Publications by authors named "Celia M T Greenwood"

130 Publications

Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals.

Genes (Basel) 2021 Aug 29;12(9). Epub 2021 Aug 29.

PhD Program in Quantitative Life Sciences, Interfaculty Studies, McGill University, Montréal, QC H3A 1E3, Canada.

High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.
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http://dx.doi.org/10.3390/genes12091349DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472734PMC
August 2021

Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regression.

Genet Epidemiol 2021 Sep 1. Epub 2021 Sep 1.

Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.

Medical research increasingly includes high-dimensional regression modeling with a need for error-in-variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error-corrected cross-validation to enable error-in-variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high-dimensional data that are only partially corrupted by measurement error. This algorithm separately optimizes the estimation of the uncorrupted and corrupted features in an iterative manner to reduce computational cost, with a specially calibrated formulation of cross-validation error. Through simulations, we show that the BDCoCoLasso algorithm successfully copes with much larger feature sets than CoCoLasso, and as expected, outperforms the naïve Lasso with enhanced estimation accuracy and consistency, as the intensity and complexity of measurement errors increase. Also, a new smoothly clipped absolute deviation penalization option is added that may be appropriate for some data sets. We apply the BDCoCoLasso algorithm to data selected from the UK Biobank. We develop and showcase the utility of covariate-adjusted genetic risk scores for body mass index, bone mineral density, and lifespan. We demonstrate that by leveraging more information than the naïve Lasso in partially corrupted data, the BDCoCoLasso may achieve higher prediction accuracy. These innovations, together with an R package, BDCoCoLasso, make error-in-variables adjustments more accessible for high-dimensional data sets. We posit the BDCoCoLasso algorithm has the potential to be widely applied in various fields, including genomics-facilitated personalized medicine research.
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http://dx.doi.org/10.1002/gepi.22430DOI Listing
September 2021

Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood.

J Child Psychol Psychiatry 2021 Aug 13. Epub 2021 Aug 13.

Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands.

Background: Polygenic risk scores (PRSs) operationalize genetic propensity toward a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection, it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance.

Methods: We computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9,247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands), and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-report and parental, teacher, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor.

Results: Seven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology.

Conclusions: The results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of nonspecificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders.
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http://dx.doi.org/10.1111/jcpp.13501DOI Listing
August 2021

Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth.

Clin Epigenetics 2021 06 26;13(1):131. Epub 2021 Jun 26.

Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada.

Background: Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner.

Results: We analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates.

Conclusion: Compared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.
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http://dx.doi.org/10.1186/s13148-021-01114-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236204PMC
June 2021

Early or Late Gestational Exposure to Maternal Immune Activation Alters Neurodevelopmental Trajectories in Mice: An Integrated Neuroimaging, Behavioral, and Transcriptional Study.

Biol Psychiatry 2021 09 23;90(5):328-341. Epub 2021 Mar 23.

Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada. Electronic address:

Background: Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental disorders later in life. The impact of the gestational timing of MIA exposure on downstream development remains unclear.

Methods: We characterized neurodevelopmental trajectories of mice exposed to the viral mimetic poly I:C (polyinosinic:polycytidylic acid) either on gestational day 9 (early) or on day 17 (late) using longitudinal structural magnetic resonance imaging from weaning to adulthood. Using multivariate methods, we related neuroimaging and behavioral variables for the time of greatest alteration (adolescence/early adulthood) and identified regions for further investigation using RNA sequencing.

Results: Early MIA exposure was associated with accelerated brain volume increases in adolescence/early adulthood that normalized in later adulthood in the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety-like, stereotypic, and sensorimotor gating behaviors observed in adolescence normalized in adulthood. MIA exposure in late gestation had less impact on anatomical and behavioral profiles. Multivariate maps associated anxiety-like, social, and sensorimotor gating deficits with volume of the dorsal and ventral hippocampus and anterior cingulate cortex, among others. The most transcriptional changes were observed in the dorsal hippocampus, with genes enriched for fibroblast growth factor regulation, autistic behaviors, inflammatory pathways, and microRNA regulation.

