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Genome-wide scan identifies genetic risk loci for depression and anxiety in IBD patients
- 1. Genome-wide scan to identify genetic risk loci for depression and anxiety in the patients with inflammatory bowel disease Svetlana Frenkel The Department of Biochemistry and Medical Genetics
- 2. Inflammatory bowel disease • Crohn's disease can affect every part of alimentary canal, but predominately affects the distal small bowel and colon • Ulcerative colitis primarily affects the colon and the rectum
- 3. The global burden of IBD From: G.G. Kaplan et al., Nature Reviews. Gastroenterology & Hepatology, 12(12):720–7 (2015)
- 4. Psychiatric comorbidity in persons with IBD From: R.A. Marrie, et al., J Psychosom Res, 101:17–23 (2017) Study groups: All IMID – Multiple sclerosis, IBD and rheumatoid artritis (n=19,572) Of those IBD (n=6,119) Control (n=97,727)
- 5. From: B.L. Bonaz et al., Gastroenterology, 144:36–49 (2013) Brain-gut interactions in IBD
- 6. Psychiatric comorbidity (PC) Inflammatory Bowel Disease (IBD) • Reduced quality of life and health anxiety • Pain and surgeries • Diet and microbiome changes • Steroid anti-inflammatory treatment Adapted from: L.M. Lix, et al., Inflamm Bowel Dis 14:1575–84 (2008) 125 150 175 200 225 0 6 12 18 24 IBDQScore Time (Month) IBD Quality of life measure Active Fluctuating Inactive
- 7. Psychiatric comorbidity (PC) Inflammatory Bowel Disease (IBD) • IBD patients with PC had first PC onset 2 years or more before the IBD diagnosis (From: J.R. Walker JR, et al. Am J Gastroenterol; 103:1989–97 (2008)) • IBD patients with lifetime mood disorders tend to have early IBD onset • PC increasing the risk of IBD relapses • Good effect of placebo and alternative medicine treatment call attention to the importance of cognition and patient expectation in the clinical responses.
- 8. Risk factors for IBD A. N. Ananthakrishnan et al., Nature Reviews Gastroenterology & Hepatology, 12(4):205–217 (2015). …and PC
- 9. Main types of genetic variations tctgactgctttttcacccatctacagtcccccttgccgtcccaagc..tggatgatttg |||×|||||||||||×||||||||||||||||||||×||||||||||××||||||||||| tctcactgctttttctcccatctacagtcccccttg.cgtcccaagcaatggatgatttg Small-scale sequence variations (<1 kbp) Large-scale sequence variations (>1 kbp) 1. Base-pair substitutions or SNPs (single nucleotide polymorphisms) Reference sequence 1. Copy number gain (duplication) 2. Copy number loss (deletion) 3. Chromosomal rearrangement (translocation) Reference sequence Reference sequence 2. Indels (short insertions and deletions)
- 10. Genetic variations associated with IBD and Depression or Anxiety disorders in ClinVar database All variants: 120 CNV: 8 Pathogenic or Likely pathogenic: 24 Risk factor: 11 All variants: 300 CNV: 131 Pathogenic or Likely pathogenic: 140 Risk factor: 7 IBD PC
- 11. Genetic variations associated with IBD and Depression or Anxiety disorders in GWAS catalog SNPs: 664 (189 intergenic) Reported genes: 1022 IBD PCSNPs: 508 (151 intergenic) Reported genes: 466
- 12. Manitoba IBD Cohort Study • In 1994, Dr. Charles N. Bernstein established the Inflammatory Bowel Disease (IBD) Clinical and Research Centre • The Manitoba IBD Cohort Study was initiated in 2002 with funding from the Canadian Institutes of Health Research. • It is a population-based study where 388 subjects who were within 7 years of diagnosis of their IBD were enrolled. • The goal of the Manitoba IBD Cohort is to determine predictors of outcomes as well as to optimize management of various aspects of IBD.
- 13. Manitoba IBD Cohort Study Antibiotic usage Smoke exposure Certain infections Hygiene Stress Academic performance Social life Employment and Income Depression and anxiety Osteoporosis Fatigue Sleep difficulties Risk of colorectal cancer Delivery mode Early childhood vaccinations Early childhood antibiotic usage Psychological functioning and quality of life IBD Nutrition Vitamin D intake Sugar intake Microbiome changes Genetic risk
- 14. Research Aim Our aim is to apply high density microarray to define CNVs architecture in IBD for understanding how they contribute to the development of PC in IBD.
