Publications by authors named "Rafal Kustra"

26 Publications

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Déterminants individuels et sociaux du test de dépistage du SRAS-CoV-2 et de l’obtention d’un résultat positif en Ontario, au Canada: une étude populationnelle.

CMAJ 2021 08;193(32):E1261-E1276

ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Département de médecine (Mishra, Chan); Institut de gestion, d'évaluation et de politiques de santé (Mishra, Chan); Institut des sciences médicales (Mishra); École de santé publique Dalla Lana (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Département des sciences statistiques (Kustra); Département de médecine familiale et communautaire (Kwong), Université de Toronto; MAP Centre for Urban Health Solutions (Mishra), Institut du savoir Li Ka Shing, Hôpital St. Michael, Unity Health Toronto; Centre des sciences de la santé Sunnybrook (Chan); Santé publique Ontario (Kwong, H. Chen); Toronto, Ont.; Département d'épidémiologie (Baral), École de santé publique Bloomberg de l'Université Johns Hopkins, Baltimore, Md.

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http://dx.doi.org/10.1503/cmaj.202608-fDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386493PMC
August 2021

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study.

Ann Epidemiol 2021 Jul 25. Epub 2021 Jul 25.

St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada; ICES, Toronto, Canada; Public Health Ontario, Toronto, Canada; Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada; Division of Infectious Diseases, Sunnybrook Health Sciences, University of Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Toronto Public Health, City of Toronto, Toronto, Canada; Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Canada; Departments of Community Health Sciences and Family Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada; Department of Community Health Sciences, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada; Capacity Planning and Analytics Division, Ontario Ministry of Health, Toronto, Canada; Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, United States.

Background: Inequities in the burden of COVID-19 were observed early in Canada and around the world suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time.

Purpose: To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January-November, 2020 using a retrospective, population-based observational study using surveillance data.

Methods: We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients.

Results: Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]:0.36-0.47) and estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI:0.16-0.28); recent immigration (0.12, 95%CI:0.09-0.16); suitable housing (0.21, 95%CI:0.14-0.30); multi-generational households (0.19, 95%CI:0.15-0.23); and essential workers (0.28, 95%CI:0.23-0.34).

Conclusions: There was rapid epidemiologic transition from higher to lower income neighbourhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.
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http://dx.doi.org/10.1016/j.annepidem.2021.07.007DOI Listing
July 2021

A disproportionate epidemic: COVID-19 cases and deaths among essential workers in Toronto, Canada.

Ann Epidemiol 2021 Jul 24;63:63-67. Epub 2021 Jul 24.

St. Michael's Hospital, University of Toronto, Toronto, Canada; Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada. Electronic address:

Shelter-in-place mandates and closure of nonessential businesses have been central to COVID19 response strategies including in Toronto, Canada. Approximately half of the working population in Canada are employed in occupations that do not allow for remote work suggesting potentially limited impact of some of the strategies proposed to mitigate COVID-19 acquisition and onward transmission risks and associated morbidity and mortality. We compared per-capita rates of COVID-19 cases and deaths from January 23, 2020 to January 24, 2021, across neighborhoods in Toronto by proportion of the population working in essential services. We used person-level data on laboratory-confirmed COVID-19 community cases and deaths, and census data for neighborhood-level attributes. Cumulative per-capita rates of COVID-19 cases and deaths were 3.3-fold and 2.5-fold higher, respectively, in neighborhoods with the highest versus lowest concentration of essential workers. Findings suggest that the population who continued to serve the essential needs of society throughout COVID-19 shouldered a disproportionate burden of transmission and deaths. Taken together, results signal the need for active intervention strategies to complement restrictive measures to optimize both the equity and effectiveness of COVID-19 responses.
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http://dx.doi.org/10.1016/j.annepidem.2021.07.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8435380PMC
July 2021

Individual and social determinants of SARS-CoV-2 testing and positivity in Ontario, Canada: a population-wide study.

CMAJ 2021 05 27;193(20):E723-E734. Epub 2021 Apr 27.