Conclusions: Leveraging an integrated hypothesis- and data-driven approach linking brain-behavior alterations to the transcriptome, we found that MIA timing differentially affects offspring development. Exposure in late gestation leads to subthreshold deficits, whereas exposure in early gestation perturbs brain development mechanisms implicated in neurodevelopmental disorders.
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http://dx.doi.org/10.1016/j.biopsych.2021.03.017DOI Listing
September 2021

A 10-color flow cytometry panel for diagnosis and minimal residual disease in chronic lymphocytic leukemia.

Leuk Lymphoma 2021 May 21:1-8. Epub 2021 May 21.

Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada.

Diagnosis and minimal residual disease (MRD) monitoring of chronic lymphocytic leukemia (CLL) by flow cytometry currently requires multiple antibody panels. We added CD23 and CD200 to the EuroFlow lymphoid screening tube (LST) to create a 10-color modified LST (mLST) capable of diagnosing typical CLL in a single tube. We then explored if the mLST could be used for MRD by comparing its performance to the European Research Initiative on CLL (ERIC) panel using spiked cryopreserved and fresh patient samples. Over 1 year of use in our clinical laboratory, the mLST diagnosed CLL without further immunophenotyping in 56% of samples with an abnormal clone. There was good agreement in MRD results between the mLST and ERIC panels. Therefore, the mLST can streamline CLL diagnosis by reducing technician time and the number of panels required. It may have the potential to screen for MRD in laboratories without access to dedicated panels (ERIC).
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http://dx.doi.org/10.1080/10428194.2021.1919658DOI Listing
May 2021

Copy number and transcriptome alterations associated with metastatic lesion response to treatment in colorectal cancer.

Clin Transl Med 2021 04;11(4):e401

McGill University-Segal Cancer Centre, Jewish General Hospital, 3755 Côte Ste-Catherine, Montreal, Quebec, H3T 1E2, Canada.

Background: Therapeutic resistance is the main cause of death in metastatic colorectal cancer. To investigate genomic plasticity, most specifically of metastatic lesions, associated with response to first-line systemic therapy, we collected longitudinal liver metastatic samples and characterized the copy number aberration (CNA) landscape and its effect on the transcriptome.

Methods: Liver metastatic biopsies were collected prior to treatment (pre, n = 97) and when clinical imaging demonstrated therapeutic resistance (post, n = 43). CNAs were inferred from whole exome sequencing and were correlated with both the status of the lesion and overall patient progression-free survival (PFS). We used RNA sequencing data from the same sample set to validate aberrations as well as independent datasets to prioritize candidate genes.

Results: We identified a significantly increased frequency gain of a unique CN, in liver metastatic lesions after first-line treatment, on chr18p11.32 harboring 10 genes, including TYMS, which has not been reported in primary tumors (GISTIC method and test of equal proportions, FDR-adjusted p = 0.0023). CNA lesion profiles exhibiting different treatment responses were compared and we detected focal genomic divergences in post-treatment resistant lesions but not in responder lesions (two-tailed Fisher's Exact test, unadjusted p ≤ 0.005). The importance of examining metastatic lesions is highlighted by the fact that 15 out of 18 independently validated CNA regions found to be associated with PFS in this study were only identified in the metastatic lesions and not in the primary tumors.

Conclusion: This investigation of genomic-phenotype associations in a large colorectal cancer liver metastases cohort identified novel molecular features associated with treatment response, supporting the clinical importance of collecting metastatic samples in a defined clinical setting.
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http://dx.doi.org/10.1002/ctm2.401DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087915PMC
April 2021

Candidate Markers of Olaparib Response from Genomic Data Analyses of Human Cancer Cell Lines.

Cancers (Basel) 2021 Mar 15;13(6). Epub 2021 Mar 15.

Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada.