- 15. Project chart flow Samples quality control CNV calling PennCNV, iPattern, QuantiSNP Samples quality control (with number of samples removed on every qualifier) • Call rate < 95% (3 samples) • Sample relatedness (3 samples) • Sex inconsistencies (4 samples) • Population outliers (18 samples) • Samples with SD of LRR greater than three times the SD from the mean SD of LRR for an analysis batch (2 samples) • Samples with SD of BAF greater than three times the SD from the mean SD of BAF for an analysis batch (3 samples) Samples genotyping CNVs quality controlCNV quality control • Only one algorithm • <5 consecutive SNPs • <5 kb in length • PennCNV confidential score cut-off < 15, QuantiSNP Log Bayes Factor cut-off < 10, iPattern score cut-off < 1 Analysis of association of genes affected by CNV with PC in IBD. CNV-based sample quality control The number of individual CNVs in the sample is greater than three times the SD from the mean number of individual CNVs for an analysis batch (3 samples were removed) 269 samples 243 samples 246 samples Stringent CNV calls detecting
- 16. Sample genotyping and quality control Samples quality control CNV calling PennCNV, iPattern, QuantiSNP Samples quality control (with number of samples removed on every qualifier) • Call rate < 95% (3 samples) • Sample relatedness (3 samples) • Sex inconsistencies (4 samples) • Population outliers (18 samples) • Samples with SD of LRR greater than three times the SD from the mean SD of LRR for an analysis batch (2 samples) • Samples with SD of BAF greater than three times the SD from the mean SD of BAF for an analysis batch (3 samples) Samples genotyping CNVs quality controlCNV quality control • Only one algorithm • <5 consecutive SNPs • <5 kb in length • PennCNV confidential score cut-off < 15, QuantiSNP Log Bayes Factor cut-off < 10, iPattern score cut-off < 1 CNV-based sample quality control The number of individual CNVs in the sample is greater than three times the SD from the mean number of individual CNVs for an analysis batch (3 samples were removed) 269 samples 243 samples 246 samples Stringent CNV calls detecting Analysis of association of genes affected by CNV with PC in IBD.
- 17. Samples and QC Case population • Residents of the province of Manitoba, Canada (population approximately 1,150,000) • Identified as having IBD through the administrative health database of Manitoba Health Control population Two populations-scale studies, data is available on dbGaP: • KORA (Cooperative Research in the Region of Augsburg) • COGEND (Collaborative Genetic Study of Nicotine Dependence) Samples quality control • Call rate<95% • Sample relatedness • Sex inconsistencies • Population outliers • Samples with SD of LRR greater than three times the SD from the mean SD of LRR for an analysis batch. • Samples with SD of BAF greater than three times the SD from the mean SD of BAF for an analysis batch. 246 samples 2988 samples Genotyped using Illumina Infinium® Omni2.5-8 v1.3 BeadChip Genotyped using the Illumina Human OMNI 2.5M-Quad microarray
- 18. Population stratification analysis Case Control Population2Population1 Individuals with allele A, frequent in the Population 1 Individuals with allele B, frequent in the Population 2 Legend
- 19. CD patients Sex IBD+PC IBD-PC p-value* Female 36 33 0.009164 OR=2.8, 95%CI=1.2-6.6Male 14 36 IBD samples characteristics in the IBD types Individuals with IBD and psychiatric comorbidity (depression or anxiety disorders) Individuals with IBD without psychiatric comorbidity UC patients Sex IBD+PC IBD-PC p-value* Female 26 42 1 Male 21 35 * Fisher’s exact test p-value
- 20. Females IBD type IBD+PC IBD-PC p-value* CD 36 33 0.1232 UC 26 42 IBD samples characteristics in the different sex groups Males IBD type IBD+PC IBD-PC p-value* CD 14 36 0.3111 UC 21 35 * Fisher’s exact test p-value
- 21. CNV detection Samples quality control CNV calling PennCNV, iPattern, QuantiSNP Samples quality control (with number of samples removed on every qualifier) • Call rate < 95% (3 samples) • Sample relatedness (3 samples) • Sex inconsistencies (4 samples) • Population outliers (18 samples) • Samples with SD of LRR greater than three times the SD from the mean SD of LRR for an analysis batch (2 samples) • Samples with SD of BAF greater than three times the SD from the mean SD of BAF for an analysis batch (3 samples) Samples genotyping CNVs quality controlCNV quality control • Only one algorithm • <5 consecutive SNPs • <5 kb in length • PennCNV confidential score cut-off < 15, QuantiSNP Log Bayes Factor cut-off < 10, iPattern score cut-off < 1 CNV-based sample quality control The number of individual CNVs in the sample is greater than three times the SD from the mean number of individual CNVs for an analysis batch (3 samples were removed) 269 samples 243 samples 246 samples Stringent CNV calls detecting Analysis of association of genes affected by CNV with PC in IBD.
