Introduction: Atherosclerotic cardiovascular disease (ASCVD) has many well-established risk factors; clinical risk tools (CRTs) aggregate these to estimate future disease risk. In 2023 the American Heart Association endorsed a new PREVENT CRT to guide preventive management. Previous work has shown that combining CRTs with a polygenic risk score (PRS), summarising the contribution of thousands of common variants, improves risk prediction, and that statin efficacy is increased in high PRS individuals. But while genetics is known to be a major risk factor, it is not currently incorporated into PREVENT or other CRTs. Research question: Does an integrated risk tool (IRT), combining a PRS with PREVENT 10yr risk for ASCVD, improve clinical utility? Methods: We assessed IRT performance on ASCVD outcomes within the Kaiser-Permanente Research Biobank, a large, US-based health system research cohort (n~450,000, n=59,823 used for testing, including 2,543 10-year incident ASCVD cases - defined as acute myocardial infarction, ischemic stroke or fatal CHD). Results: Individuals often experienced large changes in risk: 13.6% had an IRT risk more than double or less than half their PREVENT risk; and 23.1% had an IRT risk at least 2 percentage points greater or smaller, which the HEART study showed could positively impact management decisions. These changes significantly improved prediction accuracy at a 7.5% actionable risk threshold (overall Net Reclassification Improvement 6.0% (95% CI 4.7-7.4%)). Similar results were observed across age and sex subgroups. Individuals with borderline PREVENT scores (5.0-7.5%) especially benefitted from the addition of PRS: the observed 10y ASCVD rate moved from 4.8% (4.0-5.7%) to 8.8% (7.7-10.0%) in low (bottom 20%) versus high (top 20%) PRS individuals, an odds ratio of 1.9. A primary use case for ASCVD risk prediction is to prioritise individuals for statins. Restricting to a statin-naive subset, the addition of PRS to PREVENT up-classified 337 cases per 100,000 individuals to above the 7.5% risk threshold. Statin treatment of this group would prevent up to 151 major ASCVD events per 100,000 individuals over 10 years, assuming a higher efficacy in high-PRS individuals. Conclusion: Combining a PRS with PREVENT results in substantial and beneficial changes in individual risk of ASCVD. Predictive performance is improved, both overall and especially for those at borderline risk, presenting opportunities to prevent future ASCVD events.

Case-only designs in longitudinal cohorts are a valuable resource for identifying disease-relevant genes, pathways, and novel targets influencing disease progression. This is particularly relevant in Alzheimer's disease (AD), where longitudinal cohorts measure disease "progression," defined by rate of cognitive decline. Few of the identified drug targets for AD have been clinically tractable, and phenotypic heterogeneity is an obstacle to both clinical research and basic science. In four cohorts (n = 7241), we performed genome-wide association studies (GWAS) and Mendelian randomization (MR) to discover novel targets associated with progression and assess causal relationships. We tested opportunities for patient stratification by deriving polygenic risk scores (PRS) for AD risk and severity and tested the value of these scores in predicting progression. Genome-wide association studies identified no loci associated with progression at genome-wide significance (α = 5×10-8); MR analyses provided no significant evidence of an association between cognitive decline in AD patients and protein levels in brain, cerebrospinal fluid (CSF), and plasma. Polygenic risk scores for AD risk did not reliably stratify fast from slow progressors; however, a deeper investigation found that APOE ε4 status predicts amyloid-β and tau positive versus negative patients (odds ratio for an additional APOE ε4 allele = 5.78 [95% confidence interval: 3.76-8.89], P<0.001) when restricting to a subset of patients with available CSF biomarker data. These results provided no evidence for large-effect, common-variant loci involved in the rate of memory decline, suggesting that patient stratification based on common genetic risk factors for progression may have limited utility. Where clinically relevant biomarkers suggest diagnostic heterogeneity, there is evidence that a priori identified genetic risk factors may have value in patient stratification. Mendelian randomization was less tractable due to the lack of large-effect loci, and future analyses with increased samples sizes are needed to replicate and validate our results.

