one final change - response option 5 was changed from 'almost always' to 'very frequently'. The revised CRU scale  was then tested in iteration 3; no additional changes were required, providing evidence of fit between the construct of CRU and the five items as they were interpreted by healthcare aides .Revisions were made to several of the items as a result of this phase of the study. A total of 10 (of 11) content validity surveys were returned for a response rate of 91%. One returned survey was not usable due to missing data, leaving an analytic sample of n = 9. The nine experts represented five countries: Canada (n = 3), United Kingdom n = 2), Sweden (n = 2), United States (n = 1), and Australia (n = 1). Table , Sweden all experts  score was 0.40, indicating low universal agreement on the scale by ts Table . The altA total of 1367 healthcare aides (representing 73% of those eligible to participate) working in 97 units in the 30 nursing homes completed the TREC survey. The Intraclass correlation 1, ICC(1), estimate for the data indicated that a degree of agreement existed around the group (unit and nursing home) mean for the CRU scale score (ICC1 = .1352 and .1354 when scores are aggregated to unit and nursing home levels respectively). This level of perpetual agreement however is not substantial indicating CRU is largely an individual level variable; best analyzed using classical psychometric approaches.D2i); D2 probability < 0.001 indicate multivariate outliers [Prior to conducting analyses to assess the internal structure of the CRU scale, we examined sample 3 data for univariate and multivariate outliers. To assess for univariate outliers the frequency distributions of each scale item was examined; values greater than 3 standard deviations from the mean indicate univariate outliers . Screenioutliers . No outlTo test for scale homogeneity, corrected item total correlations for the items were calculated. All corrected item-total correlations exceeded the accepted cutoff of 0.30 indicating each item was related to the overall scale  , and the Kaiser-Meyer-Olkin measure indicated acceptable sampling adequacy (0.866). From the PCA, one-dominant factor  was extracted from the scale items. Visual inspection of the scree plot  was consistent with this finding. Factor loadings were substantial, ranging from 0.610 to 0.759 . The RMSEA (0.140) did not support close fit but SRMSR (0.03) and CFI (0.977) did support close fit. Based on these findings, we rejected the simple 1-factor model.Factor loadings for all three CFA models are displayed in Table 2 test is expected to improve if a fixed parameter is freed to be estimated, suggested freeing seven of the possible ten measurement error covariances in the model . A careful re-examination of the five items comprising the scale revealed a level of content overlap with respect to two pairs of items: items 1 (give new knowledge or information) with 2 (raise awareness); and, items 3 (help change your mind) with 4 (give new ideas). We therefore considered the possibility that systematic error variance may be causing these items to group together beyond their dependence on one principal factor. We hypothesized that in addition to the five items loading onto a single factor; there would be error covariances for items 1 and 2, and items 3 and 4. We chose not to allow the errors on the remaining five pairs of items identified in the modification indices to correlate because they did not match this error theory. This error theory was also supported statistically; these two pairs of items displayed the largest standardized residuals and modification indices among all possible pairs of items . The close fit statistics also improved . We concluded based on these findings that the 1-factor model incorporating limited error theory was superior to the strict 1-factor model. However, the need to correlate errors to attain a better-fitting model raised the question of why items that overlap significantly in content are necessary in the scale. As a final modification, we therefore selected to drop item 1 and rerun model 2. We dropped item 1 based on: (1) the error theory (that item 1 had redundancy with item 2), (2) that it (item 1) received the lowest I-CVI score  in comparison to the previous two models. The close fit statistics remained relatively unchanged from model 2 . A final alternate model would be a three-item scale (without item 1 and one of items 3 or 4). However, such a model would be just identified (df = 0) and not testable.Model 2, where we correlated errors on items 1 and 2, and items 3 and 4, resulted in improved and a marginally acceptable fit  for scales intended to compare groups  . By odd-The percentage of healthcare aides providing complete data on the CRU scale  was high at 98.6% . The average time for completion of the five items was minimal (1 minute and 6 seconds).The aim of this paper was to report the psychometric properties of responses obtained with the CRU scale when used with healthcare aides in nursing homes. In line with previous studies ,58, a suStandards', adopted in this study, focuses on content, response processes, internal structure, and relations to other variables evidence to obtain a unitary and comprehensive perspective of validity. In this framework all validity contributes to construct validity and exists as a matter of degree, meaning interpretations from scores are more or less valid given a specific context. The Standards' approach therefore provides an alternative to the traditional conceptualization of validity which views validity as: (1) distinct types , and (2) existing or not.In this study, we aimed to assess the validity of the CRU scale and each of its items when completed by healthcare aides in nursing homes. A sound validity argument integrates various types of evidence to make a determination about the degree to which existing evidence and theory support the intended interpretations of scale scores for specific uses . The StaStandards) with respect to the scores and interpretations obtained from the CRU scale when completed by healthcare aides in nursing homes. While it does do not provide a complete picture of all aspects of validity, it does provide a much needed first look at several critical issues that need to be addressed before more in-depth validity studies can be undertaken with additional samples.In this study, we systematically performed several analyses to seek validity evidence  with an expert panel. This process  illustrates the importance of considering multiple evidence sources. A traditional  approach to validity assessment would have resulted in the original items being assessed for relevance by an expert panel without knowledge of misfit between the items  and the concept of CRU. However, by adopting the Standards approach and letting multiple evidence sources inform one another, we were able to pilot test a form of the CRU scale that produced more valid score interpretations, then would have been used, if a traditional approach to validity assessment was undertaken.Content validity is an important source of validity evidence; it is essential to identifying the concept being measured and is an early step in establishing construct validity. We explored content validity in a number of ways. First, we attempted to include a representative sample of items by reviewing the existing literature and modifying previously developed statements designed to capture conceptual use of knowledge in acute care hospitals with professional nurses. Second, before conducting a formal content validity assessment with experts, we assessed the appropriateness of the scale with respondents representative of those for whom it was developed . This latter activity is formally labeled as 'response processes' validity evidence in the i.e., without item 1) in assessments of CRU by healthcare aides.Our validity assessment revealed problems with two of the five items in the CRU Scale: item 1 (give new knowledge or information) and item 3 (help change your mind). The formal (expert) content validity assessment resulted in item 1 (give new knowledge or information) being rated at an unacceptable level overall with respect to its relevance to CRU. Some experts also identified