Jiazheng Zhu, Georgios Kalantzis, Ali Pazokitoroudi, Árni Freyr Gunnarsson, Hrushikesh Loya, Han Chen, Sriram Sankararaman, Pier Francesco Palamara
{"title":"利用大规模祖先重组图进行快速变异成分分析","authors":"Jiazheng Zhu, Georgios Kalantzis, Ali Pazokitoroudi, Árni Freyr Gunnarsson, Hrushikesh Loya, Han Chen, Sriram Sankararaman, Pier Francesco Palamara","doi":"10.1101/2024.08.31.610262","DOIUrl":null,"url":null,"abstract":"Recent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE relies on scalable randomized algorithms to estimate variance components and assess their statistical significance, and can be applied to multiple quantitative traits in parallel. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,374 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.","PeriodicalId":501246,"journal":{"name":"bioRxiv - Genetics","volume":"83 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast variance component analysis using large-scale ancestral recombination graphs\",\"authors\":\"Jiazheng Zhu, Georgios Kalantzis, Ali Pazokitoroudi, Árni Freyr Gunnarsson, Hrushikesh Loya, Han Chen, Sriram Sankararaman, Pier Francesco Palamara\",\"doi\":\"10.1101/2024.08.31.610262\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE relies on scalable randomized algorithms to estimate variance components and assess their statistical significance, and can be applied to multiple quantitative traits in parallel. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,374 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.\",\"PeriodicalId\":501246,\"journal\":{\"name\":\"bioRxiv - Genetics\",\"volume\":\"83 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"bioRxiv - Genetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.08.31.610262\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Genetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.31.610262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast variance component analysis using large-scale ancestral recombination graphs
Recent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE relies on scalable randomized algorithms to estimate variance components and assess their statistical significance, and can be applied to multiple quantitative traits in parallel. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,374 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.