Glaucoma and age-related macular degeneration (AMD) are the two leading causes of visual loss in the United States. We utilized a novel study design to perform a genome-wide association for both primary open angle glaucoma (POAG) and AMD. This study design utilized a two-stage process for hypothesis generation and validation, in which each disease cohort was utilized as a control for the other. A total of 400 POAG patients and 400 AMD patients were ascertained and genotyped at 500,000 loci. This study identified a novel association of complement component 7 (C7) to POAG. Additionally, an association of central corneal thickness, a known risk factor for POAG, was found to be associated with ribophorin II (RPN2). Linked monogenic loci for POAG and AMD were also evaluated for evidence of association, none of which were found to be significantly associated. However, several yielded putative associations requiring validation. Our data suggest that POAG is more genetically complex than AMD, with no common risk alleles of large effect. Age-related macular degeneration (AMD) and glaucoma are leading causes of blindness and visual disability in aging populations. Although these conditions are unrelated, each has a strong genetic component. The glaucomas are a group of diseases that are characterized by injury to the optic nerve and a corresponding pattern of visual loss. These disorders are the second most common cause of irreversible blindness and visual impairment in the United States.MYOC) OPTN) WDR36) NTF4) TBK1) ASB10) MYOC are responsible for ∼4% of cases of POAG. MYOC-associated POAG cases are usually characterized by markedly elevated IOP.OPTN have been associated with 1–2% of cases of normotensive glaucoma (NTG).WDR36, NTF4, ASB10 and TBK1 play a role in the high incidence of POAG is unclear.While the genetics of glaucoma are overall complex, a fraction of glaucoma is caused primarily by defects or mutations in single genes. Linkage studies of large families exhibiting autosomal dominant inheritance of glaucoma have mapped the chromosomal locations of multiple different genes capable of causing glaucoma GLC1A-P; . The caui.e. caused by the combined actions of multiple genes). Genes that have been reported to confer an increased risk for POAG or NTG include: CAV1/2,CDKN2B-AS1,ATOH7,SIX1,TMCO1,TLR4,SRBD1 and ELOVL5Genome-wide association studies (GWAS) have begun to identify genes that confer some risk for complex forms of glaucoma (ARMS2 and HTRA1) have been investigated as the source of the AMD risk.Genetic studies of AMD have also identified many important disease-causing genes and risk factors. Linkage analysis and positional cloning studies of pedigrees with Mendelian forms of macular degeneration have identified genes that are likely the primary cause of disease in some patients. For example, a family-based study identified fibulin 5 mutations in members of a large AMD pedigree.The goal of the current study was to use a novel GWAS design to identify additional loci involved in glaucoma and age-related macular degeneration. Importantly, there is no known comorbidity between AMD and glaucoma. We compared the SNP allele and genotype frequencies between two independent cohorts, each with 200 glaucoma patients and 200 AMD patients - a total of 800 patients. Both disease populations were selected to be without a history, clinical signs or symptoms of the other disease. Specifically, the glaucoma patients had no evidence of AMD, and the AMD patients had no evidence of glaucoma. AMD patients were then able to serve as the control  population for the glaucoma cohort and vice versa. This design also incorporated a replication phase. This second 200 versus 200 comparison allowed the validation of the statistically-supported hypotheses generated by the first 200 versus 200 comparison. As all 800 patient samples were evaluated on a genome-wide collection of SNPs (Affymetrix 500k and Mapping 5.0 arrays), a comprehensive comparison of all 400 glaucoma versus 400 AMD patients was also performed in order to maximize our ability to detect associations found through this experiment.This methodology allowed two simultaneous analyses to be performed, with both cost and time benefits. The disadvantages of this design are that it may be difficult to distinguish the ‘disease phase’ of a given association , and that the identification of associations may be confounded by similar disease pathophysiology. Much of the challenge in determining the disease corresponding to a given association was alleviated through the use of expected normal genotypes – for example, the allele and genotype frequencies available as part of HapMapThe University of Iowa's Institutional Review Board approved the research study and written informed consent was obtained from study participants. All patients were ascertained in the Department of Ophthalmology and Visual Sciences at the University of Iowa Carver College of Medicine. Blood samples were obtained from study participants and DNA was prepared using a non-organic method as previously describedThe primary and validation cohorts within each phenotype category  were matched for age, disease, gender and sub-phenotypes , bilateral choroidal neovascularization (BCNV) for AMD; normal tension glaucoma (NTG), high tension glaucoma (HTG) for glaucoma). These cohorts were designed such that the prevalence of disease, sub-phenotype, age and gender were equally represented as shown in Two normative data sets were utilized in assessing the results of the study. The first was the HapMapThe cohort of glaucoma subjects underwent complete ophthalmologic evaluation including a dilated stereoscopic examination of the optic nerve heads, Goldmann applanation tonometry, gonioscopy, optic nerve head photography, perimetry and slit lamp examination. Visual fields were assessed in using the SITA 24-2 program on the Humphrey Field Analyzer . Patients that were unable to perform automated perimetry were tested with Goldmann manual kinetic perimetry .Patients exhibiting excavation of their optic nerve head and associated glaucomatous visual field loss in at least one eye were considered to have glaucoma. Glaucomatous optic nerves were defined as nerves with cup-to-disc ratios of greater than 0.7, thinning of the neural rim, asymmetry of the optic nerve cup-to-disc ratio of >0.2, or photographic documentation of progressive loss of the neural rim. Patients were required to have visual fields of adequate quality for interpretation. For Humphrey visual fields this required a false positive rate, false negative rate and fixation loss rate of less than 33% The medical records of glaucoma patients were reviewed for evidence of macular degeneration. Subjects were excluded from the glaucoma cohort if: 1) they had a prior diagnosis of macular degeneration; 2) they take oral medications for macular degeneration (i.e. AREDS vitamins); or 3) they have a history of treatment with anti-VEGF therapies.Candidates for this project were selected from a pool of patients diagnosed with age-related macular degeneration (AMD) by a faculty ophthalmologist at the University of Iowa. Their charts and photofiles were reviewed by a retinal expert with extensive experience in AMD and AMD trials. For inclusion in this study, a patient had to have either Category 3 or 4 AMD as defined by the Age-Related Treatment Trial in both eyesPatients with evidence of myopic degeneration, chorioretinal scars in the macula, angioid streaks, or diabetic retinopathy consisting of more than five microaneursyms or hemorrhages were excluded from the study. The few patients who had equivocal findings, no photos, or poor quality photos that could not be evaluated were excluded from the study.Patients were subdivided into groups based on past or present evidence of CNV up to and including their last follow-up examination. Patients with CNV in both eyes were placed in group one. Patients with CNV in only one eye were placed into group two. Patients with no CNV and were age 70 or older were placed into group three.The AMD patients were rigorously evaluated for glaucoma to ensure that they could serve as a glaucoma depleted control group. AMD patients were examined and medical records were reviewed by an ophthalmologist that judged them to be free of glaucoma based on the following criteria: 1) history of medication to lower their intraocular pressure; 2) history of any type of glaucoma or status as a glaucoma suspect; 3) cup to disc ratio in either eye of 0.5 or greater unless they had been evaluated by the glaucoma service at UIHC and deemed not to have glaucoma; and 4) intraocular pressure of 25 mm Hg or higher. Patients with an intraocular pressure of 22–24 mm Hg in either eye were included only if there were at least three other measurements that were below this mark.Genome-wide SNP genotypes were obtained for all samples with the Affymetrix 500 K set and Mapping 5.0 arrays  using the manufacturer's recommended protocols. The BRLMMSNPs with a call rate less than 85% were eliminated from the analysis. All SNP genotypes were analyzed for Hardy-Weinberg Equilibrium (HWE). A p-value threshold of 0.001 was used to identify SNPs not in HWE. Deviations from HWE may be caused by genuine associations or genotyping errors. Thus all SNPs determined not to be in HWE were manually inspected for evidence of genotyping errors.−7 was used as the threshold for statistical significance of the associations−5 were classified as suggestive of an association. Only SNPs that were reproducibly associated in both cohorts were considered significant. A cluster of at least three statistically significant (or suggestive) SNPs was required to define an associated (or suggestive) locus, with an inter-SNP distance of at most 200 kb. Associated loci were classified as AMD or POAG using a Fisher's Exact test versus the control populations. To classify the locus, a p-value <0.05 was required for a disease population in both the primary and validation cohorts, with the other disease yielding non-significant p-values (p>0.05).Gender is a known confounder for both glaucoma and AMD. The balanced construction of our populations, however, was designed to minimize gender as a covariate. The Cochran-Armitage test for trend was used to evaluate the genetic variations between the groups of discrete traits  with a correction for population stratification Age, gender, and sub-phenotype(s) were evenly distributed within each 200×200 comparison see . The absEight hundred patients were genotyped and analyzed in a genome-wide association study design. The resulting associations are summarized in −16. Also of note based upon previously published results, we carefully evaluated several recently published glaucoma associations As Because both populations were selected from the extant clinical population, the relative gender distribution differs slightly between the two disease populations. While the difference is small, we performed a logistic regression as part of the association analysis to address this issue. The resulting p-values were virtually identical to the original analysis, with the expected decrease in significance due to the additional degree of freedom.The genome-wide association analysis identified one glaucoma-associated loci, as shown in a priori candidates for association with POAG. Of note, the novel glaucoma-associated regions described above do not overlap this set of published monogenic glaucoma intervals. In this association study, only one of the previously linked loci, GLC1N, exhibited an association (p = 0.00013) to the SNP rs872476. This SNP lies within the THSD4 gene, which is expressed in several ocular tissues including the trabecular meshwork and optic nerve (data not shown).All previously reported loci for monogenic forms of POAG  is one of several quantitative traits reported to be a risk factor for glaucoma. Patients with thin corneas were shown to have a higher prevalence of glaucomaThis study identified the two previously reported AMD loci CFH−5. The associated SNPs localize within the first intron of the CAMK2A gene, a calcium-dependent serine/threonine kinase. The MCDR3 locus contains a cluster of eight associated SNPs with a peak p-value of 2×10−4. This associated locus contains