- 22. CNV detection: microarray technology • Array comparative genomic hybridization (Array CGH) • SNP microarray (single nucleotide variants)
- 23. CNV detection: Array CGH Е. Karampetsou et al., J Clin Med. 3(2): 663–678. (2014)
- 24. CNV detection: SNP array Е. Karampetsou et al., J Clin Med. 3(2): 663–678. (2014)
- 25. Copy number states C. Alkan et al., Nat Rev Genet, 12(5): 363–376 (2011) AA A- AB BB B- AAB AAA ABB BBB AAAA AAAB ABBB BBBB AABB AA BB SNP probes →→→
- 26. CNV detection software • cnvPartition (Illumina) • QuantiSNP (Oxford University) • Partek GS v6.2 (Partek) • JMP Genomics v7.0 (JMP/SAS) • SNP & CN Variation Suite (CNAM) (Golden Helix) • Nexus Copy Number (BioDiscovery) • PennCNV (University of Pennsylvania) • Exemplar for CN (Sapio) • ArrayAssist (Stratagene) • iPattern (TCAG, SickKids)
- 27. Stringent CNV calls detection Samples quality control CNV calling PennCNV, iPattern, QuantiSNP Samples quality control (with number of samples removed on every qualifier) • Call rate < 95% (3 samples) • Sample relatedness (3 samples) • Sex inconsistencies (4 samples) • Population outliers (18 samples) • Samples with SD of LRR greater than three times the SD from the mean SD of LRR for an analysis batch (2 samples) • Samples with SD of BAF greater than three times the SD from the mean SD of BAF for an analysis batch (3 samples) Samples genotyping CNVs quality controlCNV quality control • Only one algorithm • <5 consecutive SNPs • <5 kb in length • PennCNV confidential score cut-off < 15, QuantiSNP Log Bayes Factor cut-off < 10, iPattern score cut-off < 1 CNV-based sample quality control The number of individual CNVs in the sample is greater than three times the SD from the mean number of individual CNVs for an analysis batch (3 samples were removed) 269 samples 243 samples 246 samples Stringent CNV calls detection Analysis of association of genes affected by CNV with PC in IBD.
- 28. Number of CNV detected by three algorithms • The CNV calling was conducted using three algorithms (QuantiSNP, PennCNV and iPattern) • Minimal CNV length = 5000bp, minimal number of probes (SNPs) = 5. • PennCNV confidential score cut-off = 15, QuantiSNP Log Bayes Factor cut-off = 10, iPattern score cut-off = 1 5552 424 iPattern PennCNV QuantiSNP 140 1086 4432 2224 1140 The number of individual CNVs in the sample is greater than three times the SD from the mean number of individual CNVs for an analysis batch • 3 samples were removed • 5826 CNVs left
- 29. CNVs distribution by length 1257 211 38 9 719 170 21 2 IBD+PC 1807 269 46 16 996 229 31 5 IBD-PC
- 30. Proportion of gene-overlapping CNVs Deletions Duplications IBD-PCIBD+PC 476 135 30 7 781 76 8 2 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <100 kbp 100-500 kbp 500-1000 kbp >1 Mbp genic deletions no genic deletions 415 125 21 2 304 45 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <100 kbp 100-500 kbp 500-1000 kbp >1 Mbp genic duplications no genic duplications 567 168 31 5 429 61 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <100 kbp 100-500 kbp 500-1000 kbp >1 Mbp genic duplications no genic duplications 671 171 37 14 1136 98 9 2 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <100 kbp 100-500 kbp 500-1000 kbp >1 Mbp genic deletions no genic deletions
- 31. Project chart flow Samples quality control CNV calling PennCNV, iPattern, QuantiSNP Samples quality control (with number of samples removed on every qualifier) • Call rate < 95% (3 samples) • Sample relatedness (3 samples) • Sex inconsistencies (4 samples) • Population outliers (18 samples) • Samples with SD of LRR greater than three times the SD from the mean SD of LRR for an analysis batch (2 samples) • Samples with SD of BAF greater than three times the SD from the mean SD of BAF for an analysis batch (3 samples) Samples genotyping CNVs quality controlCNV quality control • Only one algorithm • <5 consecutive SNPs • <5 kb in length • PennCNV confidential score cut-off < 15, QuantiSNP Log Bayes Factor cut-off < 10, iPattern score cut-off < 1 CNV-based sample quality control The number of individual CNVs in the sample is greater than three times the SD from the mean number of individual CNVs for an analysis batch (3 samples were removed) 269 samples 243 samples 246 samples Stringent CNV calls detecting Analysis of association of genes affected by CNV with PC in IBD.
