从大规模家族数据中估算出的遗传责任改善了重度抑郁症的遗传预测、风险评分分析和基因图谱绘制。

IF 8.1 1区 生物学 Q1 GENETICS & HEREDITY
American journal of human genetics Pub Date : 2024-11-07 Epub Date: 2024-10-28 DOI:10.1016/j.ajhg.2024.09.009
Morten Dybdahl Krebs, Kajsa-Lotta Georgii Hellberg, Mischa Lundberg, Vivek Appadurai, Henrik Ohlsson, Emil Pedersen, Jette Steinbach, Jamie Matthews, Richard Border, Sonja LaBianca, Xabier Calle, Joeri J Meijsen, Andrés Ingason, Alfonso Buil, Bjarni J Vilhjálmsson, Jonathan Flint, Silviu-Alin Bacanu, Na Cai, Andy Dahl, Noah Zaitlen, Thomas Werge, Kenneth S Kendler, Andrew J Schork
{"title":"从大规模家族数据中估算出的遗传责任改善了重度抑郁症的遗传预测、风险评分分析和基因图谱绘制。","authors":"Morten Dybdahl Krebs, Kajsa-Lotta Georgii Hellberg, Mischa Lundberg, Vivek Appadurai, Henrik Ohlsson, Emil Pedersen, Jette Steinbach, Jamie Matthews, Richard Border, Sonja LaBianca, Xabier Calle, Joeri J Meijsen, Andrés Ingason, Alfonso Buil, Bjarni J Vilhjálmsson, Jonathan Flint, Silviu-Alin Bacanu, Na Cai, Andy Dahl, Noah Zaitlen, Thomas Werge, Kenneth S Kendler, Andrew J Schork","doi":"10.1016/j.ajhg.2024.09.009","DOIUrl":null,"url":null,"abstract":"<p><p>Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":"2494-2509"},"PeriodicalIF":8.1000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568754/pdf/","citationCount":"0","resultStr":"{\"title\":\"Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression.\",\"authors\":\"Morten Dybdahl Krebs, Kajsa-Lotta Georgii Hellberg, Mischa Lundberg, Vivek Appadurai, Henrik Ohlsson, Emil Pedersen, Jette Steinbach, Jamie Matthews, Richard Border, Sonja LaBianca, Xabier Calle, Joeri J Meijsen, Andrés Ingason, Alfonso Buil, Bjarni J Vilhjálmsson, Jonathan Flint, Silviu-Alin Bacanu, Na Cai, Andy Dahl, Noah Zaitlen, Thomas Werge, Kenneth S Kendler, Andrew J Schork\",\"doi\":\"10.1016/j.ajhg.2024.09.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.</p>\",\"PeriodicalId\":7659,\"journal\":{\"name\":\"American journal of human genetics\",\"volume\":\" \",\"pages\":\"2494-2509\"},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568754/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American journal of human genetics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ajhg.2024.09.009\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of human genetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.ajhg.2024.09.009","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

摘要

大型生物库样本为整合广泛的表型、家族记录和分子遗传学数据来研究复杂的性状和疾病提供了机会。我们介绍了皮尔森-艾特肯家族遗传风险评分(Pearson-Aitken Family Genetic Risk Scores,PA-FGRS),这是一种通过扩展的、有年龄删减的家谱记录中的诊断模式来估计疾病责任的方法。然后,我们利用 iPSYCH2015 病例队列研究(包含 30,949 个 MDD 病例、39,655 个随机人群对照和 200 多万个亲属),将该方法应用于研究典型的复杂疾病--重性抑郁症(MDD)。我们的研究表明,将从家庭记录中估算出的 PA-FGRS 责任与病例的分子基因型相结合,可以改进三方面的研究。与多基因评分相比,结合 PA-FGRS 负荷可改善 MDD 的分类,确定与合并症、复发和严重程度相关的 MDD 临床异质性的强大遗传贡献,并可提高全基因组关联研究的能力。我们的方法灵活易用,我们的研究方法可推广到其他数据集和其他复杂的性状和疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression.

Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
14.70
自引率
4.10%
发文量
185
审稿时长
1 months
期刊介绍: The American Journal of Human Genetics (AJHG) is a monthly journal published by Cell Press, chosen by The American Society of Human Genetics (ASHG) as its premier publication starting from January 2008. AJHG represents Cell Press's first society-owned journal, and both ASHG and Cell Press anticipate significant synergies between AJHG content and that of other Cell Press titles.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信