GWAS of HCC Proposed Statistical Approach Mendelian Randomization and Mediation Analysis. Chris Amos Manal Hassan Lewis Roberts Donghui Li

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1 GWAS of HCC Proposed Statistical Approach Mendelian Randomization and Mediation Analysis Chris Amos Manal Hassan Lewis Roberts Donghui Li

2 Overall Design of GWAS Study Aim 1 (DISCOVERY PHASE): To genotype ~700K SNPs and >240K functional exonic SNPs using the Illumina Human OmniExpress and ExomeChip in 1715 HCC patients and 3254 controls. Aim 2 (VALIDATION PHASE): To replicate the top SNPs identified from Specific Aim1 in an independent sample of 3000 HCC cases and 3000 controls pooled from 18 U.S. institutions. Aim 3 (EXTRA VALIDATION): To validate the significant SNPs related to genetic susceptibility of HCV-induced HCC in NCIfunded follow-up study.

3 Previous Study HBV + HCC GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers PLoS Genet 8(7): e We discovered two novel associations at rs (HLA- DQA1/DRB1) on 6p21.32 (OR = 1.30, P = ) and rs (GRIK1) on 21q21.3 (OR = 0.84, P = )

4 Previous Study of HCC HCV+ Genome-wide association study identifies a susceptibility locus for HCV-induced hepatocellular carcinoma Nature Genetics 43, (2011) Analyzed 673 cases and 2,596 controls. We found a previously unidentified locus in the 5 flanking region of MICA on 6p21.33 (rs , P combined = , odds ratio = 1.39) Affects MICA expression

5 Genetic Influences our Design can Interrogate HCV-HCC HCV+HCC A B G C D HCV- healthy controls E HCV+ No cirrhosis No HCC HCV+ Cirrhosis No HCC [A] HCC in absence of HCV infection, [B] HCV-related HCC), [C] HCC in patients with chronic HCV infection, [D] Cirrhosis and HCV to HCC [E] HCV infection risk [F] risk for Cirrhosis from HCV [G] HCC +/- (HCV, Cirrhosis) F

6 Sources of Samples

7 Proposed sample sizes and ORs

8 Experience of Oncoarray Failures Figure 1. Failure rates (<95% of SNPs called) for 441,202 samples genotyped by CIDR across multiple tissue types. The overall failure rate was 3.17%

9 Oncoarray Consortium Collaboration Cluster file, lists of excluded SNPs, methods for QC, ancestry calculation, imputation shared on the Oncoarray wiki User: onco Password: gameon Google Groups: groups.google.com/forum/#!forum/oncoarry

10 SNP QC Exclusions QC exclusions from Cambridge data (plus Lung) were combined to create a single list of failed SNPs. Exclusion Reason Excluded SNPs Duplicate SNPS - sequence and position same Best SNP retained 765 Zeroed_by_v2c_cluster - No Genotype calls 5011 Call_rate_all_consortia below 95% 6701 HWE_all_consortia 1994 Duplicate_concordance below 98% (combined score from up to 5250 duplicates) 766 Failed_Cluster_Plot Call rate < 95% or HWE fails in one dataset 7092 TOTAL 38855

11 SNP Exclusions from Imputation Additional SNPs were excluded from the imputation input files Genotypes will still be analysed - will also possibly have imputed calls for same SNPs Imputation exclusions reason Number of SNPs Rare SNPs (MAF <1% Europeans) and call rate below 98% 3163 Frequency differs from expected frequency in 1000 Genomes 5928 Non-ideal cluster plot - possible CNV or ethnic specific effects 1131 Duplicate position - (not handled by Impute software) Also 14,608 Oncoarray QC ed SNPs unmatched to a Phase G variant (3,292 do match to a Phase 1 variant) Most of rest are rare but still 1,699 of those have frequency > 5% in BCAC Included in Impute files as Type 3 variants in theory missing calls are not imputed

12 SNP QC Exclusions 22,867 cluster plots were checked for a second time, 18,244 scored as failed SNP call rate below 98% is a strong indicator of poor SNPs cluster plot checking removed ~75% of these Exclusions/ Inclusions by Call Rate Call Rate Excluded SNPs 95% % % % % 8071