- 32. Fisher’s Exact Test and Bonferroni’s method of FDR correction for p-value adjustment Analysis of association of genes affected by CNV with PC in IBD Genes overlapped by at least one CNV in case or control (7087 genes) Compare the numbers of deletion or duplications overlapped each gene in IBD+PC and control populations Compare the numbers of deletion or duplications overlapped each gene in IBD-PC and control populations 4364 genes overlapped by deletions 4440 genes overlapped by duplications
- 33. Fisher’s Exact Test and Bonferroni’s method of FDR correction for p-value adjustment Analysis of association of genes affected by CNV with PC in IBD Overlapped by Deletions Overlapped by Duplications Compare the numbers of deletion or duplications overlapped each gene in IBD+PC and control populations Genes: 54 Loci: 16 Genes: 7 Loci: 5
- 34. Fisher’s Exact Test and Bonferroni’s method of FDR correction for p-value adjustment Analysis of association of genes affected by CNV with PC in IBD Overlapped by Deletions Overlapped by Duplications Compare the numbers of deletion or duplications overlapped each gene in IBD+PC and control populations Compare the numbers of deletion or duplications overlapped each gene in IBD-PC and control populations Genes: 22 32 19 Loci: 3 13 17 Genes: 3 4 13 Loci: 2 3 7
- 35. CNV-overlapped genes, associated with IBD+PC Gene symbol IBD+PC Control Odds Ratio (95% CI) Padj deletions 8p23.1 (chr8:7242715-7881478) FAM90A7P, FAM90A10P, DEFB107A, DEFB105A, DEFB106A, DEFB104A, SPAG11A 5/97 (5.2%) 6/2988 (0.2%) 26.9 (6.4-107.7) 4.8×10-2 DEFB103A, DEFB4A, ZNF705B 5/97 (5.2%) 5/2988 (0.17%) 32.3 (7.3-142.7) 2.7×10-2 FAM66E, USP17L8, USP17L3 5/97 (5.2%) 1/2988 (0.03%) 161.4 (17.8-7250.9) 7.1×10-4 10p11.1 (chr10:38675824-38995349) LOC399744 6/97 (6.2%) 4/2988 (0.13%) 48.8 (11.4-238.1) 6.9×10-4 15q11.2 (chr15:21974835-22585470) CXADRP2, POTEB, NF1P2 7/97 (7.2%) 9/2988 (0.3%) 25.6 (7.9-79.1) 9.7×10-4 LOC727924, OR4M2, OR4M4 8/97 (8.2%) 24/2988 (0.8%) 11.1 (4.2-26.4) 1.8×10-2 OR4N3P 8/97 (8.2%) 21/2988 (0.7%) 12.7 (4.7-30.8) 7.8×10-3 REREP3 7/97 (7.2%) 9/2988 (0.3%) 25.6 (7.9-79.1) 9.7×10-4 Duplications 1p36.11 (chr1:25598276-25659509) RHD 7/97 (7.2%) 4/2988 (0.1%) 57.6 (14.4-273.7) 3.2×10-5 1p13.3 (chr1:110221506-110234286) GSTM1, GSTM2 4/97 (4.1%) 0/2988 (0%) Inf (20.8-Inf) 4.1×10-3 Green are pseudogenes and RNA genes, purple are protein coding genes with unknown functions
- 36. Genes associated with IBD+PC Reactome GO Molecular functions GO Biological Process KEGG Node shape represents gene set source Node size is proportional to the size of the functional gene set The functional network of CNV-overlapped genes, associated with IBD+PC β-Defensins (n=6) OR4M2 OR4N4 GSTM1 GSTM2 RHD Metabolism Transport Olfactory signaling Antibacterial response
- 37. The functional network of CNV-overlapped genes, associated with IBD+PC β-Defensins (n=6) OR4M2 OR4N4 GSTM1 GSTM2 RHD Metabolism Transport Olfactory signaling Antibacterial response
- 38. Summary • PC is significantly more frequent among female CD patients. • Frequency and proportion of CNVs in the groups of IBD patients with and without PC is similar. • 25 CNV-overlapped genes were associated with PC in IBD. • These genes are functionally related to the immune system, metabolic and transport activity, and to the perception of smells. • The RHD gene is involved in the Dopamine and Serotonin transmembrane transport.
