Genetic Epidemiology

Qualitative models of Alzheimers pathology often posit that amyloid accumulation follows a sigmoid curve, indicating that the rate of deposition wanes over time. Longitudinal PET data now allow us to investigate amyloid accumulation trajectories with greater detail and over longer follow-up periods. We combine inferences from simulated amyloid trajectories, empirical PET data from the Alzheimers Disease Neuroimaging Initiative (ADNI), and the sampled iterative local approximation algorithm (SILA) to assess whether amyloid accumulation reaches a physiologic ceiling. We find that SILA reliably detects a ceiling, when present, across a range of simulated scenarios that impose a sigmoid shape. When fit to empirical data from ADNI, however, SILA does not appear to indicate the presence of a ceiling. Thus, we conclude that amyloid trajectories may not reach a physiologic ceiling during the stages of Alzheimers disease typically observed while patients remain under follow-up in cohort studies. Fits using SILA indicate that illustrative models of biomarker cascades, while useful tools for conceptualizing and interrogating pathologic processes, may not represent the shapes of amyloid trajectories accurately. Summary for General PublicAmyloid, a protein implicated in Alzheimers disease, is thought to reach a plateau in the brain, but methods that estimate how amyloid changes over time suggest it grows unabated. Gantenberg et al. use one such method and simulations to argue that amyloid does not reach a plateau during the typical course of Alzheimers.

Phenome-wide association studies (PheWAS) enable systematic exploration of relationships between genetic variants and clinical phenotypes derived from electronic health records (EHRs). Conventional regression-based PheWAS treats phenotypes separately and relies on binary phenotype representations, which limits statistical power for rare variants and rare phenotypes and reduces the ability to detect associations with phenotypes that are distributed across clinical codes. To address this limitation, we first developed EmbedPheScan, a phenotype embedding-based prioritization framework that summarizes the phenotypic profiles of rare loss-of-function variant carriers in a continuous embedding space. We then proposed EA-PheWAS by combining these embedding-derived signals with conventional regression-based PheWAS results using the aggregated Cauchy association test. Using the UK Biobank whole-exome sequencing and EHR data, we show that the proposed methods maintain appropriate false-positive control. We then performed genome-wide phenome scans across all genes and across biologically defined gene classes to evaluate EA-PheWAS relative to conventional PheWAS and EmbedPheScan, consistently finding that EA-PheWAS outperformed the other two methods. We illustrate the utility of EA-PheWAS focusing on four genes representing distinct scenarios, including strong-effect disease genes (PKD1, PKD2), genes with large numbers of rare LoF carriers (NF1), and genes with extremely sparse carrier counts (FBN1).

Summary statistics fine-mapping methods offer advantages over classical methods, including avoiding data-sharing constraints and improved modelling of correlated variables and sparse effects. However, its performance has not been comprehensively evaluated in breast cancer using real-world data. Previous multinomial stepwise regression (MNR) fine-mapping analyses for breast cancer identified 196 credible sets. Here, we apply summary statistics fine-mapping, compare methods, and assess parameters influencing performance. Using summary statistics from the Breast Cancer Association Consortium, we compared finiMOM, SuSiE, and FINEMAP to published MNR results across 129 regions. Performance was assessed by recall using in-sample and out-of-sample LD. Discordant credible sets were examined for technical factors, and target genes were defined using the INQUISIT pipeline. SuSiE showed the closest agreement with MNR. Results varied across regions depending on the assumed number of causal variants (L), with higher values reducing recall and no single L maximising performance. At optimal L per region, SuSiE identified 8,192 CCVs in 244 credible sets, with recall of 88%, 86%, and 72% for overall, ER-positive, and ER-negative breast cancer. Thirty MNR sets were missed. Discordance was partially explained by allele flips, imputation quality, and array heterogeneity. Fifty-two MNR-identified genes, including BRCA2, WNT7B and CREBBP were not recovered, while additional candidate genes were identified. Using out-of-sample LD reduced recall by 3% but identified novel variants. Fine-mapping results vary across methods, and no single approach is sufficient. The choice of L strongly influences results, and combining analytical approaches with functional validation can improve causal variant identification.

