Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression

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; iPSYCH Study Consortium,; 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

Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression

Am J Hum Genet. 2024 Oct 22:S0002-9297(24)00369-0. doi: 10.1016/j.ajhg.2024.09.009. Online ahead of print.

Authors

Morten Dybdahl Krebs¹, Kajsa-Lotta Georgii Hellberg², Mischa Lundberg², Vivek Appadurai², Henrik Ohlsson³, Emil Pedersen⁴, Jette Steinbach⁴, Jamie Matthews⁵, Richard Border⁵, Sonja LaBianca², Xabier Calle², Joeri J Meijsen²; iPSYCH Study Consortium,; Andrés Ingason², Alfonso Buil², Bjarni J Vilhjálmsson⁶, Jonathan Flint⁷, Silviu-Alin Bacanu⁸, Na Cai⁹, Andy Dahl¹⁰, Noah Zaitlen¹¹, Thomas Werge², Kenneth S Kendler⁸, Andrew J Schork¹²

Affiliations

¹ Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark. Electronic address: [email protected].
² Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark.
³ Center for Primary Health Care Research, Lund University, Malmö, Sweden.
⁴ NCRR - National Centre for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark.
⁵ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark; NCRR - National Centre for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark; Department of Biomedicine, Aarhus University, Aarhus, Denmark.
⁷ Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.
⁹ Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
¹⁰ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
¹¹ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Neurology, University of California, Los Angeles, Los Angeles, CA 90024, USA.
¹² Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark; Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark. Electronic address: [email protected].

PMID: 39471805
DOI: 10.1016/j.ajhg.2024.09.009

Abstract

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.

Keywords: depression; family genetic risk scores; genealogy; genetic liability; genetic risk profiles; pedigree.