item 1 as having content overlap with the concept of instrumental research utilization. The ICC  measure of agreement further supported item 1 needing removal and/or revision; ICC  increased substantially when item 1 was removed from the scale (0.317 with item 1 to 0.793 without item 1). While the bivariate correlation between item 1 and instrumental research utilization was low - moderate (0.295), of the five scale items, it correlated the strongest with instrumental research utilization, lending some empirical support to the expert panel's assessment of the item . Other issues with item 1 also emerged in our analysis. For example, item 1 had the second lowest factor loading in the PCA  received a perfect relevance score in the formal content validity assessment Table . HoweverRelations with other variables evidence also added to the construct validity argument for the CRU scale. Statistically significant bivariate correlations Table  between df = 0) and thus, not testable. Item parceling  has been used by others to deal with issues around local dependence and lack of unidimensionality. This was not an option here given the small number of items in the CRU Scale; by parceling items 3 and 4 along with removal of item 1, the model would remain 'just identified' and not testable.While the items comprising the CRU scale were originally selected to cluster on one dimension (CRU) they were also intentionally selected to be non-redundant, allowing each item to focus on a slightly different feature of CRU. The intended 'clustering' of the items onto a factor renders the factor model the most appropriate model for assessing the internal structure of the CRU scale but the purposefully non-redundant nature of items meant that the scale would not function perfectly as a factor model. We employed three factor models: Model 1 with the five items loading onto a single factor, Model 2 with the five items loading onto a single factor with correlated errors between two sets of items , and Model 3 with four items (item 1 was removed) loading onto a single factor with correlated errors between one set of items (items 3 and 4). A fourth model with one of items 3 or 4 also removed (in addition to item 1) would have been the next logical alternative model. However, this model would be just identified  in addition to the scale's internal consistency. Third, the internal structure analyses revealed information about how each of the five items in the CRU scale relate to the latent concept of CRU. These findings suggest that research  investigating approaches to deriving an overall latent score for the CRU scale  is needed. Fourth, we conducted the expert panel content validity assessment and the pilot test concurrently. This prevented us from making revisions to the scale based on the expert panel assessment before pilot testing the scale. Fifth, the data used in sample 3 (pilot test) of this study has a naturally occurring multi-level nature  which could have a biasing effect on the analyses reported here; the ICC(1) values for CRU scale score however revealed CRU is largely an individual concept in this dataset supporting our choice of analyses and limiting any potential bias in this regard. Finally, because this was the first administration of the CRU scale, it has not yet been used in studies of research utilization interventions. Therefore, it is not known whether the scale is sensitive to and able to detect changes in CRU over time. Despite these limitations, the CRU scale addresses an important gap in health services research - the ability to assess healthcare aides' conceptual use of research findings. To date, research utilization has been measured predominantly as an omnibus or general concept. Failure to measure CRU results in: (1) an underestimate of the extent to which healthcare providers use research in practice and, (2) a lack of understanding of the true research utilization process.i.e., without item 1: give new knowledge or information) to yield sound assessments of CRU by healthcare aides. Future research should first include exploration of item 1 as a possible distinguished aspect of CRU and revision to the wording of item 3 (help change your mind), followed by investigation of: (1) reasons for differences in CRU scale scores by first language spoken, (2) latent scale scoring, (3) variables that predict and are outcomes to CRU , and (4) longitudinal work to determine whether the CRU Scale and its items are sensitive to changes in levels of CRU.The CRU scale assessed in this paper showed acceptable beginning psychometric properties with respect to responses from healthcare aides in nursing homes whose first language was English. The analyses of validity, reliability, and acceptability are promising. These findings, however, are not generalizable beyond healthcare aides in Canadian nursing homes that speak English as their first language. Based on our findings, we recommend only using the four-item version of the CRU scale (Abbreviations used in this manuscript include: (1) (CRU): Conceptual Research Utilization; (2) (TREC): Translating Research in Elder Care; (3) (EFA): Exploratory Factor Analysis; (4) (PCA): Principal Components Analysis; (5) (CFA): Confirmatory Factor Analysis; (6) (I-CVI): Item-Content Validity Index; (7) (S-CVI): Scale-Content Validity Index; (8) (RMSEA): Root Mean Square Error of Approximation; (9) (SRMR): Standardized Root Mean Square Residual; (10) (CFI): Comparative Fit Index; (10) (ICC): Intraclass Correlation.The authors declare that they have no competing interests.All authors participated in designing the study. JES and CAE developed the CRU scale. JES performed the statistical analysis and drafted the manuscript. CAE, CNC, and MG provided valuable advice throughout the study. All authors provided critical commentary on the manuscript and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/11/107/prepubThe CRU Scale. The CRU scale as presented to the expert panel and used in the pilot testClick here for fileCRU Scores by First Language. A summary of scores on the CRU items and scale score according to whether or not English was the healthcare aides first languageClick here for fileCFA Model 1 Additional Diagnostics. A summary of standardized residuals and modification indices for CFA Model 1Click here for file


Mus musculus, called b-congenic, by replacing the androgen-binding protein aAbpa27 allele in the C3H/HeJ genome with the bAbpa27 allele from DBA/2J. We and other researchers used this b-congenic strain and its C3H counterpart, the a-congenic strain, to test the hypothesis that, given the choice between signals from two strains with different a27 alleles on the same genetic background, test subjects would prefer the homosubspecific one. It was our purpose in undertaking this study to characterize the segment transferred from DBA to the C3H background in producing the b-congenic strain on which a role for ABPA27 in behavior has been predicated. We determined the size of the chromosome 7 segment transferred from DBA and the genes it contains that might influence preference. We found that the “functional" DBA segment is about 1% the size of the mouse haploid genome and contains at least 29 genes expressed in salivary glands, however, only three of these encode proteins identified in the mouse salivary proteome. At least two of the three genes Abpa27, Abpbg26 and Abpbg27 encoding the subunits of androgen-binding protein ABP dimers evolved under positive selection and the third one may have also. In the sense that they are subunits of the same two functional entities, the ABP dimers, we propose that their evolutionary histories might not be independent of each other.Two decades ago, we developed a congenic strain of  Androgen-binding protein (Abp) gene in order to test the hypothesis that mouse salivary androgen-binding protein (ABP) is a signal for mouse mate recognition. ABP is a dimeric protein, composed of two subunits originally described as an alpha subunit, encoded by Abpa, connected by disulfide bridging to either a beta or a gamma subunit, encoded by Abpb and Abpg, respectively (these are now a large gene family with a new nomenclature described below). The dimer is capable of binding male sex steroid