- 40. Future directions: The influence of diet on the genetic risk of psychiatric comorbidity in inflammatory bowel disease PCIBD Dietary factors: Sugar intake Specific food avoidance
- 41. Future directions: Exploring association between host genetics and microbiome in pediatric Crohn’s disease Microbiome changes Genetic factors
- 42. Acknowledgements The Centre for Applied Genomics Bhooma Thiruvahindrapuram John Wei Stephen W Scherer Department of Biochemistry and Medical Genetics and The George and Fay Yee Centre for Healthcare Innovation Pingzhao Hu Qin Kuang IBD Clinical and Research Centre Charles N Bernstein Michael Sargent Department of Biochemistry and Medical Genetics and Molecular Diagnostic Laboratory, Diagnostic Services of Manitoba Elizabeth Spriggs Division of Biostatistics Wenxin Jiang Dr. John Wilkins, HSC's Director of. Research
- 43. IBD+PC IBD-PC CD: UC: 8p23.1 deletion: Samples characteristics Gene symbol IBD+PC Control Odds Ratio (95% CI) Padj FAM90A7P, FAM90A10P, DEFB107A, DEFB105A, DEFB106A, DEFB104A, SPAG11A 5/97 (5.2%) 6/2988 (0.2%) 26.9 (6.4-107.7) 4.8×10-2 DEFB103A, DEFB4A, ZNF705B 5/97 (5.2%) 5/2988 (0.17%) 32.3 (7.3-142.7) 2.7×10-2 FAM66E, USP17L8, USP17L3 5/97 (5.2%) 1/2988 (0.03%) 161.4 (17.8-7250.9) 7.1×10-4 F M M F F MF
- 44. IBD+PC IBD-PC CD: UC: 10p11.1 deletion: Samples characteristics Gene symbol IBD+PC Control Odds Ratio (95% CI) Padj LOC399744 6/97 (6.2%) 4/2988 (0.13%) 48.8 (11.4-238.1) 6.9×10-4 F MF F F F F F F M M
- 45. IBD+PC IBD-PC CD: UC: 15q11.2 deletion: Samples characteristics Gene symbol IBD+PC Control Odds Ratio (95% CI) Padj CXADRP2, POTEB, NF1P2 7/97 (7.2%) 9/2988 (0.3%) 25.6 (7.9-79.1) 9.7×10-4 LOC727924, OR4M2, OR4M4 8/97 (8.2%) 24/2988 (0.8%) 11.1 (4.2-26.4) 1.8×10-2 OR4N3P 8/97 (8.2%) 21/2988 (0.7%) 12.7 (4.7-30.8) 7.8×10-3 REREP3 7/97 (7.2%) 9/2988 (0.3%) 25.6 (7.9-79.1) 9.7×10-4 F MM F F F M M M MMM M M
- 46. IBD+PC IBD-PC CD: UC: 1p36.11 duplication: Samples characteristics Gene symbol IBD+PC Control Odds Ratio (95% CI) Padj RHD 7/97 (7.2%) 4/2988 (0.1%) 57.6 (14.4-273.7) 3.2×10-5 F F F F MF F F F M M
- 47. IBD+PC IBD-PC CD: UC: 1p13.3 duplication: Samples characteristics Gene symbol IBD+PC Control Odds Ratio (95% CI) Padj GSTM1, GSTM2 4/97 (4.1%) 0/2988 (0%) Inf (20.8-Inf) 4.1×10-3 F MF M M M
Editor's Notes
- Hello My name is Svetlana Frenkel I am postdoctoral researcher, working under supervision of Dr Pingzhao Hu from the department of Biochemistry and Medical Genetics and Dr Charles Bernstein, the director of the IBD Clinical and Research Center. Today we will talk about the role of copy number variations in the development of psychiatric comorbidity in the patients with inflammatory bowel disease.
- Inflammatory bowel disease, also known as IBD, is one of the immune-mediated inflammatory diseases. IBD and its complications have a severe impact on a patient’s quality of life. This disease is also associated with high costs for diagnosis and treatment. There are two main categories of IBD: Crohn's disease and Ulcerative colitis Crohn's disease can affect any part of the intestines, and usually involves trAnsmural inflammation. Ulcerative colitis is usually limited to the colonic mucosa. Both variants of the disease are characterized by periods of symptomatic relapse and remission.
- Higher rates of IBD are seen in industrialized countries, with greater prevalence among Caucasians, but over the last few decades many more cases were found in developing countries.
- It was observed, that people with chronic diseases, such as immune-mediated inflammatory diseases have high risk of psychiatric disorders. In particular, the prevalence of depression and anxiety in persons with IBD is much higher than in the general population. For illustration here I used the observations that were made using the data from the Manitoba Centre for Health Policy, covering almost thirty-year period. In summary, groups of individuals with immune-mediated inflammatory diseases were compared with matched controls. More than six thousands of these people had IBD. High frequency of depression, anxiety, bipolar disorders and schizophrenia was found in IBD population.