Alzheimer’s disease (AD) is a complex, multifactorial disease that is incurable in the aging population. The success of genome-wide association studies (GWAS), as a cross sectional design, to identify novel genetic risk factors for AD in recent years is well known; however, case-control studies are less likely to uncover genetic factors that influence other aspects of the disease, such as progression and onset. In order to identify common and rare genetic variants associated with rates of cognitive decline in AD, we aim at systematically analyzing the largest collection of longitudinal and GWAS data (N = 7,241), across non-Hispanic white individuals from multicenter cohorts (Knight-ADRC, ADNI, NACC, and GSK) with at least 1.5 years of follow up after being diagnosed with AD. We also aim at exploring if genetic risk factors, alone or interacting with other genetic factors, are associated rate of cognitive decline in patients with AD when aggregated together into a single Polygenic Risk Score (PRS). Furthermore, external proteomics data will be combined with the genetics data for Mendelian Randomization (MR) analyses in order to discover causal biomarkers for cognitive decline and AD risk. Preliminary results found a suggestive signal in chr21 (min P = 8.7e-8) approaching close to the genome-wide significance threshold. Moreover, some other suggestive signals were detected in chr2 (min P = 3.8e-06) and chr1 (min P = 3.7e-06) that warrant further investigation. Further results and key findings of the post-GWAS analyses will be presented during the AAIC meeting. We have leveraged these datasets to generate prediction models for rate of cognitive decline in AD. The identification of AD-specific genetic variants and biomarkers will enable the characterization of appropriate therapeutic targets for slowing or halting the AD progression.

Contemporary clinical trials often have complex logistics: they are run across multiple centers/countries and involve a lot of uncertainties about disease prevalence rates, patient characteristics and regulatory requirements, all of which can vary across countries. As a result, planning and delivering such studies on-time has long been recognized as a challenge in the pharmaceutical industry. One well-known approach to modeling recruitment in complex trials, which we use as a starting point of our work is a Poisson-Gamma stochastic model. COVID-19 pandemic brought additional layer of complexity to recruitment modeling: as many countries went into shutdown, sponsors were forced with the dilemma of replanning study delivery rebalancing their portfolios "on the fly." That translated into the need to adapt the recruitment model to account for effect of COVID-19. In this work we present an approach that blends Poisson–Gamma model, real-time recruitment data updates and epidemiological modeling of COVID-19 spread. At the core of the methodology is sophisticated Bayesian hierarchical model predicting how pandemic intensity affected parameters of Poisson-gamma model. We also build an app conveniently showing many publicly available COVID models in one place. The work is a result of intense collaboration among many statisticians, data scientists and clinical operations professionals.

Introduction: Alzheimer's disease (AD) is a progressive and irreversible neurological disease. The genetics and molecular mechanisms underpinning differential cognitive decline in AD are not well understood; the genetics of AD risk have been studied far more assiduously. Materials and Methods: Two phase III clinical trials measuring cognitive decline over 48 weeks using Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog, n = 2060) and Clinical Dementia Rating-Sum of Boxes (CDR-SB, n = 1996) were retrospectively genotyped. A Genome-Wide Association Study (GWAS) was performed to identify and replicate genetic variants associated with cognitive decline. The relationship between polygenic risk score (PRS) and cognitive decline was tested to investigate the predictive power of aggregating many variants of individually small effect. Results: No loci met candidate gene or genome-wide significance. PRS explained a very small percentage of variance in rates of cognitive decline (ADAS-cog: 0.54%). Conclusions: These results suggest that incorporating genetic information in the prediction of cognitive decline in AD currently appears to have limited utility in clinical trials, consistent with small effect sizes estimated elsewhere. If AD progression is more heritable soon after disease onset, genetics may have more clinical utility.