A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. Cellular control of isoform abundances is critical for multiple aspects of biology and is only partially regulated by transcript levels. While long-read sequencing facilitates transcript quantification, quantifying the resulting protein isoforms on a large scale is a major challenge, complicating biological interpretation of transcript alterations. Standard "bottom up" mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map onto more than one isoform. We introduce PAQu, a novel Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance even when peptide mapping is ambiguous. PAQu offers several advantages over existing methods in a unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, and provides a rigorous framework for hypothesis testing. Extensive simulations show that PAQu consistently outperforms competing methods in detecting differentially expressed protein isoforms and estimating their abundances. We use PAQu to investigate differences in isoform abundance levels between people with schizophrenia and control subjects, confirming a long held hypothesis that levels of the C4A isoform of Complement Component 4 are increased in schizophrenia while C4B is not. These results demonstrate that PAQu can identify significant variations in isoform abundance levels not previously possible.

Background: Elevated lipoprotein(a) [Lp(a)] is a known risk factor for several cardiovascular-related diseases established from multiple genetic and observational studies. However, the underlying mechanisms mediating the effects of Lp(a) levels on cardiovascular disease risk and major adverse cardiovascular events (MACE) are unclear. The aim of this study was to identify proteins downstream of Lp(a) using mendelian randomization (MR) - a genetic causal inference approach. Methods: A two-sample MR was performed by initially identifying Lp(a) genetic instruments based on data from genome wide association studies (GWAS) of Lp(a) blood concentrations. These instruments were then tested for association with proteins from proteomic pQTL data (Olink from UK Biobank, 2940 proteins and SomaScan from deCODE, 4907 proteins). Results: A total of 521 proteins associated with Lp(a) were identified. Using pathway enrichment analysis, the following MACE-relevant pathways were identified comprising a total of 91 Lp(a) downstream proteins: oxidized phospholipid-related, chemotaxis of immune cells and endothelial cell activation, pro-inflammatory monocyte activation, neutrophil activity, coagulation, and lipid metabolism. Conclusion: The results suggest that the influence of Lp(a) treatments is primarily through modifying inflammation rather than lipid-lowering, thus providing insight into the mechanistic framework which mediates the effects of elevated Lp(a) on atherosclerotic cardiovascular disease.

Individuals with admixed ancestry comprise a significant proportion of populations of the Americas. Statistical methods have been developed to specifically leverage local ancestry inference to enhance the power and interpretability of genome-wide association studies in admixed populations. However, no such methods currently exist to test for rare-variant aggregate associations. Here we present LANTERN (Leveraging local ANcestry Tracts to Enhance Rare variaNt aggregate associations), a method that infers the alleles that lie on each ancestral haplotype and conducts rare-variant aggregate association testing in a generalized linear mixed model framework. Through simulation studies we demonstrated that LANTERN achieves proper control of Type 1 error while boosting power to detect associations when causal alleles predominately lie on one ancestral haplotype. Using data from a cohort of African American participants from the Jackson Heart Study, LANTERN identified two genes known to be involved in red-blood cell (RBC) biology when local ancestry information was incorporated. Specifically, a burden of rare alleles on European ancestral haplotypes in EPO was associated with both hemoglobin levels (HGB) and RBC counts, whereas a burden of rare alleles on African ancestral haplotypes in EPB42 was associated with HGB and RBC. In summary, LANTERN (i) allows for the identification of ancestry-specific rare-variant associations; and (ii) enhances rare-variant association signals compared to an analysis that ignores local ancestry. LANTERN is implemented in R and is freely available on GitHub.

