Agrawal, Arpana

al., et

Anokhin, Andrey

Barr, Peter B.

Johnson, Emma C.

English

Digital Commons@Becker

Using Polygenic Scores for Identifying Individuals at Increased Risk of Substance Use Disorders in Clinical and Population Samples  Supplemental Information    2 1.	GENOTYPING	AND	QUALITY	CONTROL	 3	COGA	 3	FinnTwin12	 3	2.	PROBLEMATIC	ALCOHOL	USE	META	ANALYSIS	 4	3.	CALCULATING	AUC	FOR	PRS	 8	4.	PREVALENCE	OF	AUD	ACROSS	PRS	IN	COGA	 9	5.	RESTRICTING	AGE	RANGE	IN	COGA	 10	REFERENCES	 12	   3 1. Genotyping and Quality Control  COGA  Participants from the Collaborative Study on the Genetics of Alcoholism (COGA) 1, 2 were identified through alcohol treatment programs at six U.S. sites and were invited to participate if they had a sufficiently large family (usually sibships > 3 with parents available) with two or more members in the COGA catchment area. The Institutional Review Boards at all sites approved this study and written consent was obtained from all participants. Genotyping of the COGA samples was conducted across different phases of data collection. European ancestry (EA) samples were genotyped at multiple sites, including: (1) Center for Inherited Disease Research using the Illumina HumanHap1M array 3; (2) Genome Technology Access Center at Washington University School of Medicine using the Illumina OmniExpress 4; and (3) Rutgers University using the Affymetrix Smokescreen array 5. All A/T and C/G SNPs were removed and a common set of ~47,000 SNPs were used to assess duplicate samples and revise the reported pedigree structure. Family structures were altered as needed, and SNP genotypes were tested for Mendelian inconsistencies 6 with the revised family structure. Genotype inconsistencies were set to missing.  Data were imputed to 1000 Genomes (Phase 3, version 5) using SHAPEIT 7 and then Minimac3 8. Samples were imputed separately due to different variant contents on each array. In addition, the two datasets genotyped on the Smokescreen genotyping array were also imputed separately, due to different processing pipelines used by the genotyping laboratory. Prior to imputation, variants with missing rates > 5%, MAF < 3% and HWE p values < 0.0001 were excluded, resulting in a total of 26,124,746 variants. Principal components were computed from GWAS data using Eigenstrat 9 and 1000 Genome reference panel. Individual ancestry was assigned using the YRI, CEU, JPT and CHB populations to set reference points. Genotypic data are available via controlled access on the Database of Genotypes and Phenotypes (dbGaP) through NIH (dbGaP; phs000125.v1.p1; phs000763.v1.p1; and phs000976.v1.p1).  FinnTwin12   Twins enrolled into Finntwin12 were born between 1983 and 1987, identified through Finland’s Central Population Registry (CPR). Ascertainment was exhaustive, and twins in all pairs with both twins alive, resident in Finland and enrolled in normal public schools, were invited to participate. The Helsinki University Central Hospital District’s Ethical Committee and Indiana University’s Institutional Review Board approved the FinnTwin12 study. Genotyping was conducted using the Human670-QuadCustom Illumina BeadChip at the Wellcome Trust Sanger Institute 10. Quality control steps included removing SNPs with minor allele frequency (MAF) <1%, genotyping success rate <95%, or Hardy–Weinberg equilibrium p < 1 × 10−6, and removing individuals with genotyping success rate <95%, a mismatch between phenotypic and genotypic gender, excess relatedness (outside of known families), and heterozygosity outliers. Genotypes were imputed to the 1,000 Genomes Phase 3 reference panel 11 reference panel using ShapeIT 12 for phasing and IMPUTE2 13 for imputation, resulting in 13,688,418 autosomal SNPs for analyses. Data are available through the Biobank of the National Institute for Health and Welfare in Finland. See Kaprio et al. 2019 for specifics on data access.   Common HapMap3 SNPs across sample and GWAS  Total # of HapMap3 SNPs in PRS  COGA FT12 RISK PC PRS 1,110,737 1,098,765 GSCAN DPW PRS 1,110,910 1,099,480 PROB ALC PRS 1,094,954 1,083,002      4 2. Problematic Alcohol use Meta Analysis   In order to maximize the predictive power for polygenic scores (PRS) for problematic alcohol use, we meta-analyzed summary statistics from two recent large scale GWAS of problematic alcohol use. The first GWAS was conducted using the problem subscale of the Alcohol Use Disorder Identification Test (AUDIT-P) on ~120k individuals in the UK Biobank. The AUDIT-P is made up of items 4-10 (1-3 reflect consumptions, or AUDIT-C) of the full AUDIT. Scores on individual items range from 0 to 4, resulting in an overall range of 0 to 28 for the AUDIT-P. Items include:  1. How often during the last year have you found that you were unable to stop drinking once you had started?  