ICES Central (Sundaram, Calzavara, Hamilton, Djebli, Rosella, Watson, H. Chen, B. Chen, Kwong); Department of Medicine (Mishra, Chan); Institute of Health Policy, Management and Evaluation (Mishra, Chan); Institute of Medical Science (Mishra); Dalla Lana School of Public Health (Kustra, Chan, Hamilton, Djebli, Rosella, Watson, H. Chen, Kwong); Department of Statistical Sciences (Kustra); and Department of Family and Community Medicine (Kwong), University of Toronto; MAP Centre for Urban Health Solutions (Mishra), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto; Sunnybrook Health Sciences Centre (Chan); Public Health Ontario (Kwong, H. Chen); University Health Network (Kwong), Toronto, Ont.; Department of Epidemiology (Baral), Johns Hopkins Bloomberg School of Public Health, Baltimore, Md

Background: Optimizing the public health response to reduce the burden of COVID-19 necessitates characterizing population-level heterogeneity of risks for the disease. However, heterogeneity in SARS-CoV-2 testing may introduce biased estimates depending on analytic design. We aimed to explore the potential for collider bias in a large study of disease determinants, and evaluate individual, environmental and social determinants associated with SARS-CoV-2 testing and diagnosis among residents of Ontario, Canada.

Methods: We explored the potential for collider bias and characterized individual, environmental and social determinants of being tested and testing positive for SARS-CoV-2 infection using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those with a diagnosis, we used separate analytic designs to compare predictors of people testing positive versus negative; symptomatic people testing positive versus testing negative; and people testing positive versus people not testing positive (i.e., testing negative or not being tested). Our analyses included tests conducted between Mar. 1 and June 20, 2020.

Results: Of 14 695 579 people, we found that 758 691 were tested for SARS-CoV-2, of whom 25 030 (3.3%) had a positive test result. The further the odds of testing from the null, the more variability we generally observed in the odds of diagnosis across analytic design, particularly among individual factors. We found that there was less variability in testing by social determinants across analytic designs. Residing in areas with the highest household density (adjusted odds ratio [OR] 1.86, 95% confidence interval [CI] 1.75-1.98), highest proportion of essential workers (adjusted OR 1.58, 95% CI 1.48-1.69), lowest educational attainment (adjusted OR 1.33, 95% CI 1.26-1.41) and highest proportion of recent immigrants (adjusted OR 1.10, 95% CI 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design.

Interpretation: Where testing is limited, our results suggest that risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment in and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation and structural racism.
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http://dx.doi.org/10.1503/cmaj.202608DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177943PMC
May 2021

Viral hepatitis C cascade of care: A population-level comparison of immigrant and long-term residents.

Liver Int 2021 08 19;41(8):1775-1788. Epub 2021 Apr 19.

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Background & Aims: Viral hepatitis C represents a major global burden, particularly among immigrant-receiving countries such as Canada, where knowledge of disparities in hepatitis C virus among immigrant groups for micro-elimination efforts is lacking. We quantify the hepatitis C cascades of care among immigrants and long-term residents prior to the introduction of direct-acting antiviral medications.

Methods: Using laboratory and health administrative records, we described the hepatitis C virus cascades of care in terms of diagnosis, engagement with care, treatment initiation, and clearance in Ontario, Canada (1997-2014). We stratified the cascade by immigrant and long-term resident groups and identify drivers at each stage using multivariable Poisson regression.

Results: We included 940 245 individuals in the study with an estimated hepatitis C prevalence of 167 923 (1.4%) overall, 23 759 (0.7%) among all immigrants, and 6019 (1.1%) among immigrants from hepatitis C endemic countries. Overall there were 104 616 individuals with reactive antibody results, 73 861 tested for viral RNA, 52 388 with viral RNA detected, 50 805 genotyped, 13 159 on treatment and 3919 with evidence of viral clearance. Compared to long-term residents, immigrants showed increased nucleic-acid testing (aRR: 1.09 [95%CI: 1.08, 1.10]), treatment initiation (aRR: 1.46 [95%CI: 1.38, 1.54]), and higher clearance rates (aRR: 1.07 [95%CI: 1.03, 1.11]).

