Xuechan Li, John Pura, Andrew Allen, Kouros Owzar, Jianfeng Lu, Matthew Harms, Jichun Xie
{"title":"DYNATE:通过嵌入在聚合树中的多个测试来定位稀有的关联区域。","authors":"Xuechan Li, John Pura, Andrew Allen, Kouros Owzar, Jianfeng Lu, Matthew Harms, Jichun Xie","doi":"10.1002/gepi.22542","DOIUrl":null,"url":null,"abstract":"<p>Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosis study and identified a new gene, <i>EPG5</i>, harboring possibly pathogenic mutations.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 1","pages":"42-55"},"PeriodicalIF":1.7000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree\",\"authors\":\"Xuechan Li, John Pura, Andrew Allen, Kouros Owzar, Jianfeng Lu, Matthew Harms, Jichun Xie\",\"doi\":\"10.1002/gepi.22542\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosis study and identified a new gene, <i>EPG5</i>, harboring possibly pathogenic mutations.</p>\",\"PeriodicalId\":12710,\"journal\":{\"name\":\"Genetic Epidemiology\",\"volume\":\"48 1\",\"pages\":\"42-55\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genetic Epidemiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gepi.22542\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genetic Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gepi.22542","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree
Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosis study and identified a new gene, EPG5, harboring possibly pathogenic mutations.
期刊介绍:
Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations.
Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.