2. How often during the last year have you failed to do what was normally expected of you because of your drinking 3. How often during the last year have you needed a first drink in the morning to get yourself going after a heavy drinking session?  4. How often during the last year have you had a feeling of guilt or remorse after drinking?  5. How often during the last year have you been unable to remember what happened the night before because you had been drinking? 6. Have you or someone else been injured as the result of your drinking? 7. Has a relative, friend, or a doctor or other health worker been concerned about your drinking or suggested you cut down?   The second GWAS was the Psychiatric Genetics Consortium’s (PGC) GWAS of alcohol dependence (AD) in ~45K individuals (European ancestry results only). This GWAS focused on lifetime diagnosis (or meeting the criteria for lifetime diagnosis on a clinical interview) of AD. As both COGA and FT12 were included in the initial PGC GWAS, we recalculated summary statistics with each cohort removed. The resulting meta-analyses was a GWAS of problematic alcohol use in ~160K individuals.  We first estimate the SNP-based heritability (h2SNP) and genetic correlations (rg) between each of the GWAS (UKB AUDIT-P, PGC AD with COGA removed, and PGC AD with FT12 removed) using LD score regression 15. Table S1 provides the estimates for h2SNP and rg. Both of the  PGC AD GWAS were sufficiently correlated with UKB AUDIT-P to justify meta-analysis.  To combine the results from these two sets of PGC summary statistics with the UKB AUDIT-P GWAS, we utilized a sample size based meta-analysis in METAL 16. In addition to the quality control metrics in the original GWASs, we constrained to SNPS with a MAF > 0.01. Independent SNPs (r2 < 0.1 ) that met genome-wide significance (GWS) for each meta-analysis are presented in Table S2. For the NO COGA META, there were 13 significant SNPs, all on chromosome 4. The significant SNPs for the NO FT12 META were also on chromosome 4, however there was one additional independent SNP. In addition to these top SNPs, the Manhattan plots and QQ plots are available in Figures S1 and S2.  Follow up with the meta-analyzed results in LDSC revealed significant h2SNP in the both meta-analyses with COGA excluded (META NO COGA h2SNP = 0.0565; SE = 0.0039; p = 1.46e-47) and with FT12 excluded (META NO FT12 h2SNP = 0.058; SE = 0.0039; p = 5.02e-50). We see modest inflation in the test statistics (META NO COGA Mean χ2 = 1.1656; META NO FT12 Mean χ2 = 1.174). This genomic inflation appears to be the result of polygenic signal rather than population stratification, as the LDSC intercept is near one for each meta-analysis (META NO COGA Intercept = 1.0105, SE = 0.0065; META NO FT12 Intercept = 1.0108, SE = 0.0064). These GWAS meta-analysis results have a very high genetic correlation (rg = 0.9783, SE = 0.0017)   Table S1: LDSC Estimates for GWAS included in Meta-Analysis   h2 rg (SE)  GWAS h2SNP SE p 1 2 3 1 UKB AUDIT-P 0.0573 0.0050 2.15e-05 - - - 2 PGC (no COGA) 0.0888 0.0209 7.17e-08 0.5869 (0.1229)  - - 3 PGC (no FT12) 0.0975 0.0181 2.10e-30 0.6349 (0.1137)  0.9825 (0.0221)  -     5    Table S2: Top SNPs from Meta-Analysis of PGC and UKB      NO COGA META NO FT12 META CHR POS RSID Nearest Gene Z P MAF Z P MAF 4 100239319 rs1229984 ADH1B -6.77 1.26E-11 0.03 -7.13 9.78E-13 0.03 4 100244221 rs3811802 ADH1B -8.02 1.02E-15 0.47 -8.13 4.18E-16 0.47 4 100252560 rs3114045 ADH1C - - - -5.61 2.08E-08 0.13 4 100262242 rs141973904 ADH1C -5.55 2.87E-08 0.02 -6.40 1.52E-10 0.02 4 100282103 rs4699743 ADH1C -5.97 2.30E-09 0.08 -6.33 2.44E-10 0.08 4 103385336 rs531685993 AF213884.1 5.75 8.89E-09 0.09 - - - 4 99713350 rs144198753 BTF3P13 -9.73 2.33E-22 0.01 -9.73 2.33E-22 0.01 4 39411407 rs62310819 KLB -5.47 4.50E-08 0.20 -5.52 3.38E-08 0.20 4 39413780 rs28712821 KLB 6.59 4.27E-11 0.39 - - - 4 39414993 rs11940694 KLB - - - -6.67 2.62E-11 0.39 4 99941138 rs146788033 METAP1 9.57 1.03E-21 0.02 9.71 2.77E-22 0.02 4 39393801 rs6842066 RNU6-887P 6.00 2.01E-09 0.42 - - - 4 39400998 rs13110790 RNU6-887P - - - 6.74 1.58E-11 0.42 4 99630017 rs193099203 RP11-1299A16.1 -8.08 6.61E-16 0.01 -8.08 6.61E-16 0.01 4 100186847 rs138423208 RP11-696N14.1 6.70 2.16E-11 0.05 6.60 4.20E-11 0.05 4 103198082 rs13135092 SLC39A8 7.77 7.69E-15 0.09 7.92 2.39E-15 0.09 4 103283117 rs34333163 SLC39A8 - - - 6.06 1.33E-09 0.08        6       Figure S1: QQ Plots for UK Biobank and PGC GWAS Meta Analyses QQ plots for observed versus expected p-values in meta analysis results with COGA (left) and FT12 (right) excluded.  