BACKGROUND: The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression. METHODS: We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 x 10(-6) ) and a candidate threshold (1.6 x 10(-4) ). RESULTS: No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLAB*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99). CONCLUSIONS: We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.

Background The prevalence of depression is higher in individuals suffering from autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Epidemiological findings point to a bi-directional relationship - that depression increases the risk of developing an autoimmune disease, and vice-versa. Shared genetic etiology is a plausible explanation for the overlap between depression and autoimmune diseases. In this study we tested whether genetic variation in the Major Histocompatibility Complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression. Method We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 Human Leukocyte Antigen (HLA) alleles and four Complement Component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium (PGC) Major Depressive Disorder (MDD) working group and the UK Biobank (UKB). In the 26 PGC-MDD studies, cases met a lifetime diagnosis of MDD, determined by a structured diagnostic interview. In the UKB, cases and controls were identified from an online mental health questionnaire. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants in each study and performed an inverse-variance weighted meta-analysis across the PGC-MDD and UKB samples, applying both a conservative region-wide significance threshold (3.9-e6) and a candidate threshold (1.6e-4). Results No HLA alleles or C4 haplotypes were associated with depression at the conservative threshold in the PGC, UKB or meta-analysis. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold in the meta-analysis. Under the conservative threshold, 70 SNPs were detected in the UKB and 143 SNPs were detected in the meta-analysis, mirroring previous findings from highly powered GWAS of depression. Discussion We found no evidence that HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia, confer risk for depression. These results indicate that autoimmune diseases and depression do not share common risk loci of moderate or large effect in the MHC.

Background: It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation. Methods: Using data on mothers (N = 7486) and children (N = 7508) from the Avon Longitudinal Study of Parents and Children, we: (i) examined the association of polygenic risk scores for a range of sociodemographic and lifestyle characteristics and health conditions related to continued participation; (ii) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and selfreported smoking differed in the largest sample with genetic data and a subsample who participated in a recent follow-up; and (iii) determined the proportion of variation in participation explained by common genetic variants, using genome-wide data. Results: We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation; and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data [ odds ratio (OR) for ever smoking per standard deviation (SD) increase in polygenic score: 0.85, 95% confidence interval (CI): 0.81, 0.89} and subsample (OR: 0.96, 95% CI: 0.89, 1.03). In genome-wide analysis, single nucleotide polymorphism based heritability explained 18-32% of variability in participation. Conclusions: Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

Background It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation. Methods Using data on mothers (N=7,486) and children (N=7,508) from the Avon Longitudinal Study of Parents and Children, we 1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, 2) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a sub-sample who participated in a recent follow-up and 3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data. Results We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, ADHD and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single nucleotide polymorphism based heritability explained 17-31% of variability in participation. Conclusions Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

Objective Reduced mental health (MH) is prevalent in rheumatoid arthritis (RA). Although longitudinal studies are limited, there is evidence that depression is associated with worse disease outcomes. We evaluated reciprocal relationships between MH, RA severity, and genetic risks for depression for 2 years in a well-characterized cohort of RA patients.Methods We evaluated 520 early RA patients previously enrolled to two clinical trials. MH was measured using the short form-36 MH domain and mental component summary scores (MCS). MCS/MH associations over 2 years with disease activity (disease activity score on a 28-joint count), disability (health assessment questionnaire), pain visual analog scale scores, and a weighted genetic risk score for depression were tested using linear mixed-effects and regression models.Results Poorer MH was associated with worse RA outcomes. Lower MCS scores (indicating worse MH) were seen in patients with a greater genetic risk for depression (weighted genetic risk score: coefficient = -1.21, p = .013). Lower baseline MCS was associated with lower 2-year improvements in disease activity score on a 28-joint count (coefficient = -0.02, p < .001), pain (coefficient = -0.33, p < .001), and health assessment questionnaire (coefficient = -0.01, p = .006). Baseline MCS was associated with changes in the swollen joint count (coefficient = -0.09, p < .001) and patient global assessment (coefficient = -0.28, p < .001) but not the tender joint count (p = .983) and erythrocyte sedimentation rate (p = .973). Only baseline pain visual analog scale (coefficient = -0.07, p = .002) was associated with 2-year changes in MCS.Conclusions Reduced baseline MH was associated with lower improvements in disease activity, disability, and pain for 2 years, supporting current national guidelines recommending screening for depression in RA. Pain had a bidirectional relationship with MH. Depression genetic risk had a significant association with MH.

