TY - JOUR
T1 - Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression
AU - Dybdahl Krebs, Morten
AU - Georgii Hellberg, Kajsa Lotta
AU - Lundberg, Mischa
AU - Appadurai, Vivek
AU - Ohlsson, Henrik
AU - Pedersen, Emil
AU - Steinbach, Jette
AU - Matthews, Jamie
AU - Border, Richard
AU - LaBianca, Sonja
AU - Calle, Xabier
AU - Meijsen, Joeri J.
AU - Ingason, Andrés
AU - Buil, Alfonso
AU - Vilhjálmsson, Bjarni J.
AU - Flint, Jonathan
AU - Bacanu, Silviu Alin
AU - Cai, Na
AU - Dahl, Andy
AU - Zaitlen, Noah
AU - Werge, Thomas
AU - Kendler, Kenneth S.
AU - Schork, Andrew J.
AU - iPSYCH Study Consortium
PY - 2024/11/7
Y1 - 2024/11/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85207813267&partnerID=8YFLogxK
U2 - 10.1016/j.ajhg.2024.09.009
DO - 10.1016/j.ajhg.2024.09.009
M3 - Journal article
C2 - 39471805
AN - SCOPUS:85207813267
SN - 0002-9297
VL - 111
SP - 2494
EP - 2509
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
IS - 11
ER -