Conclusions: Hepatitis C virus is more prevalent among long-term residents compared to immigrants overall, however, immigrants from endemic countries are an important subgroup to consider for future screening and linkage to care initiatives. These findings are prior to the introduction of newer medications and provide a population-based benchmark for follow-up studies and evaluation of treatment programs and surveillance activities.
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http://dx.doi.org/10.1111/liv.14840DOI Listing
August 2021

Validating viral hepatitis B and C diagnosis codes: a retrospective analysis using Ontario's health administrative data.

Can J Public Health 2021 Jun 8;112(3):502-512. Epub 2021 Jan 8.

Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.

Objective: We aimed to determine the criterion validity of using diagnosis codes for hepatitis B virus (HBV) and hepatitis C virus (HCV) to identify infections.

Methods: Using linked laboratory and administrative data in Ontario, Canada, from January 2004 to December 2014, we validated HBV/HCV diagnosis codes against laboratory-confirmed infections. Performance measures (sensitivity, specificity, and positive predictive value) were estimated via cross-validated logistic regression and we explored variations by varying time windows from 1 to 5 years before (i.e., prognostic prediction) and after (i.e., diagnostic prediction) the date of laboratory confirmation. Subgroup analyses were performed among immigrants, males, baby boomers, and females to examine the robustness of these measures.

Results: A total of 1,599,023 individuals were tested for HBV and 840,924 for HCV, with a resulting 41,714 (2.7%) and 58,563 (7.0%) infections identified, respectively. HBV/HCV diagnosis codes ± 3 years of laboratory confirmation showed high specificity (99.9% HBV; 99.8% HCV), moderate positive predictive value (70.3% HBV; 85.8% HCV), and low sensitivity (12.8% HBV; 30.8% HCV). Varying the time window resulted in limited changes to performance measures. Diagnostic models consistently outperformed prognostic models. No major differences were observed among subgroups.

Conclusion: HBV/HCV codes should not be the only source used for monitoring the population burden of these infections, due to low sensitivity and moderate positive predictive values. These results underscore the importance of ongoing laboratory and reportable disease surveillance systems for monitoring viral hepatitis in Ontario.
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http://dx.doi.org/10.17269/s41997-020-00435-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076389PMC
June 2021

Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations.

PLoS One 2021 7;16(1):e0244746. Epub 2021 Jan 7.

ICES, Toronto, Ontario, Canada.

Objective: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10th revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada.

Study Design And Setting: Influenza and RSV laboratory data from the 2014-15, 2015-16, 2016-17 and 2017-18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms.

Results: 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%).

Conclusion: We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244746PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790248PMC
May 2021

Heterogeneity in testing, diagnosis and outcome in SARS-CoV-2 infection across outbreak settings in the Greater Toronto Area, Canada: an observational study.

CMAJ Open 2020 Oct-Dec;8(4):E627-E636. Epub 2020 Oct 9.

MAP Centre for Urban Health Solutions (Wang, Ma, Yiu, Landsman, Luong, Hwang, Mishra), St. Michael's Hospital, University of Toronto; ICES (Calzavara, Kwong); Division of Infectious Diseases, Department of Medicine (Chan, Mishra), University of Toronto; Division of Infectious Diseases (Chan), Sunnybrook Health Sciences Centre, University of Toronto; Dalla Lana School of Public Health (Kustra), University of Toronto; Department of Family and Community Medicine (Kwong), Faculty of Medicine, University of Toronto, Toronto, Ont.; MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology (Boily), Faculty of Medicine, Imperial College, London, UK; Division of General Internal Medicine (Hwang), Department of Medicine, University of Toronto; Department of Medicine (Straus), St. Michael's Hospital, University of Toronto, Toronto, Ont.; Bloomberg School of Public Health (Baral), Johns Hopkins University, Baltimore, Md.

Background: Congregate settings have been disproportionately affected by coronavirus disease 2019 (COVID-19). Our objective was to compare testing for, diagnosis of and death after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across 3 settings (residents of long-term care homes, people living in shelters and the rest of the population).