the CCT5 gene, part of the chaperonin containing TCP1 complex, and the first exon of the FAM173B gene. The MCDR4 locus contains three associated SNPs with a peak p-value of 0.002. There are no annotated genes within the MCDR4 associated SNP cluster.A set of previously reported AMD causing or AMD risk associated loci .disease classification of each individual association  is not immediately apparent. In such instances, it is possible to utilize the expected allele and genotype frequencies based upon other studies such as the HapMap projectProvided that two diseases do not share risk associations, the study design used here allows the identification of SNPs that are statistically associated to one of the diseases or the other. However, the The primary advantage of the two-disease study design is that genotyping costs can be conserved in the initial phase of the study (genotyping of the primary cohorts). This allows hypotheses to be developed for both diseases for a specific population without genotyping an additional normal control cohort. The power to detect association is based upon the size of the two disease cohorts. Hypotheses developed in the initial phase, can then be pursued using a validation cohort. A control cohort can be included for limited genotyping at loci of interest and to clarify the specific disease associations. In this study two control cohorts were utilized – the CEU population from HapMap and a set of normal patients from the same clinic population as the POAG and AMD cohorts. The allele and genotype frequencies from the normal cohorts were in close agreement with each other.In this study, no single, large risk allele for glaucoma on the order of the CFH AMD risk allele was detected. Therefore it is likely that this disorder is very genetically heterogeneous and that no single locus with a high genotype relative risk (GRR) exists for POAG in the population studied. A key factor in the power to detect associated loci in a GWAS analysis is the genotype relative risk. The GRR reported for CFH and ARMS2  contribute substantially to the ability to detect those associationsCAV1/2,CDKN2B-AS1,ATOH7,SIX1,TMCO1,TLR4,SRBD1 and ELOVL5The findings highlight the genetic complexity of glaucoma. Known Mendelian genes account for between three to five percent of glaucoma, with the remainder of disease most likely resulting from a combination of many risk factors, each with a small effect. Previous GWAS reports have identified several glaucoma risk associated genes , a tissue of critical relevance to glaucoma. Our data indicate that there are several SNPs within the C7 gene that are significantly associated with glaucoma. Four associated SNPs are located in an intron. An additional SNP encodes an amino acid change (Ser389Thr), which is phylogenetically highly conserved in many vertebrates, including mammals, fish, amphibians, and birds. Chou-Fasman modeling predicts that this mutation results in a change in the secondary structure of C7 and may affect the activity of the protein. C7 is required for assembly of C5b-9.We and others have demonstrated that complement components, including the C1q, C3, and the C5b-9 complex, are deposited in the glaucomatous retina and optic nerve headIn addition to evaluating SNP associations for the primary phenotypes, it is also of interest to identify associations with known disease risk factors, subclasses of disease, and disease complications. For example, central corneal thickness (CCT) has been shown to be a risk factor for glaucomaThis study reports a genome-wide association study in primary open angle glaucoma. A novel association of POAG to a variation in complement component 7 was identified. In addition, an association of central corneal thickness, a quantitative risk factor for glaucoma, to a SNP in the ribophorin II gene was identified. This study utilized a novel design to maximize the utility of the available genotyping resources and patient cohorts. This design was validated through the observation of AMD associations to the most significant AMD-risk associated alleles – Tyr402His in CFH and Arg69Ser in ARMS2. However, further investigation of this study design is needed to better characterize the power to detect bona fide associations and to correctly reject false associations in comparison to a traditional case-control design. Future work includes focused genotyping in additional large cohorts to further validate the genetic associations.


The high mortality rates that follow the onset of acute kidney injury (AKI) are well recognised. However, the mode of death in patients with AKI remains relatively under-studied, particularly in general hospitalised populations who represent the majority of those affected. We sought to describe the primary cause of death in a large group of prospectively identified patients with AKI.st October 2010 and 31st October 2011 were identified by real-time, hospital-wide, electronic AKI reporting based on the Acute Kidney Injury Network (AKIN) diagnostic criteria. Using this system we are able to generate a prospective database of all AKI cases that includes demographic, outcome and hospital coding data. For those patients that died during hospital admission, cause of death was derived from the Medical Certificate of Cause of Death.All patients sustaining AKI at our centre between 1During the study period there were 3,930 patients who sustained AKI; 62.0% had AKI stage 1, 20.6% had stage 2 and 17.4% stage 3. In-hospital mortality rate was 21.9% (859 patients). Cause of death could be identified in 93.4% of cases. There were three main disease categories accounting for three quarters of all mortality; sepsis (41.1%), cardiovascular disease (19.2%) and malignancy (12.9%). The major diagnosis leading to sepsis was pneumonia, whilst cardiovascular death was largely a result of heart failure and ischaemic heart disease. AKI was the primary cause of death in only 3% of cases.Mortality associated with AKI remains high, although cause of death is usually concurrent illness. Specific strategies to improve outcomes may therefore need to target not just the management of AKI but also the most relevant co-existing conditions. Acute Kidney Injury (AKI) is an important condition affecting up to 22% of hospitalised patients Currently, there are no specific therapies that are effective for AKI, and attempts to improve outcomes have focussed on improving basic elements of AKI management The Royal Derby Hospital is a 1,139 bedded teaching hospital that provides all major medical and surgical specialties (excepting neuro- and cardiothoracic surgery). The renal unit is a tertiary referral centre, serving a population of 700,000. There is a central chemical pathology laboratory for all inpatient and outpatient samples. A compensated kinetic Jaffe method with an inter-assay coefficient of variance of 2.3% at 96 µmol/l  was used to measure all serum creatinine values throughout the study period .2We have developed a real-time, hospital-wide, electronic reporting system for AKI based on the Acute Kidney Injury Network (AKIN) diagnostic criteria This system allows prospective data collection for all cases of AKI at our centre. A daily electronic report of all AKI episodes is automatically generated that details patient location , patient age, and baseline creatinine. These data are supplemented by highest AKI stage, last serum creatinine in stay , length of hospital stay and whether the patient survived to hospital discharge. Co-morbidity (based on ICD-10 coding) and Charlson score are also included. Approval to use anonymised data in this way was obtained from the National Information Governance Board. Patients who did not sustain AKI were not included.For those patients with AKI that died, CoD was taken from an internal database that records details from all Medical Certificates of Cause of Death (MCCD), the completion of which is governed by UK law. At our centre, a policy exists to ensure consistency in MCCD completion. Certain deaths must be reported to HM Coroner ; in other cases in which the CoD was apparent the MCCD is issued directly. For the latter group, the certificate is completed by doctor with a minimum of one year of experience and the CoD is determined after discussion with the attending physician in charge of the case. For those cases that are examined by HM Coroner, CoD are issued after a postmortem examination or after the completion of an inquest.Immediately after the completion of the MCCD the CoD are sent to a clinical auditor who enters these into an electronic database. CoD for patients who subsequently undergo postmortem examination are also collated. To ensure data quality, the CoD database is cross-checked monthly against an automatically generated list of all in-hospital deaths to identify cases in which CoD is missing; data for these cases is sourced from the MCCD records or notification of death correspondence. CoD summary data are also generated and reviewed on a monthly basis by a committee of senior doctors representing all major specialities. This group identifies anomalies in MCCD CoD results, can review specific cases and then correct entries in CoD database as appropriate.Parametric data are presented as mean±standard deviation and non-parametric data as median (inter-quartile range). Chi-squared test was used to compare categorical data and t-test or Mann-Whitney test to compare continuous data depending on whether data were parametric or non-parametric. Binary logistic regression was used to test significant univariate associations with in-hospital mortality. In-hospital mortality was chosen as CoD were only available for patients who died in hospital. P-values of <0.05 were considered significant. All analyses were performed using SPSSv.19.st October 2010 until 31st October 2011 there were 3,930 patients who sustained AKI. 2437 patients (62.0%) had AKI stage 1, 811 (20.6%) patients stage 2 and 682 (17.4%) stage 3. Overall in-hospital mortality rate was 21.9% (859 patients) and mortality rate at 30 days post AKI was 23.8% (931 patients). Median age was 80 years (IQR 16) and the vast majority of patients  were admitted to hospital as an emergency. 2558 (65.1%) patients had community acquired AKI with the remainder sustaining AKI during their hospital admission. 68% of patients had a Charlson index of one or greater and median score was 1 (IQR 3). The proportion of patients receiving RRT was low at 2.5% (99 patients). Males and females were equally represented  and reflecting our centre’s catchment population, 3532 (89.9%) of patients were Caucasian.From 1CoD could be identified in 802 cases (93.4%). Reasons for unavailability of CoD were that MCCD was issued elsewhere , cases still awaiting inquest  and missing data in 32 cases (3.7%).There were three main disease categories accounting for three quarters of all mortality; sepsis , cardiovascular disease  and malignancy . These data are summarised in Of those that died, 145 (16.8%) cases underwent post-mortem examination. The frequency of specific CoD in this subset was very similar to the overall group. Pneumonia remained the most common CoD in 49 (33.8%), with cardiovascular disease again being the second most common . As previously, cardiovascular disease comprised mainly of ischaemic heart disease  and heart failure . The third most common CoD was malignancy . Results of 23 (15.9%) post-mortem examinations were not available at time of data collection.AKI was the primary CoD in only 27 (3.1%) cases; 14 of these cases had sepsis as a precipitating cause and 8 were in association with advanced malignancy. Only two MCCD were completed as AKI without a precipitating event. The small proportion of patients with AKI as the primary CoD was consistent with a median final creatinine in non-survivors of 168 µmol/l (IQR 116 µmol/l); 797 (92.8%) of non-surviving patients had a final serum creatinine of <400 µmol/l. Although this demonstrates that the majority of patients did not die as a direct result of uraemia, 580 (76.2%) patients had not recovered renal function by the time of death . In 98 patients, there were no creatinine data to assess recovery.Despite studying a group of patients who all had AKI and in whom less than a quarter of non-survivors had