hormones and progesterone with a relatively high specificity and affinity , bAbpa in M. m. musculus (eastern Europe to northern China) and cAbpa in M. m.castaneus  Abpa monomorphism, resulting from a different allele having been fixed in each of three different subspecies, suggested that ABP might have a role in mediating subspecies recognition and that idea stimulated our interest in producing strains congenic for different alleles of Abpa. The existence of a relatively narrow house mouse hybrid zone between M. m. domesticus and M. m. musculus in Europe influenced us to choose the alleles from those two subspecies to produce congenic strains.Strains of mice that differ in only one gene on an otherwise identical genetic background provide a powerful tool for investigating the function of the isolated gene. Twenty years ago, our laboratory developed a mouse strain congenic for a salivary affinity . ThoughtbAbpa allele) to C3H  which possesses the aAbpa allele and then backcrossed the F1 to the C3H parent strain, selecting the aAbpa/bAbpa heterozygote for subsequent backcrosses to C3H. Backcrossing was continued for 16 generations before an intercross was made. The resulting congenic strain was named “b-congenic" and was used in conjunction with the C3H strain (by default “a-congenic") to provide sources of signals in various types of behavioural tests. A locus such as Abpa, which has been selected for 16 generations, is expected to retain flanking heterozygosity for an average combined distance of 12 cM (i.e. 12 map units), with a standard deviation of 8 cM We crossed DBA/2J  genes, which encode the vast majority of proteins secreted into urine, are on chromosome 4 while the Abp genes are on chromosome 7. Because salivas were the sources of the signals used in these tests, we conclude that the product of the gene of interest must be secreted into saliva. This has been previously shown for the three ABP subunits: alpha, beta and gamma One important observation from early studies was that subjects do not show a preference for urine from the congenic strains Abp gene region in the mouse is comprised of 3 Mb of DNA on the proximal (with respect to the centromere) end of mouse chromosome 7 and contains 30 alpha (Abpa) paralogs, as well as 34 beta-gamma (Abpbg) paralogs for a total of 64 Abp genes bAbpa allele, now called bAbpa27Abpbg27, originally Abpb) ∼7.6 kb away, while its nearest distal neighbour, Abpa30 is 76.8 kb away. And what of other genes in the Abp cluster? Even though the entire family (3 Mb) comprises 0.1% of the mouse genome, it could nonetheless easily fit into the smallest estimate for the region transferred from DBA. When one considers the larger estimate of the segment transferred (40 Mb), the possibility that other genes on the transferred chromosomal segment might have some effect on the mate choice seen in the behavioral experiments has to be considered.While these behavioral studies were in progress, genomic studies were underway that ultimately revealed that the Abpa27, Abpbg26, and Abpbg27. This alters our notion of how mouse salivary androgen-binding proteins interact to influence mouse mate preference with at least two, and perhaps three, subunits of ABP dimers comprising the salivary signal.This project was motivated by two goals: 1) to complement our studies with congenic strains by identifying the genes transferred onto the C3H background that might contribute to the preference seen in mouse behavioural tests; and 2) to investigate the possibility that the signal that stimulates mate choice might be complex, with ABPA27 interacting with another protein(s). We used the mouse genome and genomic sequences of individual strains to determine the size and gene content of the chromosome 7 segment donated by DBA to the b-congenic strain. We combined this information with studies of mouse salivary gland ESTs Abp gene region spanning coordinates (cds) ∼32 to ∼35 Mb of DBA chromosome 7 came from M. m. musculus, with a small contribution from M. m. castaneus  and the Mouse Phylogeny Viewer (MPV) suggest that essentially the entire 3 Mb sequence .Abp region, Fkrp, Plaur and Aplp1 were sequenced in the b-congenic strain and found to have DBA SNPs  in the two strains. Thus it is not possible to determine exactly the left boundary of the transferred portion. Two genes on the distal side of the Abp region, Lrp3 and Ccne1 have the DBA SNPs in b-congenic, while the more distal gene Siglece has C3H SNPs in b-congenic. This suggests that a crossover occurred between cds ∼38.8 and ∼50.9 during backcrossing. There is a large gap between cds ∼39.5 and ∼46 Mb that begins almost immediately to the right of Ccne1 , where the two parental strains differ in their subspecies genetic contribution, to the crossover somewhere proximal to estimate . Gene(s)We used two different analyses to search for candidate genes: 1. Searching ESTs expressed in mouse salivary glands These two analyses are complementary and allowed us to make the most thorough search possible with the data currently available. For the first analysis, we downloaded the ESTs reported from a study of gene expressions in all three major salivary glands, parotid, sublingual and submandibular, evaluated in both sexes in the BALB/c strain There were 26 genes within the segment described above . Ten of bg26, bg27 and a27 Abp paralogs that had salivary gland cDNAs identified by Klk1 and Klk1b5 in both sexes, and 12 other Klk1b family genes in males only). All these genes map distally to Siglece and thus outside the region transferred from DBA to the b-congenic strain.Data from male and female mouse saliva proteomes Abp genes, a27, bg27 and bg26 map in the chromosome segment transferred from DBA and encode proteins found in saliva. C3H and DBA cDNA and protein sequences of these genes were published previously bg26 has the most nonsynonymous substitutions . Such introgression or homoplastic mutations might even have been fixed due to positive selection, and the gene trees for a27 and bg26 are curiously similar insofar as it is the M. m. domesticus branch that is out of place in both. While some investigators feel that the evolutionary relationships among the three full Palearctic species of Mus may be tenuous (e.g. http://www.ucl.ac.uk/discussions/viewtopic.php?t  =  7850). Thus the gene tree was the appropriate choice regardless of the status of the species tree.The phylogeny of polytomy . Two of ide tree . On the ate tree , File S1ous e.g. , PAML's a27 and bg26 were analyzed with CODEML using their gene phylogenies as the guide trees, we again obtained significant signs of positive selection within these five murid rodents .When ies tree  and the Abp orthologs from various taxa have been analyzed to identify specific sites under selection in each. Four amino acid sites in ABPA27 were identified as positively selected at a BEB posterior probability threshold of 90%, regardless of which tree was used. In the case of ABPBG26, however, eleven sites were identified as positively selected at a BEB posterior probability threshold of 90% when the species tree was used as the guide tree but only two when the gene tree was used. Thus, the gene tree produced a more conservative result. In spite of the non-significant CODEML result for ABPBG27, three amino acid sites were identified as positively selected at a BEB posterior probability threshold of 95%. Whether or not to report positively selected amino acid sites obtained with the BEB method when the CODEML result is non-significant is controversial . While the outcome (M8 - M8a) was not statistically significant, the current test is underpowered Because of the evidence for positive selection on ABPA27 and ABPBG26, we predicted positively selected amino acid sites using a Bayes empirical Bayes (BEB) method ; Table 1bg27 gene. We attempted to amplify segments from Mus caroli  genomic DNA using the bg26 and bg27 primer sets, and sequenced a candidate from the bg26 amplification. Attempts to verify orthology of the bg26 candidate using BLAT suggested that it was more closely related to bg27. While that seemed to qualify this as a sixth sequence for the purpose of re-running the bg27 CODEML analysis, the M. caroli sequence is radically different from any of