Methods: We conducted a population-based prospective cohort study involving individuals tested for SARS-CoV-2 in the Greater Toronto Area between Jan. 23, 2020, and May 20, 2020. We sourced person-level data from COVID-19 surveillance and reporting systems in Ontario. We calculated cumulatively diagnosed cases per capita, proportion tested, proportion tested positive and case-fatality proportion for each setting. We estimated the age- and sex-adjusted rate ratios associated with setting for test positivity and case fatality using quasi-Poisson regression.

Results: Over the study period, a total of 173 092 individuals were tested for and 16 490 individuals were diagnosed with SARS-CoV-2 infection. We observed a shift in the proportion of cumulative cases from all cases being related to travel to cases in residents of long-term care homes (20.4% [3368/16 490]), shelters (2.3% [372/16 490]), other congregate settings (20.9% [3446/16 490]) and community settings (35.4% [5834/16 490]), with cumulative travel-related cases at 4.1% (674/16490). Cumulatively, compared with the rest of the population, the diagnosed cases per capita was 64-fold and 19-fold higher among long-term care home and shelter residents, respectively. By May 20, 2020, 76.3% (21 617/28 316) of long-term care home residents and 2.2% (150 077/6 808 890) of the rest of the population had been tested. After adjusting for age and sex, residents of long-term care homes were 2.4 (95% confidence interval [CI] 2.2-2.7) times more likely to test positive, and those who received a diagnosis of COVID-19 were 1.4-fold (95% CI 1.1-1.8) more likely to die than the rest of the population.

Interpretation: Long-term care homes and shelters had disproportionate diagnosed cases per capita, and residents of long-term care homes diagnosed with COVID-19 had higher case fatality than the rest of the population. Heterogeneity across micro-epidemics among specific populations and settings may reflect underlying heterogeneity in transmission risks, necessitating setting-specific COVID-19 prevention and mitigation strategies.
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http://dx.doi.org/10.9778/cmajo.20200213DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567509PMC
January 2021

CFH and ARMS2 Polymorphisms Interact with Zinc Supplements in Cognitive Impairment in the Women's Health Initiative Hormone Trial.

J Alzheimers Dis 2018 ;66(2):707-715

Faculty of Medicine, Division of Hematology, University of Toronto, Toronto, Canada.

Background: An interaction between genetic variants in complement factor H (CFH) and age-related maculopathy susceptibility 2 (ARMS2) and high-dose zinc supplementation on progression to advanced age-related macular degeneration (AMD) exists. Because cognitive impairment (CI) is associated with AMD, we used data from the Women's Health Initiative (WHI) to search for a zinc/genetics interaction.

Objective: To study the interaction of chronic zinc supplementation with genetic variants in CFH and ARMS2 on the development of CI.

Background: Zinc dietary supplements, CFH and ARMS2 genotypes, and serial mental status was analyzed in participants with available genetic data (n = 7,483). Cognition was assessed using the Modified Mini-Mental State Examination. The development of CI over 5 years was analyzed by genotype and zinc intake using a repeated measures logistic regression model.

Results: Zinc supplementation of approximately 15 mg/day was associated with decreased development of CI in women with 1 or 2 CFH and no ARMS2 risk alleles (OR = 0.46: 1 CFH risk allele; 0.20: 2 CFH risk alleles; p = 0.002).

Conclusion: Low-dose zinc (approximately 15 mg) is associated with reduced CI in women with 2 CFH and 0 ARMS2 AMD risk alleles. This interaction is opposite in direction to that observed in AMD, where patients with 2 CFH and 0 ARMS2 risk alleles had increased progression to neovascular AMD if treated with 80 mg/day of zinc. This may be due to a zinc dose-response or to a fundamental difference in the role of zinc in the progression of early CI versus advanced AMD.
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http://dx.doi.org/10.3233/JAD-180673DOI Listing
October 2019

Reply to Vickers: Pharmacogenetics and progression to neovascular age-related macular degeneration-Evidence supporting practice change.

Proc Natl Acad Sci U S A 2018 06 7;115(25):E5640-E5641. Epub 2018 Jun 7.

Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.