recovered their baseline renal function, only 102 (11.9%) of the MCCDs included AKI in any section, signifying under-reporting of AKI.We compared survivors and non-survivors to identify factors associated with in-hospital mortality. Mortality rates associated with different AKI stages are summarised in 2 = 0.14, Hosmer and Lemeshow test for goodness of fit p = 0.08). The full results of this model are displayed in Binary logistic regression including all significant univariable associations revealed that increasing AKI stage remained associated with in-hospital mortality after accounting for the effects of age and co-morbidity . In keeping with other studies, sepsis remained the most common CoD, and this predominantly consisted of pneumonia. Ischaemic heart disease and cardiac failure were also prevalent. These results emphasise the important effect of co-existing conditions on patient outcomes. There is also an increasing evidence base that points towards the crucial effects of organ cross-talk in AKI, in which the development of AKI has a negative impact on distant organ function In the absence of specific treatments for AKI, attempts have been made to develop quality improvement strategies to combat the deficiencies that are both common and well documented in the routine clinical care of patients with AKI In contrast to previous studies in which it was rarely implicated, malignancy was the third most common CoD in our population. There may be several factors contributing to this change over time. It may reflect the increasing age and frailty of patients undergoing chemotherapy who are particularly vulnerable to AKI in the setting of neutropaenic sepsis, fluid depletion or nephrotoxic insults The factors associated with AKI in our population were similar to previously reported, and include age, co-morbid conditions , the severity of AKI and developing AKI during a hospital stay. The severity of AKI retained a strong association with mortality even after the effects of age and co-morbid conditions were considered. However, as we have previously reported, there was less of a difference in mortality rates between AKI stages 2 and 3 in this heterogenous group of AKI patients from a general hospitalised population The under reporting of AKI on MCCD is a novel observation, but is consistent with the concept that AKI is often under-recognised in routine clinical practice This study does have some potential weaknesses. As an observational study, it is not possible to attribute causality to any of the observed associations. Using hospital coding data to derive co-morbidity does have potential limitations with respect to accuracy, although the use of ICD-10 provides a standardised method to collect relatively detailed clinical information. In addition, we did not have access to details regarding the severity of acute illness or other biochemical variables, which were therefore not included in the analysis of factors associated with mortality. Finally, it must be acknowledged that the CoD derived from MCCD may not always be accurate, as in the absence of a post-mortem examination it is completed according to ‘the best of the doctor’s knowledge and belief’. However, the validity of this approach is supported by a very similar pattern of CoD in the subset of those that underwent post-mortem examination.In conclusion, we provide a current description of the leading CoD in patients with AKI from a general hospitalised population, as well as important associations with mortality. These data should help inform future strategies to improve outcomes in this high risk group that target not just AKI but the most relevant associated conditions.


Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks. Text mining and information extraction can be seen as the challenge of converting information hidden in text into manageable data. We have used text mining to automatically extract clinically relevant terms from 5543 psychiatric patient records and map these to disease codes in the International Classification of Disease ontology (ICD10). Mined codes were supplemented by existing coded data. For each patient we constructed a phenotypic profile of associated ICD10 codes. This allowed us to cluster patients together based on the similarity of their profiles. The result is a patient stratification based on more complete profiles than the primary diagnosis, which is typically used. Similarly we investigated comorbidities by looking for pairs of disease codes cooccuring in patients more often than expected. Our high ranking pairs were manually curated by a medical doctor who flagged 93 candidates as interesting. For a number of these we were able to find genes/proteins known to be associated with the diseases using the OMIM database. The disease-associated proteins allowed us to construct protein networks suspected to be involved in each of the phenotypes. Shared proteins between two associated diseases might provide insight to the disease comorbidity. With the consolidation of EPR systems in modern healthcare, massive amounts of clinical data and phenotype data are gradually becoming available for researchers EPR systems document patient morbidity, treatment and care over time. They comprise different types of structured and unstructured data, ranging from coded diagnoses, ordinary physiological measures, biobank data, laboratory test results over medication prescriptions, and treatment plans, to free text notes such as admission notes, discharge notes and nursing notes We focus here on the assigned structured diagnosis codes and the free text notes. In our Danish setting, assigned codes are coded in the EPR according to the International Classification of Disease version 10 (ICD10), and are ultimately reported to the discharge registries for reimbursement. This process has known (but poorly quantified) biases since codes result in different reimbursement sums There is an increasing focus on the research potential of both structured and textual data collected in EPR systems and registries. Examples of this work is classical database knowledge discovery and association mining Independently of the research assisted by the information presented in the patient records, several approaches have been developed to discover novel disease associations, either based on shared disease causing genes or on overlapping pathways Here we describe a strategy for exploring EPR data from a patient cohort in the context of subsequent systems biology analysis. By mining the free-text parts of the EPR from a psychiatric hospital we are able to augment the disease information assigned in structured formats as ICD10 codes, and thus obtain a much richer phenotype profile of each patient. Treating these profiles as phenotype vectors We based our study on a corpus of 5,543 patient records from the Sct. Hans Hospital  collected in the period 1998–2008. A manually curated subset of the records was used to assess the precision of the text mining approach. From structured fields in the EPR, we extracted 31,662 ICD10 codes, representing 351 different level 3 codes and corresponding to 2.7 unique codes associated to each patient on average. In the selected text found in the EPR our text mining approach matched 218,963 text strings to strings in a compiled dictionary of ICD10 terms and generated term variants see  weightedThe precision of our text mining was quantitatively assessed by manually checking all 2,724 mining hits for 48 patients . The valICD10 is organized into 22 chapters according to disease areas see . To discWhen including both the assigned and the mined codes from the textual records we capture many symptomatic descriptions for diseases. As seen on In our attempt to identify pairs of interesting unexpected co-morbidities, as well as general trends of correlation, we investigated pairs of ICD10 code vectors in patient space (columns in the patient-ICD10 association matrix). We used two measures to rank the 226,801 possible pairs of the 674 ICD10 codes, according to their co-association, compared to what would be randomly expected. Pairs were sorted based on p-values and a cut-off was imposed based on a comorbidity score and a false discovery rate of 1% see . The resUsing the comorbidity score as a similarity measure we clustered all 674 ICD10 codes and created a corresponding heatmap of the comorbidity scores for the ICD10 pairs. Our approach will, and should, for the most part return trivial or already known co-morbidities. This is a result of the non-independence of ICD10 codes. These will to a certain extent be expected to correlate according to anatomical and functional similarity, which again is what the taxonomy of the ICD10 classification attempts to capture. This is also reflected in −3 (−6). To confirm these associations, which primarily came from text mining, we checked the surrounding textual contexts of all the mining associations to check their validity. For the 12 overlaps a medical doctors looked for confirmation in the full EPR record. In the case of migraine, in some cases a more correct clinical description would have been ‘headache’, and for alopecia some cases covered fear of or delusion of hair loss. The corrected contingency numbers were 168 (migraine), 26 , 9 (both), and results in a comorbidity score of 0.4 and a p-value of 2.81×10−6. Of the remaining 9 patients with migraine and alopecia, six are women aged 21–63 and three are men aged between 47 and 54.Disease correlations may or may not have genetic causes. To identify a possible molecular basis for the flagged pairs, we extracted genes implicated in those particular diseases when a good mapping from ICD10 to OMIM was possible see . We then−3 . This ma−4.The observed comorbidity may reflect different side effects from medication The comorbidity may also have an etiological cause that relates to schizophrenia, the primary disease of the patients. It has previously been shown that schizophrenia is associated with celiac disease, i.e. the highly under-diagnosed condition of gluten allergy In a specific hospital corpus the most important level of stratification is generally based on the primary diagnosis, or inclusion, which dictates treatment and care. The stratification can be very specific and based on lab results and tests for molecular markers, such as in the case of hormone receptor variants in breast cancer In the patient-ICD10 association matrix each patient is represented as a vector of associated ICD10 codes in the space of all the 674 ICD10 codes. We calculated cosine similarity As EPR systems become the norm in modern health care, focus is naturally turned to exploring this treasure trove of data for improving health care and research The enrichment of existing structured patient data by text mining significantly expands phenotype profiles, both within the specific pathology of the corpus, but especially into other disease areas. We present one example of comorbidity between two diseases that are very often not coded in the record by the physician, but show up in the patient record text and are later picked up by mining. The enrichment from mining is also visible in our attempts to stratify patients, where potential is shown for uncovering additional layers of the population structure. More detailed stratification of patient cohorts could help improve population homogeneity and signal strength in genome wide association studies, and lead to increased power in case-control studies The procedure described here represents, in our opinion, a practical non-hypothesis driven approach for extracting valuable information from patient records for any patient corpus where manual inspection and ICD10 association would turn into an otherwise impossible task. Furthermore, we show how this information can be used in researching disease comorbidity and patient stratification and how it can be mapped to the underlying systems biology revealing possible causes for the observed correlations.The results obtained from a data driven approach like this one will obviously depend on the composition and domain of the patient corpus and on the amount and quality of the available data. In that sense, some of the found correlations and results will be domain or cohort specific, and do not necessarily translate to general population wide conclusions. In the case of patient stratification, this is inherently true. Even in these cases however, novel correlations can still be highly valuable and suggest hypothesis for causality within the cohort in terms of treatments, procedures, responses, and co-morbidities that are not necessarily genetically founded.Patient data was analyzed anonymously and the project was ethically approved by the Danish National Board of Health (No. J. nr. 7-604-04-2/33/EHE).http://www.whocc.no/atc) for medication usage, patient care notes from nurses and doctors, admission and personal information, etc. A corpus was created from the relevant tables of the Sct. Hans EPR, containing all unique text entries for each patient that were verified and signed by a physician. To each entry we assign an entry date, the note type, and the text. The note type identifies the type of text entry, such as the epicrisis, discharge note, treatment note, nursing note etc. A few non-medical notetypes such as ‘Social worker’ notes were excluded. In total, the corpus contains text for 4,765 patients with an average of 25,000 words per patient. In addition, we extracted all ICD10 codes assigned to patients that were stored in a structured format.The patient population data was collected from the Sct. Hans Mental Health Centre, in Roskilde, Denmark. All analyses were performed on an anonymous data set. A total of 5,543 patients were followed from 1998–2008, and their records stored in an EPR database. 