the original five. More problematic is the observation in M. caroli of two, closely related a27 paralogs in the clade containing a26p, a27 and a30pbg26-a26p> and <bg27-a27> modules in M. caroli with their counterparts in the other species and subspecies of Mus is obscured by the possibility that their evolutionary history is different in the M. caroli genome than in those of Palearctic species used in this study.It is possible that adding taxa to the CODEML analysis would have tipped the scales from nonsignificant to significant for the a27 and bg26, and the species tree analysis for bg27  served as the foundation of a number of studies aimed at testing the hypothesis that, given the choice between signals from two strains with different ba27 allele, as originally intended. The alternative hypothesis is that they are attributable to the affect of another, closely linked gene that is also expressed in saliva and that differs significantly in the two parental strains. Another possibility that must be considered is that one or more Abp genes (a27 and/or another Abp gene) is responsible for the preference test results but those results were also influenced by the expression of an additional, non-Abp gene that also encodes a protein secreted into saliva and that differed significantly in the parental strains.We undertook the project reported here to determine the physical and genetic nature of the chromosome 7 segment transferred from DBA onto the C3H genome to produce the b-congenic strain. We employed the information in the mouse genome Abp genes had been predicted from biochemical and genetic studies Abpa, that encoded the alpha subunit of ABP was the only one encoding that subunit common to two dimeric forms found in mouse salivas: alpha-beta and alpha-gamma  was listed as showing a medium level of expression in microarray analysis of the sublingual and submandibular glands, however, there was no evidence of a transcript for this Abp gene in: 1) extensive testing of parotid gland, sublingual gland and submaxillary gland Abph (Abpa2) expression in sublingual and submandibular glands Abp genes (see below) to the probes produced from the Abph transcript for the study. Of the 64 Abp paralogs in the mouse genome there is only evidence that three of them, bg26, bg27 and a27, are transcribed in the three salivary glands Abp paralogs.One protein, a27, bg26 and bg27 are the only genes identified on the segment of chromosome 7 transferred to the b-congenic strain that could have influenced the results of the Y-maze preference tests Abpbg genes by comparing their gene phylogenies and by using the CODEML program to look for the footprints of adaptive evolution.It appears, in light of the proteome data M. m. domesticus and M. m. musculus made secondary contact Our CODEML analysis supports the notion that both subunits, A27 and BG26, have a history of adaptive evolution driven by positive selection on a few sites at the surface of one face of the dimer they form. We suggest that these two genes evolved rapidly as subunits that form one of the dimers secreted in relatively large quantity into the salivas of mice and that their evolutionary histories may not be independent. This is not unexpected given that at least A27 appears to be involved in incipient reinforcement on the hybrid zone where bg27 was non-significant, however, there were three sites in the sequence of BG27 identified as positively selected at a BEB posterior probability threshold of >95%. As noted above, whether to accept such sites as selected, given the non-significant CODEML result, is controversial but it is nonetheless interesting that two of the three sites are at the surface of one face of the dimer formed with the A27 subunit resulting in a model that looks very similar to that of the A27-BG26 dimer . DNA sequence traces were edited with Chromas 2.3 (http://www.technelysium.com.au). DNA sequence alignment, coding region assembly, and in silico translation were done using the DNAsis Max program 2.0 (Hitachi).Polymerase chain reaction (PCR) was run as previously described www.ncbi.nlm.nih.gov/geo/). Data was downloaded and cross-referenced with Amersham Codelink UniSet Mouse 1 Bioarray, and protein information for each probe target was gathered by searching probe accession numbers on MGI (http://www.informatics.jax.org/), the international database resource for the laboratory mouse. The data in the region of interest was also sorted on the sample signal intensity from the array, which ranged from 0 to 250–400 in the six data sets, and only those with values of 10 or above were retained for this study.Salivary gland protein expression data sets were obtained by searching “mouse salivary gland" on the NCBI Gene Expression Omnibus website  threading program . Sites under positive selection were mapped onto the structural models using PYMOL and incorporated into a figure.Positive selection was assessed in the program CODEML in the PAML package Table S1Gene information.(XLS)Click here for additional data file.Table S2Sequence gaps (regions with low/no SNP density in the Mouse Diversity array and lacking UCSC genes).(XLS)Click here for additional data file.Table S3ESTs found in one or more of three glands of the two sexes of BALB/c mice .(DOC)Click here for additional data file.Table S4Chromosome 7 encoded proteins found in mouse saliva proteomes .(XLS)Click here for additional data file.File S1Retained and eliminated proteins (represented by ESTs and cDNAs).(DOC)Click here for additional data file.File S2Protein sequences from Sanger Data and from sequencing.(TXT)Click here for additional data file.File S3Sequences of Abp coding regions used in CODEML analysis.(TXT)Click here for additional data file.File S4Unrooted trees for Abpa27, Abpbg26 and Abpbg27. Trees were made as in Mus spretus ortholog.(TIF)Click here for additional data file.File S5Details of Materials and Methods.(DOC)Click here for additional data file.


Combination of different agents is widely used in clinic to combat complex diseases with improved therapy and reduced side effects. However, the identification of effective drug combinations remains a challenging task due to the huge number of possible combinations among candidate drugs that makes it impractical to screen putative combinations.Drug Combination Predictor) to predict possible drug combinations by exploiting the topological features of the drug cocktail network. Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach.In this work, we construct a 'drug cocktail network' using all the known effective drug combinations extracted from the Drug Combination Database (DCDB), and propose a network-based approach to investigate drug combinations. Our results show that the agents in an effective combination tend to have more similar therapeutic effects and share more interaction partners. Based on our observations, we further develop a statistical approach termed as DCPred (The drug cocktail network constructed in this work provides useful insights into the underlying rules of effective drug combinations and offer important clues to accelerate the future discovery of new drug combinations. Recently, it is becoming a popular and promising strategy to new drug discovery, especially for treating complex diseases, e.g. cancer -3. For eeriments . Agrawalosophila . In addi in vivo . Recentlratively . Despiteratively . In a drratively ,12. Accoratively . Furtherin silico methods that are capable of discovering new drug combinations prior to combination synthesis and practical test in the lab. Owing to the completion of human genome sequencing projects and the advancement of molecular medicine, extensive system biology efforts have been made to discover new combinations based on molecular interaction networks [et al. have recently found that the protein dynamics in response to drug combination can be accurately described by a linear superposition of the dynamics under the corresponding individual drugs [et al. devised an efficient search algorithm originated from information theory for optimization of drug combinations based on the sequential decoding algorithms [Therefore, it is necessary to develop effective networks ,15 in thnetworks -19. Nevenetworks . In thisal drugs . Their