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http://dx.doi.org/10.1073/pnas.1804781115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016766PMC
June 2018

Cell-Free DNA Modification Dynamics in Abiraterone Acetate-Treated Prostate Cancer Patients.

Clin Cancer Res 2018 07 3;24(14):3317-3324. Epub 2018 Apr 3.

The Krembil Family Epigenetics Laboratory, The Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Primary resistance to abiraterone acetate (AA), a key medication for the treatment of metastatic castration-resistant prostate cancer, occurs in 20% to 40% of patients. We aim to identify predictive biomarkers for AA-treatment response and understand the mechanisms related to treatment resistance. We used the Infinium Human Methylation 450K BeadChip to monitor modification profiles of cell-free circulating DNA (cfDNA) in 108 plasma samples collected from 33 AA-treated patients. Thirty cytosines showed significant modification differences (FDR Q < 0.05) between AA-sensitive and AA-resistant patients during the treatment, of which 21 cytosines were differentially modified prior to treatment. In addition, AA-sensitive patients, but not AA-resistant patients, lost interindividual variation of cfDNA modification shortly after starting AA treatment, but such variation returned to initial levels in the later phases of treatment. Our findings provide a list of potential biomarkers for predicting AA-treatment response, highlight the prognostic value of using cytosine modification variance as biomarkers, and shed new insights into the mechanisms of prostate cancer relapse in AA-sensitive patients. .
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http://dx.doi.org/10.1158/1078-0432.CCR-18-0101DOI Listing
July 2018

and genetic risk determines progression to neovascular age-related macular degeneration after antioxidant and zinc supplementation.

Proc Natl Acad Sci U S A 2018 01 8;115(4):E696-E704. Epub 2018 Jan 8.

Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.

We evaluated the influence of an antioxidant and zinc nutritional supplement [the Age-Related Eye Disease Study (AREDS) formulation] on delaying or preventing progression to neovascular AMD (NV) in persons with age-related macular degeneration (AMD). AREDS subjects ( = 802) with category 3 or 4 AMD at baseline who had been treated with placebo or the AREDS formulation were evaluated for differences in the risk of progression to NV as a function of () and () genotype groups. We used published genetic grouping: a two-SNP haplotype risk-calling algorithm to assess , and either the single SNP rs10490924 or 372_815del443ins54 to mark risk. Progression risk was determined using the Cox proportional hazard model. Genetics-treatment interaction on NV risk was assessed using a multiiterative bootstrap validation analysis. We identified strong interaction of genetics with AREDS formulation treatment on the development of NV. Individuals with high and no risk alleles and taking the AREDS formulation had increased progression to NV compared with placebo. Those with low risk and high risk had decreased progression risk. Analysis of and genotype groups from a validation dataset reinforces this conclusion. Bootstrapping analysis confirms the presence of a genetics-treatment interaction and suggests that individual treatment response to the AREDS formulation is largely determined by genetics. The AREDS formulation modifies the risk of progression to NV based on individual genetics. Its use should be based on patient-specific genotype.
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http://dx.doi.org/10.1073/pnas.1718059115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789949PMC
January 2018

DNA modification study of major depressive disorder: beyond locus-by-locus comparisons.

Biol Psychiatry 2015 Feb 1;77(3):246-255. Epub 2014 Jul 1.

Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan. Electronic address:

Background: Major depressive disorder (MDD) exhibits numerous clinical and molecular features that are consistent with putative epigenetic misregulation. Despite growing interest in epigenetic studies of psychiatric diseases, the methodologies guiding such studies have not been well defined.

Methods: We performed DNA modification analysis in white blood cells from monozygotic twins discordant for MDD, in brain prefrontal cortex, and germline (sperm) samples from affected individuals and control subjects (total N = 304) using 8.1K CpG island microarrays and fine mapping. In addition to the traditional locus-by-locus comparisons, we explored the potential of new analytical approaches in epigenomic studies.

