{"title":"利用双胞胎数据比较认知能力的因素模型和网络模型","authors":"Jacob Knyspel, Robert Plomin","doi":"10.1016/j.intell.2024.101833","DOIUrl":null,"url":null,"abstract":"<div><p>Network models have become a popular alternative to factor models for analysing the phenotypic relationships among cognitive abilities. Studies have begun to compare these models directly to one another using cognitive ability data, although such a comparison has so far not extended to genetics. Our aim with this study was therefore to compare factor and network models of cognitive abilities first at a phenotypic level and then at a genetic level. We analyzed data from the Twins Early Development Study that were collected using 14 cognitive ability measures from 11,290 twins in the UK aged 12 years old. We conducted phenotypic and genetic analyses in which numerous factor and network models were tested, including a novel network twin model. Factor and network models both provided useful representations of the phenotypic and genetic relationships among cognitive abilities. Surprisingly, several relationships among cognitive abilities within the genetic networks were negative, which suggests that these cognitive abilities might share some genetic variants with inverse effects, although more research is currently needed to confirm this. Implications for future genomic research are discussed.</p></div>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0160289624000278/pdfft?md5=4fce9f2261c54e23fad5b161a8d215c5&pid=1-s2.0-S0160289624000278-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Comparing factor and network models of cognitive abilities using twin data\",\"authors\":\"Jacob Knyspel, Robert Plomin\",\"doi\":\"10.1016/j.intell.2024.101833\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Network models have become a popular alternative to factor models for analysing the phenotypic relationships among cognitive abilities. Studies have begun to compare these models directly to one another using cognitive ability data, although such a comparison has so far not extended to genetics. Our aim with this study was therefore to compare factor and network models of cognitive abilities first at a phenotypic level and then at a genetic level. We analyzed data from the Twins Early Development Study that were collected using 14 cognitive ability measures from 11,290 twins in the UK aged 12 years old. We conducted phenotypic and genetic analyses in which numerous factor and network models were tested, including a novel network twin model. Factor and network models both provided useful representations of the phenotypic and genetic relationships among cognitive abilities. Surprisingly, several relationships among cognitive abilities within the genetic networks were negative, which suggests that these cognitive abilities might share some genetic variants with inverse effects, although more research is currently needed to confirm this. Implications for future genomic research are discussed.</p></div>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0160289624000278/pdfft?md5=4fce9f2261c54e23fad5b161a8d215c5&pid=1-s2.0-S0160289624000278-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0160289624000278\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160289624000278","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
在分析认知能力之间的表型关系时,网络模型已成为因子模型的一种流行替代方法。已有研究开始利用认知能力数据对这些模型进行直接比较,但这种比较迄今尚未扩展到遗传学领域。因此,我们本研究的目的是首先在表型层面,然后在遗传层面对认知能力的因子模型和网络模型进行比较。我们分析了 "双胞胎早期发育研究"(Twins Early Development Study)中的数据,这些数据是通过对英国 11,290 对 12 岁双胞胎的 14 项认知能力测量而收集的。我们进行了表型和遗传分析,测试了多种因子和网络模型,包括一种新型网络双胞胎模型。因子模型和网络模型都对认知能力之间的表型和遗传关系提供了有用的表征。令人惊讶的是,遗传网络中认知能力之间的一些关系是负的,这表明这些认知能力可能共享一些具有反向效应的遗传变异,尽管目前还需要更多的研究来证实这一点。本文讨论了未来基因组研究的意义。
Comparing factor and network models of cognitive abilities using twin data
Network models have become a popular alternative to factor models for analysing the phenotypic relationships among cognitive abilities. Studies have begun to compare these models directly to one another using cognitive ability data, although such a comparison has so far not extended to genetics. Our aim with this study was therefore to compare factor and network models of cognitive abilities first at a phenotypic level and then at a genetic level. We analyzed data from the Twins Early Development Study that were collected using 14 cognitive ability measures from 11,290 twins in the UK aged 12 years old. We conducted phenotypic and genetic analyses in which numerous factor and network models were tested, including a novel network twin model. Factor and network models both provided useful representations of the phenotypic and genetic relationships among cognitive abilities. Surprisingly, several relationships among cognitive abilities within the genetic networks were negative, which suggests that these cognitive abilities might share some genetic variants with inverse effects, although more research is currently needed to confirm this. Implications for future genomic research are discussed.