70% of the patients  are from the Copenhagen area, 61% of these are males. The average age is 30 years. The records are a mixture of structured diagnose assignments of ICD10 codes, ATC codes , downloaded from the Danish National Board of Health the 2ructures  in the D0, downloFor relevant reviews on methods in text mining see e.g. . The comFor each disease we created a vector mapping its presence or absence from a patient record. This resulted in 22 vectors for each disease chapter. The pair-wise overlap between vectors was quantified by calculating the cosine of the angle between normalized vector pairs For the purpose of exploring comorbidity between ICD10 codes we used two measures to rank the 226,801 possible ((674*674-674)/2) pairs of different codes, according to how often they come together in patients, compared to what would be randomly expected assuming no a-priori correlations. The two measures represent our desire to ensure statistical significance, while focusing on pairs with a noticeably increased co-association.A and nB). To make the tendency to favor pairs of low prevalence ICD10 codes less pronounced, a pseudo-count of 1 is added to nominator and denominator. Since we take log2 of this ratio, a cut-off value of 1.0 means we restrict our focus to pairs with a higher than two fold (approximately) over co-association. This comorbidity measure is very similar to the one used by Hidalgo et al. First, for each pair of ICD10 codes A and B, the patient corpus is divided and counted in the four categories: A & B, A NOT B, B NOT A and NOT A NOT B, according to their association to A and B. Using this, p-values are calculated using Fishers exact test, and the pairs are sorted accordingly. We then filtered this list by imposing a cut-off value of 1.0 of a comorbidity score between diseases A and B defined as:Finally we used a Benjamini-Hockberg false discovery rate method http://www.cms.gov/ICD10/) to map the ICD codes to their previous version. With the mappings in place, OMIM was parsed for phenotypic descriptions of defects in genes, as described in Lage et al., 2007 clinical synopsis field was extracted for retrieving phenotypic descriptions regarding a certain disease. Additional information was retrieved from the morbid map tables, a map of disorders included in OMIM that have the syndrome name, chromosomal localization, and name of the disease causing gene. A manual curation step by a medical doctor ensured that each ICD10 code to be included in the analysis was assigned the correct OMIM entries.There is no direct mapping between ICD10 codes and the OMIM −3.For each disease, a network was generated by taking the disease causing genes extracted from OMIM and determining their first order interactions in a human protein interaction network of refined experimental proteomics data. This procedure is described in detail elsewhere By looking at the Patient-ICD10 matrix by rows, or patient vectors in ICD10 space, we can stratify patients based on the similarity of their ICD10 associations. Instead of a binary association of a given code to a given patient, we weighted the significance of ICD10 occurrences using the term frequency – inverse document frequency measure (TF-IDF) Figure S1All disease-disease correlations. Heatmap of all 674 level 3 ICD10 codes found in the corpus. Chapter colors are highlighted next to the ICD10 codes. Diseases that occur often together have red color in the heatmap, while those with lower than expected co-occurrence are colored blue. The color label shows the log2 change of comorbidity between two diseases when compared to the expected level.(PDF)Click here for additional data file.Figure S2Protein interaction network. The putative single zinc finger transcription factor protein HR involved in alopecia and the Estrogen Receptor (ESR1) have thyroid hormone receptor (THRA) as a shared interaction partner.(EPS)Click here for additional data file.Dataset S1Comorbidity candidate lists. The complete list of 802 candidate comorbidity pairs resulting from sorting disease pairs on p-value, truncating based on comorbidity score (ln2(ratio)) and imposing a Benjamini Hochberg false discovery rate (FDR) of 1%. Also the list containing the 93 surprising co-morbidities flagged in manual curation by a medical doctor. Finally a table showing how the members of the 26 clusters in (XLS)Click here for additional data file.Text S1Supplementary text. Detailed information about ICD10 dictionary generation and the text mining procedure and validation. Also additional information about genetic overlaps between ICD10 pairs.(DOC)Click here for additional data file.


A ranking of the models with respect toparsimonious data representation is provided, enabling discrimination betweencandidate models and the biological hypotheses underlying them. We conclude thata previously published model fitted spurious effects in the data. Moreover, thediscrimination analysis suggests that the reported data does not support theconclusion that a desensitization mechanism leads to the rapid attenuation ofHog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports amodel where an integrator feedback shuts down the pathway. This conclusion isalso supported by dedicated experiments that can exclusively be predicted bythose models including an integrator feedback.In systems biology uncertainty about biological processes translates intoalternative mathematical model candidates. Here, the goal is to generate, fitand discriminate several candidate models that represent different hypothesesfor feedback mechanisms responsible for downregulating the response of the Sho1branch of the yeast high osmolarity glycerol (HOG) signaling pathway afterinitial stimulation. Implementing and testing these candidate models by hand isa tedious and error-prone task. Therefore, we automatically generated a set ofcandidate models of the Sho1 branch with the tool modelMaGe is an open source project and is distributed under theGnu General Public License (GPL) and is available from http://modelmage.org. Dynamic models of complex biochemical networks have become an indispensable tool inbiochemical and genetic research Possible combinations of uncertain structures and kinetics directly translate intoalternative mathematical models. Generating and managing such candidate models posesa considerable challenge to the modeler. This is mainly because of the combinatorialcomplexity of model alternatives that often renders it a tedious and error-pronetask to implement and handle each model individually. Currently, there is no toolthat automatically generates, implements, manages and discriminates a specificuser-defined set of candidate models that differ in both structure and kinetics.Another debated issue is model documentation modelMaGe that automatically generates candidate models basedon a single master model and specified modifications In order to handle uncertainty in kinetics and model structure, we developed the toolThe aim of this study was to elucidate which mechanism(s) could be responsible forshutting down the response of the Sho1 branch of the high osmolarity glycerol (HOG)signaling pathway in yeast, a question that was also addressed in a recent paperThere are, however, experimental observations and theoretical considerations thatargue against such a scenario. It is well known that the HOG pathway is a perfectadaptor: following adaptation to high osmolarity the signaling pathway is shut offmodelMaGe. Our analysis suggests that the reported data doesnot support the conclusion that a negative feedback of activated Hog1 on theupstream Sho1 leads to rapid attenuation of Hog1 signaling. The data rather supportsa model where an integrator feedback shuts down the pathway. This conclusion is alsosupported by dedicated triple osmo-shock experiments that can exclusively bepredicted by those models including an integrator feedback.The aim of this study is to systematically explore several hypotheses for thefeedback mechanisms in the Sho1 branch of the HOG pathway and test which of thoseare best supported by the data published by Hao et al. (2007) with by a modeldiscrimination analysis. This endeavor is largely facilitated by the use of the toolWe developed a master model that includes the best fitting model of Hao et al.(2007) as well as a set of other candidate models. In line with the purpose ofthis study, we keep the models as simple as possible and abstract from conceptslike volume change, turgor, transcription, etc. that are known to be involved inHOG signaling and osmo-adaptation In short, osmo-adaptation in yeast by the Sho1 branch of the HOG pathwayfunctions through activation of several membrane proteins involving Sho1 Sho1a) to desensitized Sho1 (Shoi) stimulates the production of intracellular glycerol , that activates the signaling pathway, rather than just theexternal osmolarity. Therefore, Signal is defined as thedifference between OuterOsmolarity andGlycerol. Accumulation of Glycerol canalso be achieved by constitutive production of glycerol and impaired outflowthrough closure of the glycerol channel Fps1, which is also subject toregulation   and simplifications thereof.Simplifications are achieved by leaving out components and/or using simplerreaction kinetics. The two leftmost branches include the feedback whereP-Hog1 mediates conversion of active Sho1(Sho1a) into inactive Sho1 (Sho1i)   the model by Hao et al.