Results: In the microarray experiment, we detected a number of nominally significant DNA modification differences in MDD and validated selected targets using bisulfite pyrosequencing. Some MDD epigenetic changes, however, overlapped across brain, blood, and sperm more often than expected by chance. We also demonstrated that stratification for disease severity and age may increase the statistical power of epimutation detection. Finally, a series of new analytical approaches, such as DNA modification networks and machine-learning algorithms using binary and quantitative depression phenotypes, provided additional insights on the epigenetic contributions to MDD.

Conclusions: Mapping epigenetic differences in MDD (and other psychiatric diseases) is a complex task. However, combining traditional and innovative analytical strategies may lead to identification of disease-specific etiopathogenic epimutations.
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http://dx.doi.org/10.1016/j.biopsych.2014.06.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4277915PMC
February 2015

Functional-network-based gene set analysis using gene-ontology.

PLoS One 2013 13;8(2):e55635. Epub 2013 Feb 13.

State Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, P.R. China.

To account for the functional non-equivalence among a set of genes within a biological pathway when performing gene set analysis, we introduce GOGANPA, a network-based gene set analysis method, which up-weights genes with functions relevant to the gene set of interest. The genes are weighted according to its degree within a genome-scale functional network constructed using the functional annotations available from the gene ontology database. By benchmarking GOGANPA using a well-studied P53 data set and three breast cancer data sets, we will demonstrate the power and reproducibility of our proposed method over traditional unweighted approaches and a competing network-based approach that involves a complex integrated network. GOGANPA's sole reliance on gene ontology further allows GOGANPA to be widely applicable to the analysis of any gene-ontology-annotated genome.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0055635PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572115PMC
August 2013

5-hmC in the brain is abundant in synaptic genes and shows differences at the exon-intron boundary.

Nat Struct Mol Biol 2012 Oct 9;19(10):1037-43. Epub 2012 Sep 9.

The Krembil Family Epigenetics Laboratory, The Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

The 5-methylcytosine (5-mC) derivative 5-hydroxymethylcytosine (5-hmC) is abundant in the brain for unknown reasons. Here we characterize the genomic distribution of 5-hmC and 5-mC in human and mouse tissues. We assayed 5-hmC by using glucosylation coupled with restriction-enzyme digestion and microarray analysis. We detected 5-hmC enrichment in genes with synapse-related functions in both human and mouse brain. We also identified substantial tissue-specific differential distributions of these DNA modifications at the exon-intron boundary in human and mouse. This boundary change was mainly due to 5-hmC in the brain but due to 5-mC in non-neural contexts. This pattern was replicated in multiple independent data sets and with single-molecule sequencing. Moreover, in human frontal cortex, constitutive exons contained higher levels of 5-hmC relative to alternatively spliced exons. Our study suggests a new role for 5-hmC in RNA splicing and synaptic function in the brain.
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http://dx.doi.org/10.1038/nsmb.2372DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465469PMC
October 2012

Symptom clusters in a population-based ambulatory cancer cohort validated using bootstrap methods.

Eur J Cancer 2012 Nov 26;48(16):3073-81. Epub 2012 May 26.

Ontario Cancer Institute and Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada.

Background: Cluster identification has emerged as a priority for symptom research. Variation in statistical approaches has hampered the identification of common clusters that should be targeted for intervention. The purpose of this study was to identify and validate common symptom clusters in a large population-based cohort of ambulatory cancer subjects.

Methods: This descriptive, factor analysis study used bootstrap methods to derive a stable factor structure to identify symptom clusters in a population-based sample of cancer patients. Subjects were identified from a provincial symptom database and linked to other provincial databases. Symptom clusters were validated using confirmatory factor analysis in a randomly selected portion of the sample and model fit examined using common goodness of fit criteria.

Results: The cluster cohort included 14,247 subjects. Three symptom clusters were identified: fatigue-sickness symptoms (tiredness, nausea, drowsiness and shortness of breath), emotional distress (depression and anxiety), and a poor sense of well-being (appetite and well-being). These clusters were stable across most sub-populations in the cohort.

Conclusion: The identification of common symptom clusters using robust statistical methods will help to yield targets to improve symptom management and identify populations at risk for worse disease outcomes.
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http://dx.doi.org/10.1016/j.ejca.2012.04.008DOI Listing
November 2012

The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer.

