Genetic Epidemiology最新文献

{"title":"Correction to the 2024 Annual Meeting of the International Genetic Epidemiology Society","authors":"","doi":"10.1002/gepi.22599","DOIUrl":"https://doi.org/10.1002/gepi.22599","url":null,"abstract":"<p>(2024), The 2024 Annual Meeting of the International Genetic Epidemiology Society. Genetic Epidemiology, 48: 344-398. https://doi.org/10.1002/gepi.22598</p><p>In the originally-published article, several abstracts were inadvertently left out. They appear on the following pages.</p><p>We apologize for this error.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Associations of Persistent Opioid Use After Surgery Point to OPRM1 but Not Other Opioid-Related Loci as the Main Driver of Opioid Use Disorder","authors":"Aubrey C. Annis,&nbsp;Vidhya Gunaseelan,&nbsp;Albert V. Smith,&nbsp;Gonçalo R. Abecasis,&nbsp;Daniel B. Larach,&nbsp;Matthew Zawistowski,&nbsp;Stephan G. Frangakis,&nbsp;Chad M. Brummett","doi":"10.1002/gepi.22588","DOIUrl":"10.1002/gepi.22588","url":null,"abstract":"<p>Persistent opioid use after surgery is a common morbidity outcome associated with subsequent opioid use disorder, overdose, and death. While phenotypic associations have been described, genetic associations remain unidentified. Here, we conducted the largest genetic study of persistent opioid use after surgery, comprising ~40,000 non-Hispanic, European-ancestry Michigan Genomics Initiative participants (3198 cases and 36,321 surgically exposed controls). Our study primarily focused on the reproducibility and reliability of 72 genetic studies of opioid use disorder phenotypes. Nominal associations (<i>p</i> &lt; 0.05) occurred at 12 of 80 unique (<i>r</i>² &lt; 0.8) signals from these studies. Six occurred in <i>OPRM1</i> (most significant: rs79704991-T, OR = 1.17, <i>p</i> = 8.7 × 10⁻⁵), with two surviving multiple testing correction. Other associations were rs640561-<i>LRRIQ3</i> (<i>p</i> = 0.015), rs4680-<i>COMT</i> (<i>p</i> = 0.016), rs9478495 (<i>p</i> = 0.017, intergenic), rs10886472-<i>GRK5</i> (<i>p</i> = 0.028), rs9291211-<i>SLC30A9/BEND4</i> (<i>p</i> = 0.043), and rs112068658-<i>KCNN1</i> (<i>p</i> = 0.048). Two highly referenced genes, <i>OPRD1</i> and <i>DRD2/ANKK1,</i> had no signals in MGI. Associations at previously identified <i>OPRM1</i> variants suggest common biology between persistent opioid use and opioid use disorder, further demonstrating connections between opioid dependence and addiction phenotypes. Lack of significant associations at other variants challenges previous studies' reliability.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-Mapping the Results From Genome-Wide Association Studies of Primary Biliary Cholangitis Using SuSiE and h2-D2","authors":"Aida Gjoka,&nbsp;Heather J. Cordell","doi":"10.1002/gepi.22592","DOIUrl":"10.1002/gepi.22592","url":null,"abstract":"<p>The main goal of fine-mapping is the identification of relevant genetic variants that have a causal effect on some trait of interest, such as the presence of a disease. From a statistical point of view, fine mapping can be seen as a variable selection problem. Fine-mapping methods are often challenging to apply because of the presence of linkage disequilibrium (LD), that is, regions of the genome where the variants interrogated have high correlation. Several methods have been proposed to address this issue. Here we explore the ‘Sum of Single Effects’ (SuSiE) method, applied to real data (summary statistics) from a genome-wide meta-analysis of the autoimmune liver disease primary biliary cholangitis (PBC). Fine-mapping in this data set was previously performed using the FINEMAP program; we compare these previous results with those obtained from SuSiE, which provides an arguably more convenient and principled way of generating ‘credible sets’, that is set of predictors that are correlated with the response variable. This allows us to appropriately acknowledge the uncertainty when selecting the causal effects for the trait. We focus on the results from SuSiE-RSS, which fits the SuSiE model to summary statistics, such as z-scores, along with a correlation matrix. We also compare the SuSiE results to those obtained using a more recently developed method, h2-D2, which uses the same inputs. Overall, we find the results from SuSiE-RSS and, to a lesser extent, h2-D2, to be quite concordant with those previously obtained using FINEMAP. The resulting genes and biological pathways implicated are therefore also similar to those previously obtained, providing valuable confirmation of these previously reported results. Detailed examination of the credible sets identified suggests that, although for the majority of the loci (33 out of 56) the results from SuSiE-RSS seem most plausible, there are some loci (5 out of 56 loci) where the results from h2-D2 seem more compelling. Computer simulations suggest that, overall, SuSiE-RSS generally has slightly higher power, better precision, and better ability to identify the true number of causal variants in a region than h2-D2, although there are some scenarios where the power of h2-D2 is higher. Thus, in real data analysis, the use of complementary approaches such as both SuSiE and h2-D2 is potentially warranted.