- The likely mechanisms of this association are related to the interactions between gut and brain. These interactions are known as gut-brain axis and include neural, hormonal and immune communication links.
- How the IBD can provoke the psychiatric comorbidity? Reduced quality of life, surgeries and health anxiety can trigger depression and anxiety disorders in predisposed persons. Diet changes, such as high sugar intake, and microbiome dysbalance can impact the psychiatric condition. In addition, treatment with steroids is a risk factor for the development of psychiatric comorbidity. The majority of the studies suggest that disease activity is the key factor of depression and anxiety, and not the IBD itself or IBD subtypes. This influence is also supported by good response of psychiatric comorbidity in IBD to non-steroid anti-inflammatory treatment, such as anti (TNF) alpha, therapy with immunomodulators or probiotics. However, according to many studies, large part of IBD patients report a negative impact of the disease to their life and mood even between flare-ups.
- In opposite, It was found in a few studies including study conducted in our University that in many cases depression aroused one or more years before IBD. It also was found that patients with PC in IBD tend to have earlier IBD onset. In the periods of remission psychiatric disorders increases the risk of relapses. And in addition, good effect of placebo and success of altErnative medicine treatment of IBD support the important role of central nervous system in the disease.
- Genetics is only one of the possible causes of IBD. Multiple environmental factors have been linked to the disease. Currently, the development of IBD is explained by imbalanced interactions between gut microbiota and human immune system. This imbalance can be related to genetic factors and to environmental influences on the gut microbes and human body. In addition, there are evidences, that the environmental factors, such as diet, vitamin D, physical activity, stress and sleep, play a role in the development of depression and anxiety disorders. Microbiome changes were linked to stress in the mice models. Stress also increases intestinal permeability, that allows bacteria to cross the epithelial barrier and to activate the immune response.
- Overall, genetic variation can be divided into different forms based on their size and type. Small-scale sequence variations include base-pair substitutions (or SNPs) and indels. Large-scale sequence variations can be either copy number variation (loss or gain), or chromosomal rearrangement (also named translocation). Copy number variations normally cover about 12% of the human genome. Altered number of gene copies can affect gene expression.
- Today, one hundred and twenty genetic variants are linked to IBD in ClinVar database. Twenty four of them are pathogenic or likely pathogenic. For psychiatric disorders these numbers are larger. Today you can find three hundreds variants associated only with depression or anxiety disorders in ClinVar. Almost half of these variants are copy number variations, and almost half of these variants are pathogenic. Many genetic variants linked to IBD or to Psychiatric disorders are not included in ClinVar database.
- In another large curated database, G-WAS catalog, we can find many SNPs significantly associated with both categories of disorders, and many genes reported. Here I included only SNPs and genes linked to depression, anxiety and bipolar disorders.
- One of the world famous centers of IBD research is here, in the University of Manitoba. The Manitoba IBD Cohort Study was initiated in twenty ou two. Three hundreds eighty eight IBD patients were enrolled and followed with annual interviews and surveys. The goal of the Manitoba IBD Cohort Study is to determine predictors of outcomes and to optimize the disease treatment. The Manitoba IBD Cohort Study is one of the few follow up IBD studies in the world conducted over several years with very low drop-off level. Besides, The Manitoba IBD Cohort Study has the most detailed patients data, including their mood conditions, evaluated few times during the follow-up period.
- The detailed data collected during a few years allowed to perform numerous research projects, related to the IBD risk factors, comorbidity and different types of outcomes. Many risk factors have been studied, including genetic and family predisposition, medication and vaccination, diet and microbiome changes, stress, smoking and hygiene. Many kinds of complications, comorbidities and outcomes were also explored, such as risk of osteoporosis, cancer and psychiatric comorbidity.
- This project is a part of the Manitoba IBD research. The aim of the project is to define the relationships between CNVs and psychiatric comorbidity in IBD individuals
- The main steps of the project includes: data collection and genotyping, with corresponding sample quality control, CNV detection by three different algorithms and quality control, detection of stringent CNV, with an additional sample quality control, and Analysis of statistical association of CNV affected genes with PC in IBD.
- So, first I will talk about samples, genotyping and quality control.
- For our study, we genotyped blood samples from two hundreds sixty nine individuals from the Manitoba IBD Cohort Study. As controls, we used almost three thousand samples from healthy individuals from two different population-scale studies. High-resolution SNP array platforms were used in both cases. Quality control at this point was related mostly to genotyping quality. Genetically related samples and population outliers were also removed.