7     Figure S2: Manhattan Plots for UK Biobank and PGC GWAS Meta Analyses Manhattan plots of –log10 p-values for SNPS in meta-analysis with COGA (top) and FT12 (bottom) excluded.   8 3. Calculating AUC for PRS    While there is increasing focus on using the extreme ends of the polygenic risk continuum to as a potential way of identifying those at increased risk 17, 18, this method is not without its limitations. One critique, is that even though individuals at the top of the distribution may demonstrate increased risk, this does not mean that PRS are useful for clinical purposes 19. We therefore calculated the area under the curve (AUC) from receiver operating characteristic (ROC) curves as an additional check on any potential utility of PRS for alcohol use disorders (AUD) 20 using the pROC package in R21. ROC curves compare the sensitivity (true positive rate) and specificity (false positive rate) to identify an indicators ability to correctly classify an individual as having a disease/disorder or not.  Where the true positive rate (sensitivity) equals:  𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑒 =  𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 +  𝐹𝑎𝑙𝑠𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠  The false positive rate is therefore equal to:  𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑒 =  𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 +  𝑇𝑟𝑢𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠          9 4. Prevalence of AUD across PRS in COGA      Figure S3: Prevalence of AUD Across PRS Continuum Mean Prevalence of mild, moderate, and severe AUD in COGA GSCAN DPW, PROB ALC, and RISK PC PRSs. Red vertical line denotes 95% point in the continuum. Fitted lines include least squares line and a loess curve.  10 5. Restricting Age Range in COGA   Because the age ranges in COGA and FT12 varied so widely, we ran a series of sensitivity analyses to ensure that differences across samples were not the result of age differences. We reran all of the analyses in the manuscript restricting the COGA sample to those aged 18 to 30 years old. The results of predictive power, increase in prevalence of AUD across septiles, and joint influence of PRS are not substantively different from the results in the full sample.    Table S3: Odds Ratios for Those at Extreme End of the PRS Continuum in COGA (ages 18-30)  Sample Phenotype Split  N Cases OR 95 % CI Low 95 % CI High  Mild AUD 80% 355 1.81 1.45 2.26 COGA Mild AUD 90% 172 1.55 1.15 2.08  Mild AUD 95% 86 1.44 0.96 2.15         Moderate AUD 80% 212 1.67 1.33 2.10 COGA Moderate AUD 90% 107 1.61 1.19 2.19  Moderate AUD 95% 54 1.47 0.97 2.22         Severe AUD 80% 122 1.57 1.20 2.05 COGA Severe AUD 90% 64 1.60 1.13 2.27  Severe AUD 95% 30 1.25 0.77 2.02        All models control for sex, age at last interview, and first 10 principal components. Models for COGA also included data collection site and genotyping array. N Cases = number of individuals who meet criteria for a given level of AUD and are in the top portion of the split.      11     Figure S4: Parameter Estimates for PRS in Independent and Joint Models Parameter estimates from linear mixed models for AUD symptoms regressed on GSCAN DPW, PROB ALC, and RISK PC PRS in COGA (age restricted). Independent = model with only corresponding PRS. Conditional = model with all PRS included. Adjusted for age, sex, first 10 ancestral principal components, genotyping array, and data collection site.  12 References  1. Begleiter H, Reich T, Hesselbrock V, Porjesz B, Li TK, Schuckit MA, . . . Rice JP. The Collaborative Study on the Genetics of Alcoholism. Alcohol Health & Research World. 1995;19:228-36. 2. Edenberg HJ. The collaborative study on the genetics of alcoholism: an update. Alcohol research & health : the journal of the National Institute on Alcohol Abuse and Alcoholism. 2002;26(3):214-8. 3. 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Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples

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Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples

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