Hum Genet 2010 Jul 1;128(1):89-101. Epub 2010 May 1.

Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, ON, Canada.

Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered.
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http://dx.doi.org/10.1007/s00439-010-0828-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2885303PMC
July 2010

Data-fusion in clustering microarray data: balancing discovery and interpretability.

IEEE/ACM Trans Comput Biol Bioinform 2010 Jan-Mar;7(1):50-63

Department of Public Health Sciences, University of Toronto, Health Sciences Building, Toronto, Ontario, Canada.

While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.
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http://dx.doi.org/10.1109/TCBB.2007.70267DOI Listing
May 2010

Predictive modeling in case-control single-nucleotide polymorphism studies in the presence of population stratification: a case study using Genetic Analysis Workshop 16 Problem 1 dataset.

BMC Proc 2009 Dec 15;3 Suppl 7:S60. Epub 2009 Dec 15.

Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, M5T 3M7, Canada.

In this paper, we apply the gradient-boosting machine predictive model to the rheumatoid arthritis data for predicting the case-control status. QQ-plot suggests severe population stratification. In univariate genome-wide association studies, a correction factor for ethnicity confounding can be derived. Here we propose a novel strategy to deal with population stratification in the context of multivariate predictive modeling. We address the problem by clustering the subjects on the axes of genetic variations, and building a predictive model separately in each cluster. This allows us to control ethnicity without explicitly including it in the model, which could marginalize the genetic signal we are trying to discover. Clustering not only leads to more similar ethnicity groups but also, as our results show, increases the accuracy of our model when compared to the non-clustered approach. The highest accuracy is achieved with the model adjusted for population stratification, when the genetic axes of variation are included among the set of predictors, although this may be misleading given the confounding effects.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795961PMC
http://dx.doi.org/10.1186/1753-6561-3-s7-s60DOI Listing
December 2009

EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis.

Bioinformatics 2008 Jul 21;24(14):1603-10. Epub 2008 May 21.

Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.

Motivation: We developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data.

Results: Using simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman-Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs.

Availability: The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRF.
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http://dx.doi.org/10.1093/bioinformatics/btn239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2638262PMC
July 2008

Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21.

Nat Genet 2008 May 30;40(5):631-7. Epub 2008 Mar 30.

Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Edinburgh EH4 2XU, UK.

In a genome-wide association study to identify loci associated with colorectal cancer (CRC) risk, we genotyped 555,510 SNPs in 1,012 early-onset Scottish CRC cases and 1,012 controls (phase 1). In phase 2, we genotyped the 15,008 highest-ranked SNPs in 2,057 Scottish cases and 2,111 controls. We then genotyped the five highest-ranked SNPs from the joint phase 1 and 2 analysis in 14,500 cases and 13,294 controls from seven populations, and identified a previously unreported association, rs3802842 on 11q23 (OR = 1.1; P = 5.8 x 10(-10)), showing population differences in risk. We also replicated and fine-mapped associations at 8q24 (rs7014346; OR = 1.19; P = 8.6 x 10(-26)) and 18q21 (rs4939827; OR = 1.2; P = 7.8 x 10(-28)). Risk was greater for rectal than for colon cancer for rs3802842 (P < 0.008) and rs4939827 (P < 0.009). Carrying all six possible risk alleles yielded OR = 2.6 (95% CI = 1.75-3.89) for CRC. These findings extend our understanding of the role of common genetic variation in CRC etiology.
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http://dx.doi.org/10.1038/ng.133DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2778004PMC
May 2008

Efficient p-value estimation in massively parallel testing problems.

Biostatistics 2008 Oct 27;9(4):601-12. Epub 2008 Feb 27.

Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada.