(2007) both in its original form as well as in the simplified version withMichaelis-Menten kinetics (C10 and C6a)performed best. In fact, the fits are even better than with the parameter setfrom the original publication  showed damped oscillations in theP-Hog1 concentrations  this model is rankedhighest.Recent publications on the Hog1 dynamics upon osmotic shock in yeast with a muchhigher time resolution odel C5c . In termP-Hog1 one hour after stimulation was above 5%the total protein concentration. We employed the 5% threshold, because weconsider this value close to the measurement error, i.e. a measured value of5% of the maximum is practically zero. Therefore, we treated simulatedvalues below 5% of the possible maximum as zero and therefore perfectlyadapted. Strikingly, only those models that did not include an integratorfeedback  werenot able to show perfect adaptation according to this criterion.To challenge a critical qualitative property, we tested which of the modelcandidates did or did not show perfect adaptation behavior by comparing initialand steady-state simulated Hog1 activation after adaptation. Models wereconsidered not to show perfect adaptation when their simulated steady-statevalue of C5c model was under-fitted by predicting and comparingsimulations to additionally measured data of P-Hog1 timecourses after repeated osmotic shock with 0.4 M KCl for both,C5c and the C10 model  wasalready desensitized after the second shock ,i.e. Sho1i showed no response at all .The generated models comprised the best model of Hao et al. (2007) as well as otheralternatives including several types of transient and/or integrator feedbacks. Theset of candidate models was automatically generated and fitted to data given in Haoet al. (2007). In addition, AICc the three-parameterC5 model approximates the data better in terms of parsimonythan the 20-parameter C10 model. The original model seems to fitspurious effects in the data, indicating that it was over-fitted. Instead, ourparsimonious three-parameter model could predict the triple shock Hog1 activationprofile better than the C10 model with the original parameter set.We found also a new parameter set for the original C10 model thatfitted the data best, but was ranked worst according to the AICc,because of its high number of parameters. The C10 model with thenew parameter set was able to predict the triple shock Hog1 activation profile, butonly at the expense of the feedback mechanism that was actually proposed. Therefore,we conclude that even though Hao et al. (2007) show that Hog1 phosphorylates Sho1and thereby dampens its own response, the single- and double-shock data they providedo not support the hypothesis that it is this desensitisation mechanism which leadsto rapid attenuation of Hog1 signaling in the Sho1 branch. Our model discriminationanalysis rather supports the hypothesis that there is a negative integrator feedbackacting through glycerol accumulation. This could be tested by measuring internalglycerol concentration for the Ssk2/22 mutant as it has been done for the wild typeC5c model that does not include glycerol effluxcan be interpreted with both an activation of glycerol production as well as fastclosure of the glycerol channel. However, we do not refute that the proposednegative feedback of Hog1 onto Sho1 modulates the Hog1 response and may serve otherfunctions than Hog1 deactivation, e.g. stability of the response, noise filtering,inhibiting crosstalk to other pathways or dose-response alignment, as suggested forthe pheromone pathway We show that according to the modelMaGe is a useful tool that facilitatessystematic testing a set of candidate models, making the modeling process and itsresults transparent to the community in an easy and comprehensible manner.We also conclude that modelMaGe is simple: model alternatives aregenerated from a master model that includes all alternatives of interest. Themaster model is the only place that is meant to be manipulated by the modeler,which avoids errors that are introduced by handling several models at the sametime. The general workflow is depicted in The main idea of modelMaGe is a two stepprocess. The first step is to create a master model in CopasiCellDesignerSemanticSBMLwww.modelMaGe.org.Generation of candidate models in Copasi filesthat can readily be simulated by appropriate tools, e.g. Copasior CellDesigner  SSR is sum of squared residuals,k the number of parameters and n thenumber of data points. The AICc is an information-theory based measure ofparsimonious data representation that incorporates the goodness of the fit(SSR) as well as the complexity of the model(k) and is used to rank the candidate models, therebygiving an objective measure for model selection and discrimination. There alsoexists a web-based version of modelMaGe (http://modelmage.org). For a detailed discussion on the AIC andits usage in model discrimination please refer to The generated models come as a set of both SBML and Designer . When daP-Hog1 was 0.92  were grown in synthetic complete medium on a rotary shaker at 225 rpmat 30°C until reaching an optical density of 1.0 measured at 600 nm. Cellswere osmotically shocked as noted with KCl from a 4M stock solution. Samples of1 ml were taken and cells harvested by centrifugation at 14000 rpm for 30 s andthe pellet frozen in liquid nitrogen. Times given in the data are the times offreezing. Total protein extracts were made from the frozen cell pellets byboiling for 6 min in 60 µl extraction buffer . Protein samples were separated using SDS-PAGE (Tris-Cl) andtransferred to nitrocellulose. Phosphorylated and total amounts of Hog1 proteinwere detected using antibodies #9211 and #yC-20(SantaCruz Biotechnology) respectively. The membranes were processed for infraredfluorescent detection using secondary antibodies #926-32223(LI-COR biosciences)and #926-32214(LI-COR biosciences) respectively, and scanned for bothfluorescent channels using an ODYSSEY IR-scanner(LI-COR biosciences). The signalfrom phosphorylated Hog1 was divided with the total Hog1 protein signal. Themeasurements were repeated three times with independent cell cultures Click here for additional data file.


In a two stage genome-wide association study (2S-GWAS), a sample of cases and controls is allocated into two groups, and genetic markers are analyzed sequentially with respect to these groups. For such studies, experimental design considerations have primarily focused on minimizing study cost as a function of the allocation of cases and controls to stages, subject to a constraint on the power to detect an associated marker. However, most treatments of this problem implicitly restrict the set of feasible designs to only those that allocate the same proportions of cases and controls to each stage. In this paper, we demonstrate that removing this restriction can improve the cost advantages demonstrated by previous 2S-GWAS designs by up to 40%. Additionally, we consider designs that maximize study power with respect to a cost constraint, and show that recalculated power maximizing designs can recover a substantial amount of the planned study power that might otherwise be lost if study funding is reduced. We provide open source software for calculating cost minimizing or power maximizing 2S-GWAS designs. Genome-wide association studies (GWAS) have become ubiquitous in complex disease genetics. While the tools to conduct these studies have improved substantially, the cost of conducting them remains expensive. This is despite the plummeting cost per genotype, and is a result of the increasing number of markers being interrogated with each successive generation of genotyping chip. Identifying efficient study designs thus remains important.One popular efficient design for GWAS is the two stage GWAS (2S-GWAS), which has been used for investigations of a wide range of diseases, such as type 2 diabetes In this paper, we are concerned with computing experimental designs for 2S-GWAS. Our work is based upon that of Skol et al We extend this work in two ways. First, we improve the cost efficiency of the 2S-GWAS by defining a procedure that allows different proportions of cases and controls to be assigned to stages, and developing software to compute minimum cost, power constrained designs for the unrestricted 2S-GWAS. We demonstrate that the unrestricted 2S-GWAS can be substantially more cost effective than designs that restrict case and control allocation proportions to be equal. In the studies we present here, which use relatively modest differences of case and control sample sizes, we achieve up to a 40% relative cost advantage as compared to the 2S-GWAS designs computed by CaTS and 80% compared to one stage designs.Second, and based upon our success in improving the cost effectiveness of 2S-GWAS designs relative to a power constraint, we consider 2S-GWAS designs that maximize power with respect to a cost constraint. Calculating such designs may be useful for maximizing the utility of studies that are cost constrained rather than designed to meet a given level of power, or are subject to reductions in funding relative to that required to achieve a given level of power in a cost minimizing 2S-GWAS. We examine the latter case, and demonstrate that substantial amounts of power can be retained by recalculating power maximizing experimental designs subject to cost constraints, even for large reductions in planned cost.http://www.bioinformatics.org/stanhope/2SGWASdesign/.To support our results and assist applied researchers, our 2S-GWAS design software is included with this paper (Code S1) and available at Let the total number of cases and controls be Stage 1 of the 2S-GWAS proceeds by comparing allele frequencies at each marker, using the allocated cases and controls. For each marker showing significant differences in allele frequencies between cases and controls in stage 1  experimental design, the proportions of cases and controls allocated to stages are implicitly constrained to be the same. In this paper we have expanded the framework for 2S-GWAS originally proposed by Skol et al Based on this result, we extended our analysis to the problem of computing maximum power 2S-GWAS designs, subject to a cost constraint. We demonstrated that when a study budget is reduced below that of a minimum cost 2S-GWAS design meeting a targeted level of power, recomputing a maximum power 2S-GWAS design subject to the new cost constraint can retain much of the desired power. Relative to the original minimum cost design, power retention is achieved by allocating fewer cases and controls to stage 1, and passing fewer markers from stage 1 to stage 2. That is, the reduction in cost is compensated for by collecting less information in stage 1, focusing on fewer markers in stage 2, and then using a more powerful stage 2-specific test.We note that the results achieved here are in some respects obvious - removal of a constraint from an optimization problem always weakly results in improvements in performance. However, the extent to which this is true for 2S-GWAS has not been made explicit in previous studies. Additionally, many questions pertaining to such improvements, such as why the optimal designs changed as experimental parameters changed, could only be understood by investigating the problem geometry. Related to such investigations, our studies of the sensitivity of 2S-GWAS designs to batch effects or genetic heterogeneity between stages demonstrated that our unconstrained 2S-GWAS designs are not substantially different (in that respect) from those that constrain case and control sample proportions to be equal. The gains in efficiency related to removing the sample proportion constraint do not come at the cost of higher sensitivity to batch effects.Because most 2S-GWAS designs constrain sample allocation proportions to be the same across cases and controls, we suggest the results presented here may have implications beyond our particular study. For example, in http://www.bioinformatics.org/stanhope/2SGWASdesign/.We recommend that when designing a 2S-GWAS, investigators think carefully about the relative number of controls to cases, genotyping costs, and disease allele frequencies. To assist in doing do, our programs for identifying optimal two-stage GWAS designs are provided in Code S1 or alternatively at Appendix S1Supporting derivations. This appendix provides mathematical details omitted in the main text, including the proof of Theorem 1; the Fisher information stage weighting calculation; and the stage 2 critical value and power calculations.(PDF)Click here for additional data file.Code S1Supporting software. This file contains all codes necessary to calculate cost minimizing and power maximizing two-stage GWAS designs with unequal proportions of cases and controls allocated to stages. Instructions are provided for their compilation and use, as well as example calculations.(GZ)Click here for additional data file.Table S1Experimental conditions for 2S-GWAS design calculations. Experiments are described in terms of disease prevalences (PDF)Click here for additional data file.Table S2Cost minimizing 2S-GWAS designs and their performance characteristics, . For experimental conditions with (PDF)Click here for additional data file.Table S3Cost minimizing 2S-GWAS designs and their performance characteristics, . For experimental conditions with (PDF)Click here for additional data file.Table S4Power maximizing two stage GWAS designs and their performance characteristics, . For all experimental conditions with (PDF)Click here for additional data file.