- I want to explain now why the population outliers should be removed from the data set. Different populations, such as races and distant ethnical groups were physically separated during large periods of time. Mating was limited by population during many generations. And this led to accumulating of some genetic alleles in the population. Now, if two populations are different by any allele frequency and by prevalence of any disease on the same time, this allele can be associated with disease by mistake. To reduce the influence of ethnicity-related genetic alleles we conducted population stratification analysis and removed eighteen population outliers from IBD population.
- Depression and anxiety disorders are widely reported to be more frequent in women than in men. Likewise, sex biases have been observed in the susceptibility-to and the severity of chronic inflammatory disorders. The molecular and cellular mechanisms responsible for these sex-specific differences both in immune responses and in depression and anxiety disorder are not completely understood. We divided IBD population onto four groups by sex and IBD type and compared the frequency of PC between male and female groups of patients with CD and between male and female groups of patients with UC. It was found, that among CD patients PC is significantly more frequent in females. In UC patients such bias was not observed. This result support recent discoveries of other research groups.
- We also analyzed frequency of PC in the groups of the same-sex patients with different IBD types. No significant difference was observed in this case.
- Now let’s talk about the method of detection of the copy number variations from the microarray genotypes.
- Microarray technologies are based on binding of small fragments of tested sequence with fluorescent labels to the probes fixed on the solid surface – a microarray chip. Fully complementary strands bind strongly to the fixed probes and form the fluorescent signal in the certain position of the chip. Partially complementary strands bind weakly and washed out during the preparation. Then the intensity of the fluorescent signals on the chip Can be analyzed using special scanners. This technology is represented mainly by array comparative genomic hybridization and SNP microarrays.
- Array comparative genomic hybridization platforms or Array CGH - are based on the principle of comparative hybridization of two labelled samples, test and reference. Difference in fluorescence intensity, or log ratio, corresponds to copy number gain or loss in comparison to the reference genome. Array CGH allows detection of copy number changes on a genome wide and high-resolution scale.
- SNP array technology is based on the discrimination between the two possible for a specific position SNP alleles, A or B. Every SNP is presented by two spots, one for allele A, and another for allele B. The tested sequence is labelled by fluorescent dyes, and fragments of this sequence bind strongly only to one of two allele-specific spots. The total fluorescent intensity is compared to the reference genomes collection, or to the intensity observed in the rest of the tested population. B allele frequency is calculated in addition to the intensity ratio, and used as measure of the allelic intensity ratio of two alleles, A and B.
- You can see here the scheme of typical signals observed in different copy number states. The Log ratio values from the array CGH are presented on the top. The Log ratio and B allele frequency values from the SNP array are presented on the bottom. Every point corresponds to the microarray probe along the chromosome. The log ratio from the SNP array is more noisy than the ratio from the array CGH. B allele frequency of 0 represents the genotype A, B allele frequency of 1 represents the genotype B, And B allele frequency of 0.5 represents the genotype A/B. Normally, the Log ratio values are close to zero, and three levels of B allele frequency are presented. In the case of copy number loss, log ratio level is low and only alleles A and B are presented, or B allele frequency is not clustered. In the case of copy number gain, log ratio level is high, and additional levels of B allele frequency values are presented.
- There are many software products that can be used for CNV detection. For our project we used three of them: QuantiSNP, Penn CNV, And iPattern These algorithms are based on different computational methods of the copy number variation detection from Log ratio alone, together with B allele frequency or using additional data, such as population B allele frequency and genetic distance between SNP probes.
- So, three algorithms were applied for CNV detection. After removal of small CNVs with low confidence scores we had almost fifteen thousand CNVs in summary.
- We removed CNVs detected by only one algorithm and kept about 7 thousands stringent CNVs, which were detected by two or three different algorithms. Then we performed additional sample quality control based on the number of CNV in the sample. We removed three samples with very large amount of CNV. Finally, almost six thousands CNVs in two hundred forty three samples left for analysis.
- We found more deletions than duplications in both groups of IBD patients with and without psychiatric comorbidity. There were more short CNVs than medium and long variations in both groups. But the proportion of CNVs of different length in two disease groups was similar.
- In average, about half of these CNVs overlapped one or more genes. The proportion of genic CNVs was higher for long CNVs and lower for short ones. But still, there were not significant difference between two diseases groups on the proportion of genic CNVs.
- On the next step, we analysed the association of CNV-affected genes with IBD.
