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. 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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.
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.