Abstract Background: Cardiometabolic diseases arise from metabolic dysfunction that develops decades before clinical onset. Conventional genetic risk models are typically derived in middle-aged or older populations, where genetic effects are confounded by cumulative environmental exposures, chronic comorbidities, and clinical interventions. Whether the life stage at which genetic liability is modelled influences the biological signal captured by polygenic scores remains unclear, particularly in underrepresented populations. We therefore tested whether genetic liability modelled in early adulthood, a period of relative physiological stability, is associated with cardiometabolic risk across the life course in Asian populations. Methods: We developed a polygenic score for metabolic syndrome, GenMetS, using data from 1,368 Singaporean women aged 18-45 years. The model integrates 15 established polygenic scores for metabolic traits and applies elastic-net penalized regression to optimize variant weights. GenMetS was evaluated in five cohorts comprising 670,952 individuals aged 0-94 years across population-based and disease-enriched settings, including Asian and European ancestry groups. Associations with metabolic traits, cardiometabolic diseases, multimorbidity, and early-life growth patterns were assessed. Results: In Asian populations, GenMetS explained 5.0-12.4% of the variance in metabolic syndrome in adults and 10.3% in children, with negligible performance in European populations (R squared < 0.001). Higher GenMetS was associated with increased odds of cardiometabolic diseases, including type 2 diabetes, heart failure, and stroke (odds ratios 1.32-1.52 per standard deviation). In UK Biobank participants of Asian ancestry, GenMetS improved discrimination of cardiometabolic multimorbidity beyond age alone. Associations were consistent across sexes. In children, higher GenMetS was associated with obesogenic growth trajectories and increased abdominal adiposity. Conclusions: Genetic liability to metabolic dysfunction modelled in early adulthood captures a stable biological signal associated with metabolic traits, disease risk, and multimorbidity from childhood to adulthood in Asian populations. These findings indicate that the life stage of model derivation shapes the biological signal captured by polygenic scores and support the development of life-stage and ancestry-informed approaches for cardiometabolic risk assessment and prevention.

Several recent studies have quantified signed linkage disequilibrium (LD) among mutations in genomic datasets, often reporting positive LD, particularly among mutations presumed to be less deleterious, such as synonymous variants. In this article, we investigate two potential sources of this positive LD: the focus on rare alleles, as adopted in several previous studies, and errors arising in the mapping of short-read sequences onto a reference genome. Using coalescent simulations, we extend previous theoretical results of the effect of focusing on rare alleles, and show that derived alleles present at similar frequencies tend to be in positive LD. Reanalyzing datasets from Capsella grandiflora and Drosophila melanogaster, we show that LD among synonymous derived alleles vanishes in the absence of any conditioning on frequency, while LD between mutations categorized as potentially deleterious by the SIFT4G program stays positive. However, we show that in both cases, this positive LD may be at least partly caused by the potential mismapping of a small fraction of sequences in some individuals, which could be a consequence of structural variants that are absent from the reference genome. Overall, these results show that average signed LD among mutations can be strongly affected by technical artifacts even if these concern only a minority of variants. Finally, we discuss other possible sources of positive LD among deleterious mutations.

Drug targets supported by human genetic evidence have significantly higher approval rates, making genome-wide association studies a valuable resource for drug candidate prioritisation. Transcriptome-wide association study signature-matching is an emerging in silico approach that integrates GWAS data with expression quantitative trait loci to generate a disease gene expression signature, which is then compared against drug perturbation databases such as the Connectivity Map. Despite recent adoption, there is no consensus on optimal methodology. Here, we systematically benchmark key parameters, including TWAS method, eQTL tissue model, similarity metric, gene set size, and CMap cell line, using LDL cholesterol, familial combined hyperlipidemia, and asthma as proof-of-concept traits. We demonstrate that while TWAS signature-matching can successfully prioritise known first-line treatments, performance is highly sensitive to parameter choice; for instance, the selection of the cell line used for drug signatures alone can dramatically alter drug prioritisation. Based on these findings, we propose a best-practice framework for robust, genetically-informed drug prioritisation using TWAS signature-matching.