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GWASBrewer: An R Package for Simulating Realistic GWAS Summary Statistics","authors":"Jean Morrison","doi":"10.1002/gepi.22594","DOIUrl":"10.1002/gepi.22594","url":null,"abstract":"<p>Many statistical genetics analysis methods make use of GWAS summary statistics. Best statistical practice requires evaluating these methods in realistic simulation experiments. However, simulating summary statistics by first simulating individual genotype and phenotype data is extremely computationally demanding. This high cost may force researchers to conduct overly simplistic simulations that fail to accurately measure method performance. Alternatively, summary statistics can be simulated directly from their theoretical distribution. Although this is a common need among statistical genetics researchers, no software packages exist for comprehensive GWAS summary statistic simulation. We present <span>GWASBrewer</span>, an open source R package for direct simulation of GWAS summary statistics. We show that statistics simulated by \u0000<span>GWASBrewer</span> have the same distribution as statistics generated from individual level data, and can be produced at a fraction of the computational expense. Additionally, \u0000<span>GWASBrewer</span> can simulate standard error estimates, something that is typically not done when sampling summary statistics directly. \u0000<span>GWASBrewer</span> is highly flexible, allowing the user to simulate data for multiple traits connected by causal effects and with complex distributions of effect sizes. We demonstrate example uses of \u0000<span>GWASBrewer</span> for evaluating Mendelian randomization, polygenic risk score, and heritability estimation methods.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies","authors":"Runzhe Li,&nbsp;Mo Li,&nbsp;Ni Zhao","doi":"10.1002/gepi.22596","DOIUrl":"10.1002/gepi.22596","url":null,"abstract":"<div>\u0000 \u0000 <p>Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations","authors":"Pratik Ramprasad,&nbsp;Jingchen Ren,&nbsp;Wei Pan","doi":"10.1002/gepi.22595","DOIUrl":"10.1002/gepi.22595","url":null,"abstract":"<p>Transcriptome-wide association studies (TWAS) aim to uncover genotype–phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Powerful Rare-Variant Association Analysis of Secondary Phenotypes","authors":"Hanyun Liu,&nbsp;Hong Zhang","doi":"10.1002/gepi.22589","DOIUrl":"10.1002/gepi.22589","url":null,"abstract":"<div>\u0000 \u0000 <p>Most genome-wide association studies are based on case-control designs, which provide abundant resources for secondary phenotype analyses. However, such studies suffer from biased sampling of primary phenotypes, and the traditional statistical methods can lead to seriously distorted analysis results when they are applied to secondary phenotypes without accounting for the biased sampling mechanism. To our knowledge, there are no statistical methods specifically tailored for rare variant association analysis with secondary phenotypes. In this article, we proposed two novel joint test statistics for identifying secondary-phenotype-associated rare variants based on prospective likelihood and retrospective likelihood, respectively. We also exploit the assumption of gene-environment independence in retrospective likelihood to improve the statistical power and adopt a two-step strategy to balance statistical power and robustness. Simulations and a real-data application are conducted to demonstrate the superior performance of our proposed methods.