sgorithms . More regorithms -20.In this work, we study the drug combinations in terms of their therapeutic similarity and the network topology of a drug cocktail network constructed from the effective drug combinations deposited in the Drug Combination Database (DCDB) . We findTS) (as defined in Equation 3) between the two drugs linked by the edge, while the grey edges indicate that the two drugs linked by the edge have totally different therapeutical effects. In addition, we found 102 drugs that have at least two neighbors in the drug cocktail network, which we termed as "star drugs" hereafter and 91 of which have target protein annotations in DrugBank.In this study, we extracted 239 known effective pairwise drug combinations from DCDB . The infP(x) of nodes in the drug cocktail network having x connections to other nodes can be described as:Since most of biological networks are scale-free networks , we analc = 2.1 and α = 1.9 in this case.where As the drug cocktail network shown in Figure TS) score was calculated for each drug pair, and the average of all TS scores was used as the TS score for the whole drug cocktail network. The random combination networks were generated by randomly shuffling the edges while still preserving the degree for each node [P-value was calculated as the ratio that the TSs of random combination networks are larger than that of the drug cocktail network during the 1000 randomizations. The results are shown in Table P-values of the drug cocktail network across ATC code levels 1-4 are all equal to 0, strongly suggesting that the real drug combinations significantly differ from the random combination networks. Note that the 5th ATC code level was not considered here, as there is only one drug combination having identical ATC codes for all the five levels in the drug cocktail network. This means that the 5th ATC code level is not suitable for performing statistical analysis and thus it is not included in the analysis.To test our hypothesis that the drugs in one combination tend to have similar therapeutic effects, the drug cocktail network was compared against random combination networks. For this purpose, a therapeutic similarity  two drugs have, the more likely they are effective combinations. Another important observation is that the combinations between drugs sharing similar therapeutic effects and star drugs are more likely effective combinations.Moreover, we also investigated the distribution of neighbor drug pairs of star drugs Figure  and 4B, d Figure . From FiIn various networks, the hub nodes are generally considered to play important roles . TherefoIn addition, we investigated the proteins targeted by the 13 hub drugs in the drug cocktail network that have target information. By mapping all proteins targeted by the drugs in the drug cocktail network to the human protein-protein interaction network retrieved from STRING database , we founAs shown in Figure TS only, DCPred2 considering TS and drugs with at least 2 neighbors, and DCPred3 considering TS and drugs with at least 3 neighbors. In the case of DCPred2 and DCPred3, all possible drug combinations were ranked in ascending order according to the p-value by equation (4), and the top ones were considered as putative effective drug combinations. While in the case of DCPred1, all possible drug combinations were ranked in descending order according to the TS value by equation (3), and the top ones were considered as putative effective drug combinations. The ranking list of drug combinations can be found in the additional files   and cell surface kinases  to reduce blood flow to the tumor for the treatment of patients with advanced renal cell carcinoma [et al. demonstrated that complementary inhibition of VEGF signaling has synergistic therapeutic effects, and this combination therapy has promising clinical activity over ovarian cancer [th one is the combination of thalidomide and lenalidomide. Thalidomide has been successfully introduced to treat multiple myeloma and its analogue, lenalidomide, is also effective in relapsed refractory myeloma [Of the top 35 ranked drug combinations inferred by our models, 63% of them (22/35) are known effective drug combinations according to DCDB, and 37% (13/35) do not have any annotations in DCDB  hub drugs usually have similar and even the same therapeutic effects as their neighbors; 2) target proteins of the hub drugs are often membrane or membrane-associated proteins; 3) the components in effective drug combinations usually have more similar therapeutic effects, making the drug cocktail network significantly different from the random combination networks.From the above observations, we consequently developed a new statistical approach to infer and rank possible effective drug combinations by taking into account drugs with at least two or three drug neighbors. As a result, our DCPred2 and DCPred3 models achieved the AUC scores of 0.88 and 0.92, respectively, demonstrating a good performance. We further applied these models to rank all the possible drug combinations and found that the top ranked combinations are more likely to be effective combinations, according to the cross-reference to the literature or the similarity of their ATC codes. In particular, four combinations in the top 35 rankings have been verified as effective combinations by the literature search. We also show that there is a better chance for another 3 combinations to be effective combinations in terms of the pharmacological similarity. Our results in this study provide useful insights into the underlying mechanisms of effective drug combinations and hence important clues for efficiently reducing the search space of possible combinations within the approved drugs. Our approach may be further useful for developing more accurate models. The DCPred models are anticipated to be applied to screen more effective drug combinations with clinical importance.Furthermore, the concentration of each drug in a combination is a crucial factor in the study of drug combination. However, it is currently difficult to utilize the dosage information of drugs without the knowledge of their quantitative dose-response profiles (e.g. drug induced gene/protein expression data) under different drug concentrations, due to the limited availability of such data. We will investigate drug combinations from this perspective in the future, when more data regarding drug concentrations become available.TS) between the two drugs linked by the edge. The gray edge means that there is no therapeutic similarity between the two drugs.The annotations of drug combinations were retrieved from a newly released Drug Combination Database (DCDB) . This isHuman protein-protein interactions (PPIs) with high confidence from STRING  were usek-th level drug therapeutic similarity (kS) between two drugs is defined using the ATC codes of these two drugs:The Anatomical Therapeutic Chemical (ATC) Classification System, which includes 5 different hierarchical levels, was used to classify drugs into different groups according to the organ they acted on and the therapeutic chemical characteristics. The k(d) ATCdenotes all the ATC codes at the k-th level of drug d. Note that a drug has five levels of ATC codes. A score, TS, is used to define the therapeutic similarity between two drugs:where n ranges from 1 to 5. In this study, n = 3 is adopted considering that only a few drugs have the same ATC codes at the 5th level.where 1 dand d2 with respective n1 and n2 partners have m in common in the drug cocktail network, there will be three groups in the neighborhood of the two drugs, i.e. (1) m drugs that are the neighbors of both drug d1 and d2; (2) n1 - m partners that are the neighbors of drug d1 only; and (3) n2 - m partners are the neighbors of drug d2 only [N drugs in the drug combination network, then a p-value between d1 and d2 can be calculated using the following equation:We assume that two drugs are more likely to be combined if they share a large number of common drugs in the drug cocktail network. For example, if two drugs  d2 only . Supposep-value computed by equation (4) will be closer to 0, which means they are more likely to be combined. We use the equation (4) to compute the p-values for all possible