13 SNP QC Exclusions CNV effects Copy number variable regions often cause SNPs to be excluded e.g. of 17,469 Oncoarray SNPs in known CNV Regions for Europeans 38% excluded CNV Regions from stringent map from Zarrei et al (Nature Genetics March 2015)- Combined most reliable entries from sequencing and chip studies in Database of Genomic Variants

14 SNP QC Exclusions by nomination category Oncoarray SNPs either came from the Illumina HumanCore GWAS backbone of common SNPs or were nominated by one or more sites. All SNPs By nomination type: HumanCore Backbone Breast Cimba Ovary Prostate Colorectal Lung Total SNPs Failed QC Failure Rate 7.3% 3.0% 6.3% 7.3% 8.5% 17.2% 15.7% 8.4% Nomination category Oncoarray SNPs QC Exclusions % Exomechip % Candidate list % Rare-variant lists % Fine-Mapping %

15 Analytical plan Perform standard GWAS using log-additive models or trend tests adjusting for eigenvectors Perform Imputation to 1000 genomes (and additional samples at Univ Michigan server) Use PanCan for additional controls (may not be done by University of Washington) Select top SNPs and those tagging candidates for replication with Illumina iselect (Golden Gate has been discontinued), or seek an extension of existing CIDR application for additional genotyping of samples Perform Mendelian Randomization and Mediation

16 Mendelian Randomization: Background Use Genotype as instrumental variants when it is Strong predictor of the exposure (X) No direct effect on the outcome (Y) G Independent from the unobserved confounders (due to Mendel s Law) X Z Measured Confounder U Y E [ Y X ] = E[ Y E[ X G] G] E[X G] can be from prior study Indirect association assumed to be mediated completely through X Homogeneity of exposure effect X on phenotype risk Y

17 Current activities in cancer and MR GAME-ON work based on existing GWAS data e.g. Telomere, BMI, height, lipid traits, glycemic traits, Vit D, etc Systematic large scale analysis based on twosample MR on risk factors for lung, kidney, prostate, head and neck cancers Methodological developments in MR

18 Systematic Two-sample Mendelian Randomization: Telomere length example Design of instruments Search NHGRI GWAS catalogue 15 January SNPs identified Validation (GWAS of telomere length (N~10,000) Instruments 17 SNPs 22 SNPs excluded (not associated with telomere length) SNP lookups public databases SNP lookups non-public databases In silico lookups in 25 public databases 130 disease studies GWAS catalogue 32 diseases 656 nondisease traits 71 disease GWAS included Outcomes 81 diseases 656 nondisease traits 369,184 cases 864,564 controls 59 excluded (did not respond / declined) Exclusion of duplicates Exclusion of studies with <200 cases Haycock, Smith, Bristol group

19 Systematic Two-sample Mendelian Randomization: Telomere length example No. of Cancer cases Head & neck cancer 7000 Squamous cell lung cancer 3275 Follicular lymphoma 212 Breast cancer Ovarian cancer Ovarian cancer (serous invasive) 8371 Prostate cancer Non-Hodgkin lymphoma 253 Upper gastrointestinal cancers 3523 Nasopharyngeal carcinoma 1860 Colorectal cancer Lung cancer Kidney cancer Testicular germ cell cancer 985 Melanoma Chronic myeloid leukemia 201 Pancreatic cancer 1896 Lung adenocarcinoma 3442 Neuroblastoma 2101 Thyroid cancer 649 Ovarian cancer (serous LMP) 979 Bladder cancer 7712 Multiple myeloma 4692 Chronic lymphocytic leukemia 2883 Glioma 692 Autoimmune diseases Celiac disease Sarcoidosis Wegeners granulomatosis Juvenile idiopathic arthritis Rheumatoid arthritis Psoriasis Ulcerative colitis Multiple sclerosis Crohn's disease Amyotrophic lateral sclerosis Periodontitis Systemic lupus erythematosus Vitiligo Kawasaki disease Cardiovascular diseases Coronary heart disease Large vessel ischaemic stroke 2167 Ischemic stroke Cardioembolic ischemic stroke 2365 Small vessel ischaemic stroke 1894 Heart failure 2526 Psychiatric / neurological diseases Alzheimer's Parkinsons Schizophrenia Autism Major depressive disorder Bipolar disorder Anorexia Nervosa Lung disease Interstitial lung disease Idiopathic pulmonary fibrosis Other diseases Endometriosis Chronic kidney disease Type 2 diabetes Paget's disease Odds ratio (95 % CI) per 650 base pair increase in telomere length P heterogeneity < NA.12 < < NA NA NA < < <