The results of behavioural experiments provide important information about the structure and information-processing abilities of the visual system. Nevertheless, if we want to infer from behavioural data how the visual system operates, it is important to know how different learning protocols affect performance and to devise protocols that minimise noise in the response of experimental subjects. The purpose of this work was to investigate how reinforcement schedule and individual variability affect the learning process in a colour discrimination task. Free-flying bumblebees were trained to discriminate between two perceptually similar colours. The target colour was associated with sucrose solution, and the distractor could be associated with water or quinine solution throughout the experiment, or with one substance during the first half of the experiment and the other during the second half. Both acquisition and final performance of the discrimination task (measured as proportion of correct choices) were determined by the choice of reinforcer during the first half of the experiment: regardless of whether bees were trained with water or quinine during the second half of the experiment, bees trained with quinine during the first half learned the task faster and performed better during the whole experiment. Our results confirm that the choice of stimuli used during training affects the rate at which colour discrimination tasks are acquired and show that early contact with a strongly aversive stimulus can be sufficient to maintain high levels of attention during several hours. On the other hand, bees which took more time to decide on which flower to alight were more likely to make correct choices than bees which made fast decisions. This result supports the existence of a trade-off between foraging speed and accuracy, and highlights the importance of measuring choice latencies during behavioural experiments focusing on cognitive abilities. Ever since the pioneering research of Karl von Frisch Apis mellifera, and bumblebees, Bombus terrestris, in colour discrimination tasks increases if differential, rather than absolute conditioning is used during training Previous work has shown that the experimental protocol affects visual performance in behavioural tests. Thus, the performance of honeybees, The schedule of reinforcement can affect learning directly, if it determines the strength of the associative connections formed in the brain, and indirectly, if it affects the internal state of individual . For instance, there is increasing evidence that, in colour discrimination tasks, the probability that a bee makes a correct decision increases with the time they invest in making the choice Bombus terrestris) housed in a single-chamber nesting box (30×20×25 cm) connected via a Plexiglass tube to a flight arena (70×70×35 cm), with floors and walls lined with grey cardboard (Canson Mi-Teintes ref. 431). The flight arena was illuminated with two Philips TL-D90 Graphica 36w/965 white light tubes and one Philips TL-D 36w BLB UV light tube, 75 cm above its floor. Tube flicker was converted to 1,200 Hz. To obtain homogeneous illumination, the flight cage was covered by a wire mosquito net and illumination was diffused by one sheet of Rosco 216 UV-transmitting white diffusion screen .Experiments were conducted indoors with bumblebees  sucrose solution from randomly distributed artificial flowers (transparent Plexiglass cubes: 4×4×4 cm). The number of bees collecting nectar between sessions was highly variable, ranging between 5 and 25 for large colonies. All tested bees were individually marked with number tags. During sessions, only the experimental subject was allowed in the flight arena.Bees had Artificial flowers were set on top of cardboard squares (7×7 cm), cut from coloured papers . One colour, the CS+, was used for target flowers and the other, the CS-, for distractor flowers. Each colour was used as CS+ for half of the bees and CS- for the other half within each experimental group. The spectral properties of incident light, as well as the reflectance spectra of the grey background and brown colour stimuli , were meForty bees were allocated to four possible treatments, 10 bees per treatment, in pseudo-random order to avoid correlations between time and treatment: in each group of four consecutive bees, one bee was randomly assigned to each treatment. Upon entering the flight arena during experimental sessions, individually marked bumblebees found eight target and eight distractor artificial flowers haphazardly distributed throughout the arena . In each1 during phase 1 and US-2 during phase 2. US-1 and US-2 could be either distilled water (W) or 0.12 M quinine hydrochloride dihydrate solution (Q), in a full factorial design. Thus, the four experimental groups of bees were characterised by the combination US-1/US-2, as follows: WW, WQ, QW and QQ . Training took place during trials 1–14 (phase 1) and 16–29 (phase 2), and bees were tested in trials 15 and 30. Distractor flowers, CS-, contained US-W and QQ .Irrespective of treatment, the target colour stimulus (CS+) was paired with 60% (w/w) sucrose solution with a 0.5 probability , while all eight distractor flowers were paired with the US-. We chose to reward only half of the CS+ flowers  because we planned to test bee performance after training in the absence of rewarded flowers  and partial reinforcement schedules typically lead to more robust performance in extinction To measure the accuracy of individual foraging strategies, and its progress as a result of learning, we recorded the number of target and distractor flowers on which bees landed in each trial, noting for each visit to distractor flowers whether bees contacted the US- with their proboscis (hereafter referred to as drinking opportunities). Because we could not always detect whether bees actually drank from the US-, the number of drinking opportunities must be seen as an upper bound to the number of US- ingestions. To measure the time that bees spent making decisions we videotaped trials 14 and 29 . A frame-to-frame analysis of the recordings provided, for each bee, the median time elapsed since the bee left a flower until it landed on the following one. We refer to this time as choice latency.Honeybees are unable to discriminate sucrose solution and quinine remotely by olfactory cues To investigate the effect of reinforcement schedule on visual learning by free-flying bumblebees, we looked at the interrelationship between US-, choice latency, acquisition and final performance in the discrimination task .1 and US-2 as fixed factors , and the choice latency during trial 14 as a covariate. Because choice latency in trial 29 was highly correlated with choice latency in trial 14 (see results), we used the average of these two measures in subsequent analyses where choice latency was included as a covariate –the average is less noisy than either measure alone.We used a general linear model (GLM) to evaluate the within-individual consistency of choice latencies and their dependence on the US-. Specifically, we performed a GLM with choice latency during trial 29 as a dependent variable, the choice of US-1 was used as a fixed factor and choice latency as a covariate. For phase 2, we analysed the effect of US-1, US-2 and their interaction on the proportion of correct choices during blocks 4–6, with choice latency as a covariate. The interactions between block and treatment and block and choice latency were included in both analyses.We pooled the 28 training trials in six blocks  and calculated, for each bee, the proportion of correct choices in each block of trials. The effect of US- on learning rate was studied with repeated-measures analyses of variance (ANOVA) on the correct choices over the three blocks of an experimental phase, having treatment as a fixed factor and choice latency as a covariate. For phase 1, the dependent variable  was the proportion of correct choices during blocks 1–3, the US-1 for block 3; US-1, US-2 and their interaction for block 6) as a fixed factor and average choice latency as a covariate. While this analysis focuses on the ability of bees to discriminate between target and distractor flowers at the end of training, the previous analysis (acquisition) investigates the rate at which discrimination ability was acquired.Due to our inability to unambiguously assign flower choices during the extinction tests (low inter-observer repeatability), we used the proportion of correct choices during the last block of each experimental phase as a proxy for final performance. The proportion of correct choices  was analysed with a GLM having treatment  as fixed factor(s). For these analyses, we determined statistical significance from type II log-likelihood ratio tests.To study how the maximum number of US- ingestions changed through time as a function of treatment, we performed generalised linear models with Poisson distributions and logarithmic link functions on the number of drinking opportunities at distractor flowers, having treatment . If the probabilities of selecting sucrose flowers were greater than 0.5, the data would provide evidence of remote discrimination.All analyses were performed with Statistica v. 10.1,35 = 39.87, p<0.000001– 1,35 = 0.50, p = 0.48), during phase 2  or their interaction . As we will see below, choice latency was strongly correlated with performance. Therefore, and in agreement with previous studies, individuals were consistent in their choice of foraging strategy within the continuum from fast-inaccurate to slow-accurate.Bumblebees and honeybees are known to face a trade-off between increasing the speed at which they solve colour-discrimination tasks and the accuracy of their choices 1, but reached a plateau after the second block when distractor flowers offered water . Besides, the choice of aversive reinforcer during phase 1  and its interaction with block of trials  had no-significant statistical effects on performance during phase 1, although bumblebees trained with quinine tended to perform better than bumblebees trained with water . However, the relationship between choice latency and proportion of correct choices changed throughout phase 1 . Early on (block 1), there was little effect of choice latency on the proportion of correct choices . Besides, the overall proportion of correct choices during phase 2 was higher for bees trained with quinine during phase 1 , or on the rate at which the proportion of correct choices changed from block to block . Furthermore, the proportion of correct choices during phase 2 was unaffected by the interaction between the choice of negative reinforcer during phases 1 and 2  or the triple interaction between block of trials, US-1 and US-2 . However, the positive relationship between choice latencies and proportion of correct choices persisted during phase 2 , although this time the