The benefit of PARP inhibitor olaparib in relapsed and advanced high-grade serous ovarian carcinoma (HGSOC) is well established especially in mutation carriers. Identification of additional biomarkers can help expand the population of patients most likely to benefit from olaparib treatment. To identify candidate markers of olaparib response we analyzed genomic and in vitro olaparib response data from two independent groups of cancer cell lines. Using pan-cancer cell lines ( = 896) from the Genomics of Drug Sensitivity in Cancer database, we applied linear regression methods to identify statistically significant gene predictors of olaparib response based on mRNA expression. We then analyzed whole exome sequencing and mRNA gene expression data from our collection of 18 HGSOC cell lines previously classified as sensitive, intermediate, or resistant based on in vitro olaparib response for mutations, copy number variation and differential expression of candidate olaparib response genes. We identify genes previously associated with olaparib response (, ), and discover novel candidate olaparib sensitivity genes with known functions including interaction with PARP1 (, ) and involvement in homologous recombination DNA repair (). Further investigations at experimental and clinical levels are required to validate novel candidates, and ultimately determine their efficacy as potential biomarkers of olaparib sensitivity.
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http://dx.doi.org/10.3390/cancers13061296DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998846PMC
March 2021

A Polygenic Risk Score to Predict Future Adult Short Stature Among Children.

J Clin Endocrinol Metab 2021 06;106(7):1918-1928

Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada.

Context: Adult height is highly heritable, yet no genetic predictor has demonstrated clinical utility compared to mid-parental height.

Objective: To develop a polygenic risk score for adult height and evaluate its clinical utility.

Design: A polygenic risk score was constructed based on meta-analysis of genomewide association studies and evaluated on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort.

Subjects: Participants included 442 599 genotyped White British individuals in the UK Biobank and 941 genotyped child-parent trios of European ancestry in the ALSPAC cohort.

Interventions: None.

Main Outcome Measures: Standing height was measured using stadiometer; Standing height 2 SDs below the sex-specific population average was considered as short stature.

Results: Combined with sex, a polygenic risk score captured 71.1% of the total variance in adult height in the UK Biobank. In the ALSPAC cohort, the polygenic risk score was able to identify children who developed adulthood short stature with an area under the receiver operating characteristic curve (AUROC) of 0.84, which is close to that of mid-parental height. Combining this polygenic risk score with mid-parental height or only one of the child's parent's height could improve the AUROC to at most 0.90. The polygenic risk score could also substitute mid-parental height in age-specific Khamis-Roche height predictors and achieve an equally strong discriminative power in identifying children with a short stature in adulthood.

Conclusions: A polygenic risk score could be considered as an alternative or adjunct to mid-parental height to improve screening for children at risk of developing short stature in adulthood in European ancestry populations.
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http://dx.doi.org/10.1210/clinem/dgab215DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266463PMC
June 2021

A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity.

Nat Med 2021 04 25;27(4):659-667. Epub 2021 Feb 25.

Anaesthesiology and Intensive Care Medicine, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.

To identify circulating proteins influencing Coronavirus Disease 2019 (COVID-19) susceptibility and severity, we undertook a two-sample Mendelian randomization (MR) study, rapidly scanning hundreds of circulating proteins while reducing bias due to reverse causation and confounding. In up to 14,134 cases and 1.2 million controls, we found that an s.d. increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (odds ratio (OR) = 0.54, P = 7 × 10), hospitalization (OR = 0.61, P = 8 × 10) and susceptibility (OR = 0.78, P = 8 × 10). Measuring OAS1 levels in 504 individuals, we found that higher plasma OAS1 levels in a non-infectious state were associated with reduced COVID-19 susceptibility and severity. Further analyses suggested that a Neanderthal isoform of OAS1 in individuals of European ancestry affords this protection. Thus, evidence from MR and a case-control study support a protective role for OAS1 in COVID-19 adverse outcomes. Available pharmacological agents that increase OAS1 levels could be prioritized for drug development.
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http://dx.doi.org/10.1038/s41591-021-01281-1DOI Listing
April 2021

Improved prediction of fracture risk leveraging a genome-wide polygenic risk score.