We use polygenic risk scores (PRSs) for schizophrenia (SCZ) and bipolar disorder (BPD) to predict smoking, and addiction to nicotine, alcohol or drugs in individuals not diagnosed with psychotic disorders. Using PRSs for 144 609 subjects, including 10 036 individuals admitted for in‐patient addiction treatment and 35 754 smokers, we find that diagnoses of various substance use disorders and smoking associate strongly with PRSs for SCZ ( P = 5.3 × 10 −50 –1.4 × 10 −6 ) and BPD ( P = 1.7 × 10 −9 –1.9 × 10 −3 ), showing shared genetic etiology between psychosis and addiction. Using standardized scores for SCZ and BPD scaled to a unit increase doubling the risk of the corresponding disorder, the odds ratios for alcohol and substance use disorders range from 1.19 to 1.31 for the SCZ‐PRS, and from 1.07 to 1.29 for the BPD‐PRS. Furthermore, we show that as regular smoking becomes more stigmatized and less prevalent, these biological risk factors gain importance as determinants of the behavior.

BACKGROUND:Depression and the autoimmune disorders are comorbid-the two classes of disorders overlap in the same individuals at a higher frequency than chance. The immune system may influence the pathological processes underlying depression; understanding the origins of this comorbidity may contribute to dissecting the mechanisms underlying these disorders. METHOD:We used population cohort data from the 1958 British birth cohort study (the National Child Development Study) to investigate the ages at onset of depression and 23 autoimmune disorders. We used self-report data to ascertain life-time history of depression, autoimmune disorders and their ages at onset. We modelled the effect of depression onset on subsequent autoimmune disorder onset, and vice versa, and incorporated polygenic risk scores for depression and autoimmune disorder risk. RESULTS:In our analytic sample of 8174 individuals, 315 reported ever being diagnosed with an autoimmune disorder (3.9%), 1499 reported ever experiencing depression (18.3%). There was significant comorbidity between depression and the autoimmune disorders (OR = 1.66, 95% CI = 1.27-2.15). Autoimmune disorder onset associated with increased subsequent hazard of depression onset (HR = 1.39, 95% CI = 1.11-1.74, P = 0.0037), independently of depression genetic risk. Finally, depression increased subsequent hazard of autoimmune disorder onset (HR = 1.40, 95% CI = 1.09-1.80, P = 0.0095), independently of autoimmune disorder genetic risk. DISCUSSION:Our results point to a bidirectional relationship between depression and the autoimmune disorders. This suggests that shared risk factors may contribute to this relationship, including both common environmental exposures that increase baseline inflammation levels, and shared genetic factors.