combinations and then rank the values in ascending order. As drug pairs with lower p-values are more likely to be combined, the prediction of effective drug combinations can be made given a certain p-value threshold. We term this framework that explores the drug cocktail network and predicts possible drug combination as DCPred (Drug Combination Predictor) and assess its performance for inferring effective drug combinations based on the curated drug combinations dataset.If two drugs share more common drugs compared with all of their neighbors, the The authors declare that they have no competing interests.KJX implemented the computational method, carried out data analysis, and drafted the manuscript under the direction of XMZ and JS. JS and XMZ guided and coordinated the project, and improved the presentation of the manuscript by copy-editing and fixing language issues. All authors read, revised and approved the final manuscript.The annotation of the neighbor drugs of DB00999 (Hydrochlorothiazide).Click here for fileThe annotation of the neighbor drugs of DB00072 (Trastuzumab).Click here for fileThe ranking of 1181 possible combinations by the DCPred2 model.Click here for fileThe ranking of 419 possible combinations by the DCPred3 model.Click here for filePrediction performance of the DCPred1 model at varying thresholds, as measured by Sensitivity, Specificity and Accuracy.Click here for filePrediction performance of the DCPred2 model at varying thresholds, as measured by Sensitivity, Specificity and Accuracy.Click here for filePrediction performance of the DCPred3 model at varying thresholds, as measured by Sensitivity, Specificity and Accuracy.Click here for file


MTHFD1 rs1076991 differed significantly between the ALL and control population. Analyzing the subtypes of the disease the GG genotype increased only the risk of B-cell ALL . The GG genotype of the rs3776455 SNP in the MTRR gene was associated with a significantly reduced risk to ALL , which resulted mainly from the reduced risk to B-cell and hyperdiploid-ALL. The TC genotype of the rs9909104 SNP in the SHMT1 gene was associated with a lower survival rate comparing it to the TT genotype . The BN-BMLA confirmed the main findings of the frequentist-based analysis and showed structural interactional maps and the probabilities of the different structural association types of the relevant SNPs especially in the hyperdiploid-ALL, involving additional SNPs in genes like TYMS, DHFR and GGH. We also investigated the statistical interactions and redundancies using structural model properties. These results gave further evidence that polymorphisms in the folate pathway could influence the ALL risk and the effectiveness of the therapy. It was also shown that in gene association studies the BN-BMLA could be a useful supplementary to the traditional frequentist-based statistical method.In this study we investigated whether polymorphisms in the folate pathway influenced the risk of childhood acute lymphoblastic leukemia (ALL) or the survival rate of the patients. For this we selected and genotyped 67 SNPs in 15 genes in the folate pathway in 543 children with ALL and 529 controls. The results were evaluated by gender adjusted logistic regression and by the Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) methods. Bayesian structure based odds ratios for the relevant variables and interactions were also calculated. Altogether 9 SNPs in 8 genes were associated with altered susceptibility to ALL. After correction for multiple testing, two associations remained significant. The genotype distribution of the  Acute lymphoblastic leukemia (ALL) is the most frequent haematopoetic malignancy in childhood worldwide MTHFD1, MTRR, MTR, MTHFR, DHFR, GGH, SLCO1B1) could influence the available folate in the cells, and therefore might alter the susceptibility to ALL and the response to the therapy DHFR, MTHFD1, MTHFR, MTRR, MTR, SHMT1, TYMS, ABCB1, FPGS, GGH, GSTP1, SLCO1B1, SLC19A1, SLC22A8, TPMT) in the folate metabolic pathway  malignant cell production  pathway .Recently, several methods have been described and used for evaluating multiple interactions in multifactorial diseases  patients of Hungarian (Caucasian) origin treated according to the ALL Berlin-Frankfurt-Münster (BFM) 90, 95 or ALL IC-BFM 2002 chemotherapy protocols in ten Hungarian centres. The patients were diagnosed with ALL between 1990 and 2010. See more details about the characteristics of patients in Risk group stratifications were determined according to the study protocols.The 529 control patients (median years of age 12.5 (1.5–59)) of the same ethnicity and from the same geographical region as the patients were randomly selected from healthy blood donors and from minor outpatients from the Orthopaedic Department in the Budai Children's Hospital, and from the Urological Department of Heim Pal Pediatric Hospital, Budapest. None of the controls have had childhood ALL or any other types of cancers previously.Written informed consent was obtained from the study participants or from the next of kin, caretakers, or guardians on the behalf of the minors/children participants involved in the study. All the associated documents have been stored. The study was conducted according to the principles expressed in the Declaration of Helsinki and the whole study including the informed consent procedure were approved by the Hungarian Scientific and Research Ethics Committee of the Medical Research Council www.ncbi.nlm.nih.gov. Accessed: 2013 Jun 20.) we selected and genotyped 67 single nucleotide polymorphisms in 15 key genes encoding the main enzymes directly  or indirectly  involved in the folate cycle through which the DNA synthesis, the DNA methylation and repair mechanisms can be regulated (Based on the relevant scientific publication results (candidate gene association and GWA studies) and the dbSNP database of the National Center for Biotechnology Information  or relevant results published previously in the scientific literature Two of the selected SNPs were monomorphic and one of them did not meet the requirements of Hardy-Weinberg Equilibrium, and hence 64 single nucleotide polymorphisms were involved in the analyses.The genomic DNA of ALL patients was obtained from peripheral blood in a retrospective manner using QIAmp DNA Blood Maxi Kit . The healthy control DNA was isolated with iPrep PureLink gDNA Blood Kit, iPrep Purification Instrument .Single nucleotide polymorphisms (SNPs) were genotyped by Sequenom iPLEX Gold MassARRAY technology at the McGill University and Génome Québec Innovation Centre, Montréal, Canada. Only SNPs with genotyping call rate over 90% were included in the analysis.Gender adjusted logistic regression model was applied to obtain odds ratios (ORs) and 95% confidence intervals (CIs) to estimate risks for each polymorphism to childhood ALL in the case-control data. Calculation of the genotype frequency and the allele positivity was performed by IBM SPSS Statistics software, version 21.0., and the allele frequency was analysed by Medcalc Version 12.4.0.The statistical analyses were performed not only for the overall ALL population but also for the clinical subtypes -ALL).http://ihg.gsf.de/cgi-bin/hw/hwa1.pl. Accessed: 2013 Jun 20.  