Genome Med 2021 02 3;13(1):16. Epub 2021 Feb 3.

Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada.

Background: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction.

Methods: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors.

Results: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072.

Conclusions: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.
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http://dx.doi.org/10.1186/s13073-021-00838-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860212PMC
February 2021

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.

Gigascience 2021 01;10(1)

Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada.

Background: The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance.

Methods: Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction.

Results: Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks.

Conclusions: This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
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http://dx.doi.org/10.1093/gigascience/giaa155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821710PMC
January 2021

Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability.

Mol Psychiatry 2021 06 7;26(6):2663-2676. Epub 2021 Jan 7.

The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.

Genomic copy number variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well. Here, we identified all CNVs > 50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of intelligence quotient when duplicated or deleted, respectively. Effect sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect size on intelligence occur de novo at the same frequency. We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways.
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http://dx.doi.org/10.1038/s41380-020-00985-zDOI Listing
June 2021

Neuropsychiatric symptoms are early indicators of an upcoming metabolic decline in Alzheimer's disease.

Transl Neurodegener 2021 01 4;10(1). Epub 2021 Jan 4.

Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.

Background: Neuropsychiatric symptoms (NPS) are increasingly recognized as early non-cognitive manifestations in the Alzheimer's disease (AD) continuum. However, the role of NPS as an early marker of pathophysiological progression in AD remains unclear. Dominantly inherited AD (DIAD) mutation carriers are young individuals who are destined to develop AD in future due to the full penetrance of the genetic mutation. Hence, the study of DIAD mutation carriers enables the evaluation of the associations between pure AD pathophysiology and metabolic correlates of NPS without the confounding effects of co-existing pathologies. In this longitudinal study, we aimed to identify regional brain metabolic dysfunctions associated with NPS in cognitively intact DIAD mutation carriers.

Methods: We stratified 221 cognitively intact participants from the Dominantly Inherited Alzheimer's Network according to their mutation carrier status. The interactions of NPS measured by the Neuropsychiatric Inventory-Questionnaire (NPI-Q), age, and estimated years to symptom onset (EYO) as a function of metabolism measured by [F]flurodeoxyglucose ([F]FDG) positron emission tomography, were evaluated by the mixed-effects regression model with family-level random effects in DIAD mutation carriers and non-carriers. Exploratory factor analysis was performed to identify the neuropsychiatric subsyndromes in DIAD mutation carriers using the NPI-Q sub-components. Then the effects of interactions between specific neuropsychiatric subsyndromes and EYO on metabolism were evaluated with the mixed-effects regression model.

Results: A total of 119 mutation carriers and 102 non-carriers were studied. The interaction of higher NPI-Q and shorter EYO was associated with more rapid declines of global and regional [F]FDG uptake in the posterior cingulate and ventromedial prefrontal cortices, the bilateral parietal lobes and the right insula in DIAD mutation carriers. The neuropsychiatric subsyndromes of agitation, disinhibition, irritability and depression interacted with the EYO to drive the [F]FDG uptake decline in the DIAD mutation carriers. The interaction of NPI and EYO was not associated with [F]FDG uptake in DIAD mutation non-carriers.

Conclusions: The NPS in cognitively intact DIAD mutation carriers may be a clinical indicator of subsequent metabolic decline in brain networks vulnerable to AD, which supports the emerging conceptual framework that NPS represent early manifestations of neuronal injury in AD. Further studies using different methodological approaches to identify NPS in preclinical AD are needed to validate our findings.
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http://dx.doi.org/10.1186/s40035-020-00225-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780680PMC
January 2021

High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients.

Am J Hypertens 2021 04;34(3):282-290

Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada.

Background: The risk that coronavirus disease 2019 (COVID-19) patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital.

Methods And Results: This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical, and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset, and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared with discharged COVID-19 patients.