BACKGROUND: Polygenic risk scores (PRSs) have successfully summarized genome- wide effects of genetic variants in schizophrenia with significant predictive power. In a clinical sample of first- episode psychosis (FEP) patients, we estimated the ability of PRSs to discriminate case-control status and to predict the development of schizophrenia as opposed to other psychoses.METHODS: The sample (445 case and 265 control subjects) was genotyped on the Illumina HumanCore Exome BeadChip with an additional 828 control subjects of African ancestry genotyped on the Illumina Multi- Ethnic Genotyping Array. To calculate PRSs, we used the results from the latest Psychiatric Genomics Consortium schizophrenia meta- analysis. We examined the association of PRSs with case- control status and with schizophrenia versus other psychoses in European and African ancestry FEP patients and in a second sample of 248 case subjects with chronic psychosis.RESULTS: PRS had good discriminative ability of case- control status in FEP European ancestry individuals (9.4% of the variance explained, p < 10(-26)), but lower in individuals of African ancestry (R-2 = 1.1%, p = .004). Furthermore, PRS distinguished European ancestry case subjects who went on to acquire a schizophrenia diagnosis from those who developed other psychotic disorders (R-2 = 9.2%, p =.002).CONCLUSIONS: PRS was a powerful predictor of case-control status in a European sample of patients with FEP, even though a large proportion did not have an established diagnosis of schizophrenia at the time of assessment. PRS was significantly different between those case subjects who developed schizophrenia from those who did not, although the discriminative accuracy may not yet be sufficient for clinical utility in FEP.

A meta-analysis of publicly available summary statistics on multiple sclerosis combined with three Nordic multiple sclerosis cohorts (21,079 cases, 371,198 controls) revealed seven sequence variants associating with multiple sclerosis, not reported previously. Using polygenic risk scores based on public summary statistics of variants outside the major histocompatibility complex region we quantified genetic overlap between common autoimmune diseases in Icelanders and identified disease clusters characterized by autoantibody presence/absence. As multiple sclerosis-polygenic risk scores captures the risk of primary biliary cirrhosis and vice versa ( P = 1.6 × 10 −7 , 4.3 × 10 −9 ) we used primary biliary cirrhosis as a proxy-phenotype for multiple sclerosis, the idea being that variants conferring risk of primary biliary cirrhosis have a prior probability of conferring risk of multiple sclerosis. We tested 255 variants forming the primary biliary cirrhosis-polygenic risk score and found seven multiple sclerosis-associating variants not correlated with any previously established multiple sclerosis variants. Most of the variants discovered are close to or within immune-related genes. One is a low-frequency missense variant in TYK2 , another is a missense variant in MTHFR that reduces the function of the encoded enzyme affecting methionine metabolism, reported to be dysregulated in multiple sclerosis brain.

Drug addiction is a costly and recurring healthcare problem, necessitating a need to understand risk factors and mechanisms of addiction, and to identify new biomarkers. To date, genome-wide association studies (GWAS) for heroin addiction have been limited; moreover they have been restricted to examining samples of European and African-American origin due to difficulty of recruiting samples from other populations. This is the first study to test a Han Chinese population; we performed a GWAS on a homogeneous sample of 370 Han Chinese subjects diagnosed with heroin dependence using the DSM-IV criteria and 134 ethnically matched controls. Analysis using the diagnostic criteria of heroin dependence yielded suggestive evidence for association between variants in the genes CCDC42 (coiled coil domain 42; p = 2.8x10(-7)) and BRSK2 (BR serine/threonine 2; p = 4.110(-6)). In addition, we found evidence for risk variants within the ARHGEF10 (Rho guanine nucleotide exchange factor 10) gene on chromosome 8 and variants in a region on chromosome 20q13, which is gene-poor but has a concentration of mRNAs and predicted miRNAs. Gene-based association analysis identified genome-wide significant association between variants in CCDC42 and heroin addiction. Additionally, when we investigated shared risk variants between heroin addiction and risk of other addiction-related and psychiatric phenotypes using polygenic risk scores, we found a suggestive relationship with variants predicting tobacco addiction, and a significant relationship with variants predicting schizophrenia. Our genome wide association study of heroin dependence provides data in a novel sample, with functionally plausible results and evidence of genetic data of value to the field.

Background Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD. Method The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them. Results PRS significantly predicted depression, explaining 1.1% of variance in phenotype ( p = 1.9 × 10 −6 ). SLEs and CT were also associated with MDD status ( p = 2.19 × 10 −4 and p = 5.12 × 10 −20 , respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found ( p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples. Conclusions CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.