relationship no longer changed with prolonged training .During phase 2 (blocks 4 to 6), the rate at which the proportion of correct choices increased from block to block was greater for bees which had been trained with quinine as US- during phase 1 than for bees trained with water during phase 1 , bees which had been trained with quinine made a higher proportion of correct choices than bees trained with water during phase 1 , and longer choice latencies lead to greater proportions of correct choices . The same factors determined the proportion of correct choices at the end of phase 2 (block 6). Specifically, after controlling for the positive effect of choice latency , the proportion of correct responses in block 6 was affected by the choice of negative reinforcer during phase 1 , but not by the choice of negative reinforcer during phase 2  or their interaction .The effects of US-1 and 2.75±0.45 when quinine solution was used as US-1. This difference was statistically significant . Most US- drinking opportunities were concentrated on the first few trials – in phase 1 we recorded only two drinking opportunities after the fifth trial. During phase 2, the number of US- drinking opportunities we recorded was low if US-1 = US-2  and similar to the number recorded during phase 1 otherwise . The main effects of US-1 and US-2 were not statistically significant , but the interaction was highly significant . Once again, most US- drinking opportunities took place at the beginning of phase 2: we recorded only three drinking opportunities at distractor flowers in the last 10 trials of phase 2. Drinking opportunities at distractor flowers were therefore tightly linked to the novelty of the US-.Bees spent little time at distractor flowers, and never consumed the ca. 25 µl of US- that they offered. After excluding a bee from the WW group which had 52 US- drinking opportunities during phase 1 (and none in phase 2), the number (mean ± standard error) of drinking opportunities at distractor flowers during phase 1 was 1.05±0.22 when water was used as US-None of the bees learned to discriminate between colourless target and distractor flowers. Over the last five trials, the proportion of target flowers visited ranged from 0.43 to0.54 and never departed significantly from 0.5 . It follows that, in our experimental setup, bumblebees were unable to discriminate sucrose and quinine solutions remotely by olfactory cues.Our results confirm that the choice of reinforcer can affect the process of learning a colour-discrimination task by free-flying bees Bees use different neural pathways to solve different visual tasks. For instance, the colour channel implicated depends on whether bees detect motion cues or stationary targets Before the start of the experiments, bees were trained to collect 20% sucrose solution from colourless flowers. Thus, when bees first landed on a distractor flower during phase 1, it seems likely that they directly drank the reward offered by the flower. Most bees in groups QW and QQ showed a strong aversive response the first time that they encountered quinine. They dashed away from the flower, entered a bout of frenzy activity and, occasionally, returned to their nest and refused to forage for extended periods of time. This response disappeared quickly, normally after as few as one or two visits to distractor flowers, and was never observed among bees encountering water. Therefore, drinking quinine solution has a stronger aversive value for free-flying bees than drinking water. Throughout most of the experiment, however, bees did not drink from the US-: all but a few US- drinking opportunities were concentrated in the first trials of phases 1 and 2 . It is possible that, after experiencing that not all flowers contained sucrose solution, bees inspected the contents of flowers prior to ingesting it. For instance, bees may have checked with their antennae the contents of the flowers, immediately departing from flowers without sucrose solution: although bees cannot detect quinine with their antennae Previous studies have found that bees are more likely to learn a difficult colour discrimination task if the distractor stimulus, CS-, is paired with a noxious substance such as quinine than if it is paired with water 1, the effect of quinine on learning could not be mediated by the trial-by-trial aversive value of quinine. Instead, it must have resulted from a mid-term effect of early exposure to quinine. As we have seen, it seems likely that bees did not ingest water or quinine after the initial trials, and that both stimuli had, in the absence of ingestion, similar aversive values. Our results could be explained if ingestion of quinine by QW and QQ bees at the start of the experiment had an arousal effect. The hypothesis that visual learning is enhanced by general arousal can readily be tested. For example, bees could be divided in two groups, presented during pre-training with a mixture of colourless flowers containing nectar and water or nectar and quinine. According to the arousal hypothesis, bees exposed to quinine during pre-training should learn faster a subsequent colour discrimination task than bees exposed to water, even if both groups were trained with water (or quinine) as US-. Furthermore, the same effect should be obtained if another noxious stimulus was used during pre-training, or if the learning task involved a different domain, such as shape or odour discrimination.If quinine solution has an immediate aversive effect at the level of gustatory receptors B. terrestrisA. melliferaA. melliferaA. mellifera and B. terrestrisOur results contrast with those reported by Chittka and colleagues One of the clearest results of our study was that bees which took more time to choose made more accurate choices, and that bees were consistent in their choice of foraging strategy. This result has important conceptual and methodological implications and confirms a number of previous studies The effect of choice latency on performance is not restricted to free-flying bees facing visual tasks. Rather, it seems to be a general phenomenon, common to many cognitive domains and species. For instance, humans solving an interval-timing discrimination took longer to respond when the task was difficult, and were more likely to choose the correct response after long than after short choice latencies, a finding that has been linked to attentional processes Partial-reinforcement schedules do not seem to enhance the robustness of bee behaviour during extinction tests. Rather, they appear to lead to erratic behaviour during these tests and are therefore to be avoided.The aversive value of quinine solution may be, to some extent, under the behavioural control of bees. In particular, it seems likely that bees can use their antennae to reject water and quinine flowers without experiencing the aversive value of quinine. To tease apart the effects of attention and aversiveness of the US on learning, it is important to use an experimental protocol where bees cannot visit distractor flowers without experiencing the aversive value of their reinforcer. Enclosing the flower reward in such a way that bees must insert their proboscis through a narrow opening to access it may impede them from using antennal receptors. Alternatively, non-gustatory aversive stimuli – such as electric shocks – could be associated with distractor flowers.Much of the variability in performance was explained by differences in choice latency . Differe


CYP1A1 polymorphism in cases versus controls. After 1:3 matching of 207 cases to 621 controls by age, family history of breast cancer and menopausal status, fungicide exposure was not significantly associated with an increased risk of breast cancer . Moreover, no statistically significant interactions between fungicide exposure and CYP1A1*2A were observed. Gene-environment interactions were identified. Though interpretations of findings are challenged by uncertainty of exposure assignment and small sample sizes, this study does provide grounds for further research.Scientific certainty regarding environmental toxin-related etiologies of breast cancer, particularly among women with genetic polymorphisms in estrogen metabolizing enzymes, is lacking. Fungicides have been recognized for their carcinogenic potential, yet there is a paucity of epidemiological studies examining the health risks of these agents. The association between agricultural fungicide exposure and breast cancer risk was examined in a secondary analysis of a province-wide breast cancer case-control study in Prince Edward Island (PEI) Canada. Specific objectives were: (1) to derive and examine the level of association between estimated fungicide exposures, and breast cancer risk among women in PEI; and (2) to assess the potential for gene-environment interactions between fungicide exposure and a  Breast cancer is the most common female cancer in North America, excluding non-melanoma skin cancer ,2. BreasCYP1A1 polymorphisms and breast cancer risk has been extensively studied. Several recent meta-analyses have reported that there is no over-all association with breast cancer risk for most of the common CYP1A1 polymorphisms. Associations in subpopulations or with specific environmental exposures, however, may still be important [CYP1A1 polymorphisms and expression may occur through either changes in metabolism of estrogen or through changes in the metabolism of the environmental toxin. With regards to estrogen metabolism, CYP1A1 has been predominantly identified as being involved in the formation of the 2-hydroxyestrogens, which exhibit lower estrogenic activity than the 4-hydroxyestrogens and 16-alpha-hydroxy estrogens, metabolites that are believed to increase cancer risk [Numerous epidemiological studies have assessed associations between pesticide exposures and breast cancer incidence ,5,6. Themportant ,14,15,16cer risk . Similarcer risk ,19,20.CYP1A1, a cytochrome p450 enzyme that is involved in estrogen and toxin metabolism [CYP1A1 subfamily has been shown to interact with many environmental contaminants, most commonly through either induction in response to exposure or through involvement in metabolism [In this study, we chose to explore the possible interaction between a polymorphism in tabolism , and funtabolism ,22. Due tabolism ,8.The present investigation is a further analysis of a recent case-control study that examined estrogen-metabolizing enzyme polymorphisms in relation to breast cancer incidence among women in Prince Edward Island (PEI) . PEI proCYP1A1 enzyme polymorphisms and fungicides. The study design and participant recruitment were approved by the Human Ethics Committee of the University of Prince Edward Island and the Ethics Committee of the Queen Elizabeth Hospital, Charlottetown, PEI, Canada.Specific objectives of this investigation were: (1) to assess the extent of association between breast