method with type I error rate of 5%  The statistical power was calculated by R, using an own implementation of the Genetic Power Calculator of Purcell and Sham Linkage disequilibrium and haplotypes were calculated with Haploview version 4.2. Odds ratios for haplotypes were obtained by MedCalc 12.4.0 software.The survival analysis was carried out with IBM SPSS Statistics Software version 21. The survival rates were estimated with the Kaplan-Meier Method.Associations and interactions were also investigated by the MDR method In our previous studies, the application of the BN-BMLA were explained in details a posteriori probability of the strongly relevant variable sets with respect to a target variable (e.g. the variable describing the case/control status of the patients in a genetic association study) a posteriori probability of the strong relevance (posterior) ranges from 0 to 1 and a posterior of 1 means that the target (e.g. immunophenotypes of ALL) definitely has a dependency relationship with a predictor (e.g. SNP), whereas 0 means there is no such relationship. Strong relevance has two types: direct relevance  and pure interaction (e.g. a SNP with an epistatic effect).The BN-BMLA method applies a Bayesian approach, which means that it computes the strong relevance, other forms of structural associations exist. In this context, we examined statistical interaction and redundancy. In a structural approach, statistical interaction means that SNPs tend to occur together more often in the model as strongly relevant variables than it is expected according to the assumption of their independent relevance  were investigated.SHMT1, rs1128503 ABCB1, not listed in ABCB1, not listed in Out of the 67 germline polymorphisms, 64 were statistically evaluated since two of them were monomorphic  for the investigated polymorphisms are reported in ABCB1, DHFR, FPGS, MTHFD1, MTR, SHMT1, TYMS, MTRR) reached the p<0.05 values (MTHFD1 and MTRR genes reached the FDR = 5% (p≤1.21E-03) significance threshold  in 8 genes  for the GG genotype; power = 0.64). Subsequently, it was investigated whether this SNP was associated with the clinical characteristics of ALL. As can be seen in The genotype distribution of the MTRR rs3776455 GG genotype differed between the ALL and control population ; power = 0.51). Further analysis of the genotype distribution showed that the GG genotype was associated with a significantly reduced risk to ALL ).The frequency of the MTHFD1 gene were found to influence the risk of ALL in our population. The ACTA haplotype  and found that the rs3776455 SNP in MTRR gene, was in strong linkage with other six SNPs  in the gene. It suggests that the rs3776455 SNP can determine the status of the other six SNPs, and thus can be regarded as a tagSNP.Linkage disequilibrium coefficients , and rs1004474 in TYMS (0.66). The SNP rs3776455 in the MTRR gene was selected as the best univariate model by MDR with an overall accuracy score of 0.5724. MDR found the other two SNPs relevant only in interaction as described later. A few other SNPs also showed low probabilities of relevance in ALL susceptibility  had around 0.65 probability of being associated to ALL, but only rs3776455 had a moderately high probability of strong relevance, meaning that the associations were mainly affected through this SNP.According to the BN-BMLA the probability that rs2236225 and rs745686 in the MTHFD1 gene showed association to ALL with probabilities between 0.57–0.67, mediated by rs1076991. Both in the overall ALL and the B-cell lineage sample groups, all investigated SNPs in SLCO1B1 (SLC21A6) were associated to ALL with probabilities between 0.51–0.55, while the probabilities of the direct relevance of these SNPs were below 0.25. This means that in the case of this gene the exact structural characteristics of the relations between these SNPs and the B-cell ALL risk could not be determined but these results indicated a possible role of this gene in the disease.In the B-cell lineage sample group, three SNPs  in TYMS gene showed a relatively high probability of association (between 0.64–0.74), but only rs2853533 and rs1004474 SNPs had moderately high value of strong relevance, meaning that the associations were mediated through these SNPs.In the case of the T-cell lineage sample group the SNPs in TYMS gene were related to HD-ALL with probabilities of 0.72–0.77 mediated through the effect of rs1004474; and SNPs in MTRR  were associated (0.79–0.82) via rs1532268 and rs3776455 . MTRR, rs1222809 in DHFR, rs11545078 and rs3780127 in GGH were in pure interaction with hyperdiploid ALL through rs1004474 in TYMS. Note, that directed edges represent only probabilistic relationships between the variables which are not necessary causal.However, in the hyperdiploid sample group some SNPs had moderately high probabilities for pure interaction . MDR showed that the SNPs rs1222809 in DHFR, rs11545078 and rs3780127 in GGH were relevant only in models containing three variables. These models consisted of the previously discussed interacting SNPs rs1532268 in MTRR and rs1004474 in TYMS and one of the following additional terms: rs1222809 in DHFR, rs11545078 and rs3780127 in GGH with accuracy scores 0.6513, 0.6566 and 0.6566, respectively (whereas the best model with three elements had 0.6842).We also analyzed the interaction of these SNPs with MDR. The interaction of SNPs rs1532268 in We computed the statistical interactions and redundancies between all variables in case of all sample groups. MTHFD1 and rs3776455 in MTRR showed a weak redundancy (Redundancy ratio (RR) = 0.12, see MTHFD1 showed a weak redundancy with several SNPs in SLCO1B1, namely rs17328763 (RR = 0.24), rs11045819 (RR = 0.16), rs11045818 (RR = 0.13) and rs11045823 (RR = 0.11). See In the whole patient group, rs1076991 in MTR  = 1.14, including rs11545078; and IR = 1.12, including rs3780127). In line with this, the two SNPs in GGH showed a strong redundancy (RR = 0.85). It must be noted, however, that this dataset was relatively small, thus the exact characterization of these effects needs further validation.SNPs in the structurally interacting component described above also had a strong statistical interaction in the hypotheses of the strongly relevant variable sets in hyperdiploid ALL see . This coBayesian odds ratios and corresponding credible intervals were investigated for relevant SNPs in order to characterize their effect on ALL.MTHFD1 rs1076991 the results confirmed its significant effect on the susceptibility to ALL. Both the AG and the GG genotype increased the risk of ALL with credible intervals of (1.44–1.52) and (1.88–2.01), respectively , and the posterior distribution curves of the Bayesian odds ratios were similarly peaked. For T-cell ALL patients the MTHFD1 rs1076991 had a similar effect although not as significant as in the previous cases. The posterior distribution curve was flat indicating that different dependency models entailed somewhat different odds ratios  and the GG  genotypes  with moderately high probability (0.75), whereas the GG genotype had a protective effect (0.53–0.54) with a similar probability (0.75). In contrast, in the HD-ALL subgroup the related Bayesian odds ratios of MTRR rs3776465 indicated strong effects with narrow credible intervals. The AG genotype increased the risk of HD-ALL (1.2–1.27), while the GG genotype had a remarkable protective effect (0.09–0.2).A similar structural uncertainty could be observed in the case of B-cell ALL patients, although the posterior distribution of Bayesian odds ratios was only bimodal. The effect of AG genotype of TYMS rs1004474 and MTRR rs1532268 shown in TYMS rs1004474 had a negligible effect on the risk of HD-ALL both in case of GA and GG genotypes (with respect to AA) with credible intervals of (0.75–0.97) and (0.94–1.10) respectively (MTRR rs1532268 led to a slightly increased risk to HD-ALL (1.28–1.40 and 0.97–1.72 with respect to GG), however the credible interval of the latter was exceedingly wide  showed nominally significant association with the risk to ALL.Altogether 9 SNPs  in 8 genes  and the rs1801131  did not show any association with the susceptibility to ALL or with the survival rates of the patients in our population. Although there are several reports showing associations between these polymorphisms and ALL risk The two most frequently investigated non-synonym SNPs in the MTHFD1 gene and B-cell ALL risk. The G allele significantly increased the risk to the disease especially in homozygous form. The rs1076991 SNP is located in the promoter region of the gene. Using a luciferase reporter gene assay, Carroll et al. found a significant effect of this polymorphism on the MTHFD1 promoter activity. The construct with A allele had only 38% of the activity than that of with the G allele  polymorphism of the MTRR gene. The gene codes for the methionine synthase reductase enzyme which plays an important role in the folate pathway. The methylation of homocysteine to methionine is catalyzed by MTR (methionine synthase) using vitamin B12 as a cofactor, in which the MTR may become inactivated due to the oxidation of vitamin B12 cofactor. MTRR could catalyze reductive methylation of vitamin B12 by using SAM as a methyl donor, leading to the