Conclusions: Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.
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http://dx.doi.org/10.1093/ajh/hpaa225DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7799245PMC
April 2021

Statistical power in COVID-19 case-control host genomic study design.

Genome Med 2020 12 28;12(1):115. Epub 2020 Dec 28.

Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada.

The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.
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http://dx.doi.org/10.1186/s13073-020-00818-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768597PMC
December 2020

Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening.

Genet Med 2021 03 28;23(3):508-515. Epub 2020 Oct 28.

Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.

Purpose: Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants.

Methods: We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants.

Results: While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32-10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06-5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14-7.46) increased odds of identifying rare variant heterozygotes.

Conclusion: Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.
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http://dx.doi.org/10.1038/s41436-020-01007-7DOI Listing
March 2021

Effect Sizes of Deletions and Duplications on Autism Risk Across the Genome.

Am J Psychiatry 2021 01 11;178(1):87-98. Epub 2020 Sep 11.

Université de Montréal, Montreal (Douard, Zeribi, Schramm, Tamer, Loum, Nowak, Lord, Moreau, Huguet, Jacquemont); UHC Sainte-Justine Research Center, Montreal (Douard, Zeribi, Schramm, Tamer, Loum, Nowak, Saci, Lord, Rodríguez-Herreros, Jean-Louis, Moreau, Huguet, Jacquemont); Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal (Schramm, Greenwood); Sensory-Motor Laboratory, Jules-Gonin Eye Hospital, University of Lausanne, Lausanne, Switzerland (Rodríguez-Herreros); Department of Forensic and Neurodevelopmental Sciences (Loth) and Center for Population Neuroscience and Stratified Medicine (Schumann), Institute of Psychiatry, Psychology, and Neuroscience, King's College London; Hospital for Sick Children and Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto (Pausova); Departments of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal (Elsabbagh); Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, and Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia (Almasy); Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston (Glahn); Human Genetics and Cognitive Functions, Institut Pasteur, Université de Paris, Paris (Bourgeron); Département de Sciences de la Décision, HEC Montreal, Montreal (Labbe); Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto (Paus); Departments of Psychology and Psychiatry, University of Toronto, Toronto (Paus); Centre de Recherche de CIUSSS-NIM, Montreal (Mottron); Département de Psychiatrie, Université de Montréal, Montreal (Mottron); Department of Epidemiology, Biostatistics, and Occupational Health, Gerald Bronfman Department of Oncology, and Department of Human Genetics, McGill University, Montreal (Greenwood).

Objective: Deleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, levels of autism risk conferred by most rare CNVs remain unknown. The authors recently developed statistical models to estimate the effect size on IQ of all CNVs, including undocumented ones. In this study, the authors extended this model to autism susceptibility.

Methods: The authors identified CNVs in two autism populations (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models were used to test nine quantitative variables associated with genes encompassed in CNVs to explain their effects on IQ, autism susceptibility, and behavioral domains.

Results: The "probability of being loss-of-function intolerant" (pLI) best explains the effect of CNVs on IQ and autism risk. Deleting 1 point of pLI decreases IQ by 2.6 points in autism and unselected populations. The effect of duplications on IQ is threefold smaller. Autism susceptibility increases when deleting or duplicating any point of pLI. This is true for individuals with high or low IQ and after removing de novo and known recurrent neuropsychiatric CNVs. When CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behavior, and phonological memory, whereas both equally affect motor skills.

Conclusions: Autism risk conferred by duplications is less influenced by IQ compared with deletions. The model applied in this study, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk and IQ loss. These models will help to interpret CNVs identified in the clinic.
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http://dx.doi.org/10.1176/appi.ajp.2020.19080834DOI Listing
January 2021

A Preclinical Trial and Molecularly Annotated Patient Cohort Identify Predictive Biomarkers in Homologous Recombination-deficient Pancreatic Cancer.

Clin Cancer Res 2020 10 14;26(20):5462-5476. Epub 2020 Aug 14.

Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) arising in patients with a germline or (g) mutation may be sensitive to platinum and PARP inhibitors (PARPi). However, treatment stratification based on g mutational status alone is associated with heterogeneous responses.

Experimental Design: We performed a seven-arm preclinical trial consisting of 471 mice, representing 12 unique PDAC patient-derived xenografts, of which nine were g mutated. From 179 patients whose PDAC was whole-genome and transcriptome sequenced, we identified 21 cases with homologous recombination deficiency (HRD), and investigated prognostic biomarkers.

Results: We found that biallelic inactivation of / is associated with genomic hallmarks of HRD and required for cisplatin and talazoparib (PARPi) sensitivity. However, HRD genomic hallmarks persisted in xenografts despite the emergence of therapy resistance, indicating the presence of a genomic scar. We identified tumor polyploidy and a low Ki67 index as predictors of poor cisplatin and talazoparib response. In patients with HRD PDAC, tumor polyploidy and a basal-like transcriptomic subtype were independent predictors of shorter survival. To facilitate clinical assignment of transcriptomic subtype, we developed a novel pragmatic two-marker assay (GATA6:KRT17).

Conclusions: In summary, we propose a predictive and prognostic model of g-mutated PDAC on the basis of HRD genomic hallmarks, Ki67 index, tumor ploidy, and transcriptomic subtype.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-1439DOI Listing
October 2020

Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models.

Genet Epidemiol 2020 11 11;44(8):825-840. Epub 2020 Aug 11.

Lady Davis Institute, Jewish General Hospital, Montreal, Canada.

It is challenging to estimate the phenotypic impact of the structural genome changes known as copy-number variations (CNVs), since there are many unique CNVs which are nonrecurrent, and most are too rare to be studied individually. In recent work, we found that CNV-aggregated genomic annotations, that is, specifically the intolerance to mutation as measured by the pLI score (probability of being loss-of-function intolerant), can be strong predictors of intellectual quotient (IQ) loss. However, this aggregation method only estimates the individual CNV effects indirectly. Here, we propose the use of hierarchical Bayesian models to directly estimate individual effects of rare CNVs on measures of intelligence. Annotation information on the impact of major mutations in genomic regions is extracted from genomic databases and used to define prior information for the approach we call HBIQ. We applied HBIQ to the analysis of CNV deletions and duplications from three datasets and identified several genomic regions containing CNVs demonstrating significant deleterious effects on IQ, some of which validate previously known associations. We also show that several CNVs were identified as deleterious by HBIQ even if they have a zero pLI score, and the converse is also true. Furthermore, we show that our new model yields higher out-of-sample concordance (78%) for predicting the consequences of carrying known recurrent CNVs compared with our previous approach.
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http://dx.doi.org/10.1002/gepi.22344DOI Listing
November 2020

Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study.

PLoS Med 2020 07 2;17(7):e1003152. Epub 2020 Jul 2.

Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada.

Background: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program.

Methods And Findings: A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk.

Conclusions: Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.
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http://dx.doi.org/10.1371/journal.pmed.1003152DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331983PMC
July 2020

Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data.

Sci Rep 2020 06 16;10(1):9747. Epub 2020 Jun 16.

Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Canada.

Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to identify gene signatures that are related to a certain disease from high-dimensional gene expression data. The expression of genes may have grouping structures, for example, a group of co-regulated genes that have similar biological functions tend to have similar expressions. Thus it is preferable to take the grouping structure into consideration to select features. In this paper, we propose a Bayesian Robit regression method with Hyper-LASSO priors (shortened by BayesHL) for feature selection in high dimensional genomic data with grouping structure. The main features of BayesHL include that it discards more aggressively unrelated features than LASSO, and it makes feature selection within groups automatically without a pre-specified grouping structure. We apply BayesHL in gene expression analysis to identify subsets of genes that contribute to the 5-year survival outcome of endometrial cancer (EC) patients. Results show that BayesHL outperforms alternative methods (including LASSO, group LASSO, supervised group LASSO, penalized logistic regression, random forest, neural network, XGBoost and knockoff) in terms of predictive power, sparsity and the ability to uncover grouping structure, and provides insight into the mechanisms of multiple genetic pathways leading to differentiated EC survival outcome.
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http://dx.doi.org/10.1038/s41598-020-66466-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297975PMC
June 2020

Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of Virulence.

mSphere 2020 06 10;5(3). Epub 2020 Jun 10.

Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, Quebec, Canada

comprises more than 2,600 serovars. Very few environmental and uncommon serovars have been characterized for their potential role in virulence and human infections. A complementary and systematic high-throughput analysis of virulence was used to elucidate the association between genetic and phenotypic variations across isolates. The goal was to develop a strategy for the classification of isolates as a benchmark and predict virulence levels of isolates. Thirty-five phylogenetically distant strains of unknown virulence were selected from the Foodborne Syst-OMICS (SalFoS) collection, representing 34 different serovars isolated from various sources. Isolates were evaluated for virulence in 4 complementary models of infection to compare virulence traits with the genomics data, including interactions with human intestinal epithelial cells, human macrophages, and amoeba. testing was conducted using the mouse model of systemic infection. Significant correlations were identified between the different models. We identified a collection of novel hypothetical and conserved proteins associated with isolates that generate a high burden. We also showed that blind prediction of virulence of 33 additional strains based on the pan-genome was high in the mouse model of systemic infection (82% agreement) and in the human epithelial cell model (74% agreement). These complementary approaches enabled us to define virulence potential in different isolates and present a novel strategy for risk assessment of specific strains and for better monitoring and source tracking during outbreaks. species are bacteria that are a major source of foodborne disease through contamination of a diversity of foods, including meat, eggs, fruits, nuts, and vegetables. More than 2,600 different serovars have been identified, and only a few of them are associated with illness in humans. Despite the fact that they are genetically closely related, there is enormous variation in the virulence of different isolates of Identification of foodborne pathogens is a lengthy process based on microbiological, biochemical, and immunological methods. Here, we worked toward new ways of integrating whole-genome sequencing (WGS) approaches into food safety practices. We used WGS to build associations between virulence and genetic diversity within 83 isolates representing 77 different serovars. Our work demonstrates the potential of combining a genomics approach and virulence tests to improve the diagnostics and assess risk of human illness associated with specific isolates.
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http://dx.doi.org/10.1128/mSphere.00293-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289705PMC
June 2020

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Biometrics 2021 06 5;77(2):424-438. Epub 2020 Jun 5.

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.
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http://dx.doi.org/10.1111/biom.13307DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8359306PMC
June 2021

Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.

PLoS Genet 2020 05 4;16(5):e1008766. Epub 2020 May 4.

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada.

Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix).
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http://dx.doi.org/10.1371/journal.pgen.1008766DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224575PMC
May 2020

Maternal Prenatal Mood, Pregnancy-Specific Worries, and Early Child Psychopathology: Findings From the DREAM BIG Consortium.

J Am Acad Child Adolesc Psychiatry 2021 01 8;60(1):186-197. Epub 2020 Apr 8.

McGill University Faculty of Medicine, Montreal, Quebec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Centre for Child Development and Mental Health, Jewish General Hospital, Montreal, Quebec, Canada. Electronic address:

Objective: Few studies have attempted to identify how distinct dimensions of maternal prenatal affective symptoms relate to offspring psychopathology. We defined latent dimensions of women's prenatal affective symptoms and pregnancy-specific worries to examine their association with early offspring psychopathology in three prenatal cohorts.