Alternative-splicing events (ASE) increase transcriptomic variability and play key roles in biological functions. The contribution of ASE to bipolar disorder (BD) remains largely unexplored. We performed a Transcriptome-Wide Alternative-Splicing Analysis (TWASA) to identify ASEs and genes potentially involved in BD. The study comprised 635 individuals: a discovery sample (DS) of 31 individuals from eight multiplex BD families (16 BD cases; 15 unaffected relatives), and a replication sample (RS) of 604 subjects (372 BD cases; 232 controls). Sequencing was conducted on RNA from lymphoblastoid cell lines (DS) and whole blood (RS). TWASA was performed using VAST-TOOLS (VT), rMATS (RM), and MAJIQ/MOCCASIN (MCC). Gene-set association analyses of genes containing ASEs were performed across six psychiatric disorders. Novel ASE (nASE) were investigated in the DS using FRASER. Limited gene overlap was observed across TWASA tools. MCC identified 2,031 complex ASEs involving 1,508 genes, showing the strongest genetic association with BD across psychiatric phenotypes. Prioritization of MCC-identified ASE genes yielded 441 candidates, including DOCK2 as top candidate from the DS. Replication was obtained for 98 genes, five with an identical ASE, and four (RBM26, QKI, ANKRD36, and TATDN2) showing a concordant percentage-spliced-in direction with the DS. Finally, 578 nASE were identified in the DS, with no evidence of familial segregation or differences in ASE types. This first TWASA in BD reveals tool-specific variability, complex ASE for genes specifically associated with BD, and novel candidate genes for BD. Alternative transcript isoform abundance may represent a mechanism contributing to BD pathophysiology.

IntroductionNeuromuscular disorders (NMDs) encompass a broad group of conditions that primarily affect the peripheral nervous system. They are often caused by genetic alterations that impair skeletal muscle function and result in debilitating symptoms. Obtaining an accurate molecular diagnosis remains a challenge, potentially because variants in genes that have yet to be identified as causal. We therefore used advanced computational methods to study the genetic architecture of NMDs and to identify key features that distinguish NMD genes from other genes in the broader genome. MethodsCurated genes implicated in NMDs (n = 639; GeneTable of NMDs) were obtained and merged with a comprehensive set of genomic features for human autosomal protein-coding genes. Machine-learning-based feature selection and ranking were performed using Boruta, along with complementary analytical approaches. These analyses were used to identify the most important genic features (n = 134, subcategories: gene complexity, genetic variation, expression patterns, and other general gene traits) for discriminating NMD genes from other genes in the genome ResultsNMD genes exhibit enriched expression in disease-relevant tissues, including skeletal muscle and heart. Additionally, compared with other protein-coding genes, these genes exhibit increased transcriptomic complexity (e.g., longer transcripts and more unique isoforms), contain more short tandem repeats, and show greater variation in conservation across model organisms. ConclusionsThis study identified several key genomic features that may distinguish NMD genes from the rest of the genome. This may enhance the identification of novel causal genes and could ultimately facilitate earlier diagnosis and medical management for affected individuals.