20 Association of traits curated by the GWAS catalog with risk of lung cancer in 11,000 cases and 26,000 controls* 1 1. smoking behaviours 2. mean corpuscular haemoglobin 3. hematology traits 4. mean corpuscular volume telomere length *estimated by two-sample Mendelian randomization, using summary genetic data from ILCCO/TRICL and the GWAS catalog

21 Correction for pleiotropy: MR Egger regression Pleiotropy is an increasing concern with increasing number of variants identified for each trait Based on the Egger regression, the pleiotropy effect can be expressed as an intercept, while the slope remains valid for MR

22 Baron and Kenny Steps of Mediation Analysis Show X is correlated with Y (path c): Y = cx + ε X c Y Show X is correlated with M (path a): M = ax + ε a M b Show M affects Y controlling for X (path b): Y = bm + c X + ε X c Y Assess the effect of X on Y controlling for M (path c ): Complete mediation: path c is zero Partial mediation: path c is not zero X: initial variable Y: outcome variable M: mediator c: the total effect of X on Y c : the direct effect of X on Y ab: the indirect effect of X on Y

23 Measuring Mediation We are interested in testing the indirect effect ab H 0 : ab = 0 vs H 1 : ab 0 Sobel test: Z Sobel = b 2 s ab 2 a + a 2 s 2 b Where, s a is the standard error of a and s b is the standard error of b. Note: the coefficients obtained from logistic regressions need to be standardized before the Sobel test is applied.

24 Comments on Mediation Mediator must be causal to the outcome and not the other way around. Mediation analyses is model-dependent. Mediation analyses used to understand the mechanism through which the initial variable affects the outcome. Some argue that association between outcome and mediator should not use initial variable. This is okay only when one has complete mediation. The coefficients obtained from the logistic regression were standardized to make them comparable before applying the Sobel test.

25 Measuring Mediation We are interested in testing the indirect effect ab H 0 : ab = 0 vs H 1 : ab 0 Sobel test: Z Sobel = b 2 s ab 2 a + a 2 s 2 b Where, s a is the standard error of a and s b is the standard error of b. Note: the coefficients obtained from logistic regressions need to be standardized before the Sobel test is applied.

26 Model 1: Mediating Effect of PKYRS on the Relationship of the SNP and Lung Cancer The indirect effect of the SNP on lung cancer risk is significant: SNP c= (p<0.0001) Lung Cancer ab = , p = The PKYRS only explains a small portion of the effect of the SNP on lung cancer risk (7.6%). a= (p=0.0009) PKYRS b= (p<0.0001) The SNP rs is directly associated with lung cancer, and also associated with lung cancer through its effect on smoking. SNP p: p value c = (p<0.0001) Lung Cancer

27 Conclusions GWAS data will provide a useful avenue for discovering variations associating with liver cancer risk Collecting and harmonizing epidemiological data provides an opportunity for evaluate relevance of risk factors on liver cancer risk Performing Mendelian randomization provides insights into the relevance of risk factors and removes concerns about confounding, but may suffer from decreased power if effects on predictor measured in a different study population Mendelian randomization only works if a risk factors is influenced by genetic factors Mediation analysis provides further insights into the relationship between SNPs or other risk factors, intermediate risk factors and outcomes Newer approaches to mediation analysis allow for case/control sampling and will have to be used to allow for potential biases