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; 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; iPSYCH Study Consortium

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

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 ZaitlenThomas Werge, Kenneth S. Kendler, Andrew J. Schork^*, iPSYCH Study Consortium

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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.

Original language	English
Journal	American Journal of Human Genetics
Volume	111
Issue	11
Pages (from-to)	2494-2509
Number of pages	16
ISSN	0002-9297
DOIs	https://doi.org/10.1016/j.ajhg.2024.09.009
Publication status	Published - 7 Nov 2024

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Dybdahl Krebs, M., Georgii Hellberg, K. L., Lundberg, M., Appadurai, V., Ohlsson, H., Pedersen, E., Steinbach, J., Matthews, J., Border, R., LaBianca, S., Calle, X., Meijsen, J. J., Ingason, A., Buil, A., Vilhjálmsson, B. J., Flint, J., Bacanu, S. A., Cai, N., Dahl, A., ... iPSYCH Study Consortium (2024). Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression. American Journal of Human Genetics, 111(11), 2494-2509. https://doi.org/10.1016/j.ajhg.2024.09.009

@article{dbe52c7e790a4a1690b13df426e90836,

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

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.",

author = "{Dybdahl Krebs}, Morten and {Georgii Hellberg}, {Kajsa Lotta} and Mischa Lundberg and Vivek Appadurai and Henrik Ohlsson and Emil Pedersen and Jette Steinbach and Jamie Matthews and Richard Border and Sonja LaBianca and Xabier Calle and Meijsen, {Joeri J.} and Andr{\'e}s Ingason and Alfonso Buil and Vilhj{\'a}lmsson, {Bjarni J.} and Jonathan Flint and Bacanu, {Silviu Alin} and Na Cai and Andy Dahl and Noah Zaitlen and Thomas Werge and Kendler, {Kenneth S.} and Schork, {Andrew J.} and {iPSYCH Study Consortium}",

year = "2024",

month = nov,

day = "7",

doi = "10.1016/j.ajhg.2024.09.009",

language = "English",

volume = "111",

pages = "2494--2509",

journal = "American Journal of Human Genetics",

issn = "0002-9297",

publisher = "Cell Press",

number = "11",

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Dybdahl Krebs, M, Georgii Hellberg, KL, Lundberg, M, Appadurai, V, Ohlsson, H, Pedersen, E , Steinbach, J, Matthews, J, Border, R, LaBianca, S, Calle, X, Meijsen, JJ, Ingason, A, Buil, A, Vilhjálmsson, BJ, Flint, J, Bacanu, SA, Cai, N, Dahl, A, Zaitlen, N, Werge, T, Kendler, KS, Schork, AJ & iPSYCH Study Consortium 2024, 'Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression', American Journal of Human Genetics, vol. 111, no. 11, pp. 2494-2509. https://doi.org/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. / Dybdahl Krebs, Morten; Georgii Hellberg, Kajsa Lotta; Lundberg, Mischa et al.
In: American Journal of Human Genetics, Vol. 111, No. 11, 07.11.2024, p. 2494-2509.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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 -

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

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