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethical, Legal, and Social Implications of Gene-Environment Interaction Research","authors":"Stephanie Calluori,&nbsp;Kaitlin Kirkpatrick Heimke,&nbsp;Charlisse Caga-anan,&nbsp;David Kaufman,&nbsp;Leah E. Mechanic,&nbsp;Kimberly A. McAllister","doi":"10.1002/gepi.22591","DOIUrl":"10.1002/gepi.22591","url":null,"abstract":"<p>Many complex disorders are impacted by the interplay of genetic and environmental factors. In gene-environment interactions (GxE), an individual's genetic and epigenetic makeup impacts the response to environmental exposures. Understanding GxE can impact health at the individual, community, and population levels. The rapid expansion of GxE research in biomedical studies for complex diseases raises many unique ethical, legal, and social implications (ELSIs) that have not been extensively explored and addressed. This review article builds on discussions originating from a workshop held by the National Institute of Environmental Health Sciences (NIEHS) and the National Human Genome Research Institute (NHGRI) in January 2022, entitled: “Ethical, Legal, and Social Implications of Gene-Environment Interaction Research.” We expand upon multiple key themes to inform broad recommendations and general guidance for addressing some of the most unique and challenging ELSI in GxE research. Key takeaways include strategies and approaches for establishing sustainable community partnerships, incorporating social determinants of health and environmental justice considerations into GxE research, effectively communicating and translating GxE findings, and addressing privacy and discrimination concerns in all GxE research going forward. Additional guidelines, resources, approaches, training, and capacity building are required to further support innovative GxE research and multidisciplinary GxE research teams.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142307513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis","authors":"Marie-Sophie C. Ogloblinsky,&nbsp;Ozvan Bocher,&nbsp;Chaker Aloui,&nbsp;Anne-Louise Leutenegger,&nbsp;Ozan Ozisik,&nbsp;Anaïs Baudot,&nbsp;Elisabeth Tournier-Lasserve,&nbsp;Helen Castillo-Madeen,&nbsp;Daniel Lewinsohn,&nbsp;Donald F. Conrad,&nbsp;Emmanuelle Génin,&nbsp;Gaëlle Marenne","doi":"10.1002/gepi.22593","DOIUrl":"10.1002/gepi.22593","url":null,"abstract":"<div>\u0000 \u0000 <p>The introduction of Next-Generation Sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The Population Sampling Probability (PSAP) method was developed to meet this aim but only for coding variants in exome data. Here, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large data sets of exomes and genomes from the general population. PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than 50% of non-coding ClinVar variants were among the top 10 variants of the genome. On real sequencing data from six patients with Cerebral Small Vessel Disease and nine patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases.</p></div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Ancestry Standardization Approaches for a Transancestry Colorectal Cancer Polygenic Risk Score","authors":"Elisabeth A. Rosenthal,&nbsp;Li Hsu,&nbsp;Minta Thomas,&nbsp;Ulrike Peters,&nbsp;Christopher Kachulis,&nbsp;Karynne Patterson,&nbsp;Gail P. Jarvik","doi":"10.1002/gepi.22590","DOIUrl":"10.1002/gepi.22590","url":null,"abstract":"<div>\u0000 \u0000 <p>Colorectal cancer (CRC) is a complex disease with monogenic, polygenic and environmental risk factors. Polygenic risk scores (PRSs) aim to identify high polygenic risk individuals. Due to differences in genetic background, PRS distributions vary by ancestry, necessitating standardization. We compared four <i>post-hoc</i> methods using the All of Us Research Program Whole Genome Sequence data for a transancestry CRC PRS. We contrasted results from linear models trained on A. the entire data or an ancestrally diverse subset AND B. covariates including principal components of ancestry or admixture. Standardization with the training subset also adjusted the variance. All methods performed similarly within ancestry, OR (95% C.I.) per s.d. change in PRS: African 1.5 (1.02, 2.08), Admixed American 2.2 (1.27, 3.85), European 1.6 (1.43, 1.89), and Middle Eastern 1.1 (0.71, 1.63). Using admixture and an ancestrally diverse training set provided distributions closest to standard Normal. Training a model on ancestrally diverse participants, adjusting both the mean and variance using admixture as covariates, created standard Normal <i>z</i>-scores, which can be used to identify patients at high polygenic risk. These scores can be incorporated into comprehensive risk calculation including other known risk factors, allowing for more precise risk estimates.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142307512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}