Method: Data were used from three cohorts of the DREAM-BIG consortium: Avon Longitudinal Study of Parents and Children (ALSPAC [N = 12,515]), Generation R (N = 6,803), and the Canadian prenatal cohort Maternal Adversity, Vulnerability, and Neurodevelopment (MAVAN [N = 578]). Maternal prenatal affective symptoms and pregnancy-specific worries were assessed using different measures in each cohort. Through confirmatory factor analyses, we determined whether comparable latent dimensions of prenatal maternal affective symptoms existed across the cohorts. We used structural equation models to examine cohort-specific associations between these dimensions and offspring psychopathology at 4 to 8 years of age (general psychopathology, specific internalizing and externalizing previously derived using confirmatory factor analyses). Cohort-based estimates were meta-analyzed using inverse variance-weighing.

Results: Four prenatal maternal factors were similar in all cohorts: a general affective symptoms factor and three specific factors-an anxiety/depression factor, a somatic factor, and a pregnancy-specific worries factor. In meta-analyses, both the general affective symptoms factor and pregnancy-specific worries factor were independently associated with offspring general psychopathology. The general affective symptoms factor was further associated with offspring specific internalizing problems. There were no associations with specific externalizing problems.

Conclusion: These replicated findings of independent and adverse effects for prenatal general affective symptoms and pregnancy-specific worries on child mental health support the need for specific interventions in pregnancy.
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http://dx.doi.org/10.1016/j.jaac.2020.02.017DOI Listing
January 2021

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes.

Cardiovasc Diabetol 2020 01 30;19(1):12. Epub 2020 Jan 30.

Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.

Background: Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS.

Methods: We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms.

Results: In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10).

Conclusions: Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.
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http://dx.doi.org/10.1186/s12933-020-0988-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993460PMC
January 2020

At the interface.

Genet Epidemiol 2020 03 10;44(2):119-124. Epub 2020 Jan 10.

Department of Clinical Epidemiology, Lady Davis Institute for Medical Research, Montreal, Quebec, Canada.

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http://dx.doi.org/10.1002/gepi.22277DOI Listing
March 2020

Inactivation of Interferon Regulatory Factor 1 Causes Susceptibility to Colitis-Associated Colorectal Cancer.

Sci Rep 2019 12 11;9(1):18897. Epub 2019 Dec 11.

Department of Biochemistry, McGill University, Montreal, QC, Canada.

The mechanisms linking chronic inflammation of the gut (IBD) and increased colorectal cancer susceptibility are poorly understood. IBD risk is influenced by genetic factors, including the IBD5 locus (human 5q31), that harbors the IRF1 gene. A cause-to-effect relationship between chronic inflammation and colorectal cancer, and a possible role of IRF1 were studied in Irf1 mice in a model of colitis-associated colorectal cancer (CA-CRC) induced by azoxymethane and dextran sulfate. Loss of Irf1 causes hyper-susceptibility to CA-CRC, with early onset and increased number of tumors leading to rapid lethality. Transcript profiling (RNA-seq) and immunostaining of colons shows heightened inflammation and enhanced enterocyte proliferation in Irf1 mutants, prior to appearance of tumors. Considerable infiltration of leukocytes is seen in Irf1 colons at this early stage, and is composed primarily of proinflammatory Gr1 Cd11b myeloid cells and other granulocytes, as well as CD4 lymphoid cells. Differential susceptibility to CA-CRC of Irf1 vs. B6 controls is fully transferable through hematopoietic cells as observed in bone marrow chimera studies. Transcript signatures seen in Irf1 mice in response to AOM/DSS are enriched in clinical specimens from patients with IBD and with colorectal cancer. In addition, IRF1 expression in the colon is significantly decreased in late stage colorectal cancer (stages 3, 4) and is associated with poorer prognosis. This suggests that partial or complete loss of IRF1 expression alters the type, number, and function of immune cells in situ during chronic inflammation, possibly via the creation of a tumor-promoting environment.
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http://dx.doi.org/10.1038/s41598-019-55378-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906452PMC
December 2019

MDiNE: a model to estimate differential co-occurrence networks in microbiome studies.

Bioinformatics 2020 03;36(6):1840-1847

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.

Motivation: The human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa through precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates.

Results: We propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modeled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa. The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn's disease.

Availability And Implementation: MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz824DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075537PMC
March 2020
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