{"title":"评估整合多人群数据的三种全基因组关联分析策略。","authors":"Zhanming Zhong, Guangzhen Li, Zhiting Xu, Haonan Zeng, Jinyan Teng, Xueyan Feng, Shuqi Diao, Yahui Gao, Jiaqi Li, Zhe Zhang","doi":"10.1111/age.13394","DOIUrl":null,"url":null,"abstract":"<p>In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected <i>p</i>-values (<i>p</i><sub>C</sub>). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (<i>p</i><sub>C</sub> < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, <i>p</i><sub>C</sub> of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of <i>p</i><sub>C</sub> among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations\",\"authors\":\"Zhanming Zhong, Guangzhen Li, Zhiting Xu, Haonan Zeng, Jinyan Teng, Xueyan Feng, Shuqi Diao, Yahui Gao, Jiaqi Li, Zhe Zhang\",\"doi\":\"10.1111/age.13394\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected <i>p</i>-values (<i>p</i><sub>C</sub>). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (<i>p</i><sub>C</sub> < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, <i>p</i><sub>C</sub> of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of <i>p</i><sub>C</sub> among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.</p>\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-01-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/age.13394\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/age.13394","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
在畜牧业中,全基因组关联研究(GWAS)通常是在样本量和检测能力有限的单一人群中进行的(single-GWAS)。为了提高全基因组关联研究的检测能力,有人提出了全基因组关联研究的元分析(meta-GWAS)和全基因组关联研究的超大规模分析(mega-GWAS),分别从汇总统计或个体数据的层面整合来自多个群体的数据。然而,这些不同的策略缺乏比较,因此很难指导整合多个研究人群数据的 GWAS 的最佳实践。为了最大限度地比较多个种群的不同关联分析策略,我们对三个商品猪品种(杜洛克、约克夏和陆地猪)中的100公斤背膘厚(BFT_100)和100公斤日龄(DAYS_100)分别进行了单GWAS、元GWAS和巨GWAS分析。在控制基因组膨胀因子为 1 的基础上,我们计算了校正 p 值 (pC )。在样本量最大的约克夏中,巨型-GWAS、元-GWAS 和单一-GWAS 分别检测到 149、38 和 20 个显著的 SNPs(巨型-GWAS 检测到的 SNPs pC C 最低,其次是元-GWAS 和单一-GWAS。三种 GWAS 策略的 pC 相关性在 0.60 到 0.75 之间,meta-GWAS 和 mega-GWAS 的 SNP 效应值相关性为 0.74,均显示出良好的一致性。总之,尽管在整合单个数据或汇总统计方面存在差异,但整合多个群体的数据是复杂性状遗传论证的有效手段,尤其是超大型 GWAS 与单一 GWAS 的比较。
Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations
In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p-values (pC). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (pC < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.