- To do that, we selected more than seven thousand genes, overlapped by at least one CNV in case or control populations. For each gene, we compared the number of overlapping CNVs in the group of IBD patient with PC with the number of overlapping CNVs in control population. This comparison was made separately for deletions and duplications. Then, we compared the number of CNVs spanned over each gene in IBD patient without depression with this number in control. Again, it was made separately for deletions and duplications.
- From the comparison between IBD-with-PC and control groups we have sixty one genes which passed the significance threshold. Fifty four genes are overlapped by sixteen deletions and seven genes are overlapped by five duplications.
- The results of second comparison was used as the additional control. We detected the genes, passed the significance threshold in the comparison of IBD-without-PC and control groups. Then we removed these genes from the lists of genes, detected in the first comparison. This way we have the list of CNV-overlapped genes, associated with IBD-with-PC, but not associated with IBD-without-PC. These are twenty two genes, overlapped by three deletions and three genes, overlapped by two duplications. These five deletions and duplications significantly overrepresented in the group of IBD patient with psychiatric comorbidity.
- These genes, including pseudogenes, are presented in this table. Fifteen protein coding genes are well investigated and linked to different molecular functions or disorders. As you can see, two of three deletions overlapped large groups of genes. First group of genes includes six members of beta-defensins gene family. These genes have been linked to IBD in many studies. In the second group of genes, some annotations are available only for two genes encoding olfactory receptor proteins. These genes are members of a large family of G-protein-coupled receptors. All three genes overlapped by duplications are well annotated. There are two genes encode mu-type of glutathione-transferases, functionally involved in the numerous metabolic processes. And this is the gene encodes Rh Blood Group D Antigen, highly immunogenic protein on the surface of red blood cells. But also, this gene is expressed in other tissues and functionally related to the transmembrane transport.
- This network shows the functional gene sets connected to two lists of genes associated to IBD with PC. The gene lists are displayed by these triangle nodes. Other nodes represent gene sets – groups of genes, linked to the same function, or disease, or any condition. These gene set were obtained from four functional gene set libraries: Gene ontology biological processes, gene ontology molecular functions, KEGG and Reactome pathways. The gene sets from different libraries are presented by nodes of different shapes. The node size represent the number of genes in gene set. Gene sets smaller than ten genes and larges then one thousand genes were removed. The connection between nodes here represent one or more CNV-overlapped genes present in both gene sets. Overall, ninety two gene sets were linked to gene lists by 11 genes, and other genes and pseudogenes were not found in these libraries.
- Of all functional gene sets described in these gene set libraries, only two can link out findings to the development of psychiatric comorbidity. According to the Gene-ontology-molecular-functions-database, the RHD gene is involved in the Dopamine and Serotonin transmembrane transport.
- In conclusion, I want to summarize our findings: We found that psychiatric comorbidity in the patients with Crohn’s disease is significantly more frequent in women. Overall Frequency and proportion of CNVs in the groups of IBD patients with and without PC is similar. We found twenty five genes overlapped by five CNVs and associated with PC in IBD. These genes are functionally related to the immune processes, metabolic and transport activity, and to the smell perception. The RHD gene is involved in the Dopamine and Serotonin transmembrane transport.
- Our results demonstrate that genetic predisposition to PC in IBD can involve more than one cellular function and is related to many genes. This situation is very similar to other immune-related diseases and other psychiatric disorders, where genetic predisposition is explained by many genes with weak phenotype effect. The possible accumulation of weak gene effects can lead to the total loss of any cellular function. It also possible, that loss of one cellular function is still not enough to the disease development. And finally, it is possible, that effect of some genes and function losses can be seen only under certain environment conditions. Further analysis will be performed using joint association methods to find the possible combinations of genes with weak effect. This analysis can help to reveal the gene-gene and gene-environment interactions.
- We are planning to continue this project and include the information about patients’ dietary habits as environmental factor, that may modify the genetic risk of psychiatric comorbidity. For example, high sugar intake has been associated with both conditions in previous studies. We will study, how the sugar intake level interacts with CNV-affected genes in IBD patient with or without depression or anxiety disorders.
- Another research related to gene-environment interactions in IBD is the project, planned and already funded by Children’s Hospital Research Institute. We are going to explore the association between host genetics and microbiome in pediatric Crohn’s disease. The project involves the simultaneous analysis of patients’ genetic features and the characteristics of their intestinal microbiome using advanced machine-learning methodology.
- To finish my presentation, I want to thank my supervisors, Dr. Pingzhao Hu and Dr. Charles Bernstein, We all also want to thank Dr. John Wilkins, the HSC's Director of. Research, for his great support and all my collaborators from the University of Manitoba, University or Toronto and SickKIds for great help. Thank you.