Introduction: Hypertrophic Cardiomyopathy (HCM) is a disease defined by the development of left ventricle hypertrophy. One of the most commonly mutated genes in HCM is cardiac myosin binding protein C (MYBPC3). MYBPC3 protein localizes to the cardiomyocyte sarcomere, but studies have reported detection of both MYBPC3 RNA and protein in non-cardiomyocyte cell populations. Therefore, it was unclear if MYBPC3 expression in non-cardiomyocyte cell populations altered the development of cardiomyopathy caused by MYBPC3 protein deficiency. Methods: We utilized genetically modified murine models with germline deletion of Mybpc3 exons 3 to 5 (Mybpc3-/-) or cardiomyocyte specific deletion of Mybpc3 exons 3 to 5 (Mybpc3fl/fl ; Myh6-Cre). Gene expression was assessed using quantitative RT-PCR. Whole tissue protein levels were assessed using immunoblots. Immunohistochemistry and proximity ligation assays were performed to evaluate in situ protein expression. Echocardiography was utilized to measure left ventricular structure and function. Results: Mybpc3 mRNA was detected in multiple organs including the heart, lung and blood from both humans and mice. Utilizing transgenic murine models with germline or cardiomyocyte specific deletion of Mybpc3 exons 3-5, we discovered that the Mybpc3 mRNA detected in extracardiac locations originated primarily from cardiomyocytes. Likewise, MYBPC3 protein was identified in myocardial tissue but not in other organs and cardiomyocytes were the only cell population in myocardial tissue that had detectable MYBPC3 protein. Importantly, cardiomyocyte deletion of Mybpc3 caused similar pathological myocardial remodeling and alterations in left ventricular function compared to germline deletion of Mybpc3 in all cell populations. Conclusions: Our results show that cardiomyocytes are the primary cell source of Mybpc3 mRNA detected in extracardiac organs and they are the principal cell type responsible for the cardiomyopathy caused by MYBPC3 protein deficiency. These results suggest that selective targeting of cardiomyocytes should be the most efficient approach to treat cardiomyopathies associated with MYBPC3 deficiency.

Background: Short read sequencing technologies have dominated the field of human whole genome sequencing in the past years in terms of cost, throughput, and accuracy. However, thanks to recent technological evolution, long read approaches have become increasingly competitive and complementary to short reads. With the gap in the cost per genome closing slowly between both approaches, long reads might replace short read sequencing in future research and clinical applications. Still, comprehensive evaluation is necessary to conclude on the performance and general advantages of each technology. Results: In this study, we compared the latest chemistries of major suppliers of short and long read technologies: Illumina short reads, Illumina Complete Long Reads (ICLR), Pacific Biosciences HiFi reads (PacBio), and Oxford Nanopore Technologies long reads (ONT). Using the HG002 human reference sample and established bioinformatics guidelines, we assessed their variant calling performance against the latest available truth sets at different levels of coverage. For single nucleotide variant detection, all technologies were equivalent. Despite the latest improvements in chemistry, indel calling with ONT continues to lag in accuracy behind other technologies. In contrast, long reads delivered a clear advantage in structural variant detection, surpassing short reads in both accuracy and sensitivity. The hybrid ICLR approach achieved intermediate performance, narrowing the gap between short and long read sequencing. Furthermore, long reads enhanced haplotype-phasing resolution, enabling the phasing of over 80% of the genome. Conclusions: These findings highlight the specific strengths and limitations of recent sequencing technologies, aiding the decision-making in future research projects, technological platforms development, and clinical applications.

Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.

Socioeconomic inequalities in health and well-being are a major public health concern, particularly in ageing populations. Education is a key determinant shaping multiple aspects of health outcomes. We used cross-sectional data from wave 9 of the German sample (n=4,148) of the Survey of Health, Ageing and Retirement in Europe (SHARE) to test whether formal education is associated with well-being in later adulthood, with health literacy, self-rated health, and preventive health behaviours as possible mediators. Our results showed that education was positively associated with greater well-being, but only via indirect pathways. Specifically, self-rated health, health literacy, and fruit and vegetable consumption mediated the relationship between education and well-being accounting for 54.7, 24.7, and 12.6 percent of the total effect, respectively. In addition, there were significant positive correlations between education and health literacy, as well as high-intensity physical activity, daily fruit and vegetable consumption, more preventive health check-ups, and less smoking. In contrast, alcohol consumption was more common among those with higher levels of education. All health behaviours and health literacy were correlated directly or indirectly (i.e., mediated by health) with well-being. These findings highlight the importance of examining indirect pathways linking education to well-being in later life. Interventions aimed at improving health literacy and promoting healthy behaviours may help reduce educational inequalities in quality of life among older adults.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.