activation of MTR.The other significant association of this study is the decreased B-cell ALL risk associated with the GG genotype of the rs3776455 SNP in the MTRR gene is the rs1801394, which causes a non-synonym isoleucine-to-methionine change at position 22 (A66G). The variant enzyme has a lower affinity for MTR Presently, there is no information about the possible function of the SNP. It does not change consensus sequence, and it has not earlier been identified as a phenotype influencing variant. The most studied SNP in the SHMT1 gene was weakly associated with a reduced risk to ALL and the minor allele in homozygous form with a reduced survival rate. Methotrexate (MTX) is an important part of the ALL chemotherapy. It is an antifolate drug and polymorphisms in the folate pathway probably influence the response to and toxicity of the MTX therapy. Our result is in line with those, which showed that variants in the folate pathway associated with decreased disease risk are often associated with poorer response to MTX therapy SHMT1 gene is the rs1979277 (1420G/A), which earlier has been found to be associated with decreased MTX sensitivity in pediatric ALL 2 is only 0.13, it means that the linkage is partly one-sided, i.e. the rs9909104 C allele always occurs together with the rs1979277 A allele, but in the other way it is not true. That can be the explanation how it is possible, that the rs9909104 SNP is associated with a reduced survival rate in our population, and possible with decreased MTX response, but the rs1979277 SNP did not. Because in the study of de Jonge et al The rs9909104 SNP in the Next to the frequentist-based analysis, we also evaluated our results with the BN-BMLA method. We showed in our previous papers MTHFD1 gene, since it showed strong relevance to B-cell ALL and that of the rs3776455 in the MTRR which showed strong relevance to HD-ALL and association to ALL. The two SNPs also showed a slight redundancy, implying that they have similar, although independent effect.In the present study BN-BMLA confirmed the role of the rs1076991 SNP in the The main strength of the BN-BMLA is that it can suggest multivariate structural features of the different dependency types between the variables. In this dataset such structural features could only be revealed in HD-ALL. It is contrary to the expectations, since one would have thought that polymorphisms in genes in an important metabolic pathway, which has been shown to have a role in the pathogenesis of ALL, must be in interactions. A possible explanation can be that the effect sizes of the particular interactions are too low and larger populations would be needed for their detection. HD-ALL might have a more homogenous genetic background, than B-cell or T-cell ALL, and thus detection of it required smaller population. Similar findings have been experienced in our previous study MTRR rs1532268 SNP were associated with a slightly increased risk to HD-ALL, while they turned to be protective when occurred together with a given allele of a gene (TYMS) in the same metabolic pathway. Logically, this can especially happen, when a pathway, like the folate, is responsible for a sensitive balance of several important metabolites. Variations in this pathway can lead to both accumulation and depletion of these metabolites leading to different consequences. This result also suggests a possible reason for the inconclusive results of the different studies investigating only the effect of single SNPs, and shows the importance of the systems biologic approach.A particular feature of the gene-gene interactions was also revealed by the BN-BMLA. Certain genotypes of the TYMS, DHFR, GGH and SNPs, and also provided possible explanations, what their roles could be in the gene-gene interactions. The results were also confirmed by MDR, a statistical method frequently used for detecting multiple interactions in vitro studies, although it will not be an easy task.Similarly to the previous studies, the BN-BMLA named further genes like The heterogeneous results in the different subtypes of the ALL shown in This study has several strengths and limitations. First, although our sample set contains similar rate of relapsed patients to what was observed in the whole population MTHFR, rs1801131 - MTHFR, rs2236225 – MTHFD1, rs1801394 - MTRR, rs1979277 – SHMT1).It is well known that association studies suffer from a possibility of false positive results. To reduce this possibility we applied multiple test correction and also the BN-BMLA method, which offers an automated and normative solution for the multiple hypothesis testing problems In conclusion, in our study population the rs1076991 SNP in the MTHFD1 gene and the rs3776455 in the MTRR gene significantly influenced the risk of ALL. The heterozygous form of the rs9909104 SNP in the SHMT1 gene was associated with a lower survival rate. Next to these results several other SNPs in different genes in the folate metabolic pathway showed nominally significant associations with altered risk of ALL. The results were also evaluated by the BN-BMLA method, which confirmed the main findings and detected structural features of the different dependency types between the SNPs in the hyperdiploid subtype of ALL. These results give further evidence that polymorphisms in the folate pathway can influence the ALL risk and the effectiveness of the therapy. We also showed several advantageous features of the BN-BMLA method, and we demonstrated that in gene association studies it might be useful supplementary to traditional frequentist statistical methods. The tool is available at a public website Figure S1Posterior distribution curves for Bayesian odds ratios of rs1076991 (MTHFD1) and rs3776455 (MTRR) with respect to ALL and its subtypes. Each curve depicts the outline of a histogram of possible odds ratio values within the 95% credible interval  corresponding to genotype AG or GG (given AA as a reference). Bayesian odds ratio values are shown on the horizontal axis, whereas related probability values are displayed on the vertical axis.(TIF)Click here for additional data file.Figure S2Individual effect of rs1004474 (TYMS) and rs1532268 (MTRR).(TIF)Click here for additional data file.Figure S3The power of the chi-square test of association for all SNPs in different sample groups.(PDF)Click here for additional data file.Figure S4Power calculations for varying effect sizes based on typical minor allele frequencies in different sample groups in the study. The power of the chi-square test of association is calculated for different genetic models (rows) in different sample groups representing different sample sizes (columns) for SNPs with typical minor allele frequencies. For example, in case of B-cell ALL, a SNP with minor allele frequency of 0.4 and with a recessive genotype relative risk of 2 would result in a power of 0.75 using a recessive genetic model. The calculated powers are adjusted using the same multiple hypothesis testing correction as described in (PDF)Click here for additional data file.Table S1Functions and names of the key enzymes and transporter molecules involved in the folate metabolism.(DOC)Click here for additional data file.Table S2Hardy–Weinberg equilibrium test for each of the studied polymorphisms.(DOC)Click here for additional data file.Table S3Genotype and minor allele frequencies (MAF) in patient (ALL) and control groups.(DOC)Click here for additional data file.Table S4Summary of the nominally significant results according to the frequentist analysis.(DOC)Click here for additional data file.Table S52) of the Linkage disequilibrium coefficients (D' and rMTHFD1 and MTRR gene polymorphisms.(DOC)Click here for additional data file.Table S6Probability of strong relevance to ALL susceptibility in different subgroups according to the BN-BMLA.(XLS)Click here for additional data file.Table S7Structural features of different dependence types between variables.(DOC)Click here for additional data file.Table S8A posteriori probabilities of the different dependence types in ALL susceptibility.(XLS)Click here for additional data file.Table S9Joint effect of TYMS rs1004474 and MTRR rs1532268 according to the Bayesian effect size analysis.(DOC)Click here for additional data file.