We present a new method to efficiently estimate very large numbers of p-values using empirically constructed null distributions of a test statistic. The need to evaluate a very large number of p-values is increasingly common with modern genomic data, and when interaction effects are of interest, the number of tests can easily run into billions. When the asymptotic distribution is not easily available, permutations are typically used to obtain p-values but these can be computationally infeasible in large problems. Our method constructs a prediction model to obtain a first approximation to the p-values and uses Bayesian methods to choose a fraction of these to be refined by permutations. We apply and evaluate our method on the study of association between 2-way interactions of genetic markers and colorectal cancer using the data from the first phase of a large, genome-wide case-control study. The results show enormous computational savings as compared to evaluating a full set of permutations, with little decrease in accuracy.
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http://dx.doi.org/10.1093/biostatistics/kxm053DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2536722PMC
October 2008

Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24.

Nat Genet 2007 Aug 8;39(8):989-94. Epub 2007 Jul 8.

Cancer Care Ontario, 620 University Avenue, Toronto, Ontario M5G 1L7, Canada.

Using a multistage genetic association approach comprising 7,480 affected individuals and 7,779 controls, we identified markers in chromosomal region 8q24 associated with colorectal cancer. In stage 1, we genotyped 99,632 SNPs in 1,257 affected individuals and 1,336 controls from Ontario. In stages 2-4, we performed serial replication studies using 4,024 affected individuals and 4,042 controls from Seattle, Newfoundland and Scotland. We identified one locus on chromosome 8q24 and another on 9p24 having combined odds ratios (OR) for stages 1-4 of 1.18 (trend; P = 1.41 x 10(-8)) and 1.14 (trend; P = 1.32 x 10(-5)), respectively. Additional analyses in 2,199 affected individuals and 2,401 controls from France and Europe supported the association at the 8q24 locus (OR = 1.16, trend; 95% confidence interval (c.i.): 1.07-1.26; P = 5.05 x 10(-4)). A summary across all seven studies at the 8q24 locus was highly significant (OR = 1.17, c.i.: 1.12-1.23; P = 3.16 x 10(-11)). This locus has also been implicated in prostate cancer.
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http://dx.doi.org/10.1038/ng2089DOI Listing
August 2007

A factor analysis model for functional genomics.

BMC Bioinformatics 2006 Apr 21;7:216. Epub 2006 Apr 21.

Public Health Sciences, University of Toronto, Toronto, ON, Canada.

Background: Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories.

Results: We propose a factor analysis model (FAM) for functional genomics and give a two-step algorithm, using genome-wide expression data for yeast and a subset of Gene-Ontology Biological Process functional annotations. We show that the predictive performance of our method is comparable to the current best approach while our total computation time was faster by a factor of 4000. We discuss the unique challenges in performance evaluation of algorithms used for genome-wide functions genomics. Finally, we discuss extensions to our method that can incorporate the inherent correlation structure of the functional categories to further improve predictive performance.

Conclusion: Our factor analysis model is a computationally efficient technique for functional genomics and provides a clear and unified statistical framework with potential for incorporating important gene ontology information to improve predictions.
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http://dx.doi.org/10.1186/1471-2105-7-216DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1468435PMC
April 2006

The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework.

Neuroimage 2002 Apr;15(4):747-71

Department of Radiology, University of Minnesota, Minneapolis, Minnesota 55455, USA.

We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training-test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility SNR metrics for the associated SPMs. We demonstrate the utility of this framework across the wide range of performance metrics obtained from [(15)O]water PET studies of 12 age- and sex-matched data sets performing different motor tasks (8 subjects/set). For the 12 data sets we apply NPAIRS with both univariate and multivariate data-analysis approaches to: (1) demonstrate that this framework may be used to obtain reproducible SPMs from any data-analysis approach on a common Z-score scale (rSPM[Z]); (2) demonstrate that the histogram of a rSPM[Z] image may be modeled as the sum of a data-analysis-dependent noise distribution and a task-dependent, Gaussian signal distribution that scales monotonically with our reproducibility performance metric; (3) explore the relation between prediction and reproducibility performance metrics with an emphasis on bias-variance tradeoffs for flexible, multivariate models; and (4) measure the broad range of reproducibility SNRs and the significant influence of individual subjects. A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects. We propose the NPAIRS framework as a validation tool for testing and optimizing methodological choices and tools in functional neuroimaging.
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http://dx.doi.org/10.1006/nimg.2001.1034DOI Listing
April 2002
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