Objective The decline of the perinatal demise rate is slowing and demises are often unexplained. Significant research has been done regarding diagnostic yield and genetic causes of demise, but little is known about how Geneticist involvement impacts outcomes. The goal of the study was to evaluate post-mortem genetic testing practices and effects of the geneticists involvement. Methods Retrospective data from 111 perinatal demise cases was examined, including rates of prenatal genetic counseling, post-delivery genetics consult, genetic testing, and autopsy investigation. Results In this cohort 54% received genetic testing and 25% received a genetics consult. When compared to those without, cases with genetic specialist involvement were associated with significant increases in testing uptake (p=0.007), diagnostic yield (p<0.001), and patient education (p<0.001). Second trimester stillbirths and those with fewer ultrasound (US) abnormalities were less likely to receive genetic testing (both p values <0.001) and consults (p<0.001, p=0.020). Conclusion Though it was not possible to avoid ascertainment bias, this data demonstrates that geneticist involvement correlates with a higher rate of testing, greater diagnostic yield, and more thorough counseling. These findings underscore the importance of integrating genetics providers into perinatal postmortem healthcare teams.

PurposeDiagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. MethodsWe performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. ResultsThe overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes.. A model without fixed effects attributed [~]10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. ConclusionAcross five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome- and genome-sequencing diagnostic workflows may help reduce inter-site differences.

Proteome-wide association studies (PWAS) typically link genetically predicted protein levels to disease using cis-pQTLs, which can be limited by low cis-heritability for disease-critical genes under negative selection and by tagging due to co-regulation among nearby genes. Trans-pQTLs provide complementary information when large sample sizes are available to detect weak polygenic effects, enabling associations between trans-predicted protein levels and disease. We developed PolyPWAS, a functionally informed, summary statistics-based framework for associating both cis- and trans-predicted protein levels to disease. PolyPWAS integrates 96 functional annotations with proteome-wide pleiotropy to improve protein prediction, while correcting for PCs of predicted protein levels to limit tagging effects. We applied PolyPWAS to 2.8K plasma proteins measured in 34K UKB-PPP participants, analyzing GWAS summary statistics for 88 diseases and complex traits (average N=336K). Trans-predicted protein levels explained 21% of disease heritability (vs. 9.6% for cis-predicted protein levels), leveraging a 24% relative improvement in trans-prediction accuracy from functional priors. Trans-PWAS identified more significant protein-disease associations (and more conditionally significant associations) than cis-PWAS. Cis and trans associations showed only modest excess overlap (1.18, 95% CI: 1.11-1.26). Accordingly, combining evidence from cis and trans associations improved disease gene prioritization evaluated using gene sets from rare variant association studies (+11% relative improvement) and PoPS (+7.0% relative improvement) relative to cis-only approaches. PWAS associations to disease replicated across protein level cohorts, with strong UKB-PPP/deCODE concordance after adjusting for cohort-specific prediction accuracy. We provide examples where trans-regulatory effects link multiple disease-critical genes, underscoring the importance of integrating cis- and trans-regulatory effects to map protein-mediated disease biology.

The Million Veteran Program (MVP) represents the largest and one of the most diverse single cohorts associated with longitudinal Electronic Health Record data (EHR) data. We profiled a subset of samples from MVP using the Illumina Infinium MethylationEPIC Beadchip (EPIC array) to generate one of the largest single cohort methylation dataset to-date. Methylation profiles were analyzed for 45,460 total individuals, with the most populous ancestries composed of 27,455 Europeans, 11,798 African Americans, and 4,859 Admixed Americans. We detail the strict quality control standards implemented to ensure the most robust method of methylation profiling of the MVP cohort. This dataset was then applied to evaluate the effects of smoking exposure on DNA methylation in MVP participants. Ancestry-stratified epigenome-wide association studies (EWAS) of smoking status (ever/never) were performed using over 750,000 probes with certifiable signal. Our multi-ancestry meta-analysis demonstrates replicability with existing EWAS and identifies 3,207 novel probe-smoking associations unlocked via the depth and breadth of data in this cohort.