cancer risk among PEI women and exposure to fungicides, derived using agricultural census data, land use files and geographic information systems (GIS) technology; (2) to explore the possibility of gene-environment interactions between breast cancer risk, et al. [et al. [The study population was the same population described by Cribb et al. . Inciden [et al. , 1,216 c [et al. .Fungicide exposure levels were derived using 1991 Canadian Agricultural Census data and census boundary files. The Agricultural Census provides data on the number of hectares in each consolidated census subdivision (CCS) that were treated with any fungicides. With these data, a continuous spatial surface of exposure, the percentage of land per CCS to which fungicides were applied, was developed using geographic information system (GIS) based methods . The meaSpecific data on the type of fungicide used was not available from the census. All Canadian farm operators that produce agricultural products intended for sale, regardless of income, are asked to complete the Canadian Census of Agriculture. This census year was chosen to represent a 10 year estimated latency period between fungicide exposure and breast cancer diagnosis . MoreoveParticipants provided their postal code, not residential address, in the original case-control study questionnaire. Thus, postal code, as a surrogate for residence, was mapped to the corresponding CCS using GIS, giving a percentage of land to which fungicide was applied for each participant’s CCS.To allow for evaluation of multiple perspectives on the exposure disease relationship, fungicide exposure was analyzed as a continuous, binary, and categorical variable. The continuous exposure variable (percentage of land to which fungicide was applied) for each participant’s CCS was transformed into binary and categorical variables by assessing the frequency pattern of exposure among controls for notable changes in slope or breaks in the curve with cutoff assigned to minimize exposure misclassification. Using this technique, the binary variable was developed by assigning CCS with 10% or less of land treated with fungicides as low exposure and greater than 10% as high exposure. The histogram of exposure demonstrated a cluster of study participants below 10% and a scattered distribution of participants above 10%. Moreover, the selection of 10% as the cut-off point identified 14% of cases and 18% of controls as having high exposure. Selecting a higher cut-off point would have resulted in subgroups that were too small for analysis and selecting a lower cut-off point might not have isolated those individuals with high exposure. The categorical variables determined high exposure as greater than 10%, medium exposure as greater than or equal to 4.5% but less than or equal to 10%, and low exposure was determined to be less than 4.5%. These cutoff points were similarly determined by assessing the pattern of exposure distribution. As the objective of the original case-control study was to assess the influence of single nucleotide genetic polymorphisms on risk of breast cancer, there was no direct measurement of fungicide exposure during the initial study, nor structured collection of histories of past personal, residential or occupational exposures to fungicides as part of the questionnaire .et al. [CYP1A1*2A polymorphism (T3801C) was completed by PCR-RFLP according to the methods described by Bailey et al. [® assay for CYP1A1 using primers and probes purchased from Applied Biosystems Inc (Assay number A-015943). The CYP1A1*2A data were categorized into (1) homozygous referent and (2) heterozygous and homozygous variant alleles such that any influence of the variant allele was captured in the non-referent category. The heterozygous and homozygous variant alleles were grouped together in this manner to ensure sufficient sample size for comparison in the variant group.Detailed methods for obtaining the genetic polymorphism data can be found in the original paper . Buccal et al. . Genotypy et al.  and thenDescriptive statistics were calculated to compare effectiveness of matching variables  and to assess breast cancer risk factor information. Crude odds ratios, t-tests and chi-square tests were examined to determine which variables were statistically significant associated with breast cancer risk. Covariates that did not change the effect estimate of fungicides on breast cancer risk by more than 10% were not included in the final logistic regression model. As cases and controls in this study were frequency matched, the analysis utilized unconditional logistic regression and included the matching variables . Two modModel I Analysis of the association between fungicide exposure and breast cancer risk adjusting for matching variables. Separate models were run for fungicide exposure as a binary, categorical and continuous variable.Model II Analysis of the association between CYP1A1 as a main effect and breast cancer risk, adjusting for matching variables. We explored the potential for a gene-environment interaction between the three fungicide exposure variables and the CYP1A1*2A allele by adding a multiplicative interaction term to the model and assessing the significance of the p value  of the interaction term. All analyses were performed using SAS v 9.2 [AS v 9.2 .p value = 0.09) to ever use oral contraceptives (OC)  . As noneAfter adjusting for matching variables in an unconditional multivariable logistic regression model, there was no statistically significant association between any of the fungicide exposure variables and breast cancer risk . In bothCYP1A1*2A and breast cancer risk was observed to be 0.78 (95% CI 0.55–1.13). Product terms between CYP1A1*2A and fungicide exposure were not significant in any of the exposure models  was smallest for the continuous exposure*CYP1A1*2A product term (0.14) and greatest for the categorical exposure*CYP1A1*2A product term (0.71) suggesting that the results were highly influenced by the low power in the exposure subgroups  and chlorothalonil (DS-3701) are more toxic than the parent compounds. In this case, women with the variant allele may be at an increased risk of breast cancer due to an elevated concentration of these toxic metabolites. The possible relationships or interactions are therefore complex, and it is not possible to speculate from the current data on biological mechanisms or the significance of the putative association.In seeking an explanation for our findings, it is necessary to consider the dual role of abolites . In thisabolites . This laThe primary strength of this investigation is the analysis of an understudied class of pesticides in a relatively high-exposure intensity geographical region. Moreover, the availability of detailed lifestyle and covariate data from the PEI case-control study allowed this investigation to evaluate potential confounding due to established breast cancer risk factors and the presence of genetic polymorphism data facilitated the analysis of gene-environment interactions.In addition, PEI provides a valuable study setting due to the presence of a national healthcare system and relatively homogenous population. All residents of the island are provided with universal access to the national healthcare system, thus eliminating any financial or insurance barriers to screening or treatment. At the time of the study, the Queen Elizabeth Hospital was the primary oncology clinic and all breast cancer diagnoses on the island were recorded in the PEI cancer registry. During the study time period, 345 breast cancer diagnoses were recorded in the PEI cancer registry. The case-control study recruited 207 (60%) of these breast cancer cases, the geographic distribution of residential addresses of the case population was examined and shown to be representative of the overall pattern of disease on the island. In contrast to communities lacking a national healthcare system, women who reside in PEI may be more likely to seek routine mammography screening. Rates of mammography screening have been reported at 56% on the island . The QEHThe present investigation is, however, challenged by some exposure misclassification, and small sample size. While the use of the Agricultural Census data was a feasible means of assessing regional levels of fungicide exposure retrospectively, these data did not permit analysis of individual level exposure to specific fungicides. Moreover, reliance on postal code rather than individual residential address may have inappropriately assigned certain individuals to a CCS if they lived at the border of the CCS or collected their mail in a different location than where they resided, leading to errors in the percentage of hectares receiving fungicides assigned to some participants. These limitations highlight the challenges of utilizing a geographic based approach to exposure assessment. The inclusion of time-activity personal exposure histories and measurements of specific fungicides in environmental media would enhance precision of such analyses in future studies, but was not possible in this retrospective analysis of secondary data.There are other considerations that must be taken into account in interpreting these results. PEI is relatively small, and given the extent to which fungicides are sprayed across the Island, the entire population may have in fact been exposed to elevated fungicide concentrations, as suggested by fungicide ambient air quality studies conducted by Environment Canada . The incCY1A1*2A polymorphisms and fungicide exposure also lacked sufficient power to make conclusive observations about the findings.Lastly, the study was underpowered to detect associations between the estimated fungicide exposure and breast cancer risk, although the sample size was limited by the sample size of the original study. Utilizing the percent of exposure among cases and controls from the binary exposure variable, the study had 25% power to detect the observed associations, thus the likelihood of a Type 2 error is notably high. Moreover, the analysis of the interaction between While there is limited data on relationships between breast cancer risk and exposure to insecticides and herbicides, there is a greater paucity of data for fungicides despite evidence of their toxic and endocrine disrupting properties. This investigation provided a valuable initial opportunity to explore such associations in relation to genetic predisposition and breast cancer risk in a region of documented intensive agricultural fungicide application. Fungicide exposure was not found to be significant associated with an increased risk of breast cancer, and no statistically significant gene-environment interactions were identified. Environmental fungicide metrics would have been enhanced by more precise exposure assessment methodologies. Further investigation into the health effects of this class of pesticides is warranted.